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Code Issues Releases Activity

Android: Switch default library used for Whisper voice typing (#11881)

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This commit is contained in:
Henry Heino
2025-02-27 10:31:13 -08:00
committed by GitHub
parent 502c929c88
commit 7f51712311
528 changed files with 175296 additions and 2232 deletions
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3
.eslintignore
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@@ -811,12 +811,11 @@ packages/app-mobile/services/e2ee/crypto.js
packages/app-mobile/services/plugins/PlatformImplementation.js
packages/app-mobile/services/profiles/index.js
packages/app-mobile/services/voiceTyping/VoiceTyping.js
packages/app-mobile/services/voiceTyping/utils/splitWhisperText.test.js
packages/app-mobile/services/voiceTyping/utils/splitWhisperText.js
packages/app-mobile/services/voiceTyping/utils/unzip.android.js
packages/app-mobile/services/voiceTyping/utils/unzip.js
packages/app-mobile/services/voiceTyping/vosk.android.js
packages/app-mobile/services/voiceTyping/vosk.js
packages/app-mobile/services/voiceTyping/whisper.test.js
packages/app-mobile/services/voiceTyping/whisper.js
packages/app-mobile/setupQuickActions.js
packages/app-mobile/tools/buildInjectedJs/BundledFile.js

3
.gitignore vendored
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@@ -786,12 +786,11 @@ packages/app-mobile/services/e2ee/crypto.js
packages/app-mobile/services/plugins/PlatformImplementation.js
packages/app-mobile/services/profiles/index.js
packages/app-mobile/services/voiceTyping/VoiceTyping.js
packages/app-mobile/services/voiceTyping/utils/splitWhisperText.test.js
packages/app-mobile/services/voiceTyping/utils/splitWhisperText.js
packages/app-mobile/services/voiceTyping/utils/unzip.android.js
packages/app-mobile/services/voiceTyping/utils/unzip.js
packages/app-mobile/services/voiceTyping/vosk.android.js
packages/app-mobile/services/voiceTyping/vosk.js
packages/app-mobile/services/voiceTyping/whisper.test.js
packages/app-mobile/services/voiceTyping/whisper.js
packages/app-mobile/setupQuickActions.js
packages/app-mobile/tools/buildInjectedJs/BundledFile.js

1
cspell.json
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@@ -33,6 +33,7 @@
"/packages/app-desktop/build/",
"/packages/app-desktop/utils/checkForUpdatesUtilsTestData.ts",
"/packages/app-desktop/vendor/",
"/packages/app-mobile/android/vendor/",
"/packages/app-mobile/ios/Pods/",
"/packages/app-mobile/lib/rnInjectedJs",
"/packages/app-mobile/pluginAssets",

38
packages/app-mobile/android/app/build.gradle
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@@ -70,6 +70,13 @@ def enableProguardInReleaseBuilds = false
def jscFlavor = 'org.webkit:android-jsc:+'
android {
externalNativeBuild {
cmake {
path file('src/main/cpp/CMakeLists.txt')
version '3.22.1'
}
}
ndkVersion rootProject.ext.ndkVersion
buildToolsVersion rootProject.ext.buildToolsVersion
compileSdk rootProject.ext.compileSdkVersion
@@ -81,12 +88,17 @@ android {
targetSdkVersion rootProject.ext.targetSdkVersion
versionCode 2097764
versionName "3.3.1"
ndk {
abiFilters "armeabi-v7a", "x86", "arm64-v8a", "x86_64"
}
ndk {
abiFilters "armeabi-v7a", "x86", "arm64-v8a", "x86_64"
}
// Needed to fix: The number of method references in a .dex file cannot exceed 64K
multiDexEnabled true
externalNativeBuild {
cmake {
cppFlags '-DCMAKE_BUILD_TYPE=Release'
}
}
}
signingConfigs {
debug {
@@ -95,14 +107,14 @@ android {
keyAlias 'androiddebugkey'
keyPassword 'android'
}
release {
if (project.hasProperty('JOPLIN_RELEASE_STORE_FILE')) {
storeFile file(JOPLIN_RELEASE_STORE_FILE)
storePassword JOPLIN_RELEASE_STORE_PASSWORD
keyAlias JOPLIN_RELEASE_KEY_ALIAS
keyPassword JOPLIN_RELEASE_KEY_PASSWORD
}
}
release {
if (project.hasProperty('JOPLIN_RELEASE_STORE_FILE')) {
storeFile file(JOPLIN_RELEASE_STORE_FILE)
storePassword JOPLIN_RELEASE_STORE_PASSWORD
keyAlias JOPLIN_RELEASE_KEY_ALIAS
keyPassword JOPLIN_RELEASE_KEY_PASSWORD
}
}
}
buildTypes {
debug {
@@ -127,10 +139,6 @@ dependencies {
} else {
implementation jscFlavor
}
// Needed for Whisper speech-to-text
implementation 'com.microsoft.onnxruntime:onnxruntime-android:latest.release'
implementation 'com.microsoft.onnxruntime:onnxruntime-extensions-android:latest.release'
}
apply from: file("../../node_modules/@react-native-community/cli-platform-android/native_modules.gradle"); applyNativeModulesAppBuildGradle(project)

64
packages/app-mobile/android/app/src/main/cpp/CMakeLists.txt Normal file
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@@ -0,0 +1,64 @@
# 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("joplin")
# 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.
whisperWrapper.cpp
utils/WhisperSession.cpp
utils/findLongestSilence.cpp
utils/findLongestSilence_test.cpp
)
set(WHISPER_LIB_DIR ${CMAKE_SOURCE_DIR}/../../../../vendor/whisper.cpp)
# Based on the Whisper.cpp Android example:
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -O3 ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -fvisibility=hidden -fvisibility-inlines-hidden -ffunction-sections -fdata-sections")
# Whisper: See https://stackoverflow.com/a/76290722
add_subdirectory(${WHISPER_LIB_DIR} ./whisper)
# Directories for header files
target_include_directories(
${CMAKE_PROJECT_NAME}
PUBLIC
${PROJECT_BASE_DIR}/shared
${WHISPER_LIB_DIR}/include
)
# Specifies libraries CMake should link to your target library. You
# can link libraries from various origins, such as libraries defined in this
# build script, prebuilt third-party libraries, or Android system libraries.
target_link_libraries(${CMAKE_PROJECT_NAME}
whisper
# List libraries link to the target library
android
log
)

154
packages/app-mobile/android/app/src/main/cpp/utils/WhisperSession.cpp Normal file
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@@ -0,0 +1,154 @@
#include "WhisperSession.h"
#include <utility>
#include <sstream>
#include <algorithm>
#include "whisper.h"
#include "findLongestSilence.h"
#include "androidUtil.h"
WhisperSession::WhisperSession(const std::string& modelPath, std::string lang, std::string prompt)
: lang_ {std::move(lang)}, prompt_ {std::move(prompt)} {
whisper_context_params contextParams = whisper_context_default_params();
// Lifetime(pModelPath): Whisper.cpp creates a copy of pModelPath and stores it in a std::string.
// whisper_init_from_file_with_params doesn't seem to otherwise save pModelPath. As such, it's
// safe to pass a pointer to a std::string's representation:
const char *pModelPath = modelPath.c_str();
pContext_ = whisper_init_from_file_with_params(pModelPath, contextParams);
if (pContext_ == nullptr) {
throw std::runtime_error("Unable to initialize the Whisper context.");
}
}
WhisperSession::~WhisperSession() {
if (pContext_ != nullptr) {
whisper_free(pContext_);
}
}
whisper_full_params
WhisperSession::buildWhisperParams_() {
whisper_full_params params = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
// WHISPER_SAMPLING_BEAM_SEARCH is an alternative to greedy:
// params.beam_search = { .beam_size = 2 };
params.print_realtime = false;
// Disable timestamps: They make creating custom Whisper models more difficult:
params.print_timestamps = false;
params.no_timestamps = true;
params.print_progress = false;
params.translate = false;
params.offset_ms = 0;
params.single_segment = true;
// Avoid non-speech tokens (e.g. "(crackle)"). For now, this is disabled because it seems to
// cause increased hallucinations (e.g. repeated "Thank you"s).
// params.suppress_nst = true;
params.temperature = 0; // Initial randomness
// There's also a temperature_inc variable, which is used when decoding fails (Whisper increases
// the temperature by temperature_inc and retries).
// Following the whisper streaming example in setting prompt_tokens to nullptr
// when using VAD (Voice Activity Detection)
params.initial_prompt = prompt_.c_str();
params.prompt_tokens = nullptr;
params.prompt_n_tokens = 0;
// Lifetime: lifetime(params) < lifetime(lang_) = lifetime(this).
params.language = lang_.c_str();
return params;
}
std::string
WhisperSession::transcribe_(const std::vector<float>& audio, size_t transcribeCount) {
int minTranscribeLength = WHISPER_SAMPLE_RATE / 2; // 0.5s
if (transcribeCount < minTranscribeLength) {
return "";
}
whisper_full_params params = buildWhisperParams_();
whisper_reset_timings(pContext_);
transcribeCount = std::min(audio.size(), transcribeCount);
if (whisper_full(pContext_, params, audio.data(), transcribeCount) != 0) {
throw std::runtime_error("Failed to run Whisper (non-zero exit status).");
} else {
whisper_print_timings(pContext_);
}
// Tokens to be used as a prompt for the next run of Whisper
unsigned int segmentCount = whisper_full_n_segments(pContext_);
// Build the results
std::stringstream results;
for (int i = 0; i < segmentCount; i++) {
results << " " << whisper_full_get_segment_text(pContext_, i);
}
std::string result = results.str();
LOGD("Transcribed: %s (audio len %.2f)", result.c_str(), audio.size() / (float) WHISPER_SAMPLE_RATE);
return result;
}
std::string
WhisperSession::splitAndTranscribeBefore_(int transcribeUpTo, int trimTo) {
std::string result = transcribe_(audioBuffer_, transcribeUpTo);
// Trim
LOGI("Trim to %.2f s, transcribe to %.2f s", (float) trimTo / WHISPER_SAMPLE_RATE, (float) transcribeUpTo / WHISPER_SAMPLE_RATE);
audioBuffer_ = std::vector(audioBuffer_.begin() + trimTo, audioBuffer_.end());
return result;
}
std::string
WhisperSession::transcribeNextChunk(const float *pAudio, int sizeAudio) {
std::string finalizedContent;
// Update the local audio buffer
for (int i = 0; i < sizeAudio; i++) {
audioBuffer_.push_back(pAudio[i]);
}
// Does the audio buffer need to be split somewhere?
int maximumSamples = WHISPER_SAMPLE_RATE * 25;
if (audioBuffer_.size() >= maximumSamples) {
float minSilenceSeconds = 0.3f;
auto silenceRange = findLongestSilence(
audioBuffer_, WHISPER_SAMPLE_RATE, minSilenceSeconds, maximumSamples
);
// In this case, the audio is long enough that it needs to be split somewhere. If there's
// no suitable pause available, default to splitting in the middle.
int halfBufferSize = audioBuffer_.size() / 2;
int transcribeTo = silenceRange.isValid ? silenceRange.start : halfBufferSize;
int trimTo = silenceRange.isValid ? silenceRange.end : halfBufferSize;
finalizedContent = splitAndTranscribeBefore_(transcribeTo, trimTo);
} else if (audioBuffer_.size() > WHISPER_SAMPLE_RATE * 3) {
// Allow brief pauses to create new paragraphs:
float minSilenceSeconds = 2.0f;
auto splitPoint = findLongestSilence(
audioBuffer_, WHISPER_SAMPLE_RATE, minSilenceSeconds, maximumSamples
);
if (splitPoint.isValid) {
int tolerance = WHISPER_SAMPLE_RATE / 20; // 0.05s
bool isCompletelySilent = splitPoint.start < tolerance && splitPoint.end > audioBuffer_.size() - tolerance;
if (isCompletelySilent) {
audioBuffer_.clear();
} else {
finalizedContent = splitAndTranscribeBefore_(splitPoint.start, splitPoint.end);
}
}
}
previewText_ = transcribe_(audioBuffer_, audioBuffer_.size());
return finalizedContent;
}
std::string WhisperSession::getPreview() {
return previewText_;
}

27
packages/app-mobile/android/app/src/main/cpp/utils/WhisperSession.h Normal file
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@@ -0,0 +1,27 @@
#pragma once
#include <string>
#include "whisper.h"
class WhisperSession {
public:
WhisperSession(const std::string& modelPath, std::string lang, std::string prompt);
~WhisperSession();
std::string transcribeNextChunk(const float *pAudio, int sizeAudio);
std::string getPreview();
private:
// Current preview state
std::string previewText_;
whisper_full_params buildWhisperParams_();
std::string transcribe_(const std::vector<float>& audio, size_t samplesToTranscribe);
std::string splitAndTranscribeBefore_(int transcribeUpTo, int trimTo);
whisper_context *pContext_;
const std::string lang_;
const std::string prompt_;
std::vector<float> audioBuffer_;
};

10
packages/app-mobile/android/app/src/main/cpp/utils/androidUtil.h Normal file
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@@ -0,0 +1,10 @@
#pragma once
#include <android/log.h>
// Use macros for these rather than functions. Functions generate a "may be unsafe"
// warning because the compiler can't check that the first argument is a string
// literal.
#define LOGW(...) __android_log_print(ANDROID_LOG_WARN, "Whisper::JNI", __VA_ARGS__);
#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, "Whisper::JNI", __VA_ARGS__);
#define LOGD(...) __android_log_print(ANDROID_LOG_DEBUG, "Whisper::JNI", __VA_ARGS__);

111
packages/app-mobile/android/app/src/main/cpp/utils/findLongestSilence.cpp Normal file
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@@ -0,0 +1,111 @@
#include "findLongestSilence.h"
#include "androidUtil.h"
static void highpass(std::vector<float>& data, int sampleRate) {
// Highpass filter. See https://en.wikipedia.org/wiki/High-pass_filter and
// the example in whisper.cpp/streaming.
float highpassCutoffHz = 60.0f;
float RC = 1.0f / (2 * 3.1416f * highpassCutoffHz);
float timePerSample = 1.0f / sampleRate;
float alpha = RC / (RC + timePerSample);
float lastInput = data[0];
for (int i = 1; i < data.size(); i++) {
float currentInput = data[i];
data[i] = alpha * data[i - 1] + alpha * (currentInput - lastInput);
lastInput = currentInput;
}
}
SilenceRange findLongestSilence(
const std::vector<float>& audioData,
int sampleRate,
float minSilenceLengthSeconds,
int maxSilencePosition
) {
int bestCandidateLength = 0;
int bestCandidateStart = -1;
int bestCandidateEnd = -1;
int currentCandidateStart = -1;
std::vector<float> processedAudio { audioData };
highpass(processedAudio, sampleRate);
// Break into windows of size `windowSize`:
int windowSize = 256;
int windowsPerSecond = sampleRate / windowSize;
int quietWindows = 0;
// Finishes the current candidate for longest silence
auto finalizeCandidate = [&] (int currentOffset) {
bool hasCandidate = currentCandidateStart >= 0;
if (!hasCandidate) {
return;
}
int currentCandidateLength = currentOffset - currentCandidateStart;
if (currentCandidateLength > bestCandidateLength && currentCandidateStart <= maxSilencePosition) {
bestCandidateLength = currentCandidateLength;
bestCandidateStart = currentCandidateStart;
bestCandidateEnd = currentOffset;
LOGD("New best candidate with length %d", currentCandidateLength);
}
currentCandidateStart = -1;
};
int windowOffset;
for (windowOffset = 0; windowOffset < processedAudio.size() && windowOffset <= maxSilencePosition; windowOffset += windowSize) {
int rollingAverageSize = 24;
float threshold = static_cast<float>(rollingAverageSize) / 80.0f;
// Count the number of samples that (when averaged with the nearby samples)
// are below some threshold value.
float absSum = 0;
int silentSamples = 0;
for (int i = windowOffset; i < windowOffset + windowSize && i < processedAudio.size(); i++) {
absSum += abs(processedAudio[i]);
bool isSumComplete = i - rollingAverageSize >= windowOffset;
if (isSumComplete) {
absSum -= abs(processedAudio[i - rollingAverageSize]);
if (absSum < threshold) {
silentSamples++;
}
}
}
// The window should be considered "quiet" if enough samples were below the threshold.
// Don't require all of them to be to allow clicks and pops.
if (silentSamples >= windowSize * 3 / 4) {
quietWindows ++;
} else {
quietWindows = 0;
}
int minQuietWindows = static_cast<int>(windowsPerSecond * minSilenceLengthSeconds);
if (quietWindows >= minQuietWindows && currentCandidateStart == -1) {
// Found a candidate. Start it.
currentCandidateStart = windowOffset;
} else if (quietWindows == 0) {
// Ended a candidate. Is it better than the best?
finalizeCandidate(windowOffset);
}
}
finalizeCandidate(windowOffset);
// Return the best candidate.
if (bestCandidateLength == 0) {
return { .isValid = false, .start = 0, .end = 0 };
} else {
return {
.isValid=true,
.start=bestCandidateStart,
.end=bestCandidateEnd
};
}
}

24
packages/app-mobile/android/app/src/main/cpp/utils/findLongestSilence.h Normal file
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@@ -0,0 +1,24 @@
#pragma once
#include <vector>
#include <optional>
#include <tuple>
struct SilenceRange {
bool isValid;
int start;
int end;
};
SilenceRange findLongestSilence(
const std::vector<float>& audioData,
int sampleRate,
// Minimum length of silence in seconds
float minSilenceLengthSeconds,
// Doesn't check for silence at a position greater than maximumSilenceStart
int maximumSilenceStart
);

169
packages/app-mobile/android/app/src/main/cpp/utils/findLongestSilence_test.cpp Normal file
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@@ -0,0 +1,169 @@
#include "findLongestSilence_test.h"
#include "findLongestSilence.h"
#include "androidUtil.h"
#include <string>
#include <vector>
#include <sstream>
#include <cmath>
#include <random>
static void testTones();
static void testToneWithPause();
static void testSilence();
static void testNoise();
static void fail(const std::string& message);
struct GeneratedAudio {
std::vector<float> data;
int sampleRate;
int sampleCount;
};
using AudioGenerator = std::function<const float(float)>;
static GeneratedAudio makeAudio(const AudioGenerator& generator, int sampleRate, float duration);
static void expectNoSilence(const GeneratedAudio& audio, const std::string& testLabel);
static void expectSilenceBetween(const GeneratedAudio& audio, float startTimeSeconds, float stopTimeSeconds, const std::string& testLabel);
void findLongestSilence_test() {
testTones();
testToneWithPause();
testSilence();
testNoise();
}
static void testTones() {
for (int frequency = 440; frequency < 1600; frequency += 300) {
std::stringstream messageBuilder;
messageBuilder << "Should not find silence in tone with frequency " << frequency << " HZ.";
auto audioTone = makeAudio([frequency](float t) {
// Also set the amplitude to 0.2f (to more closely match mic input).
return std::sin(t * static_cast<float>(frequency)) * 0.2f;
}, 15000, 10.0f);
expectNoSilence(audioTone, messageBuilder.str());
}
auto lowFrequencyTone = makeAudio([](float t) {
return std::sin(t * 8) * 0.3f;
}, 15000, 10.0f);
expectSilenceBetween(lowFrequencyTone, 0.0f, 10.0f, "Should find silence in a very low-frequency tone");
}
static void testToneWithPause() {
auto audioToneWithPause = makeAudio([](float t) {
if (t < 5.0f || t > 6.0f) {
return std::sin(t * 880);
} else {
return 0.0f;
}
}, 15000, 11.0f);
expectSilenceBetween(audioToneWithPause, 5.0f, 6.0f, "Should find silence when completely silent in a region");
auto audioToneWithTwoPauses = makeAudio([](float t) {
if (t < 1.0f || (t > 8.0f && t < 10.0f)) {
return 0.0f;
} else {
return std::sin(t * 880);
}
}, 15000, 20.0f);
expectSilenceBetween(audioToneWithPause, 5.0f, 6.0f, "Should find silence when completely silent in a region");
}
static void testSilence() {
auto silence = makeAudio([](float t) {
return 0.0f;
}, 16000, 10.0f);
expectSilenceBetween(silence, 0.0f, 10.0f, "Should find silence in a completely silent signal");
}
static void testNoise() {
std::minstd_rand randomness {2};
std::uniform_real_distribution noiseGenerator {-1.0, 1.0};
auto quietNoise = makeAudio([&](float t) {
return noiseGenerator(randomness) * 0.02f;
}, 16000, 5.0f);
expectSilenceBetween(quietNoise, 0.0f, 5.0f, "Should find silence in a tone with low-amplitude noise");
}
static void fail(const std::string& message) {
throw std::runtime_error(message);
}
static GeneratedAudio makeAudio(const AudioGenerator& generator, int sampleRate, float duration) {
std::vector<float> result { };
int numSamples = static_cast<int>(static_cast<float>(sampleRate) * duration);
for (int i = 0; i < numSamples; i++) {
float time = static_cast<float>(i) / static_cast<float>(sampleRate);
result.push_back(generator(time));
}
return {
.data=result,
.sampleRate=sampleRate,
.sampleCount=numSamples,
};
}
static void logTestPass(const std::string& message) {
LOGI("Test PASS: %s", message.c_str());
}
static float samplesToSeconds(int samples, int sampleRate) {
return static_cast<float>(samples) / static_cast<float>(sampleRate);
}
static void expectNoSilence(const GeneratedAudio& audio, const std::string& testLabel) {
auto silence = findLongestSilence(
audio.data,
audio.sampleRate,
0.02f,
audio.sampleCount
);
if (silence.isValid) {
std::stringstream errorBuilder;
float startSeconds = samplesToSeconds(silence.start, audio.sampleRate);
float stopSeconds = samplesToSeconds(silence.end, audio.sampleRate);
errorBuilder << "Error: Found silence between " << startSeconds << "s and " << stopSeconds << "s";
errorBuilder << ": " << testLabel;
fail(errorBuilder.str());
}
logTestPass(testLabel);
}
static void expectSilenceBetween(const GeneratedAudio& audio, float startTimeSeconds, float stopTimeSeconds, const std::string& testLabel) {
auto silenceResult = findLongestSilence(
audio.data,
audio.sampleRate,
0.02f,
audio.sampleCount
);
if (!silenceResult.isValid) {
fail("Error: No silence found: " + testLabel);
}
auto checkEndpoint = [&] (int actualValueSamples, float expectedValueSeconds, const std::string& description) {
float actualValueSeconds = samplesToSeconds(actualValueSamples, audio.sampleRate);
float tolerance = 0.1f; // 100ms
if (std::abs(expectedValueSeconds - actualValueSeconds) > tolerance) {
std::stringstream messageBuilder;
messageBuilder << "Error: Silence " << description << " mismatch: ";
messageBuilder << "got " << actualValueSeconds << "s expected " << expectedValueSeconds << "s. ";
messageBuilder << testLabel;
fail(messageBuilder.str());
}
};
checkEndpoint(silenceResult.start, startTimeSeconds, "start time");
checkEndpoint(silenceResult.end, stopTimeSeconds, "stop time");
logTestPass(testLabel);
}

3
packages/app-mobile/android/app/src/main/cpp/utils/findLongestSilence_test.h Normal file
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@@ -0,0 +1,3 @@
#pragma once
void findLongestSilence_test();

125
packages/app-mobile/android/app/src/main/cpp/whisperWrapper.cpp Normal file
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@@ -0,0 +1,125 @@
// Write C++ code here.
//
// Do not forget to dynamically load the C++ library into your application.
//
// For instance,
//
// In MainActivity.java:
// static {
// System.loadLibrary("joplin");
// }
//
// Or, in MainActivity.kt:
// companion object {
// init {
// System.loadLibrary("joplin")
// }
// }
#include <jni.h>
#include <memory>
#include <string>
#include <sstream>
#include <android/log.h>
#include "whisper.h"
#include "utils/WhisperSession.h"
#include "utils/androidUtil.h"
#include "utils/findLongestSilence_test.h"
void log_android(enum ggml_log_level level, const char* message, void* user_data) {
android_LogPriority priority = level == 4 ? ANDROID_LOG_ERROR : ANDROID_LOG_INFO;
__android_log_print(priority, "Whisper::JNI::cpp", "%s", message);
}
jstring stringToJava(JNIEnv *env, const std::string& source) {
return env->NewStringUTF(source.c_str());
}
std::string stringToCXX(JNIEnv *env, jstring jString) {
const char *jStringChars = env->GetStringUTFChars(jString, nullptr);
std::string result { jStringChars };
env->ReleaseStringUTFChars(jString, jStringChars);
return result;
}
void throwException(JNIEnv *env, const std::string& message) {
jclass errorClass = env->FindClass("java/lang/Exception");
env->ThrowNew(errorClass, message.c_str());
}
extern "C"
JNIEXPORT jlong JNICALL
Java_net_cozic_joplin_audio_NativeWhisperLib_00024Companion_init(
JNIEnv *env,
jobject thiz,
jstring modelPath,
jstring language,
jstring prompt
) {
whisper_log_set(log_android, nullptr);
try {
auto *pSession = new WhisperSession(
stringToCXX(env, modelPath), stringToCXX(env, language), stringToCXX(env, prompt)
);
return (jlong) pSession;
} catch (const std::exception& exception) {
LOGW("Failed to init whisper: %s", exception.what());
throwException(env, exception.what());
return 0;
}
}
extern "C"
JNIEXPORT void JNICALL
Java_net_cozic_joplin_audio_NativeWhisperLib_00024Companion_free(JNIEnv *env, jobject thiz,
jlong pointer) {
std::free(reinterpret_cast<WhisperSession *>(pointer));
}
extern "C"
JNIEXPORT jstring JNICALL
Java_net_cozic_joplin_audio_NativeWhisperLib_00024Companion_fullTranscribe(JNIEnv *env,
jobject thiz,
jlong pointer,
jfloatArray audio_data) {
auto *pSession = reinterpret_cast<WhisperSession *> (pointer);
jfloat *pAudioData = env->GetFloatArrayElements(audio_data, nullptr);
jsize lenAudioData = env->GetArrayLength(audio_data);
std::string result;
try {
LOGD("Starting Whisper, transcribe %d", lenAudioData);
result = pSession->transcribeNextChunk(pAudioData, lenAudioData);
auto preview = pSession->getPreview();
LOGD("Ran Whisper. Got %s (preview %s)", result.c_str(), preview.c_str());
} catch (const std::exception& exception) {
LOGW("Failed to run whisper: %s", exception.what());
throwException(env, exception.what());
}
// JNI_ABORT: "free the buffer without copying back the possible changes", pass 0 to copy
// changes (there should be no changes)
env->ReleaseFloatArrayElements(audio_data, pAudioData, JNI_ABORT);
return stringToJava(env, result);
}
extern "C"
JNIEXPORT jstring JNICALL
Java_net_cozic_joplin_audio_NativeWhisperLib_00024Companion_getPreview(
JNIEnv *env, jobject thiz, jlong pointer
) {
auto *pSession = reinterpret_cast<WhisperSession *> (pointer);
return stringToJava(env, pSession->getPreview());
}
extern "C"
JNIEXPORT void JNICALL
Java_net_cozic_joplin_audio_NativeWhisperLib_00024Companion_runTests(JNIEnv *env, jobject thiz) {
try {
findLongestSilence_test();
} catch (const std::exception& exception) {
LOGW("Failed to run tests: %s", exception.what());
throwException(env, exception.what());
}
}

16
packages/app-mobile/android/app/src/main/java/net/cozic/joplin/audio/AudioRecorder.kt
View File

@@ -21,7 +21,7 @@ class AudioRecorder(context: Context) : Closeable {
private var bufferWriteOffset = 0
// Accessor must not modify result
val bufferedData: FloatArray get() = buffer.sliceArray(0 until bufferWriteOffset)
private val bufferedData: FloatArray get() = buffer.sliceArray(0 until bufferWriteOffset)
val bufferLengthSeconds: Double get() = bufferWriteOffset.toDouble() / sampleRate
init {
@@ -74,11 +74,16 @@ class AudioRecorder(context: Context) : Closeable {
}
// Pulls all available data from the audio recorder's buffer
fun pullAvailable() {
return read(maxBufferSize, AudioRecord.READ_NON_BLOCKING)
fun pullAvailable(): FloatArray {
read(maxBufferSize, AudioRecord.READ_NON_BLOCKING)
val result = bufferedData
buffer.fill(0.0f, 0, maxBufferSize);
bufferWriteOffset = 0
return result
}
fun pullNextSeconds(seconds: Double) {
fun pullNextSeconds(seconds: Double):FloatArray {
val remainingSize = maxBufferSize - bufferWriteOffset
val requestedSize = (seconds * sampleRate).toInt()
@@ -87,7 +92,8 @@ class AudioRecorder(context: Context) : Closeable {
advanceStartBySamples(maxBufferSize / 3)
}
return read(requestedSize, AudioRecord.READ_BLOCKING)
read(requestedSize, AudioRecord.READ_BLOCKING)
return pullAvailable()
}
override fun close() {

54
packages/app-mobile/android/app/src/main/java/net/cozic/joplin/audio/NativeWhisperLib.kt Normal file
View File

@@ -0,0 +1,54 @@
package net.cozic.joplin.audio
import java.io.Closeable
class NativeWhisperLib(
modelPath: String,
languageCode: String,
prompt: String,
) : Closeable {
companion object {
init {
System.loadLibrary("joplin")
}
external fun runTests(): Unit;
// TODO: The example whisper.cpp project transfers pointers as Longs to the Kotlin code.
// This seems unsafe. Try changing how this is managed.
private external fun init(modelPath: String, languageCode: String, prompt: String): Long;
private external fun free(pointer: Long): Unit;
private external fun fullTranscribe(pointer: Long, audioData: FloatArray): String;
private external fun getPreview(pointer: Long): String;
}
private var closed = false
private val pointer: Long = init(modelPath, languageCode, prompt)
fun transcribe(audioData: FloatArray): String {
if (closed) {
throw Exception("Cannot transcribe using a closed session")
}
return fullTranscribe(pointer, audioData)
}
fun getPreview(): String {
if (closed) {
throw Exception("Cannot get preview from a closed session")
}
return getPreview(pointer)
}
override fun close() {
if (closed) {
throw Exception("Cannot close a whisper session twice")
}
closed = true
free(pointer)
}
}

122
packages/app-mobile/android/app/src/main/java/net/cozic/joplin/audio/SpeechToTextConverter.kt
View File

@@ -1,110 +1,33 @@
package net.cozic.joplin.audio
import ai.onnxruntime.OnnxTensor
import ai.onnxruntime.OrtEnvironment
import ai.onnxruntime.OrtSession
import ai.onnxruntime.extensions.OrtxPackage
import android.annotation.SuppressLint
import android.content.Context
import android.util.Log
import java.io.Closeable
import java.nio.FloatBuffer
import java.nio.IntBuffer
import kotlin.time.DurationUnit
import kotlin.time.measureTimedValue
class SpeechToTextConverter(
modelPath: String,
locale: String,
prompt: String,
recorderFactory: AudioRecorderFactory,
private val environment: OrtEnvironment,
context: Context,
) : Closeable {
private val recorder = recorderFactory(context)
private val session: OrtSession = environment.createSession(
modelPath,
OrtSession.SessionOptions().apply {
// Needed for audio decoding
registerCustomOpLibrary(OrtxPackage.getLibraryPath())
},
)
private val languageCode = Regex("_.*").replace(locale, "")
private val decoderInputIds = when (languageCode) {
// Add 50363 to the end to omit timestamps
"en" -> intArrayOf(50258, 50259, 50359)
"fr" -> intArrayOf(50258, 50265, 50359)
"es" -> intArrayOf(50258, 50262, 50359)
"de" -> intArrayOf(50258, 50261, 50359)
"it" -> intArrayOf(50258, 50274, 50359)
"nl" -> intArrayOf(50258, 50271, 50359)
"ko" -> intArrayOf(50258, 50264, 50359)
"th" -> intArrayOf(50258, 50289, 50359)
"ru" -> intArrayOf(50258, 50263, 50359)
"pt" -> intArrayOf(50258, 50267, 50359)
"pl" -> intArrayOf(50258, 50269, 50359)
"id" -> intArrayOf(50258, 50275, 50359)
"hi" -> intArrayOf(50258, 50276, 50359)
// Let Whisper guess the language
else -> intArrayOf(50258)
}
private var whisper = NativeWhisperLib(
modelPath,
languageCode,
prompt,
)
fun start() {
recorder.start()
}
private fun getInputs(data: FloatArray): MutableMap<String, OnnxTensor> {
fun intTensor(value: Int) = OnnxTensor.createTensor(
environment,
IntBuffer.wrap(intArrayOf(value)),
longArrayOf(1),
)
fun floatTensor(value: Float) = OnnxTensor.createTensor(
environment,
FloatBuffer.wrap(floatArrayOf(value)),
longArrayOf(1),
)
val audioPcmTensor = OnnxTensor.createTensor(
environment,
FloatBuffer.wrap(data),
longArrayOf(1, data.size.toLong()),
)
val decoderInputIdsTensor = OnnxTensor.createTensor(
environment,
IntBuffer.wrap(decoderInputIds),
longArrayOf(1, decoderInputIds.size.toLong())
)
return mutableMapOf(
"audio_pcm" to audioPcmTensor,
"max_length" to intTensor(412),
"min_length" to intTensor(0),
"num_return_sequences" to intTensor(1),
"num_beams" to intTensor(1),
"length_penalty" to floatTensor(1.1f),
"repetition_penalty" to floatTensor(3f),
"decoder_input_ids" to decoderInputIdsTensor,
// Required for timestamps
"logits_processor" to intTensor(1)
)
}
// TODO .get() fails on older Android versions
@SuppressLint("NewApi")
private fun convert(data: FloatArray): String {
val (inputs, convertInputsTime) = measureTimedValue {
getInputs(data)
}
val (outputs, getOutputsTime) = measureTimedValue {
session.run(inputs, setOf("str"))
}
val mainOutput = outputs.get("str").get().value as Array<Array<String>>
outputs.close()
Log.i("Whisper", "Converted ${data.size / 16000}s of data in ${
getOutputsTime.toString(DurationUnit.SECONDS, 2)
} converted inputs in ${convertInputsTime.inWholeMilliseconds}ms")
return mainOutput[0][0]
Log.d("Whisper", "Pre-transcribe data of size ${data.size}")
val result = whisper.transcribe(data)
Log.d("Whisper", "Post transcribe. Got $result")
return result;
}
fun dropFirstSeconds(seconds: Double) {
@@ -114,23 +37,26 @@ class SpeechToTextConverter(
val bufferLengthSeconds: Double get() = recorder.bufferLengthSeconds
fun expandBufferAndConvert(seconds: Double): String {
recorder.pullNextSeconds(seconds)
// Also pull any extra available data, in case the speech-to-text converter
// is lagging behind the audio recorder.
recorder.pullAvailable()
return convert(recorder.bufferedData)
fun convertNext(seconds: Double): String {
val buffer = recorder.pullNextSeconds(seconds)
val result = convert(buffer)
dropFirstSeconds(seconds)
return result
}
// Converts as many seconds of buffered data as possible, without waiting
fun expandBufferAndConvert(): String {
recorder.pullAvailable()
return convert(recorder.bufferedData)
fun convertRemaining(): String {
val buffer = recorder.pullAvailable()
return convert(buffer)
}
fun getPreview(): String {
return whisper.getPreview()
}
override fun close() {
Log.d("Whisper", "Close")
recorder.close()
session.close()
whisper.close()
}
}

32
packages/app-mobile/android/app/src/main/java/net/cozic/joplin/audio/SpeechToTextPackage.kt
View File

@@ -1,6 +1,5 @@
package net.cozic.joplin.audio
import ai.onnxruntime.OrtEnvironment
import com.facebook.react.ReactPackage
import com.facebook.react.bridge.LifecycleEventListener
import com.facebook.react.bridge.NativeModule
@@ -24,7 +23,6 @@ class SpeechToTextPackage : ReactPackage {
class SpeechToTextModule(
private var context: ReactApplicationContext,
) : ReactContextBaseJavaModule(context), LifecycleEventListener {
private var environment: OrtEnvironment? = null
private val executorService: ExecutorService = Executors.newFixedThreadPool(1)
private val sessionManager = SpeechToTextSessionManager(executorService)
@@ -32,21 +30,24 @@ class SpeechToTextPackage : ReactPackage {
override fun onHostResume() { }
override fun onHostPause() { }
override fun onHostDestroy() {
environment?.close()
override fun onHostDestroy() { }
@ReactMethod
fun runTests(promise: Promise) {
try {
NativeWhisperLib.runTests()
promise.resolve(true)
} catch (exception: Throwable) {
promise.reject(exception)
}
}
@ReactMethod
fun openSession(modelPath: String, locale: String, promise: Promise) {
fun openSession(modelPath: String, locale: String, prompt: String, promise: Promise) {
val appContext = context.applicationContext
// Initialize environment as late as possible:
val ortEnvironment = environment ?: OrtEnvironment.getEnvironment()
if (environment != null) {
environment = ortEnvironment
}
try {
val sessionId = sessionManager.openSession(modelPath, locale, ortEnvironment, appContext)
val sessionId = sessionManager.openSession(modelPath, locale, prompt, appContext)
promise.resolve(sessionId)
} catch (exception: Throwable) {
promise.reject(exception)
@@ -69,8 +70,8 @@ class SpeechToTextPackage : ReactPackage {
}
@ReactMethod
fun expandBufferAndConvert(sessionId: Int, duration: Double, promise: Promise) {
sessionManager.expandBufferAndConvert(sessionId, duration, promise)
fun convertNext(sessionId: Int, duration: Double, promise: Promise) {
sessionManager.convertNext(sessionId, duration, promise)
}
@ReactMethod
@@ -78,6 +79,11 @@ class SpeechToTextPackage : ReactPackage {
sessionManager.convertAvailable(sessionId, promise)
}
@ReactMethod
fun getPreview(sessionId: Int, promise: Promise) {
sessionManager.getPreview(sessionId, promise)
}
@ReactMethod
fun closeSession(sessionId: Int, promise: Promise) {
sessionManager.closeSession(sessionId, promise)

18
packages/app-mobile/android/app/src/main/java/net/cozic/joplin/audio/SpeechToTextSessionManager.kt
View File

@@ -1,6 +1,5 @@
package net.cozic.joplin.audio
import ai.onnxruntime.OrtEnvironment
import android.content.Context
import com.facebook.react.bridge.Promise
import java.util.concurrent.Executor
@@ -21,13 +20,13 @@ class SpeechToTextSessionManager(
fun openSession(
modelPath: String,
locale: String,
environment: OrtEnvironment,
prompt: String,
context: Context,
): Int {
val sessionId = nextSessionId++
sessions[sessionId] = SpeechToTextSession(
SpeechToTextConverter(
modelPath, locale, recorderFactory = AudioRecorder.factory, environment, context,
modelPath, locale, prompt, recorderFactory = AudioRecorder.factory, context,
)
)
return sessionId
@@ -87,9 +86,9 @@ class SpeechToTextSessionManager(
}
// Waits for the next [duration] seconds to become available, then converts
fun expandBufferAndConvert(sessionId: Int, duration: Double, promise: Promise) {
fun convertNext(sessionId: Int, duration: Double, promise: Promise) {
this.concurrentWithSession(sessionId, promise::reject) { session ->
val result = session.converter.expandBufferAndConvert(duration)
val result = session.converter.convertNext(duration)
promise.resolve(result)
}
}
@@ -97,7 +96,14 @@ class SpeechToTextSessionManager(
// Converts all available recorded data
fun convertAvailable(sessionId: Int, promise: Promise) {
this.concurrentWithSession(sessionId, promise::reject) { session ->
val result = session.converter.expandBufferAndConvert()
val result = session.converter.convertRemaining()
promise.resolve(result)
}
}
fun getPreview(sessionId: Int, promise: Promise) {
this.concurrentWithSession(sessionId, promise::reject) { session ->
val result = session.converter.getPreview()
promise.resolve(result)
}
}

9
packages/app-mobile/android/vendor/.gitignore vendored Normal file
View File

@@ -0,0 +1,9 @@
whisper.cpp/.gitmodules
whisper.cpp/scripts/
whisper.cpp/samples/
whisper.cpp/tests/
whisper.cpp/models/
whisper.cpp/examples/
whisper.cpp/.*/
whisper.cpp/bindings/
whisper.cpp/**/*.Dockerfile

7
packages/app-mobile/android/vendor/README.md vendored Normal file
View File

@@ -0,0 +1,7 @@
# Vendored Android packages
This directory contains upstream packages that can't be added as direct dependencies (e.g. through `npm`).
## whisper.cpp
`whisper.cpp` provides voice typing capabilities. It can be updated by replacing the contents of the `whisper.cpp` directory with the latest content from https://github.com/ggerganov/whisper.cpp. To decrease the size of the `whisper.cpp` directory, some files are ignored by the `.gitignore`.

60
packages/app-mobile/android/vendor/whisper.cpp/.gitignore vendored Normal file
View File

@@ -0,0 +1,60 @@
*.o
*.a
*.d
.cache/
.coreml/
.test/
.venv/
.vs/
.vscode/
.DS_Store
.vimspector.json
/CMakeSettings.json
/talk-llama.dSYM/
build/
build-*/
# SPM
.build/
.swiftpm
*.metallib
ggml-metal-embed.metal
ggml-metal-embed.metal.tmp
/main
/stream
/command
/talk
/talk-llama
/bench
/quantize
/server
/lsp
arm_neon.h
sync.sh
libwhisper.a
libwhisper.so
compile_commands.json
examples/arm_neon.h
examples/whisper.objc/whisper.objc.xcodeproj/xcshareddata
examples/whisper.objc/whisper.objc.xcodeproj/xcuserdata/
examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcuserdata
extra/bench-gg.txt
models/*.mlmodel
models/*.mlmodelc
models/*.mlpackage
bindings/java/.gradle/
bindings/java/.idea/
.idea/
benchmark_results.csv
cmake-build-debug/
.cxx/
.gradle/
local.properties

510
packages/app-mobile/android/vendor/whisper.cpp/AUTHORS vendored Normal file
View File

@@ -0,0 +1,510 @@
# date: Tue Feb 4 13:03:35 EET 2025
# this file is auto-generated by scripts/gen-authors.sh
0/0 <zero@imaskeleton.me>
0cc4m <picard12@live.de>
0xsourcecode <134374803+0xsourcecode@users.noreply.github.com>
65a <10104049+65a@users.noreply.github.com>
AIWintermuteAI <32562299+AIWintermuteAI@users.noreply.github.com>
AT <manyoso@users.noreply.github.com>
Aarni Koskela <akx@iki.fi>
Aaron Pham <29749331+aarnphm@users.noreply.github.com>
Aaron Taylor <aaron@exphat.com>
Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
Abitofevrything <54505189+abitofevrything@users.noreply.github.com>
Adam Jones <domdomegg+git@gmail.com>
Adrien Gallouët <adrien@gallouet.fr>
Adrien Gallouët <angt@huggingface.co>
AfryMask <AfryMask@163.com>
Ahmad Bilal <ahmad.bilal@empglabs.com>
Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com>
AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
AidanBeltonS <aidan.belton@codeplay.com>
Akarshan Biswas <akarshan.biswas@gmail.com>
Akarshan Biswas <akarshanbiswas@fedoraproject.org>
Akash Mahajan <akash7190@gmail.com>
Akash Mahajan <akashmjn@stanford.edu>
Al Hoang <3811822-hoanga@users.noreply.gitlab.com>
Alan <unknown>
Albert Jin <albert.jin@gmail.com>
Alberto Cabrera Pérez <alberto.cabrera@codeplay.com>
Alberto Cabrera Pérez <alberto.cabrera@intel.com>
Aleksander Andrzejewski <18704749+aleksanderandrzejewski@users.noreply.github.com>
Alex Azarov <alex@azarov.by>
Alex Bacart <13940752+alex-bacart@users.noreply.github.com>
Alex Evgrashin <aevgrashin@yandex.ru>
Alex O'Connell <35843486+acon96@users.noreply.github.com>
Alexandr Graschenkov <alexandr.graschenkov91@gmail.com>
Alexandru Mariuti <alex@mariuti.com>
Alexey Kharlamov <alexey@kharlamov.biz>
Alfredo Montesinos <alfredo.montesinos@g.austincc.edu>
Ali Alameh <ali.alameh@isae.edu.lb>
Alter <0x7c48@gmail.com>
Ananta Bastola <anantarajbastola@gmail.com>
Andreas Kieslinger <47689530+aendk@users.noreply.github.com>
Andreas Lubbe <git@lubbe.org>
Andreu Huguet <andreuhuguet@gmail.com>
Andrew Huynh <a5thuynh@gmail.com>
Andrew Minh Nguyen <40281306+amqdn@users.noreply.github.com>
Andrew S <andrews54757@gmail.com>
Andy Maloney <asmaloney@gmail.com>
Anton Kostin <masguit42@users.noreply.github.com>
Artyom Mezin <psycho.fading@gmail.com>
Asad Memon <asad.lionpk@gmail.com>
Ashraful Islam <ashraful.meche@gmail.com>
AsukaMinato <asukaminato@nyan.eu.org>
AustinMroz <austinmroz@utexas.edu>
Avik Sengupta <avik@sengupta.net>
Bader-eddine Ouaich <49657842+baderouaich@users.noreply.github.com>
Baffin Lee <baffinlee@gmail.com>
Ben Ashbaugh <ben.ashbaugh@intel.com>
Ben Nortier <bjnortier@gmail.com>
Benjamin Heiniger <benjamin.heiniger@bluewin.ch>
Bernhard M. Wiedemann <githubbmwprimary@lsmod.de>
Binozo <70137898+Binozo@users.noreply.github.com>
Bo-Yi Wu <appleboy.tw@gmail.com>
Boris Bliznioukov <blib@mail.com>
Borislav Stanimirov <b.stanimirov@abv.bg>
Brad Murray <59848399+bradmurray-dt@users.noreply.github.com>
Brian Murray <brian@bmurray.ca>
CRD716 <crd716@gmail.com>
Canis Lupus <Canis-UK@users.noreply.github.com>
Carlos Zoido <mrgalleta@gmail.com>
Carolinabanana <140120812+Carolinabanana@users.noreply.github.com>
CarterLi999 <664681047@qq.com>
ChangSeok Oh <shivamidow@users.noreply.github.com>
Changyeon Kim <cyzero.kim@samsung.com>
Chaoqun <27287694+OpenWaygate@users.noreply.github.com>
Charles Xu <63788048+chaxu01@users.noreply.github.com>
Charles Xu <charles.xu@arm.com>
Chen Xi <xi2.chen@intel.com>
Chen Xi <xixichen08@foxmail.com>
Chenguang Li <87689256+noemotiovon@users.noreply.github.com>
Chia-Hsiang Cheng <88014292+garychia@users.noreply.github.com>
Chidi Williams <williamschidi1@gmail.com>
Chris Elrod <elrodc@gmail.com>
Christian <12550267+iceychris@users.noreply.github.com>
Christian Kastner <ckk@kvr.at>
Clifford Heath <clifford.heath@gmail.com>
Clint Herron <hanclinto@gmail.com>
Colin <github@whoisc.cc>
Conrad Kramer <conrad@conradkramer.com>
Corey Earwood <iamcgn+github@gmail.com>
CrispStrobe <154636388+CrispStrobe@users.noreply.github.com>
DAN™ <dranger003@gmail.com>
DGdev91 <DGdev91@users.noreply.github.com>
Damian Czaja <trojan295@protonmail.com>
Dan Johansson <164997844+eddnjjn@users.noreply.github.com>
Dan Johansson <dan.johansson@arm.com>
Daniel Bevenius <daniel.bevenius@gmail.com>
Daniel Valdivia <18384552+dvaldivia@users.noreply.github.com>
Daniel Ziegenberg <daniel@ziegenberg.at>
Daniele <57776841+daniandtheweb@users.noreply.github.com>
Dave <dave-fl@users.noreply.github.com>
Dave Airlie <airlied@gmail.com>
Dave Airlie <airlied@redhat.com>
Daven Sanassy <daven@vochlea.co.uk>
David <dnhkng@gmail.com>
David Thorpe <djt@mutablelogic.com>
DavidKorczynski <david@adalogics.com>
Davidson Francis <davidsondfgl@gmail.com>
Dener Stassun <denerstassun@gmail.com>
Dibakar Gope <dibakar.gope@arm.com>
Didzis Gosko <didzis@users.noreply.github.com>
Diego Devesa <slarengh@gmail.com>
Digipom <admin@digipom.com>
Dimo <dimo@ieee.org>
Djip007 <3705339+Djip007@users.noreply.github.com>
Djip007 <djip.perois@free.fr>
Dody Suria Wijaya <dodysw@gmail.com>
Dou Xinpeng <15529241576@163.com>
Dou Xinpeng <81913537+Dou-Git@users.noreply.github.com>
Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com>
Duncan McConnell <ddmcconnell4@gmail.com>
Egor Egorov <me@egorfine.com>
Elkana Bardugo <ttv200@gmail.com>
Emmanuel Schmidbauer <eschmidbauer@gmail.com>
Engininja2 <139037756+Engininja2@users.noreply.github.com>
Eric Curtin <ericcurtin17@gmail.com>
Eric Swanson <eswanson@alloscomp.com>
Eric Tendian <erictendian@gmail.com>
Eric Zhang <34133756+EZForever@users.noreply.github.com>
Erik Scholz <Green-Sky@users.noreply.github.com>
Evan Jones <evan.q.jones@gmail.com>
Evan Martin <evan.martin@gmail.com>
Eve <139727413+netrunnereve@users.noreply.github.com>
Evgeny Kuznetsov <evgeny@kuznetsov.md>
F1L1P <78918286+F1L1Pv2@users.noreply.github.com>
Faisal Zaghloul <quic_fzaghlou@quicinc.com>
Fangjun Kuang <csukuangfj@gmail.com>
Felix <stenbackfelix@gmail.com>
Finn Voorhees <finnvoorhees@gmail.com>
FirstTimeEZ <179362031+FirstTimeEZ@users.noreply.github.com>
FlippFuzz <41221030+FlippFuzz@users.noreply.github.com>
Frankie Robertson <frankier@users.noreply.github.com>
Gang Chen <goncha@gmail.com>
Gavin Cai <gavin1818@hotmail.com>
George Hindle <george@georgehindle.com>
Georgi Gerganov <ggerganov@gmail.com>
Gilad S <7817232+giladgd@users.noreply.github.com>
Gilad S <giladgd@users.noreply.github.com>
Gilad S. <7817232+giladgd@users.noreply.github.com>
GitAritron <103900385+GitAritron@users.noreply.github.com>
GiviMAD <GiviMAD@users.noreply.github.com>
Gleicon Moraes <gleicon@gmail.com>
Gregor Jasny <gjasny@googlemail.com>
Guillaume Wenzek <gwenzek@users.noreply.github.com>
HY. Kelvin Lee <34256578+hykelvinlee42@users.noreply.github.com>
Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
Hang <bebound@gmail.com>
Haus1 <haus.xda@gmail.com>
Herman Semenov <GermanAizek@yandex.ru>
HimariO <dsfhe49854@gmail.com>
Hong Bo PENG <penghb@cn.ibm.com>
Hrishikesh Barman <geekodour@users.noreply.github.com>
Hugo <hugo@whynothugo.nl>
Ian Bicking <ian@ianbicking.org>
Ian Bull <irbull@eclipsesource.com>
Ihar Hrachyshka <ihrachys@redhat.com>
Ikko Ashimine <eltociear@gmail.com>
Ikko Eltociear Ashimine <eltociear@gmail.com>
InconsolableCellist <23345188+InconsolableCellist@users.noreply.github.com>
Ismatulla Mansurov <47342870+sapoepsilon@users.noreply.github.com>
Ivan <nekotekina@gmail.com>
Ivan Filipov <159561759+vanaka11@users.noreply.github.com>
Ivan Gorin <ivangorin21@gmail.com>
Ivo von Putzer Reibegg <ivo.putzer@gmail.com>
JJ <103335846+computerscienceiscool@users.noreply.github.com>
Jack Mousseau <jmousseau@users.noreply.github.com>
JacobLinCool <jacoblincool@gmail.com>
Jakub Ráček <blizzcz@gmail.com>
Jared Van Bortel <jared@nomic.ai>
Jay Binks <jaybinks@gmail.com>
Jayant <jayantyadav202@gmail.com>
Jeff Bolz <jbolz@nvidia.com>
Jeroen Mostert <jeroen.mostert@cm.com>
Jhen-Jie Hong <developer@jhen.me>
Jhen-Jie Hong <iainst0409@gmail.com>
JidongZhang-THU <1119708529@qq.com>
Jo Liss <joliss42@gmail.com>
Joe Todd <joe.todd@codeplay.com>
Johan <jr.raffin@gmail.com>
Johannes Gäßler <johannesg@5d6.de>
John Balis <phobossystems@gmail.com>
JohnnyB <jboero@users.noreply.github.com>
Jonathan Soo <jcsoo@agora.com>
Jonno <1160532+razodactyl@users.noreply.github.com>
Joonas Pihlajamaa <joonas.pihlajamaa@iki.fi>
Jose <34888496+Jerry-Master@users.noreply.github.com>
Josh Bleecher Snyder <josharian@gmail.com>
Josscii <jossciiweiyi@gmail.com>
Judd <foldl@users.noreply.github.com>
Jumper775 <78500318+jumpers775@users.noreply.github.com>
Jun Hee Yoo <contact.jhyoo@gmail.com>
Junil Kim <logyourself@gmail.com>
Justina Cho <justcho5@gmail.com>
Justine Tunney <jtunney@gmail.com>
Justine Tunney <jtunney@mozilla.com>
KITAITI Makoto <KitaitiMakoto@gmail.com>
KP Kaiser <kirk@zothcorp.com>
Kamilake <exjang0@gmail.com>
Karol Kontny <82021046+kkontny@users.noreply.github.com>
Karthick <j.karthic2004@gmail.com>
Kartik Saranathan <278928+Kartiku@users.noreply.github.com>
Kasumi <90275229+kasumi-1@users.noreply.github.com>
Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Kendrick Taylor <kendrick@circuitsix.com>
Kevin Brothaler <admin@digipom.com>
Kevin Gibbons <bakkot@gmail.com>
Konosuke Sakai <konosuke@konosuke.work>
Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
Kreijstal <rainb@tfwno.gf>
Kylin <56434533+KyL0N@users.noreply.github.com>
LBlue <153975653+lbluep@users.noreply.github.com>
Larry Battle <larry.battle.tech@gmail.com>
Laytan Laats <laytanlaats@hotmail.com>
Leo Moll <leo.moll@yeasoft.com>
Lexevolution <31176843+Lexevolution@users.noreply.github.com>
LittleLoli <26589867+WhichWho@users.noreply.github.com>
Lucas Zanek <57494138+LucasZNK@users.noreply.github.com>
Luis Herrera <herrera-luis@users.noreply.github.com>
Lukas Rist <glaslos@gmail.com>
M. A. Ali <73258591+MightyStud@users.noreply.github.com>
M. Eren Akbiyik <erenakbiyik@gmail.com>
Ma Mingfei <mingfei.ma@intel.com>
Maciek <maciek.mab122@gmail.com>
Mahesh Madhav <67384846+heshpdx@users.noreply.github.com>
Marcin Mielniczuk <marmistrz.dev@zoho.eu>
Mark Karpelès <MagicalTux@users.noreply.github.com>
Mark Zhuang <zhuangqiubin@gmail.com>
Markus Tavenrath <mtavenrath@users.noreply.github.com>
Martin Delille <martin@delille.org>
Martin Warnaar <martinwarnaar@gmail.com>
Masaya, Kato <62578291+msy-kato@users.noreply.github.com>
Matheus de Sousa <23645013+keyehzy@users.noreply.github.com>
Mathieu Baudier <mbaudier@argeo.org>
Mathijs de Bruin <mathijs@mathijsfietst.nl>
Matija Pevec <mightymatth@users.noreply.github.com>
Matt Stephenson <mstephenson6@users.noreply.github.com>
Max Krasnyansky <max.krasnyansky@gmail.com>
Max Krasnyansky <quic_maxk@quicinc.com>
Maximiliano Levi <8160966+maxilevi@users.noreply.github.com>
Meng, Hengyu <hengyu.meng@intel.com>
Mengqing Cao <cmq0113@163.com>
Michael Podvitskiy <podvitskiymichael@gmail.com>
Michael Rienstra <mrienstra@gmail.com>
Mikhail Grigorev <sleuthhound@gmail.com>
Mohammadreza Hendiani <hendiani.mohammadreza@gmail.com>
Mohit Agarwal <mohit@sdf.org>
Molly Sophia <mollysophia379@gmail.com>
Murilo Santana <mvrilo@gmail.com>
NETZkultur GmbH <mulholland@netzkultur.de>
Natsu <chino@hotococoa.moe>
Neil Chudleigh <nchudleigh@users.noreply.github.com>
Neo Zhang <14088817+arthw@users.noreply.github.com>
Neo Zhang Jianyu <jianyu.zhang@intel.com>
Neuman Vong <neuman.vong@gmail.com>
Nicholai Tukanov <nicholaitukanov@gmail.com>
Nicholas Albion <nalbion@yahoo.com>
Nico Bosshard <nico@bosshome.ch>
Nicolò Scipione <nicolo.scipione@codeplay.com>
Niels Mayer <Niels.Mayer@gmail.com>
Nikita Sarychev <42014488+sARY77@users.noreply.github.com>
Nikolaj Olsson <nikse.dk@gmail.com>
Okabintaro <103938900+Okabintaro@users.noreply.github.com>
Oleg Sidorov <me@whitebox.io>
Oleg Sidorov <oleg@sidorov.nl>
Olivier Chafik <ochafik@users.noreply.github.com>
Ondrej Kokes <ondrej.kokes@gmail.com>
Ouadie EL FAROUKI <ouadie.elfarouki@codeplay.com>
PAB <pierreantoine.bannier@gmail.com>
Paul Tsochantaris <ptsochantaris@icloud.com>
Pedro Probst <pprobst@insiberia.net>
Peng <hzp1024@qq.com>
Peter <peter277@users.noreply.github.com>
Philipp Zabel <philipp.zabel@gmail.com>
Philippe Normand <phil@base-art.net>
Philippe Normand <philn@igalia.com>
Plamen Minev <pacominev@gmail.com>
Prashant Vithule <119530321+Vithulep@users.noreply.github.com>
Przemysław Pawełczyk <przemoc@gmail.com>
Qianhe Chen <54462604+chenqianhe@users.noreply.github.com>
R0CKSTAR <xiaodong.ye@mthreads.com>
R0CKSTAR <yeahdongcn@gmail.com>
Radoslav Gerganov <rgerganov@gmail.com>
Radosław Gryta <radek.gryta@gmail.com>
Rahul Vadhyar <107788610+RahulVadhyar@users.noreply.github.com>
Raiya Araki <83504221+rai62@users.noreply.github.com>
Reinforce-II <fate@eastal.com>
Reinis Muiznieks <muiznieks.reinis@gmail.com>
RelatedTitle <r3latedtitle@gmail.com>
Rémy Oudompheng <oudomphe@phare.normalesup.org>
RhinoDevel <RhinoDevel@users.noreply.github.com>
Rich Jones <miserlou@gmail.com>
Robert Ormandi <52251610+ormandi@users.noreply.github.com>
Robin <robin.xw@hotmail.com>
Roddur Dasgupta <roddurd@gmail.com>
Roland Rabien <figbug@gmail.com>
Romain Biessy <romain.biessy@codeplay.com>
Ronsor <ronsor@ronsor.pw>
Rotem Dan <rotemdan@gmail.com>
Ryan Hitchman <hitchmanr@gmail.com>
Ryan Metcalfe <107415876+RyanMetcalfeInt8@users.noreply.github.com>
RyanChang <ftes90015@gmail.com>
SRHMorris <69468379+SRHMorris@users.noreply.github.com>
SXX <sxx1136965276@gmail.com>
Sacha Arbonel <sacha.arbonel@hotmail.fr>
Salman Faroz <stsfaroz@gmail.com>
Salvatore Mesoraca <s.mesoraca16@gmail.com>
Sam <49637763+Onlyartist9@users.noreply.github.com>
Sam Pullara <spullara@gmail.com>
Samuel Durante <44513615+samueldurantes@users.noreply.github.com>
Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Sandro Hanea <40202887+sandrohanea@users.noreply.github.com>
Sergio López <slp@redhat.com>
Sergio López <slp@sinrega.org>
Shanshan Shen <467638484@qq.com>
Shijie <821898965@qq.com>
Shupei Fan <dymarkfan@outlook.com>
Siddharth Ramakrishnan <srr2141@columbia.edu>
Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Simon Moisselin <simon.moisstoll@gmail.com>
Sindre Sorhus <sindresorhus@gmail.com>
Slava Primenko <primenko.s@gmail.com>
Srihari-mcw <96763064+Srihari-mcw@users.noreply.github.com>
Stavros Panakakis <53979866+Stavrospanakakis@users.noreply.github.com>
Stefan Sydow <s.sydow@heinlein-video.de>
Stefan Sydow <stefan@sydow.email>
Syahmi Azhar <prsyahmi@gmail.com>
Syed Jafri <syedjafri97@gmail.com>
Sơn Phan Trung <phantrungson17@gmail.com>
Taisei Mima <bhbstar.me@gmail.com>
Takeshi Inoue <inoue.takeshi@gmail.com>
Tamotsu Takahashi <ttakah+github@gmail.com>
Taras Glek <taras@thegp.com>
Tauseef Mohiuddin <35351464+tauseefmohammed2@users.noreply.github.com>
Thamster <Thamster@users.noreply.github.com>
Thijs Raymakers <thijs@raymakers.nl>
Thomas Fitzsimmons <fitzsim@fitzsim.org>
Tiago Fassoni <tiagofassoni@users.noreply.github.com>
Tienshiao Ma <tienshiao@tienshiao.org>
Tim Miller <drasticactions@users.noreply.github.com>
Timothy Cronin <40186632+4imothy@users.noreply.github.com>
Tobrun <tobrun.van.nuland@gmail.com>
Todd <taf2@users.noreply.github.com>
Toliver <teejae@gmail.com>
Tong Li <31761981+litongjava@users.noreply.github.com>
Tony Wasserka <4840017+neobrain@users.noreply.github.com>
Topping1 <78745143+Topping1@users.noreply.github.com>
Travis Cline <travis.cline@gmail.com>
UEXTM.com <84163508+uextm@users.noreply.github.com>
UsernamesLame <156965854+UsernamesLame@users.noreply.github.com>
Vadim Peretokin <vperetokin@hey.com>
Valentin Gosu <1454649+valenting@users.noreply.github.com>
Vin Misra <vinith@alum.mit.edu>
Vulcan <93451215+trholding@users.noreply.github.com>
WhiteOlivierus <36532695+WhiteOlivierus@users.noreply.github.com>
William Tambellini <william.tambellini@gmail.com>
William Tambellini <wtambellini@sdl.com>
Wilson Silva <wilson.dsigns@gmail.com>
Xiang (Kevin) Li <kevinli020508@gmail.com>
Xiao-Yong Jin <jinxiaoyong@gmail.com>
XiaotaoChen <chenxiaotao1234@gmail.com>
Xingchen Song(宋星辰) <xingchensong1996@163.com>
Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com>
Xuan Son Nguyen <thichthat@gmail.com>
Yajing Tang <phillis@google.com>
Yang Shen <aplshenyang@gmail.com>
Yunès <jean.baptiste.yunes@free.fr>
Yuri Khrustalev <ykhrustalev@users.noreply.github.com>
Yusuf Redžić <48274562+redzic@users.noreply.github.com>
ZaBlazzingZephyrus <119159668+blazingzephyr@users.noreply.github.com>
Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com>
Zhiyuan Li <lizhiyuan@uniartisan.com>
Zhiyuan Li <uniartisan2017@gmail.com>
Zigfrid Zvezdin <ziggerZZ@gmail.com>
Zollner <24618122+Zolliner@users.noreply.github.com>
a3sh <38979186+A3shTnT@users.noreply.github.com>
ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com>
agray3 <agray3@users.noreply.github.com>
ai-at-home <149282006+ai-at-home@users.noreply.github.com>
aldorof <aldorof@users.noreply.github.com>
alonfaraj <alonfaraj@gmail.com>
amd-dwang <dong.wang@amd.com>
amritahs-ibm <amritahs@linux.vnet.ibm.com>
andypayne <apayne@gmail.com>
ardfork <134447697+ardfork@users.noreply.github.com>
arizhih <40765267+arizhih@users.noreply.github.com>
automaticcat <daogiatuank54@gmail.com>
bandoti <141645996+bandoti@users.noreply.github.com>
be-next <jerome.ramette@gmail.com>
bert hubert <bert@hubertnet.nl>
billyct <billy_allen@126.com>
bmwl <brian.marshall@tolko.com>
bobqianic <129547291+bobqianic@users.noreply.github.com>
bocytko <bocytko+github@gmail.com>
boolemancer <48014766+boolemancer@users.noreply.github.com>
boolemancer <boolemancer@gmail.com>
bradmit <151883577+bradmit@users.noreply.github.com>
brunofaustino <b.fa.amorim@gmail.com>
bssrdf <merlintiger@hotmail.com>
byte-6174 <88070277+byte-6174@users.noreply.github.com>
cdosoftei <ciprian.dosoftei@gmail.com>
clach04 <Chris.Clark@actian.com>
compilade <113953597+compilade@users.noreply.github.com>
compilade <git@compilade.net>
conradg <conradjgodfrey@gmail.com>
crummyh <elijah@crums.us>
ddpasa <112642920+ddpasa@users.noreply.github.com>
denersc <denerstassun@gmail.com>
dscripka <dscripka@users.noreply.github.com>
duthils <duthils@duthils.net>
ecneladis <ecneladis@users.noreply.github.com>
faker <nspyia2002@gmail.com>
fitzsim <fitzsim@fitzsim.org>
fj-y-saito <85871716+fj-y-saito@users.noreply.github.com>
fraxy-v <65565042+fraxy-v@users.noreply.github.com>
genevera (she/her) <genevera@users.noreply.github.com>
geniusnut <geniusnut@gmail.com>
gilbertgong <gilbert.gong@gmail.com>
gn64 <yukikaze.jp@gmail.com>
goldwaving <77494627+goldwaving@users.noreply.github.com>
greeshmay <greeshmay@gmail.com>
haopeng <657407891@qq.com>
hipudding <huafengchun@gmail.com>
hsinhoyeh <yhh92u@gmail.com>
hydai <z54981220@gmail.com>
iamthad <thadeus.j.fleming@gmail.com>
issixx <46835150+issixx@users.noreply.github.com>
james wolf <contractorwolf@hotmail.com>
jdomke <28772296+jdomke@users.noreply.github.com>
jettoblack <jettoblack@gmail.com>
jiez <373447296@qq.com>
joecryptotoo <80373433+joecryptotoo@users.noreply.github.com>
jorismertz <35079666+jorismertz@users.noreply.github.com>
junchao-loongson <68935141+junchao-loongson@users.noreply.github.com>
junkfood <69683722+JunkFood02@users.noreply.github.com>
jwijffels <jwijffels@bnosac.be>
k.h.lai <adrian.k.h.lai@outlook.com>
kamranjon <kamranjon@gmail.com>
katsu560 <katsu560oo-@docomo.ne.jp>
kennethge <57784063+kenneth-ge@users.noreply.github.com>
keyehzy <msamuel@aluno.puc-rio.br>
kunnis <kunnis@users.noreply.github.com>
l3utterfly <gc.pthzfoldr@gmail.com>
leejet <leejet714@gmail.com>
leo-pony <nengjunma@outlook.com>
lhez <quic_lih@quicinc.com>
litong <31761981+litongjava@users.noreply.github.com>
liuwei-git <14815172+liuwei-git@users.noreply.github.com>
lnyan <lkwq007@gmail.com>
luoyu-intel <yu.luo@intel.com>
m.bell <m.bell@techsmith.com>
mahorozte <41834471+mahorozte@users.noreply.github.com>
mashizora <30516315+mashizora@users.noreply.github.com>
matt23654 <matthew.webber@protonmail.com>
matteo <matteogeniaccio@yahoo.it>
mgrachten <maarten@grachten.eu>
mkiol <mkiol@users.noreply.github.com>
mky_coder <47767389+mkycoder@users.noreply.github.com>
novag <7754358+novag@users.noreply.github.com>
pajowu <pajowu@pajowu.de>
pengxin99 <pengxin.yuan@intel.com>
petterreinholdtsen <pere-github@hungry.com>
polarmoon <90010972+polarmoon@users.noreply.github.com>
rlapray <lapray.romain@gmail.com>
sandrohanea <40202887+sandrohanea@users.noreply.github.com>
semiformal-net <84111142+semiformal-net@users.noreply.github.com>
shibukazu <61775791+shibukazu@users.noreply.github.com>
shikokuchuo <53399081+shikokuchuo@users.noreply.github.com>
slaren <slarengh@gmail.com>
slashlib <slashlib@users.noreply.github.com>
snadampal <87143774+snadampal@users.noreply.github.com>
someone13574 <81528246+someone13574@users.noreply.github.com>
st-gr <38470677+st-gr@users.noreply.github.com>
stduhpf <stephduh@live.fr>
stormofice <58337328+stormofice@users.noreply.github.com>
texmex76 <40733439+texmex76@users.noreply.github.com>
thefinaldegree <thefinaldegree@gmail.com>
thewh1teagle <61390950+thewh1teagle@users.noreply.github.com>
toboil-features <160222185+toboil-features@users.noreply.github.com>
trixirt <trix@redhat.com>
ulatekh <ulatekh@yahoo.com>
undef <undefdev@gmail.com>
uvos <devnull@uvos.xyz>
uvos <philipp@uvos.xyz>
valVk <valVk@users.noreply.github.com>
venkr <venkateshrameshkumar+1@gmail.com>
vicalloy <zbirder@gmail.com>
wangshuai09 <391746016@qq.com>
woachk <24752637+woachk@users.noreply.github.com>
xctan <axunlei@gmail.com>
xdrudis <xavierdrudis@yahoo.es>
yuri@FreeBSD <yuri@FreeBSD>
zhangjixiong <code.zjx@gmail.com>
zhentaoyu <zhentao.yu@intel.com>
zhouwg <6889919+zhouwg@users.noreply.github.com>
zhouwg <zhouwg2000@gmail.com>
谢乃闻 <sienaiwun@users.noreply.github.com>
布客飞龙 <562826179@qq.com>
Артём Земляк <azemlyak@smart-consulting.ru>

185
packages/app-mobile/android/vendor/whisper.cpp/CMakeLists.txt vendored Normal file
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cmake_minimum_required(VERSION 3.5) # for add_link_options and implicit target directories.
project("whisper.cpp" C CXX)
project("whisper.cpp" VERSION 1.7.4)
include(CheckIncludeFileCXX)
set(SOVERSION 1)
#set(CMAKE_WARN_DEPRECATED YES)
set(CMAKE_WARN_UNUSED_CLI YES)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
endif()
# Add path to modules
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
set(WHISPER_STANDALONE ON)
include(git-vars)
# configure project version
configure_file(${CMAKE_SOURCE_DIR}/bindings/javascript/package-tmpl.json ${CMAKE_SOURCE_DIR}/bindings/javascript/package.json @ONLY)
else()
set(WHISPER_STANDALONE OFF)
endif()
if (EMSCRIPTEN)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
option(WHISPER_WASM_SINGLE_FILE "whisper: embed WASM inside the generated whisper.js" ON)
# TODO: without these, we get the following error:
# wasm-ld: error: --shared-memory is disallowed by whisper.cpp.o because it was not compiled with 'atomics' or 'bulk-memory' features.
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -pthread -s TOTAL_STACK=5242880")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread -s TOTAL_STACK=5242880")
else()
if (MINGW)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
else()
set(BUILD_SHARED_LIBS_DEFAULT ON)
endif()
endif()
option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
#
# option list
#
# general
option(WHISPER_CCACHE "whisper: use ccache if available" ON)
# debug
option(WHISPER_ALL_WARNINGS "whisper: enable all compiler warnings" ON)
option(WHISPER_ALL_WARNINGS_3RD_PARTY "whisper: enable all compiler warnings in 3rd party libs" OFF)
# build
option(WHISPER_FATAL_WARNINGS "whisper: enable -Werror flag" OFF)
# sanitizers
option(WHISPER_SANITIZE_THREAD "whisper: enable thread sanitizer" OFF)
option(WHISPER_SANITIZE_ADDRESS "whisper: enable address sanitizer" OFF)
option(WHISPER_SANITIZE_UNDEFINED "whisper: enable undefined sanitizer" OFF)
# extra artifacts
option(WHISPER_BUILD_TESTS "whisper: build tests" ${WHISPER_STANDALONE})
option(WHISPER_BUILD_EXAMPLES "whisper: build examples" ${WHISPER_STANDALONE})
option(WHISPER_BUILD_SERVER "whisper: build server example" ${WHISPER_STANDALONE})
# 3rd party libs
option(WHISPER_CURL "whisper: use libcurl to download model from an URL" OFF)
option(WHISPER_SDL2 "whisper: support for libSDL2" OFF)
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
option(WHISPER_FFMPEG "whisper: support building and linking with ffmpeg libs (avcodec, swresample, ...)" OFF)
endif()
option(WHISPER_COREML "whisper: enable Core ML framework" OFF)
option(WHISPER_COREML_ALLOW_FALLBACK "whisper: allow non-CoreML fallback" OFF)
option(WHISPER_OPENVINO "whisper: support for OpenVINO" OFF)
# Required for relocatable CMake package
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
# override ggml options
set(GGML_CCACHE ${WHISPER_CCACHE})
set(GGML_SANITIZE_THREAD ${WHISPER_SANITIZE_THREAD})
set(GGML_SANITIZE_ADDRESS ${WHISPER_SANITIZE_ADDRESS})
set(GGML_SANITIZE_UNDEFINED ${WHISPER_SANITIZE_UNDEFINED})
set(GGML_ALL_WARNINGS ${WHISPER_ALL_WARNINGS})
set(GGML_FATAL_WARNINGS ${WHISPER_FATAL_WARNINGS})
# transition helpers
function (whisper_option_depr TYPE OLD NEW)
if (${OLD})
message(${TYPE} "${OLD} is deprecated and will be removed in the future.\nUse ${NEW} instead\n")
set(${NEW} ON)
endif()
endfunction()
whisper_option_depr(FATAL_ERROR WHISPER_CUBLAS GGML_CUDA)
whisper_option_depr(WARNING WHISPER_CUDA GGML_CUDA)
whisper_option_depr(WARNING WHISPER_KOMPUTE GGML_KOMPUTE)
whisper_option_depr(WARNING WHISPER_METAL GGML_METAL)
whisper_option_depr(WARNING WHISPER_METAL_EMBED_LIBRARY GGML_METAL_EMBED_LIBRARY)
whisper_option_depr(WARNING WHISPER_NATIVE GGML_NATIVE)
whisper_option_depr(WARNING WHISPER_OPENMP GGML_OPENMP)
whisper_option_depr(WARNING WHISPER_RPC GGML_RPC)
whisper_option_depr(WARNING WHISPER_SYCL GGML_SYCL)
whisper_option_depr(WARNING WHISPER_SYCL_F16 GGML_SYCL_F16)
#
# build the library
#
if (NOT TARGET ggml)
add_subdirectory(ggml)
# ... otherwise assume ggml is added by a parent CMakeLists.txt
endif()
add_subdirectory(src)
#
# install
#
include(GNUInstallDirs)
include(CMakePackageConfigHelpers)
set(WHISPER_BUILD_NUMBER ${BUILD_NUMBER})
set(WHISPER_BUILD_COMMIT ${BUILD_COMMIT})
set(WHISPER_INSTALL_VERSION ${CMAKE_PROJECT_VERSION})
set(WHISPER_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header files")
set(WHISPER_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files")
set(WHISPER_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files")
get_directory_property(WHISPER_TRANSIENT_DEFINES COMPILE_DEFINITIONS)
set_target_properties(whisper PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/whisper.h)
install(TARGETS whisper LIBRARY PUBLIC_HEADER)
configure_package_config_file(
${CMAKE_CURRENT_SOURCE_DIR}/cmake/whisper-config.cmake.in
${CMAKE_CURRENT_BINARY_DIR}/whisper-config.cmake
INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/whisper
PATH_VARS
WHISPER_INCLUDE_INSTALL_DIR
WHISPER_LIB_INSTALL_DIR
WHISPER_BIN_INSTALL_DIR )
write_basic_package_version_file(
${CMAKE_CURRENT_BINARY_DIR}/whisper-version.cmake
VERSION ${WHISPER_INSTALL_VERSION}
COMPATIBILITY SameMajorVersion)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/whisper-config.cmake
${CMAKE_CURRENT_BINARY_DIR}/whisper-version.cmake
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/whisper)
configure_file(cmake/whisper.pc.in
"${CMAKE_CURRENT_BINARY_DIR}/whisper.pc"
@ONLY)
install(FILES "${CMAKE_CURRENT_BINARY_DIR}/whisper.pc"
DESTINATION lib/pkgconfig)
#
# programs, examples and tests
#
if (WHISPER_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
#include(CTest)
#add_subdirectory(tests)
endif ()
if (WHISPER_BUILD_EXAMPLES)
add_subdirectory(examples)
endif()

21
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@@ -0,0 +1,21 @@
MIT License
Copyright (c) 2023-2024 The ggml authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

19
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@@ -0,0 +1,19 @@
// swift-tools-version:5.5
import PackageDescription
let package = Package(
name: "whisper",
platforms: [
.macOS(.v12),
.iOS(.v14),
.watchOS(.v4),
.tvOS(.v14)
],
products: [
.library(name: "whisper", targets: ["whisper"]),
],
targets: [
.systemLibrary(name: "whisper", pkgConfig: "whisper"),
]
)

679
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@@ -0,0 +1,679 @@
# whisper.cpp
![whisper.cpp](https://user-images.githubusercontent.com/1991296/235238348-05d0f6a4-da44-4900-a1de-d0707e75b763.jpeg)
[![Actions Status](https://github.com/ggerganov/whisper.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/whisper.cpp/actions)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Conan Center](https://shields.io/conan/v/whisper-cpp)](https://conan.io/center/whisper-cpp)
[![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/)
> [!NOTE]
> New maintenance roadmap: https://github.com/ggerganov/whisper.cpp/discussions/2788
Stable: [v1.7.4](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.7.4) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
- Plain C/C++ implementation without dependencies
- Apple Silicon first-class citizen - optimized via ARM NEON, Accelerate framework, Metal and [Core ML](#core-ml-support)
- AVX intrinsics support for x86 architectures
- [VSX intrinsics support for POWER architectures](#power-vsx-intrinsics)
- Mixed F16 / F32 precision
- [Integer quantization support](#quantization)
- Zero memory allocations at runtime
- [Vulkan support](#vulkan-gpu-support)
- Support for CPU-only inference
- [Efficient GPU support for NVIDIA](#nvidia-gpu-support)
- [OpenVINO Support](#openvino-support)
- [Ascend NPU Support](#ascend-npu-support)
- [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/include/whisper.h)
Supported platforms:
- [x] Mac OS (Intel and Arm)
- [x] [iOS](examples/whisper.objc)
- [x] [Android](examples/whisper.android)
- [x] [Java](bindings/java/README.md)
- [x] Linux / [FreeBSD](https://github.com/ggerganov/whisper.cpp/issues/56#issuecomment-1350920264)
- [x] [WebAssembly](examples/whisper.wasm)
- [x] Windows ([MSVC](https://github.com/ggerganov/whisper.cpp/blob/master/.github/workflows/build.yml#L117-L144) and [MinGW](https://github.com/ggerganov/whisper.cpp/issues/168)]
- [x] [Raspberry Pi](https://github.com/ggerganov/whisper.cpp/discussions/166)
- [x] [Docker](https://github.com/ggerganov/whisper.cpp/pkgs/container/whisper.cpp)
The entire high-level implementation of the model is contained in [whisper.h](include/whisper.h) and [whisper.cpp](src/whisper.cpp).
The rest of the code is part of the [`ggml`](https://github.com/ggerganov/ggml) machine learning library.
Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications.
As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: [whisper.objc](examples/whisper.objc)
https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4
You can also easily make your own offline voice assistant application: [command](examples/command)
https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4
On Apple Silicon, the inference runs fully on the GPU via Metal:
https://github.com/ggerganov/whisper.cpp/assets/1991296/c82e8f86-60dc-49f2-b048-d2fdbd6b5225
## Quick start
First clone the repository:
```bash
git clone https://github.com/ggerganov/whisper.cpp.git
```
Navigate into the directory:
```
cd whisper.cpp
```
Then, download one of the Whisper [models](models/README.md) converted in [`ggml` format](#ggml-format). For example:
```bash
sh ./models/download-ggml-model.sh base.en
```
Now build the [whisper-cli](examples/cli) example and transcribe an audio file like this:
```bash
# build the project
cmake -B build
cmake --build build --config Release
# transcribe an audio file
./build/bin/whisper-cli -f samples/jfk.wav
```
---
For a quick demo, simply run `make base.en`.
The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
For detailed usage instructions, run: `./build/bin/whisper-cli -h`
Note that the [whisper-cli](examples/cli) example currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool.
For example, you can use `ffmpeg` like this:
```bash
ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
```
## More audio samples
If you want some extra audio samples to play with, simply run:
```
make -j samples
```
This will download a few more audio files from Wikipedia and convert them to 16-bit WAV format via `ffmpeg`.
You can download and run the other models as follows:
```
make -j tiny.en
make -j tiny
make -j base.en
make -j base
make -j small.en
make -j small
make -j medium.en
make -j medium
make -j large-v1
make -j large-v2
make -j large-v3
make -j large-v3-turbo
```
## Memory usage
| Model | Disk | Mem |
| ------ | ------- | ------- |
| tiny | 75 MiB | ~273 MB |
| base | 142 MiB | ~388 MB |
| small | 466 MiB | ~852 MB |
| medium | 1.5 GiB | ~2.1 GB |
| large | 2.9 GiB | ~3.9 GB |
## POWER VSX Intrinsics
`whisper.cpp` supports POWER architectures and includes code which
significantly speeds operation on Linux running on POWER9/10, making it
capable of faster-than-realtime transcription on underclocked Raptor
Talos II. Ensure you have a BLAS package installed, and replace the
standard cmake setup with:
```bash
# build with GGML_BLAS defined
cmake -B build -DGGML_BLAS=1
cmake --build build --config Release
./build/bin/whisper-cli [ .. etc .. ]
## Quantization
`whisper.cpp` supports integer quantization of the Whisper `ggml` models.
Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.
Here are the steps for creating and using a quantized model:
```bash
# quantize a model with Q5_0 method
cmake -B build
cmake --build build --config Release
./build/bin/quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0
# run the examples as usual, specifying the quantized model file
./build/bin/whisper-cli -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav
```
## Core ML support
On Apple Silicon devices, the Encoder inference can be executed on the Apple Neural Engine (ANE) via Core ML. This can result in significant
speed-up - more than x3 faster compared with CPU-only execution. Here are the instructions for generating a Core ML model and using it with `whisper.cpp`:
- Install Python dependencies needed for the creation of the Core ML model:
```bash
pip install ane_transformers
pip install openai-whisper
pip install coremltools
```
- To ensure `coremltools` operates correctly, please confirm that [Xcode](https://developer.apple.com/xcode/) is installed and execute `xcode-select --install` to install the command-line tools.
- Python 3.10 is recommended.
- MacOS Sonoma (version 14) or newer is recommended, as older versions of MacOS might experience issues with transcription hallucination.
- [OPTIONAL] It is recommended to utilize a Python version management system, such as [Miniconda](https://docs.conda.io/en/latest/miniconda.html) for this step:
- To create an environment, use: `conda create -n py310-whisper python=3.10 -y`
- To activate the environment, use: `conda activate py310-whisper`
- Generate a Core ML model. For example, to generate a `base.en` model, use:
```bash
./models/generate-coreml-model.sh base.en
```
This will generate the folder `models/ggml-base.en-encoder.mlmodelc`
- Build `whisper.cpp` with Core ML support:
```bash
# using CMake
cmake -B build -DWHISPER_COREML=1
cmake --build build -j --config Release
```
- Run the examples as usual. For example:
```text
$ ./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/jfk.wav
...
whisper_init_state: loading Core ML model from 'models/ggml-base.en-encoder.mlmodelc'
whisper_init_state: first run on a device may take a while ...
whisper_init_state: Core ML model loaded
system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | COREML = 1 |
...
```
The first run on a device is slow, since the ANE service compiles the Core ML model to some device-specific format.
Next runs are faster.
For more information about the Core ML implementation please refer to PR [#566](https://github.com/ggerganov/whisper.cpp/pull/566).
## OpenVINO support
On platforms that support [OpenVINO](https://github.com/openvinotoolkit/openvino), the Encoder inference can be executed
on OpenVINO-supported devices including x86 CPUs and Intel GPUs (integrated & discrete).
This can result in significant speedup in encoder performance. Here are the instructions for generating the OpenVINO model and using it with `whisper.cpp`:
- First, setup python virtual env. and install python dependencies. Python 3.10 is recommended.
Windows:
```powershell
cd models
python -m venv openvino_conv_env
openvino_conv_env\Scripts\activate
python -m pip install --upgrade pip
pip install -r requirements-openvino.txt
```
Linux and macOS:
```bash
cd models
python3 -m venv openvino_conv_env
source openvino_conv_env/bin/activate
python -m pip install --upgrade pip
pip install -r requirements-openvino.txt
```
- Generate an OpenVINO encoder model. For example, to generate a `base.en` model, use:
```
python convert-whisper-to-openvino.py --model base.en
```
This will produce ggml-base.en-encoder-openvino.xml/.bin IR model files. It's recommended to relocate these to the same folder as `ggml` models, as that
is the default location that the OpenVINO extension will search at runtime.
- Build `whisper.cpp` with OpenVINO support:
Download OpenVINO package from [release page](https://github.com/openvinotoolkit/openvino/releases). The recommended version to use is [2023.0.0](https://github.com/openvinotoolkit/openvino/releases/tag/2023.0.0).
After downloading & extracting package onto your development system, set up required environment by sourcing setupvars script. For example:
Linux:
```bash
source /path/to/l_openvino_toolkit_ubuntu22_2023.0.0.10926.b4452d56304_x86_64/setupvars.sh
```
Windows (cmd):
```powershell
C:\Path\To\w_openvino_toolkit_windows_2023.0.0.10926.b4452d56304_x86_64\setupvars.bat
```
And then build the project using cmake:
```bash
cmake -B build -DWHISPER_OPENVINO=1
cmake --build build -j --config Release
```
- Run the examples as usual. For example:
```text
$ ./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/jfk.wav
...
whisper_ctx_init_openvino_encoder: loading OpenVINO model from 'models/ggml-base.en-encoder-openvino.xml'
whisper_ctx_init_openvino_encoder: first run on a device may take a while ...
whisper_openvino_init: path_model = models/ggml-base.en-encoder-openvino.xml, device = GPU, cache_dir = models/ggml-base.en-encoder-openvino-cache
whisper_ctx_init_openvino_encoder: OpenVINO model loaded
system_info: n_threads = 4 / 8 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 | COREML = 0 | OPENVINO = 1 |
...
```
The first time run on an OpenVINO device is slow, since the OpenVINO framework will compile the IR (Intermediate Representation) model to a device-specific 'blob'. This device-specific blob will get
cached for the next run.
For more information about the OpenVINO implementation please refer to PR [#1037](https://github.com/ggerganov/whisper.cpp/pull/1037).
## NVIDIA GPU support
With NVIDIA cards the processing of the models is done efficiently on the GPU via cuBLAS and custom CUDA kernels.
First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-downloads
Now build `whisper.cpp` with CUDA support:
```
cmake -B build -DGGML_CUDA=1
cmake --build build -j --config Release
```
## Vulkan GPU support
Cross-vendor solution which allows you to accelerate workload on your GPU.
First, make sure your graphics card driver provides support for Vulkan API.
Now build `whisper.cpp` with Vulkan support:
```
cmake -B build -DGGML_VULKAN=1
cmake --build build -j --config Release
```
## BLAS CPU support via OpenBLAS
Encoder processing can be accelerated on the CPU via OpenBLAS.
First, make sure you have installed `openblas`: https://www.openblas.net/
Now build `whisper.cpp` with OpenBLAS support:
```
cmake -B build -DGGML_BLAS=1
cmake --build build -j --config Release
```
## Ascend NPU support
Ascend NPU provides inference acceleration via [`CANN`](https://www.hiascend.com/en/software/cann) and AI cores.
First, check if your Ascend NPU device is supported:
**Verified devices**
| Ascend NPU | Status |
|:-----------------------------:|:-------:|
| Atlas 300T A2 | Support |
Then, make sure you have installed [`CANN toolkit`](https://www.hiascend.com/en/software/cann/community) . The lasted version of CANN is recommanded.
Now build `whisper.cpp` with CANN support:
```
cmake -B build -DGGML_CANN=1
cmake --build build -j --config Release
```
Run the inference examples as usual, for example:
```
./build/bin/whisper-cli -f samples/jfk.wav -m models/ggml-base.en.bin -t 8
```
*Notes:*
- If you have trouble with Ascend NPU device, please create a issue with **[CANN]** prefix/tag.
- If you run successfully with your Ascend NPU device, please help update the table `Verified devices`.
## Docker
### Prerequisites
- Docker must be installed and running on your system.
- Create a folder to store big models & intermediate files (ex. /whisper/models)
### Images
We have two Docker images available for this project:
1. `ghcr.io/ggerganov/whisper.cpp:main`: This image includes the main executable file as well as `curl` and `ffmpeg`. (platforms: `linux/amd64`, `linux/arm64`)
2. `ghcr.io/ggerganov/whisper.cpp:main-cuda`: Same as `main` but compiled with CUDA support. (platforms: `linux/amd64`)
### Usage
```shell
# download model and persist it in a local folder
docker run -it --rm \
-v path/to/models:/models \
whisper.cpp:main "./models/download-ggml-model.sh base /models"
# transcribe an audio file
docker run -it --rm \
-v path/to/models:/models \
-v path/to/audios:/audios \
whisper.cpp:main "./main -m /models/ggml-base.bin -f /audios/jfk.wav"
# transcribe an audio file in samples folder
docker run -it --rm \
-v path/to/models:/models \
whisper.cpp:main "./main -m /models/ggml-base.bin -f ./samples/jfk.wav"
```
## Installing with Conan
You can install pre-built binaries for whisper.cpp or build it from source using [Conan](https://conan.io/). Use the following command:
```
conan install --requires="whisper-cpp/[*]" --build=missing
```
For detailed instructions on how to use Conan, please refer to the [Conan documentation](https://docs.conan.io/2/).
## Limitations
- Inference only
## Real-time audio input example
This is a naive example of performing real-time inference on audio from your microphone.
The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continuously.
More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
```bash
cmake -B build -DWHISPER_SDL2=ON
cmake --build build --config Release
./build/bin/whisper-stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
```
https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.mp4
## Confidence color-coding
Adding the `--print-colors` argument will print the transcribed text using an experimental color coding strategy
to highlight words with high or low confidence:
```bash
./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/gb0.wav --print-colors
```
<img width="965" alt="image" src="https://user-images.githubusercontent.com/1991296/197356445-311c8643-9397-4e5e-b46e-0b4b4daa2530.png">
## Controlling the length of the generated text segments (experimental)
For example, to limit the line length to a maximum of 16 characters, simply add `-ml 16`:
```text
$ ./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 16
whisper_model_load: loading model from './models/ggml-base.en.bin'
...
system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 |
main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
[00:00:00.000 --> 00:00:00.850] And so my
[00:00:00.850 --> 00:00:01.590] fellow
[00:00:01.590 --> 00:00:04.140] Americans, ask
[00:00:04.140 --> 00:00:05.660] not what your
[00:00:05.660 --> 00:00:06.840] country can do
[00:00:06.840 --> 00:00:08.430] for you, ask
[00:00:08.430 --> 00:00:09.440] what you can do
[00:00:09.440 --> 00:00:10.020] for your
[00:00:10.020 --> 00:00:11.000] country.
```
## Word-level timestamp (experimental)
The `--max-len` argument can be used to obtain word-level timestamps. Simply use `-ml 1`:
```text
$ ./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 1
whisper_model_load: loading model from './models/ggml-base.en.bin'
...
system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 |
main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
[00:00:00.000 --> 00:00:00.320]
[00:00:00.320 --> 00:00:00.370] And
[00:00:00.370 --> 00:00:00.690] so
[00:00:00.690 --> 00:00:00.850] my
[00:00:00.850 --> 00:00:01.590] fellow
[00:00:01.590 --> 00:00:02.850] Americans
[00:00:02.850 --> 00:00:03.300] ,
[00:00:03.300 --> 00:00:04.140] ask
[00:00:04.140 --> 00:00:04.990] not
[00:00:04.990 --> 00:00:05.410] what
[00:00:05.410 --> 00:00:05.660] your
[00:00:05.660 --> 00:00:06.260] country
[00:00:06.260 --> 00:00:06.600] can
[00:00:06.600 --> 00:00:06.840] do
[00:00:06.840 --> 00:00:07.010] for
[00:00:07.010 --> 00:00:08.170] you
[00:00:08.170 --> 00:00:08.190] ,
[00:00:08.190 --> 00:00:08.430] ask
[00:00:08.430 --> 00:00:08.910] what
[00:00:08.910 --> 00:00:09.040] you
[00:00:09.040 --> 00:00:09.320] can
[00:00:09.320 --> 00:00:09.440] do
[00:00:09.440 --> 00:00:09.760] for
[00:00:09.760 --> 00:00:10.020] your
[00:00:10.020 --> 00:00:10.510] country
[00:00:10.510 --> 00:00:11.000] .
```
## Speaker segmentation via tinydiarize (experimental)
More information about this approach is available here: https://github.com/ggerganov/whisper.cpp/pull/1058
Sample usage:
```py
# download a tinydiarize compatible model
./models/download-ggml-model.sh small.en-tdrz
# run as usual, adding the "-tdrz" command-line argument
./build/bin/whisper-cli -f ./samples/a13.wav -m ./models/ggml-small.en-tdrz.bin -tdrz
...
main: processing './samples/a13.wav' (480000 samples, 30.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, tdrz = 1, timestamps = 1 ...
...
[00:00:00.000 --> 00:00:03.800] Okay Houston, we've had a problem here. [SPEAKER_TURN]
[00:00:03.800 --> 00:00:06.200] This is Houston. Say again please. [SPEAKER_TURN]
[00:00:06.200 --> 00:00:08.260] Uh Houston we've had a problem.
[00:00:08.260 --> 00:00:11.320] We've had a main beam up on a volt. [SPEAKER_TURN]
[00:00:11.320 --> 00:00:13.820] Roger main beam interval. [SPEAKER_TURN]
[00:00:13.820 --> 00:00:15.100] Uh uh [SPEAKER_TURN]
[00:00:15.100 --> 00:00:18.020] So okay stand, by thirteen we're looking at it. [SPEAKER_TURN]
[00:00:18.020 --> 00:00:25.740] Okay uh right now uh Houston the uh voltage is uh is looking good um.
[00:00:27.620 --> 00:00:29.940] And we had a a pretty large bank or so.
```
## Karaoke-style movie generation (experimental)
The [whisper-cli](examples/cli) example provides support for output of karaoke-style movies, where the
currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script.
This requires to have `ffmpeg` installed.
Here are a few _"typical"_ examples:
```bash
./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts
source ./samples/jfk.wav.wts
ffplay ./samples/jfk.wav.mp4
```
https://user-images.githubusercontent.com/1991296/199337465-dbee4b5e-9aeb-48a3-b1c6-323ac4db5b2c.mp4
---
```bash
./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/mm0.wav -owts
source ./samples/mm0.wav.wts
ffplay ./samples/mm0.wav.mp4
```
https://user-images.githubusercontent.com/1991296/199337504-cc8fd233-0cb7-4920-95f9-4227de3570aa.mp4
---
```bash
./build/bin/whisper-cli -m ./models/ggml-base.en.bin -f ./samples/gb0.wav -owts
source ./samples/gb0.wav.wts
ffplay ./samples/gb0.wav.mp4
```
https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a0cd-f28a317987ba.mp4
---
## Video comparison of different models
Use the [scripts/bench-wts.sh](https://github.com/ggerganov/whisper.cpp/blob/master/scripts/bench-wts.sh) script to generate a video in the following format:
```bash
./scripts/bench-wts.sh samples/jfk.wav
ffplay ./samples/jfk.wav.all.mp4
```
https://user-images.githubusercontent.com/1991296/223206245-2d36d903-cf8e-4f09-8c3b-eb9f9c39d6fc.mp4
---
## Benchmarks
In order to have an objective comparison of the performance of the inference across different system configurations,
use the [whisper-bench](examples/bench) tool. The tool simply runs the Encoder part of the model and prints how much time it
took to execute it. The results are summarized in the following Github issue:
[Benchmark results](https://github.com/ggerganov/whisper.cpp/issues/89)
Additionally a script to run whisper.cpp with different models and audio files is provided [bench.py](scripts/bench.py).
You can run it with the following command, by default it will run against any standard model in the models folder.
```bash
python3 scripts/bench.py -f samples/jfk.wav -t 2,4,8 -p 1,2
```
It is written in python with the intention of being easy to modify and extend for your benchmarking use case.
It outputs a csv file with the results of the benchmarking.
## `ggml` format
The original models are converted to a custom binary format. This allows to pack everything needed into a single file:
- model parameters
- mel filters
- vocabulary
- weights
You can download the converted models using the [models/download-ggml-model.sh](models/download-ggml-model.sh) script
or manually from here:
- https://huggingface.co/ggerganov/whisper.cpp
- https://ggml.ggerganov.com
For more details, see the conversion script [models/convert-pt-to-ggml.py](models/convert-pt-to-ggml.py) or [models/README.md](models/README.md).
## [Bindings](https://github.com/ggerganov/whisper.cpp/discussions/categories/bindings)
- [x] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) | [#310](https://github.com/ggerganov/whisper.cpp/discussions/310)
- [x] JavaScript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309)
- React Native (iOS / Android): [whisper.rn](https://github.com/mybigday/whisper.rn)
- [x] Go: [bindings/go](bindings/go) | [#312](https://github.com/ggerganov/whisper.cpp/discussions/312)
- [x] Java:
- [GiviMAD/whisper-jni](https://github.com/GiviMAD/whisper-jni)
- [x] Ruby: [bindings/ruby](bindings/ruby) | [#507](https://github.com/ggerganov/whisper.cpp/discussions/507)
- [x] Objective-C / Swift: [ggerganov/whisper.spm](https://github.com/ggerganov/whisper.spm) | [#313](https://github.com/ggerganov/whisper.cpp/discussions/313)
- [exPHAT/SwiftWhisper](https://github.com/exPHAT/SwiftWhisper)
- [x] .NET: | [#422](https://github.com/ggerganov/whisper.cpp/discussions/422)
- [sandrohanea/whisper.net](https://github.com/sandrohanea/whisper.net)
- [NickDarvey/whisper](https://github.com/NickDarvey/whisper)
- [x] Python: | [#9](https://github.com/ggerganov/whisper.cpp/issues/9)
- [stlukey/whispercpp.py](https://github.com/stlukey/whispercpp.py) (Cython)
- [AIWintermuteAI/whispercpp](https://github.com/AIWintermuteAI/whispercpp) (Updated fork of aarnphm/whispercpp)
- [aarnphm/whispercpp](https://github.com/aarnphm/whispercpp) (Pybind11)
- [abdeladim-s/pywhispercpp](https://github.com/abdeladim-s/pywhispercpp) (Pybind11)
- [x] R: [bnosac/audio.whisper](https://github.com/bnosac/audio.whisper)
- [x] Unity: [macoron/whisper.unity](https://github.com/Macoron/whisper.unity)
## Examples
There are various examples of using the library for different projects in the [examples](examples) folder.
Some of the examples are even ported to run in the browser using WebAssembly. Check them out!
| Example | Web | Description |
| --------------------------------------------------- | ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- |
| [whisper-cli](examples/cli) | [whisper.wasm](examples/whisper.wasm) | Tool for translating and transcribing audio using Whisper |
| [whisper-bench](examples/bench) | [bench.wasm](examples/bench.wasm) | Benchmark the performance of Whisper on your machine |
| [whisper-stream](examples/stream) | [stream.wasm](examples/stream.wasm) | Real-time transcription of raw microphone capture |
| [whisper-command](examples/command) | [command.wasm](examples/command.wasm) | Basic voice assistant example for receiving voice commands from the mic |
| [whisper-server](examples/server) | | HTTP transcription server with OAI-like API |
| [whisper-talk-llama](examples/talk-llama) | | Talk with a LLaMA bot |
| [whisper.objc](examples/whisper.objc) | | iOS mobile application using whisper.cpp |
| [whisper.swiftui](examples/whisper.swiftui) | | SwiftUI iOS / macOS application using whisper.cpp |
| [whisper.android](examples/whisper.android) | | Android mobile application using whisper.cpp |
| [whisper.nvim](examples/whisper.nvim) | | Speech-to-text plugin for Neovim |
| [generate-karaoke.sh](examples/generate-karaoke.sh) | | Helper script to easily [generate a karaoke video](https://youtu.be/uj7hVta4blM) of raw audio capture |
| [livestream.sh](examples/livestream.sh) | | [Livestream audio transcription](https://github.com/ggerganov/whisper.cpp/issues/185) |
| [yt-wsp.sh](examples/yt-wsp.sh) | | Download + transcribe and/or translate any VOD [(original)](https://gist.github.com/DaniruKun/96f763ec1a037cc92fe1a059b643b818) |
| [wchess](examples/wchess) | [wchess.wasm](examples/wchess) | Voice-controlled chess |
## [Discussions](https://github.com/ggerganov/whisper.cpp/discussions)
If you have any kind of feedback about this project feel free to use the Discussions section and open a new topic.
You can use the [Show and tell](https://github.com/ggerganov/whisper.cpp/discussions/categories/show-and-tell) category
to share your own projects that use `whisper.cpp`. If you have a question, make sure to check the
[Frequently asked questions (#126)](https://github.com/ggerganov/whisper.cpp/discussions/126) discussion.

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# whisper.cpp for SYCL
[Background](#background)
[OS](#os)
[Intel GPU](#intel-gpu)
[Linux](#linux)
[Environment Variable](#environment-variable)
[Known Issue](#known-issue)
[Todo](#todo)
## Background
SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators�such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17.
oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms.
Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs.
To avoid re-inventing the wheel, this code refers other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel� DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL.
The whisper.cpp for SYCL is used to support Intel GPUs.
For Intel CPU, recommend to use whisper.cpp for X86 (Intel MKL build).
## OS
|OS|Status|Verified|
|-|-|-|
|Linux|Support|Ubuntu 22.04|
|Windows|Ongoing| |
## Intel GPU
|Intel GPU| Status | Verified Model|
|-|-|-|
|Intel Data Center Max Series| Support| Max 1550|
|Intel Data Center Flex Series| Support| Flex 170|
|Intel Arc Series| Support| Arc 770|
|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7|
## Linux
### Setup Environment
1. Install Intel GPU driver.
a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html).
Note: for iGPU, please install the client GPU driver.
b. Add user to group: video, render.
```
sudo usermod -aG render username
sudo usermod -aG video username
```
Note: re-login to enable it.
c. Check
```
sudo apt install clinfo
sudo clinfo -l
```
Output (example):
```
Platform #0: Intel(R) OpenCL Graphics
`-- Device #0: Intel(R) Arc(TM) A770 Graphics
Platform #0: Intel(R) OpenCL HD Graphics
`-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
```
2. Install Intel� oneAPI Base toolkit.
a. Please follow the procedure in [Get the Intel� oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
Recommend to install to default folder: **/opt/intel/oneapi**.
Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder.
b. Check
```
source /opt/intel/oneapi/setvars.sh
sycl-ls
```
There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**.
Output (example):
```
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
```
2. Build locally:
```
mkdir -p build
cd build
source /opt/intel/oneapi/setvars.sh
#for FP16
#cmake .. -DWHISPER_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DWHISPER_SYCL_F16=ON
#for FP32
cmake .. -DWHISPER_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#build example/main only
#cmake --build . --config Release --target main
#build all binary
cmake --build . --config Release -v
```
or
```
./examples/sycl/build.sh
```
Note:
- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
### Run
1. Put model file to folder **models**
2. Enable oneAPI running environment
```
source /opt/intel/oneapi/setvars.sh
```
3. List device ID
Run without parameter:
```
./build/bin/ls-sycl-device
or
./build/bin/main
```
Check the ID in startup log, like:
```
found 4 SYCL devices:
Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3,
max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136
Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2,
max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280
Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0,
max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280
Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0,
max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136
```
|Attribute|Note|
|-|-|
|compute capability 1.3|Level-zero running time, recommended |
|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
4. Set device ID and execute whisper.cpp
Set device ID = 0 by **GGML_SYCL_DEVICE=0**
```
GGML_SYCL_DEVICE=0 ./build/bin/main -m models/ggml-base.en.bin -f samples/jfk.wav
```
or run by script:
```
./examples/sycl/run_whisper.sh
```
5. Check the device ID in output
Like:
```
Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
```
## Environment Variable
#### Build
|Name|Value|Function|
|-|-|-|
|WHISPER_SYCL|ON (mandatory)|Enable build with SYCL code path. <br>For FP32/FP16, WHISPER_SYCL=ON is mandatory.|
|WHISPER_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path.For FP32, do not set it.|
|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path|
|CMAKE_CXX_COMPILER|icpx|use icpx for SYCL code path|
#### Running
|Name|Value|Function|
|-|-|-|
|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output|
|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
## Known Issue
- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
Miss to enable oneAPI running environment.
Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`.
- Hang during startup
llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block.
Solution: add **--no-mmap**.
## Todo
- Support to build in Windows.
- Support multiple cards.

5
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module whisper [system] {
header "whisper.h"
link "whisper"
export *
}

4
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#pragma once
#include <whisper.h>

28
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name: Close inactive issues
on:
schedule:
- cron: "42 0 * * *"
# Fine-grant permission
# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
permissions:
issues: write
jobs:
close-issues:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
steps:
- uses: actions/stale@v5
with:
exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap"
days-before-issue-stale: 30
days-before-issue-close: 14
stale-issue-label: "stale"
close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
days-before-pr-stale: -1
days-before-pr-close: -1
operations-per-run: 10000
repo-token: ${{ secrets.GITHUB_TOKEN }}

16
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# Set the default compile features and properties for a target.
if (NOT TARGET)
message(FATAL_ERROR "TARGET not set before including DefaultTargetOptions")
endif()
target_compile_features(${TARGET}
PRIVATE
cxx_std_11
)
set_target_properties(${TARGET}
PROPERTIES
EXPORT_COMPILE_COMMANDS ON
RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin"
)

163
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# From
# https://github.com/snikulov/cmake-modules/blob/master/FindFFmpeg.cmake
#
# vim: ts=2 sw=2
# - Try to find the required ffmpeg components(default: AVFORMAT, AVUTIL, AVCODEC)
#
# Once done this will define
# FFMPEG_FOUND - System has the all required components.
# FFMPEG_INCLUDE_DIRS - Include directory necessary for using the required components headers.
# FFMPEG_LIBRARIES - Link these to use the required ffmpeg components.
# FFMPEG_DEFINITIONS - Compiler switches required for using the required ffmpeg components.
#
# For each of the components it will additionally set.
# - AVCODEC
# - AVDEVICE
# - AVFORMAT
# - AVFILTER
# - AVUTIL
# - POSTPROC
# - SWSCALE
# the following variables will be defined
# <component>_FOUND - System has <component>
# <component>_INCLUDE_DIRS - Include directory necessary for using the <component> headers
# <component>_LIBRARIES - Link these to use <component>
# <component>_DEFINITIONS - Compiler switches required for using <component>
# <component>_VERSION - The components version
#
# Copyright (c) 2006, Matthias Kretz, <kretz@kde.org>
# Copyright (c) 2008, Alexander Neundorf, <neundorf@kde.org>
# Copyright (c) 2011, Michael Jansen, <kde@michael-jansen.biz>
#
# Redistribution and use is allowed according to the terms of the BSD license.
# For details see the accompanying COPYING-CMAKE-SCRIPTS file.
include(FindPackageHandleStandardArgs)
# The default components were taken from a survey over other FindFFMPEG.cmake files
if (NOT FFmpeg_FIND_COMPONENTS)
set(FFmpeg_FIND_COMPONENTS AVFORMAT AVCODEC AVUTIL SWRESAMPLE)
endif()
#
### Macro: set_component_found
#
# Marks the given component as found if both *_LIBRARIES AND *_INCLUDE_DIRS is present.
#
macro(set_component_found _component )
if (${_component}_LIBRARIES AND ${_component}_INCLUDE_DIRS)
message(DEBUG " - ${_component} found.")
set(${_component}_FOUND TRUE)
else ()
message(DEBUG " - ${_component} not found.")
endif ()
endmacro()
#
### Macro: find_component
#
# Checks for the given component by invoking pkgconfig and then looking up the libraries and
# include directories.
#
macro(find_component _component _pkgconfig _library _header)
if (NOT WIN32)
# use pkg-config to get the directories and then use these values
# in the FIND_PATH() and FIND_LIBRARY() calls
find_package(PkgConfig)
if (PKG_CONFIG_FOUND)
pkg_check_modules(PC_${_component} ${_pkgconfig})
message(STATUS "Pkgconfig found: ${PC_${_component}_INCLUDEDIR}")
message(STATUS "Pkgconfig found: ${PC_${_component}_INCLUDE_DIRS}")
message(STATUS "${PC_${_component}_CFLAGS}")
endif ()
endif (NOT WIN32)
find_path(${_component}_INCLUDE_DIRS ${_header}
HINTS
${PC_${_component}_INCLUDEDIR}
${PC_${_component}_INCLUDE_DIRS}
PATH_SUFFIXES
ffmpeg
)
# CMake's default is to search first for shared libraries and then for static libraries.
# Todo later: add option to prefer static libs over dynamic:
find_library(${_component}_LIBRARIES NAMES ${_library} lib${_library}.a
HINTS
${PC_${_component}_LIBDIR}
${PC_${_component}_LIBRARY_DIRS}
)
set(${_component}_DEFINITIONS ${PC_${_component}_CFLAGS_OTHER} CACHE STRING "The ${_component} CFLAGS.")
set(${_component}_VERSION ${PC_${_component}_VERSION} CACHE STRING "The ${_component} version number.")
set_component_found(${_component})
mark_as_advanced(
${_component}_INCLUDE_DIRS
${_component}_LIBRARIES
${_component}_DEFINITIONS
${_component}_VERSION)
endmacro()
# Check for cached results. If there are skip the costly part.
if (NOT FFMPEG_LIBRARIES)
# Check for all possible component.
find_component(AVCODEC libavcodec avcodec libavcodec/avcodec.h)
find_component(AVFORMAT libavformat avformat libavformat/avformat.h)
find_component(AVDEVICE libavdevice avdevice libavdevice/avdevice.h)
#find_component(AVRESAMPLE libavresample avresample libavresample/avresample.h) # old name for swresample
find_component(AVUTIL libavutil avutil libavutil/avutil.h)
find_component(AVFILTER libavfilter avfilter libavfilter/avfilter.h)
find_component(SWSCALE libswscale swscale libswscale/swscale.h)
find_component(POSTPROC libpostproc postproc libpostproc/postprocess.h)
find_component(SWRESAMPLE libswresample swresample libswresample/swresample.h)
# Check if the required components were found and add their stuff to the FFMPEG_* vars.
foreach (_component ${FFmpeg_FIND_COMPONENTS})
if (${_component}_FOUND)
# message(STATUS "Required component ${_component} present.")
set(FFMPEG_LIBRARIES ${FFMPEG_LIBRARIES} ${${_component}_LIBRARIES})
set(FFMPEG_DEFINITIONS ${FFMPEG_DEFINITIONS} ${${_component}_DEFINITIONS})
list(APPEND FFMPEG_INCLUDE_DIRS ${${_component}_INCLUDE_DIRS})
else ()
# message(STATUS "Required component ${_component} missing.")
endif ()
endforeach ()
# Build the include path with duplicates removed.
if (FFMPEG_INCLUDE_DIRS)
list(REMOVE_DUPLICATES FFMPEG_INCLUDE_DIRS)
endif ()
# cache the vars.
set(FFMPEG_INCLUDE_DIRS ${FFMPEG_INCLUDE_DIRS} CACHE STRING "The FFmpeg include directories." FORCE)
set(FFMPEG_LIBRARIES ${FFMPEG_LIBRARIES} CACHE STRING "The FFmpeg libraries." FORCE)
set(FFMPEG_DEFINITIONS ${FFMPEG_DEFINITIONS} CACHE STRING "The FFmpeg cflags." FORCE)
mark_as_advanced(FFMPEG_INCLUDE_DIRS
FFMPEG_LIBRARIES
FFMPEG_DEFINITIONS)
endif ()
# Now set the noncached _FOUND vars for the components.
# whisper.cpp does not need SWSCALE
foreach (_component AVCODEC AVDEVICE AVFORMAT AVRESAMPLE AVUTIL POSTPROCESS)
set_component_found(${_component})
endforeach ()
# Compile the list of required vars
set(_FFmpeg_REQUIRED_VARS FFMPEG_LIBRARIES FFMPEG_INCLUDE_DIRS)
foreach (_component ${FFmpeg_FIND_COMPONENTS})
list(APPEND _FFmpeg_REQUIRED_VARS ${_component}_LIBRARIES ${_component}_INCLUDE_DIRS)
endforeach ()
# Give a nice error message if some of the required vars are missing.
find_package_handle_standard_args(FFmpeg DEFAULT_MSG ${_FFmpeg_REQUIRED_VARS})

60
packages/app-mobile/android/vendor/whisper.cpp/cmake/build-info.cmake vendored Normal file
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set(BUILD_NUMBER 0)
set(BUILD_COMMIT "unknown")
set(BUILD_COMPILER "unknown")
set(BUILD_TARGET "unknown")
# Look for git
find_package(Git)
if(NOT Git_FOUND)
find_program(GIT_EXECUTABLE NAMES git git.exe)
if(GIT_EXECUTABLE)
set(Git_FOUND TRUE)
message(STATUS "Found Git: ${GIT_EXECUTABLE}")
else()
message(WARNING "Git not found. Build info will not be accurate.")
endif()
endif()
# Get the commit count and hash
if(Git_FOUND)
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE HEAD
OUTPUT_STRIP_TRAILING_WHITESPACE
RESULT_VARIABLE RES
)
if (RES EQUAL 0)
set(BUILD_COMMIT ${HEAD})
endif()
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE COUNT
OUTPUT_STRIP_TRAILING_WHITESPACE
RESULT_VARIABLE RES
)
if (RES EQUAL 0)
set(BUILD_NUMBER ${COUNT})
endif()
endif()
if(MSVC)
set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
else()
execute_process(
COMMAND sh -c "$@ --version | head -1" _ ${CMAKE_C_COMPILER}
OUTPUT_VARIABLE OUT
OUTPUT_STRIP_TRAILING_WHITESPACE
)
set(BUILD_COMPILER ${OUT})
execute_process(
COMMAND ${CMAKE_C_COMPILER} -dumpmachine
OUTPUT_VARIABLE OUT
OUTPUT_STRIP_TRAILING_WHITESPACE
)
set(BUILD_TARGET ${OUT})
endif()

22
packages/app-mobile/android/vendor/whisper.cpp/cmake/git-vars.cmake vendored Normal file
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find_package(Git)
# the commit's SHA1
execute_process(COMMAND
"${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_SHA1
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
# the date of the commit
execute_process(COMMAND
"${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_DATE
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
# the subject of the commit
execute_process(COMMAND
"${GIT_EXECUTABLE}" log -1 --format=%s
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)

65
packages/app-mobile/android/vendor/whisper.cpp/cmake/whisper-config.cmake.in vendored Normal file
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set(WHISPER_VERSION @WHISPER_INSTALL_VERSION@)
set(WHISPER_BUILD_COMMIT @WHISPER_BUILD_COMMIT@)
set(WHISPER_BUILD_NUMBER @WHISPER_BUILD_NUMBER@)
set(WHISPER_SHARED_LIB @BUILD_SHARED_LIBS@)
set(GGML_BLAS @GGML_BLAS@)
set(GGML_CUDA @GGML_CUDA@)
set(GGML_METAL @GGML_METAL@)
set(GGML_HIPBLAS @GGML_HIPBLAS@)
set(GGML_ACCELERATE @GGML_ACCELERATE@)
@PACKAGE_INIT@
set_and_check(WHISPER_INCLUDE_DIR "@PACKAGE_WHISPER_INCLUDE_INSTALL_DIR@")
set_and_check(WHISPER_LIB_DIR "@PACKAGE_WHISPER_LIB_INSTALL_DIR@")
set_and_check(WHISPER_BIN_DIR "@PACKAGE_WHISPER_BIN_INSTALL_DIR@")
# Ensure transient dependencies satisfied
find_package(Threads REQUIRED)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
endif()
if (GGML_BLAS)
find_package(BLAS REQUIRED)
endif()
if (GGML_CUDA)
find_package(CUDAToolkit REQUIRED)
endif()
if (GGML_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
endif()
if (GGML_HIPBLAS)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
endif()
find_library(whisper_LIBRARY whisper
REQUIRED
HINTS ${WHISPER_LIB_DIR})
set(_whisper_link_deps "Threads::Threads" "@WHISPER_EXTRA_LIBS@")
set(_whisper_transient_defines "@WHISPER_TRANSIENT_DEFINES@")
add_library(whisper UNKNOWN IMPORTED)
set_target_properties(whisper
PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${WHISPER_INCLUDE_DIR}"
INTERFACE_LINK_LIBRARIES "${_whisper_link_deps}"
INTERFACE_COMPILE_DEFINITIONS "${_whisper_transient_defines}"
IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
IMPORTED_LOCATION "${whisper_LIBRARY}"
INTERFACE_COMPILE_FEATURES cxx_std_11
POSITION_INDEPENDENT_CODE ON )
check_required_components(whisper)

10
packages/app-mobile/android/vendor/whisper.cpp/cmake/whisper.pc.in vendored Normal file
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prefix=@CMAKE_INSTALL_PREFIX@
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir=${prefix}/include
Name: whisper
Description: Port of OpenAI's Whisper model in C/C++
Version: @PROJECT_VERSION@
Libs: -L${libdir} -lggml -lggml-base -lwhisper
Cflags: -I${includedir}

1
packages/app-mobile/android/vendor/whisper.cpp/ggml/.gitignore vendored Normal file
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src/ggml-metal-embed.metal

343
packages/app-mobile/android/vendor/whisper.cpp/ggml/CMakeLists.txt vendored Normal file
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cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
project("ggml" C CXX)
include(CheckIncludeFileCXX)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
endif()
if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
set(GGML_STANDALONE ON)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
# configure project version
# TODO
else()
set(GGML_STANDALONE OFF)
endif()
if (EMSCRIPTEN)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
option(GGML_WASM_SINGLE_FILE "ggml: embed WASM inside the generated ggml.js" ON)
else()
if (MINGW)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
else()
set(BUILD_SHARED_LIBS_DEFAULT ON)
endif()
endif()
# remove the lib prefix on win32 mingw
if (WIN32)
set(CMAKE_STATIC_LIBRARY_PREFIX "")
set(CMAKE_SHARED_LIBRARY_PREFIX "")
set(CMAKE_SHARED_MODULE_PREFIX "")
endif()
option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
option(GGML_BACKEND_DL "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
#
# option list
#
# TODO: mark all options as advanced when not GGML_STANDALONE
if (APPLE)
set(GGML_METAL_DEFAULT ON)
set(GGML_BLAS_DEFAULT ON)
set(GGML_BLAS_VENDOR_DEFAULT "Apple")
else()
set(GGML_METAL_DEFAULT OFF)
set(GGML_BLAS_DEFAULT OFF)
set(GGML_BLAS_VENDOR_DEFAULT "Generic")
endif()
if (CMAKE_CROSSCOMPILING OR DEFINED ENV{SOURCE_DATE_EPOCH})
message(STATUS "Setting GGML_NATIVE_DEFAULT to OFF")
set(GGML_NATIVE_DEFAULT OFF)
else()
set(GGML_NATIVE_DEFAULT ON)
endif()
# defaults
if (NOT GGML_LLAMAFILE_DEFAULT)
set(GGML_LLAMAFILE_DEFAULT OFF)
endif()
if (NOT GGML_CUDA_GRAPHS_DEFAULT)
set(GGML_CUDA_GRAPHS_DEFAULT OFF)
endif()
# general
option(GGML_STATIC "ggml: static link libraries" OFF)
option(GGML_NATIVE "ggml: optimize the build for the current system" ${GGML_NATIVE_DEFAULT})
option(GGML_LTO "ggml: enable link time optimization" OFF)
option(GGML_CCACHE "ggml: use ccache if available" ON)
# debug
option(GGML_ALL_WARNINGS "ggml: enable all compiler warnings" ON)
option(GGML_ALL_WARNINGS_3RD_PARTY "ggml: enable all compiler warnings in 3rd party libs" OFF)
option(GGML_GPROF "ggml: enable gprof" OFF)
# build
option(GGML_FATAL_WARNINGS "ggml: enable -Werror flag" OFF)
# sanitizers
option(GGML_SANITIZE_THREAD "ggml: enable thread sanitizer" OFF)
option(GGML_SANITIZE_ADDRESS "ggml: enable address sanitizer" OFF)
option(GGML_SANITIZE_UNDEFINED "ggml: enable undefined sanitizer" OFF)
# instruction set specific
if (GGML_NATIVE OR NOT GGML_NATIVE_DEFAULT)
set(INS_ENB OFF)
else()
set(INS_ENB ON)
endif()
option(GGML_CPU_HBM "ggml: use memkind for CPU HBM" OFF)
option(GGML_CPU_AARCH64 "ggml: use runtime weight conversion of Q4_0 to Q4_X_X" ON)
option(GGML_AVX "ggml: enable AVX" ${INS_ENB})
option(GGML_AVX_VNNI "ggml: enable AVX-VNNI" OFF)
option(GGML_AVX2 "ggml: enable AVX2" ${INS_ENB})
option(GGML_AVX512 "ggml: enable AVX512F" OFF)
option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF)
option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF)
option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16" OFF)
if (NOT MSVC)
# in MSVC F16C and FMA is implied with AVX2/AVX512
option(GGML_FMA "ggml: enable FMA" ${INS_ENB})
option(GGML_F16C "ggml: enable F16C" ${INS_ENB})
# MSVC does not seem to support AMX
option(GGML_AMX_TILE "ggml: enable AMX-TILE" OFF)
option(GGML_AMX_INT8 "ggml: enable AMX-INT8" OFF)
option(GGML_AMX_BF16 "ggml: enable AMX-BF16" OFF)
endif()
option(GGML_LASX "ggml: enable lasx" ON)
option(GGML_LSX "ggml: enable lsx" ON)
option(GGML_RVV "ggml: enable rvv" ON)
option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
set(GGML_CPU_ARM_ARCH "" CACHE STRING "ggml: CPU architecture for ARM")
if (WIN32)
set(GGML_WIN_VER "0x602" CACHE STRING "ggml: Windows version")
endif()
# ggml core
set(GGML_SCHED_MAX_COPIES "4" CACHE STRING "ggml: max input copies for pipeline parallelism")
option(GGML_CPU "ggml: enable CPU backend" ON)
# 3rd party libs / backends
option(GGML_ACCELERATE "ggml: enable Accelerate framework" ON)
option(GGML_BLAS "ggml: use BLAS" ${GGML_BLAS_DEFAULT})
set(GGML_BLAS_VENDOR ${GGML_BLAS_VENDOR_DEFAULT} CACHE STRING
"ggml: BLAS library vendor")
option(GGML_LLAMAFILE "ggml: use LLAMAFILE" ${GGML_LLAMAFILE_DEFAULT})
option(GGML_CUDA "ggml: use CUDA" OFF)
option(GGML_MUSA "ggml: use MUSA" OFF)
option(GGML_CUDA_FORCE_MMQ "ggml: use mmq kernels instead of cuBLAS" OFF)
option(GGML_CUDA_FORCE_CUBLAS "ggml: always use cuBLAS instead of mmq kernels" OFF)
option(GGML_CUDA_F16 "ggml: use 16 bit floats for some calculations" OFF)
set (GGML_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
"ggml: max. batch size for using peer access")
option(GGML_CUDA_NO_PEER_COPY "ggml: do not use peer to peer copies" OFF)
option(GGML_CUDA_NO_VMM "ggml: do not try to use CUDA VMM" OFF)
option(GGML_CUDA_FA_ALL_QUANTS "ggml: compile all quants for FlashAttention" OFF)
option(GGML_CUDA_GRAPHS "ggml: use CUDA graphs (llama.cpp only)" ${GGML_CUDA_GRAPHS_DEFAULT})
option(GGML_HIP "ggml: use HIP" OFF)
option(GGML_HIP_GRAPHS "ggml: use HIP graph, experimental, slow" OFF)
option(GGML_HIP_NO_VMM "ggml: do not try to use HIP VMM" ON)
option(GGML_HIP_UMA "ggml: use HIP unified memory architecture" OFF)
option(GGML_VULKAN "ggml: use Vulkan" OFF)
option(GGML_VULKAN_CHECK_RESULTS "ggml: run Vulkan op checks" OFF)
option(GGML_VULKAN_DEBUG "ggml: enable Vulkan debug output" OFF)
option(GGML_VULKAN_MEMORY_DEBUG "ggml: enable Vulkan memory debug output" OFF)
option(GGML_VULKAN_SHADER_DEBUG_INFO "ggml: enable Vulkan shader debug info" OFF)
option(GGML_VULKAN_PERF "ggml: enable Vulkan perf output" OFF)
option(GGML_VULKAN_VALIDATE "ggml: enable Vulkan validation" OFF)
option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF)
option(GGML_KOMPUTE "ggml: use Kompute" OFF)
option(GGML_METAL "ggml: use Metal" ${GGML_METAL_DEFAULT})
option(GGML_METAL_USE_BF16 "ggml: use bfloat if available" OFF)
option(GGML_METAL_NDEBUG "ggml: disable Metal debugging" OFF)
option(GGML_METAL_SHADER_DEBUG "ggml: compile Metal with -fno-fast-math" OFF)
option(GGML_METAL_EMBED_LIBRARY "ggml: embed Metal library" ${GGML_METAL})
set (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING
"ggml: metal minimum macOS version")
set (GGML_METAL_STD "" CACHE STRING "ggml: metal standard version (-std flag)")
option(GGML_OPENMP "ggml: use OpenMP" ON)
option(GGML_RPC "ggml: use RPC" OFF)
option(GGML_SYCL "ggml: use SYCL" OFF)
option(GGML_SYCL_F16 "ggml: use 16 bit floats for sycl calculations" OFF)
set (GGML_SYCL_TARGET "INTEL" CACHE STRING
"ggml: sycl target device")
set (GGML_SYCL_DEVICE_ARCH "" CACHE STRING
"ggml: sycl device architecture")
option(GGML_OPENCL "ggml: use OpenCL" OFF)
option(GGML_OPENCL_PROFILING "ggml: use OpenCL profiling (increases overhead)" OFF)
option(GGML_OPENCL_EMBED_KERNELS "ggml: embed kernels" ON)
option(GGML_OPENCL_USE_ADRENO_KERNELS "ggml: use optimized kernels for Adreno" ON)
# toolchain for vulkan-shaders-gen
set (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")
# extra artifacts
option(GGML_BUILD_TESTS "ggml: build tests" ${GGML_STANDALONE})
option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
#
# dependencies
#
set(CMAKE_C_STANDARD 11)
set(CMAKE_C_STANDARD_REQUIRED true)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED true)
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
#
# build the library
#
add_subdirectory(src)
#
# tests and examples
#
if (GGML_BUILD_TESTS)
enable_testing()
add_subdirectory(tests)
endif ()
if (GGML_BUILD_EXAMPLES)
add_subdirectory(examples)
endif ()
#
# install
#
include(GNUInstallDirs)
include(CMakePackageConfigHelpers)
# all public headers
set(GGML_PUBLIC_HEADERS
include/ggml.h
include/ggml-cpu.h
include/ggml-alloc.h
include/ggml-backend.h
include/ggml-blas.h
include/ggml-cann.h
include/ggml-cuda.h
include/ggml-kompute.h
include/ggml-opt.h
include/ggml-metal.h
include/ggml-rpc.h
include/ggml-sycl.h
include/ggml-vulkan.h
include/gguf.h)
set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
#if (GGML_METAL)
# set_target_properties(ggml PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/src/ggml-metal.metal")
#endif()
install(TARGETS ggml LIBRARY PUBLIC_HEADER)
install(TARGETS ggml-base LIBRARY)
if (GGML_STANDALONE)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/ggml.pc.in
${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
@ONLY)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
DESTINATION share/pkgconfig)
endif()
#
# Create CMake package
#
# Generate version info based on git commit.
if(NOT DEFINED GGML_BUILD_NUMBER)
find_program(GIT_EXE NAMES git git.exe REQUIRED NO_CMAKE_FIND_ROOT_PATH)
execute_process(COMMAND ${GIT_EXE} rev-list --count HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE GGML_BUILD_NUMBER
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if(GGML_BUILD_NUMBER EQUAL 1)
message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
endif()
execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE GGML_BUILD_COMMIT
OUTPUT_STRIP_TRAILING_WHITESPACE
)
endif()
# Capture variables prefixed with GGML_.
set(variable_set_statements
"
####### Expanded from @GGML_VARIABLES_EXPANED@ by configure_package_config_file() #######
####### Any changes to this file will be overwritten by the next CMake run #######
")
set(GGML_SHARED_LIB ${BUILD_SHARED_LIBS})
get_cmake_property(all_variables VARIABLES)
foreach(variable_name IN LISTS all_variables)
if(variable_name MATCHES "^GGML_")
string(REPLACE ";" "\\;"
variable_value "${${variable_name}}")
set(variable_set_statements
"${variable_set_statements}set(${variable_name} \"${variable_value}\")\n")
endif()
endforeach()
set(GGML_VARIABLES_EXPANDED ${variable_set_statements})
# Create the CMake package and set install location.
set(GGML_INSTALL_VERSION 0.0.${GGML_BUILD_NUMBER})
set(GGML_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header files")
set(GGML_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files")
set(GGML_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files")
configure_package_config_file(
${CMAKE_CURRENT_SOURCE_DIR}/cmake/ggml-config.cmake.in
${CMAKE_CURRENT_BINARY_DIR}/ggml-config.cmake
INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ggml
PATH_VARS GGML_INCLUDE_INSTALL_DIR
GGML_LIB_INSTALL_DIR
GGML_BIN_INSTALL_DIR)
write_basic_package_version_file(
${CMAKE_CURRENT_BINARY_DIR}/ggml-version.cmake
VERSION ${GGML_INSTALL_VERSION}
COMPATIBILITY SameMajorVersion)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml-config.cmake
${CMAKE_CURRENT_BINARY_DIR}/ggml-version.cmake
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ggml)

54
packages/app-mobile/android/vendor/whisper.cpp/ggml/cmake/BuildTypes.cmake vendored Normal file
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@@ -0,0 +1,54 @@
# Add new build types
# ReleaseGG - Release with enabled asserts
SET(CMAKE_CXX_FLAGS_RELEASEGG
"-O3"
CACHE STRING "Flags used by the c++ compiler during release builds with enabled asserts."
FORCE )
SET(CMAKE_C_FLAGS_RELEASEGG
"-O3"
CACHE STRING "Flags used by the compiler during release builds with enabled asserts."
FORCE )
SET(CMAKE_EXE_LINKER_FLAGS_RELEASEGG
""
CACHE STRING "Flags used for linking binaries during release builds with enabled asserts."
FORCE )
SET(CMAKE_SHARED_LINKER_FLAGS_RELEASEGG
""
CACHE STRING "Flags used by the shared libraries linker during release builds with enabled asserts."
FORCE )
MARK_AS_ADVANCED(
CMAKE_CXX_FLAGS_RELEASEGG
CMAKE_C_FLAGS_RELEASEGG
CMAKE_EXE_LINKER_FLAGS_RELEASEGG
CMAKE_SHARED_LINKER_FLAGS_RELEASEGG )
# RelWithDebInfoGG - RelWithDebInfo with enabled asserts
SET(CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
"-O2 -g"
CACHE STRING "Flags used by the c++ compiler during release builds with debug symbols and enabled asserts."
FORCE )
SET(CMAKE_C_FLAGS_RELWITHDEBINFOGG
"-O2 -g"
CACHE STRING "Flags used by the compiler during release builds with debug symbols and enabled asserts."
FORCE )
SET(CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
""
CACHE STRING "Flags used for linking binaries during release builds with debug symbols and enabled asserts."
FORCE )
SET(CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG
""
CACHE STRING "Flags used by the shared libraries linker during release builds with debug symbols and enabled asserts."
FORCE )
MARK_AS_ADVANCED(
CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
CMAKE_C_FLAGS_RELWITHDEBINFOGG
CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG )
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo" "ReleaseGG" "RelWithDebInfoGG")
endif()

22
packages/app-mobile/android/vendor/whisper.cpp/ggml/cmake/GitVars.cmake vendored Normal file
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@@ -0,0 +1,22 @@
find_package(Git)
# the commit's SHA1
execute_process(COMMAND
"${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_SHA1
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
# the date of the commit
execute_process(COMMAND
"${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_DATE
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
# the subject of the commit
execute_process(COMMAND
"${GIT_EXECUTABLE}" log -1 --format=%s
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)

147
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@GGML_VARIABLES_EXPANDED@
@PACKAGE_INIT@
set_and_check(GGML_INCLUDE_DIR "@PACKAGE_GGML_INCLUDE_INSTALL_DIR@")
set_and_check(GGML_LIB_DIR "@PACKAGE_GGML_LIB_INSTALL_DIR@")
set_and_check(GGML_BIN_DIR "@PACKAGE_GGML_BIN_INSTALL_DIR@")
find_package(Threads REQUIRED)
find_library(GGML_LIBRARY ggml
REQUIRED
HINTS ${GGML_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH)
add_library(ggml::ggml UNKNOWN IMPORTED)
set_target_properties(ggml::ggml
PROPERTIES
IMPORTED_LOCATION "${GGML_LIBRARY}")
find_library(GGML_BASE_LIBRARY ggml-base
REQUIRED
HINTS ${GGML_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH)
add_library(ggml::ggml-base UNKNOWN IMPORTED)
set_target_properties(ggml::ggml-base
PROPERTIES
IMPORTED_LOCATION "${GGML_BASE_LIBRARY}")
if (NOT GGML_SHARED_LIB)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${ACCELERATE_FRAMEWORK})
endif()
if (GGML_OPENMP)
find_package(OpenMP REQUIRED)
list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
endif()
if (GGML_CPU_HBM)
find_library(memkind memkind REQUIRED)
list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES memkind)
endif()
if (GGML_BLAS)
find_package(BLAS REQUIRED)
list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${BLAS_LIBRARIES})
list(APPEND GGML_CPU_INTERFACE_LINK_OPTIONS ${BLAS_LINKER_FLAGS})
endif()
if (GGML_CUDA)
find_package(CUDAToolkit REQUIRED)
endif()
if (GGML_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
list(APPEND GGML_METAL_INTERFACE_LINK_LIBRARIES
${FOUNDATION_LIBRARY} ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
endif()
if (GGML_VULKAN)
find_package(Vulkan REQUIRED)
list(APPEND GGML_VULKAN_INTERFACE_LINK_LIBRARIES Vulkan::Vulkan)
endif()
if (GGML_HIP)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
list(APPEND GGML_HIP_INTERFACE_LINK_LIBRARIES hip::host roc::rocblas roc::hipblas)
endif()
if (GGML_SYCL)
find_package(DNNL)
if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES DNNL::dnnl)
endif()
if (WIN32)
find_package(IntelSYCL REQUIRED)
find_package(MKL REQUIRED)
list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
endif()
endif()
endif()
set(_ggml_all_targets "")
foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
REQUIRED
HINTS ${GGML_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH)
message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
set_target_properties(ggml::${_ggml_backend}
PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
INTERFACE_COMPILE_FEATURES c_std_90
POSITION_INDEPENDENT_CODE ON)
string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
if(is_cpu_variant)
list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml" "ggml::ggml-base")
set_target_properties(ggml::${_ggml_backend}
PROPERTIES
INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
if(GGML_CPU_INTERFACE_LINK_OPTIONS)
set_target_properties(ggml::${_ggml_backend}
PROPERTIES
INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
endif()
else()
list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml" "ggml::ggml-base")
set_target_properties(ggml::${_ggml_backend}
PROPERTIES
INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
set_target_properties(ggml::${_ggml_backend}
PROPERTIES
INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
endif()
endif()
list(APPEND _ggml_all_targets ggml::${_ggml_backend})
endforeach()
add_library(ggml::all INTERFACE IMPORTED)
set_target_properties(ggml::all
PROPERTIES
INTERFACE_LINK_LIBRARIES "${_ggml_all_targets}")
check_required_components(ggml)

76
packages/app-mobile/android/vendor/whisper.cpp/ggml/include/ggml-alloc.h vendored Normal file
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#pragma once
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend * ggml_backend_t;
// Tensor allocator
struct ggml_tallocr {
ggml_backend_buffer_t buffer;
void * base;
size_t alignment;
size_t offset;
};
GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
GGML_API void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
// Graph allocator
/*
Example usage:
ggml_gallocr_t galloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
// optional: create a worst-case graph and reserve the buffers to avoid reallocations
ggml_gallocr_reserve(galloc, build_graph(max_batch));
// allocate the graph
struct ggml_cgraph * graph = build_graph(batch);
ggml_gallocr_alloc_graph(galloc, graph);
printf("compute buffer size: %zu bytes\n", ggml_gallocr_get_buffer_size(galloc, 0));
// evaluate the graph
ggml_backend_graph_compute(backend, graph);
*/
// special tensor flags for use with the graph allocator:
// ggml_set_input(): all input tensors are allocated at the beginning of the graph in non-overlapping addresses
// ggml_set_output(): output tensors are never freed and never overwritten
typedef struct ggml_gallocr * ggml_gallocr_t;
GGML_API ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft);
GGML_API ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs);
GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc);
// pre-allocate buffers from a measure graph - does not allocate or modify the graph
// call with a worst-case graph to avoid buffer reallocations
// not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
// returns false if the buffer allocation failed
GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
GGML_API bool ggml_gallocr_reserve_n(
ggml_gallocr_t galloc,
struct ggml_cgraph * graph,
const int * node_buffer_ids,
const int * leaf_buffer_ids);
// automatic reallocation if the topology changes when using a single buffer
// returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)
GGML_API bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id);
// Utils
// Create a buffer and allocate all the tensors in a ggml_context
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend);
#ifdef __cplusplus
}
#endif

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packages/app-mobile/android/vendor/whisper.cpp/ggml/include/ggml-backend.h vendored Normal file
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#pragma once
#include "ggml.h"
#include "ggml-alloc.h"
#ifdef GGML_BACKEND_SHARED
# if defined(_WIN32) && !defined(__MINGW32__)
# ifdef GGML_BACKEND_BUILD
# define GGML_BACKEND_API __declspec(dllexport) extern
# else
# define GGML_BACKEND_API __declspec(dllimport) extern
# endif
# else
# define GGML_BACKEND_API __attribute__ ((visibility ("default"))) extern
# endif
#else
# define GGML_BACKEND_API extern
#endif
#ifdef __cplusplus
extern "C" {
#endif
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend_event * ggml_backend_event_t;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
typedef struct ggml_backend_reg * ggml_backend_reg_t;
typedef struct ggml_backend_device * ggml_backend_dev_t;
//
// Backend 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_max_size (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_is_host (ggml_backend_buffer_type_t buft);
GGML_API ggml_backend_dev_t ggml_backend_buft_get_device (ggml_backend_buffer_type_t buft);
//
// Backend buffer
//
enum ggml_backend_buffer_usage {
GGML_BACKEND_BUFFER_USAGE_ANY = 0,
GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
GGML_BACKEND_BUFFER_USAGE_COMPUTE = 2,
};
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_max_size (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 enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage (ggml_backend_buffer_t buffer);
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);
// tensor copy between different backends
GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
//
// Backend (stream)
//
GGML_API ggml_guid_t ggml_backend_guid(ggml_backend_t backend);
GGML_API const char * ggml_backend_name(ggml_backend_t backend);
GGML_API void ggml_backend_free(ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API size_t ggml_backend_get_max_size(ggml_backend_t backend);
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);
// "offset" refers to the offset in tensor->data for setting/getting data
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 void ggml_backend_tensor_memset( struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
// NOTE: will be removed, use device version instead
GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
GGML_API bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
// asynchronous copy
// the copy is performed after all the currently queued operations in backend_src
// backend_dst will wait for the copy to complete before performing other operations
// automatic fallback to sync copy if async is not supported
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
GGML_API ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend);
//
// Events
//
GGML_API ggml_backend_event_t ggml_backend_event_new(ggml_backend_dev_t device);
GGML_API void ggml_backend_event_free(ggml_backend_event_t event);
GGML_API void ggml_backend_event_record(ggml_backend_event_t event, ggml_backend_t backend);
GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event);
GGML_API void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event);
//
// Backend device
//
enum ggml_backend_dev_type {
// CPU device using system memory
GGML_BACKEND_DEVICE_TYPE_CPU,
// GPU device using dedicated memory
GGML_BACKEND_DEVICE_TYPE_GPU,
// accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
GGML_BACKEND_DEVICE_TYPE_ACCEL
};
// functionality supported by the device
struct ggml_backend_dev_caps {
// asynchronous operations
bool async;
// pinned host buffer
bool host_buffer;
// creating buffers from host ptr
bool buffer_from_host_ptr;
// event synchronization
bool events;
};
// all the device properties
struct ggml_backend_dev_props {
const char * name;
const char * description;
size_t memory_free;
size_t memory_total;
enum ggml_backend_dev_type type;
struct ggml_backend_dev_caps caps;
};
GGML_API const char * ggml_backend_dev_name(ggml_backend_dev_t device);
GGML_API const char * ggml_backend_dev_description(ggml_backend_dev_t device);
GGML_API void ggml_backend_dev_memory(ggml_backend_dev_t device, size_t * free, size_t * total);
GGML_API enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device);
GGML_API void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_dev_props * props);
GGML_API ggml_backend_reg_t ggml_backend_dev_backend_reg(ggml_backend_dev_t device);
GGML_API ggml_backend_t ggml_backend_dev_init(ggml_backend_dev_t device, const char * params);
GGML_API ggml_backend_buffer_type_t ggml_backend_dev_buffer_type(ggml_backend_dev_t device);
GGML_API ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device);
GGML_API ggml_backend_buffer_t ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size);
GGML_API bool ggml_backend_dev_supports_op(ggml_backend_dev_t device, const struct ggml_tensor * op);
GGML_API bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buffer_type_t buft);
GGML_API bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op);
//
// Backend (reg)
//
GGML_API const char * ggml_backend_reg_name(ggml_backend_reg_t reg);
GGML_API size_t ggml_backend_reg_dev_count(ggml_backend_reg_t reg);
GGML_API ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index);
GGML_API void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name);
// Common functions that may be obtained using ggml_backend_reg_get_proc_address
// Split buffer type for tensor parallelism
typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split);
// Set the number of threads for the backend
typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads);
// Get additional buffer types provided by the device (returns a NULL-terminated array)
typedef ggml_backend_buffer_type_t * (*ggml_backend_dev_get_extra_bufts_t)(ggml_backend_dev_t device);
// Set the abort callback for the backend
typedef void (*ggml_backend_set_abort_callback_t)(ggml_backend_t backend, ggml_abort_callback abort_callback, void * abort_callback_data);
// Get a list of feature flags supported by the backend (returns a NULL-terminated array)
struct ggml_backend_feature {
const char * name;
const char * value;
};
typedef struct ggml_backend_feature * (*ggml_backend_get_features_t)(ggml_backend_reg_t reg);
//
// Backend registry
//
GGML_API void ggml_backend_device_register(ggml_backend_dev_t device);
// Backend (reg) enumeration
GGML_API size_t ggml_backend_reg_count(void);
GGML_API ggml_backend_reg_t ggml_backend_reg_get(size_t index);
GGML_API ggml_backend_reg_t ggml_backend_reg_by_name(const char * name);
// Device enumeration
GGML_API size_t ggml_backend_dev_count(void);
GGML_API ggml_backend_dev_t ggml_backend_dev_get(size_t index);
GGML_API ggml_backend_dev_t ggml_backend_dev_by_name(const char * name);
GGML_API ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type);
// Direct backend (stream) initialization
// = ggml_backend_dev_init(ggml_backend_dev_by_name(name), params)
GGML_API ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params);
// = ggml_backend_dev_init(ggml_backend_dev_by_type(type), params)
GGML_API ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params);
// = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU) OR ggml_backend_dev_by_type(CPU), NULL)
GGML_API ggml_backend_t ggml_backend_init_best(void);
// Load a backend from a dynamic library and register it
GGML_API ggml_backend_reg_t ggml_backend_load(const char * path);
// Unload a backend if loaded dynamically and unregister it
GGML_API void ggml_backend_unload(ggml_backend_reg_t reg);
// Load all known backends from dynamic libraries
GGML_API void ggml_backend_load_all(void);
GGML_API void ggml_backend_load_all_from_path(const char * dir_path);
//
// Backend scheduler
//
// The backend scheduler allows for multiple backend devices to be used together
// Handles compute buffer allocation, assignment of tensors to backends, and copying of tensors between backends
// The backends are selected based on:
// - the backend that supports the operation
// - the location of the pre-allocated tensors (e.g. the weights)
/*
Example usage:
// operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
// preferrably to run on the same backend as the buffer
ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
// initialize buffers from a max size graph (optional)
reserve_graph = build_graph(sched, max_batch_size);
// manually assign nodes to a backend (optional, should not be needed in most cases)
struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
ggml_backend_sched_reserve(sched, reserve_graph);
// compute
graph = build_graph(sched); // the graph and its tensors are single-use in terms of allocation, multi-use in terms of computation
for (int i = 0; i < 10; ++i) {
ggml_backend_sched_graph_compute(sched, graph); // on the first iteration the graph is allocated automatically
}
// if there are graph inputs:
graph = build_graph(sched); // get a new graph that is not allocated (the metadata for the old graph is freed once ggml_free is called)
ggml_backend_sched_reset(sched); // clear the allocation of the previous graph
ggml_backend_sched_alloc_graph(sched, graph); // explicitly allocate the new graph but do not execute it
ggml_backend_tensor_set(input_tensor, ...); // copy data to the newly allocated graph tensors
ggml_backend_sched_graph_compute(sched, graph); // execute the graph
// as an alternative to the above it is also possible to assign the inputs to a dedicated context and
// allocate them statically via ggml_backend_alloc_ctx_tensors
}
*/
typedef struct ggml_backend_sched * ggml_backend_sched_t;
// Evaluation callback for each node in the graph (set with ggml_backend_sched_set_eval_callback)
// when ask == true, the scheduler wants to know if the user wants to observe this node
// this allows the scheduler to batch nodes together in order to evaluate them in a single call
//
// when ask == false, the scheduler is passing the node tensor to the user for observation
// if the user returns false, the scheduler will cancel the graph compute
//
typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
// Initialize a backend scheduler, backends with low index are given priority over backends with high index
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success
GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
GGML_API ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
// Get the number of splits of the last graph
GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
// Allocate and compute graph on the backend scheduler
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
// Reset all assignments and allocators - must be called before changing the node backends or allocating a new graph.
// This in effect deallocates all tensors that were previously allocated and leaves them with dangling pointers.
// The correct way to use this API is to discard the deallocated tensors and create new ones.
GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);
// Set a callback to be called for each resulting node during graph compute
GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
//
// Utils
//
struct ggml_backend_graph_copy {
ggml_backend_buffer_t buffer;
struct ggml_context * ctx_allocated;
struct ggml_context * ctx_unallocated;
struct ggml_cgraph * graph;
};
// Copy a graph to a different backend
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);
// 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);
// Tensor initialization
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
// CPU buffer types are always available
GGML_API 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);
#ifdef __cplusplus
}
#endif

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#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_blas_init(void);
GGML_BACKEND_API bool ggml_backend_is_blas(ggml_backend_t backend);
// number of threads used for conversion to float
// for openblas and blis, this will also set the number of threads used for blas operations
GGML_BACKEND_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_blas_reg(void);
#ifdef __cplusplus
}
#endif

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/*
* Copyright (c) 2023-2024 The ggml authors
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*/
#pragma once
#include "ggml-backend.h"
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
/**
* @brief Maximum number of CANN devices supported.
*/
#define GGML_CANN_MAX_DEVICES 16
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cann_reg(void);
/**
* @brief Initializes the CANN backend for a specified device.
*
* This function initializes the CANN backend for the given device.
* It verifies the device index, allocates a context, and creates a backend
* instance.
*
* @param device The index of the device to initialize.
* @return A pointer to the initialized backend instance, or nullptr on failure.
*/
GGML_BACKEND_API ggml_backend_t ggml_backend_cann_init(int32_t device);
/**
* @brief Checks if a given backend is a CANN backend.
*
* This function verifies if the provided backend is a CANN backend by comparing
* its GUID with the CANN backend's GUID.
*
* @param backend The backend instance to check.
* @return True if the backend is a CANN backend, false otherwise.
*/
GGML_BACKEND_API bool ggml_backend_is_cann(ggml_backend_t backend);
/**
* @brief Retrieves the CANN buffer type for a specified device.
*
* This function initializes and returns the buffer type interface associated
* with the given device. It ensures thread-safe access using a mutex.
*
* @param device The device index for which to retrieve the buffer type.
* @return A pointer to the buffer type interface for the specified device, or
* nullptr if the device index is out of range.
*/
GGML_BACKEND_API ggml_backend_buffer_type_t
ggml_backend_cann_buffer_type(int32_t device);
/**
* @brief Retrieves the number of CANN devices available.
*
* This function returns the number of CANN devices available based on
* information obtained from `ggml_cann_info()`.
*
* @return The number of CANN devices available.
*/
GGML_BACKEND_API int32_t ggml_backend_cann_get_device_count(void);
/**
* @brief pinned host buffer for use with the CPU backend for faster copies between CPU and NPU.
*
* @return A pointer to the host buffer type interface.
*/
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void);
/**
* @brief Retrieves the description of a specific CANN device.
*
* This function sets the specified device, retrieves the SoC name,
* and writes it into the provided description buffer.
*
* @param device The device index to retrieve the description for.
* @param description Pointer to a buffer where the description will be written.
* @param description_size Size of the description buffer.
*/
GGML_BACKEND_API void ggml_backend_cann_get_device_description(
int32_t device, char* description, size_t description_size);
/**
* @brief Retrieves the memory information of a specific CANN device.
*
* This function sets the specified device, retrieves the free and total
* memory information of the specified type (ACL_HBM_MEM), and stores them
* in the provided pointers.
*
* @param device The device index to retrieve memory information for.
* @param free Pointer to a variable where the free memory size will be stored.
* @param total Pointer to a variable where the total memory size will be
* stored.
*/
GGML_BACKEND_API void ggml_backend_cann_get_device_memory(int32_t device,
size_t* free,
size_t* total);
#ifdef __cplusplus
}
#endif

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#pragma once
#ifndef __cplusplus
#error "This header is for C++ only"
#endif
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "gguf.h"
#include <memory>
// Smart pointers for ggml types
// ggml
struct ggml_context_deleter { void operator()(ggml_context * ctx) { ggml_free(ctx); } };
struct gguf_context_deleter { void operator()(gguf_context * ctx) { gguf_free(ctx); } };
typedef std::unique_ptr<ggml_context, ggml_context_deleter> ggml_context_ptr;
typedef std::unique_ptr<gguf_context, gguf_context_deleter> gguf_context_ptr;
// ggml-alloc
struct ggml_gallocr_deleter { void operator()(ggml_gallocr_t galloc) { ggml_gallocr_free(galloc); } };
typedef std::unique_ptr<ggml_gallocr_t, ggml_gallocr_deleter> ggml_gallocr_ptr;
// ggml-backend
struct ggml_backend_deleter { void operator()(ggml_backend_t backend) { ggml_backend_free(backend); } };
struct ggml_backend_buffer_deleter { void operator()(ggml_backend_buffer_t buffer) { ggml_backend_buffer_free(buffer); } };
struct ggml_backend_event_deleter { void operator()(ggml_backend_event_t event) { ggml_backend_event_free(event); } };
struct ggml_backend_sched_deleter { void operator()(ggml_backend_sched_t sched) { ggml_backend_sched_free(sched); } };
typedef std::unique_ptr<ggml_backend, ggml_backend_deleter> ggml_backend_ptr;
typedef std::unique_ptr<ggml_backend_buffer, ggml_backend_buffer_deleter> ggml_backend_buffer_ptr;
typedef std::unique_ptr<ggml_backend_event, ggml_backend_event_deleter> ggml_backend_event_ptr;
typedef std::unique_ptr<ggml_backend_sched, ggml_backend_sched_deleter> ggml_backend_sched_ptr;

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#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
// the compute plan that needs to be prepared for ggml_graph_compute()
// since https://github.com/ggerganov/ggml/issues/287
struct ggml_cplan {
size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()`
uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
int n_threads;
struct ggml_threadpool * threadpool;
// abort ggml_graph_compute when true
ggml_abort_callback abort_callback;
void * abort_callback_data;
};
// numa strategies
enum ggml_numa_strategy {
GGML_NUMA_STRATEGY_DISABLED = 0,
GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
GGML_NUMA_STRATEGY_ISOLATE = 2,
GGML_NUMA_STRATEGY_NUMACTL = 3,
GGML_NUMA_STRATEGY_MIRROR = 4,
GGML_NUMA_STRATEGY_COUNT
};
GGML_BACKEND_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
GGML_BACKEND_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
GGML_BACKEND_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
GGML_BACKEND_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
GGML_BACKEND_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
GGML_BACKEND_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
GGML_BACKEND_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
GGML_BACKEND_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
GGML_BACKEND_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
GGML_BACKEND_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value);
GGML_BACKEND_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
GGML_BACKEND_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
GGML_BACKEND_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
GGML_BACKEND_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value);
GGML_BACKEND_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params);
GGML_BACKEND_API void ggml_threadpool_free (struct ggml_threadpool * threadpool);
GGML_BACKEND_API int ggml_threadpool_get_n_threads (struct ggml_threadpool * threadpool);
GGML_BACKEND_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool);
GGML_BACKEND_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool);
// ggml_graph_plan() has to be called before ggml_graph_compute()
// when plan.work_size > 0, caller must allocate memory for plan.work_data
GGML_BACKEND_API struct ggml_cplan ggml_graph_plan(
const struct ggml_cgraph * cgraph,
int n_threads, /* = GGML_DEFAULT_N_THREADS */
struct ggml_threadpool * threadpool /* = NULL */ );
GGML_BACKEND_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
// same as ggml_graph_compute() but the work data is allocated as a part of the context
// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
GGML_BACKEND_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
//
// system info
//
// x86
GGML_BACKEND_API int ggml_cpu_has_sse3 (void);
GGML_BACKEND_API int ggml_cpu_has_ssse3 (void);
GGML_BACKEND_API int ggml_cpu_has_avx (void);
GGML_BACKEND_API int ggml_cpu_has_avx_vnni (void);
GGML_BACKEND_API int ggml_cpu_has_avx2 (void);
GGML_BACKEND_API int ggml_cpu_has_f16c (void);
GGML_BACKEND_API int ggml_cpu_has_fma (void);
GGML_BACKEND_API int ggml_cpu_has_avx512 (void);
GGML_BACKEND_API int ggml_cpu_has_avx512_vbmi(void);
GGML_BACKEND_API int ggml_cpu_has_avx512_vnni(void);
GGML_BACKEND_API int ggml_cpu_has_avx512_bf16(void);
GGML_BACKEND_API int ggml_cpu_has_amx_int8 (void);
// ARM
GGML_BACKEND_API int ggml_cpu_has_neon (void);
GGML_BACKEND_API int ggml_cpu_has_arm_fma (void);
GGML_BACKEND_API int ggml_cpu_has_fp16_va (void);
GGML_BACKEND_API int ggml_cpu_has_dotprod (void);
GGML_BACKEND_API int ggml_cpu_has_matmul_int8(void);
GGML_BACKEND_API int ggml_cpu_has_sve (void);
GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes
// other
GGML_BACKEND_API int ggml_cpu_has_riscv_v (void);
GGML_BACKEND_API int ggml_cpu_has_vsx (void);
GGML_BACKEND_API int ggml_cpu_has_wasm_simd (void);
GGML_BACKEND_API int ggml_cpu_has_llamafile (void);
// Internal types and functions exposed for tests and benchmarks
typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
const void * GGML_RESTRICT y, size_t by, int nrc);
struct ggml_type_traits_cpu {
ggml_from_float_t from_float;
ggml_vec_dot_t vec_dot;
enum ggml_type vec_dot_type;
int64_t nrows; // number of rows to process simultaneously
};
GGML_BACKEND_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type);
GGML_BACKEND_API void ggml_cpu_init(void);
//
// CPU backend
//
GGML_BACKEND_API ggml_backend_t ggml_backend_cpu_init(void);
GGML_BACKEND_API bool ggml_backend_is_cpu (ggml_backend_t backend);
GGML_BACKEND_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads);
GGML_BACKEND_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
GGML_BACKEND_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
#ifdef __cplusplus
}
#endif

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#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
#ifdef GGML_USE_HIP
#define GGML_CUDA_NAME "ROCm"
#define GGML_CUBLAS_NAME "hipBLAS"
#elif defined(GGML_USE_MUSA)
#define GGML_CUDA_NAME "MUSA"
#define GGML_CUBLAS_NAME "muBLAS"
#else
#define GGML_CUDA_NAME "CUDA"
#define GGML_CUBLAS_NAME "cuBLAS"
#endif
#define GGML_CUDA_MAX_DEVICES 16
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_cuda_init(int device);
GGML_BACKEND_API bool ggml_backend_is_cuda(ggml_backend_t backend);
// device buffer
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
// split tensor buffer that splits matrices by rows across multiple devices
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
GGML_BACKEND_API int ggml_backend_cuda_get_device_count(void);
GGML_BACKEND_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
GGML_BACKEND_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
GGML_BACKEND_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
GGML_BACKEND_API void ggml_backend_cuda_unregister_host_buffer(void * buffer);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cuda_reg(void);
#ifdef __cplusplus
}
#endif

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#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif
#define GGML_KOMPUTE_MAX_DEVICES 16
struct ggml_vk_device {
int index;
int type; // same as VkPhysicalDeviceType
size_t heapSize;
const char * name;
const char * vendor;
int subgroupSize;
uint64_t bufferAlignment;
uint64_t maxAlloc;
};
struct ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count);
bool ggml_vk_get_device(struct ggml_vk_device * device, size_t memoryRequired, const char * name);
bool ggml_vk_has_vulkan(void);
bool ggml_vk_has_device(void);
struct ggml_vk_device ggml_vk_current_device(void);
//
// backend API
//
// forward declaration
typedef struct ggml_backend * ggml_backend_t;
GGML_BACKEND_API ggml_backend_t ggml_backend_kompute_init(int device);
GGML_BACKEND_API bool ggml_backend_is_kompute(ggml_backend_t backend);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_kompute_reg(void);
#ifdef __cplusplus
}
#endif

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// Note: this description is outdated
//
// An interface allowing to compute ggml_cgraph with Metal
//
// This is a fully functional interface that extends ggml with GPU support for Apple devices.
// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, etc.)
//
// How it works?
//
// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
//
// You only need to make sure that all memory buffers that you used during the graph creation
// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
// used during the graph evaluation to determine the arguments of the compute kernels.
//
// Synchronization between device and host memory (for example for input and output tensors)
// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
//
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#include <stddef.h>
#include <stdbool.h>
struct ggml_tensor;
struct ggml_cgraph;
#ifdef __cplusplus
extern "C" {
#endif
//
// backend API
// user-code should use only these functions
//
GGML_BACKEND_API ggml_backend_t ggml_backend_metal_init(void);
GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_DEPRECATED(
GGML_BACKEND_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size),
"obsoleted by the new device interface - https://github.com/ggerganov/llama.cpp/pull/9713");
GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
GGML_BACKEND_API 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
// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
GGML_BACKEND_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
// capture all command buffers committed the next time `ggml_backend_graph_compute` is called
GGML_BACKEND_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_metal_reg(void);
#ifdef __cplusplus
}
#endif

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#ifndef GGML_OPENCL_H
#define GGML_OPENCL_H
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
//
// backend API
//
GGML_BACKEND_API ggml_backend_t ggml_backend_opencl_init(void);
GGML_BACKEND_API bool ggml_backend_is_opencl(ggml_backend_t backend);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_opencl_reg(void);
#ifdef __cplusplus
}
#endif
#endif // GGML_OPENCL_H

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// This file contains functionality for training models using GGML.
// It is not strictly needed vs. just vanilla GGML but it provides a more high-level interface for common needs such as datasets.
// At the bottom of this file especially there are relatively high-level functions that are suitable use or adaptation in user code.
//
// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de)
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif
struct ggml_opt_dataset;
struct ggml_opt_context;
struct ggml_opt_result;
typedef struct ggml_opt_dataset * ggml_opt_dataset_t;
typedef struct ggml_opt_context * ggml_opt_context_t;
typedef struct ggml_opt_result * ggml_opt_result_t;
// ====== Loss ======
// built-in loss types, i.e. the built-in quantities minimized by the optimizer
// custom loss types can be defined via mean or sum which simply reduce the outputs for all datapoints to a single value
enum ggml_opt_loss_type {
GGML_OPT_LOSS_TYPE_MEAN,
GGML_OPT_LOSS_TYPE_SUM,
GGML_OPT_LOSS_TYPE_CROSS_ENTROPY,
GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR,
};
// ====== Dataset ======
GGML_API ggml_opt_dataset_t ggml_opt_dataset_init(
int64_t ne_datapoint, // number of elements per datapoint
int64_t ne_label, // number of elements per label
int64_t ndata, // total number of datapoints/labels
int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset);
// get underlying tensors that store the data
GGML_API struct ggml_tensor * ggml_opt_dataset_data (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata]
GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label, ndata]
// shuffle idata first datapoints from dataset with RNG from opt_ctx, shuffle all datapoints if idata is negative
GGML_API void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata);
// get batch at position ibatch from dataset and copy the data to data_batch and labels_batch
GGML_API void ggml_opt_dataset_get_batch(
ggml_opt_dataset_t dataset,
struct ggml_tensor * data_batch, // shape = [ne_datapoint, ndata_batch]
struct ggml_tensor * labels_batch, // shape = [ne_label, ndata_batch]
int64_t ibatch);
// ====== Model / Context ======
enum ggml_opt_build_type {
GGML_OPT_BUILD_TYPE_FORWARD,
GGML_OPT_BUILD_TYPE_GRAD,
GGML_OPT_BUILD_TYPE_OPT,
};
// parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
struct ggml_opt_optimizer_params {
// AdamW optimizer parameters
struct {
float alpha; // learning rate
float beta1;
float beta2;
float eps; // epsilon for numerical stability
float wd; // weight decay for AdamW, use 0.0f to disable
} adamw;
};
// callback to calculate optimizer parameters prior to a backward pass
// userdata can be used to pass arbitrary data
typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata);
// returns the default optimizer params (constant)
// userdata is not used
GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata);
// parameters for initializing a new optimization context
struct ggml_opt_params {
ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs
struct ggml_context * ctx_compute; // created in user code, holds non-static tensors
// the forward graph is defined by inputs and outputs
// those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts
struct ggml_tensor * inputs;
struct ggml_tensor * outputs;
enum ggml_opt_loss_type loss_type;
enum ggml_opt_build_type build_type;
int32_t opt_period; // after how many gradient accumulation steps an optimizer step should be done
ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
void * get_opt_pars_ud; // userdata for calculating optimizer parameters
};
// get parameters for an optimization context with defaults set where possible
// parameters for which no sensible defaults exist are supplied as arguments to this function
GGML_API ggml_opt_params ggml_opt_default_params(
ggml_backend_sched_t backend_sched,
struct ggml_context * ctx_compute,
struct ggml_tensor * inputs,
struct ggml_tensor * outputs,
enum ggml_opt_loss_type loss_type);
GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params);
GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx);
// set gradients to zero, initilize loss, and optionally reset the optimizer
GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer);
// get underlying tensors that store data
GGML_API struct ggml_tensor * ggml_opt_inputs( ggml_opt_context_t opt_ctx); // forward graph input tensor
GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor
GGML_API struct ggml_tensor * ggml_opt_labels( ggml_opt_context_t opt_ctx); // labels to compare outputs against
GGML_API struct ggml_tensor * ggml_opt_loss( ggml_opt_context_t opt_ctx); // scalar tensor that contains the loss
GGML_API struct ggml_tensor * ggml_opt_pred( ggml_opt_context_t opt_ctx); // predictions made by outputs
GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels
GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node);
// ====== Optimization Result ======
GGML_API ggml_opt_result_t ggml_opt_result_init();
GGML_API void ggml_opt_result_free(ggml_opt_result_t result);
GGML_API void ggml_opt_result_reset(ggml_opt_result_t result);
// get data from result, uncertainties are optional and can be ignored by passing NULL
GGML_API void ggml_opt_result_ndata( ggml_opt_result_t result, int64_t * ndata); // writes 1 value, number of datapoints
GGML_API void ggml_opt_result_loss( ggml_opt_result_t result, double * loss, double * unc); // writes 1 value
GGML_API void ggml_opt_result_pred( ggml_opt_result_t result, int32_t * pred); // writes ndata values
GGML_API void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc); // writes 1 value
// ====== Computation ======
// do forward pass, increment result if not NULL
GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
// do forward pass, increment result if not NULL, do backward pass
GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
// ############################################################################
// ## The high-level functions start here. They do not depend on any private ##
// ## functions or structs and can be copied to and adapted for user code. ##
// ############################################################################
// ====== Intended Usage ======
//
// 1. Select the appropriate loss for your problem.
// 2. Create a dataset and set the data for the "data" tensor. Also set the "labels" tensor if your loss needs them.
// Setting the shard size to 1 will be fine, it's the granularity with which data is shuffled/loaded (bigger values are faster).
// 3. Create a GGML graph for your model with no_alloc == true. Use two separate contexts for the tensors.
// The first context should contain the model parameters and inputs and be allocated statically in user code.
// The second context should contain all other tensors and will be (re)allocated automatically.
// Due to this automated allocation the data of the second context is not defined when accessed in user code.
// Note that the second dimension of the inputs/outputs are interpreted as the number of datapoints in those tensors.
// 4. Call ggml_opt_fit. If you need more control you can use ggml_opt_epoch instead.
// signature for a callback while evaluating opt_ctx on dataset, called after an evaluation
typedef void (*ggml_opt_epoch_callback)(
bool train, // true after training evaluation, false after validation evaluation
ggml_opt_context_t opt_ctx,
ggml_opt_dataset_t dataset,
ggml_opt_result_t result, // result associated with the dataset subsection
int64_t ibatch, // number of batches that have been evaluated so far
int64_t ibatch_max, // total number of batches in this dataset subsection
int64_t t_start_us); // time at which the evaluation on the dataset subsection was started
// do training on front of dataset, do evaluation only on back of dataset
GGML_API void ggml_opt_epoch(
ggml_opt_context_t opt_ctx,
ggml_opt_dataset_t dataset,
ggml_opt_result_t result_train, // result to increment during training, ignored if NULL
ggml_opt_result_t result_eval, // result to increment during evaluation, ignored if NULL
int64_t idata_split, // data index at which to split training and evaluation
ggml_opt_epoch_callback callback_train,
ggml_opt_epoch_callback callback_eval);
// callback that prints a progress bar on stderr
GGML_API void ggml_opt_epoch_callback_progress_bar(
bool train,
ggml_opt_context_t opt_ctx,
ggml_opt_dataset_t dataset,
ggml_opt_result_t result,
int64_t ibatch,
int64_t ibatch_max,
int64_t t_start_us);
// fit model defined by inputs and outputs to dataset
GGML_API void ggml_opt_fit(
ggml_backend_sched_t backend_sched, // backend scheduler for constructing the compute graphs
ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs
ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch]
ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used
ggml_opt_dataset_t dataset, // dataset with data and optionally also labels
enum ggml_opt_loss_type loss_type, // loss to minimize
ggml_opt_get_optimizer_params get_opt_pars, // callback to get optimizer params, userdata is pointer to epoch (of type int64_t)
int64_t nepoch, // how many times the dataset should be iterated over
int64_t nbatch_logical, // datapoints optimizer step, must be a multiple of ndata_batch in inputs/outputs
float val_split, // fraction of the dataset to use for validation, must be in [0.0f, 1.0f)
bool silent); // whether or not info prints to stderr should be suppressed
#ifdef __cplusplus
}
#endif

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#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
#define GGML_RPC_MAX_SERVERS 16
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint);
GGML_BACKEND_API bool ggml_backend_is_rpc(ggml_backend_t backend);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint);
GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total);
GGML_BACKEND_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_reg(void);
GGML_BACKEND_API ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint);
#ifdef __cplusplus
}
#endif

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//
// MIT license
// Copyright (C) 2024 Intel Corporation
// SPDX-License-Identifier: MIT
//
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#define GGML_SYCL_NAME "SYCL"
#define GGML_SYCL_MAX_DEVICES 48
#ifdef __cplusplus
extern "C" {
#endif
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_sycl_init(int device);
GGML_BACKEND_API bool ggml_backend_is_sycl(ggml_backend_t backend);
// devide buffer
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);
// split tensor buffer that splits matrices by rows across multiple devices
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
GGML_BACKEND_API void ggml_backend_sycl_print_sycl_devices(void);
GGML_BACKEND_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len);
GGML_BACKEND_API void ggml_backend_sycl_get_device_description(int device,
char *description,
size_t description_size);
GGML_BACKEND_API int ggml_backend_sycl_get_device_count();
GGML_BACKEND_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
// SYCL doesn't support registering host memory, keep here for reference
// GGML_BACKEND_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
// GGML_BACKEND_API void ggml_backend_sycl_unregister_host_buffer(void * buffer);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_sycl_reg(void);
#ifdef __cplusplus
}
#endif

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#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
#define GGML_VK_NAME "Vulkan"
#define GGML_VK_MAX_DEVICES 16
GGML_BACKEND_API void ggml_vk_instance_init(void);
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_vk_init(size_t dev_num);
GGML_BACKEND_API bool ggml_backend_is_vk(ggml_backend_t backend);
GGML_BACKEND_API int ggml_backend_vk_get_device_count(void);
GGML_BACKEND_API void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
GGML_BACKEND_API void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_vk_reg(void);
#ifdef __cplusplus
}
#endif

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// This file contains functionality related to "GGUF" files, the binary file format used by ggml.
// GGUF files have the following structure:
//
// 1. File magic "GGUF" (4 bytes).
// 2. File version (uint32_t).
// 3. Number of ggml tensors in file (int64_t).
// 4. Number of key-value-pairs in file (int64_t).
// 5. For each KV pair:
// 1. The key (string).
// 2. The value type (gguf_type).
// 3a. If the value type is GGUF_TYPE_ARRAY:
// 1. The type of the array (gguf_type).
// 2. The number of elements in the array (uint64_t).
// 3. The binary representation of each element in the array.
// 3b. Otherwise:
// 1. The binary representation of the value.
// 6. For each ggml tensor:
// 1. The tensor name (string).
// 2. The number of dimensions of the tensor (uint32_t).
// 3. For each dimension:
// 1. The size of the tensor in the dimension (int64_t).
// 4. The tensor data type (ggml_type).
// 5. The tensor data offset in the tensor data binary blob (uint64_t).
// 7. The tensor data binary blob (optional, aligned).
//
// Strings are serialized as the string length (uint64_t) followed by the C string without the null terminator.
// All enums are stored as int32_t.
// All bool values are stored as int8_t.
// If the special key "general.alignment" (uint32_t) is defined it is used for alignment,
// otherwise GGUF_DEFAULT_ALIGNMENT is used.
//
// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de)
#pragma once
#include "ggml.h"
#include <stdbool.h>
#include <stdint.h>
#define GGUF_MAGIC "GGUF"
#define GGUF_VERSION 3
#define GGUF_KEY_GENERAL_ALIGNMENT "general.alignment"
#define GGUF_DEFAULT_ALIGNMENT 32
#ifdef __cplusplus
extern "C" {
#endif
// types that can be stored as GGUF KV data
enum gguf_type {
GGUF_TYPE_UINT8 = 0,
GGUF_TYPE_INT8 = 1,
GGUF_TYPE_UINT16 = 2,
GGUF_TYPE_INT16 = 3,
GGUF_TYPE_UINT32 = 4,
GGUF_TYPE_INT32 = 5,
GGUF_TYPE_FLOAT32 = 6,
GGUF_TYPE_BOOL = 7,
GGUF_TYPE_STRING = 8,
GGUF_TYPE_ARRAY = 9,
GGUF_TYPE_UINT64 = 10,
GGUF_TYPE_INT64 = 11,
GGUF_TYPE_FLOAT64 = 12,
GGUF_TYPE_COUNT, // marks the end of the enum
};
struct gguf_context;
struct gguf_init_params {
bool no_alloc;
// if not NULL, create a ggml_context and allocate the tensor data in it
struct ggml_context ** ctx;
};
GGML_API struct gguf_context * gguf_init_empty(void);
GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
//GGML_API struct gguf_context * gguf_init_from_buffer(..);
GGML_API void gguf_free(struct gguf_context * ctx);
GGML_API const char * gguf_type_name(enum gguf_type type);
GGML_API uint32_t gguf_get_version (const struct gguf_context * ctx);
GGML_API size_t gguf_get_alignment (const struct gguf_context * ctx);
GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
GGML_API int64_t gguf_get_n_kv(const struct gguf_context * ctx);
GGML_API int64_t gguf_find_key(const struct gguf_context * ctx, const char * key); // returns -1 if key is not found
GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int64_t key_id);
GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int64_t key_id);
GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id);
// will abort if the wrong type is used for the key
GGML_API uint8_t gguf_get_val_u8 (const struct gguf_context * ctx, int64_t key_id);
GGML_API int8_t gguf_get_val_i8 (const struct gguf_context * ctx, int64_t key_id);
GGML_API uint16_t gguf_get_val_u16 (const struct gguf_context * ctx, int64_t key_id);
GGML_API int16_t gguf_get_val_i16 (const struct gguf_context * ctx, int64_t key_id);
GGML_API uint32_t gguf_get_val_u32 (const struct gguf_context * ctx, int64_t key_id);
GGML_API int32_t gguf_get_val_i32 (const struct gguf_context * ctx, int64_t key_id);
GGML_API float gguf_get_val_f32 (const struct gguf_context * ctx, int64_t key_id);
GGML_API uint64_t gguf_get_val_u64 (const struct gguf_context * ctx, int64_t key_id);
GGML_API int64_t gguf_get_val_i64 (const struct gguf_context * ctx, int64_t key_id);
GGML_API double gguf_get_val_f64 (const struct gguf_context * ctx, int64_t key_id);
GGML_API bool gguf_get_val_bool(const struct gguf_context * ctx, int64_t key_id);
GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int64_t key_id);
GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id);
GGML_API size_t gguf_get_arr_n (const struct gguf_context * ctx, int64_t key_id);
// get raw pointer to the first element of the array with the given key_id
// for bool arrays, note that they are always stored as int8 on all platforms (usually this makes no difference)
GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id);
// get ith C string from array with given key_id
GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int64_t key_id, size_t i);
GGML_API int64_t gguf_get_n_tensors (const struct gguf_context * ctx);
GGML_API int64_t gguf_find_tensor (const struct gguf_context * ctx, const char * name); // returns -1 if the tensor is not found
GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int64_t tensor_id);
GGML_API const char * gguf_get_tensor_name (const struct gguf_context * ctx, int64_t tensor_id);
GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int64_t tensor_id);
GGML_API size_t gguf_get_tensor_size (const struct gguf_context * ctx, int64_t tensor_id);
// removes key if it exists, returns id that the key had prior to removal (-1 if it didn't exist)
GGML_API int64_t gguf_remove_key(struct gguf_context * ctx, const char * key);
// overrides an existing KV pair or adds a new one, the new KV pair is always at the back
GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);
GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val);
GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);
GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);
GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val);
GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val);
GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);
GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
// creates a new array with n elements of the given type and copies the corresponding number of bytes from data
GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, size_t n);
// creates a new array with n strings and copies the corresponding strings from data
GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, size_t n);
// set or add KV pairs from another context
GGML_API void gguf_set_kv(struct gguf_context * ctx, const struct gguf_context * src);
// add tensor to GGUF context, tensor name must be unique
GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
// after changing a tensor's type, the offsets of all tensors with higher indices are immediately recalculated
// in such a way that the tensor data remains as one contiguous block (except for padding)
GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
// assumes that at least gguf_get_tensor_size bytes can be read from data
GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data);
// writing gguf files can be done in 3 ways:
//
// - write the entire gguf_context to a binary file in a single pass:
//
// gguf_write_to_file(ctx, fname, /*only_meta =*/ false);
//
// - write only the meta data to a file, then re-open the file and append the tensor data:
//
// gguf_write_to_file(ctx, fname, /*only_meta =*/ true);
// FILE * f = fopen(fname, "ab");
// fwrite(f, ...); // write tensor data
// fclose(f);
//
// - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
//
// FILE * f = fopen(fname, "wb");
// const size_t size_meta = gguf_get_meta_size(ctx);
// fseek(f, size_meta, SEEK_SET);
// fwrite(f, ...); // write tensor data
// void * data = malloc(size_meta);
// gguf_get_meta_data(ctx, data);
// rewind(f);
// fwrite(data, 1, data, f);
// free(data);
// fclose(f);
//
// write the entire context to a binary file
GGML_API bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
// get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
// writes the meta data to pointer "data"
GGML_API void gguf_get_meta_data(const struct gguf_context * ctx, void * data);
#ifdef __cplusplus
}
#endif

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include(CheckCXXCompilerFlag)
add_compile_definitions(GGML_SCHED_MAX_COPIES=${GGML_SCHED_MAX_COPIES})
# enable libstdc++ assertions for debug builds
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
endif()
if (NOT MSVC)
if (GGML_SANITIZE_THREAD)
add_compile_options(-fsanitize=thread)
link_libraries (-fsanitize=thread)
endif()
if (GGML_SANITIZE_ADDRESS)
add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
link_libraries (-fsanitize=address)
endif()
if (GGML_SANITIZE_UNDEFINED)
add_compile_options(-fsanitize=undefined)
link_libraries (-fsanitize=undefined)
endif()
endif()
function(ggml_get_flags CCID CCVER)
set(C_FLAGS "")
set(CXX_FLAGS "")
if (CCID MATCHES "Clang")
set(C_FLAGS -Wunreachable-code-break -Wunreachable-code-return)
set(CXX_FLAGS -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi)
if (
(CCID STREQUAL "Clang" AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR
(CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0)
)
list(APPEND C_FLAGS -Wdouble-promotion)
endif()
elseif (CCID STREQUAL "GNU")
set(C_FLAGS -Wdouble-promotion)
set(CXX_FLAGS -Wno-array-bounds)
if (CCVER VERSION_GREATER_EQUAL 8.1.0)
list(APPEND CXX_FLAGS -Wextra-semi)
endif()
endif()
set(GF_C_FLAGS ${C_FLAGS} PARENT_SCOPE)
set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE)
endfunction()
if (GGML_FATAL_WARNINGS)
if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
list(APPEND C_FLAGS -Werror)
list(APPEND CXX_FLAGS -Werror)
elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
add_compile_options(/WX)
endif()
endif()
if (GGML_ALL_WARNINGS)
if (NOT MSVC)
list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
-Werror=implicit-int -Werror=implicit-function-declaration)
list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn)
list(APPEND C_FLAGS ${WARNING_FLAGS})
list(APPEND CXX_FLAGS ${WARNING_FLAGS})
ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
"$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
else()
# todo : msvc
set(C_FLAGS "")
set(CXX_FLAGS "")
endif()
endif()
if (GGML_LTO)
include(CheckIPOSupported)
check_ipo_supported(RESULT result OUTPUT output)
if (result)
set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE)
else()
message(WARNING "IPO is not supported: ${output}")
endif()
endif()
if (GGML_CCACHE)
find_program(GGML_CCACHE_FOUND ccache)
find_program(GGML_SCCACHE_FOUND sccache)
if (GGML_CCACHE_FOUND OR GGML_SCCACHE_FOUND)
if(GGML_CCACHE_FOUND)
set(GGML_CCACHE_VARIANT ccache)
else()
set(GGML_CCACHE_VARIANT sccache)
endif()
# TODO: should not be set globally
set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${GGML_CCACHE_VARIANT}")
set(ENV{CCACHE_SLOPPINESS} time_macros)
message(STATUS "${GGML_CCACHE_VARIANT} found, compilation results will be cached. Disable with GGML_CCACHE=OFF.")
else()
message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF")
endif ()
endif()
# this version of Apple ld64 is buggy
execute_process(
COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
ERROR_VARIABLE output
OUTPUT_QUIET
)
if (output MATCHES "dyld-1015\.7")
add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)
endif()
# architecture specific
# TODO: probably these flags need to be tweaked on some architectures
# feel free to update the Makefile for your architecture and send a pull request or issue
message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
if (MSVC)
string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR)
message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}")
else ()
set(CMAKE_GENERATOR_PLATFORM_LWR "")
endif ()
if (NOT MSVC)
if (GGML_STATIC)
add_link_options(-static)
if (MINGW)
add_link_options(-static-libgcc -static-libstdc++)
endif()
endif()
if (GGML_GPROF)
add_compile_options(-pg)
endif()
endif()
if (MINGW)
# Target Windows 8 for PrefetchVirtualMemory
add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
endif()
#
# POSIX conformance
#
# clock_gettime came in POSIX.1b (1993)
# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
# posix_memalign came in POSIX.1-2001 / SUSv3
# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)
# Somehow in OpenBSD whenever POSIX conformance is specified
# some string functions rely on locale_t availability,
# which was introduced in POSIX.1-2008, forcing us to go higher
if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
add_compile_definitions(_XOPEN_SOURCE=700)
else()
add_compile_definitions(_XOPEN_SOURCE=600)
endif()
# Data types, macros and functions related to controlling CPU affinity and
# some memory allocation are available on Linux through GNU extensions in libc
if (CMAKE_SYSTEM_NAME MATCHES "Linux" OR CMAKE_SYSTEM_NAME MATCHES "Android")
add_compile_definitions(_GNU_SOURCE)
endif()
# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,
# and on macOS its availability depends on enabling Darwin extensions
# similarly on DragonFly, enabling BSD extensions is necessary
if (
CMAKE_SYSTEM_NAME MATCHES "Darwin" OR
CMAKE_SYSTEM_NAME MATCHES "iOS" OR
CMAKE_SYSTEM_NAME MATCHES "tvOS" OR
CMAKE_SYSTEM_NAME MATCHES "DragonFly"
)
add_compile_definitions(_DARWIN_C_SOURCE)
endif()
# alloca is a non-standard interface that is not visible on BSDs when
# POSIX conformance is specified, but not all of them provide a clean way
# to enable it in such cases
if (CMAKE_SYSTEM_NAME MATCHES "FreeBSD")
add_compile_definitions(__BSD_VISIBLE)
endif()
if (CMAKE_SYSTEM_NAME MATCHES "NetBSD")
add_compile_definitions(_NETBSD_SOURCE)
endif()
if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
add_compile_definitions(_BSD_SOURCE)
endif()
if (WIN32)
add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
endif()
# ggml
if (GGML_BACKEND_DL AND NOT BUILD_SHARED_LIBS)
message(FATAL_ERROR "GGML_BACKEND_DL requires BUILD_SHARED_LIBS")
endif()
add_library(ggml-base
../include/ggml.h
../include/ggml-alloc.h
../include/ggml-backend.h
../include/ggml-cpp.h
../include/ggml-opt.h
../include/gguf.h
ggml.c
ggml-alloc.c
ggml-backend.cpp
ggml-opt.cpp
ggml-threading.cpp
ggml-threading.h
ggml-quants.c
ggml-quants.h
gguf.cpp)
target_include_directories(ggml-base PRIVATE .)
add_library(ggml
ggml-backend-reg.cpp)
target_link_libraries(ggml PUBLIC ggml-base)
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
target_link_libraries(ggml PRIVATE dl)
endif()
function(ggml_add_backend_library backend)
if (GGML_BACKEND_DL)
add_library(${backend} MODULE ${ARGN})
# write the shared library to the output directory
set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
add_dependencies(ggml ${backend})
else()
add_library(${backend} ${ARGN})
target_link_libraries(ggml PUBLIC ${backend})
install(TARGETS ${backend} LIBRARY)
endif()
target_link_libraries(${backend} PRIVATE ggml-base)
target_include_directories(${backend} PRIVATE ..)
if (${BUILD_SHARED_LIBS})
target_compile_definitions(${backend} PRIVATE GGML_BACKEND_BUILD)
target_compile_definitions(${backend} PUBLIC GGML_BACKEND_SHARED)
endif()
if(NOT GGML_AVAILABLE_BACKENDS)
set(GGML_AVAILABLE_BACKENDS "${backend}"
CACHE INTERNAL "List of backends for cmake package")
else()
list(FIND GGML_AVAILABLE_BACKENDS "${backend}" has_backend)
if(has_backend EQUAL -1)
set(GGML_AVAILABLE_BACKENDS "${GGML_AVAILABLE_BACKENDS};${backend}"
CACHE INTERNAL "List of backends for cmake package")
endif()
endif()
endfunction()
function(ggml_add_backend backend)
string(TOUPPER "GGML_${backend}" backend_id)
if (${backend_id})
string(TOLOWER "ggml-${backend}" backend_target)
add_subdirectory(${backend_target})
message(STATUS "Including ${backend} backend")
if (NOT GGML_BACKEND_DL)
string(TOUPPER "GGML_USE_${backend}" backend_use)
target_compile_definitions(ggml PUBLIC ${backend_use})
endif()
endif()
endfunction()
function(ggml_add_cpu_backend_variant tag_name)
set(GGML_CPU_TAG_NAME ${tag_name})
# other: OPENMP LLAMAFILE CPU_HBM
foreach (feat NATIVE
AVX AVX2 AVX_VNNI FMA F16C
AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16
AMX_TILE AMX_INT8 AMX_BF16)
set(GGML_${feat} OFF)
endforeach()
foreach (feat ${ARGN})
set(GGML_${feat} ON)
endforeach()
ggml_add_cpu_backend_variant_impl(${tag_name})
endfunction()
ggml_add_backend(CPU)
if (GGML_CPU_ALL_VARIANTS)
if (NOT GGML_BACKEND_DL)
message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL")
endif()
ggml_add_cpu_backend_variant(sandybridge AVX)
ggml_add_cpu_backend_variant(haswell AVX F16C AVX2 FMA)
ggml_add_cpu_backend_variant(skylakex AVX F16C AVX2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake AVX F16C AVX2 FMA AVX_VNNI)
if (NOT MSVC)
# MSVC doesn't support AMX
ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
endif()
elseif (GGML_CPU)
ggml_add_cpu_backend_variant_impl("")
endif()
ggml_add_backend(BLAS)
ggml_add_backend(CANN)
ggml_add_backend(CUDA)
ggml_add_backend(HIP)
ggml_add_backend(Kompute)
ggml_add_backend(METAL)
ggml_add_backend(MUSA)
ggml_add_backend(RPC)
ggml_add_backend(SYCL)
ggml_add_backend(Vulkan)
ggml_add_backend(OpenCL)
foreach (target ggml-base ggml)
target_include_directories(${target} PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)
target_compile_features (${target} PRIVATE c_std_11 cxx_std_17) # don't bump
endforeach()
target_link_libraries(ggml-base PRIVATE Threads::Threads)
find_library(MATH_LIBRARY m)
if (MATH_LIBRARY)
if (NOT WIN32 OR NOT DEFINED ENV{ONEAPI_ROOT})
target_link_libraries(ggml-base PRIVATE m)
endif()
endif()
if (CMAKE_SYSTEM_NAME MATCHES "Android")
target_link_libraries(ggml-base PRIVATE dl)
endif()
if (BUILD_SHARED_LIBS)
foreach (target ggml-base ggml)
set_target_properties(${target} PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(${target} PRIVATE GGML_BUILD)
target_compile_definitions(${target} PUBLIC GGML_SHARED)
endforeach()
endif()

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107
packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-amx/CMakeLists.txt vendored Normal file
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if (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
(NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$") AND
CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 11.0)
message(STATUS "Using AMX")
file(GLOB GGML_HEADERS_AMX "*.h")
list(APPEND GGML_HEADERS_AMX "../../include/ggml-amx.h")
file(GLOB GGML_SOURCES_AMX "*.cpp")
add_library(ggml-amx
${GGML_HEADERS_AMX}
${GGML_SOURCES_AMX})
target_link_libraries(ggml-amx PRIVATE ggml-base)
target_include_directories(ggml-amx PRIVATE . ..)
# this is duplicated from the CPU backend, since the AMX backend also depends on the architecture flags
# TODO: integrate AMX backend into the CPU backend
if (MSVC)
# instruction set detection for MSVC only
if (GGML_NATIVE)
# TODO: improve, should not reference files from the parent folder
include(../ggml-cpu/cmake/FindSIMD.cmake)
endif ()
if (GGML_AVX512)
list(APPEND ARCH_FLAGS /arch:AVX512)
# MSVC has no compile-time flags enabling specific
# AVX512 extensions, neither it defines the
# macros corresponding to the extensions.
# Do it manually.
if (GGML_AVX512_VBMI)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VBMI__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VBMI__>)
endif()
if (GGML_AVX512_VNNI)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VNNI__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
endif()
if (GGML_AVX512_BF16)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512BF16__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512BF16__>)
endif()
if (GGML_AMX_TILE)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AMX_TILE__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AMX_TILE__>)
endif()
if (GGML_AMX_INT8)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AMX_INT8__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AMX_INT8__>)
endif()
if (GGML_AMX_BF16)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AMX_BF16__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AMX_BF16__>)
endif()
elseif (GGML_AVX2)
list(APPEND ARCH_FLAGS /arch:AVX2)
elseif (GGML_AVX)
list(APPEND ARCH_FLAGS /arch:AVX)
endif()
else()
if (GGML_NATIVE)
list(APPEND ARCH_FLAGS -march=native)
endif()
if (GGML_F16C)
list(APPEND ARCH_FLAGS -mf16c)
endif()
if (GGML_FMA)
list(APPEND ARCH_FLAGS -mfma)
endif()
if (GGML_AVX)
list(APPEND ARCH_FLAGS -mavx)
endif()
if (GGML_AVX2)
list(APPEND ARCH_FLAGS -mavx2)
endif()
if (GGML_AVX512)
list(APPEND ARCH_FLAGS -mavx512f)
list(APPEND ARCH_FLAGS -mavx512dq)
list(APPEND ARCH_FLAGS -mavx512bw)
endif()
if (GGML_AVX512_VBMI)
list(APPEND ARCH_FLAGS -mavx512vbmi)
endif()
if (GGML_AVX512_VNNI)
list(APPEND ARCH_FLAGS -mavx512vnni)
endif()
if (GGML_AVX512_BF16)
list(APPEND ARCH_FLAGS -mavx512bf16)
endif()
if (GGML_AMX_TILE)
list(APPEND ARCH_FLAGS -mamx-tile)
endif()
if (GGML_AMX_INT8)
list(APPEND ARCH_FLAGS -mamx-int8)
endif()
if (GGML_AMX_BF16)
list(APPEND ARCH_FLAGS -mamx-bf16)
endif()
endif()
target_compile_options(ggml-amx PRIVATE ${ARCH_FLAGS})
else()
set(GGML_AMX OFF PARENT_SCOPE)
message(WARNING "AMX requires x86 and gcc version > 11.0. Turning off GGML_AMX.")
endif()

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#pragma once
#include "ggml.h"
// hack until AMX is moved into the CPU backend
#include "../ggml-cpu/ggml-cpu-impl.h" // <immintrin.h>
#include <algorithm>
#include <memory>
#include <type_traits>
#if defined(_OPENMP)
#include <omp.h>
#endif
#define TILE_M 16
#define TILE_N 16
#define TILE_K 32
#define VNNI_BLK 4
#define AMX_BLK_SIZE 32
#define TMM0 0
#define TMM1 1
#define TMM2 2
#define TMM3 3
#define TMM4 4
#define TMM5 5
#define TMM6 6
#define TMM7 7
// parallel routines
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
inline T div_up(T x, T y) { return (x + y - 1) / y; }
template <typename T>
inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
#if 0
// onednn partition pattern
T& n_my = n_end;
if (nth <= 1 || n == 0) {
n_start = 0;
n_my = n;
} else {
T n1 = div_up(n, nth);
T n2 = n1 - 1;
T T1 = n - n2 * nth;
n_my = ith < T1 ? n1 : n2;
n_start = ith <= T1 ? ith*n1 : T1 * n1 + (ith - T1) * n2;
}
n_end += n_start;
#else
// pytorch aten partition pattern
T n_my = div_up(n, nth);
n_start = ith * n_my;
n_end = std::min(n_start + n_my, n);
#endif
}
template <typename func_t>
inline void parallel_for(int nth, int n, const func_t& f) {
#if defined(_OPENMP)
#pragma omp parallel num_threads(nth)
{
//int nth = omp_get_num_threads();
int ith = omp_get_thread_num();
int tbegin, tend;
balance211(n, nth, ith, tbegin, tend);
f(tbegin, tend);
}
#else
f(0, n);
GGML_UNUSED(nth);
#endif
}
// quantized types that have AMX support
inline bool qtype_has_amx_kernels(const enum ggml_type type) {
// TODO: fix padding for vnni format
return (type == GGML_TYPE_Q4_0) ||
(type == GGML_TYPE_Q4_1);
//(type == GGML_TYPE_Q8_0) ||
//(type == GGML_TYPE_Q4_K) ||
//(type == GGML_TYPE_Q5_K) ||
//(type == GGML_TYPE_Q6_K) ||
//(type == GGML_TYPE_IQ4_XS);
}
// ggml backend context
struct ggml_backend_amx_context {
int n_threads = GGML_DEFAULT_N_THREADS;
std::unique_ptr<char[]> work_data;
size_t work_size = 0;
};

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#include "ggml-amx.h"
#include "ggml-amx/common.h"
#include "ggml-amx/mmq.h"
#include "ggml-backend-impl.h"
#include "ggml-impl.h"
#if defined(__gnu_linux__)
#include <sys/syscall.h>
#include <unistd.h>
#endif
#include <cstdlib>
#include <cstring>
#include <memory>
#if defined(__AMX_INT8__)
// AMX buffer interface
static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
}
static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) {
return (void *)(buffer->context);
}
static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
memset((char *)tensor->data + offset, value, size);
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
if (qtype_has_amx_kernels(tensor->type)) {
ggml_backend_amx_convert_weight(tensor, data, offset, size);
} else {
memcpy((char *)tensor->data + offset, data, size);
}
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(!qtype_has_amx_kernels(tensor->type));
memcpy(data, (const char *)tensor->data + offset, size);
GGML_UNUSED(buffer);
}
static bool ggml_backend_amx_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)) {
if (qtype_has_amx_kernels(src->type)) {
ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_backend_amx_get_alloc_size(dst));
} else {
memcpy(dst->data, src->data, ggml_nbytes(src));
}
return true;
}
return false;
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
memset(buffer->context, value, buffer->size);
}
static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
/* .free_buffer = */ ggml_backend_amx_buffer_free_buffer,
/* .get_base = */ ggml_backend_amx_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
/* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_amx_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_amx_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor,
/* .clear = */ ggml_backend_amx_buffer_clear,
/* .reset = */ NULL,
};
static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "AMX";
GGML_UNUSED(buft);
}
static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
void * data = aligned_alloc(TENSOR_ALIGNMENT, size);
if (data == NULL) {
fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
return NULL;
}
return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size);
}
static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
return TENSOR_ALIGNMENT;
GGML_UNUSED(buft);
}
static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) {
return ggml_backend_amx_get_alloc_size(tensor);
GGML_UNUSED(buft);
}
static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
return false;
GGML_UNUSED(buft);
}
ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() {
static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = {
/* .iface = */ {
/* .get_name = */ ggml_backend_amx_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size,
/* .is_host = */ ggml_backend_amx_buffer_type_is_host,
},
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0),
/* .context = */ NULL,
};
return &ggml_backend_buffer_type_amx;
}
// backend interface
static const char * ggml_backend_amx_name(ggml_backend_t backend) {
return "AMX";
GGML_UNUSED(backend);
}
static void ggml_backend_amx_free(ggml_backend_t backend) {
ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context;
delete ctx;
delete backend;
}
static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context;
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * node = cgraph->nodes[i];
switch (node->op) {
case GGML_OP_MUL_MAT:
ggml_backend_amx_mul_mat(ctx, node);
break;
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
break;
default:
fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node));
GGML_ASSERT(false);
}
}
return GGML_STATUS_SUCCESS;
GGML_UNUSED(backend);
}
static struct ggml_backend_i ggml_backend_amx_i = {
/* .get_name = */ ggml_backend_amx_name,
/* .free = */ ggml_backend_amx_free,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ NULL,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_amx_graph_compute,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
static ggml_guid_t ggml_backend_amx_guid() {
static ggml_guid guid = { 0x13, 0xb8, 0xa4, 0xc4, 0xba, 0xfe, 0x51, 0x67, 0x87, 0x44, 0x55, 0x15, 0xb2, 0x35, 0x62, 0x3e };
return &guid;
}
#define ARCH_GET_XCOMP_PERM 0x1022
#define ARCH_REQ_XCOMP_PERM 0x1023
#define XFEATURE_XTILECFG 17
#define XFEATURE_XTILEDATA 18
static bool ggml_amx_init() {
#if defined(__gnu_linux__)
if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) {
fprintf(stderr, "AMX is not ready to be used!\n");
return false;
}
return true;
#elif defined(_WIN32)
return true;
#endif
}
ggml_backend_t ggml_backend_amx_init() {
// invoke a Linux system call to request access to AMX features
ggml_amx_init();
// backend context
ggml_backend_amx_context * ctx = new ggml_backend_amx_context;
// ggml amx backend
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_amx_guid(),
/* .interface = */ ggml_backend_amx_i,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0),
/* .context = */ ctx,
};
return backend;
}
bool ggml_backend_is_amx(ggml_backend_t backend) {
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_amx_guid());
}
void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) {
GGML_ASSERT(ggml_backend_is_amx(backend_amx));
ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend_amx->context;
ctx->n_threads = n_threads;
}
// device interface
static const char * ggml_backend_amx_device_get_name(ggml_backend_dev_t dev) {
return "AMX";
GGML_UNUSED(dev);
}
static const char * ggml_backend_amx_device_get_description(ggml_backend_dev_t dev) {
return "Intel Advanced Matrix Extensions";
GGML_UNUSED(dev);
}
static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
// TODO
*free = 0;
*total = 0;
GGML_UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_ACCEL;
GGML_UNUSED(dev);
}
static void ggml_backend_amx_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_amx_device_get_name(dev);
props->description = ggml_backend_amx_device_get_description(dev);
props->type = ggml_backend_amx_device_get_type(dev);
ggml_backend_amx_device_get_memory(dev, &props->memory_free, &props->memory_total);
// `buffer_from_host_ptr` is intended to be used in mmap, when memory layout unchanged
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ false,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_amx_device_init(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_amx_init();
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_amx_device_get_buffer_type(ggml_backend_dev_t dev) {
return ggml_backend_amx_buffer_type();
GGML_UNUSED(dev);
}
static bool ggml_backend_amx_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
// handle only 2d gemm for now
auto is_contiguous_2d = [](const struct ggml_tensor * t) {
return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1;
};
switch (op->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_MUL_MAT: {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const enum ggml_type type = src0->type;
const int64_t ne0 = op->ne[0];
// amx kernels enables for Q4_0, Q4_1, Q8_0, F16
// Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256
bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16);
bool can_use_amx =
is_contiguous_2d(src0) && // src0 must be contiguous
is_contiguous_2d(src1) && // src1 must be contiguous
src1->type == GGML_TYPE_F32 && // src1 must be float32
has_amx_kernels && // with amx kernel impls
ne0 % (TILE_N * 2) == 0; // out_features is 32x
return can_use_amx;
}
default:
return false;
}
GGML_UNUSED(dev);
}
static bool ggml_backend_amx_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name;
GGML_UNUSED(dev);
}
static const struct ggml_backend_device_i ggml_backend_amx_device_i = {
/* .get_name = */ ggml_backend_amx_device_get_name,
/* .get_description = */ ggml_backend_amx_device_get_description,
/* .get_memory = */ ggml_backend_amx_device_get_memory,
/* .get_type = */ ggml_backend_amx_device_get_type,
/* .get_props = */ ggml_backend_amx_device_get_props,
/* .init_backend = */ ggml_backend_amx_device_init,
/* .get_buffer_type = */ ggml_backend_amx_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ NULL,
/* .supports_op = */ ggml_backend_amx_device_supports_op,
/* .supports_buft = */ ggml_backend_amx_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// backend reg interface
static const char * ggml_backend_amx_reg_get_name(ggml_backend_reg_t reg) {
return "AMX";
GGML_UNUSED(reg);
}
static size_t ggml_backend_amx_reg_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_amx_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_device ggml_backend_amx_device = {
/* .iface = */ ggml_backend_amx_device_i,
/* .reg = */ reg,
/* .context = */ nullptr,
};
return &ggml_backend_amx_device;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static void * ggml_backend_amx_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_amx_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(name);
}
static const struct ggml_backend_reg_i ggml_backend_amx_reg_i = {
/* .get_name = */ ggml_backend_amx_reg_get_name,
/* .get_device_count = */ ggml_backend_amx_reg_get_device_count,
/* .get_device = */ ggml_backend_amx_reg_get_device,
/* .get_proc_address = */ ggml_backend_amx_get_proc_address,
};
ggml_backend_reg_t ggml_backend_amx_reg(void) {
static struct ggml_backend_reg ggml_backend_amx_reg = {
/* .iface = */ ggml_backend_amx_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_amx_reg;
}
#else // if defined(__AMX_INT8__)
ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void) {
return nullptr;
}
bool ggml_backend_is_amx(ggml_backend_t backend) {
GGML_UNUSED(backend);
return false;
}
ggml_backend_t ggml_backend_amx_init(void) {
fprintf(stderr, "GGML is not compiled with AMX support!\n");
return nullptr;
}
void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) {
fprintf(stderr, "GGML is not compiled with AMX support!\n");
GGML_UNUSED(backend_amx);
GGML_UNUSED(n_threads);
}
ggml_backend_reg_t ggml_backend_amx_reg(void) {
return nullptr;
}
#endif

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#pragma once
#include "common.h"
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif
size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor);
void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst);
#ifdef __cplusplus
}
#endif

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#pragma once
// ggml-backend internal header
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
#define GGML_BACKEND_API_VERSION 1
//
// Backend buffer type
//
struct ggml_backend_buffer_type_i {
const char * (*get_name) (ggml_backend_buffer_type_t buft);
// allocate a buffer of this type
ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
// tensor alignment
size_t (*get_alignment) (ggml_backend_buffer_type_t buft);
// (optional) max buffer size that can be allocated (defaults to SIZE_MAX)
size_t (*get_max_size) (ggml_backend_buffer_type_t buft);
// (optional) data size needed to allocate the tensor, including padding (defaults to ggml_nbytes)
size_t (*get_alloc_size)(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor);
// (optional) check if tensor data is in host memory and uses standard ggml tensor layout (defaults to false)
bool (*is_host) (ggml_backend_buffer_type_t buft);
};
struct ggml_backend_buffer_type {
struct ggml_backend_buffer_type_i iface;
ggml_backend_dev_t device;
void * context;
};
//
// Backend buffer
//
struct ggml_backend_buffer_i {
// (optional) free the buffer
void (*free_buffer) (ggml_backend_buffer_t buffer);
// base address of the buffer
void * (*get_base) (ggml_backend_buffer_t buffer);
// (optional) initialize a tensor in the buffer (eg. add tensor extras)
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
// tensor data access
void (*memset_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
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);
// (optional) tensor copy: dst is in the buffer, src may be in any buffer, including buffers from a different backend (return false if not supported)
bool (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst);
// clear the entire buffer
void (*clear) (ggml_backend_buffer_t buffer, uint8_t value);
// (optional) reset any internal state due to tensor initialization, such as tensor extras
void (*reset) (ggml_backend_buffer_t buffer);
};
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_buffer_type_t buft;
void * context;
size_t size;
enum ggml_backend_buffer_usage usage;
};
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
ggml_backend_buffer_type_t buft,
struct ggml_backend_buffer_i iface,
void * context,
size_t size);
// do not use directly, use ggml_backend_tensor_copy instead
GGML_API bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst);
// multi-buffer
// buffer that contains a collection of buffers
GGML_API ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers);
GGML_API bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer);
GGML_API void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
//
// Backend (stream)
//
struct ggml_backend_i {
const char * (*get_name)(ggml_backend_t backend);
void (*free)(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_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
// (optional) complete all pending operations (required if the backend supports async operations)
void (*synchronize)(ggml_backend_t backend);
// (optional) graph plans (not used currently)
// 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);
// update the plan with a new graph - this should be faster than creating a new plan when the graph has the same topology
void (*graph_plan_update) (ggml_backend_t backend, ggml_backend_graph_plan_t plan, const struct ggml_cgraph * cgraph);
// compute the graph with the plan
enum ggml_status (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph (always async if supported by the backend)
enum ggml_status (*graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
// (optional) event synchronization
// record an event on this stream
void (*event_record)(ggml_backend_t backend, ggml_backend_event_t event);
// wait for an event on on a different stream
void (*event_wait) (ggml_backend_t backend, ggml_backend_event_t event);
};
struct ggml_backend {
ggml_guid_t guid;
struct ggml_backend_i iface;
ggml_backend_dev_t device;
void * context;
};
struct ggml_backend_event {
struct ggml_backend_device * device;
void * context;
};
//
// Backend device
//
// Note: if additional properties are needed, we should add a struct with all of them
// the current functions to obtain the properties can remain, since they are more convenient for often used properties
struct ggml_backend_device_i {
// device name: short identifier for this device, such as "CPU" or "CUDA0"
const char * (*get_name)(ggml_backend_dev_t dev);
// device description: short informative description of the device, could be the model name
const char * (*get_description)(ggml_backend_dev_t dev);
// device memory in bytes
void (*get_memory)(ggml_backend_dev_t dev, size_t * free, size_t * total);
// device type
enum ggml_backend_dev_type (*get_type)(ggml_backend_dev_t dev);
// device properties
void (*get_props)(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props);
// backend (stream) initialization
ggml_backend_t (*init_backend)(ggml_backend_dev_t dev, const char * params);
// preferred buffer type
ggml_backend_buffer_type_t (*get_buffer_type)(ggml_backend_dev_t dev);
// (optional) host buffer type (in system memory, typically this is a pinned memory buffer for faster transfers between host and device)
ggml_backend_buffer_type_t (*get_host_buffer_type)(ggml_backend_dev_t dev);
// (optional) buffer from pointer: create a buffer from a host pointer (useful for memory mapped models and importing data from other libraries)
ggml_backend_buffer_t (*buffer_from_host_ptr)(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size);
// check if the backend can compute an operation
bool (*supports_op)(ggml_backend_dev_t dev, const struct ggml_tensor * op);
// check if the backend can use tensors allocated in a buffer type
bool (*supports_buft)(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft);
// (optional) check if the backend wants to run an operation, even if the weights are allocated in an incompatible buffer
// these should be expensive operations that may benefit from running on this backend instead of the CPU backend
bool (*offload_op)(ggml_backend_dev_t dev, const struct ggml_tensor * op);
// (optional) event synchronization
ggml_backend_event_t (*event_new) (ggml_backend_dev_t dev);
void (*event_free) (ggml_backend_dev_t dev, ggml_backend_event_t event);
void (*event_synchronize) (ggml_backend_dev_t dev, ggml_backend_event_t event);
};
struct ggml_backend_device {
struct ggml_backend_device_i iface;
ggml_backend_reg_t reg;
void * context;
};
//
// Backend (reg)
//
struct ggml_backend_reg_i {
const char * (*get_name)(ggml_backend_reg_t reg);
// enumerate available devices
size_t (*get_device_count)(ggml_backend_reg_t reg);
ggml_backend_dev_t (*get_device)(ggml_backend_reg_t reg, size_t index);
// (optional) get a pointer to a function in the backend
// backends can add custom functions that are not part of the standard ggml-backend interface
void * (*get_proc_address)(ggml_backend_reg_t reg, const char * name);
};
struct ggml_backend_reg {
int api_version; // initialize to GGML_BACKEND_API_VERSION
struct ggml_backend_reg_i iface;
void * context;
};
// Internal backend registry API
GGML_API void ggml_backend_register(ggml_backend_reg_t reg);
// Add backend dynamic loading support to the backend
// Initialize the backend
typedef ggml_backend_reg_t (*ggml_backend_init_t)(void);
// Optional: obtain a score for the backend based on the system configuration
// Higher scores are preferred, 0 means the backend is not supported in the current system
typedef int (*ggml_backend_score_t)(void);
#ifdef GGML_BACKEND_DL
# ifdef __cplusplus
# define GGML_BACKEND_DL_IMPL(reg_fn) \
extern "C" { \
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_init(void); \
} \
ggml_backend_reg_t ggml_backend_init(void) { \
return reg_fn(); \
}
# define GGML_BACKEND_DL_SCORE_IMPL(score_fn) \
extern "C" { \
GGML_BACKEND_API int ggml_backend_score(void); \
} \
int ggml_backend_score(void) { \
return score_fn(); \
}
# else
# define GGML_BACKEND_DL_IMPL(reg_fn) \
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_init(void); \
ggml_backend_reg_t ggml_backend_init(void) { \
return reg_fn(); \
}
# define GGML_BACKEND_DL_SCORE_IMPL(score_fn) \
GGML_BACKEND_API int ggml_backend_score(void); \
int ggml_backend_score(void) { \
return score_fn(); \
}
# endif
#else
# define GGML_BACKEND_DL_IMPL(reg_fn)
# define GGML_BACKEND_DL_SCORE_IMPL(score_fn)
#endif
#ifdef __cplusplus
}
#endif

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-backend-reg.cpp vendored Normal file
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#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include <algorithm>
#include <codecvt>
#include <cstring>
#include <filesystem>
#include <locale>
#include <memory>
#include <string>
#include <type_traits>
#include <vector>
#ifdef _WIN32
# define WIN32_LEAN_AND_MEAN
# ifndef NOMINMAX
# define NOMINMAX
# endif
# include <windows.h>
#elif defined(__APPLE__)
# include <mach-o/dyld.h>
# include <dlfcn.h>
#else
# include <dlfcn.h>
# include <unistd.h>
#endif
// Backend registry
#ifdef GGML_USE_CPU
#include "ggml-cpu.h"
#endif
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_SYCL
#include "ggml-sycl.h"
#endif
#ifdef GGML_USE_VULKAN
#include "ggml-vulkan.h"
#endif
#ifdef GGML_USE_OPENCL
#include "ggml-opencl.h"
#endif
#ifdef GGML_USE_BLAS
#include "ggml-blas.h"
#endif
#ifdef GGML_USE_RPC
#include "ggml-rpc.h"
#endif
#ifdef GGML_USE_CANN
#include "ggml-cann.h"
#endif
#ifdef GGML_USE_KOMPUTE
#include "ggml-kompute.h"
#endif
// disable C++17 deprecation warning for std::codecvt_utf8
#if defined(__clang__)
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
#endif
static std::wstring utf8_to_utf16(const std::string & str) {
std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
return converter.from_bytes(str);
}
static std::string utf16_to_utf8(const std::wstring & str) {
std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
return converter.to_bytes(str);
}
#if defined(__clang__)
# pragma clang diagnostic pop
#endif
#ifdef _WIN32
using dl_handle = std::remove_pointer_t<HMODULE>;
struct dl_handle_deleter {
void operator()(HMODULE handle) {
FreeLibrary(handle);
}
};
static dl_handle * dl_load_library(const std::wstring & path) {
// suppress error dialogs for missing DLLs
DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
HMODULE handle = LoadLibraryW(path.c_str());
SetErrorMode(old_mode);
return handle;
}
static void * dl_get_sym(dl_handle * handle, const char * name) {
DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
void * p = (void *) GetProcAddress(handle, name);
SetErrorMode(old_mode);
return p;
}
#else
using dl_handle = void;
struct dl_handle_deleter {
void operator()(void * handle) {
dlclose(handle);
}
};
static void * dl_load_library(const std::wstring & path) {
dl_handle * handle = dlopen(utf16_to_utf8(path).c_str(), RTLD_NOW | RTLD_LOCAL);
return handle;
}
static void * dl_get_sym(dl_handle * handle, const char * name) {
return dlsym(handle, name);
}
#endif
using dl_handle_ptr = std::unique_ptr<dl_handle, dl_handle_deleter>;
struct ggml_backend_reg_entry {
ggml_backend_reg_t reg;
dl_handle_ptr handle;
};
struct ggml_backend_registry {
std::vector<ggml_backend_reg_entry> backends;
std::vector<ggml_backend_dev_t> devices;
ggml_backend_registry() {
#ifdef GGML_USE_CUDA
register_backend(ggml_backend_cuda_reg());
#endif
#ifdef GGML_USE_METAL
register_backend(ggml_backend_metal_reg());
#endif
#ifdef GGML_USE_SYCL
register_backend(ggml_backend_sycl_reg());
#endif
#ifdef GGML_USE_VULKAN
register_backend(ggml_backend_vk_reg());
#endif
#ifdef GGML_USE_OPENCL
register_backend(ggml_backend_opencl_reg());
#endif
#ifdef GGML_USE_CANN
register_backend(ggml_backend_cann_reg());
#endif
#ifdef GGML_USE_BLAS
register_backend(ggml_backend_blas_reg());
#endif
#ifdef GGML_USE_RPC
register_backend(ggml_backend_rpc_reg());
#endif
#ifdef GGML_USE_KOMPUTE
register_backend(ggml_backend_kompute_reg());
#endif
#ifdef GGML_USE_CPU
register_backend(ggml_backend_cpu_reg());
#endif
}
~ggml_backend_registry() {
// FIXME: backends cannot be safely unloaded without a function to destroy all the backend resources,
// since backend threads may still be running and accessing resources from the dynamic library
for (auto & entry : backends) {
if (entry.handle) {
entry.handle.release(); // NOLINT
}
}
}
void register_backend(ggml_backend_reg_t reg, dl_handle_ptr handle = nullptr) {
if (!reg) {
return;
}
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n",
__func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg));
#endif
backends.push_back({ reg, std::move(handle) });
for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) {
register_device(ggml_backend_reg_dev_get(reg, i));
}
}
void register_device(ggml_backend_dev_t device) {
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
#endif
devices.push_back(device);
}
ggml_backend_reg_t load_backend(const std::wstring & path, bool silent) {
dl_handle_ptr handle { dl_load_library(path) };
if (!handle) {
if (!silent) {
GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(path).c_str());
}
return nullptr;
}
auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
if (score_fn && score_fn() == 0) {
if (!silent) {
GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, utf16_to_utf8(path).c_str());
}
return nullptr;
}
auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init");
if (!backend_init_fn) {
if (!silent) {
GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, utf16_to_utf8(path).c_str());
}
return nullptr;
}
ggml_backend_reg_t reg = backend_init_fn();
if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) {
if (!silent) {
if (!reg) {
GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, utf16_to_utf8(path).c_str());
} else {
GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n",
__func__, utf16_to_utf8(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
}
}
return nullptr;
}
GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), utf16_to_utf8(path).c_str());
register_backend(reg, std::move(handle));
return reg;
}
void unload_backend(ggml_backend_reg_t reg, bool silent) {
auto it = std::find_if(backends.begin(), backends.end(),
[reg](const ggml_backend_reg_entry & entry) { return entry.reg == reg; });
if (it == backends.end()) {
if (!silent) {
GGML_LOG_ERROR("%s: backend not found\n", __func__);
}
return;
}
if (!silent) {
GGML_LOG_DEBUG("%s: unloading %s backend\n", __func__, ggml_backend_reg_name(reg));
}
// remove devices
devices.erase(
std::remove_if(devices.begin(), devices.end(),
[reg](ggml_backend_dev_t dev) { return ggml_backend_dev_backend_reg(dev) == reg; }),
devices.end());
// remove backend
backends.erase(it);
}
};
static ggml_backend_registry & get_reg() {
static ggml_backend_registry reg;
return reg;
}
// Internal API
void ggml_backend_register(ggml_backend_reg_t reg) {
get_reg().register_backend(reg);
}
void ggml_backend_device_register(ggml_backend_dev_t device) {
get_reg().register_device(device);
}
// Backend (reg) enumeration
static bool striequals(const char * a, const char * b) {
for (; *a && *b; a++, b++) {
if (std::tolower(*a) != std::tolower(*b)) {
return false;
}
}
return *a == *b;
}
size_t ggml_backend_reg_count() {
return get_reg().backends.size();
}
ggml_backend_reg_t ggml_backend_reg_get(size_t index) {
GGML_ASSERT(index < ggml_backend_reg_count());
return get_reg().backends[index].reg;
}
ggml_backend_reg_t ggml_backend_reg_by_name(const char * name) {
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
ggml_backend_reg_t reg = ggml_backend_reg_get(i);
if (striequals(ggml_backend_reg_name(reg), name)) {
return reg;
}
}
return nullptr;
}
// Device enumeration
size_t ggml_backend_dev_count() {
return get_reg().devices.size();
}
ggml_backend_dev_t ggml_backend_dev_get(size_t index) {
GGML_ASSERT(index < ggml_backend_dev_count());
return get_reg().devices[index];
}
ggml_backend_dev_t ggml_backend_dev_by_name(const char * name) {
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
if (striequals(ggml_backend_dev_name(dev), name)) {
return dev;
}
}
return nullptr;
}
ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) {
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_type(dev) == type) {
return dev;
}
}
return nullptr;
}
// Convenience functions
ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) {
ggml_backend_dev_t dev = ggml_backend_dev_by_name(name);
if (!dev) {
return nullptr;
}
return ggml_backend_dev_init(dev, params);
}
ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params) {
ggml_backend_dev_t dev = ggml_backend_dev_by_type(type);
if (!dev) {
return nullptr;
}
return ggml_backend_dev_init(dev, params);
}
ggml_backend_t ggml_backend_init_best(void) {
ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
if (!dev) {
dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
}
if (!dev) {
return nullptr;
}
return ggml_backend_dev_init(dev, nullptr);
}
// Dynamic loading
ggml_backend_reg_t ggml_backend_load(const char * path) {
return get_reg().load_backend(utf8_to_utf16(path), false);
}
void ggml_backend_unload(ggml_backend_reg_t reg) {
get_reg().unload_backend(reg, true);
}
static std::wstring get_executable_path() {
#if defined(__APPLE__)
// get executable path
std::vector<char> path;
uint32_t size;
while (true) {
size = path.size();
if (_NSGetExecutablePath(path.data(), &size) == 0) {
break;
}
path.resize(size);
}
std::string base_path(path.data(), size);
// remove executable name
auto last_slash = base_path.find_last_of('/');
if (last_slash != std::string::npos) {
base_path = base_path.substr(0, last_slash);
}
return utf8_to_utf16(base_path + "/");
#elif defined(__linux__) || defined(__FreeBSD__)
std::string base_path = ".";
std::vector<char> path(1024);
while (true) {
// get executable path
# if defined(__linux__)
ssize_t len = readlink("/proc/self/exe", path.data(), path.size());
# elif defined(__FreeBSD__)
ssize_t len = readlink("/proc/curproc/file", path.data(), path.size());
# endif
if (len == -1) {
break;
}
if (len < (ssize_t) path.size()) {
base_path = std::string(path.data(), len);
// remove executable name
auto last_slash = base_path.find_last_of('/');
if (last_slash != std::string::npos) {
base_path = base_path.substr(0, last_slash);
}
break;
}
path.resize(path.size() * 2);
}
return utf8_to_utf16(base_path + "/");
#elif defined(_WIN32)
std::vector<wchar_t> path(MAX_PATH);
DWORD len = GetModuleFileNameW(NULL, path.data(), path.size());
if (len == 0) {
return {};
}
std::wstring base_path(path.data(), len);
// remove executable name
auto last_slash = base_path.find_last_of('\\');
if (last_slash != std::string::npos) {
base_path = base_path.substr(0, last_slash);
}
return base_path + L"\\";
#else
return {};
#endif
}
static std::wstring backend_filename_prefix() {
#ifdef _WIN32
return L"ggml-";
#else
return L"libggml-";
#endif
}
static std::wstring backend_filename_suffix() {
#ifdef _WIN32
return L".dll";
#else
return L".so";
#endif
}
static std::wstring path_separator() {
#ifdef _WIN32
return L"\\";
#else
return L"/";
#endif
}
static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, const char * user_search_path) {
// enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths
// TODO: search system paths
std::wstring file_prefix = backend_filename_prefix() + utf8_to_utf16(name) + L"-";
std::vector<std::wstring> search_paths;
if (user_search_path == nullptr) {
search_paths.push_back(L"." + path_separator());
search_paths.push_back(get_executable_path());
} else {
search_paths.push_back(utf8_to_utf16(user_search_path) + path_separator());
}
int best_score = 0;
std::wstring best_path;
namespace fs = std::filesystem;
for (const auto & search_path : search_paths) {
if (!fs::exists(search_path)) {
continue;
}
fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
for (const auto & entry : dir_it) {
if (entry.is_regular_file()) {
std::wstring filename = entry.path().filename().wstring();
std::wstring ext = entry.path().extension().wstring();
if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
dl_handle_ptr handle { dl_load_library(entry.path().wstring()) };
if (!handle && !silent) {
GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
}
if (handle) {
auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
if (score_fn) {
int s = score_fn();
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
#endif
if (s > best_score) {
best_score = s;
best_path = entry.path().wstring();
}
} else {
if (!silent) {
GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
}
}
}
}
}
}
}
if (best_score == 0) {
// try to load the base backend
for (const auto & search_path : search_paths) {
std::wstring path = search_path + backend_filename_prefix() + utf8_to_utf16(name) + backend_filename_suffix();
if (fs::exists(path)) {
return get_reg().load_backend(path, silent);
}
}
return nullptr;
}
return get_reg().load_backend(best_path, silent);
}
void ggml_backend_load_all() {
ggml_backend_load_all_from_path(nullptr);
}
void ggml_backend_load_all_from_path(const char * dir_path) {
#ifdef NDEBUG
bool silent = true;
#else
bool silent = false;
#endif
ggml_backend_load_best("blas", silent, dir_path);
ggml_backend_load_best("cann", silent, dir_path);
ggml_backend_load_best("cuda", silent, dir_path);
ggml_backend_load_best("hip", silent, dir_path);
ggml_backend_load_best("kompute", silent, dir_path);
ggml_backend_load_best("metal", silent, dir_path);
ggml_backend_load_best("rpc", silent, dir_path);
ggml_backend_load_best("sycl", silent, dir_path);
ggml_backend_load_best("vulkan", silent, dir_path);
ggml_backend_load_best("opencl", silent, dir_path);
ggml_backend_load_best("musa", silent, dir_path);
ggml_backend_load_best("cpu", silent, dir_path);
// check the environment variable GGML_BACKEND_PATH to load an out-of-tree backend
const char * backend_path = std::getenv("GGML_BACKEND_PATH");
if (backend_path) {
ggml_backend_load(backend_path);
}
}

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-blas/CMakeLists.txt vendored Normal file
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if (GGML_STATIC)
set(BLA_STATIC ON)
endif()
#if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22)
# set(BLA_SIZEOF_INTEGER 8)
#endif()
set(BLA_VENDOR ${GGML_BLAS_VENDOR})
find_package(BLAS)
if (BLAS_FOUND)
message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}")
ggml_add_backend_library(ggml-blas
ggml-blas.cpp
)
if (${GGML_BLAS_VENDOR} MATCHES "Apple")
add_compile_definitions(ACCELERATE_NEW_LAPACK)
add_compile_definitions(ACCELERATE_LAPACK_ILP64)
add_compile_definitions(GGML_BLAS_USE_ACCELERATE)
elseif ("${BLAS_INCLUDE_DIRS}" STREQUAL "")
# BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake.
# see https://gitlab.kitware.com/cmake/cmake/-/issues/20268
find_package(PkgConfig REQUIRED)
if (${GGML_BLAS_VENDOR} MATCHES "Generic")
pkg_check_modules(DepBLAS blas)
elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS")
# As of openblas v0.3.22, the 64-bit is named openblas64.pc
pkg_check_modules(DepBLAS openblas64)
if (NOT DepBLAS_FOUND)
pkg_check_modules(DepBLAS openblas)
endif()
elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME")
add_compile_definitions(GGML_BLAS_USE_BLIS)
pkg_check_modules(DepBLAS blis)
elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS")
pkg_check_modules(DepBLAS blas-atlas)
elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS")
pkg_check_modules(DepBLAS flexiblas_api)
elseif (${GGML_BLAS_VENDOR} MATCHES "Intel")
add_compile_definitions(GGML_BLAS_USE_MKL)
# all Intel* libraries share the same include path
pkg_check_modules(DepBLAS mkl-sdl)
elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC")
# this doesn't provide pkg-config
# suggest to assign BLAS_INCLUDE_DIRS on your own
if ("${NVHPC_VERSION}" STREQUAL "")
message(WARNING "Better to set NVHPC_VERSION")
else()
set(DepBLAS_FOUND ON)
set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include")
endif()
endif()
if (DepBLAS_FOUND)
set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS})
else()
message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically"
" detected by pkgconfig, trying to find cblas.h from possible paths...")
find_path(BLAS_INCLUDE_DIRS
NAMES cblas.h
HINTS
/usr/include
/usr/local/include
/usr/include/openblas
/opt/homebrew/opt/openblas/include
/usr/local/opt/openblas/include
/usr/include/x86_64-linux-gnu/openblas/include
)
endif()
endif()
message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}")
target_compile_options(ggml-blas PRIVATE ${BLAS_LINKER_FLAGS})
if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel"))
add_compile_definitions(GGML_BLAS_USE_MKL)
endif()
target_link_libraries (ggml-blas PRIVATE ${BLAS_LIBRARIES})
target_include_directories(ggml-blas PRIVATE ${BLAS_INCLUDE_DIRS})
else()
message(ERROR "BLAS not found, please refer to "
"https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
" to set correct GGML_BLAS_VENDOR")
endif()

517
packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-blas/ggml-blas.cpp vendored Normal file
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#include "ggml-impl.h"
#include "ggml-blas.h"
#include "ggml-backend-impl.h"
#include <future>
#include <vector>
#include <cstring>
#if defined(GGML_BLAS_USE_ACCELERATE)
# include <Accelerate/Accelerate.h>
#elif defined(GGML_BLAS_USE_MKL)
# include <mkl.h>
#elif defined(GGML_BLAS_USE_BLIS)
# include <blis.h>
#elif defined(GGML_BLAS_USE_NVPL)
# include <nvpl_blas.h>
#else
# include <cblas.h>
#endif
struct ggml_backend_blas_context {
int n_threads = GGML_DEFAULT_N_THREADS;
std::unique_ptr<char[]> work_data;
size_t work_size = 0;
#ifndef GGML_USE_OPENMP
std::vector<std::future<void>> tasks;
#endif
};
static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
const enum ggml_type type = src0->type;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
const int64_t ne_plane = ne01*ne00;
const size_t desired_wsize = type == GGML_TYPE_F32 ? 0 : ne03*ne02*ne_plane*sizeof(float);
if (ctx->work_size < desired_wsize) {
ctx->work_data.reset(new char[desired_wsize]);
ctx->work_size = desired_wsize;
}
void * wdata = ctx->work_data.get();
// convert src0 to float
if (type != GGML_TYPE_F32) {
const auto * type_traits = ggml_get_type_traits(type);
ggml_to_float_t const to_float = type_traits->to_float;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
float * const wplane = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane;
const int min_cols_per_thread = 4096;
const int min_rows_per_thread = std::max((int)(min_cols_per_thread/ne00), 1);
const int n_threads = std::max(std::min(ctx->n_threads, (int)(ne01/min_rows_per_thread)), 1);
#ifdef GGML_USE_OPENMP
#pragma omp parallel for num_threads(n_threads)
for (int64_t i01 = 0; i01 < ne01; i01++) {
to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
}
#else
for (int i = 1; i < n_threads; i++) {
const int64_t start = i*ne01/n_threads;
const int64_t end = (i + 1)*ne01/n_threads;
if (start < end) {
ctx->tasks.push_back(std::async(std::launch::async, [=]() {
for (int64_t i01 = start; i01 < end; i01++) {
to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
}
}));
}
}
{
// reuse the current thread for the first task
const int64_t start = 0;
const int64_t end = ne01/n_threads;
for (int64_t i01 = start; i01 < end; i01++) {
to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
}
}
#endif
}
}
#ifndef GGML_USE_OPENMP
// wait for all tasks to finish
for (auto & task : ctx->tasks) {
task.get();
}
ctx->tasks.clear();
#endif
}
#if defined(OPENBLAS_VERSION)
openblas_set_num_threads(ctx->n_threads);
#endif
#if defined(GGML_BLAS_USE_BLIS)
bli_thread_set_num_threads(ctx->n_threads);
#endif
#if defined(GGML_BLAS_USE_NVPL)
nvpl_blas_set_num_threads(ctx->n_threads);
#endif
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
if (type != GGML_TYPE_F32) {
x = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane;
}
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne1, ne01, ne10,
1.0f, y, ne10,
x, ne00,
0.0f, d, ne01);
}
}
}
static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
GGML_ASSERT(ne0 == ne00);
GGML_ASSERT(ne1 == ne10);
GGML_ASSERT(ne2 == ne02);
GGML_ASSERT(ne02 == ne12);
GGML_ASSERT(ne3 == ne13);
GGML_ASSERT(ne03 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == sizeof(float));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
// GGML_ASSERT(nb0 <= nb1);
// GGML_ASSERT(nb1 <= nb2);
// GGML_ASSERT(nb2 <= nb3);
// Arguments to ggml_compute_forward_out_prod (expressed as major,minor)
// src0: (k,n)
// src1: (k,m)
// dst: (m,n)
//
// Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f)
// Also expressed as (major,minor)
// a: (m,k): so src1 transposed
// b: (k,n): so src0
// c: (m,n)
//
// However, if ggml_is_transposed(src1) is true, then
// src1->data already contains a transposed version, so sgemm mustn't
// transpose it further.
int n = src0->ne[0];
int k = src0->ne[1];
int m = src1->ne[0];
CBLAS_TRANSPOSE transposeA;
int lda;
if (!ggml_is_transposed(src1)) {
transposeA = CblasTrans;
lda = m;
} else {
transposeA = CblasNoTrans;
lda = k;
}
float * a = (float *) ((char *) src1->data);
float * b = (float *) ((char *) src0->data);
float * c = (float *) ((char *) dst->data);
cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n);
GGML_UNUSED(ctx);
}
// backend interface
static const char * ggml_backend_blas_get_name(ggml_backend_t backend) {
return "BLAS";
GGML_UNUSED(backend);
}
static void ggml_backend_blas_free(ggml_backend_t backend) {
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
delete ctx;
delete backend;
}
static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * node = cgraph->nodes[i];
switch (node->op) {
case GGML_OP_MUL_MAT:
ggml_backend_blas_mul_mat(ctx, node);
break;
case GGML_OP_OUT_PROD:
ggml_backend_blas_out_prod(ctx, node);
break;
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
break;
default:
GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node));
}
}
return GGML_STATUS_SUCCESS;
GGML_UNUSED(backend);
}
static struct ggml_backend_i blas_backend_i = {
/* .get_name = */ ggml_backend_blas_get_name,
/* .free = */ ggml_backend_blas_free,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ NULL,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_blas_graph_compute,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
static ggml_guid_t ggml_backend_blas_guid(void) {
static ggml_guid guid = { 0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d };
return &guid;
}
ggml_backend_t ggml_backend_blas_init(void) {
ggml_backend_blas_context * ctx = new ggml_backend_blas_context;
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_blas_guid(),
/* .interface = */ blas_backend_i,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_blas_reg(), 0),
/* .context = */ ctx,
};
#if defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
if (openblas_get_parallel() != OPENBLAS_OPENMP) {
GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
}
#endif
#if defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
#endif
return backend;
}
bool ggml_backend_is_blas(ggml_backend_t backend) {
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_blas_guid());
}
void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads) {
GGML_ASSERT(ggml_backend_is_blas(backend_blas));
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context;
ctx->n_threads = n_threads;
}
// device interface
static const char * ggml_backend_blas_device_get_name(ggml_backend_dev_t dev) {
return "BLAS";
GGML_UNUSED(dev);
}
static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t dev) {
#if defined(GGML_BLAS_USE_ACCELERATE)
return "Accelerate";
#elif defined(GGML_BLAS_USE_MKL)
return "MKL";
#elif defined(GGML_BLAS_USE_BLIS)
return "BLIS";
#elif defined(GGML_BLAS_USE_NVPL)
return "NVPL";
#elif defined(OPENBLAS_VERSION)
return "OpenBLAS";
#else
return "BLAS";
#endif
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
// TODO
*free = 0;
*total = 0;
GGML_UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_ACCEL;
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_blas_device_get_name(dev);
props->description = ggml_backend_blas_device_get_description(dev);
props->type = ggml_backend_blas_device_get_type(dev);
ggml_backend_blas_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ true,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_blas_device_init_backend(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_blas_init();
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_backend_dev_t dev) {
return ggml_backend_cpu_buffer_type();
GGML_UNUSED(dev);
}
static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
GGML_UNUSED(dev);
GGML_UNUSED(max_tensor_size);
}
static bool ggml_backend_blas_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
switch (op->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_MUL_MAT:
{
// BLAS usually is only faster for large matrices
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = op->ne[0];
const int64_t ne1 = op->ne[1];
// TODO: find the optimal value
const int64_t min_batch = 32;
return ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src1->type == GGML_TYPE_F32 &&
(ne0 >= min_batch && ne1 >= min_batch && ne10 >= min_batch) &&
(src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL);
}
case GGML_OP_OUT_PROD:
return op->src[0]->type == GGML_TYPE_F32 &&
op->src[1]->type == GGML_TYPE_F32 &&
ggml_is_matrix(src0) &&
ggml_is_matrix(src1) &&
ggml_is_contiguous(src0) &&
(ggml_is_contiguous(src1) || ggml_is_transposed(src1)) &&
(src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL);
default:
return false;
}
GGML_UNUSED(dev);
}
static bool ggml_backend_blas_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return ggml_backend_buft_is_host(buft);
GGML_UNUSED(dev);
}
static const struct ggml_backend_device_i ggml_backend_blas_device_i = {
/* .get_name = */ ggml_backend_blas_device_get_name,
/* .get_description = */ ggml_backend_blas_device_get_description,
/* .get_memory = */ ggml_backend_blas_device_get_memory,
/* .get_type = */ ggml_backend_blas_device_get_type,
/* .get_props = */ ggml_backend_blas_device_get_props,
/* .init_backend = */ ggml_backend_blas_device_init_backend,
/* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_host_ptr,
/* .supports_op = */ ggml_backend_blas_device_supports_op,
/* .supports_buft = */ ggml_backend_blas_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// backend reg interface
static const char * ggml_backend_blas_reg_get_name(ggml_backend_reg_t reg) {
return "BLAS";
GGML_UNUSED(reg);
}
static size_t ggml_backend_blas_reg_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_blas_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_device ggml_backend_blas_device = {
/* .iface = */ ggml_backend_blas_device_i,
/* .reg = */ reg,
/* .context = */ nullptr,
};
return &ggml_backend_blas_device;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static void * ggml_backend_blas_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_blas_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(name);
}
static const struct ggml_backend_reg_i ggml_backend_blas_reg_i = {
/* .get_name = */ ggml_backend_blas_reg_get_name,
/* .get_device_count = */ ggml_backend_blas_reg_get_device_count,
/* .get_device = */ ggml_backend_blas_reg_get_device,
/* .get_proc_address = */ ggml_backend_blas_get_proc_address,
};
ggml_backend_reg_t ggml_backend_blas_reg(void) {
static struct ggml_backend_reg ggml_backend_blas_reg = {
/* .api_version = */ GGML_BACKEND_API_VERSION,
/* .iface = */ ggml_backend_blas_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_blas_reg;
}
GGML_BACKEND_DL_IMPL(ggml_backend_blas_reg)

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cann/CMakeLists.txt vendored Normal file
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if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOME})
set(CANN_INSTALL_DIR $ENV{ASCEND_TOOLKIT_HOME})
message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}")
endif()
# Auto-detech Soc type and Soc version, if detect failed, will abort build
set(SOC_VERSION "")
function(detect_ascend_soc_type SOC_VERSION)
execute_process(
COMMAND bash -c "npu-smi info|awk -F' ' 'NF > 0 && NR==7 {print $3}'"
OUTPUT_VARIABLE npu_info
RESULT_VARIABLE npu_result
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if("${npu_info}" STREQUAL "" OR ${npu_result})
message(FATAL_ERROR "Auto-detech ascend soc type failed, please specify manually or check ascend device working normally.")
endif()
set(${SOC_VERSION} "Ascend${npu_info}" PARENT_SCOPE)
endfunction()
if(NOT SOC_TYPE)
detect_ascend_soc_type(SOC_VERSION)
set(SOC_TYPE "${SOC_VERSION}")
message(STATUS "CANN: SOC_VERSION auto-detected is:${SOC_VERSION}")
endif()
string(TOLOWER ${SOC_TYPE} SOC_VERSION) # SOC_VERSION need lower
# Construct Soc specify compile option: ASCEND_#Soc_Major_SN. Such as ASCEND_910B, ASCEND_310P.
string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
if (CANN_INSTALL_DIR)
# Only Support Linux.
if (NOT UNIX)
message(FATAL_ERROR "CANN: CANN toolkit supports unix but not ${CMAKE_SYSTEM_NAME}")
endif()
# Supported platforms: x86-64, arm64
if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64")
elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64")
else()
message(FATAL_ERROR "CANN: CANN toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}")
endif()
# Set header and libs
set(CANN_INCLUDE_DIRS
${CANN_INSTALL_DIR}/include
${CANN_INSTALL_DIR}/include/aclnn
${CANN_INSTALL_DIR}/acllib/include
)
add_subdirectory(kernels)
list(APPEND CANN_LIBRARIES
ascendcl
nnopbase
opapi
acl_op_compiler
ascendc_kernels
)
file(GLOB GGML_SOURCES_CANN "*.cpp")
ggml_add_backend_library(ggml-cann ${GGML_SOURCES_CANN})
target_link_libraries(ggml-cann PRIVATE ${CANN_LIBRARIES})
target_include_directories(ggml-cann PRIVATE ${CANN_INCLUDE_DIRS})
target_link_directories(ggml-cann PRIVATE ${CANN_INSTALL_DIR}/lib64)
target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
message(STATUS "CANN: CANN_INCLUDE_DIRS = ${CANN_INCLUDE_DIRS}")
message(STATUS "CANN: CANN_LIBRARIES = ${CANN_LIBRARIES}")
else()
message(FATAL_ERROR "CANN: Can't find CANN_INSTALL_DIR, did you forget to source set_var.sh?")
endif()

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175
packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cann/acl_tensor.cpp vendored Normal file
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/*
* Copyright (c) 2023-2024 The ggml authors
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*/
#include "acl_tensor.h"
#include <algorithm>
#include <cstring>
aclDataType ggml_cann_type_mapping(ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return ACL_FLOAT;
case GGML_TYPE_F16:
return ACL_FLOAT16;
case GGML_TYPE_I8:
return ACL_INT8;
case GGML_TYPE_I16:
return ACL_INT16;
case GGML_TYPE_I32:
return ACL_INT32;
case GGML_TYPE_Q4_0:
return ACL_INT4;
case GGML_TYPE_Q8_0:
return ACL_INT8;
default:
return ACL_DT_UNDEFINED;
}
return ACL_DT_UNDEFINED;
}
aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne,
size_t* nb, int64_t dims, aclFormat format,
size_t offset) {
// If tensor is bcasted, Up to GGML_MAX_DIMS additional dimensions will be
// added.
int64_t acl_ne[GGML_MAX_DIMS * 2], acl_stride[GGML_MAX_DIMS * 2];
int64_t acl_storage_len = 0;
if (ne == nullptr) {
acl_storage_len = ggml_nbytes(tensor);
for (int i = 0; i < GGML_MAX_DIMS; i++) {
acl_ne[i] = tensor->ne[i];
// The step size of acl is in elements.
acl_stride[i] = tensor->nb[i] / ggml_element_size(tensor);
}
} else {
// With bcast
for (int i = 0; i < dims; i++) {
acl_storage_len += (ne[i] - 1) * nb[i];
acl_ne[i] = ne[i];
acl_stride[i] = nb[i] / ggml_element_size(tensor);
}
}
// Reverse ne and stride.
int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims);
std::reverse(acl_ne, acl_ne + final_dims);
std::reverse(acl_stride, acl_stride + final_dims);
aclTensor* acl_tensor = aclCreateTensor(
acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride,
offset / ggml_element_size(tensor), format, &acl_storage_len, 1,
tensor->data);
return acl_tensor;
}
bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) {
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (t1->ne[i] != t0->ne[i] && t1->ne[i] != 1) {
return true;
}
}
return false;
}
int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0,
const ggml_tensor* src1,
int64_t* bcast_src0_ne,
int64_t* bcast_src1_ne, size_t* bcast_src0_nb,
size_t* bcast_src1_nb) {
GGML_ASSERT(ggml_can_repeat(src1, src0));
int bcast_dim_cnt = 0;
for (int i = 0; i < GGML_MAX_DIMS; i++) {
int64_t nr = src0->ne[i] / src1->ne[i];
bcast_src0_ne[bcast_dim_cnt] = src0->ne[i] / nr;
bcast_src1_ne[bcast_dim_cnt] = src1->ne[i];
bcast_src0_nb[bcast_dim_cnt] = src0->nb[i];
bcast_src1_nb[bcast_dim_cnt] = src1->nb[i];
bcast_dim_cnt++;
if (nr != 1) {
// Need to add an extra dim.
bcast_src0_ne[bcast_dim_cnt] = nr;
bcast_src1_ne[bcast_dim_cnt] = 1;
bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] *
bcast_src0_ne[bcast_dim_cnt - 1];
bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] *
bcast_src1_ne[bcast_dim_cnt - 1];
bcast_dim_cnt++;
}
}
return bcast_dim_cnt;
}
int64_t ggml_cann_get_mulmat_bcast_shape(
const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne,
const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb,
int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne,
size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb) {
// input and dst shoule in same shape, except first two dims.
GGML_ASSERT(input_ne[2] == dst_ne[2]);
GGML_ASSERT(input_ne[3] == dst_ne[3]);
int bcast_dim_cnt = 0;
// For mul_mat, a dimension needs to be added before the dimension that
// weight needs to be expanded to satisfy the bcast rule of matrix
// multiplication.
for (int i = 0; i < GGML_MAX_DIMS; i++) {
int64_t nr = input_ne[i] / weight_ne[i];
// Do not use bcast in the first two dimensions because we only support
// the bcast batch dimension. Just copy them.
if (i < 2 || nr == 1) {
bcast_input_ne[bcast_dim_cnt] = input_ne[i];
bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
bcast_dst_ne[bcast_dim_cnt] = dst_ne[i];
bcast_input_nb[bcast_dim_cnt] = input_nb[i];
bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
bcast_dim_cnt++;
} else {
// Need to add an extra dim.
bcast_input_ne[bcast_dim_cnt] = nr;
bcast_dst_ne[bcast_dim_cnt] = nr;
bcast_weight_ne[bcast_dim_cnt] = 1;
bcast_input_nb[bcast_dim_cnt] = input_nb[i];
bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
bcast_dim_cnt++;
bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr;
bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr;
bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] *
bcast_input_ne[bcast_dim_cnt - 1];
bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] *
bcast_dst_ne[bcast_dim_cnt - 1];
bcast_weight_nb[bcast_dim_cnt] =
bcast_weight_nb[bcast_dim_cnt - 1] *
bcast_weight_ne[bcast_dim_cnt - 1];
bcast_dim_cnt++;
}
}
return bcast_dim_cnt;
}

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/*
* Copyright (c) 2023-2024 The ggml authors
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*/
#ifndef CANN_ACL_TENSOR_H
#define CANN_ACL_TENSOR_H
#include <algorithm>
#include <cstring>
#include <aclnn/aclnn_base.h>
#include "common.h"
/**
* @brief Maps a ggml_type to its corresponding aclDataType.
*
* @details This function takes a ggml_type as input and returns the corresponding
* aclDataType. It supports mapping for various ggml_types. If the input type
* does not match any of the predefined ggml_types, the function returns
* ACL_DT_UNDEFINED.
*
* @param type The ggml_type to be mapped.
* @return The corresponding aclDataType. If the input type is not recognized,
* ACL_DT_UNDEFINED is returned.
*/
aclDataType ggml_cann_type_mapping(ggml_type type);
/**
* @brief Creates an ACL tensor from a ggml_tensor with optional shape.
*
* @details This function creates an ACL tensor based on the properties of the
* provided ggml_tensor. It supports customer shape by adjusting dimensions
* and strides accordingly. If customer shape is applied, additional
* dimensions and strides are calculated based on the provided parameters.
*
* @param tensor Pointer to the ggml_tensor to be converted to ACL tensor.
* @param ne Pointer to an array containing dimensions. Defaults to nullptr
* if no customer shape is applied.
* @param nb Pointer to an array containing strides. Defaults to nullptr
* if no customer shape is applied.
* @param dims Number of dimensions in the tensor. Defaults to 0 if no customer
* shape is applied.
* @param format ACL tensor format. Defaults to ACL_FORMAT_ND.
* @param offset Offset in bytes for the ACL tensor data. Defaults to 0.
* @return Pointer to the created ACL tensor.
*/
aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr,
size_t* nb = nullptr, int64_t dims = 0,
aclFormat format = ACL_FORMAT_ND,
size_t offset = 0);
/**
* @brief Template for creating an ACL tensor from provided parameters. typename TYPE
* should be size_t or float.
*
* @details This function creates an ACL tensor using the provided data pointer,
* data type, dimensions, strides, format, offset, and additional parameters.
* It calculates necessary dimensions and strides based on the provided ne and nb
* arrays, adjusting them for the ACL tensor creation. The ACL storage length
* is also calculated based on the provided dimensions and strides.
*
* @param data_ptr Pointer to the data buffer for the ACL tensor.
* @param dtype ACL data type of the tensor.
* @param type_size Size of each element in the tensor data buffer.
* @param ne Pointer to an array containing tensor dimensions.
* @param nb Pointer to an array containing tensor strides.
* @param dims Number of dimensions of the tensor.
* @param format ACL tensor format. Defaults to ACL_FORMAT_ND.
* @param offset Offset in bytes for the ACL tensor data. Defaults to 0.
* @return Pointer to the created ACL tensor.
*/
template<typename TYPE>
aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype,
TYPE type_size, int64_t* ne, TYPE* nb,
int64_t dims,
aclFormat format = ACL_FORMAT_ND,
size_t offset = 0) {
int64_t tmp_ne[GGML_MAX_DIMS * 2];
int64_t tmp_stride[GGML_MAX_DIMS * 2];
memcpy(tmp_ne, ne, dims * sizeof(int64_t));
for (int i = 0; i < dims; i++) {
tmp_stride[i] = nb[i] / type_size;
}
std::reverse(tmp_ne, tmp_ne + dims);
std::reverse(tmp_stride, tmp_stride + dims);
int64_t acl_storage_len = 0;
for (int i = 0; i < dims; i++) {
acl_storage_len += (ne[i] - 1) * nb[i];
}
aclTensor* acl_tensor =
aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size,
format, &acl_storage_len, 1, data_ptr);
return acl_tensor;
}
/**
* @brief Checks if tensors require broadcasting based on their shapes.
*
* @details This function determines if two ggml_tensors need to be broadcasted for
* element-wise operations. Broadcasting is necessary if the shapes of the
* tensors are not identical and no dimension in either tensor equals 1.
*
* @param t0 Pointer to the first ggml_tensor.
* @param t1 Pointer to the second ggml_tensor.
* @return True if broadcasting is needed, False otherwise.
*
* @remarks This function iterates over the dimensions of t0 and t1. It checks if each
* dimension in t1 differs from t0's corresponding dimension and is not equal
* to 1. If such a dimension is found, broadcasting is required to align t1
* with t0 for element-wise operations.
*/
bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1);
/**
* @brief Computes broadcast shapes and strides for two ggml_tensors.
*
* @details This function calculates the broadcast shapes and strides for two ggml_tensors,
* following the broadcasting rules similar to numpy. It adjusts dimensions and
* strides to ensure compatibility for element-wise operations where one tensor
* can be broadcasted to match the shape of another tensor.
*
* @param src0 Pointer to the first ggml_tensor.
* @param src1 Pointer to the second ggml_tensor.
* @param bcast_ne_src0 Output array to store broadcasted dimensions for src0.
* @param bcast_ne_src1 Output array to store broadcasted dimensions for src1.
* @param bcast_nb_src0 Output array to store broadcasted strides for src0.
* @param bcast_nb_src1 Output array to store broadcasted strides for src1.
* @return Number of dimensions in the broadcasted shape.
*
* @pre ggml_can_repeat(src1, src0) must return true, indicating src1 can be broadcasted
* to match src0.
*
* @remarks This function iterates over the dimensions of src0 and src1, calculating the
* necessary broadcast dimensions and strides. If a dimension requires broadcasting
* (i.e., its size in src1 is smaller than in src0), an additional dimension is
* added with size calculated to match src0's dimension. This adjustment ensures
* that src1 can be element-wise broadcasted to src0's shape.
*
* How it works:
*
* if dim0 has padding.
* a -> (2, 2) padding = 2
* a: [[1, 2, *, *]
* [2, 3, *, *]]
* nb = (8, 4, 2)
*
* if a should bcast with b -> (2, 4)
* b' -> (2, 2, 2)
* b : [[1, 2, 3, 4, *, *]
* [5, 6, 7, 8, *, *]]
* nb = (12, 6, 1)
*
* after bcast:
* a' -> (2, 1, 2)
* a': [[[1, 2], *, *]
* [[2, 3], *, *]]
* nb = (8, 4, 2, 1)
*
* b' : [[[1, 2], [3, 4], *, *]
* [[5, 6], [7, 8], *, *]]
* nb = (12, 6, 2, 1)
* \endcode
*
* dim1 in a inserted dim, should add nb for dim1,
* and all other nb moves to next in order.
*/
int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1,
int64_t* bcast_ne_src0, int64_t* bcast_ne_src1,
size_t* bcast_nb_src0, size_t* bcast_nb_src1);
// Bcast macro to avoid duplicate code.
#define BCAST_SHAPE(src0, src1) \
int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2]; \
int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2]; \
size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2]; \
size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2]; \
int64_t bcast_dims = ggml_cann_get_bcast_shape( \
src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, bcast_##src0##_nb, \
bcast_##src1##_nb);
#define BCAST_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims
/**
* @brief Calculates broadcast shapes for matrix multiplication.
*
* @details This function computes the broadcast shapes required for matrix multiplication
* based on the input, weight, and destination tensor shapes. It ensures that the
* dimensions of weight tensors are expanded appropriately to satisfy matrix
* multiplication broadcast rules.
*
* @param input_ne Array containing the dimensions of the input tensor.
* @param weight_ne Array containing the dimensions of the weight tensor.
* @param dst_ne Array containing the dimensions of the destination tensor.
* @param input_nb Array containing the strides of the input tensor.
* @param weight_nb Array containing the strides of the weight tensor.
* @param dst_nb Array containing the strides of the destination tensor.
* @param bcast_input_ne Output array for broadcasted input tensor dimensions.
* @param bcast_weight_ne Output array for broadcasted weight tensor dimensions.
* @param bcast_dst_ne Output array for broadcasted destination tensor dimensions.
* @param bcast_input_nb Output array for broadcasted input tensor strides.
* @param bcast_weight_nb Output array for broadcasted weight tensor strides.
* @param bcast_dst_nb Output array for broadcasted destination tensor strides.
* @return The number of dimensions in the broadcasted tensors.
*
* @remarks This function iterates over the tensor dimensions and calculates the broadcast
* shapes needed for matrix multiplication. It ensures that dimensions where
* weight tensor requires expansion are appropriately handled to conform with
* broadcasting rules.
* @note compare with ggml_cann_get_bcast_shape, mul_mat broadcast need add this new dim
* before cast dim.
* @sa ggml_cann_get_bcast_shape
*/
int64_t ggml_cann_get_mulmat_bcast_shape(
const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne,
const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb,
int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne,
size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb);
// Bcast macro to avoid duplicate code.
#define BCAST_MUL_MAT_SHAPE(input, weight, dst) \
int64_t bcast_##input##_ne[GGML_MAX_DIMS * 2]; \
int64_t bcast_##weight##_ne[GGML_MAX_DIMS * 2]; \
int64_t bcast_##dst##_ne[GGML_MAX_DIMS * 2]; \
size_t bcast_##input##_nb[GGML_MAX_DIMS * 2]; \
size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2]; \
size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2]; \
int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape( \
input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, \
bcast_##input##_ne, bcast_##weight##_ne, bcast_##dst##_ne, \
bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb);
#define BCAST_MUL_MAT_PARAM(tensor) \
bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims
#endif // CANN_ACL_TENSOR_H

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#ifndef CANN_ACLNN_OPS
#define CANN_ACLNN_OPS
/**
* @file acl_tensor
* @brief This file contains related functions of ggml_tensor and acl_tensor.
* Contains conversion from ggml_tensor to acl_tensor, broadcast and other
* functions.
* @author hipudding <huafengchun@gmail.com>
* @author wangshuai09 <391746016@qq.com>
* @date July 15, 2024
*
* Copyright (c) 2023-2024 The ggml authors
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*/
#include <aclnnop/aclnn_add.h>
#include <aclnnop/aclnn_arange.h>
#include <aclnnop/aclnn_argsort.h>
#include <aclnnop/aclnn_cat.h>
#include <aclnnop/aclnn_clamp.h>
#include <aclnnop/aclnn_div.h>
#include <aclnnop/aclnn_gelu.h>
#include <aclnnop/aclnn_hardsigmoid.h>
#include <aclnnop/aclnn_hardswish.h>
#include <aclnnop/aclnn_leaky_relu.h>
#include <aclnnop/aclnn_mul.h>
#include <aclnnop/aclnn_relu.h>
#include <aclnnop/aclnn_silu.h>
#include <aclnnop/aclnn_tanh.h>
#include "acl_tensor.h"
#include "common.h"
/**
* @brief Repeats a ggml tensor along each dimension to match the dimensions
* of another tensor.
*
* @details This function repeats the elements of a source ggml tensor along
* each dimension to create a destination tensor with the specified
* dimensions. The operation is performed using the ACL backend and
* executed asynchronously on the device.
*
* @param ctx The CANN context used for operations.
* @param dst The ggml tensor representing the destination, which op is
* GGML_OP_REPEAT and specifies the desired dimensions.
*/
void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Adds two ggml tensors using the CANN backend.
*
* @details This function performs an element-wise addition of two tensors. In
* case the tensors do not have the same shape, one or both tensors
* will be broadcasted to match the shape of the other before the
* addition is performed.The formula for the operation is given by:
* \f[
* \text{dst} = \text{acl_src0} + \alpha \cdot \text{acl_src1}
* \f]
*
* @param ctx The CANN context used for operations.
* @param dst The ggml tensor representing the destination, result of the
* addition is stored at dst->data, and dst->op is `GGML_OP_ADD`
*/
void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Applies the Leaky ReLU activation function to a tensor using the CANN
* backend.
*
* @details This function computes the Leaky ReLU activation for each element of
* the input tensor. The Leaky ReLU function allows a small gradient
* when the unit is not active (i.e., when the input is negative). The
* Leaky ReLU function is defined as:
* \f[
* \text{dst} = \max(0, src) + \text{negativeSlope} \cdot \min(0,
* src)
* \f]
* `negativeSlope` is in dst->params.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the result of the Leaky ReLU
* activation is stored, which op is `GGML_OP_LEAKY_RELU`
*/
void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Concatenates multiple tensors along a specified dimension using the
* CANN backend.
*
* @param ctx The CANN context used for operations.
* @param tensorList A pointer to the list of tensors to be concatenated.
* @param dst The destination tensor where the result of the
* concatenation is stored. dst->op is `GGML_OP_CONCAT`.
* @param concat_dim The dimension along which the tensors are concatenated.
*
* @attention tensorList length should be 2 and the dimension using for concat
* default to 1.
*/
void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Generates a sequence of evenly spaced values within a specified
* interval for a ggml tensor using the CANN backend.
*
* @details This function creates a sequence of numbers over a specified i
* nterval, starting from `start`, ending before `stop`, and
* incrementing by `step`. The sequence is stored in the destination
* tensor `dst`.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the generated sequence will be stored.
* `start`, 'stop' and 'step' are in dst->op_params and dst->op is
* `GGML_OP_ARANGE`.
*/
void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the square of the elements of a ggml tensor using the CANN
* backend.
* @details The function sets the second source tensor of the destination
* tensor `dst` to be equal to the first source tensor. This is
* effectively squaring the elements since the multiplication becomes
* `element * element`.
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the squared values will be stored，
* which dst->op is `GGML_OP_SQR`.
*/
void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Applies a clamp operation to the elements of a ggml tensor using the
* CANN backend.
*
* @details This function clamps the elements of the input tensor `src` to a
* specified range defined by `min` and `max` values. The result is
* stored in the destination tensor `dst`. The operation is defined as:
* \f[
* y = \max(\min(x, max\_value), min\_value)
* \f]
* where `x` is an element of the input tensor, and `y` is the
* corresponding element in the output tensor.
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the clamped values will be stored.
* dst->op is `GGML_OP_CLAMP`, `min` and `max` value is in dst->params.
*/
void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Scales the elements of a ggml tensor by a constant factor using the
* CANN backend.
*
* @details This function multiplies each element of the input tensor `src` by
* a scaling factor `scale`, storing the result in the destination
* tensor `dst`. The operation is defined as:
* \f[
* dst = src \times scale
* \f]
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the scaled values will be stored.
* dst->op is `GGML_OP_SCALE` and `scale` value is in dst->params.
*/
void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Sorts the elements of a ggml tensor and returns the indices that
* would sort the tensor using the CANN backend.
*
* @details This function performs an argsort operation on the input tensor
* `src`. It sorts the elements of `src` in either ascending or
* descending order, depending on the `GGML_SORT_ORDER_DESC`,
* and returns the indices that would sort the original tensor.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the sorted indices will be stored.
* dst->op is `GGML_OP_ARGSORT`.
*/
void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the Layer Normalization for a ggml tensor using the CANN
* backend.
*
* @details This function applies the Layer Normalization operation on the
* input tensor `src` and stores the result in the destination tensor
* `dst`. Layer Normalization normalizes the features at each sample in
* a mini-batch independently. It is commonly used in neural networks
* to normalize the activations of a layer by adjusting and scaling
* the outputs.
* The operation is defined as:
* \f[
* \text { out }=\frac{x-\mathrm{E}[x]}{\sqrt{\text{Var}[x]+eps}}
* \f]
* `Var` defaults dst->ne[0]. `eps` is in dst->params.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the normalized values will be stored.
* @attention `Var` defaults to dst->ne[0].
*/
void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the Group Normalization for a ggml tensor using the CANN
* backend.
*
* @brief This function applies the Group Normalization operation on the input
* tensor `src` and stores the result in the destination tensor `dst`.
* Group Normalization divides the channels into groups and normalizes
* the features within each group across spatial locations.
* It is commonly used in convolutional neural networks to improve
* training stability and performance.
* The operation is defined as:
* \f[
* \text { out }=\frac{x-\mathrm{E}[x]}{\sqrt{\text{Var}[x]+eps}}
* \f]
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the normalized values will be stored.
* `n_groups` is in dst->params, which split C channel to `n_groups`.
* dst->op is `GGML_OP_GROUP_NORM`.
*
* @attention eps defaults to 1e-6f.
*/
void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the accumulation of tensors using the CANN backend.
*
* @details This function performs an accumulation operation on two tensors.
* Depending on the `inplace` flag, it either updates the destination
* tensor `dst` in place by adding `alpha * src1` to it, or it creates
* a new tensor as the result of `src0 + alpha * src1` and stores it in
* `dst`.
* The operation is defined as:
* \f[
* dst = src0 + alpha \times src1
* \f]
* if `inplace` is `true`, `src0` is equal to 'dst'.
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the accumulated values will be stored.
* `inplace` is in dst->params, and dst->op is `GGML_OP_ACC`.
*/
void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the sum of elements along the last dimension of a ggml tensor
* using the CANN backend.
*
* @details This function performs a reduction sum operation along the last
* dimension of the input tensor `src`. The result of the sum is stored
* in the destination tensor `dst`.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the reduced values will be stored。
* dst->op is `GGML_OP_SUM_ROWS`.
*
* @attention `reduce_dims` defaults to 3, which means the last dimension.
*/
void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Upsamples a ggml tensor using nearest neighbor interpolation using
* the CANN backend.
*
* @details This function performs upsampling of the input tensor `src` using
* nearest neighbor interpolation. The upsampling is applied to the
* height and width dimensions (last two dimensions) of the tensor. The
* result is stored in the destination tensor `dst`, which must have
* the appropriate dimensions for the upsampled output.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the upsampled values will be stored.
* dst->op is `GGML_OP_UPSCALE`.
*/
void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx,
ggml_tensor* dst);
/**
* @brief Pads a ggml tensor to match the dimensions of the destination tensor
* using the CANN backend.
*
* @details This function pads the input tensor `src` so that it matches the
* dimensions of the destination tensor `dst`. The amount of padding
* is calculated based on the difference in sizes between `src` and
* `dst` along each dimension. The padded tensor is stored in `dst`.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor, which specifies the target dimensions for
* padding. dst->op is `GGML_OP_PAD`.
*/
void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Executes a 2D pooling operation on a ggml tensor using the CANN
* backend.
*
* @details This function dispatches the execution of a 2D pooling operation on
* the input tensor `dst`. The type of pooling (average or max) is
* determined by the `op` parameter, which is read from the operation
* parameters of `dst`. The function supports average pooling
* (`GGML_OP_POOL_AVG`) and max pooling (`GGML_OP_POOL_MAX`). If an
* invalid operation is encountered, the function asserts a failure.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor on which the pooling operation is to be
* performed. dst->op is `GGML_OP_POOL_2D`.
*/
void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Duplicates a ggml tensor using the CANN backend.
*
* @details This function duplicates the contents of the source tensor `src` to
* the destination tensor `dst`. The function supports various tensor
* types and configurations, including handling of extra data, type
* conversions, and special cases for contiguous and non-contiguous
* tensors.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the duplicated data will be stored.
* dst->op is `GGML_OP_DUP`
*
* @attention Only support Fp16/FP32. Not support when src and dst have
* different shape and dst is no-contiguous.
* @note: This func need to simplify.
*/
void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the Root Mean Square (RMS) normalization of a ggml tensor
* using the CANN backend.
*
* @details This function applies RMS normalization to the input tensor `src`
* and stores the result in the destination tensor `dst`. RMS
* normalization involves computing the root mean square of the input
* tensor along a specified dimension and then dividing each element of
* the tensor by this value, adjusted by a small epsilon value to
* prevent division by zero.
* The operation is defined as:
* \f[
* \text{RmsNorm}\left(x_i\right)=\frac{x_i}{\text{Rms}(\mathbf{x})} g_i,
* \quad \text { where } \text{Rms}(\mathbf{x})=\sqrt{\frac{1}{n} \sum_{i=1}^n x_i^2+e p s}
* \f]
* `eps` is in dst->op_params.
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the normalized values will be stored.
* dst->op is `GGML_OP_RMS_NORM`.
*/
void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Applies a diagonal mask to the tensor with a specified value.
*
* @details This function creates a mask tensor filled with ones, then applies
* an upper triangular and lower triangular operation to it based on
* the number of past elements specified. Afterward, it adds the masked
* tensor to the destination tensor in-place.
*
* @param ctx The backend CANN context used for operations.
* @param dst The destination tensor where the result will be stored. dst->op is
* `GGML_OP_DIAG_MASK`
* @param value The value to use for masking.
*/
void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float value);
/**
* @brief Performs an image-to-column transformation on the input tensor.
*
* @details This function takes an input tensor and applies an image-to-column
* operation, converting spatial dimensions into column-like
* structures suitable for convolutional operations. It supports both
* half-precision (F16) and single-precision (F32) floating-point data
* types.
*
* @param ctx The backend CANN context for executing operations.
* @param dst The destination tensor that stores the result of the operation.
* dst->op is `GGML_OP_IM2COL`.
*/
void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes time step embeddings using sine and cosine functions.
*
* @details This function calculates time step embeddings by applying sine and
* cosine transformations to a given input tensor, which is typically
* used in temporal models like diffusion models or transformers to
* encode time information effectively.
*
* @param ctx The backend CANN context for executing operations.
* @param dst The destination tensor where the result of the embedding operation
* will be stored. dst->op is `GGML_OP_TIMESTEP_EMBEDDING`.
*/
void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_tensor* dst);
// @see ggml_cann_dup.
void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the softmax activation with optional masking.
*
* @details This function computes the softmax activation over the input tensor,
* optionally applying a mask and scaling factor. It supports both FP16
* and FP32 data types and can handle masking by broadcasting the mask
* across rows if necessary.
* The function performs the following steps:
* 1. Multiplies the input tensor by a scale factor.
* 2. Optionally casts the mask tensor to FP32 if it is in FP16 format.
* 3. Broadcasts the mask tensor if its dimensions do not match the
* input tensor's dimensions.
* 4. Adds the mask to the scaled input tensor.
* 5. Applies the softmax activation function along the specified
* dimension.
*
* @param ctx The backend CANN context for executing operations.
* @param dst The destination tensor where the result will be stored. dst->op is
* `GGML_OP_SOFTMAX`.
*/
void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Extracts specific rows from a tensor based on indices.
*
* @details This function retrieves rows from a source tensor src0 according to
* the indices provided in another tensor src1 and stores the result in
* a destination tensor (\p dst). It supports different data types
* including F32, F16, Q4_0, and Q8_0.
*
* @param ctx The backend CANN context for executing operations.
* @param dst The destination tensor where the extracted rows will be stored.
* dst->op is `GGML_OP_GET_ROWS`.
*/
void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Executes matrix multiplication for the given tensor.
*
* @details This function performs matrix multiplication on the source tensors
* associated with the destination tensor. It supports matrix
* multiplication F32, F16, and Q8_0.
*
* @param ctx The backend CANN context for executing operations.
* @param dst The destination tensor for storing the result of the matrix
* multiplication. dst->op is `GGML_OP_MUL_MAT`.
*/
void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Applies Rotary Positional Embedding (RoPE) to the input tensor.
*
* @details This function implements the RoPE mechanism, which is a method to
* encode positional information into sequence data, particularly
* useful in transformer models. It supports both F32 and F16 data
* types.
*
* @param ctx The backend CANN context for executing operations.
* @param dst The destination tensor where the RoPE-transformed data will be
* stored. dst->op is `GGML_OP_ROPE`.
*
* @note The function currently does not support cases where the n_dims is less
* than the input tensor's first dimension.
* @note The function currently does not support cases where the freq_factors is
* not NULL.
* @note The function currently does not support cases where the ext_factor is
* not equal 0.
* @note The function currently does not support cases where the freq_scale is
* not equal 1.
*/
void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst);
template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
aclTensor*, uint64_t*, aclOpExecutor**),
aclnnStatus execute(void*, uint64_t, aclOpExecutor*, aclrtStream)>
void ggml_cann_mul_div(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_tensor* src0 = dst->src[0];
ggml_tensor* src1 = dst->src[1];
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
aclTensor* acl_src0;
aclTensor* acl_src1;
aclTensor* acl_dst;
// Need bcast
if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) {
BCAST_SHAPE(src0, src1)
acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0));
acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1));
acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0));
} else {
acl_src0 = ggml_cann_create_tensor(src0);
acl_src1 = ggml_cann_create_tensor(src1);
acl_dst = ggml_cann_create_tensor(dst);
}
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
void* workspaceAddr = nullptr;
ACL_CHECK(getWorkspaceSize(acl_src0, acl_src1, acl_dst, &workspaceSize,
&executor));
if (workspaceSize > 0) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
workspaceAddr = workspace_allocator.get();
}
aclrtStream main_stream = ctx.stream();
ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
ACL_CHECK(aclDestroyTensor(acl_src0));
ACL_CHECK(aclDestroyTensor(acl_src1));
ACL_CHECK(aclDestroyTensor(acl_dst));
}
// Activation functions template.
template <aclnnStatus getWorkspaceSize(const aclTensor*, aclTensor*, uint64_t*,
aclOpExecutor**),
aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
const aclrtStream)>
void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_tensor* src = dst->src[0];
GGML_ASSERT(src->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
aclTensor* acl_src = ggml_cann_create_tensor(src);
aclTensor* acl_dst = ggml_cann_create_tensor(dst);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
void* workspaceAddr = nullptr;
ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
if (workspaceSize > 0) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
workspaceAddr = workspace_allocator.get();
}
aclrtStream main_stream = ctx.stream();
ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
ACL_CHECK(aclDestroyTensor(acl_src));
ACL_CHECK(aclDestroyTensor(acl_dst));
}
// Activation functions template for const aclTensors.
template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
uint64_t*, aclOpExecutor**),
aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
const aclrtStream)>
void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_tensor* src = dst->src[0];
GGML_ASSERT(src->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
aclTensor* acl_src = ggml_cann_create_tensor(src);
aclTensor* acl_dst = ggml_cann_create_tensor(dst);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
void* workspaceAddr = nullptr;
ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
if (workspaceSize > 0) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
workspaceAddr = workspace_allocator.get();
}
aclrtStream main_stream = ctx.stream();
ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
ACL_CHECK(aclDestroyTensor(acl_src));
ACL_CHECK(aclDestroyTensor(acl_dst));
}
#endif // CANN_ACLNN_OPS

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/*
* Copyright (c) 2023-2024 The ggml authors
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*/
#ifndef CANN_COMMON_H
#define CANN_COMMON_H
#include <acl/acl.h>
#include <cstdio>
#include <iostream>
#include <map>
#include <memory>
#include <string>
#include <vector>
#include "../include/ggml-cann.h"
#include "../include/ggml.h"
#define MATRIX_ROW_PADDING 512
#define GGML_CANN_MAX_STREAMS 8
/**
* @brief Handles CANN-related errors by printing an error message and
* terminating the program.
* @param stmt The statement that caused the error.
* @param func The function in which the error occurred.
* @param file The file in which the error occurred.
* @param line The line number at which the error occurred.
* @param msg The error message.
*/
[[noreturn]] void ggml_cann_error(const char* stmt, const char* func,
const char* file, int line, const char* msg);
/**
* @brief Checks the result of a CANN function call and invokes the error
* handler if the call fails.
* @param stmt The CANN function call to check.
* @param success The success code that indicates the call was successful.
* @param error_fn The function to call to retrieve the error message.
*/
#define ACL_CHECK_GEN(stmt, success, error_fn) \
do { \
int err_code = (stmt); \
if (err_code != (success)) { \
ggml_cann_error(#stmt, __func__, __FILE__, __LINE__, error_fn()); \
} \
} while (0);
#define ACL_CHECK(stmt) ACL_CHECK_GEN(stmt, 0, aclGetRecentErrMsg)
/**
* @brief Contains information about CANN devices.
*/
struct ggml_cann_device_info {
/**
* @brief Number of CANN devices available.
*/
int32_t device_count;
/**
* @brief Information about a single CANN device.
*/
struct cann_device_info {
int cc; /**< Compute capability. */
size_t smpb; /**< Maximum shared memory per block. */
bool vmm; /**< Virtual memory support. */
size_t vmm_granularity; /**< Granularity of virtual memory. */
size_t total_vram; /**< Total video RAM available on the device. */
};
cann_device_info devices[GGML_CANN_MAX_DEVICES] =
{}; /**< Array of CANN device information. */
};
const ggml_cann_device_info& ggml_cann_info();
void ggml_cann_set_device(int32_t device);
int32_t ggml_cann_get_device();
/**
* @brief Abstract base class for memory pools used by CANN.
*/
struct ggml_cann_pool {
/**
* @brief Virtual destructor for the memory pool.
*/
virtual ~ggml_cann_pool() = default;
/**
* @brief Allocates memory from the pool.
*
* @param size The size of the memory block to allocate.
* @param actual_size Pointer to a variable where the actual allocated size
* will be stored.
* @return Pointer to the allocated memory block.
*/
virtual void* alloc(size_t size, size_t* actual_size) = 0;
/**
* @brief Frees a previously allocated memory block.
*
* @param ptr Pointer to the memory block to free.
* @param size Size of the memory block to free.
* @note Note that all CANN opertors are running async. Make sure memory is
* still avaiable before this operator finished.
*/
virtual void free(void* ptr, size_t size) = 0;
};
/**
* @brief RAII wrapper for managing memory allocations from a CANN memory pool.
*/
struct ggml_cann_pool_alloc {
ggml_cann_pool* pool = nullptr; /**< Pointer to the memory pool. */
void* ptr = nullptr; /**< Pointer to the allocated memory block. */
size_t actual_size = 0; /**< Actual size of the allocated memory block. */
/**
* @brief Default constructor.
*/
ggml_cann_pool_alloc() = default;
/**
* @brief Constructor that initializes the memory pool.
* @param pool Reference to the memory pool.
*/
explicit ggml_cann_pool_alloc(ggml_cann_pool& pool) : pool(&pool) {}
/**
* @brief Constructor that initializes the memory pool and allocates memory.
* @param pool Reference to the memory pool.
* @param size Size of the memory block to allocate.
*/
ggml_cann_pool_alloc(ggml_cann_pool& pool, size_t size) : pool(&pool) {
alloc(size);
}
/**
* @brief Destructor that frees the allocated memory block.
*/
~ggml_cann_pool_alloc() {
if (ptr != nullptr) {
pool->free(ptr, actual_size);
}
}
/**
* @brief Allocates memory from the pool.
* @param size Size of the memory block to allocate.
* @return Pointer to the allocated memory block.
*/
void* alloc(size_t size) {
GGML_ASSERT(pool != nullptr);
GGML_ASSERT(ptr == nullptr);
ptr = pool->alloc(size, &this->actual_size);
return ptr;
}
/**
* @brief Allocates memory from a specific memory pool.
* @param pool Reference to the memory pool.
* @param size Size of the memory block to allocate.
* @return Pointer to the allocated memory block.
*/
void* alloc(ggml_cann_pool& pool, size_t size) {
this->pool = &pool;
return alloc(size);
}
/**
* @brief Gets the pointer to the allocated memory block.
* @return Pointer to the allocated memory block.
*/
void* get() { return ptr; }
// Deleted copy constructor
ggml_cann_pool_alloc(const ggml_cann_pool_alloc&) = delete;
// Deleted move constructor
ggml_cann_pool_alloc(ggml_cann_pool_alloc&&) = delete;
// Deleted copy assignment operator
ggml_cann_pool_alloc& operator=(const ggml_cann_pool_alloc&) = delete;
// Deleted move assignment operator
ggml_cann_pool_alloc& operator=(ggml_cann_pool_alloc&&) = delete;
};
/**
* @brief Context for managing CANN backend operations.
*/
struct ggml_backend_cann_context {
int32_t device; /**< Device ID. */
std::string name; /**< Name of the device. */
std::string description; /**< Description of the device. */
aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
/**
* @brief Constructor for initializing the context with a given device.
* @param device Device ID.
*/
explicit ggml_backend_cann_context(int device)
: device(device), name("CANN" + std::to_string(device)) {
ggml_cann_set_device(device);
description = aclrtGetSocName();
}
/**
* @brief Destructor for cleaning up resources.
*/
~ggml_backend_cann_context() {
ggml_cann_set_device(device);
if (copy_event != nullptr) {
ACL_CHECK(aclrtDestroyEvent(copy_event));
}
for (int i = 0; i < GGML_CANN_MAX_STREAMS; ++i) {
if (streams[i] != nullptr) {
ACL_CHECK(aclrtDestroyStream(streams[i]));
}
}
}
/**
* @brief Get or create a stream for a given index.
* @param stream Index of the stream.
* @return The stream corresponding to the given index.
*/
aclrtStream stream(int stream) {
if (streams[stream] == nullptr) {
ggml_cann_set_device(device);
ACL_CHECK(aclrtCreateStream(&streams[stream]));
}
return streams[stream];
}
/**
* @brief Get or create the default stream (index 0).
* @return The default stream.
*/
aclrtStream stream() { return stream(0); }
// TODO: each stream should have a memory pool.
std::unique_ptr<ggml_cann_pool>
mem_pool; /**< Memory pool for the device. */
/**
* @brief Create a new memory pool for a given device.
* @param device Device ID.
* @return A unique pointer to the new memory pool.
*/
static std::unique_ptr<ggml_cann_pool> new_pool_for_device(int device);
/**
* @brief Get or create the memory pool for the context.
* @return Reference to the memory pool.
*/
ggml_cann_pool& pool() {
if (mem_pool == nullptr) {
mem_pool = new_pool_for_device(device);
}
return *mem_pool;
}
};
#endif // CANN_COMMON_H

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file(GLOB SRC_FILES
get_row_f32.cpp
get_row_f16.cpp
get_row_q4_0.cpp
get_row_q8_0.cpp
quantize_f32_q8_0.cpp
quantize_f16_q8_0.cpp
quantize_float_to_q4_0.cpp
dup.cpp
)
set(ASCEND_CANN_PACKAGE_PATH ${CANN_INSTALL_DIR})
set(RUN_MODE "npu" CACHE STRING "run mode: npu/sim")
if(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
elseif(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake)
set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake)
else()
message(FATAL_ERROR "ascendc_kernel_cmake does not exist, please check whether the compiler package is installed.")
endif()
include(${ASCENDC_CMAKE_DIR}/ascendc.cmake)
ascendc_library(ascendc_kernels STATIC
${SRC_FILES}
)
message(STATUS "CANN: compile ascend kernels witch SOC_TYPE:${SOC_TYPE}, SOC_VERSION:${SOC_VERSION}, compile macro:-D${SOC_TYPE_COMPILE_OPTION}.")
ascendc_compile_definitions(ascendc_kernels PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
# ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP)

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cann/kernels/ascendc_kernels.h vendored Normal file
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#ifndef ASCENDC_KERNELS_H
#define ASCENDC_KERNELS_H
#include "aclrtlaunch_ascendc_get_row_f32.h"
#include "aclrtlaunch_ascendc_get_row_f16.h"
#include "aclrtlaunch_ascendc_get_row_q8_0.h"
#include "aclrtlaunch_ascendc_get_row_q4_0.h"
#include "aclrtlaunch_ascendc_quantize_f32_q8_0.h"
#include "aclrtlaunch_ascendc_quantize_f16_q8_0.h"
#include "aclrtlaunch_ascendc_quantize_f16_to_q4_0.h"
#include "aclrtlaunch_ascendc_quantize_f32_to_q4_0.h"
#include "aclrtlaunch_ascendc_dup_by_rows_fp16.h"
#include "aclrtlaunch_ascendc_dup_by_rows_fp32.h"
#include "aclrtlaunch_ascendc_dup_by_rows_fp32_to_fp16.h"
#include "aclrtlaunch_ascendc_dup_by_rows_fp16_to_fp32.h"
#endif // ASCENDC_KERNELS_H

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#include "kernel_operator.h"
#include <cmath>
using namespace AscendC;
#define BUFFER_NUM 2
const int64_t SUPPORTED_MAX_DIM = 65535; // currently the limit of max block dim supportted by dup kernel is 65535template <typename SRC_T, typename DST_T>
template <typename SRC_T, typename DST_T>
class DupByRows {
public:
__aicore__ inline DupByRows() {}
__aicore__ inline void init(GM_ADDR src, GM_ADDR dst, int64_t *input_ne_ub,
size_t *input_nb_ub) {
/* Dup by rows when src is contigous on first dimension and dst is
contiguous, each kernel process one row.
*/
// Input has four dims.
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
// param
num_rows = input_ne_ub[1] * input_ne_ub[2] * input_ne_ub[3];
num_elem = input_ne_ub[0];
// index for (ne[1], ne[2], ne[3]): (idx_ne1, idx_ne2, idx_ne3)
idx_ne3 = op_block_idx / (input_ne_ub[1] * input_ne_ub[2]);
idx_ne2 = (op_block_idx - idx_ne3 * (input_ne_ub[1] * input_ne_ub[2]))
/ (input_ne_ub[1]);
idx_ne1 = op_block_idx - idx_ne3 * (input_ne_ub[1] * input_ne_ub[2])
- idx_ne2 * input_ne_ub[1];
// src may not contiguous in dim [1,2,3], so stride decited by ne&nb
src_stride = input_nb_ub[3] * idx_ne3 + input_nb_ub[2] * idx_ne2
+ input_nb_ub[1] * idx_ne1;
// dst is contiguous
dst_stride = op_block_idx * (input_ne_ub[0] * sizeof(DST_T));
src_gm.SetGlobalBuffer(reinterpret_cast<__gm__ SRC_T *>(src +
src_stride));
dst_gm.SetGlobalBuffer(reinterpret_cast<__gm__ DST_T *>(dst +
dst_stride));
pipe.InitBuffer(src_queue, BUFFER_NUM, (sizeof(SRC_T) * num_elem +
32 - 1) / 32 * 32);
pipe.InitBuffer(dst_queue, BUFFER_NUM, (sizeof(DST_T) * num_elem +
32 - 1) / 32 * 32);
}
__aicore__ inline void copy_in() {
LocalTensor<SRC_T> src_local = src_queue.AllocTensor<SRC_T>();
const size_t elem_per_block = 32 / sizeof(SRC_T);
size_t tail = num_elem % elem_per_block;
size_t cpy_elements_len = tail > 0 ? num_elem + 1 : num_elem;
DataCopy(src_local, src_gm, cpy_elements_len);
src_queue.EnQue(src_local);
}
__aicore__ inline void copy_out() {
LocalTensor<DST_T> dst_local = dst_queue.DeQue<DST_T>();
#ifdef ASCEND_310P
const size_t elem_per_block = 32 / sizeof(DST_T);
size_t tail = num_elem % elem_per_block;
size_t len = num_elem & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(dst_gm, dst_local, len);
}
if(tail != 0) {
for (size_t i = tail; i < elem_per_block; i++) {
dst_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(dst_gm[len], dst_local[len], elem_per_block);
SetAtomicNone();
}
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = num_elem * sizeof(DST_T);
DataCopyPad(dst_gm, dst_local, dataCopyParams);
#endif
dst_queue.FreeTensor(dst_local);
}
__aicore__ inline void dup() {
// main process, copy one row data from src to dst.
copy_in();
LocalTensor<SRC_T> src_local = src_queue.DeQue<SRC_T>();
LocalTensor<DST_T> dst_local = dst_queue.AllocTensor<DST_T>();
int32_t BLOCK_NUM = 32 / sizeof(DST_T);
DataCopy(dst_local, src_local, (num_elem + BLOCK_NUM - 1)
/ BLOCK_NUM * BLOCK_NUM);
dst_queue.EnQue<DST_T>(dst_local);
src_queue.FreeTensor(src_local);
copy_out();
}
__aicore__ inline void dup_with_cast() {
// main process, copy one row data from src to dst.
// cast dtype from src to dst.
copy_in();
LocalTensor<SRC_T> src_local = src_queue.DeQue<SRC_T>();
LocalTensor<DST_T> dst_local = dst_queue.AllocTensor<DST_T>();
Cast(dst_local, src_local, RoundMode::CAST_NONE, num_elem);
dst_queue.EnQue<DST_T>(dst_local);
src_queue.FreeTensor(src_local);
copy_out();
}
private:
TPipe pipe;
GlobalTensor<SRC_T> src_gm;
GlobalTensor<DST_T> dst_gm;
int64_t num_rows;
int64_t num_elem;
int64_t idx_ne3;
int64_t idx_ne2;
int64_t idx_ne1;
int64_t src_stride;
int64_t dst_stride;
TQue<QuePosition::VECIN, BUFFER_NUM> src_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> dst_queue;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp16(
GM_ADDR src_gm,
GM_ADDR dst_gm,
GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm,
GM_ADDR output_ne_gm,
GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
DupByRows<half, half> op;
op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
op.dup();
}
extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp32(
GM_ADDR src_gm,
GM_ADDR dst_gm,
GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm,
GM_ADDR output_ne_gm,
GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
DupByRows<float_t, float_t> op;
op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
op.dup();
}
extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp32_to_fp16(
GM_ADDR src_gm,
GM_ADDR dst_gm,
GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm,
GM_ADDR output_ne_gm,
GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
DupByRows<float_t, half> op;
op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
op.dup_with_cast();
}
extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp16_to_fp32(
GM_ADDR src_gm,
GM_ADDR dst_gm,
GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm,
GM_ADDR output_ne_gm,
GM_ADDR output_nb_gm) {
// copy params from gm to ub.
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
DupByRows<half, float_t> op;
op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
op.dup_with_cast();
}

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#include "kernel_operator.h"
// optimize me. Use template to avoid copy code.
using namespace AscendC;
#define BUFFER_NUM 2
class GET_ROW_F16 {
public:
__aicore__ inline GET_ROW_F16() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
int64_t *input_ne_ub, size_t *input_nb_ub,
int64_t *indices_ne_ub, size_t *indices_nb_ub,
int64_t *output_ne_ub, size_t *output_nb_ub) {
// TODO, use template for F16/f32
int64_t op_block_num = GetBlockNum();
op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
indices_ne[i] = indices_ne_ub[i];
indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
output_ne[i] = output_ne_ub[i];
output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
}
// Indices has two dims. n_elements = all rows should get.
// dr, all rows should this thread get.
uint64_t n_elements =
indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
dr = n_elements / op_block_num;
uint64_t tails = n_elements % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
input_gm.SetGlobalBuffer((__gm__ half *)input);
indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
output_gm.SetGlobalBuffer((__gm__ float *)output);
uint64_t input_local_buffer_size = ((input_ne[0] * sizeof(half) + 31)
& ~31);
uint64_t output_local_buffer_size = ((input_ne[0] * sizeof(float) + 31)
& ~31);
local_buffer_elems = input_local_buffer_size / sizeof(half);
// TODO, consider long row that can't put in UB.
// All data should asign to 32. It's ok because all data is align to 32.
pipe.InitBuffer(input_queue, BUFFER_NUM, input_local_buffer_size);
pipe.InitBuffer(output_queue, BUFFER_NUM, output_local_buffer_size);
}
__aicore__ inline void copy_in(uint32_t offset, size_t len) {
size_t origin_len = len;
LocalTensor<half> input_local = input_queue.AllocTensor<half>();
const size_t elem_per_block = 32 / sizeof(half);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if(tail != 0) {
len += elem_per_block;
}
DataCopy(input_local, input_gm[offset], len);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset, size_t len) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(output_gm[offset], output_local, len);
}
if(tail != 0) {
#ifdef ASCEND_310P
for (size_t i = tail; i < elem_per_block; i++) {
output_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(output_gm[offset + len], output_local[len], elem_per_block);
SetAtomicNone();
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPad(output_gm[offset + len], output_local[len],
dataCopyParams);
#endif
}
output_queue.FreeTensor(output_local);
}
__aicore__ inline void calculate_row(int64_t idx) {
const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
const int64_t indices_ne1_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
indices_ne[0];
const int64_t indices_ne0_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
indices_ne1_idx * indices_ne[0]);
const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
indices_ne1_idx * indices_stride[1] +
indices_ne2_idx * indices_stride[2];
const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
const int64_t input_offset = selected_row_idx * input_stride[1] +
indices_ne1_idx * input_stride[2] +
indices_ne2_idx * input_stride[3];
const int64_t output_offset = indices_ne0_idx * output_stride[1] +
indices_ne1_idx * output_stride[2] +
indices_ne2_idx * output_stride[3];
copy_in(input_offset, input_ne[0]);
LocalTensor<half> input_local = input_queue.DeQue<half>();
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
Cast(output_local, input_local, RoundMode::CAST_NONE,
local_buffer_elems);
output_queue.EnQue(output_local);
copy_out(output_offset, input_ne[0]);
input_queue.FreeTensor(input_local);
}
__aicore__ inline void calculate() {
for (int64_t i = ir; i < ir + dr; i++) {
calculate_row(i);
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t indices_ne[4];
size_t indices_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
size_t local_buffer_elems;
int64_t ir;
int64_t dr;
TPipe pipe;
GlobalTensor<half> input_gm;
GlobalTensor<int32_t> indices_gm;
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
int64_t op_block_idx;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_get_row_f16(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR input_nb_gm, GM_ADDR indices_ne_gm,
GM_ADDR indices_nb_gm, GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t indices_ne_ub[4];
size_t indices_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
GET_ROW_F16 op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();
}

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#include "kernel_operator.h"
// optimize me. Use template to avoid copy code.
using namespace AscendC;
#define BUFFER_NUM 2
class GET_ROW_F32 {
public:
__aicore__ inline GET_ROW_F32() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
int64_t *input_ne_ub, size_t *input_nb_ub,
int64_t *indices_ne_ub, size_t *indices_nb_ub,
int64_t *output_ne_ub, size_t *output_nb_ub) {
int64_t op_block_num = GetBlockNum();
op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
indices_ne[i] = indices_ne_ub[i];
indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
output_ne[i] = output_ne_ub[i];
output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
}
// Indices has two dims. n_elements = all rows should get.
// dr, all rows should this thread get.
uint64_t n_elements =
indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
dr = n_elements / op_block_num;
uint64_t tails = n_elements % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
input_gm.SetGlobalBuffer((__gm__ float *)input);
indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
output_gm.SetGlobalBuffer((__gm__ float *)output);
uint64_t local_buffer_size = ((input_ne[0] * sizeof(float) + 31) & ~31);
local_buffer_elems = local_buffer_size / sizeof(float);
// TODO, consider long row that can't put in UB.
// All data should asign to 32. It's ok because all data is align to 32.
pipe.InitBuffer(input_queue, BUFFER_NUM, local_buffer_size);
pipe.InitBuffer(output_queue, BUFFER_NUM, local_buffer_size);
}
__aicore__ inline void copy_in(uint32_t offset, size_t len) {
LocalTensor<float> input_local = input_queue.AllocTensor<float>();
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if(tail != 0) {
len += elem_per_block;
}
DataCopy(input_local, input_gm[offset], len);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset, size_t len) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(output_gm[offset], output_local, len);
}
if(tail != 0) {
#ifdef ASCEND_310P
for (size_t i = tail; i < elem_per_block; i++) {
output_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(output_gm[offset + len], output_local[len], elem_per_block);
SetAtomicNone();
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPad(output_gm[offset + len], output_local[len],
dataCopyParams);
#endif
}
output_queue.FreeTensor(output_local);
}
__aicore__ inline void calculate_row(int64_t idx) {
const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
const int64_t indices_ne1_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
indices_ne[0];
const int64_t indices_ne0_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
indices_ne1_idx * indices_ne[0]);
const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
indices_ne1_idx * indices_stride[1] +
indices_ne2_idx * indices_stride[2];
const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
const int64_t input_offset = selected_row_idx * input_stride[1] +
indices_ne1_idx * input_stride[2] +
indices_ne2_idx * input_stride[3];
const int64_t output_offset = indices_ne0_idx * output_stride[1] +
indices_ne1_idx * output_stride[2] +
indices_ne2_idx * output_stride[3];
copy_in(input_offset, input_ne[0]);
LocalTensor<float> input_local = input_queue.DeQue<float>();
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
DataCopy(output_local, input_local, local_buffer_elems);
output_queue.EnQue(output_local);
copy_out(output_offset, input_ne[0]);
input_queue.FreeTensor(input_local);
}
__aicore__ inline void calculate() {
for (int64_t i = ir; i < ir + dr; i++) {
calculate_row(i);
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t indices_ne[4];
size_t indices_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
size_t local_buffer_elems;
int64_t ir;
int64_t dr;
TPipe pipe;
GlobalTensor<float> input_gm;
GlobalTensor<int32_t> indices_gm;
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
int64_t op_block_idx;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_get_row_f32(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR input_nb_gm, GM_ADDR indices_ne_gm,
GM_ADDR indices_nb_gm, GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t indices_ne_ub[4];
size_t indices_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
GET_ROW_F32 op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();
}

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp vendored Normal file
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#include "kernel_operator.h"
// optimize me. Use template to avoid copy code.
using namespace AscendC;
#ifdef ASCEND_310P // 310P not support 4bit get row
extern "C" __global__ __aicore__ void ascendc_get_row_q4_0(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
// let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed.
printf("Ascend310P not support 4bit get row.\n");
}
#else
#define BUFFER_NUM 2
#define QK4_0 32
class GET_ROW_Q4_0 {
public:
__aicore__ inline GET_ROW_Q4_0() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
int64_t *input_ne_ub, int64_t *indices_ne_ub,
size_t *indices_nb_ub, int64_t *output_ne_ub,
size_t *output_nb_ub) {
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
indices_ne[i] = indices_ne_ub[i];
indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
scale_ne[i] = input_ne_ub[i];
output_ne[i] = output_ne_ub[i];
output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
}
// one scale for a group.
scale_ne[0] /= QK4_0;
input_stride[0] = 1;
scale_stride[0] = 1;
output_stride[0] = 1;
for (int i = 1; i < 4; i++) {
input_stride[i] = input_stride[i - 1] * input_ne[i - 1];
scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
}
group_size_in_row = input_ne[0] / QK4_0;
int64_t scale_offset = input_ne[0] * input_ne[1] * input_ne[2] *
input_ne[3] / 2;
// Indices has two dims. n_elements = all rows should get.
// dr, all rows should this thread get.
uint64_t n_elements =
indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
dr = n_elements / op_block_num;
uint64_t tails = n_elements % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
input_gm.SetGlobalBuffer((__gm__ int4b_t *)input);
scale_gm.SetGlobalBuffer((__gm__ half *)(input + scale_offset));
indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
output_gm.SetGlobalBuffer((__gm__ float *)output);
pipe.InitBuffer(input_queue, BUFFER_NUM, QK4_0 * sizeof(int4b_t));
pipe.InitBuffer(cast_queue, BUFFER_NUM, QK4_0 * sizeof(half));
pipe.InitBuffer(output_queue, BUFFER_NUM, QK4_0 * sizeof(float));
}
__aicore__ inline void copy_in(uint32_t offset) {
LocalTensor<int4b_t> input_local = input_queue.AllocTensor<int4b_t>();
// 32 * sizeof(int4b_t) = 16, which is not aligned to 32, why no error?
DataCopy(input_local, input_gm[offset], QK4_0);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
DataCopy(output_gm[offset], output_local, QK4_0);
output_queue.FreeTensor(output_local);
}
__aicore__ inline void calculate_group(int64_t idx, int64_t group) {
const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
const int64_t indices_ne1_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
indices_ne[0];
const int64_t indices_ne0_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
indices_ne1_idx * indices_ne[0]);
const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
indices_ne1_idx * indices_stride[1] +
indices_ne2_idx * indices_stride[2];
const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
const int64_t input_offset = selected_row_idx * input_stride[1] +
indices_ne1_idx * input_stride[2] +
indices_ne2_idx * input_stride[3] +
group * QK4_0;
const int64_t scale_offset = selected_row_idx * scale_stride[1] +
indices_ne1_idx * scale_stride[2] +
indices_ne2_idx * scale_stride[3] + group;
const int64_t output_offset = indices_ne0_idx * output_stride[1] +
indices_ne1_idx * output_stride[2] +
indices_ne2_idx * output_stride[3] +
group * QK4_0;
copy_in(input_offset);
LocalTensor<int4b_t> input_local = input_queue.DeQue<int4b_t>();
LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
// TODO: cast more data to speed up.
Cast(cast_local, input_local, RoundMode::CAST_NONE, QK4_0);
Cast(output_local, cast_local, RoundMode::CAST_NONE, QK4_0);
// Only mul need compile by group.
half scale = scale_gm.GetValue(scale_offset);
Muls(output_local, output_local, (float)scale, QK4_0);
input_queue.FreeTensor(input_local);
cast_queue.FreeTensor(cast_local);
output_queue.EnQue(output_local);
copy_out(output_offset);
}
__aicore__ inline void calculate() {
for (int64_t i = ir; i < ir + dr; i++) {
for (int64_t j = 0; j < group_size_in_row; j++) {
calculate_group(i, j);
}
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t scale_ne[4];
size_t scale_stride[4];
int64_t indices_ne[4];
size_t indices_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
int64_t ir;
int64_t dr;
int64_t group_size_in_row;
TPipe pipe;
GlobalTensor<int4b_t> input_gm;
GlobalTensor<half> scale_gm;
GlobalTensor<int32_t> indices_gm;
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
TQue<QuePosition::VECIN, BUFFER_NUM> cast_queue;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_get_row_q4_0(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
int64_t indices_ne_ub[4];
size_t indices_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
GET_ROW_Q4_0 op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, indices_ne_ub,
indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();
}
#endif // #ifdef ASCEND_310P

191
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#include "kernel_operator.h"
// optimize me. Use template to avoid copy code.
using namespace AscendC;
#define BUFFER_NUM 2
#define QK8_0 32
class GET_ROW_Q8_0 {
public:
__aicore__ inline GET_ROW_Q8_0() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
int64_t *input_ne_ub, int64_t *indices_ne_ub,
size_t *indices_nb_ub, int64_t *output_ne_ub,
size_t *output_nb_ub) {
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
indices_ne[i] = indices_ne_ub[i];
indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
scale_ne[i] = input_ne_ub[i];
output_ne[i] = output_ne_ub[i];
output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
}
// one scale for a group.
scale_ne[0] /= QK8_0;
input_stride[0] = 1;
scale_stride[0] = 1;
output_stride[0] = 1;
for (int i = 1; i < 4; i++) {
input_stride[i] = input_stride[i - 1] * input_ne[i - 1];
scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
}
group_size_in_row = input_ne[0] / QK8_0;
int64_t scale_offset = input_ne[0] * input_ne[1] * input_ne[2] *
input_ne[3] * sizeof(int8_t);
// Indices has two dims. n_elements = all rows should get.
// dr, all rows should this thread get.
uint64_t n_elements =
indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
dr = n_elements / op_block_num;
uint64_t tails = n_elements % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
input_gm.SetGlobalBuffer((__gm__ int8_t *)input);
scale_gm.SetGlobalBuffer((__gm__ half *)(input + scale_offset));
indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
output_gm.SetGlobalBuffer((__gm__ float *)output);
pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
pipe.InitBuffer(cast_queue, BUFFER_NUM, QK8_0 * sizeof(half));
pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(float));
}
__aicore__ inline void copy_in(uint32_t offset) {
LocalTensor<int8_t> input_local = input_queue.AllocTensor<int8_t>();
DataCopy(input_local, input_gm[offset], QK8_0);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
DataCopy(output_gm[offset], output_local, QK8_0);
output_queue.FreeTensor(output_local);
}
__aicore__ inline void calculate_group(int64_t idx, int64_t group) {
const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
const int64_t indices_ne1_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
indices_ne[0];
const int64_t indices_ne0_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
indices_ne1_idx * indices_ne[0]);
const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
indices_ne1_idx * indices_stride[1] +
indices_ne2_idx * indices_stride[2];
const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
const int64_t input_offset = selected_row_idx * input_stride[1] +
indices_ne1_idx * input_stride[2] +
indices_ne2_idx * input_stride[3] +
group * QK8_0;
const int64_t scale_offset = selected_row_idx * scale_stride[1] +
indices_ne1_idx * scale_stride[2] +
indices_ne2_idx * scale_stride[3] + group;
const int64_t output_offset = indices_ne0_idx * output_stride[1] +
indices_ne1_idx * output_stride[2] +
indices_ne2_idx * output_stride[3] +
group * QK8_0;
copy_in(input_offset);
LocalTensor<int8_t> input_local = input_queue.DeQue<int8_t>();
LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
// TODO: cast more data to speed up.
Cast(cast_local, input_local, RoundMode::CAST_NONE, QK8_0);
Cast(output_local, cast_local, RoundMode::CAST_NONE, QK8_0);
// Only mul need compile by group.
half scale = scale_gm.GetValue(scale_offset);
Muls(output_local, output_local, (float)scale, QK8_0);
input_queue.FreeTensor(input_local);
cast_queue.FreeTensor(cast_local);
output_queue.EnQue(output_local);
copy_out(output_offset);
}
__aicore__ inline void calculate() {
for (int64_t i = ir; i < ir + dr; i++) {
for (int64_t j = 0; j < group_size_in_row; j++) {
calculate_group(i, j);
}
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t scale_ne[4];
size_t scale_stride[4];
int64_t indices_ne[4];
size_t indices_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
int64_t ir;
int64_t dr;
int64_t group_size_in_row;
TPipe pipe;
GlobalTensor<int8_t> input_gm;
GlobalTensor<half> scale_gm;
GlobalTensor<int32_t> indices_gm;
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
TQue<QuePosition::VECIN, BUFFER_NUM> cast_queue;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_get_row_q8_0(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
int64_t indices_ne_ub[4];
size_t indices_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
GET_ROW_Q8_0 op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, indices_ne_ub,
indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();
}

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp vendored Normal file
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#include "kernel_operator.h"
using namespace AscendC;
#ifdef ASCEND_310P
extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
// let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed.
printf("Ascend310P not support f16->8bit quantization.\n");
}
#else
#define BUFFER_NUM 2
#define QK8_0 32
class QUANTIZE_F16_Q8_0 {
public:
__aicore__ inline QUANTIZE_F16_Q8_0() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR output,
int64_t *input_ne_ub, size_t *input_nb_ub,
int64_t *output_ne_ub) {
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
output_ne[i] = output_ne_ub[i];
}
output_stride[0] = 1;
for (int i = 1; i < 4; i++) {
output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
}
scale_ne = input_ne;
scale_stride[0] = 1;
scale_stride[1] = input_ne[0] / QK8_0;
for (int i = 2; i < 4; i++) {
scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
}
// split input tensor by rows.
uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
dr = nr / op_block_num;
uint64_t tails = nr % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
group_size_in_row = scale_stride[1];
int64_t output_size = output_ne[0] * output_ne[1] * output_ne[2] *
output_ne[3] * sizeof(uint8_t);
input_gm.SetGlobalBuffer((__gm__ half *)input);
output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
scale_gm.SetGlobalBuffer((__gm__ half *)(output + output_size + ir *
group_size_in_row *
sizeof(half)));
pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(half));
pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
pipe.InitBuffer(work_queue, 1, 32);
pipe.InitBuffer(max_queue, 1, 32);
pipe.InitBuffer(abs_queue, 1, QK8_0 * sizeof(float));
pipe.InitBuffer(scale_queue, 1, 32);
pipe.InitBuffer(cast_queue ,1 ,QK8_0 * sizeof(float));
}
__aicore__ inline void copy_in(uint32_t offset) {
LocalTensor<half> input_local = input_queue.AllocTensor<half>();
DataCopy(input_local, input_gm[offset], QK8_0);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset) {
LocalTensor<int8_t> output_local = output_queue.DeQue<int8_t>();
DataCopy(output_gm[offset], output_local, QK8_0);
output_queue.FreeTensor(output_local);
}
__aicore__ inline half calculate_group(int64_t row, int64_t group) {
const int64_t i3 = row / (input_ne[1] * input_ne[2]);
const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
const int64_t i1 =
row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
const int64_t input_offset = i1 * input_stride[1] +
i2 * input_stride[2] +
i3 * input_stride[3] + QK8_0 * group;
const int64_t output_offset = i1 * output_stride[1] +
i2 * output_stride[2] +
i3 * output_stride[3] + QK8_0 * group;
copy_in(input_offset);
LocalTensor<half> input_local = input_queue.DeQue<half>();
LocalTensor<int8_t> output_local = output_queue.AllocTensor<int8_t>();
LocalTensor<float> work_local = work_queue.AllocTensor<float>();
LocalTensor<float> abs_local = abs_queue.AllocTensor<float>();
LocalTensor<float> max_local = max_queue.AllocTensor<float>();
LocalTensor<float> cast_local = cast_queue.AllocTensor<float>();
Cast(cast_local, input_local, RoundMode::CAST_NONE, QK8_0);
Abs(abs_local, cast_local, QK8_0);
ReduceMax(max_local, abs_local, work_local, QK8_0);
pipe_barrier(PIPE_ALL);
float d = max_local.GetValue(0);
d = d / ((1 << 7) - 1);
if (d != 0) {
Muls(cast_local, cast_local, 1.0f / d, QK8_0);
}
Cast(cast_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
Cast(input_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
Cast(output_local, input_local, RoundMode::CAST_ROUND, QK8_0);
output_queue.EnQue(output_local);
copy_out(output_offset);
input_queue.FreeTensor(input_local);
work_queue.FreeTensor(work_local);
abs_queue.FreeTensor(abs_local);
max_queue.FreeTensor(max_local);
cast_queue.FreeTensor(cast_local);
return (half)d;
}
__aicore__ inline void calculate() {
LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
uint32_t scale_local_offset = 0;
uint32_t scale_global_offset = 0;
for (int64_t i = ir; i < ir + dr; i++) {
for (int64_t j = 0; j < group_size_in_row; j++) {
half scale = calculate_group(i, j);
scale_local.SetValue(scale_local_offset++, scale);
if (scale_local_offset == 16) {
scale_local_offset = 0;
// TODO: OPTIMIZE ME
pipe_barrier(PIPE_ALL);
DataCopy(scale_gm[scale_global_offset], scale_local, 16);
pipe_barrier(PIPE_ALL);
scale_global_offset += 16;
}
}
}
if (scale_local_offset != 0) {
pipe_barrier(PIPE_ALL);
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = scale_local_offset * sizeof(half);
DataCopyPad(scale_gm[scale_global_offset], scale_local,
dataCopyParams);
pipe_barrier(PIPE_ALL);
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t *scale_ne;
size_t scale_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
int64_t group_size_in_row;
int64_t ir;
int64_t dr;
TPipe pipe;
GlobalTensor<half> input_gm;
GlobalTensor<half> scale_gm;
GlobalTensor<int8_t> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
TQue<QuePosition::VECIN, 1> work_queue;
TQue<QuePosition::VECOUT, 1> max_queue;
TQue<QuePosition::VECIN, 1> abs_queue;
TQue<QuePosition::VECOUT, 1> scale_queue;
TQue<QuePosition::VECOUT, 1> cast_queue;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
QUANTIZE_F16_Q8_0 op;
op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
op.calculate();
}
#endif // #ifdef ASCEND_310P

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#include "kernel_operator.h"
using namespace AscendC;
#ifdef ASCEND_310P // 310P not support f32->8bit quantization
extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
// let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed.
printf("Ascend310P not support f32->8bit quantization.\n");
}
#else
#define BUFFER_NUM 2
#define QK8_0 32
class QUANTIZE_F32_Q8_0 {
public:
__aicore__ inline QUANTIZE_F32_Q8_0() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR output,
int64_t *input_ne_ub, size_t *input_nb_ub,
int64_t *output_ne_ub) {
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
output_ne[i] = output_ne_ub[i];
}
output_stride[0] = 1;
for (int i = 1; i < 4; i++) {
output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
}
scale_ne = input_ne;
scale_stride[0] = 1;
scale_stride[1] = input_ne[0] / QK8_0;
for (int i = 2; i < 4; i++) {
scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
}
// split input tensor by rows.
uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
dr = nr / op_block_num;
uint64_t tails = nr % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
group_size_in_row = scale_stride[1];
int64_t output_size = output_ne[0] * output_ne[1] * output_ne[2] *
output_ne[3] * sizeof(uint8_t);
input_gm.SetGlobalBuffer((__gm__ float *)input);
output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
scale_gm.SetGlobalBuffer((__gm__ half *)(output + output_size +
ir * group_size_in_row *
sizeof(half)));
pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(float));
pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
pipe.InitBuffer(work_queue, 1, 32);
pipe.InitBuffer(max_queue, 1, 32);
pipe.InitBuffer(abs_queue, 1, QK8_0 * sizeof(float));
pipe.InitBuffer(cast_queue, 1, QK8_0 * sizeof(half));
pipe.InitBuffer(scale_queue, 1, 32);
}
__aicore__ inline void copy_in(uint32_t offset) {
LocalTensor<float> input_local = input_queue.AllocTensor<float>();
DataCopy(input_local, input_gm[offset], QK8_0);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset) {
LocalTensor<int8_t> output_local = output_queue.DeQue<int8_t>();
DataCopy(output_gm[offset], output_local, QK8_0);
output_queue.FreeTensor(output_local);
}
__aicore__ inline half calculate_group(int64_t row, int64_t group) {
const int64_t i3 = row / (input_ne[1] * input_ne[2]);
const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
const int64_t i1 =
row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
const int64_t input_offset = i1 * input_stride[1] +
i2 * input_stride[2] +
i3 * input_stride[3] + QK8_0 * group;
const int64_t output_offset = i1 * output_stride[1] +
i2 * output_stride[2] +
i3 * output_stride[3] + QK8_0 * group;
copy_in(input_offset);
LocalTensor<float> input_local = input_queue.DeQue<float>();
LocalTensor<int8_t> output_local = output_queue.AllocTensor<int8_t>();
LocalTensor<float> work_local = work_queue.AllocTensor<float>();
LocalTensor<float> abs_local = abs_queue.AllocTensor<float>();
LocalTensor<float> max_local = max_queue.AllocTensor<float>();
LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
Abs(abs_local, input_local, QK8_0);
ReduceMax(max_local, abs_local, work_local, QK8_0);
pipe_barrier(PIPE_ALL);
float d = max_local.GetValue(0);
d = d / ((1 << 7) - 1);
if (d != 0) {
Muls(input_local, input_local, 1.0f / d, QK8_0);
}
Cast(input_local, input_local, RoundMode::CAST_ROUND, QK8_0);
Cast(cast_local, input_local, RoundMode::CAST_ROUND, QK8_0);
Cast(output_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
output_queue.EnQue(output_local);
copy_out(output_offset);
input_queue.FreeTensor(input_local);
work_queue.FreeTensor(work_local);
abs_queue.FreeTensor(abs_local);
max_queue.FreeTensor(max_local);
cast_queue.FreeTensor(cast_local);
return (half)d;
}
__aicore__ inline void calculate() {
LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
uint32_t scale_local_offset = 0;
uint32_t scale_global_offset = 0;
for (int64_t i = ir; i < ir + dr; i++) {
for (int64_t j = 0; j < group_size_in_row; j++) {
half scale = calculate_group(i, j);
scale_local.SetValue(scale_local_offset++, scale);
if (scale_local_offset == 16) {
scale_local_offset = 0;
// TODO: OPTIMIZE ME
pipe_barrier(PIPE_ALL);
DataCopy(scale_gm[scale_global_offset], scale_local, 16);
pipe_barrier(PIPE_ALL);
scale_global_offset += 16;
}
}
}
if (scale_local_offset != 0) {
pipe_barrier(PIPE_ALL);
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = scale_local_offset * sizeof(half);
DataCopyPad(scale_gm[scale_global_offset], scale_local,
dataCopyParams);
pipe_barrier(PIPE_ALL);
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t *scale_ne;
size_t scale_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
int64_t group_size_in_row;
int64_t ir;
int64_t dr;
TPipe pipe;
GlobalTensor<float> input_gm;
GlobalTensor<half> scale_gm;
GlobalTensor<int8_t> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
TQue<QuePosition::VECIN, 1> work_queue;
TQue<QuePosition::VECOUT, 1> max_queue;
TQue<QuePosition::VECIN, 1> abs_queue;
TQue<QuePosition::VECIN, 1> cast_queue;
TQue<QuePosition::VECOUT, 1> scale_queue;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
QUANTIZE_F32_Q8_0 op;
op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
op.calculate();
}
#endif // #ifdef ASCEND_310P

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#include "kernel_operator.h"
using namespace AscendC;
#ifdef ASCEND_310P // 310P not support float->4bit quantization
extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
// let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed.
printf("Ascend310P not support f32->4bit quantization.\n");
}
extern "C" __global__ __aicore__ void ascendc_quantize_f16_to_q4_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
// let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed.
printf("Ascend310P not support f16->4bit quantization.\n");
}
#else
#define BUFFER_NUM 2
#define Group_Size 32
template <typename SRC_T>
class QUANTIZE_FLOAT_TO_Q4_0 {
public:
__aicore__ inline QUANTIZE_FLOAT_TO_Q4_0() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR output,
int64_t *input_ne_ub, size_t *input_nb_ub,
int64_t *output_ne_ub) {
// TODO: fix test_case CPY(type_src=f16,type_dst=q4_0,ne=[256,4,4,4],
// permute=[0,0,0,0]):
// [CPY] NMSE = 0.000008343 > 0.000001000 FAIL
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
// input stride of data elements
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
output_ne[i] = output_ne_ub[i];
}
// output stride of data elements
output_stride[0] = 1;
for (int i = 1; i < 4; i++) {
output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
}
// scale saved one by one after data:. [group1_scale, group2_scale, ...]
scale_ne = input_ne;
scale_stride[0] = 1;
scale_stride[1] = input_ne[0] / Group_Size;
for (int i = 2; i < 4; i++) {
scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
}
// split input tensor by rows.
uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
dr = nr / op_block_num;
uint64_t tails = nr % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
group_size_in_row = scale_stride[1];
int64_t scale_offset = output_ne[0] * output_ne[1] * output_ne[2] *
output_ne[3] * sizeof(uint8_t) / 2;
input_gm.SetGlobalBuffer((__gm__ SRC_T *)input);
output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
scale_gm.SetGlobalBuffer((__gm__ half *)(output + scale_offset + ir *
group_size_in_row *
sizeof(half)));
pipe.InitBuffer(input_queue, BUFFER_NUM, Group_Size * sizeof(SRC_T));
pipe.InitBuffer(output_queue, BUFFER_NUM,
Group_Size * sizeof(int8_t) / 2);
pipe.InitBuffer(cast_queue , 1, Group_Size * sizeof(float));
pipe.InitBuffer(work_queue, 1, Group_Size * sizeof(float));
pipe.InitBuffer(max_queue, 1, Group_Size * sizeof(float));
pipe.InitBuffer(min_queue, 1, Group_Size * sizeof(float));
pipe.InitBuffer(scale_queue, 1, Group_Size / 2 * sizeof(half));
pipe.InitBuffer(int8_queue, 1, Group_Size * sizeof(int8_t));
pipe.InitBuffer(half_queue, 1, Group_Size * sizeof(half));
}
__aicore__ inline void copy_in(uint32_t offset) {
LocalTensor<SRC_T> input_local = input_queue.AllocTensor<SRC_T>();
DataCopy(input_local, input_gm[offset], Group_Size);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset) {
// reinterpretcast Group_Size(32) * int4b_t to Group_Size / 2 * int8_t,
// and using DataCopyPad to avoid 32 bits align.
LocalTensor<int4b_t> output_local = output_queue.DeQue<int4b_t>();
LocalTensor<int8_t> output_int8_local =
output_local.ReinterpretCast<int8_t>();
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = Group_Size / 2 * sizeof(int8_t);
DataCopyPad(output_gm[offset], output_int8_local, dataCopyParams);
output_queue.FreeTensor(output_local);
}
__aicore__ inline void input_to_cast(LocalTensor<float> cast_local,
LocalTensor<float> input_local) {
DataCopy(cast_local, input_local, Group_Size);
}
__aicore__ inline void input_to_cast(LocalTensor<float> cast_local,
LocalTensor<half> input_local) {
Cast(cast_local, input_local, RoundMode::CAST_NONE, Group_Size);
}
__aicore__ inline half calculate_group(int64_t row, int64_t group) {
const int64_t i3 = row / (input_ne[1] * input_ne[2]);
const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
const int64_t i1 =
row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
const int64_t input_offset = i1 * input_stride[1] +
i2 * input_stride[2] +
i3 * input_stride[3] + Group_Size * group;
// output_offset is stride for output_gm which datatype is int8_t and
// divided by 2 is needed for int4b_t.
const int64_t output_offset = (i1 * output_stride[1] +
i2 * output_stride[2] +
i3 * output_stride[3] +
Group_Size * group) / 2;
copy_in(input_offset);
LocalTensor<SRC_T> input_local = input_queue.DeQue<SRC_T>();
LocalTensor<int4b_t> output_local = output_queue.AllocTensor<int4b_t>();
LocalTensor<float> cast_local = cast_queue.AllocTensor<float>();
LocalTensor<float> work_local = work_queue.AllocTensor<float>();
LocalTensor<float> max_local = max_queue.AllocTensor<float>();
LocalTensor<float> min_local = min_queue.AllocTensor<float>();
LocalTensor<int8_t> int8_local = int8_queue.AllocTensor<int8_t>();
LocalTensor<half> half_local = half_queue.AllocTensor<half>();
input_to_cast(cast_local, input_local);
ReduceMax(max_local, cast_local, work_local, Group_Size);
ReduceMin(min_local, cast_local, work_local, Group_Size);
const float max_value = max_local.GetValue(0);
const float min_value = min_local.GetValue(0);
float d = max_value;
if (min_value < 0 && (-1 * min_value) > max_value) {
d = min_value;
}
d = d / (-8);
if (d != 0) {
Muls(cast_local, cast_local, 1.0f / d, Group_Size);
}
// range: [-8,8] -> [0.5,16.5] -> [0,16] -> [0,15] -> [-8,7]
float scalar = 8.5f;
Adds(cast_local, cast_local, scalar, Group_Size);
Cast(cast_local, cast_local, RoundMode::CAST_FLOOR, Group_Size);
scalar = 15.0f;
Mins(cast_local, cast_local, scalar, Group_Size);
scalar = -8.0f;
Adds(cast_local, cast_local, scalar, Group_Size);
// float->half->int4b
Cast(half_local, cast_local, RoundMode::CAST_NONE, Group_Size);
Cast(output_local, half_local, RoundMode::CAST_NONE, Group_Size);
output_queue.EnQue(output_local);
copy_out(output_offset);
input_queue.FreeTensor(input_local);
work_queue.FreeTensor(work_local);
max_queue.FreeTensor(max_local);
min_queue.FreeTensor(min_local);
int8_queue.FreeTensor(int8_local);
half_queue.FreeTensor(half_local);
cast_queue.FreeTensor(cast_local);
return (half)d;
}
__aicore__ inline void calculate() {
LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
uint32_t scale_local_offset = 0;
uint32_t scale_global_offset = 0;
for (int64_t i = ir; i < ir + dr; i++) {
for (int64_t j = 0; j < group_size_in_row; j++) {
half scale = calculate_group(i, j);
scale_local.SetValue(scale_local_offset++, scale);
// Copy Group_Size/2 length data each time.
if (scale_local_offset == Group_Size / 2) {
scale_local_offset = 0;
// TODO: OPTIMIZE ME
pipe_barrier(PIPE_ALL);
DataCopy(scale_gm[scale_global_offset], scale_local,
Group_Size / 2);
pipe_barrier(PIPE_ALL);
scale_global_offset += Group_Size / 2;
}
}
}
if (scale_local_offset != 0) {
pipe_barrier(PIPE_ALL);
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = scale_local_offset * sizeof(half);
DataCopyPad(scale_gm[scale_global_offset], scale_local,
dataCopyParams);
pipe_barrier(PIPE_ALL);
}
scale_queue.FreeTensor(scale_local);
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t *scale_ne;
size_t scale_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
int64_t group_size_in_row;
int64_t ir;
int64_t dr;
TPipe pipe;
GlobalTensor<SRC_T> input_gm;
GlobalTensor<half> scale_gm;
GlobalTensor<int8_t> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
TQue<QuePosition::VECIN, BUFFER_NUM> work_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> max_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> min_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> scale_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> cast_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> int8_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> half_queue;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_quantize_f16_to_q4_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
QUANTIZE_FLOAT_TO_Q4_0<half> op;
op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
op.calculate();
}
extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0(
GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
int64_t input_ne_ub[4];
size_t input_nb_ub[4];
int64_t output_ne_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(input_nb_gm, input_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
QUANTIZE_FLOAT_TO_Q4_0<float> op;
op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
op.calculate();
}
#endif // #ifdef ASCEND_310P

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function(ggml_add_cpu_backend_variant_impl tag_name)
if (tag_name)
set(GGML_CPU_NAME ggml-cpu-${tag_name})
else()
set(GGML_CPU_NAME ggml-cpu)
endif()
ggml_add_backend_library(${GGML_CPU_NAME})
list (APPEND GGML_CPU_SOURCES
ggml-cpu/ggml-cpu.c
ggml-cpu/ggml-cpu.cpp
ggml-cpu/ggml-cpu-aarch64.cpp
ggml-cpu/ggml-cpu-aarch64.h
ggml-cpu/ggml-cpu-hbm.cpp
ggml-cpu/ggml-cpu-hbm.h
ggml-cpu/ggml-cpu-quants.c
ggml-cpu/ggml-cpu-quants.h
ggml-cpu/ggml-cpu-traits.cpp
ggml-cpu/ggml-cpu-traits.h
ggml-cpu/amx/amx.cpp
ggml-cpu/amx/amx.h
ggml-cpu/amx/mmq.cpp
ggml-cpu/amx/mmq.h
ggml-cpu/ggml-cpu-impl.h
)
target_compile_features(${GGML_CPU_NAME} PRIVATE c_std_11 cxx_std_17)
target_include_directories(${GGML_CPU_NAME} PRIVATE . ggml-cpu)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate)
if (ACCELERATE_FRAMEWORK)
message(STATUS "Accelerate framework found")
target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_ACCELERATE)
target_compile_definitions(${GGML_CPU_NAME} PRIVATE ACCELERATE_NEW_LAPACK)
target_compile_definitions(${GGML_CPU_NAME} PRIVATE ACCELERATE_LAPACK_ILP64)
target_link_libraries(${GGML_CPU_NAME} PRIVATE ${ACCELERATE_FRAMEWORK})
else()
message(WARNING "Accelerate framework not found")
endif()
endif()
if (GGML_OPENMP)
find_package(OpenMP)
if (OpenMP_FOUND)
target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_OPENMP)
target_link_libraries(${GGML_CPU_NAME} PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
else()
message(WARNING "OpenMP not found")
endif()
endif()
if (GGML_LLAMAFILE)
target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_LLAMAFILE)
list(APPEND GGML_CPU_SOURCES
ggml-cpu/llamafile/sgemm.cpp
ggml-cpu/llamafile/sgemm.h)
endif()
if (GGML_CPU_HBM)
find_library(memkind memkind REQUIRED)
message(STATUS "Using memkind for CPU HBM")
target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_CPU_HBM)
target_link_libraries(${GGML_CPU_NAME} PUBLIC memkind)
endif()
if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR
CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
(NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
message(STATUS "ARM detected")
if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang")
message(FATAL_ERROR "MSVC is not supported for ARM, use clang")
else()
check_cxx_compiler_flag(-mfp16-format=ieee GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E)
if (NOT "${GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
list(APPEND ARCH_FLAGS -mfp16-format=ieee)
endif()
if (GGML_NATIVE)
# -mcpu=native does not always enable all the features in some compilers,
# so we check for them manually and enable them if available
execute_process(
COMMAND ${CMAKE_C_COMPILER} -mcpu=native -E -v -
INPUT_FILE "/dev/null"
OUTPUT_QUIET
ERROR_VARIABLE ARM_MCPU
RESULT_VARIABLE ARM_MCPU_RESULT
)
if (NOT ARM_MCPU_RESULT)
string(REGEX MATCH "-mcpu=[^ ']+" ARM_MCPU_FLAG "${ARM_MCPU}")
endif()
if ("${ARM_MCPU_FLAG}" STREQUAL "")
set(ARM_MCPU_FLAG -mcpu=native)
message(STATUS "ARM -mcpu not found, -mcpu=native will be used")
endif()
include(CheckCXXSourceRuns)
function(check_arm_feature tag code)
set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+${tag}")
check_cxx_source_runs(
"${code}"
GGML_MACHINE_SUPPORTS_${tag}
)
if (GGML_MACHINE_SUPPORTS_${tag})
set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+${tag}" PARENT_SCOPE)
else()
set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+no${tag}" PARENT_SCOPE)
endif()
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
endfunction()
check_arm_feature(dotprod "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }")
check_arm_feature(i8mm "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }")
check_arm_feature(sve "#include <arm_sve.h>\nint main() { svfloat32_t _a, _b; volatile svfloat32_t _c = svadd_f32_z(svptrue_b8(), _a, _b); return 0; }")
list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}")
else()
if (GGML_CPU_ARM_ARCH)
list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH})
endif()
endif()
# show enabled features
if (CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows")
set(FEAT_INPUT_FILE "NUL")
else()
set(FEAT_INPUT_FILE "/dev/null")
endif()
execute_process(
COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E -
INPUT_FILE ${FEAT_INPUT_FILE}
OUTPUT_VARIABLE ARM_FEATURE
RESULT_VARIABLE ARM_FEATURE_RESULT
)
if (ARM_FEATURE_RESULT)
message(WARNING "Failed to get ARM features")
else()
foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC)
string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)
if (NOT ${feature_pos} EQUAL -1)
message(STATUS "ARM feature ${feature} enabled")
endif()
endforeach()
endif()
endif()
elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
(NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
message(STATUS "x86 detected")
if (MSVC)
# instruction set detection for MSVC only
if (GGML_NATIVE)
include(ggml-cpu/cmake/FindSIMD.cmake)
endif ()
if (GGML_AVX512)
list(APPEND ARCH_FLAGS /arch:AVX512)
# /arch:AVX512 includes: __AVX512F__, __AVX512CD__, __AVX512BW__, __AVX512DQ__, and __AVX512VL__
# MSVC has no compile-time flags enabling specific
# AVX512 extensions, neither it defines the
# macros corresponding to the extensions.
# Do it manually.
list(APPEND ARCH_DEFINITIONS GGML_AVX512)
if (GGML_AVX512_VBMI)
list(APPEND ARCH_DEFINITIONS __AVX512VBMI__)
if (CMAKE_C_COMPILER_ID STREQUAL "Clang")
list(APPEND ARCH_FLAGS -mavx512vbmi)
endif()
endif()
if (GGML_AVX512_VNNI)
list(APPEND ARCH_DEFINITIONS __AVX512VNNI__ GGML_AVX512_VNNI)
if (CMAKE_C_COMPILER_ID STREQUAL "Clang")
list(APPEND ARCH_FLAGS -mavx512vnni)
endif()
endif()
if (GGML_AVX512_BF16)
list(APPEND ARCH_DEFINITIONS __AVX512BF16__ GGML_AVX512_BF16)
if (CMAKE_C_COMPILER_ID STREQUAL "Clang")
list(APPEND ARCH_FLAGS -mavx512bf16)
endif()
endif()
if (GGML_AMX_TILE)
list(APPEND ARCH_DEFINITIONS __AMX_TILE__ GGML_AMX_TILE)
endif()
if (GGML_AMX_INT8)
list(APPEND ARCH_DEFINITIONS __AMX_INT8__ GGML_AMX_INT8)
endif()
if (GGML_AMX_BF16)
list(APPEND ARCH_DEFINITIONS __AMX_BF16__ GGML_AMX_BF16)
endif()
elseif (GGML_AVX2)
list(APPEND ARCH_FLAGS /arch:AVX2)
list(APPEND ARCH_DEFINITIONS GGML_AVX2 GGML_FMA GGML_F16C)
elseif (GGML_AVX)
list(APPEND ARCH_FLAGS /arch:AVX)
list(APPEND ARCH_DEFINITIONS GGML_AVX)
else ()
list(APPEND ARCH_FLAGS /arch:SSE4.2)
list(APPEND ARCH_DEFINITIONS GGML_SSE42)
endif()
if (GGML_AVX_VNNI)
list(APPEND ARCH_DEFINITIONS __AVXVNNI__ GGML_AVX_VNNI)
endif()
else ()
if (GGML_NATIVE)
list(APPEND ARCH_FLAGS -march=native)
else ()
list(APPEND ARCH_FLAGS -msse4.2)
list(APPEND ARCH_DEFINITIONS GGML_SSE42)
if (GGML_F16C)
list(APPEND ARCH_FLAGS -mf16c)
list(APPEND ARCH_DEFINITIONS GGML_F16C)
endif()
if (GGML_FMA)
list(APPEND ARCH_FLAGS -mfma)
list(APPEND ARCH_DEFINITIONS GGML_FMA)
endif()
if (GGML_AVX)
list(APPEND ARCH_FLAGS -mavx)
list(APPEND ARCH_DEFINITIONS GGML_AVX)
endif()
if (GGML_AVX2)
list(APPEND ARCH_FLAGS -mavx2)
list(APPEND ARCH_DEFINITIONS GGML_AVX2)
endif()
if (GGML_AVX_VNNI)
list(APPEND ARCH_FLAGS -mavxvnni)
list(APPEND ARCH_DEFINITIONS GGML_AVX_VNNI)
endif()
if (GGML_AVX512)
list(APPEND ARCH_FLAGS -mavx512f)
list(APPEND ARCH_FLAGS -mavx512cd)
list(APPEND ARCH_FLAGS -mavx512vl)
list(APPEND ARCH_FLAGS -mavx512dq)
list(APPEND ARCH_FLAGS -mavx512bw)
list(APPEND ARCH_DEFINITIONS GGML_AVX512)
endif()
if (GGML_AVX512_VBMI)
list(APPEND ARCH_FLAGS -mavx512vbmi)
list(APPEND ARCH_DEFINITIONS GGML_AVX512_VBMI)
endif()
if (GGML_AVX512_VNNI)
list(APPEND ARCH_FLAGS -mavx512vnni)
list(APPEND ARCH_DEFINITIONS GGML_AVX512_VNNI)
endif()
if (GGML_AVX512_BF16)
list(APPEND ARCH_FLAGS -mavx512bf16)
list(APPEND ARCH_DEFINITIONS GGML_AVX512_BF16)
endif()
if (GGML_AMX_TILE)
list(APPEND ARCH_FLAGS -mamx-tile)
list(APPEND ARCH_DEFINITIONS GGML_AMX_TILE)
endif()
if (GGML_AMX_INT8)
list(APPEND ARCH_FLAGS -mamx-int8)
list(APPEND ARCH_DEFINITIONS GGML_AMX_INT8)
endif()
if (GGML_AMX_BF16)
list(APPEND ARCH_FLAGS -mamx-bf16)
list(APPEND ARCH_DEFINITIONS GGML_AMX_BF16)
endif()
endif()
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
message(STATUS "PowerPC detected")
execute_process(COMMAND bash -c "grep POWER /proc/cpuinfo | head -n 1" OUTPUT_VARIABLE POWER_M)
if (${POWER_M} MATCHES "POWER10")
list(APPEND ARCH_FLAGS -mcpu=power10)
elseif (${POWER_M} MATCHES "POWER9")
list(APPEND ARCH_FLAGS -mcpu=power9)
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le")
list(APPEND ARCH_FLAGS -mcpu=powerpc64le -mtune=native)
else()
list(APPEND ARCH_FLAGS -mcpu=powerpc64 -mtune=native)
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
message(STATUS "loongarch64 detected")
list(APPEND ARCH_FLAGS -march=loongarch64)
if (GGML_LASX)
list(APPEND ARCH_FLAGS -mlasx)
endif()
if (GGML_LSX)
list(APPEND ARCH_FLAGS -mlsx)
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "riscv64")
message(STATUS "RISC-V detected")
if (GGML_RVV)
list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d)
endif()
else()
message(STATUS "Unknown architecture")
endif()
if (GGML_CPU_AARCH64)
target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_CPU_AARCH64)
endif()
message(STATUS "Adding CPU backend variant ${GGML_CPU_NAME}: ${ARCH_FLAGS} ${ARCH_DEFINITIONS}")
target_sources(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_SOURCES})
target_compile_options(${GGML_CPU_NAME} PRIVATE ${ARCH_FLAGS})
target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS})
if (GGML_BACKEND_DL)
if (GGML_NATIVE)
# the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
endif()
# The feature detection code is compiled as a separate target so that
# it can be built without the architecture flags
# Since multiple variants of the CPU backend may be included in the same
# build, using set_source_files_properties() to set the arch flags is not possible
set(GGML_CPU_FEATS_NAME ${GGML_CPU_NAME}-feats)
add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/cpu-feats-x86.cpp)
target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include)
target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARCH_DEFINITIONS})
target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED)
set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_link_libraries(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_FEATS_NAME})
endif()
if (EMSCRIPTEN)
set_target_properties(${GGML_CPU_NAME} PROPERTIES COMPILE_FLAGS "-msimd128")
endif()
endfunction()

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#include "amx.h"
#include "common.h"
#include "mmq.h"
#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include "ggml-cpu.h"
#include "ggml-cpu-traits.h"
#if defined(__gnu_linux__)
#include <sys/syscall.h>
#include <unistd.h>
#endif
#include <cstdlib>
#include <cstring>
#include <memory>
#if defined(__AMX_INT8__) && defined(__AVX512VNNI__)
// AMX type_trais
namespace ggml::cpu::amx {
class tensor_traits : public ggml::cpu::tensor_traits {
bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
size = ggml_backend_amx_desired_wsize(op);
return true;
}
bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
if (op->op == GGML_OP_MUL_MAT) {
ggml_backend_amx_mul_mat(params, op);
return true;
}
return false;
}
};
static ggml::cpu::tensor_traits * get_tensor_traits(ggml_backend_buffer_t, struct ggml_tensor *) {
static tensor_traits traits;
return &traits;
}
} // namespace ggml::cpu::amx
// AMX buffer interface
static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
}
static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) {
return (void *) (buffer->context);
}
static void ggml_backend_amx_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
tensor->extra = (void *) ggml::cpu::amx::get_tensor_traits(buffer, tensor);
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
uint8_t value, size_t offset, size_t size) {
memset((char *) tensor->data + offset, value, size);
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
const void * data, size_t offset, size_t size) {
if (qtype_has_amx_kernels(tensor->type)) {
GGML_LOG_DEBUG("%s: amx repack tensor %s of type %s\n", __func__, tensor->name, ggml_type_name(tensor->type));
ggml_backend_amx_convert_weight(tensor, data, offset, size);
} else {
memcpy((char *) tensor->data + offset, data, size);
}
GGML_UNUSED(buffer);
}
/*
// need to figure what we need to do with buffer->extra.
static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(!qtype_has_amx_kernels(tensor->type));
memcpy(data, (const char *)tensor->data + offset, size);
GGML_UNUSED(buffer);
}
static bool ggml_backend_amx_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)) {
if (qtype_has_amx_kernels(src->type)) {
ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_nbytes(dst));
} else {
memcpy(dst->data, src->data, ggml_nbytes(src));
}
return true;
}
return false;
GGML_UNUSED(buffer);
}
*/
static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
memset(buffer->context, value, buffer->size);
}
static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
/* .free_buffer = */ ggml_backend_amx_buffer_free_buffer,
/* .get_base = */ ggml_backend_amx_buffer_get_base,
/* .init_tensor = */ ggml_backend_amx_buffer_init_tensor,
/* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_amx_buffer_set_tensor,
/* .get_tensor = */ nullptr,
/* .cpy_tensor = */ nullptr,
/* .clear = */ ggml_backend_amx_buffer_clear,
/* .reset = */ nullptr,
};
static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "AMX";
GGML_UNUSED(buft);
}
static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
void * data = ggml_aligned_malloc(size);
if (data == NULL) {
fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
return NULL;
}
return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size);
}
static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
return TENSOR_ALIGNMENT;
GGML_UNUSED(buft);
}
namespace ggml::cpu::amx {
class extra_buffer_type : ggml::cpu::extra_buffer_type {
bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
// handle only 2d gemm for now
auto is_contiguous_2d = [](const struct ggml_tensor * t) {
return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1;
};
if (op->op == GGML_OP_MUL_MAT && is_contiguous_2d(op->src[0]) && // src0 must be contiguous
is_contiguous_2d(op->src[1]) && // src1 must be contiguous
op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_amx_buffer_type() &&
op->ne[0] % (TILE_N * 2) == 0 && // out_features is 32x
(qtype_has_amx_kernels(op->src[0]->type) || (op->src[0]->type == GGML_TYPE_F16))) {
// src1 must be host buffer
if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
return false;
}
// src1 must be float32
if (op->src[1]->type == GGML_TYPE_F32) {
return true;
}
}
return false;
}
ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
if (op->op == GGML_OP_MUL_MAT && op->src[0]->buffer &&
op->src[0]->buffer->buft == ggml_backend_amx_buffer_type()) {
return (ggml::cpu::tensor_traits *) op->src[0]->extra;
}
return nullptr;
}
};
} // namespace ggml::cpu::amx
static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
return ggml_backend_amx_get_alloc_size(tensor);
GGML_UNUSED(buft);
}
#define ARCH_GET_XCOMP_PERM 0x1022
#define ARCH_REQ_XCOMP_PERM 0x1023
#define XFEATURE_XTILECFG 17
#define XFEATURE_XTILEDATA 18
static bool ggml_amx_init() {
#if defined(__gnu_linux__)
if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) {
fprintf(stderr, "AMX is not ready to be used!\n");
return false;
}
return true;
#elif defined(_WIN32)
return true;
#endif
}
ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() {
static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = {
/* .iface = */ {
/* .get_name = */ ggml_backend_amx_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment,
/* .get_max_size = */ nullptr, // defaults to SIZE_MAX
/* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size,
/* .is_host = */ nullptr,
},
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
/* .context = */ new ggml::cpu::amx::extra_buffer_type(),
};
if (!ggml_amx_init()) {
return nullptr;
}
return &ggml_backend_buffer_type_amx;
}
#endif // defined(__AMX_INT8__) && defined(__AVX512VNNI__)

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#include "ggml-backend.h"
#include "ggml-cpu-impl.h"
// GGML internal header
#if defined(__AMX_INT8__) && defined(__AVX512VNNI__)
ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void);
#endif

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cpu/amx/common.h vendored Normal file
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#pragma once
#include "ggml.h"
#include "ggml-cpu-impl.h"
#include <algorithm>
#include <memory>
#include <type_traits>
#if defined(GGML_USE_OPENMP)
#include <omp.h>
#endif
#define TILE_M 16
#define TILE_N 16
#define TILE_K 32
#define VNNI_BLK 4
#define AMX_BLK_SIZE 32
#define TMM0 0
#define TMM1 1
#define TMM2 2
#define TMM3 3
#define TMM4 4
#define TMM5 5
#define TMM6 6
#define TMM7 7
// parallel routines
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
inline T div_up(T x, T y) { return (x + y - 1) / y; }
template <typename T>
inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
#if 0
// onednn partition pattern
T& n_my = n_end;
if (nth <= 1 || n == 0) {
n_start = 0;
n_my = n;
} else {
T n1 = div_up(n, nth);
T n2 = n1 - 1;
T T1 = n - n2 * nth;
n_my = ith < T1 ? n1 : n2;
n_start = ith <= T1 ? ith*n1 : T1 * n1 + (ith - T1) * n2;
}
n_end += n_start;
#else
// pytorch aten partition pattern
T n_my = div_up(n, nth);
n_start = ith * n_my;
n_end = std::min(n_start + n_my, n);
#endif
}
template <typename func_t>
inline void parallel_for(int n, const func_t& f) {
#if defined(GGML_USE_OPENMP)
#pragma omp parallel
{
int nth = omp_get_num_threads();
int ith = omp_get_thread_num();
int tbegin, tend;
balance211(n, nth, ith, tbegin, tend);
f(tbegin, tend);
}
#else
f(0, n);
#endif
}
template <typename func_t>
inline void parallel_for_ggml(const ggml_compute_params * params, int n, const func_t & f) {
int tbegin, tend;
balance211(n, params->nth, params->ith, tbegin, tend);
f(tbegin, tend);
}
// quantized types that have AMX support
inline bool qtype_has_amx_kernels(const enum ggml_type type) {
// TODO: fix padding for vnni format
return (type == GGML_TYPE_Q4_0) ||
(type == GGML_TYPE_Q4_1) ||
(type == GGML_TYPE_Q8_0) ||
(type == GGML_TYPE_Q4_K) ||
(type == GGML_TYPE_Q5_K) ||
(type == GGML_TYPE_Q6_K) ||
(type == GGML_TYPE_IQ4_XS);
}

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#pragma once
#include "common.h"
size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst);
size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor);
void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void ggml_backend_amx_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst);

100
packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cpu/cmake/FindSIMD.cmake vendored Normal file
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include(CheckCSourceRuns)
set(AVX_CODE "
#include <immintrin.h>
int main()
{
__m256 a;
a = _mm256_set1_ps(0);
return 0;
}
")
set(AVX512_CODE "
#include <immintrin.h>
int main()
{
__m512i a = _mm512_set_epi8(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0);
__m512i b = a;
__mmask64 equality_mask = _mm512_cmp_epi8_mask(a, b, _MM_CMPINT_EQ);
return 0;
}
")
set(AVX2_CODE "
#include <immintrin.h>
int main()
{
__m256i a = {0};
a = _mm256_abs_epi16(a);
__m256i x;
_mm256_extract_epi64(x, 0); // we rely on this in our AVX2 code
return 0;
}
")
set(FMA_CODE "
#include <immintrin.h>
int main()
{
__m256 acc = _mm256_setzero_ps();
const __m256 d = _mm256_setzero_ps();
const __m256 p = _mm256_setzero_ps();
acc = _mm256_fmadd_ps( d, p, acc );
return 0;
}
")
macro(check_sse type flags)
set(__FLAG_I 1)
set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
foreach (__FLAG ${flags})
if (NOT ${type}_FOUND)
set(CMAKE_REQUIRED_FLAGS ${__FLAG})
check_c_source_runs("${${type}_CODE}" HAS_${type}_${__FLAG_I})
if (HAS_${type}_${__FLAG_I})
set(${type}_FOUND TRUE CACHE BOOL "${type} support")
set(${type}_FLAGS "${__FLAG}" CACHE STRING "${type} flags")
endif()
math(EXPR __FLAG_I "${__FLAG_I}+1")
endif()
endforeach()
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
if (NOT ${type}_FOUND)
set(${type}_FOUND FALSE CACHE BOOL "${type} support")
set(${type}_FLAGS "" CACHE STRING "${type} flags")
endif()
mark_as_advanced(${type}_FOUND ${type}_FLAGS)
endmacro()
# flags are for MSVC only!
check_sse("AVX" " ;/arch:AVX")
if (NOT ${AVX_FOUND})
set(GGML_AVX OFF)
else()
set(GGML_AVX ON)
endif()
check_sse("AVX2" " ;/arch:AVX2")
check_sse("FMA" " ;/arch:AVX2")
if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND}))
set(GGML_AVX2 OFF)
else()
set(GGML_AVX2 ON)
endif()
check_sse("AVX512" " ;/arch:AVX512")
if (NOT ${AVX512_FOUND})
set(GGML_AVX512 OFF)
else()
set(GGML_AVX512 ON)
endif()

323
packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cpu/cpu-feats-x86.cpp vendored Normal file
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#include "ggml-backend-impl.h"
#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64))
#ifdef _MSC_VER
#include <intrin.h>
#endif
#include <cstring>
#include <vector>
#include <bitset>
#include <array>
#include <string>
// ref: https://cdrdv2-public.intel.com/782156/325383-sdm-vol-2abcd.pdf
struct cpuid_x86 {
bool SSE3(void) { return f_1_ecx[0]; }
bool PCLMULQDQ(void) { return f_1_ecx[1]; }
bool MONITOR(void) { return f_1_ecx[3]; }
bool SSSE3(void) { return f_1_ecx[9]; }
bool FMA(void) { return f_1_ecx[12]; }
bool CMPXCHG16B(void) { return f_1_ecx[13]; }
bool SSE41(void) { return f_1_ecx[19]; }
bool SSE42(void) { return f_1_ecx[20]; }
bool MOVBE(void) { return f_1_ecx[22]; }
bool POPCNT(void) { return f_1_ecx[23]; }
bool AES(void) { return f_1_ecx[25]; }
bool XSAVE(void) { return f_1_ecx[26]; }
bool OSXSAVE(void) { return f_1_ecx[27]; }
bool AVX(void) { return f_1_ecx[28]; }
bool F16C(void) { return f_1_ecx[29]; }
bool RDRAND(void) { return f_1_ecx[30]; }
bool MSR(void) { return f_1_edx[5]; }
bool CX8(void) { return f_1_edx[8]; }
bool SEP(void) { return f_1_edx[11]; }
bool CMOV(void) { return f_1_edx[15]; }
bool CLFSH(void) { return f_1_edx[19]; }
bool MMX(void) { return f_1_edx[23]; }
bool FXSR(void) { return f_1_edx[24]; }
bool SSE(void) { return f_1_edx[25]; }
bool SSE2(void) { return f_1_edx[26]; }
bool FSGSBASE(void) { return f_7_ebx[0]; }
bool BMI1(void) { return f_7_ebx[3]; }
bool HLE(void) { return is_intel && f_7_ebx[4]; }
bool AVX2(void) { return f_7_ebx[5]; }
bool BMI2(void) { return f_7_ebx[8]; }
bool ERMS(void) { return f_7_ebx[9]; }
bool INVPCID(void) { return f_7_ebx[10]; }
bool RTM(void) { return is_intel && f_7_ebx[11]; }
bool AVX512F(void) { return f_7_ebx[16]; }
bool AVX512DQ(void) { return f_7_ebx[17]; }
bool RDSEED(void) { return f_7_ebx[18]; }
bool ADX(void) { return f_7_ebx[19]; }
bool AVX512PF(void) { return f_7_ebx[26]; }
bool AVX512ER(void) { return f_7_ebx[27]; }
bool AVX512CD(void) { return f_7_ebx[28]; }
bool AVX512BW(void) { return f_7_ebx[30]; }
bool AVX512VL(void) { return f_7_ebx[31]; }
bool SHA(void) { return f_7_ebx[29]; }
bool PREFETCHWT1(void) { return f_7_ecx[0]; }
bool LAHF(void) { return f_81_ecx[0]; }
bool LZCNT(void) { return is_intel && f_81_ecx[5]; }
bool ABM(void) { return is_amd && f_81_ecx[5]; }
bool SSE4a(void) { return is_amd && f_81_ecx[6]; }
bool XOP(void) { return is_amd && f_81_ecx[11]; }
bool TBM(void) { return is_amd && f_81_ecx[21]; }
bool SYSCALL(void) { return is_intel && f_81_edx[11]; }
bool MMXEXT(void) { return is_amd && f_81_edx[22]; }
bool RDTSCP(void) { return is_intel && f_81_edx[27]; }
bool _3DNOWEXT(void) { return is_amd && f_81_edx[30]; }
bool _3DNOW(void) { return is_amd && f_81_edx[31]; }
bool AVX512_VBMI(void) { return f_7_ecx[1]; }
bool AVX512_VNNI(void) { return f_7_ecx[11]; }
bool AVX512_FP16(void) { return f_7_edx[23]; }
bool AVX512_BF16(void) { return f_7_1_eax[5]; }
bool AVX_VNNI(void) { return f_7_1_eax[4]; }
bool AMX_TILE(void) { return f_7_edx[24]; }
bool AMX_INT8(void) { return f_7_edx[25]; }
bool AMX_FP16(void) { return f_7_1_eax[21]; }
bool AMX_BF16(void) { return f_7_edx[22]; }
#ifdef _MSC_VER
static void cpuid(int cpu_info[4], int eax) {
__cpuid(cpu_info, eax);
}
static void cpuidex(int cpu_info[4], int eax, int ecx) {
__cpuidex(cpu_info, eax, ecx);
}
#else
static void cpuid(int cpu_info[4], int eax) {
__asm__ __volatile__(
"cpuid"
: "=a"(cpu_info[0]), "=b"(cpu_info[1]), "=c"(cpu_info[2]), "=d"(cpu_info[3])
: "a"(eax), "c"(0));
}
static void cpuidex(int cpu_info[4], int eax, int ecx) {
__asm__ __volatile__(
"cpuid"
: "=a"(cpu_info[0]), "=b"(cpu_info[1]), "=c"(cpu_info[2]), "=d"(cpu_info[3])
: "a"(eax), "c"(ecx));
}
#endif
cpuid_x86() {
std::array<int, 4> cpui;
std::vector<std::array<int, 4>> data;
// calling __cpuid with 0x0 as the function_id argument
// gets the number of the highest valid function ID.
cpuid(cpui.data(), 0);
int n_ids = cpui[0];
for (int i = 0; i <= n_ids; ++i) {
cpuidex(cpui.data(), i, 0);
data.push_back(cpui);
}
// capture vendor string
char vendor[0x20] = {};
*reinterpret_cast<int *>(vendor) = data[0][1];
*reinterpret_cast<int *>(vendor + 4) = data[0][3];
*reinterpret_cast<int *>(vendor + 8) = data[0][2];
this->vendor = vendor;
if (this->vendor == "GenuineIntel") {
is_intel = true;
} else if (this->vendor == "AuthenticAMD") {
is_amd = true;
}
// load bitset with flags for function 0x00000001
if (n_ids >= 1) {
f_1_ecx = data[1][2];
f_1_edx = data[1][3];
}
// load bitset with flags for function 0x00000007
if (n_ids >= 7) {
f_7_ebx = data[7][1];
f_7_ecx = data[7][2];
f_7_edx = data[7][3];
cpuidex(cpui.data(), 7, 1);
f_7_1_eax = cpui[0];
}
// calling __cpuid with 0x80000000 as the function_id argument
// gets the number of the highest valid extended ID.
cpuid(cpui.data(), 0x80000000);
unsigned int n_ex_ids = cpui[0];
std::vector<std::array<int, 4>> ext_data;
for (unsigned int i = 0x80000000; i <= n_ex_ids; ++i) {
cpuidex(cpui.data(), i, 0);
ext_data.push_back(cpui);
}
// load bitset with flags for function 0x80000001
if (n_ex_ids >= 0x80000001) {
f_81_ecx = ext_data[1][2];
f_81_edx = ext_data[1][3];
}
// interpret CPU brand string if reported
char brand[0x40] = {};
if (n_ex_ids >= 0x80000004) {
std::memcpy(brand, ext_data[2].data(), sizeof(cpui));
std::memcpy(brand + 16, ext_data[3].data(), sizeof(cpui));
std::memcpy(brand + 32, ext_data[4].data(), sizeof(cpui));
this->brand = brand;
}
}
bool is_intel = false;
bool is_amd = false;
std::string vendor;
std::string brand;
std::bitset<32> f_1_ecx;
std::bitset<32> f_1_edx;
std::bitset<32> f_7_ebx;
std::bitset<32> f_7_ecx;
std::bitset<32> f_7_edx;
std::bitset<32> f_7_1_eax;
std::bitset<32> f_81_ecx;
std::bitset<32> f_81_edx;
};
#if 0
void test_x86_is() {
cpuid_x86 is;
printf("CPU Vendor: %s\n", is.vendor.c_str());
printf("Brand: %s\n", is.brand.c_str());
printf("is_intel: %d\n", is.is_intel);
printf("is_amd: %d\n", is.is_amd);
printf("sse3: %d\n", is.SSE3());
printf("pclmulqdq: %d\n", is.PCLMULQDQ());
printf("ssse3: %d\n", is.SSSE3());
printf("fma: %d\n", is.FMA());
printf("cmpxchg16b: %d\n", is.CMPXCHG16B());
printf("sse41: %d\n", is.SSE41());
printf("sse42: %d\n", is.SSE42());
printf("movbe: %d\n", is.MOVBE());
printf("popcnt: %d\n", is.POPCNT());
printf("aes: %d\n", is.AES());
printf("xsave: %d\n", is.XSAVE());
printf("osxsave: %d\n", is.OSXSAVE());
printf("avx: %d\n", is.AVX());
printf("f16c: %d\n", is.F16C());
printf("rdrand: %d\n", is.RDRAND());
printf("msr: %d\n", is.MSR());
printf("cx8: %d\n", is.CX8());
printf("sep: %d\n", is.SEP());
printf("cmov: %d\n", is.CMOV());
printf("clflush: %d\n", is.CLFSH());
printf("mmx: %d\n", is.MMX());
printf("fxsr: %d\n", is.FXSR());
printf("sse: %d\n", is.SSE());
printf("sse2: %d\n", is.SSE2());
printf("fsgsbase: %d\n", is.FSGSBASE());
printf("bmi1: %d\n", is.BMI1());
printf("hle: %d\n", is.HLE());
printf("avx2: %d\n", is.AVX2());
printf("bmi2: %d\n", is.BMI2());
printf("erms: %d\n", is.ERMS());
printf("invpcid: %d\n", is.INVPCID());
printf("rtm: %d\n", is.RTM());
printf("avx512f: %d\n", is.AVX512F());
printf("rdseed: %d\n", is.RDSEED());
printf("adx: %d\n", is.ADX());
printf("avx512pf: %d\n", is.AVX512PF());
printf("avx512er: %d\n", is.AVX512ER());
printf("avx512cd: %d\n", is.AVX512CD());
printf("sha: %d\n", is.SHA());
printf("prefetchwt1: %d\n", is.PREFETCHWT1());
printf("lahf: %d\n", is.LAHF());
printf("lzcnt: %d\n", is.LZCNT());
printf("abm: %d\n", is.ABM());
printf("sse4a: %d\n", is.SSE4a());
printf("xop: %d\n", is.XOP());
printf("tbm: %d\n", is.TBM());
printf("syscall: %d\n", is.SYSCALL());
printf("mmxext: %d\n", is.MMXEXT());
printf("rdtscp: %d\n", is.RDTSCP());
printf("3dnowext: %d\n", is._3DNOWEXT());
printf("3dnow: %d\n", is._3DNOW());
printf("avx512_vbmi: %d\n", is.AVX512_VBMI());
printf("avx512_vnni: %d\n", is.AVX512_VNNI());
printf("avx512_fp16: %d\n", is.AVX512_FP16());
printf("avx512_bf16: %d\n", is.AVX512_BF16());
printf("amx_tile: %d\n", is.AMX_TILE());
printf("amx_int8: %d\n", is.AMX_INT8());
printf("amx_fp16: %d\n", is.AMX_FP16());
printf("amx_bf16: %d\n", is.AMX_BF16());
}
#endif
static int ggml_backend_cpu_x86_score() {
// FIXME: this does not check for OS support
int score = 0;
cpuid_x86 is;
#ifdef GGML_FMA
if (!is.FMA()) { return 0; }
score += 1;
#endif
#ifdef GGML_F16C
if (!is.F16C()) { return 0; }
score += 1<<1;
#endif
#ifdef GGML_SSE42
if (!is.SSE42()) { return 0; }
score += 1<<2;
#endif
#ifdef GGML_AVX
if (!is.AVX()) { return 0; }
score += 1<<4;
#endif
#ifdef GGML_AVX2
if (!is.AVX2()) { return 0; }
score += 1<<5;
#endif
#ifdef GGML_AVX_VNNI
if (!is.AVX_VNNI()) { return 0; }
score += 1<<6;
#endif
#ifdef GGML_AVX512
if (!is.AVX512F()) { return 0; }
if (!is.AVX512CD()) { return 0; }
if (!is.AVX512VL()) { return 0; }
if (!is.AVX512DQ()) { return 0; }
if (!is.AVX512BW()) { return 0; }
score += 1<<7;
#endif
#ifdef GGML_AVX512_VBMI
if (!is.AVX512_VBMI()) { return 0; }
score += 1<<8;
#endif
#ifdef GGML_AVX512_BF16
if (!is.AVX512_BF16()) { return 0; }
score += 1<<9;
#endif
#ifdef GGML_AVX512_VNNI
if (!is.AVX512_VNNI()) { return 0; }
score += 1<<10;
#endif
#ifdef GGML_AMX_INT8
if (!is.AMX_INT8()) { return 0; }
score += 1<<11;
#endif
return score;
}
GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_x86_score)
#endif // defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64))

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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp vendored Normal file
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packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cpu/ggml-cpu-aarch64.h vendored Normal file
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#pragma once
#include "ggml-cpu-traits.h"
#include "ggml.h"
// GGML internal header
ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void);

55
packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cpu/ggml-cpu-hbm.cpp vendored Normal file
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#ifdef GGML_USE_CPU_HBM
#include "ggml-backend.h"
#include "ggml-backend-impl.h"
#include "ggml-cpu.h"
#include "ggml-impl.h"
#include "ggml-cpu-hbm.h"
// buffer type HBM
#include <hbwmalloc.h>
static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "CPU_HBM";
GGML_UNUSED(buft);
}
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) {
void * ptr;
int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
if (result != 0) {
GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size);
return NULL;
}
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
buffer->buft = buft;
buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer;
return buffer;
}
ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = {
/* .iface = */ {
/* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment,
/* .get_max_size = */ nullptr, // defaults to SIZE_MAX
/* .get_alloc_size = */ nullptr, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_cpu_buffer_type_is_host,
},
/* .context = */ nullptr,
};
return &ggml_backend_cpu_buffer_type_hbm;
}
#endif

8
packages/app-mobile/android/vendor/whisper.cpp/ggml/src/ggml-cpu/ggml-cpu-hbm.h vendored Normal file
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#pragma once
#include "ggml-backend.h"
#include "ggml.h"
// GGML CPU internal header
ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);

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