Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.github/workflows/docker.yml
#	CMakeLists.txt
#	Makefile
#	README.md
#	flake.lock
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo 2024-05-28 21:57:19 +08:00
commit 4ed9ba7352
58 changed files with 2404 additions and 361 deletions

View file

@ -774,7 +774,7 @@ static struct train_params get_default_train_params() {
params.samples_start_after_nl = false;
params.use_adam = true;
params.use_flash = true;
params.use_flash = false;
params.use_scratch = true;
// only adam

View file

@ -7,8 +7,6 @@ android {
namespace = "com.example.llama"
compileSdk = 34
ndkVersion = "26.1.10909125"
defaultConfig {
applicationId = "com.example.llama"
minSdk = 33
@ -20,17 +18,6 @@ android {
vectorDrawables {
useSupportLibrary = true
}
ndk {
// Add NDK properties if wanted, e.g.
// abiFilters += listOf("arm64-v8a")
}
externalNativeBuild {
cmake {
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()
}
}
}
buildTypes {
@ -55,17 +42,6 @@ android {
composeOptions {
kotlinCompilerExtensionVersion = "1.5.1"
}
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
}
}
externalNativeBuild {
cmake {
path = file("src/main/cpp/CMakeLists.txt")
version = "3.22.1"
}
}
}
dependencies {
@ -78,6 +54,7 @@ dependencies {
implementation("androidx.compose.ui:ui-graphics")
implementation("androidx.compose.ui:ui-tooling-preview")
implementation("androidx.compose.material3:material3")
implementation(project(":llama"))
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")

View file

@ -1,5 +1,6 @@
package com.example.llama
import android.llama.cpp.LLamaAndroid
import android.util.Log
import androidx.compose.runtime.getValue
import androidx.compose.runtime.mutableStateOf
@ -9,7 +10,7 @@ import androidx.lifecycle.viewModelScope
import kotlinx.coroutines.flow.catch
import kotlinx.coroutines.launch
class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() {
companion object {
@JvmStatic
private val NanosPerSecond = 1_000_000_000.0
@ -28,7 +29,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
viewModelScope.launch {
try {
llm.unload()
llamaAndroid.unload()
} catch (exc: IllegalStateException) {
messages += exc.message!!
}
@ -44,7 +45,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
messages += ""
viewModelScope.launch {
llm.send(text)
llamaAndroid.send(text)
.catch {
Log.e(tag, "send() failed", it)
messages += it.message!!
@ -57,7 +58,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
viewModelScope.launch {
try {
val start = System.nanoTime()
val warmupResult = llm.bench(pp, tg, pl, nr)
val warmupResult = llamaAndroid.bench(pp, tg, pl, nr)
val end = System.nanoTime()
messages += warmupResult
@ -70,7 +71,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
return@launch
}
messages += llm.bench(512, 128, 1, 3)
messages += llamaAndroid.bench(512, 128, 1, 3)
} catch (exc: IllegalStateException) {
Log.e(tag, "bench() failed", exc)
messages += exc.message!!
@ -81,7 +82,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
fun load(pathToModel: String) {
viewModelScope.launch {
try {
llm.load(pathToModel)
llamaAndroid.load(pathToModel)
messages += "Loaded $pathToModel"
} catch (exc: IllegalStateException) {
Log.e(tag, "load() failed", exc)

View file

@ -2,4 +2,5 @@
plugins {
id("com.android.application") version "8.2.0" apply false
id("org.jetbrains.kotlin.android") version "1.9.0" apply false
id("com.android.library") version "8.2.0" apply false
}

View file

@ -0,0 +1 @@
/build

View file

@ -42,7 +42,7 @@ add_subdirectory(../../../../../../ build-llama)
# used in the AndroidManifest.xml file.
add_library(${CMAKE_PROJECT_NAME} SHARED
# List C/C++ source files with relative paths to this CMakeLists.txt.
llama-android.cpp)
llama-android.cpp)
# Specifies libraries CMake should link to your target library. You
# can link libraries from various origins, such as libraries defined in this

View file

@ -0,0 +1,68 @@
plugins {
id("com.android.library")
id("org.jetbrains.kotlin.android")
}
android {
namespace = "android.llama.cpp"
compileSdk = 34
defaultConfig {
minSdk = 33
testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
consumerProguardFiles("consumer-rules.pro")
ndk {
// Add NDK properties if wanted, e.g.
// abiFilters += listOf("arm64-v8a")
}
externalNativeBuild {
cmake {
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()
cppFlags("")
}
}
}
buildTypes {
release {
isMinifyEnabled = false
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
}
externalNativeBuild {
cmake {
path("src/main/cpp/CMakeLists.txt")
version = "3.22.1"
}
}
compileOptions {
sourceCompatibility = JavaVersion.VERSION_1_8
targetCompatibility = JavaVersion.VERSION_1_8
}
kotlinOptions {
jvmTarget = "1.8"
}
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
}
}
}
dependencies {
implementation("androidx.core:core-ktx:1.12.0")
implementation("androidx.appcompat:appcompat:1.6.1")
implementation("com.google.android.material:material:1.11.0")
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
}

View file

@ -0,0 +1,21 @@
# Add project specific ProGuard rules here.
# You can control the set of applied configuration files using the
# proguardFiles setting in build.gradle.
#
# For more details, see
# http://developer.android.com/guide/developing/tools/proguard.html
# If your project uses WebView with JS, uncomment the following
# and specify the fully qualified class name to the JavaScript interface
# class:
#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
# public *;
#}
# Uncomment this to preserve the line number information for
# debugging stack traces.
#-keepattributes SourceFile,LineNumberTable
# If you keep the line number information, uncomment this to
# hide the original source file name.
#-renamesourcefileattribute SourceFile

View file

@ -0,0 +1,24 @@
package android.llama.cpp
import androidx.test.platform.app.InstrumentationRegistry
import androidx.test.ext.junit.runners.AndroidJUnit4
import org.junit.Test
import org.junit.runner.RunWith
import org.junit.Assert.*
/**
* Instrumented test, which will execute on an Android device.
*
* See [testing documentation](http://d.android.com/tools/testing).
*/
@RunWith(AndroidJUnit4::class)
class ExampleInstrumentedTest {
@Test
fun useAppContext() {
// Context of the app under test.
val appContext = InstrumentationRegistry.getInstrumentation().targetContext
assertEquals("android.llama.cpp.test", appContext.packageName)
}
}

View file

@ -0,0 +1,4 @@
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android">
</manifest>

View file

@ -0,0 +1,49 @@
# For more information about using CMake with Android Studio, read the
# documentation: https://d.android.com/studio/projects/add-native-code.html.
# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
# Sets the minimum CMake version required for this project.
cmake_minimum_required(VERSION 3.22.1)
# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
# Since this is the top level CMakeLists.txt, the project name is also accessible
# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
# build script scope).
project("llama-android")
include(FetchContent)
FetchContent_Declare(
llama
GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
GIT_TAG master
)
# Also provides "common"
FetchContent_MakeAvailable(llama)
# Creates and names a library, sets it as either STATIC
# or SHARED, and provides the relative paths to its source code.
# You can define multiple libraries, and CMake builds them for you.
# Gradle automatically packages shared libraries with your APK.
#
# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
# is preferred for the same purpose.
#
# In order to load a library into your app from Java/Kotlin, you must call
# System.loadLibrary() and pass the name of the library defined here;
# for GameActivity/NativeActivity derived applications, the same library name must be
# used in the AndroidManifest.xml file.
add_library(${CMAKE_PROJECT_NAME} SHARED
# List C/C++ source files with relative paths to this CMakeLists.txt.
llama-android.cpp)
# Specifies libraries CMake should link to your target library. You
# can link libraries from various origins, such as libraries defined in this
# build script, prebuilt third-party libraries, or Android system libraries.
target_link_libraries(${CMAKE_PROJECT_NAME}
# List libraries link to the target library
llama
common
android
log)

View file

@ -81,7 +81,7 @@ static void log_callback(ggml_log_level level, const char * fmt, void * data) {
extern "C"
JNIEXPORT jlong JNICALL
Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) {
llama_model_params model_params = llama_model_default_params();
auto path_to_model = env->GetStringUTFChars(filename, 0);
@ -101,13 +101,13 @@ Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) {
Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) {
llama_free_model(reinterpret_cast<llama_model *>(model));
}
extern "C"
JNIEXPORT jlong JNICALL
Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) {
auto model = reinterpret_cast<llama_model *>(jmodel);
if (!model) {
@ -139,25 +139,25 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) {
Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) {
llama_free(reinterpret_cast<llama_context *>(context));
}
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) {
Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) {
llama_backend_free();
}
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) {
Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) {
llama_log_set(log_callback, NULL);
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_example_llama_Llm_bench_1model(
Java_android_llama_cpp_LLamaAndroid_bench_1model(
JNIEnv *env,
jobject,
jlong context_pointer,
@ -271,13 +271,13 @@ Java_com_example_llama_Llm_bench_1model(
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
}
extern "C"
JNIEXPORT jlong JNICALL
Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
// Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
@ -313,19 +313,19 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) {
Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) {
llama_backend_init();
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) {
Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) {
return env->NewStringUTF(llama_print_system_info());
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_example_llama_Llm_completion_1init(
Java_android_llama_cpp_LLamaAndroid_completion_1init(
JNIEnv *env,
jobject,
jlong context_pointer,
@ -376,7 +376,7 @@ Java_com_example_llama_Llm_completion_1init(
extern "C"
JNIEXPORT jstring JNICALL
Java_com_example_llama_Llm_completion_1loop(
Java_android_llama_cpp_LLamaAndroid_completion_1loop(
JNIEnv * env,
jobject,
jlong context_pointer,
@ -438,6 +438,6 @@ Java_com_example_llama_Llm_completion_1loop(
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
}

View file

@ -1,4 +1,4 @@
package com.example.llama
package android.llama.cpp
import android.util.Log
import kotlinx.coroutines.CoroutineDispatcher
@ -10,7 +10,7 @@ import kotlinx.coroutines.withContext
import java.util.concurrent.Executors
import kotlin.concurrent.thread
class Llm {
class LLamaAndroid {
private val tag: String? = this::class.simpleName
private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
@ -165,8 +165,8 @@ class Llm {
}
// Enforce only one instance of Llm.
private val _instance: Llm = Llm()
private val _instance: LLamaAndroid = LLamaAndroid()
fun instance(): Llm = _instance
fun instance(): LLamaAndroid = _instance
}
}

View file

@ -0,0 +1,17 @@
package android.llama.cpp
import org.junit.Test
import org.junit.Assert.*
/**
* Example local unit test, which will execute on the development machine (host).
*
* See [testing documentation](http://d.android.com/tools/testing).
*/
class ExampleUnitTest {
@Test
fun addition_isCorrect() {
assertEquals(4, 2 + 2)
}
}

View file

@ -15,3 +15,4 @@ dependencyResolutionManagement {
rootProject.name = "LlamaAndroid"
include(":app")
include(":llama")

View file

@ -68,7 +68,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);

View file

@ -741,18 +741,26 @@ int main(int argc, char ** argv) {
// display text
if (input_echo && display) {
for (auto id : embd) {
const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation);
printf("%s", token_str.c_str());
const std::string token_str = llama_token_to_piece(ctx, id, params.special);
// Console/Stream Output
fprintf(stdout, "%s", token_str.c_str());
// Record Displayed Tokens To Log
// Note: Generated tokens are created one by one hence this check
if (embd.size() > 1) {
// Incoming Requested Tokens
input_tokens.push_back(id);
} else {
// Outgoing Generated Tokens
output_tokens.push_back(id);
output_ss << token_str;
}
fflush(stdout);
}
fflush(stdout);
}
// reset color to default if there is no pending user input
if (input_echo && (int) embd_inp.size() == n_consumed) {
console::set_display(console::reset);

View file

@ -594,7 +594,7 @@
message = html`<${Probabilities} data=${data} />`
} else {
const text = isArrayMessage ?
data.map(msg => msg.content).join('').replace(/^\s+/, '') :
data.map(msg => msg.content).join('') :
data;
message = isCompletionMode ?
text :
@ -877,19 +877,30 @@
// poor mans markdown replacement
const Markdownish = (params) => {
const md = params.text
.replace(/&/g, '&amp;')
.replace(/</g, '&lt;')
.replace(/>/g, '&gt;')
.replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
.replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');
return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
const chunks = params.text.split('```');
for (let i = 0; i < chunks.length; i++) {
if (i % 2 === 0) { // outside code block
chunks[i] = chunks[i]
.replace(/&/g, '&amp;')
.replace(/</g, '&lt;')
.replace(/>/g, '&gt;')
.replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
.replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');
} else { // inside code block
chunks[i] = `<pre><code>${chunks[i]}</code></pre>`;
}
}
const restoredText = chunks.join('');
return html`<span dangerouslySetInnerHTML=${{ __html: restoredText }} />`;
};
const ModelGenerationInfo = (params) => {
@ -903,6 +914,7 @@
`
}
// simple popover impl
const Popover = (props) => {
const isOpen = useSignal(false);
@ -1054,4 +1066,3 @@
</body>
</html>

View file

@ -1,7 +1,7 @@
<!DOCTYPE html>
<html lang="en">
<head>
<title>SimpleChat (LlamaCPP, ...) </title>
<title>SimpleChat LlamaCppEtal </title>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="message" content="Save Nature Save Earth" />
@ -30,20 +30,17 @@
<hr>
<div class="sameline">
<label for="system-in">System</label>
<input type="text" name="system" id="system-in" class="flex-grow"/>
<input type="text" name="system" id="system-in" placeholder="e.g. you are a helpful ai assistant, who provides concise answers" class="flex-grow"/>
</div>
<hr>
<div id="chat-div">
<p> Enter the system prompt above, before entering/submitting any user query.</p>
<p> Enter your text to the ai assistant below.</p>
<p> Use shift+enter for inserting enter.</p>
<p> Refresh the page to start over fresh.</p>
<p> You need to have javascript enabled.</p>
</div>
<hr>
<div class="sameline">
<textarea id="user-in" class="flex-grow" rows="3"></textarea>
<textarea id="user-in" class="flex-grow" rows="3" placeholder="enter your query to the ai model here" ></textarea>
<button id="user-btn">submit</button>
</div>

View file

@ -14,11 +14,15 @@ own system prompts.
The UI follows a responsive web design so that the layout can adapt to available display space in a usable
enough manner, in general.
NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and
culling of old messages from the chat.
Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool
console.
NOTE: It doesnt set any parameters other than temperature for now. However if someone wants they can update
the js file as needed.
NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and
culling of old messages from the chat by default. However by enabling the sliding window chat logic, a crude
form of old messages culling can be achieved.
NOTE: It doesnt set any parameters other than temperature and max_tokens for now. However if someone wants
they can update the js file or equivalent member in gMe as needed.
## usage
@ -43,11 +47,33 @@ next run this web front end in examples/server/public_simplechat
### using the front end
Open this simple web front end from your local browser
* http://127.0.0.1:PORT/index.html
Once inside
* Select between chat and completion mode. By default it is set to chat mode.
* In completion mode
* logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message.
If the model requires any prefix wrt user role messages, then the end user has to
explicitly add the needed prefix, when they enter their chat message.
Similarly if the model requires any prefix to trigger assistant/ai-model response,
then the end user needs to enter the same.
This keeps the logic simple, while still giving flexibility to the end user to
manage any templating/tagging requirement wrt their messages to the model.
* the logic doesnt insert newline at the begining and end wrt the prompt message generated.
However if the chat being sent to /completions end point has more than one role's message,
then insert newline when moving from one role's message to the next role's message, so
that it can be clearly identified/distinguished.
* given that /completions endpoint normally doesnt add additional chat-templating of its
own, the above ensures that end user can create a custom single/multi message combo with
any tags/special-tokens related chat templating to test out model handshake. Or enduser
can use it just for normal completion related/based query.
* If you want to provide a system prompt, then ideally enter it first, before entering any user query.
Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting
responses with a suitable system prompt.
* if chat.add_system_begin is used
* you cant change the system prompt, after it is has been submitted once along with user query.
* you cant set a system prompt, after you have submitted any user query
@ -55,27 +81,121 @@ Once inside
* one can change the system prompt any time during chat, by changing the contents of system prompt.
* inturn the updated/changed system prompt will be inserted into the chat session.
* this allows for the subsequent user chatting to be driven by the new system prompt set above.
* Enter your query and either press enter or click on the submit button.
If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter.
* Wait for the logic to communicate with the server and get the response.
* the user is not allowed to enter any fresh query during this time.
* the user input box will be disabled and a working message will be shown in it.
* just refresh the page, to reset wrt the chat history and or system prompt and start afresh.
* Using NewChat one can start independent chat sessions.
* two independent chat sessions are setup by default.
## Devel note
### Reason behind this
The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable
by developers who may not be from web frontend background (so inturn may not be familiar with template /
end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things.
And given that the idea is also to help explore/experiment for developers, some flexibility is provided
to change behaviour easily using the devel-tools/console, for now. And skeletal logic has been implemented
to explore some of the end points and ideas/implications around them.
### General
Me/gMe consolidates the settings which control the behaviour into one object.
One can see the current settings, as well as change/update them using browsers devel-tool/console.
bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when
communicating with the server or only sends the latest user query/message.
bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the
messages that get inserted into prompt field wrt /Completion endpoint.
chatRequestOptions - maintains the list of options/fields to send along with chat request,
irrespective of whether /chat/completions or /completions endpoint.
If you want to add additional options/fields to send to the server/ai-model, and or
modify the existing options value or remove them, for now you can update this global var
using browser's development-tools/console.
iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end.
This is disabled by default. However if enabled, then in addition to latest system message, only
the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses
from the ai model will be sent to the ai-model, when querying for a new response. IE if enabled,
only user messages after the latest system message/prompt will be considered.
This specified sliding window user message count also includes the latest user query.
<0 : Send entire chat history to server
0 : Send only the system message if any to the server
>0 : Send the latest chat history from the latest system prompt, limited to specified cnt.
By using gMe's iRecentUserMsgCnt and chatRequestOptions.max_tokens one can try to control the
implications of loading of the ai-model's context window by chat history, wrt chat response to
some extent in a simple crude way.
Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js
may not be visible. Also remember that just refreshing/reloading page in browser or for that
matter clearing site data, dont directly override site caching in all cases. Worst case you may
have to change port. Or in dev tools of browser, you may be able to disable caching fully.
Concept of multiple chat sessions with different servers, as well as saving and restoring of
those across browser usage sessions, can be woven around the SimpleChat/MultiChatUI class and
its instances relatively easily, however given the current goal of keeping this simple, it has
not been added, for now.
By switching between chat.add_system_begin/anytime, one can control whether one can change
the system prompt, anytime during the conversation or only at the beginning.
read_json_early, is to experiment with reading json response data early on, if available,
so that user can be shown generated data, as and when it is being generated, rather than
at the end when full data is available.
the server flow doesnt seem to be sending back data early, atleast for request (inc options)
that is currently sent.
if able to read json data early on in future, as and when ai model is generating data, then
this helper needs to indirectly update the chat div with the recieved data, without waiting
for the overall data to be available.
### Default setup
By default things are setup to try and make the user experience a bit better, if possible.
However a developer when testing the server of ai-model may want to change these value.
Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be
just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of
full chat history. This way if there is any response with garbage/repeatation, it doesnt
mess with things beyond the next question/request/query, in some ways.
Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space
available wrt next query-response. However dont forget that the server when started should
also be started with a model context size of 1k or more, to be on safe side.
The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the
internal n_predict, for now add the same here on the client side, maybe later add max_tokens
to /completions endpoint handling code on server side.
Frequency and presence penalty fields are set to 1.2 in the set of fields sent to server
along with the user query. So that the model is partly set to try avoid repeating text in
its response.
A end-user can change these behaviour by editing gMe from browser's devel-tool/console.
## At the end
Also a thank you to all open source and open model developers, who strive for the common good.

View file

@ -48,6 +48,13 @@ button {
flex-direction: column;
}
.ul1 {
padding-inline-start: 2vw;
}
.ul2 {
padding-inline-start: 2vw;
}
* {
margin: 0.6vmin;
}

View file

@ -14,23 +14,86 @@ class ApiEP {
}
let gUsageMsg = `
<p> Enter the system prompt above, before entering/submitting any user query.</p>
<p> Enter your text to the ai assistant below.</p>
<p> Use shift+enter for inserting enter.</p>
<p> Refresh the page to start over fresh.</p>
<p class="role-system">Usage</p>
<ul class="ul1">
<li> Set system prompt above, to try control ai response charactersitic, if model supports same.</li>
<ul class="ul2">
<li> Completion mode normally wont have a system prompt.</li>
</ul>
<li> Enter your query to ai assistant below.</li>
<ul class="ul2">
<li> Completion mode doesnt insert user/role: prefix implicitly.</li>
<li> Use shift+enter for inserting enter/newline.</li>
</ul>
<li> Default ContextWindow = [System, Last Query+Resp, Cur Query].</li>
<ul class="ul2">
<li> experiment iRecentUserMsgCnt, max_tokens, model ctxt window to expand</li>
</ul>
</ul>
`;
/** @typedef {{role: string, content: string}[]} ChatMessages */
class SimpleChat {
constructor() {
/**
* Maintain in a form suitable for common LLM web service chat/completions' messages entry
* @type {{role: string, content: string}[]}
* @type {ChatMessages}
*/
this.xchat = [];
this.iLastSys = -1;
}
clear() {
this.xchat = [];
this.iLastSys = -1;
}
/**
* Recent chat messages.
* If iRecentUserMsgCnt < 0
* Then return the full chat history
* Else
* Return chat messages from latest going back till the last/latest system prompt.
* While keeping track that the number of user queries/messages doesnt exceed iRecentUserMsgCnt.
* @param {number} iRecentUserMsgCnt
*/
recent_chat(iRecentUserMsgCnt) {
if (iRecentUserMsgCnt < 0) {
return this.xchat;
}
if (iRecentUserMsgCnt == 0) {
console.warn("WARN:SimpleChat:SC:RecentChat:iRecentUsermsgCnt of 0 means no user message/query sent");
}
/** @type{ChatMessages} */
let rchat = [];
let sysMsg = this.get_system_latest();
if (sysMsg.length != 0) {
rchat.push({role: Roles.System, content: sysMsg});
}
let iUserCnt = 0;
let iStart = this.xchat.length;
for(let i=this.xchat.length-1; i > this.iLastSys; i--) {
if (iUserCnt >= iRecentUserMsgCnt) {
break;
}
let msg = this.xchat[i];
if (msg.role == Roles.User) {
iStart = i;
iUserCnt += 1;
}
}
for(let i = iStart; i < this.xchat.length; i++) {
let msg = this.xchat[i];
if (msg.role == Roles.System) {
continue;
}
rchat.push({role: msg.role, content: msg.content});
}
return rchat;
}
/**
* Add an entry into xchat
* @param {string} role
@ -57,7 +120,7 @@ class SimpleChat {
div.replaceChildren();
}
let last = undefined;
for(const x of this.xchat) {
for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) {
let entry = document.createElement("p");
entry.className = `role-${x.role}`;
entry.innerText = `${x.role}: ${x.content}`;
@ -69,17 +132,21 @@ class SimpleChat {
} else {
if (bClear) {
div.innerHTML = gUsageMsg;
gMe.show_info(div);
}
}
}
/**
* Add needed fields wrt json object to be sent wrt LLM web services completions endpoint
* Add needed fields wrt json object to be sent wrt LLM web services completions endpoint.
* The needed fields/options are picked from a global object.
* Convert the json into string.
* @param {Object} obj
*/
request_jsonstr(obj) {
obj["temperature"] = 0.7;
for(let k in gMe.chatRequestOptions) {
obj[k] = gMe.chatRequestOptions[k];
}
return JSON.stringify(obj);
}
@ -88,18 +155,27 @@ class SimpleChat {
*/
request_messages_jsonstr() {
let req = {
messages: this.xchat,
messages: this.recent_chat(gMe.iRecentUserMsgCnt),
}
return this.request_jsonstr(req);
}
/**
* Return a string form of json object suitable for /completions
* @param {boolean} bInsertStandardRolePrefix Insert "<THE_ROLE>: " as prefix wrt each role's message
*/
request_prompt_jsonstr() {
request_prompt_jsonstr(bInsertStandardRolePrefix) {
let prompt = "";
for(const chat of this.xchat) {
prompt += `${chat.role}: ${chat.content}\n`;
let iCnt = 0;
for(const chat of this.recent_chat(gMe.iRecentUserMsgCnt)) {
iCnt += 1;
if (iCnt > 1) {
prompt += "\n";
}
if (bInsertStandardRolePrefix) {
prompt += `${chat.role}: `;
}
prompt += `${chat.content}`;
}
let req = {
prompt: prompt,
@ -171,7 +247,6 @@ let gChatURL = {
'chat': `${gBaseURL}/chat/completions`,
'completion': `${gBaseURL}/completions`,
}
const gbCompletionFreshChatAlways = true;
/**
@ -291,6 +366,8 @@ class MultiChatUI {
// allow user to insert enter into their message using shift+enter.
// while just pressing enter key will lead to submitting.
if ((ev.key === "Enter") && (!ev.shiftKey)) {
let value = this.elInUser.value;
this.elInUser.value = value.substring(0,value.length-1);
this.elBtnUser.click();
ev.preventDefault();
}
@ -321,6 +398,29 @@ class MultiChatUI {
}
}
/**
* Try read json response early, if available.
* @param {Response} resp
*/
async read_json_early(resp) {
if (!resp.body) {
throw Error("ERRR:SimpleChat:MCUI:ReadJsonEarly:No body...");
}
let tdUtf8 = new TextDecoder("utf-8");
let rr = resp.body.getReader();
let gotBody = "";
while(true) {
let { value: cur, done: done} = await rr.read();
let curBody = tdUtf8.decode(cur);
console.debug("DBUG:SC:PART:", curBody);
gotBody += curBody;
if (done) {
break;
}
}
return JSON.parse(gotBody);
}
/**
* Handle user query submit request, wrt specified chat session.
* @param {string} chatId
@ -330,6 +430,14 @@ class MultiChatUI {
let chat = this.simpleChats[chatId];
// In completion mode, if configured, clear any previous chat history.
// So if user wants to simulate a multi-chat based completion query,
// they will have to enter the full thing, as a suitable multiline
// user input/query.
if ((apiEP == ApiEP.Completion) && (gMe.bCompletionFreshChatAlways)) {
chat.clear();
}
chat.add_system_anytime(this.elInSystem.value, chatId);
let content = this.elInUser.value;
@ -344,7 +452,7 @@ class MultiChatUI {
if (apiEP == ApiEP.Chat) {
theBody = chat.request_messages_jsonstr();
} else {
theBody = chat.request_prompt_jsonstr();
theBody = chat.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix);
}
this.elInUser.value = "working...";
@ -359,6 +467,7 @@ class MultiChatUI {
});
let respBody = await resp.json();
//let respBody = await this.read_json_early(resp);
console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`);
let assistantMsg;
if (apiEP == ApiEP.Chat) {
@ -376,13 +485,6 @@ class MultiChatUI {
} else {
console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`);
}
// Purposefully clear at end rather than begin of this function
// so that one can switch from chat to completion mode and sequece
// in a completion mode with multiple user-assistant chat data
// from before to be sent/occur once.
if ((apiEP == ApiEP.Completion) && (gbCompletionFreshChatAlways)) {
chat.xchat.length = 0;
}
this.ui_reset_userinput();
}
@ -462,17 +564,66 @@ class MultiChatUI {
}
let gMuitChat;
const gChatIds = [ "Default", "Other" ];
class Me {
constructor() {
this.defaultChatIds = [ "Default", "Other" ];
this.multiChat = new MultiChatUI();
this.bCompletionFreshChatAlways = true;
this.bCompletionInsertStandardRolePrefix = false;
this.iRecentUserMsgCnt = 2;
// Add needed fields wrt json object to be sent wrt LLM web services completions endpoint.
this.chatRequestOptions = {
"temperature": 0.7,
"max_tokens": 1024,
"frequency_penalty": 1.2,
"presence_penalty": 1.2,
"n_predict": 1024
};
}
/**
* @param {HTMLDivElement} elDiv
*/
show_info(elDiv) {
var p = document.createElement("p");
p.innerText = "Settings (devel-tools-console gMe)";
p.className = "role-system";
elDiv.appendChild(p);
var p = document.createElement("p");
p.innerText = `bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`;
elDiv.appendChild(p);
p = document.createElement("p");
p.innerText = `bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`;
elDiv.appendChild(p);
p = document.createElement("p");
p.innerText = `iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`;
elDiv.appendChild(p);
p = document.createElement("p");
p.innerText = `chatRequestOptions:${JSON.stringify(this.chatRequestOptions)}`;
elDiv.appendChild(p);
}
}
/** @type {Me} */
let gMe;
function startme() {
console.log("INFO:SimpleChat:StartMe:Starting...");
gMuitChat = new MultiChatUI();
for (let cid of gChatIds) {
gMuitChat.new_chat_session(cid);
gMe = new Me();
for (let cid of gMe.defaultChatIds) {
gMe.multiChat.new_chat_session(cid);
}
gMuitChat.setup_ui(gChatIds[0]);
gMuitChat.show_sessions();
gMe.multiChat.setup_ui(gMe.defaultChatIds[0], true);
gMe.multiChat.show_sessions();
}
document.addEventListener("DOMContentLoaded", startme);

View file

@ -3,40 +3,390 @@
#include <cmath>
#include <cstdio>
#include <fstream>
#include <string>
#include <vector>
int main(int argc, char ** argv) {
if (argc < 3 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH PROMPT [--ids]\n" , argv[0]);
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#include <shellapi.h> // For CommandLineToArgvW
#endif
static void print_usage_information(const char * argv0, FILE * stream) {
fprintf(stream, "usage: %s [options]\n\n", argv0);
fprintf(stream, "The tokenize program tokenizes a prompt using a given model,\n");
fprintf(stream, "and prints the resulting tokens to standard output.\n\n");
fprintf(stream, "It needs a model file, a prompt, and optionally other flags\n");
fprintf(stream, "to control the behavior of the tokenizer.\n\n");
fprintf(stream, " The possible options are:\n");
fprintf(stream, "\n");
fprintf(stream, " -h, --help print this help and exit\n");
fprintf(stream, " -m MODEL_PATH, --model MODEL_PATH path to model.\n");
fprintf(stream, " --ids if given, only print numerical token IDs, and not token strings.\n");
fprintf(stream, " The output format looks like [1, 2, 3], i.e. parseable by Python.\n");
fprintf(stream, " -f PROMPT_FNAME, --file PROMPT_FNAME read prompt from a file.\n");
fprintf(stream, " -p PROMPT, --prompt PROMPT read prompt from the argument.\n");
fprintf(stream, " --stdin read prompt from standard input.\n");
fprintf(stream, " --no-bos do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
fprintf(stream, " --log-disable disable logs. Makes stderr quiet when loading the model.\n");
}
static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) text;
(void) user_data;
}
static std::string read_prompt_from_file(const char * filepath, bool & success) {
success = false;
std::ifstream in(filepath, std::ios::binary);
if (!in) {
fprintf(stderr, "%s: could not open file '%s' for reading: %s\n", __func__, filepath, strerror(errno));
return std::string();
}
// do not assume the file is seekable (e.g. /dev/stdin)
std::stringstream buffer;
buffer << in.rdbuf();
if (in.fail()) {
fprintf(stderr, "%s: could not read the entire file '%s': %s\n", __func__, filepath, strerror(errno));
return std::string();
}
success = true;
return buffer.str();
}
//
// Function: ingest_args(...) -> vector<string>
//
// Takes argc and argv arguments, and converts them to a vector of UTF-8 encoded
// strings, as an STL vector<string>.
//
// In particular, it handles character encoding shenanigans on Windows.
//
// Note: raw_argc and raw_argv are not actually read at all on Windows.
// On Windows we call GetCommandLineW to get the arguments in wchar_t
// format, ignoring the regular argc/argv arguments to main().
//
// TODO: potential opportunity to roll common stuff into common/console.cpp
// in relation to Windows wchar_t shenanigans.
static std::vector<std::string> ingest_args(int raw_argc, char ** raw_argv) {
std::vector<std::string> argv;
// Handle Windows, if given non-ASCII arguments.
// We convert wchar_t arguments into UTF-8 char* on this platform.
// Lets you invoke 'tokenize' on Windows cmd.exe with non-ASCII characters
// without throwing tantrums.
#if defined(_WIN32)
int argc;
const LPWSTR cmdline_wargv = GetCommandLineW();
LPWSTR * wargv = CommandLineToArgvW(cmdline_wargv, &argc);
// silence unused arg warnings
(void) raw_argc;
(void) raw_argv;
for (int i = 0; i < argc; ++i) {
int length_needed = WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), 0, 0, NULL, NULL);
char * output_buf = (char *) calloc(length_needed+1, sizeof(char));
GGML_ASSERT(output_buf);
WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), output_buf, length_needed, NULL, NULL);
output_buf[length_needed] = '\0';
argv.push_back(output_buf);
free(output_buf);
}
LocalFree((HLOCAL) wargv);
#else
int argc = raw_argc;
for (int i = 0; i < argc; ++i) {
argv.push_back(raw_argv[i]);
}
#endif
GGML_ASSERT((unsigned int) argc == argv.size());
return argv;
}
//
// Function: write_utf8_cstr_to_stdout(const char *) -> <writes to stdout>
//
// writes a string to standard output; taking into account that on Windows
// to display correctly you have to use special handling. Works even if the
// user has not set a unicode code page on a Windows cmd.exe.
//
// In case of invalid UTF-8, invalid_utf8 is set to true on Windows, and something
// a human-readable is written instead.
//
// On non-Windows systems, simply printfs() the string.
static void write_utf8_cstr_to_stdout(const char * str, bool & invalid_utf8) {
invalid_utf8 = false;
#if defined(_WIN32)
// Are we in a console?
HANDLE hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
DWORD dwMode = 0;
// According to Microsoft docs:
// "WriteConsole fails if it is used with a standard handle that is redirected to a file."
// Also according to the docs, you can use GetConsoleMode to check for that.
if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {
printf("%s", str);
return;
}
// MultiByteToWideChar reports an error if str is empty, don't report
// them as invalid_utf8.
if (*str == 0) {
return;
}
int length_needed = MultiByteToWideChar(CP_UTF8, MB_ERR_INVALID_CHARS, str, strlen(str), NULL, 0);
if (length_needed == 0) {
DWORD err = GetLastError();
if (err == ERROR_NO_UNICODE_TRANSLATION) {
invalid_utf8 = true;
int len = strlen(str);
printf("<");
for (int i = 0; i < len; ++i) {
if (i > 0) {
printf(" ");
}
printf("%02x", (uint8_t) str[i]);
}
printf(">");
return;
}
GGML_ASSERT(false && "MultiByteToWideChar() failed in an unexpected way.");
}
LPWSTR wstr = (LPWSTR) calloc(length_needed+1, sizeof(*wstr));
GGML_ASSERT(wstr);
MultiByteToWideChar(CP_UTF8, 0, str, strlen(str), wstr, length_needed);
WriteConsoleW(hConsole, wstr, length_needed, NULL, NULL);
free(wstr);
#else
// TODO: reporting invalid_utf8 would be useful on non-Windows too.
// printf will silently just write bad unicode.
printf("%s", str);
#endif
}
int main(int raw_argc, char ** raw_argv) {
const std::vector<std::string> argv = ingest_args(raw_argc, raw_argv);
const int argc = argv.size();
if (argc <= 1) {
print_usage_information(argv[0].c_str(), stderr);
return 1;
}
const char * model_path = argv[1];
const char * prompt = argv[2];
//////
// Read out all the command line arguments.
//////
const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids";
// variables where to put any arguments we see.
bool printing_ids = false;
bool no_bos = false;
bool disable_logging = false;
const char * model_path = NULL;
const char * prompt_path = NULL;
const char * prompt_arg = NULL;
// track which arguments were explicitly given
// used for sanity checking down the line
bool model_path_set = false;
bool prompt_path_set = false;
bool prompt_set = false;
bool stdin_set = false;
int iarg = 1;
for (; iarg < argc; ++iarg) {
std::string arg{argv[iarg]};
if (arg == "-h" || arg == "--help") {
print_usage_information(argv[0].c_str(), stdout);
return 0;
}
else if (arg == "--ids") {
printing_ids = true;
}
else if (arg == "-m" || arg == "--model") {
if (model_path_set) {
fprintf(stderr, "Error: -m or --model specified multiple times.\n");
return 1;
}
model_path = argv[++iarg].c_str();
model_path_set = true;
}
else if (arg == "--no-bos") {
no_bos = true;
}
else if (arg == "-p" || arg == "--prompt") {
if (prompt_set) {
fprintf(stderr, "Error: -p or --prompt specified multiple times.\n");
return 1;
}
prompt_arg = argv[++iarg].c_str();
prompt_set = true;
}
else if (arg == "-f" || arg == "--file") {
if (prompt_path_set) {
fprintf(stderr, "Error: -f or --file specified multiple times.\n");
return 1;
}
prompt_path = argv[++iarg].c_str();
prompt_path_set = true;
}
else if (arg == "--stdin") {
stdin_set = true;
}
else if (arg == "--log-disable") {
disable_logging = true;
}
else {
fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str());
return 1;
}
}
//////
// Sanity check the command line arguments.
//////
// Check that we have the required stuff set.
if (model_path_set && model_path == NULL) {
fprintf(stderr, "Error: --model requires an argument.\n");
return 1;
}
if (!model_path_set) {
fprintf(stderr, "Error: must specify --model.\n");
return 1;
}
if (prompt_path_set && prompt_path == NULL) {
fprintf(stderr, "Error: --file requires an argument.\n");
return 1;
}
if (prompt_set && prompt_arg == NULL) {
fprintf(stderr, "Error: --prompt requires an argument.\n");
return 1;
}
const int prompts_set = !!(prompt_path_set) + !!(prompt_set) + !!(stdin_set);
if (prompts_set > 1) {
fprintf(stderr, "Error: --stdin, --file and --prompt are mutually exclusive.\n");
return 1;
}
// Must have some prompt.
if (prompts_set == 0) {
fprintf(stderr, "Error: must specify one of: --stdin, --file or --prompt.\n");
return 1;
}
GGML_ASSERT(model_path);
GGML_ASSERT(prompt_path || prompt_arg || stdin_set);
//////
// Figure out where will the prompt come from.
//////
std::string prompt;
if (prompt_path_set) {
bool success = false;
prompt = read_prompt_from_file(prompt_path, success);
if (!success) {
return 1;
}
} else if (prompt_set) {
prompt = prompt_arg;
} else {
GGML_ASSERT(stdin_set);
// we read stdin *after* loading model (early exit if model cannot
// be loaded, which can be a nicer user experience)
}
//////
// Start actually doing the tokenizing stuff.
//////
#ifdef LOG_DISABLE_LOGS
disable_logging = true;
#endif
if (disable_logging) {
llama_log_set(llama_log_callback_null, NULL);
}
llama_backend_init();
llama_model_params model_params = llama_model_default_params();
model_params.vocab_only = true;
llama_model * model = llama_load_model_from_file(model_path, model_params);
if (!model) {
fprintf(stderr, "Error: could not load model from file '%s'.\n", model_path);
return 1;
}
llama_context_params ctx_params = llama_context_default_params();
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (!ctx) {
fprintf(stderr, "Error: could not create context.\n");
return 1;
}
// read entire prompt from stdin?
if (stdin_set) {
GGML_ASSERT(!prompt_path_set && !prompt_set);
std::stringstream stdin_buffer;
stdin_buffer << std::cin.rdbuf();
if (std::cin.fail()) {
fprintf(stderr, "Error: could not read the entire standard input.\n");
return 1;
}
prompt = stdin_buffer.str();
}
const bool model_wants_add_bos = llama_should_add_bos_token(model);
const bool add_bos = model_wants_add_bos && !no_bos;
std::vector<llama_token> tokens;
tokens = ::llama_tokenize(model, prompt, add_bos, true);
tokens = ::llama_tokenize(model, prompt, true, true);
if (printing_ids) {
printf("[");
}
for (int i = 0; i < (int) tokens.size(); i++) {
if (printing_ids) {
printf("%d\n", tokens[i]);
if (i > 0) {
printf(", ");
}
printf("%d", tokens[i]);
} else {
printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str());
bool invalid_utf8 = false;
printf("%6d -> '", tokens[i]);
write_utf8_cstr_to_stdout(llama_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8);
if (invalid_utf8) {
printf("' (utf-8 decode failure)\n");
} else {
printf("'\n");
}
}
}
if (printing_ids) {
printf("]\n");
}
// silence valgrind
llama_free(ctx);
llama_free_model(model);
return 0;
}