ggml : use sched_yield when using BLAS + add comment

* Faster `beam_search` sampling

Refine the KV cache update logic for more intelligent and efficient updating.

* Faster `whisper_sample_token_topk`

* Update whisper.cpp

* Update whisper.cpp

* Update whisper.cpp

* Reduce `memory allocation`

* Add `pointer swapping`

* Fixed some bugs

* Update whisper.cpp

* Apply suggestions from code review

* Updated the logic for determining `two-copy`

* Updated the logic for determining `two-copy` v2

* whisper : add debug logs + coding style

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

.github/workflows/build.yml

@@ -428,15 +428,15 @@ jobs:
 
       - name: Publish package
         if: ${{ github.ref == 'refs/heads/master' }}
-        uses: gradle/gradle-build-action@v2
+        uses: gradle/gradle-build-action@v2.4.2
         with:
           arguments: publish
           build-root-directory: bindings/java
         env:
           MAVEN_USERNAME: ${{ secrets.JIRA_USER }}
           MAVEN_PASSWORD: ${{ secrets.JIRA_PASS }}
-          # MAVEN_USERNAME: ${{ secrets.OSSRH_USERNAME }}
-          # MAVEN_PASSWORD: ${{ secrets.OSSRH_TOKEN }}
+          PGP_SECRET: ${{ secrets.GPG_PRIVATE_KEY }}
+          PGP_PASSPHRASE: ${{ secrets.GPG_PASSPHRASE }}
 
   quantize:
     runs-on: ubuntu-latest

.gitignore

@@ -11,6 +11,7 @@ build/
 build-em/
 build-debug/
 build-release/
+build-rwdi/
 build-static/
 build-cublas/
 build-no-accel/

CMakeLists.txt

@@ -321,6 +321,53 @@ else()
     endif()
 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)
+add_compile_definitions(_XOPEN_SOURCE=600)
+
+# 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")
+    remove_definitions(-D_XOPEN_SOURCE=600)
+    add_compile_definitions(_XOPEN_SOURCE=700)
+endif()
+
+# Data types, macros and functions related to controlling CPU affinity
+# are available on Linux through GNU extensions in libc
+if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+    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")
+    add_compile_definitions(_DARWIN_C_SOURCE)
+endif()
+if (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 (WHISPER_PERF)
     set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_PERF)
 endif()

Makefile

@@ -42,18 +42,55 @@ CFLAGS   = -I.              -O3 -DNDEBUG -std=c11   -fPIC
 CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
 LDFLAGS  =
 
-# ref: https://github.com/ggerganov/whisper.cpp/issues/37
-ifneq ($(wildcard /usr/include/musl/*),)
-	CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
-	CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
+# clock_gettime came in POSIX.1b (1993)
+# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
+# posix_memalign came in POSIX.1-2001 / SUSv3
+# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)
+CFLAGS   += -D_XOPEN_SOURCE=600
+CXXFLAGS += -D_XOPEN_SOURCE=600
+
+# Somehow in OpenBSD whenever POSIX conformance is specified
+# some string functions rely on locale_t availability,
+# which was introduced in POSIX.1-2008, forcing us to go higher
+ifeq ($(UNAME_S),OpenBSD)
+	CFLAGS   += -U_XOPEN_SOURCE -D_XOPEN_SOURCE=700
+	CXXFLAGS += -U_XOPEN_SOURCE -D_XOPEN_SOURCE=700
+endif
+
+# Data types, macros and functions related to controlling CPU affinity
+# are available on Linux through GNU extensions in libc
+ifeq ($(UNAME_S),Linux)
+	CFLAGS   += -D_GNU_SOURCE
+	CXXFLAGS += -D_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
 ifeq ($(UNAME_S),Darwin)
 	CFLAGS   += -D_DARWIN_C_SOURCE
 	CXXFLAGS += -D_DARWIN_C_SOURCE
 endif
+ifeq ($(UNAME_S),DragonFly)
+	CFLAGS   += -D__BSD_VISIBLE
+	CXXFLAGS += -D__BSD_VISIBLE
+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
+ifeq ($(UNAME_S),FreeBSD)
+	CFLAGS   += -D__BSD_VISIBLE
+	CXXFLAGS += -D__BSD_VISIBLE
+endif
+ifeq ($(UNAME_S),NetBSD)
+	CFLAGS   += -D_NETBSD_SOURCE
+	CXXFLAGS += -D_NETBSD_SOURCE
+endif
+ifeq ($(UNAME_S),OpenBSD)
+	CFLAGS   += -D_BSD_SOURCE
+	CXXFLAGS += -D_BSD_SOURCE
+endif
 
 # OS specific
 # TODO: support Windows
@@ -67,7 +104,7 @@ endif
 #       feel free to update the Makefile for your architecture and send a pull request or issue
 ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	ifeq ($(UNAME_S),Darwin)
-		CPUINFO_CMD := sysctl machdep.cpu.features
+		CPUINFO_CMD := sysctl machdep.cpu.features machdep.cpu.leaf7_features
 	else ifeq ($(UNAME_S),Linux)
 		CPUINFO_CMD := cat /proc/cpuinfo
 	else ifneq (,$(filter MINGW32_NT% MINGW64_NT%,$(UNAME_S)))

bindings/java/build.gradle

@@ -2,6 +2,7 @@ plugins {
     id 'java'
     id 'java-library'
     id 'maven-publish'
+    id 'signing'
 }
 
 archivesBaseName = 'whispercpp'
@@ -109,4 +110,23 @@ publishing {
             }
         }
     }
+
+    repositories {
+        maven {
+            def releasesRepoUrl = 'https://s01.oss.sonatype.org/service/local/staging/deploy/maven2/'
+            def snapshotsRepoUrl = 'https://s01.oss.sonatype.org/content/repositories/snapshots/'
+            url = version.endsWith('-SNAPSHOT') ? snapshotsRepoUrl : releasesRepoUrl
+            credentials {
+                username = System.getenv("MAVEN_USERNAME")
+                password = System.getenv("MAVEN_PASSWORD")
+            }
+        }
+    }
+}
+
+signing {
+    def signingKey = System.getenv("PGP_SECRET")
+    def signingPassword = System.getenv("PGP_PASSPHRASE")
+    useInMemoryPgpKeys(signingKey, signingPassword)
+    sign publishing.publications.mavenJava
 }

examples/command/command.cpp

@@ -6,8 +6,8 @@
 // ref: https://github.com/ggerganov/whisper.cpp/issues/171
 //
 
-#include "common.h"
 #include "common-sdl.h"
+#include "common.h"
 #include "whisper.h"
 
 #include <sstream>

examples/common.cpp

@@ -792,7 +792,7 @@ bool sam_params_parse(int argc, char ** argv, sam_params & params) {
     return true;
 }
 
-void sam_print_usage(int argc, char ** argv, const sam_params & params) {
+void sam_print_usage(int /*argc*/, char ** argv, const sam_params & params) {
     fprintf(stderr, "usage: %s [options]\n", argv[0]);
     fprintf(stderr, "\n");
     fprintf(stderr, "options:\n");

examples/lsp/lsp.cpp

@@ -324,12 +324,12 @@ json register_commandset(struct whisper_context * ctx, json jparams, std::vector
     commandset_list.push_back(cs);
     return json{{"index",index}};
 }
-json seek(struct whisper_context * ctx, audio_async &audio, json params) {
+json seek(struct whisper_context * /*ctx*/, audio_async & /*audio*/, json /*params*/) {
     // whisper_state has the pertinent offsets, but there also seem to be a large
     // number of scratch buffers that would prevent rewinding context in a manner similar to llama
     // I'll give this a another pass once everything else is implemented,
     // but for now, it's unsupported
-    throw json{
+    throw json {
         {"code", -32601},
             {"message", "Seeking is not yet supported."}
     };
@@ -412,7 +412,7 @@ void process_loop(struct whisper_context * ctx, audio_async &audio, const whispe
             jobqueue.pop_front();
             // send response
             std::string data = resp.dump(-1, ' ', false, json::error_handler_t::replace);
-            fprintf(stdout, "Content-Length: %d\r\n\r\n%s\n", data.length()+1, data.c_str());
+            fprintf(stdout, "Content-Length: %d\r\n\r\n%s\n", (int)data.length()+1, data.c_str());
             std::cout.flush();
 
         }

examples/main/main.cpp

@@ -260,7 +260,7 @@ std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s
 
     return speaker;
 }
-void whisper_print_progress_callback(struct whisper_context * ctx, struct whisper_state * /*state*/, int progress, void * user_data) {
+void whisper_print_progress_callback(struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, int progress, void * user_data) {
     int progress_step = ((whisper_print_user_data *) user_data)->params->progress_step;
     int * progress_prev  = &(((whisper_print_user_data *) user_data)->progress_prev);
     if (progress >= *progress_prev + progress_step) {
@@ -492,7 +492,7 @@ bool output_csv(struct whisper_context * ctx, const char * fname, const whisper_
     return true;
 }
 
-bool output_score(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
+bool output_score(struct whisper_context * ctx, const char * fname, const whisper_params & /*params*/, std::vector<std::vector<float>> /*pcmf32s*/) {
     std::ofstream fout(fname);
     fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
 

examples/stream/stream.cpp

@@ -3,8 +3,8 @@
 // A very quick-n-dirty implementation serving mainly as a proof of concept.
 //
 
-#include "common.h"
 #include "common-sdl.h"
+#include "common.h"
 #include "whisper.h"
 
 #include <cassert>

examples/talk-llama/llama.cpp

@@ -1,11 +1,3 @@
-// Defines fileno on msys:
-#ifndef _GNU_SOURCE
-#define _GNU_SOURCE
-#include <cstddef>
-#include <cstdint>
-#include <cstdio>
-#endif
-
 #include "llama-util.h"
 #include "llama.h"
 
@@ -1164,7 +1156,7 @@ static bool llama_eval_internal(
     const llama_token * tokens,
             const int   n_tokens,
             const int   n_past,
-            const int   n_threads) {
+                  int   n_threads) {
 
     // enforce that the first token is BOS
     if (n_past == 0 && tokens[0] != llama_token_bos()) {
@@ -1190,6 +1182,8 @@ static bool llama_eval_internal(
     const int n_vocab = hparams.n_vocab;
     const int n_rot   = hparams.n_embd/hparams.n_head;
 
+    const float eps = 5e-6f; // TODO: take from hparams
+
     auto & mem_per_token = lctx.mem_per_token;
     auto & buf_compute   = lctx.buf_compute;
 
@@ -1204,7 +1198,7 @@ static bool llama_eval_internal(
     // for big prompts, if BLAS is enabled, it is better to use only one thread
     // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
     ggml_cgraph gf = {};
-    gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
+    n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
 
     struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
     ggml_set_name(embd, "embd");
@@ -1221,7 +1215,7 @@ static bool llama_eval_internal(
 
         // norm
         {
-            cur = ggml_rms_norm(ctx0, inpL);
+            cur = ggml_rms_norm(ctx0, inpL, eps);
 
             // cur = cur*attention_norm(broadcasted)
             cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
@@ -1329,7 +1323,7 @@ static bool llama_eval_internal(
         {
             // norm
             {
-                cur = ggml_rms_norm(ctx0, inpFF);
+                cur = ggml_rms_norm(ctx0, inpFF, eps);
 
                 // cur = cur*ffn_norm(broadcasted)
                 cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
@@ -1367,7 +1361,7 @@ static bool llama_eval_internal(
     // norm
     {
 
-        inpL = ggml_rms_norm(ctx0, inpL);
+        inpL = ggml_rms_norm(ctx0, inpL, eps);
 
         // inpL = inpL*norm(broadcasted)
         inpL = ggml_mul(ctx0, inpL, model.norm);
@@ -1384,8 +1378,8 @@ static bool llama_eval_internal(
     //inpL = ggml_soft_max_inplace(ctx0, inpL);
 
     // run the computation
-    ggml_build_forward_expand(&gf, inpL);
-    ggml_graph_compute       (ctx0, &gf);
+    ggml_build_forward_expand  (&gf, inpL);
+    ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
 
 #ifdef GGML_PERF
     // print timing information per ggml operation (for debugging purposes)
@@ -2488,8 +2482,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
             }
 
             struct ggml_cgraph gf = ggml_build_forward(r);
-            gf.n_threads = n_threads;
-            ggml_graph_compute(lora_ctx, &gf);
+            ggml_graph_compute_with_ctx(lora_ctx, &gf, n_threads);
 
             // we won't need these tensors again, reset the context to save memory
             ggml_free(lora_ctx);
@@ -2635,7 +2628,6 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
 
             ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
             ggml_cgraph gf{};
-            gf.n_threads = 1;
 
             ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
             kout3d->data = out;
@@ -2655,7 +2647,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
 
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
-            ggml_graph_compute(cpy_ctx, &gf);
+            ggml_graph_compute_with_ctx(cpy_ctx, &gf, 1);
 
             ggml_free(cpy_ctx);
         }
@@ -2743,7 +2735,6 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
 
             ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
             ggml_cgraph gf{};
-            gf.n_threads = 1;
 
             ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
             kin3d->data = (void *) inp;
@@ -2763,7 +2754,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
 
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
-            ggml_graph_compute(cpy_ctx, &gf);
+            ggml_graph_compute_with_ctx(cpy_ctx, &gf, 1);
 
             ggml_free(cpy_ctx);
         }

examples/talk-llama/talk-llama.cpp

@@ -1,8 +1,8 @@
 // Talk with AI
 //
 
-#include "common.h"
 #include "common-sdl.h"
+#include "common.h"
 #include "whisper.h"
 #include "llama.h"
 
@@ -649,7 +649,10 @@ int main(int argc, char ** argv) {
                 }
 
                 text_to_speak = ::replace(text_to_speak, "\"", "");
-                system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
+                int ret = system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
+                if (ret != 0) {
+                    fprintf(stderr, "%s: failed to speak\n", __func__);
+                }
 
                 audio.clear();
 

examples/talk/gpt-2.cpp

@@ -191,9 +191,9 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
     // create the ggml context
     {
         struct ggml_init_params params = {
-            .mem_size   = ctx_size,
-            .mem_buffer = NULL,
-            .no_alloc   = false,
+            /*.mem_size   =*/ ctx_size,
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ false,
         };
 
         model.ctx = ggml_init(params);

examples/talk/talk.cpp

@@ -1,8 +1,8 @@
 // Talk with AI
 //
 
-#include "common.h"
 #include "common-sdl.h"
+#include "common.h"
 #include "whisper.h"
 #include "gpt-2.h"
 
@@ -349,7 +349,10 @@ int main(int argc, char ** argv) {
                 gpt2_set_prompt(ctx_gpt, prompt_base.c_str());
 
                 text_to_speak = ::replace(text_to_speak, params.person + ": ", "");
-                system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
+                int ret = system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
+                if (ret != 0) {
+                    fprintf(stderr, "%s: system() failed!\n", __func__);
+                }
 
                 audio.clear();
 

examples/whisper.android/.idea/compiler.xml

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="CompilerConfiguration">
-    <bytecodeTargetLevel target="11" />
+    <bytecodeTargetLevel target="17" />
   </component>
 </project>

examples/whisper.android/.idea/gradle.xml

@@ -4,15 +4,15 @@
   <component name="GradleSettings">
     <option name="linkedExternalProjectsSettings">
       <GradleProjectSettings>
-        <option name="testRunner" value="GRADLE" />
-        <option name="distributionType" value="DEFAULT_WRAPPED" />
         <option name="externalProjectPath" value="$PROJECT_DIR$" />
+        <option name="gradleJvm" value="#GRADLE_LOCAL_JAVA_HOME" />
         <option name="modules">
           <set>
             <option value="$PROJECT_DIR$" />
             <option value="$PROJECT_DIR$/app" />
           </set>
         </option>
+        <option name="resolveExternalAnnotations" value="false" />
       </GradleProjectSettings>
     </option>
   </component>

examples/whisper.android/.idea/misc.xml

@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="ExternalStorageConfigurationManager" enabled="true" />
-  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" default="true" project-jdk-name="Android Studio default JDK" project-jdk-type="JavaSDK">
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_17" default="true" project-jdk-name="jbr-17" project-jdk-type="JavaSDK">
     <output url="file://$PROJECT_DIR$/build/classes" />
   </component>
   <component name="ProjectType">

examples/whisper.android/app/build.gradle

@@ -5,12 +5,12 @@ plugins {
 
 android {
     namespace 'com.whispercppdemo'
-    compileSdk 33
+    compileSdk 34
 
     defaultConfig {
         applicationId "com.whispercppdemo"
         minSdk 26
-        targetSdk 32
+        targetSdk 34
         versionCode 1
         versionName "1.0"
 
@@ -31,19 +31,19 @@ android {
         }
     }
     compileOptions {
-        sourceCompatibility JavaVersion.VERSION_1_8
-        targetCompatibility JavaVersion.VERSION_1_8
+        sourceCompatibility JavaVersion.VERSION_17
+        targetCompatibility JavaVersion.VERSION_17
     }
     kotlinOptions {
-        jvmTarget = '1.8'
+        jvmTarget = '17'
     }
     buildFeatures {
         compose true
     }
     composeOptions {
-        kotlinCompilerExtensionVersion '1.3.1'
+        kotlinCompilerExtensionVersion '1.5.0'
     }
-    ndkVersion "25.1.8937393"
+    ndkVersion "25.2.9519653"
     externalNativeBuild {
         cmake {
             path = file("src/main/jni/whisper/CMakeLists.txt")
@@ -57,19 +57,19 @@ android {
 }
 
 dependencies {
-    implementation 'androidx.activity:activity-compose:1.6.1'
-    implementation 'androidx.compose.material:material-icons-core:1.3.1'
-    implementation 'androidx.compose.material3:material3:1.0.1'
-    implementation "androidx.compose.ui:ui:1.3.2"
-    implementation "androidx.compose.ui:ui-tooling-preview:1.3.2"
-    implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.5.1'
+    implementation 'androidx.activity:activity-compose:1.7.2'
+    implementation 'androidx.compose.material:material-icons-core:1.5.0'
+    implementation 'androidx.compose.material3:material3:1.1.1'
+    implementation "androidx.compose.ui:ui:1.5.0"
+    implementation "androidx.compose.ui:ui-tooling-preview:1.5.0"
+    implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
     implementation "com.google.accompanist:accompanist-permissions:0.28.0"
-    implementation 'org.jetbrains.kotlinx:kotlinx-coroutines-core:1.6.4'
+    implementation 'org.jetbrains.kotlinx:kotlinx-coroutines-core:1.7.2'
 
     testImplementation 'junit:junit:4.13.2'
-    androidTestImplementation 'androidx.test.ext:junit:1.1.4'
-    androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.0'
-    androidTestImplementation "androidx.compose.ui:ui-test-junit4:1.3.2"
-    debugImplementation "androidx.compose.ui:ui-tooling:1.3.2"
-    debugImplementation "androidx.compose.ui:ui-test-manifest:1.3.2"
+    androidTestImplementation 'androidx.test.ext:junit:1.1.5'
+    androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
+    androidTestImplementation "androidx.compose.ui:ui-test-junit4:1.5.0"
+    debugImplementation "androidx.compose.ui:ui-tooling:1.5.0"
+    debugImplementation "androidx.compose.ui:ui-test-manifest:1.5.0"
 }

examples/whisper.android/app/src/main/java/com/whispercppdemo/ui/main/MainScreen.kt

@@ -66,7 +66,7 @@ private fun MainScreen(
 
 @Composable
 private fun MessageLog(log: String) {
-    SelectionContainer() {
+    SelectionContainer {
         Text(modifier = Modifier.verticalScroll(rememberScrollState()), text = log)
     }
 }

examples/whisper.android/app/src/main/java/com/whispercppdemo/ui/main/MainScreenViewModel.kt

@@ -47,7 +47,7 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
     }
 
     private suspend fun printSystemInfo() {
-        printMessage(String.format("System Info: %s\n", WhisperContext.getSystemInfo()));
+        printMessage(String.format("System Info: %s\n", WhisperContext.getSystemInfo()))
     }
 
     private suspend fun loadData() {

examples/whisper.android/app/src/main/java/com/whispercppdemo/ui/theme/Theme.kt

@@ -13,7 +13,7 @@ import androidx.compose.runtime.SideEffect
 import androidx.compose.ui.graphics.toArgb
 import androidx.compose.ui.platform.LocalContext
 import androidx.compose.ui.platform.LocalView
-import androidx.core.view.ViewCompat
+import androidx.core.view.WindowCompat
 
 private val DarkColorScheme = darkColorScheme(
     primary = Purple80,
@@ -55,8 +55,9 @@ fun WhisperCppDemoTheme(
     val view = LocalView.current
     if (!view.isInEditMode) {
         SideEffect {
-            (view.context as Activity).window.statusBarColor = colorScheme.primary.toArgb()
-            ViewCompat.getWindowInsetsController(view)?.isAppearanceLightStatusBars = darkTheme
+            val window = (view.context as Activity).window
+            window.statusBarColor = colorScheme.primary.toArgb()
+            WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme
         }
     }
 

examples/whisper.android/app/src/main/java/com/whispercppdemo/whisper/LibWhisper.kt

@@ -18,7 +18,9 @@ class WhisperContext private constructor(private var ptr: Long) {
 
     suspend fun transcribeData(data: FloatArray): String = withContext(scope.coroutineContext) {
         require(ptr != 0L)
-        WhisperLib.fullTranscribe(ptr, data)
+        val numThreads = WhisperCpuConfig.preferredThreadCount
+        Log.d(LOG_TAG, "Selecting $numThreads threads")
+        WhisperLib.fullTranscribe(ptr, numThreads, data)
         val textCount = WhisperLib.getTextSegmentCount(ptr)
         return@withContext buildString {
             for (i in 0 until textCount) {
@@ -126,7 +128,7 @@ private class WhisperLib {
         external fun initContextFromAsset(assetManager: AssetManager, assetPath: String): Long
         external fun initContext(modelPath: String): Long
         external fun freeContext(contextPtr: Long)
-        external fun fullTranscribe(contextPtr: Long, audioData: FloatArray)
+        external fun fullTranscribe(contextPtr: Long, numThreads: Int, audioData: FloatArray)
         external fun getTextSegmentCount(contextPtr: Long): Int
         external fun getTextSegment(contextPtr: Long, index: Int): String
         external fun getSystemInfo(): String

examples/whisper.android/app/src/main/java/com/whispercppdemo/whisper/WhisperCpuConfig.kt

@@ -0,0 +1,73 @@
+package com.whispercppdemo.whisper
+
+import android.util.Log
+import java.io.BufferedReader
+import java.io.FileReader
+
+object WhisperCpuConfig {
+    val preferredThreadCount: Int
+        // Always use at least 2 threads:
+        get() = CpuInfo.getHighPerfCpuCount().coerceAtLeast(2)
+}
+
+private class CpuInfo(private val lines: List<String>) {
+    private fun getHighPerfCpuCount(): Int = try {
+        getHighPerfCpuCountByFrequencies()
+    } catch (e: Exception) {
+        Log.d(LOG_TAG, "Couldn't read CPU frequencies", e)
+        getHighPerfCpuCountByVariant()
+    }
+
+    private fun getHighPerfCpuCountByFrequencies(): Int =
+        getCpuValues(property = "processor") { getMaxCpuFrequency(it.toInt()) }
+            .also { Log.d(LOG_TAG, "Binned cpu frequencies (frequency, count): ${it.binnedValues()}") }
+            .countDroppingMin()
+
+    private fun getHighPerfCpuCountByVariant(): Int =
+        getCpuValues(property = "CPU variant") { it.substringAfter("0x").toInt(radix = 16) }
+            .also { Log.d(LOG_TAG, "Binned cpu variants (variant, count): ${it.binnedValues()}") }
+            .countKeepingMin()
+
+    private fun List<Int>.binnedValues() = groupingBy { it }.eachCount()
+
+    private fun getCpuValues(property: String, mapper: (String) -> Int) = lines
+        .asSequence()
+        .filter { it.startsWith(property) }
+        .map { mapper(it.substringAfter(':').trim()) }
+        .sorted()
+        .toList()
+
+
+    private fun List<Int>.countDroppingMin(): Int {
+        val min = min()
+        return count { it > min }
+    }
+
+    private fun List<Int>.countKeepingMin(): Int {
+        val min = min()
+        return count { it == min }
+    }
+
+    companion object {
+        private const val LOG_TAG = "WhisperCpuConfig"
+
+        fun getHighPerfCpuCount(): Int = try {
+            readCpuInfo().getHighPerfCpuCount()
+        } catch (e: Exception) {
+            Log.d(LOG_TAG, "Couldn't read CPU info", e)
+            // Our best guess -- just return the # of CPUs minus 4.
+            (Runtime.getRuntime().availableProcessors() - 4).coerceAtLeast(0)
+        }
+
+        private fun readCpuInfo() = CpuInfo(
+            BufferedReader(FileReader("/proc/cpuinfo"))
+                .useLines { it.toList() }
+        )
+
+        private fun getMaxCpuFrequency(cpuIndex: Int): Int {
+            val path = "/sys/devices/system/cpu/cpu${cpuIndex}/cpufreq/cpuinfo_max_freq"
+            val maxFreq = BufferedReader(FileReader(path)).use { it.readLine() }
+            return maxFreq.toInt()
+        }
+    }
+}

examples/whisper.android/app/src/main/jni/whisper/jni.c

@@ -163,16 +163,12 @@ Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_freeContext(
 
 JNIEXPORT void JNICALL
 Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_fullTranscribe(
-        JNIEnv *env, jobject thiz, jlong context_ptr, jfloatArray audio_data) {
+        JNIEnv *env, jobject thiz, jlong context_ptr, jint num_threads, jfloatArray audio_data) {
     UNUSED(thiz);
     struct whisper_context *context = (struct whisper_context *) context_ptr;
     jfloat *audio_data_arr = (*env)->GetFloatArrayElements(env, audio_data, NULL);
     const jsize audio_data_length = (*env)->GetArrayLength(env, audio_data);
 
-    // Leave 2 processors free (i.e. the high-efficiency cores).
-    int max_threads = max(1, min(8, get_nprocs() - 2));
-    LOGI("Selecting %d threads", max_threads);
-
     // The below adapted from the Objective-C iOS sample
     struct whisper_full_params params = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
     params.print_realtime = true;
@@ -181,7 +177,7 @@ Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_fullTranscribe(
     params.print_special = false;
     params.translate = false;
     params.language = "en";
-    params.n_threads = max_threads;
+    params.n_threads = num_threads;
     params.offset_ms = 0;
     params.no_context = true;
     params.single_segment = false;

examples/whisper.android/app/src/main/res/values/colors.xml

@@ -1,10 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<resources>
-    <color name="purple_200">#FFBB86FC</color>
-    <color name="purple_500">#FF6200EE</color>
-    <color name="purple_700">#FF3700B3</color>
-    <color name="teal_200">#FF03DAC5</color>
-    <color name="teal_700">#FF018786</color>
-    <color name="black">#FF000000</color>
-    <color name="white">#FFFFFFFF</color>
-</resources>

examples/whisper.android/build.gradle

@@ -1,6 +1,6 @@
 // Top-level build file where you can add configuration options common to all sub-projects/modules.
 plugins {
-    id 'com.android.application' version '7.3.1' apply false
-    id 'com.android.library' version '7.3.1' apply false
-    id 'org.jetbrains.kotlin.android' version '1.7.10' apply false
+    id 'com.android.application' version '8.1.1' apply false
+    id 'com.android.library' version '8.1.1' apply false
+    id 'org.jetbrains.kotlin.android' version '1.9.0' apply false
 }

examples/whisper.android/gradle/wrapper/gradle-wrapper.properties

@@ -1,6 +1,6 @@
 #Wed Dec 14 10:37:24 EST 2022
 distributionBase=GRADLE_USER_HOME
-distributionUrl=https\://services.gradle.org/distributions/gradle-7.4-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.2-bin.zip
 distributionPath=wrapper/dists
 zipStorePath=wrapper/dists
 zipStoreBase=GRADLE_USER_HOME

ggml-cuda.cu

@@ -4086,7 +4086,8 @@ static __global__ void rope_neox_f32(const float * x, float * dst, const int nco
     dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
 }
 
-static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p, const float block_p, const float theta_scale) {
+static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p0,
+                                    const float p_delta, const int p_delta_rows, const float theta_scale, const int n_ctx) {
     const int col = blockDim.x*blockIdx.x + threadIdx.x;
     const int half_n_dims = ncols/4;
 
@@ -4098,8 +4099,9 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
     const int i = row*ncols + col;
 
     const float col_theta_scale = powf(theta_scale, col);
+    const float p = p0 + p_delta*(row/p_delta_rows);
 
-    const float theta = p*col_theta_scale;
+    const float theta = min(p, p_delta*(n_ctx - 2))*col_theta_scale;
     const float sin_theta = sinf(theta);
     const float cos_theta = cosf(theta);
 
@@ -4109,7 +4111,7 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
     dst[i + 0]           = x0*cos_theta - x1*sin_theta;
     dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
 
-    const float block_theta = block_p*col_theta_scale;
+    const float block_theta = max(p - p_delta*(n_ctx - 2), 0.f)*col_theta_scale;
     const float sin_block_theta = sinf(block_theta);
     const float cos_block_theta = cosf(block_theta);
 
@@ -4984,12 +4986,13 @@ static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, co
     rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
 }
 
-static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p, const float block_p, const float theta_scale, cudaStream_t stream) {
-    GGML_ASSERT(nrows % 4 == 0);
-    const dim3 block_dims(4*CUDA_ROPE_BLOCK_SIZE, 1, 1);
-    const int num_blocks_x = (ncols + 4*CUDA_ROPE_BLOCK_SIZE - 1) / (4*CUDA_ROPE_BLOCK_SIZE);
+static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
+                              const float p_delta, const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
+    GGML_ASSERT(ncols % 4 == 0);
+    const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
+    const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
     const dim3 block_nums(num_blocks_x, nrows, 1);
-    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p, block_p, theta_scale);
+    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale, n_ctx);
 }
 
 static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
@@ -5723,22 +5726,18 @@ inline void ggml_cuda_op_rope(
     memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
 
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
 
     // compute
     if (is_glm) {
-        const float p = (((mode & 1) == 0 ? n_past + i02 : i02)) * freq_scale;
-        const float id_p = min(p, n_ctx - 2.f);
-        const float block_p = max(p - (n_ctx - 2.f), 0.f);
-        rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, id_p, block_p, theta_scale, cudaStream_main);
+        rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, n_ctx, cudaStream_main);
     } else if (is_neox) {
         GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
-        const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
         rope_neox_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
     } else {
-        const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
         rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
     }
 
@@ -6400,10 +6399,7 @@ void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_ten
     GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
     GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
 
-    const int mode = ((int32_t *) dst->op_params)[2];
-    const bool is_glm = mode & 4;
-
-    ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm
+    ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, true);
 }
 
 void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {

ggml-metal.m

@@ -327,7 +327,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
 
 void * ggml_metal_host_malloc(size_t n) {
     void * data = NULL;
-    const int result = posix_memalign((void **) &data, getpagesize(), n);
+    const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
     if (result != 0) {
         metal_printf("%s: error: posix_memalign failed\n", __func__);
         return NULL;
@@ -401,7 +401,7 @@ bool ggml_metal_add_buffer(
             }
         }
 
-        const size_t size_page = getpagesize();
+        const size_t size_page = sysconf(_SC_PAGESIZE);
 
         size_t size_aligned = size;
         if ((size_aligned % size_page) != 0) {
@@ -1141,7 +1141,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&freq_base  length:sizeof(float) atIndex:21];
                             [encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];
 
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
                         } break;
                     case GGML_OP_DUP:
                     case GGML_OP_CPY:

ggml-metal.metal

@@ -220,14 +220,10 @@ kernel void kernel_norm(
         }
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
-    //// broadcast
-    //if (tpitg == 0) {
-    //    sum[0] /= ne00;
-    //}
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    const float mean  = sum[0];
+    const float mean  = sum[0] / ne00;
 
     // recenter and VARIANCE
+    threadgroup_barrier(mem_flags::mem_threadgroup);
     device float * y = dst + tgpig*ne00;
     sum[tpitg] = 0.0f;
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
@@ -235,12 +231,6 @@ kernel void kernel_norm(
         sum[tpitg] += y[i00] * y[i00];
     }
 
-    //// VARIANCE
-    //// parallel sum
-    //sum[tpitg] = 0.0f;
-    //for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-    //    sum[tpitg] += y[i00] * y[i00];
-    //}
     // reduce
     threadgroup_barrier(mem_flags::mem_threadgroup);
     for (uint i = ntg/2; i > 0; i /= 2) {
@@ -249,12 +239,7 @@ kernel void kernel_norm(
         }
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
-    //// broadcast
-    //if (tpitg == 0) {
-    //    sum[0] /= ne00;
-    //}
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    const float variance = sum[0];
+    const float variance = sum[0] / ne00;
 
     const float scale = 1.0f/sqrt(variance + eps);
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
@@ -262,7 +247,6 @@ kernel void kernel_norm(
     }
 }
 
-
 kernel void kernel_rms_norm(
         device const  void * src0,
         device       float * dst,
@@ -630,7 +614,6 @@ kernel void kernel_mul_mat_f16_f32(
             }
         }
     }
-
 }
 
 kernel void kernel_alibi_f32(
@@ -699,25 +682,27 @@ kernel void kernel_rope(
         constant       int & mode,
         constant     float & freq_base,
         constant     float & freq_scale,
-        uint3 tpig[[thread_position_in_grid]]) {
-    const int64_t i3 = tpig[2];
-    const int64_t i2 = tpig[1];
-    const int64_t i1 = tpig[0];
+        uint  tiitg[[thread_index_in_threadgroup]],
+        uint3 tptg[[threads_per_threadgroup]],
+        uint3 tgpig[[threadgroup_position_in_grid]]) {
+    const int64_t i3 = tgpig[2];
+    const int64_t i2 = tgpig[1];
+    const int64_t i1 = tgpig[0];
 
     const bool is_neox = mode & 2;
-    const float theta_scale = pow(freq_base, -2.0f/n_dims);
 
     const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
 
-    float theta = freq_scale * (float)p;
+    const float theta_0 = freq_scale * (float)p;
+    const float inv_ndims = -1.f/n_dims;
 
     if (!is_neox) {
-        for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+        for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
+
+            const float theta = theta_0 * pow(freq_base, inv_ndims*i0);
             const float cos_theta = cos(theta);
             const float sin_theta = sin(theta);
 
-            theta *= theta_scale;
-
             device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
             device       float * dst_data  = (device float *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
@@ -729,12 +714,12 @@ kernel void kernel_rope(
         }
     } else {
         for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
-            for (int64_t ic = 0; ic < n_dims; ic += 2) {
+            for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) {
+
+                const float theta = theta_0 * pow(freq_base, inv_ndims*ic - ib);
                 const float cos_theta = cos(theta);
                 const float sin_theta = sin(theta);
 
-                theta *= theta_scale;
-
                 const int64_t i0 = ib*n_dims + ic/2;
 
                 device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);

ggml.c

@@ -1,4 +1,3 @@
-#define _GNU_SOURCE // Defines CLOCK_MONOTONIC on Linux
 #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
 
 #include "ggml.h"
@@ -107,6 +106,9 @@ typedef void * thread_ret_t;
 #include <sys/stat.h>
 #include <unistd.h>
 
+#endif
+#ifdef GGML_USE_CPU_HBM
+#include <hbwmalloc.h>
 #endif
 
 // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
@@ -196,9 +198,15 @@ typedef void * thread_ret_t;
 #define GGML_ALIGNED_FREE(ptr)    _aligned_free(ptr)
 #else
 inline static void * ggml_aligned_malloc(size_t size) {
+    if (size == 0) {
+        GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n");
+        return NULL;
+    }
     void * aligned_memory = NULL;
-#ifdef GGML_USE_METAL
-    int result = posix_memalign(&aligned_memory, getpagesize(), size);
+#ifdef GGML_USE_CPU_HBM
+    int result = hbw_posix_memalign(&aligned_memory, 16, size);
+#elif GGML_USE_METAL
+    int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size);
 #else
     int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
 #endif
@@ -219,8 +227,12 @@ inline static void * ggml_aligned_malloc(size_t size) {
     return aligned_memory;
 }
 #define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size)
+#ifdef GGML_USE_CPU_HBM
+#define GGML_ALIGNED_FREE(ptr)    if(NULL != ptr) hbw_free(ptr)
+#else
 #define GGML_ALIGNED_FREE(ptr)    free(ptr)
 #endif
+#endif
 
 #define UNUSED GGML_UNUSED
 #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
@@ -4572,6 +4584,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
         return NULL;
     }
 
+    // allow to call ggml_init with 0 size
+    if (params.mem_size == 0) {
+        params.mem_size = GGML_MEM_ALIGN;
+    }
+
     const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN);
 
     *ctx = (struct ggml_context) {
@@ -4774,7 +4791,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
 
     size_t obj_alloc_size = 0;
 
-    if (view_src == NULL && ctx->no_alloc == false) {
+    if (view_src == NULL && !ctx->no_alloc) {
         if (ctx->scratch.data != NULL) {
             // allocate tensor data in the scratch buffer
             if (ctx->scratch.offs + data_size > ctx->scratch.size) {
@@ -5475,7 +5492,7 @@ static struct ggml_tensor * ggml_mul_impl(
     }
 
     if (inplace) {
-        GGML_ASSERT(is_node == false);
+        GGML_ASSERT(!is_node);
     }
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
@@ -5518,7 +5535,7 @@ static struct ggml_tensor * ggml_div_impl(
     }
 
     if (inplace) {
-        GGML_ASSERT(is_node == false);
+        GGML_ASSERT(!is_node);
     }
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
@@ -17266,10 +17283,18 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         } else {
             // wait for other threads to finish
             const int last = node_n;
-            do {
-                //sched_yield();
+            while (true) {
+                // TODO: this sched_yield can have significant impact on the performance - either positive or negative
+                //       depending on the workload and the operating system.
+                //       since it is not clear what is the best approach, it should potentially become user-configurable
+                //       ref: https://github.com/ggerganov/ggml/issues/291
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+                sched_yield();
+#endif
+
                 node_n = atomic_load(&state->shared->node_n);
-            } while (node_n == last);
+                if (node_n != last) break;
+            };
         }
 
         // check if we should stop
@@ -19962,7 +19987,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
         struct ggml_tensor * data = NULL;
 
-        if (params.no_alloc == false) {
+        if (!params.no_alloc) {
             data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);
 
             ok = ok && data != NULL;
@@ -20003,7 +20028,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             }
 
             // point the data member to the appropriate location in the binary blob using the tensor infos
-            if (params.no_alloc == false) {
+            if (!params.no_alloc) {
               //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
                 cur->data = (char *) data->data + ctx->infos[i].offset;               // offset from data
             }

models/download-ggml-model.sh

@@ -22,7 +22,28 @@ function get_script_path() {
 models_path="$(get_script_path)"
 
 # Whisper models
-models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small.en-tdrz" "small" "medium.en" "medium" "large-v1" "large" )
+models=( 
+    "tiny.en"
+    "tiny"
+    "tiny-q5_1"
+    "tiny.en-q5_1"
+    "base.en"
+    "base"
+    "base-q5_1"
+    "base.en-q5_1"
+    "small.en"
+    "small.en-tdrz"
+    "small"
+    "small-q5_1"
+    "small.en-q5_1"
+    "medium"
+    "medium.en"
+    "medium-q5_0"
+    "medium.en-q5_0"
+    "large-v1"
+    "large"
+    "large-q5_0"
+)
 
 # list available models
 function list_models {

whisper.cpp

@@ -18,6 +18,7 @@
 #include <cstring>
 #include <fstream>
 #include <map>
+#include <set>
 #include <string>
 #include <thread>
 #include <vector>
@@ -117,6 +118,21 @@ static void byteswap_tensor(ggml_tensor * tensor) {
 #define WHISPER_USE_SCRATCH
 #define WHISPER_MAX_SCRATCH_BUFFERS 16
 
+//
+// ggml helpers
+//
+
+static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
+    struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
+
+    if (plan.work_size > 0) {
+        buf.resize(plan.work_size);
+        plan.work_data = buf.data();
+    }
+
+    ggml_graph_compute(graph, &plan);
+}
+
 // available whisper models
 enum e_model {
     MODEL_UNKNOWN,
@@ -537,6 +553,7 @@ struct whisper_kv_cache {
 
     struct ggml_context * ctx;
 
+    // buf points to the memory allocated for both ggml_tensor 'k' and 'v' (see kv_cache_init)
     std::vector<uint8_t> buf;
 
     int n; // number of tokens currently in the cache
@@ -602,7 +619,7 @@ struct whisper_sequence {
 
 // TAGS: WHISPER_DECODER_INIT
 struct whisper_decoder {
-    // each decoders keeps its own KV-cache
+    // each decoder keeps its own KV-cache
     whisper_kv_cache kv_self;
 
     // the currently generated sequence of tokens
@@ -622,6 +639,24 @@ struct whisper_decoder {
     std::vector<whisper_token> tokens_tmp; // used for whisper_decode calls
 };
 
+// replace std::pair by using customized pair struct (reason: std::pair is very slow)
+template<typename A, typename B>
+struct whisper_pair {
+    A first;
+    B second;
+
+    // Define a constructor that takes two arguments.
+    whisper_pair(const A& a, const B& b) : first(a), second(b) {}
+    // Define a constructor that takes no argument.
+    whisper_pair() : first(A()), second(B()) {}
+};
+
+// beam-search helpers
+struct kv_buf {
+    std::vector<uint8_t> k;
+    std::vector<uint8_t> v;
+};
+
 struct whisper_state {
     int64_t t_sample_us = 0;
     int64_t t_encode_us = 0;
@@ -641,8 +676,12 @@ struct whisper_state {
 
     whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
 
+    // buffer for swapping KV caches between decoders during beam-search
+    std::vector<kv_buf> kv_swap_bufs;
+
     // memory buffers used by encode / decode contexts
     std::vector<uint8_t> buf_compute;
+    std::vector<uint8_t> buf_work;
     std::vector<uint8_t> buf_scratch[WHISPER_MAX_SCRATCH_BUFFERS];
 
     int    buf_last = 0;
@@ -655,7 +694,7 @@ struct whisper_state {
     std::vector<whisper_token>   prompt_past;
 
     // work container used to avoid memory allocations
-    std::vector<std::pair<double, whisper_vocab::id>> logits_id;
+    std::vector<whisper_pair<double, whisper_vocab::id>> logits_id;
 
     mutable std::mt19937 rng; // used for sampling at t > 0.0
 
@@ -1807,8 +1846,8 @@ static bool whisper_encode_internal(
         {
             struct ggml_cgraph gf = {};
 
-            ggml_build_forward_expand  (&gf, cur);
-            ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
+            ggml_build_forward_expand(&gf, cur);
+            ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
 
             //ggml_graph_print(&gf);
         }
@@ -1893,7 +1932,7 @@ static bool whisper_encode_internal(
             ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcross, v));
         }
 
-        ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
+        ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
         //ggml_graph_print(&gf);
     }
 
@@ -2306,8 +2345,8 @@ static bool whisper_decode_internal(
 
     // run the computation
     {
-        ggml_build_forward_expand  (&gf, logits);
-        ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
+        ggml_build_forward_expand(&gf, logits);
+        ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
     }
 
     // extract logits for all N tokens
@@ -3975,17 +4014,21 @@ static std::vector<whisper_token_data> whisper_sample_token_topk(
 
     auto & logits_id = state.logits_id;
 
-    logits_id.clear();
+    logits_id.resize(n_logits);
     for (int i = 0; i < n_logits; ++i) {
-        logits_id.push_back({ logits[i], i });
+        logits_id[i].first = logits[i];
+        logits_id[i].second = i;
     }
 
-    std::partial_sort(
-            logits_id.begin(),
-            logits_id.begin() + k, logits_id.end(),
-            [](const std::pair<double, whisper_token> & a, const std::pair<double, whisper_token> & b) {
-                return a.first > b.first;
-            });
+    {
+        using pair_type = std::remove_reference<decltype(logits_id)>::type::value_type;
+        std::partial_sort(
+                logits_id.begin(),
+                logits_id.begin() + k, logits_id.end(),
+                [](const pair_type & a, const pair_type & b) {
+            return a.first > b.first;
+        });
+    }
 
     std::vector<whisper_token_data> result;
     result.reserve(k);
@@ -4080,6 +4123,115 @@ static void whisper_sequence_score(
     }
 }
 
+static bool whisper_kv_swap_fast(
+                   std::vector<int> & view,
+                    whisper_decoder   src[],
+                std::vector<kv_buf> & kv_swap_bufs,
+                          const int & n_decoders) {
+    WHISPER_PRINT_DEBUG("%s: n_decoders %d\n", __func__, n_decoders);
+
+    // (decoder->buffer->decoder or decoder->buffer + decoder->decoder)
+    std::set<int> two_copy; // decoder indices require two copies to safely modify KV caches
+
+    // (buffer->decoder or decoder->decoder)
+    std::set<int> one_copy; // decoder indices require one copy to safely modify KV caches
+
+    // (decoder<->decoder)
+    std::set<int> p_swap_set; // decoder indices able to swap KV-cache pointers
+    std::vector<whisper_pair<int, int>> p_swap_vec;
+    p_swap_vec.reserve(n_decoders);
+
+    // see https://github.com/ggerganov/whisper.cpp/wiki
+    for (int i = 0; i < n_decoders; i++) {
+        // zero-copy (no modification)
+        if (i == view[i] || view[i] < 0) {
+            continue;
+        }
+
+        bool is_one_copy = true;
+        // since we modify data sequentially, we only consider decoder indices after current index
+        for (int j = i + 1; j < n_decoders; j++) {
+            if (i == view[j]) {
+                // detect symmetric diagram
+                if (j == view[i]) {
+                    p_swap_set.insert(i);
+                    p_swap_set.insert(j);
+                    p_swap_vec.emplace_back(i, j);
+                } else {
+                    two_copy.insert(i);
+                    is_one_copy = false;
+                }
+                break;
+            }
+        }
+        if (is_one_copy) {
+            one_copy.insert(i);
+        }
+    }
+
+    kv_swap_bufs.resize(n_decoders);
+
+    for (int i = 0; i < n_decoders; i++) {
+        kv_swap_bufs[i].k.resize(ggml_nbytes(src[i].kv_self.k));
+        kv_swap_bufs[i].v.resize(ggml_nbytes(src[i].kv_self.v));
+    }
+
+    for (auto & i : two_copy) {
+        // make a copy of KV caches
+        WHISPER_PRINT_DEBUG("%s: store KV cache into swap: idx %d\n", __func__, i);
+        memcpy(kv_swap_bufs[i].k.data(), src[i].kv_self.k->data, kv_swap_bufs[i].k.size());
+        memcpy(kv_swap_bufs[i].v.data(), src[i].kv_self.v->data, kv_swap_bufs[i].v.size());
+    }
+
+    // since two-copy decoder KV caches are protected by kv_swap_bufs, modify them first
+    for (auto & i : two_copy) {
+        // skip the decoder indices that require pointer swapping
+        if (p_swap_set.find(i) != p_swap_set.end()) {
+            continue;
+        }
+
+        if (two_copy.find(view[i]) != two_copy.end()) {
+            // modify KV caches of decoder using data from kv_swap_bufs
+            WHISPER_PRINT_DEBUG("%s: two-copy decoder using   swap buffers: swap[%d] -> %d\n", __func__, view[i], i);
+            memcpy(src[i].kv_self.k->data, kv_swap_bufs[view[i]].k.data(), kv_swap_bufs[view[i]].k.size());
+            memcpy(src[i].kv_self.v->data, kv_swap_bufs[view[i]].v.data(), kv_swap_bufs[view[i]].v.size());
+        } else {
+            // modify KV caches of decoder using data from correspond decoder KV caches directly
+            WHISPER_PRINT_DEBUG("%s: two-copy decoder without swap buffers:      %d  -> %d\n", __func__, view[i], i);
+            memcpy(src[i].kv_self.k->data, src[view[i]].kv_self.k->data, ggml_nbytes(src[view[i]].kv_self.k));
+            memcpy(src[i].kv_self.v->data, src[view[i]].kv_self.v->data, ggml_nbytes(src[view[i]].kv_self.v));
+        }
+    }
+
+    // then modify one-copy decoder KV caches
+    for (auto & i : one_copy) {
+        // skip the decoder indices that require pointer swapping
+        if (p_swap_set.find(i) != p_swap_set.end()) {
+            continue;
+        }
+
+        if (two_copy.find(view[i]) != two_copy.end()) {
+            // modify KV caches of decoder using data from kv_swap_bufs
+            WHISPER_PRINT_DEBUG("%s: one-copy decoder using   swap buffers: swap[%d] -> %d\n", __func__, view[i], i);
+            memcpy(src[i].kv_self.k->data, kv_swap_bufs[view[i]].k.data(), kv_swap_bufs[view[i]].k.size());
+            memcpy(src[i].kv_self.v->data, kv_swap_bufs[view[i]].v.data(), kv_swap_bufs[view[i]].v.size());
+        } else {
+            // modify KV caches of decoder using data from correspond decoder KV caches directly
+            WHISPER_PRINT_DEBUG("%s: one-copy decoder without swap buffers:      %d  -> %d\n", __func__, view[i], i);
+            memcpy(src[i].kv_self.k->data, src[view[i]].kv_self.k->data, ggml_nbytes(src[view[i]].kv_self.k));
+            memcpy(src[i].kv_self.v->data, src[view[i]].kv_self.v->data, ggml_nbytes(src[view[i]].kv_self.v));
+        }
+    }
+
+    // swap the pointers
+    for (auto & i : p_swap_vec) {
+        WHISPER_PRINT_DEBUG("%s: swap pointers: %d <-> %d\n", __func__, i.first, i.second);
+        std::swap(src[i.first].kv_self, src[i.second].kv_self);
+    }
+
+    return true;
+}
+
 int whisper_full_with_state(
         struct whisper_context * ctx,
           struct whisper_state * state,
@@ -4243,14 +4395,6 @@ int whisper_full_with_state(
     std::vector<whisper_token> prompt;
     prompt.reserve(whisper_n_text_ctx(ctx));
 
-    // beam-search helpers
-    struct kv_buf {
-        std::vector<uint8_t> k;
-        std::vector<uint8_t> v;
-    };
-
-    std::vector<kv_buf> kv_bufs;
-
     struct beam_candidate {
         int decoder_idx;
         int seek_delta;
@@ -4399,23 +4543,7 @@ int whisper_full_with_state(
             for (int i = 0, n_max = whisper_n_text_ctx(ctx)/2 - 4; i < n_max; ++i) {
                 const int64_t t_start_sample_us = ggml_time_us();
 
-                // store the KV caches of all decoders when doing beam-search
                 if (params.strategy == whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH) {
-                    kv_bufs.resize(n_decoders_cur);
-                    for (int j = 0; j < n_decoders_cur; ++j) {
-                        auto & decoder = state->decoders[j];
-
-                        if (decoder.completed || decoder.failed) {
-                            continue;
-                        }
-
-                        kv_bufs[j].k.resize(ggml_nbytes(decoder.kv_self.k));
-                        kv_bufs[j].v.resize(ggml_nbytes(decoder.kv_self.v));
-
-                        memcpy(kv_bufs[j].k.data(), decoder.kv_self.k->data, kv_bufs[j].k.size());
-                        memcpy(kv_bufs[j].v.data(), decoder.kv_self.v->data, kv_bufs[j].v.size());
-                    }
-
                     beam_candidates.clear();
                 }
 
@@ -4463,6 +4591,7 @@ int whisper_full_with_state(
                     });
 
                     uint32_t cur_c = 0;
+                    std::vector<int> decoder_idx(n_decoders_cur, -1);
 
                     for (int j = 0; j < n_decoders_cur; ++j) {
                         auto & decoder = state->decoders[j];
@@ -4481,12 +4610,13 @@ int whisper_full_with_state(
                         decoder.seek_delta = cur.seek_delta;
                         decoder.has_ts     = cur.has_ts;
 
-                        memcpy(decoder.kv_self.k->data, kv_bufs[cur.decoder_idx].k.data(), kv_bufs[cur.decoder_idx].k.size());
-                        memcpy(decoder.kv_self.v->data, kv_bufs[cur.decoder_idx].v.data(), kv_bufs[cur.decoder_idx].v.size());
-
+                        decoder_idx[j] = cur.decoder_idx;
                         WHISPER_PRINT_DEBUG("%s: beam search: decoder %d: from decoder %d: token = %10s, plog = %8.5f, sum_logprobs = %8.5f\n",
                                 __func__, j, cur.decoder_idx, ctx->vocab.id_to_token.at(decoder.sequence.tokens.back().id).c_str(), decoder.sequence.tokens.back().plog, decoder.sequence.sum_logprobs_all);
                     }
+
+                    // update KV caches
+                    whisper_kv_swap_fast(decoder_idx, state->decoders, state->kv_swap_bufs, n_decoders_cur);
                 }
 
                 // update the decoder state
@@ -5111,7 +5241,8 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
     // b: N*N*sizeof(float)
     // c: N*N*sizeof(float)
     // when F16 is used, there is an extra work buffer of size N*N*sizeof(float)
-    std::vector<char> buf(4llu*N_max*N_max*sizeof(float) + 4*512);
+    std::vector<uint8_t> buf (3llu*N_max*N_max*sizeof(float) + 3*ggml_tensor_overhead());
+    std::vector<uint8_t> work(1llu*N_max*N_max*sizeof(float) + 1*ggml_tensor_overhead());
 
     // put a bunch of random data in the buffer
     for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
@@ -5166,12 +5297,12 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
             double tsum = 0.0;
 
             // heat-up
-            ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
+            ggml_graph_compute_helper(work, &gf, n_threads);
 
             for (int i = 0; i < n_max; ++i) {
                 const int64_t t0 = ggml_time_us();
 
-                ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
+                ggml_graph_compute_helper(work, &gf, n_threads);
 
                 const int64_t t1 = ggml_time_us();