bench : fix Windows linkage by moving ggml benches in whisper lib ..

CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required (VERSION 3.0)
 
-project(whisper.cpp VERSION 1.0.4)
+project(whisper.cpp VERSION 1.1.0)
 
 # Add path to modules
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")

Makefile

@@ -133,8 +133,8 @@ ifdef WHISPER_OPENBLAS
 	LDFLAGS += -lopenblas
 endif
 ifdef WHISPER_GPROF
-	CFLAGS  += -pg
-	CXXFLAGS  += -pg
+	CFLAGS   += -pg
+	CXXFLAGS += -pg
 endif
 ifneq ($(filter aarch64%,$(UNAME_M)),)
 endif

README.md

@@ -4,7 +4,7 @@
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 [![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/)
 
-[Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
+Stable: [v1.0.4](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.0.4) / Beta: [v1.1.0](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.1.0) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
 
 High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
 

bindings/go/whisper.go

@@ -147,16 +147,6 @@ func (ctx *Context) Whisper_decode(tokens []Token, past, threads int) error {
 	}
 }
 
-// whisper_sample_best() returns the token with the highest probability
-func (ctx *Context) Whisper_sample_best() TokenData {
-	return TokenData(C.whisper_sample_best((*C.struct_whisper_context)(ctx)))
-}
-
-// whisper_sample_timestamp() returns the most probable timestamp token
-func (ctx *Context) Whisper_sample_timestamp(is_initial bool) TokenData {
-	return TokenData(C.whisper_sample_timestamp((*C.struct_whisper_context)(ctx), C.bool(is_initial)))
-}
-
 // Convert the provided text into tokens. The tokens pointer must be large enough to hold the resulting tokens.
 // Returns the number of tokens on success
 func (ctx *Context) Whisper_tokenize(text string, tokens []Token) (int, error) {

bindings/javascript/package.json

@@ -1,6 +1,6 @@
 {
   "name": "whisper.cpp",
-  "version": "1.0.4",
+  "version": "1.1.0",
   "description": "Whisper speech recognition",
   "main": "whisper.js",
   "scripts": {

examples/bench/bench.cpp

@@ -7,6 +7,7 @@
 // command-line parameters
 struct whisper_params {
     int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t what = 0; // what to benchmark: 0 - whisper ecoder, 1 - memcpy, 2 - ggml_mul_mat
 
     std::string model = "models/ggml-base.en.bin";
 };
@@ -23,6 +24,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         }
         else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
         else if (arg == "-m" || arg == "--model")   { params.model     = argv[++i]; }
+        else if (arg == "-w" || arg == "--what")    { params.what     = atoi(argv[++i]); }
         else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             whisper_print_usage(argc, argv, params);
@@ -41,16 +43,14 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -h,       --help        [default] show this help message and exit\n");
     fprintf(stderr, "  -t N,     --threads N   [%-7d] number of threads to use during computation\n", params.n_threads);
     fprintf(stderr, "  -m FNAME, --model FNAME [%-7s] model path\n",                                  params.model.c_str());
+    fprintf(stderr, "  -w N,     --what N      [%-7d] what to benchmark:\n",                          params.what);
+    fprintf(stderr, "                           %-7s  0 - whisper encoder\n",                         "");
+    fprintf(stderr, "                           %-7s  1 - memcpy\n",                                  "");
+    fprintf(stderr, "                           %-7s  2 - ggml_mul_mat\n",                            "");
     fprintf(stderr, "\n");
 }
 
-int main(int argc, char ** argv) {
-    whisper_params params;
-
-    if (whisper_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
+int whisper_bench_encoder(const whisper_params & params) {
     // whisper init
 
     struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
@@ -92,3 +92,22 @@ int main(int argc, char ** argv) {
 
     return 0;
 }
+
+int main(int argc, char ** argv) {
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    int ret = -1;
+
+    switch (params.what) {
+        case 0: ret = whisper_bench_encoder(params);                break;
+        case 1: ret = whisper_bench_memcpy(params.n_threads);       break;
+        case 2: ret = whisper_bench_ggml_mul_mat(params.n_threads); break;
+        default: fprintf(stderr, "error: unknown benchmark: %d\n", params.what); break;
+    }
+
+    return ret;
+}

examples/stream/stream.cpp

@@ -434,9 +434,9 @@ int main(int argc, char ** argv) {
 
     const int n_new_line = !use_vad ? params.length_ms / params.step_ms - 1 : 1; // number of steps to print new line
 
-    params.no_timestamps = !use_vad;
-    params.no_context    = use_vad;
-    params.max_tokens    = 0;
+    params.no_timestamps  = !use_vad;
+    params.no_context    |= use_vad;
+    params.max_tokens     = 0;
 
     // init audio
 
@@ -486,7 +486,7 @@ int main(int argc, char ** argv) {
                 params.no_timestamps ? 0 : 1);
 
         if (!use_vad) {
-            fprintf(stderr, "%s: n_new_line = %d\n", __func__, n_new_line);
+            fprintf(stderr, "%s: n_new_line = %d, no_context = %d\n", __func__, n_new_line, params.no_context);
         } else {
             fprintf(stderr, "%s: using VAD, will transcribe on speech activity\n", __func__);
         }

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

@@ -73,8 +73,7 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
         printMessage("Loading model...\n")
         val models = application.assets.list("models/")
         if (models != null) {
-            val inputstream = application.assets.open("models/" + models[0])
-            whisperContext = WhisperContext.createContextFromInputStream(inputstream)
+            whisperContext = WhisperContext.createContextFromAsset(application.assets, "models/" + models[0])
             printMessage("Loaded model ${models[0]}.\n")
         }
 

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

@@ -1,5 +1,6 @@
 package com.whispercppdemo.whisper
 
+import android.content.res.AssetManager
 import android.os.Build
 import android.util.Log
 import kotlinx.coroutines.*
@@ -56,6 +57,15 @@ class WhisperContext private constructor(private var ptr: Long) {
             }
             return WhisperContext(ptr)
         }
+
+        fun createContextFromAsset(assetManager: AssetManager, assetPath: String): WhisperContext {
+            val ptr = WhisperLib.initContextFromAsset(assetManager, assetPath)
+
+            if (ptr == 0L) {
+                throw java.lang.RuntimeException("Couldn't create context from asset $assetPath")
+            }
+            return WhisperContext(ptr)
+        }
     }
 }
 
@@ -87,6 +97,7 @@ private class WhisperLib {
 
         // JNI methods
         external fun initContextFromInputStream(inputStream: InputStream): Long
+        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)

examples/whisper.android/app/src/main/jni/whisper/Whisper.mk

@@ -1,5 +1,5 @@
 WHISPER_LIB_DIR := $(LOCAL_PATH)/../../../../../../../
-LOCAL_LDLIBS    := -llog
+LOCAL_LDLIBS    := -landroid -llog
 
 # Make the final output library smaller by only keeping the symbols referenced from the app.
 ifneq ($(APP_OPTIM),debug)

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

@@ -1,4 +1,6 @@
 #include <jni.h>
+#include <android/asset_manager.h>
+#include <android/asset_manager_jni.h>
 #include <android/log.h>
 #include <stdlib.h>
 #include <sys/sysinfo.h>
@@ -9,6 +11,7 @@
 #define TAG "JNI"
 
 #define LOGI(...) __android_log_print(ANDROID_LOG_INFO,     TAG, __VA_ARGS__)
+#define LOGW(...) __android_log_print(ANDROID_LOG_WARN,     TAG, __VA_ARGS__)
 
 static inline int min(int a, int b) {
     return (a < b) ? a : b;
@@ -91,6 +94,52 @@ Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_initContextFromInputSt
     return (jlong) context;
 }
 
+static size_t asset_read(void *ctx, void *output, size_t read_size) {
+    return AAsset_read((AAsset *) ctx, output, read_size);
+}
+
+static bool asset_is_eof(void *ctx) {
+    return AAsset_getRemainingLength64((AAsset *) ctx) <= 0;
+}
+
+static void asset_close(void *ctx) {
+    AAsset_close((AAsset *) ctx);
+}
+
+static struct whisper_context *whisper_init_from_asset(
+        JNIEnv *env,
+        jobject assetManager,
+        const char *asset_path
+) {
+    LOGI("Loading model from asset '%s'\n", asset_path);
+    AAssetManager *asset_manager = AAssetManager_fromJava(env, assetManager);
+    AAsset *asset = AAssetManager_open(asset_manager, asset_path, AASSET_MODE_STREAMING);
+    if (!asset) {
+        LOGW("Failed to open '%s'\n", asset_path);
+        return NULL;
+    }
+
+    whisper_model_loader loader = {
+            .context = asset,
+            .read = &asset_read,
+            .eof = &asset_is_eof,
+            .close = &asset_close
+    };
+
+    return whisper_init(&loader);
+}
+
+JNIEXPORT jlong JNICALL
+Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_initContextFromAsset(
+        JNIEnv *env, jobject thiz, jobject assetManager, jstring asset_path_str) {
+    UNUSED(thiz);
+    struct whisper_context *context = NULL;
+    const char *asset_path_chars = (*env)->GetStringUTFChars(env, asset_path_str, NULL);
+    context = whisper_init_from_asset(env, assetManager, asset_path_chars);
+    (*env)->ReleaseStringUTFChars(env, asset_path_str, asset_path_chars);
+    return (jlong) context;
+}
+
 JNIEXPORT jlong JNICALL
 Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_initContext(
         JNIEnv *env, jobject thiz, jstring model_path_str) {

examples/whisper.swiftui/README.md

@@ -10,3 +10,5 @@ To use:
 5. Select the "release" build configuration under "Run", then deploy and run to your device.
 
 [^1]: I recommend the tiny, base or small models for running on an iOS device.
+
+![image](https://user-images.githubusercontent.com/1991296/212539216-0aef65e4-f882-480a-8358-0f816838fd52.png)

extra/bench-all.sh

@@ -12,6 +12,18 @@ fi
 
 models=( "tiny" "base" "small" "medium" "large" )
 
+printf "\n"
+printf "Running memcpy benchmark with 1 thread\n"
+printf "\n"
+
+./bench -w 1 -t 1 2>&1
+
+printf "\n"
+printf "Running ggml_mul_mat benchmark with " $n_threads " threads\n"
+printf "\n"
+
+./bench -w 2 -t $n_threads 2>&1
+
 printf "\n"
 printf "Running benchmark for all models\n"
 printf "This can take a while!\n"
@@ -56,4 +68,3 @@ for model in "${models[@]}"; do
 
     printf "| <todo> | <todo> | $config | $model | $n_threads | $load_time | $encode_time | $commit |\n"
 done
-

ggml.c

@@ -4373,7 +4373,9 @@ static void ggml_compute_forward_mul_mat_f32(
     if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
         GGML_ASSERT(nb10 == sizeof(float));
 
-        if (params->ith != 0) return;
+        if (params->ith != 0) {
+            return;
+        }
 
         if (params->type == GGML_TASK_INIT) {
             return;
@@ -4616,7 +4618,9 @@ static void ggml_compute_forward_mul_mat_f16_f32(
     if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
         GGML_ASSERT(nb10 == sizeof(float));
 
-        if (params->ith != 0) return;
+        if (params->ith != 0) {
+            return;
+        }
 
         if (params->type == GGML_TASK_INIT) {
             return;
@@ -7054,7 +7058,7 @@ struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cg
 #ifdef __APPLE__
 
 //#include <os/lock.h>
-
+//
 //typedef os_unfair_lock ggml_lock_t;
 //
 //#define ggml_lock_init(x)    UNUSED(x)
@@ -7161,6 +7165,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             if (state->params.ith < state->params.nth) {
                 ggml_compute_forward(&state->params, state->node);
             }
+
             state->node = NULL;
         } else {
             break;
@@ -7205,6 +7210,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                 .node   = NULL,
                 .shared = &state_shared,
             };
+
             int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
             assert(rc == 0);
             UNUSED(rc);
@@ -7273,7 +7279,8 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                                 node->src1->type == GGML_TYPE_F32) {
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
                                 if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
-                                    node->n_tasks = 1;
+                                    node->n_tasks = 1; // TODO: this actually is doing nothing
+                                                       //       the threads are still spinning
                                     cur = sizeof(float)*(node->src0->ne[0]*node->src0->ne[1]);
                                 } else {
                                     cur = sizeof(ggml_fp16_t)*ggml_nelements(node->src1);

whisper.cpp

@@ -3091,10 +3091,10 @@ static std::vector<whisper_token_data> whisper_sample_token_topk(
     std::vector<whisper_token_data> result;
     result.reserve(k);
 
-    whisper_token tid;
+    whisper_token tid = vocab.token_beg;
 
-    float pt;
-    float ptsum;
+    float pt    = 0.0;
+    float ptsum = 0.0;
 
     {
         double sum_ts = 0.0;
@@ -3801,6 +3801,7 @@ int whisper_full(
 
                     if (tokens_cur[i].id > whisper_token_beg(ctx) && !params.single_segment) {
                         const auto t1 = seek + 2*(tokens_cur[i].tid - whisper_token_beg(ctx));
+
                         if (!text.empty()) {
                             const auto tt0 = params.speed_up ? 2*t0 : t0;
                             const auto tt1 = params.speed_up ? 2*t1 : t1;
@@ -4059,6 +4060,145 @@ float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int
 
 // =================================================================================================
 
+//
+// Temporary interface needed for exposing ggml interface
+// Will be removed in the future when ggml becomes a separate library
+//
+
+WHISPER_API int whisper_bench_memcpy(int n_threads) {
+    ggml_time_init();
+
+    size_t n    = 50;
+    size_t arr  = n_threads > 0 ? 1024 : n_threads; // trick to avoid compiler optimizations
+
+    // 1 GB array
+    const size_t size = arr*1024llu*1024llu;
+
+    char * src = (char *) malloc(size);
+    char * dst = (char *) malloc(size);
+
+    for (size_t i = 0; i < size; i++) src[i] = i;
+
+    memcpy(dst, src, size); // heat-up
+
+    double tsum = 0.0;
+
+    for (size_t i = 0; i < n; i++) {
+        const int64_t t0 = ggml_time_us();
+
+        memcpy(dst, src, size);
+
+        const int64_t t1 = ggml_time_us();
+
+        tsum += (t1 - t0)*1e-6;
+
+        src[0] = rand();
+    }
+
+    fprintf(stderr, "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
+
+    // needed to prevent the compile from optimizing the memcpy away
+    {
+        double sum = 0.0;
+
+        for (size_t i = 0; i < size; i++) sum += dst[i];
+
+        fprintf(stderr, "sum:    %s\n", sum == -536870910.00 ? "ok" : "error");
+    }
+
+    free(src);
+    free(dst);
+
+    return 0;
+}
+
+WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads) {
+    ggml_time_init();
+
+    const int n_max = 128;
+
+    const std::vector<size_t> sizes = {
+        64, 128, 256, 512, 1024, 2048, 4096,
+    };
+
+    const size_t N_max = sizes.back();
+
+    // a: N*N*sizeof(float)
+    // 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*256);
+
+    for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
+
+    for (int j = 0; j < (int) sizes.size(); j++) {
+        int n_fp16 = 0;
+        int n_fp32 = 0;
+
+        // GFLOPS/s
+        double s_fp16 = 0.0;
+        double s_fp32 = 0.0;
+
+        const size_t N = sizes[j];
+
+        for (int k = 0; k < 2; ++k) {
+            const ggml_type wtype = k == 0 ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+            double & s = k == 0 ? s_fp16 : s_fp32;
+            int    & n = k == 0 ? n_fp16   : n_fp32;
+
+            struct ggml_init_params gparams = {
+                /*.mem_size   =*/ buf.size(),
+                /*.mem_buffer =*/ buf.data(),
+            };
+
+            struct ggml_context * ctx0 = ggml_init(gparams);
+
+            struct ggml_tensor * a = ggml_new_tensor_2d(ctx0, wtype,         N, N);
+            struct ggml_tensor * b = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, N);
+
+            struct ggml_tensor * c = ggml_mul_mat(ctx0, a, b);
+
+            struct ggml_cgraph gf = ggml_build_forward(c);
+
+            gf.n_threads = n_threads;
+
+            double tsum = 0.0;
+
+            // heat-up
+            ggml_graph_compute(ctx0, &gf);
+
+            for (int i = 0; i < n_max; ++i) {
+                const int64_t t0 = ggml_time_us();
+
+                ggml_graph_compute(ctx0, &gf);
+
+                const int64_t t1 = ggml_time_us();
+
+                tsum += (t1 - t0)*1e-6;
+                n++;
+
+                if (tsum > 1.0 && n >= 3) {
+                    break;
+                }
+            }
+
+            ggml_free(ctx0);
+
+            s = ((2.0*N*N*N*n)/tsum)*1e-9;
+        }
+
+        fprintf(stderr, "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f GFLOPS (%3d runs)\n",
+            N, N, s_fp16, n_fp16, s_fp32, n_fp32);
+    }
+
+    return 0;
+}
+
+// =================================================================================================
+
+// =================================================================================================
+
 //
 // Experimental stuff below
 //

whisper.h

@@ -245,7 +245,7 @@ extern "C" {
         int duration_ms;        // audio duration to process in ms
 
         bool translate;
-        bool no_context;        // do not use initial prompt for the decoder (if any)
+        bool no_context;        // do not use past transcription (if any) as initial prompt for the decoder
         bool single_segment;    // force single segment output (useful for streaming)
         bool print_special;     // print special tokens (e.g. <SOT>, <EOT>, <BEG>, etc.)
         bool print_progress;    // print progress information
@@ -350,6 +350,13 @@ extern "C" {
     // Get the probability of the specified token in the specified segment.
     WHISPER_API float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token);
 
+    ////////////////////////////////////////////////////////////////////////////
+
+    // Temporary helpers needed for exposing ggml interface
+
+    WHISPER_API int whisper_bench_memcpy(int n_threads);
+    WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads);
+
 #ifdef __cplusplus
 }
 #endif