whisper : print log when using distilled models

* Clarify doc about where to compile from

* Update examples/stream/README.md

* Update examples/stream/README.md

* Update README.md

---------

Co-authored-by: AI @ Home <>
Co-authored-by: bobqianic <129547291+bobqianic@users.noreply.github.com>

.devops/cublas.Dockerfile

@@ -0,0 +1,28 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG CUDA_VERSION=11.7.1
+
+# Target the CUDA build image
+ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_CUDA_DEV_CONTAINER} as build
+
+# Unless otherwise specified, we make a fat build.
+ARG CUDA_DOCKER_ARCH=all
+
+RUN apt-get update && \
+    apt-get install -y build-essential git cmake
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
+# Enable cuBLAS
+ENV WHISPER_CUBLAS=1
+
+RUN make
+
+ENTRYPOINT ["/app/main"]

.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 }}
+          PGP_SECRET: ${{ secrets.GPG_PRIVATE_KEY }}
-          # MAVEN_PASSWORD: ${{ secrets.OSSRH_TOKEN }}
+          PGP_PASSPHRASE: ${{ secrets.GPG_PASSPHRASE }}
 
   quantize:
     runs-on: ubuntu-latest

.gitignore

@@ -46,3 +46,5 @@ models/*.mlpackage
 bindings/java/.gradle/
 bindings/java/.idea/
 .idea/
+
+benchmark_results.csv

CMakeLists.txt

@@ -1,4 +1,4 @@
-cmake_minimum_required (VERSION 3.0)
+cmake_minimum_required (VERSION 3.5)
 
 project(whisper.cpp VERSION 1.4.2)
 
@@ -35,6 +35,12 @@ endif()
 
 # options
 
+if (APPLE)
+    set(WHISPER_METAL_DEFAULT ON)
+else()
+    set(WHISPER_METAL_DEFAULT OFF)
+endif()
+
 option(BUILD_SHARED_LIBS              "whisper: build shared libs" ${BUILD_SHARED_LIBS_DEFAULT})
 
 option(WHISPER_ALL_WARNINGS           "whisper: enable all compiler warnings"                   ON)
@@ -58,6 +64,8 @@ option(WHISPER_OPENVINO               "whisper: support for OpenVINO" OFF)
 
 if (APPLE)
     option(WHISPER_NO_ACCELERATE         "whisper: disable Accelerate framework" OFF)
+    option(WHISPER_METAL                 "whisper: use Metal"                    ${WHISPER_METAL_DEFAULT})
+    option(WHISPER_METAL_NDEBUG          "whisper: disable Metal debugging"      OFF)
     option(WHISPER_COREML                "whisper: enable Core ML framework"     OFF)
     option(WHISPER_COREML_ALLOW_FALLBACK "whisper: allow non-CoreML fallback"    OFF)
 else()
@@ -109,10 +117,38 @@ if (APPLE)
             set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${ACCELERATE_FRAMEWORK})
             set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
         else()
-            message(WARNING "Accelerate framework not found")
+            message(FATAL_ERROR "Accelerate framework not found")
         endif()
     endif()
 
+    if (WHISPER_METAL)
+        find_library(FOUNDATION_LIBRARY         Foundation              REQUIRED)
+        find_library(METAL_FRAMEWORK            Metal                   REQUIRED)
+        find_library(METALKIT_FRAMEWORK         MetalKit                REQUIRED)
+
+        if (METAL_FRAMEWORK)
+            message(STATUS "Metal framework found")
+
+            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS}
+                ${FOUNDATION_LIBRARY}
+                ${METAL_FRAMEWORK}
+                ${METALKIT_FRAMEWORK}
+                )
+            set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_METAL)
+
+            if (WHISPER_METAL_NDEBUG)
+                set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_METAL_NDEBUG)
+            endif()
+        else()
+            message(FATAL_ERROR "Metal framework not found")
+        endif()
+
+        set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
+
+        # copy ggml-metal.metal to bin directory
+        configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
+    endif()
+
     if (WHISPER_COREML)
         find_library(FOUNDATION_FRAMEWORK Foundation)
         find_library(COREML_FRAMEWORK CoreML)
@@ -122,7 +158,7 @@ if (APPLE)
 
             set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_USE_COREML)
         else()
-            message(WARNING "CoreML framework not found")
+            message(FATAL_ERROR "CoreML framework not found")
         endif()
 
         if (WHISPER_COREML_ALLOW_FALLBACK)
@@ -145,13 +181,13 @@ if (WHISPER_BLAS)
             include_directories($ENV{OPENBLAS_PATH}/include)
             set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${BLAS_LIBRARIES})
         else ()
-            message(WARNING "BLAS library was not found. Environment variable OPENBLAS_PATH not defined.")
+            message(FATAL_ERROR "BLAS library was not found. Environment variable OPENBLAS_PATH not defined.")
         endif ()
     else ()
         set(BLA_STATIC 1)
         set(BLA_VENDOR ${WHISPER_BLAS_VENDOR})
-        #    set(BLA_PREFER_PKGCONFIG 1)
         set(BLA_SIZEOF_INTEGER 8)
+        set(BLA_PREFER_PKGCONFIG 1)
         find_package(BLAS)
 
         if(BLAS_FOUND)
@@ -162,7 +198,7 @@ if (WHISPER_BLAS)
             include_directories(${BLAS_INCLUDE_DIRS})
             set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${BLAS_LIBRARIES})
         else()
-            message(WARNING "BLAS library was not found")
+            message(FATAL_ERROR "BLAS library was not found")
         endif()
     endif ()
 endif ()
@@ -177,7 +213,7 @@ if (WHISPER_CUBLAS)
 
         enable_language(CUDA)
 
-        set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
+        set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
 
         add_compile_definitions(GGML_USE_CUBLAS)
 
@@ -188,7 +224,7 @@ if (WHISPER_CUBLAS)
         endif()
 
     else()
-        message(WARNING "cuBLAS not found")
+        message(FATAL_ERROR "cuBLAS not found")
     endif()
 endif()
 
@@ -219,7 +255,7 @@ if (WHISPER_HIPBLAS)
         endif()
         set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ggml-rocm)
     else()
-        message(WARNING "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm")
+        message(FATAL_ERROR "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm")
     endif()
 endif()
 
@@ -228,13 +264,13 @@ if (WHISPER_CLBLAST)
     if (CLBlast_FOUND)
         message(STATUS "CLBlast found")
 
-        set(GGML_OPENCL_SOURCES ggml-opencl.cpp ggml-opencl.h)
+        set(GGML_SOURCES_OPENCL ggml-opencl.cpp ggml-opencl.h)
 
         add_compile_definitions(GGML_USE_CLBLAST)
 
         set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} clblast)
     else()
-        message(WARNING "CLBlast not found")
+        message(FATAL_ERROR "CLBlast not found")
     endif()
 endif()
 
@@ -321,6 +357,53 @@ 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 ${WHISPER_EXTRA_FLAGS} -DGGML_PERF)
 endif()
@@ -379,8 +462,11 @@ set(TARGET whisper)
 add_library(${TARGET}
     ggml.h
     ggml.c
-    ${GGML_CUDA_SOURCES}
+    ggml-alloc.h
-    ${GGML_OPENCL_SOURCES}
+    ggml-alloc.c
+    ${GGML_SOURCES_METAL}
+    ${GGML_SOURCES_CUDA}
+    ${GGML_SOURCES_OPENCL}
     whisper.h
     whisper.cpp
     )
@@ -421,9 +507,15 @@ if (BUILD_SHARED_LIBS)
         WHISPER_BUILD
         GGML_BUILD
         )
+
+    if (WHISPER_METAL)
+        # TODO: I think this should make ggml-metal.m "see" the ggml-metal.metal file from the "bin" directory
+        #       but for some reason it does not work here like it does in llama.cpp
+        set_target_properties(${TARGET} PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
+    endif()
 endif()
 
-if (GGML_CUDA_SOURCES)
+if (GGML_SOURCES_CUDA)
     message(STATUS "GGML CUDA sources found, configuring CUDA architecture")
     set_property(TARGET whisper PROPERTY CUDA_ARCHITECTURES OFF)
     set_property(TARGET whisper PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto")
@@ -439,10 +531,13 @@ target_compile_definitions(${TARGET} PUBLIC
 
 set_target_properties(${TARGET} PROPERTIES PUBLIC_HEADER "whisper.h")
 
+include(GNUInstallDirs)
+
 install(TARGETS ${TARGET}
     LIBRARY  DESTINATION lib
     ARCHIVE  DESTINATION lib/static
     RUNTIME  DESTINATION bin
+    RESOURCE DESTINATION bin
     PUBLIC_HEADER DESTINATION include
     )
 

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
+# clock_gettime came in POSIX.1b (1993)
-ifneq ($(wildcard /usr/include/musl/*),)
+# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
-	CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
+# posix_memalign came in POSIX.1-2001 / SUSv3
-	CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
+# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)
+CFLAGS   += -D_XOPEN_SOURCE=600
+CXXFLAGS += -D_XOPEN_SOURCE=600
+
+# Somehow in OpenBSD whenever POSIX conformance is specified
+# some string functions rely on locale_t availability,
+# which was introduced in POSIX.1-2008, forcing us to go higher
+ifeq ($(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)))
@@ -145,6 +182,16 @@ ifdef WHISPER_COREML_ALLOW_FALLBACK
 endif
 endif
 
+ifndef WHISPER_NO_METAL
+	ifeq ($(UNAME_S),Darwin)
+		WHISPER_METAL := 1
+
+		CFLAGS   += -DGGML_USE_METAL
+		CXXFLAGS += -DGGML_USE_METAL
+		LDFLAGS  += -framework Foundation -framework Metal -framework MetalKit
+	endif
+endif
+
 ifdef WHISPER_OPENBLAS
 	CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
 	LDFLAGS += -lopenblas
@@ -251,6 +298,11 @@ $(info )
 ggml.o: ggml.c ggml.h ggml-cuda.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
+ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
+	$(CC)  $(CFLAGS)   -c $< -o $@
+
+WHISPER_OBJ += ggml-alloc.o
+
 whisper.o: whisper.cpp whisper.h ggml.h ggml-cuda.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
@@ -266,6 +318,13 @@ whisper-encoder-impl.o: coreml/whisper-encoder-impl.m coreml/whisper-encoder-imp
 WHISPER_OBJ += whisper.o whisper-encoder.o whisper-encoder-impl.o
 endif
 
+ifdef WHISPER_METAL
+ggml-metal.o: ggml-metal.m ggml-metal.h
+	$(CC) $(CFLAGS) -c $< -o $@
+
+WHISPER_OBJ += ggml-metal.o
+endif
+
 libwhisper.a: ggml.o $(WHISPER_OBJ)
 	$(AR) rcs libwhisper.a ggml.o $(WHISPER_OBJ)
 
@@ -297,8 +356,8 @@ quantize: examples/quantize/quantize.cpp ggml.o $(WHISPER_OBJ) $(SRC_COMMON)
 stream: examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
 	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o stream $(CC_SDL) $(LDFLAGS)
 
-command: examples/command/command.cpp examples/grammar-parser.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+command: examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
-	$(CXX) $(CXXFLAGS) examples/command/command.cpp examples/grammar-parser.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o command $(CC_SDL) $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o command $(CC_SDL) $(LDFLAGS)
 
 lsp: examples/lsp/lsp.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
 	$(CXX) $(CXXFLAGS) examples/lsp/lsp.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o lsp $(CC_SDL) $(LDFLAGS)

README.md

@@ -11,14 +11,14 @@ Beta: [v1.4.2](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.4.2) / S
 High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
 
 - Plain C/C++ implementation without dependencies
-- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate framework and [Core ML](https://github.com/ggerganov/whisper.cpp#core-ml-support)
+- Apple Silicon first-class citizen - optimized via ARM NEON, Accelerate framework, Metal and [Core ML](https://github.com/ggerganov/whisper.cpp#core-ml-support)
 - AVX intrinsics support for x86 architectures
 - VSX intrinsics support for POWER architectures
 - Mixed F16 / F32 precision
 - [4-bit and 5-bit integer quantization support](https://github.com/ggerganov/whisper.cpp#quantization)
 - Low memory usage (Flash Attention)
 - Zero memory allocations at runtime
-- Runs on the CPU
+- Support for CPU-only inference
 - [Partial GPU support for NVIDIA via cuBLAS](https://github.com/ggerganov/whisper.cpp#nvidia-gpu-support-via-cublas)
 - [Partial OpenCL GPU support via CLBlast](https://github.com/ggerganov/whisper.cpp#opencl-gpu-support-via-clblast)
 - [BLAS CPU support via OpenBLAS](https://github.com/ggerganov/whisper.cpp#blas-cpu-support-via-openblas)
@@ -50,6 +50,10 @@ You can also easily make your own offline voice assistant application: [command]
 
 https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4
 
+On Apple Silicon, the inference runs fully on the GPU via Metal:
+
+https://github.com/ggerganov/whisper.cpp/assets/1991296/c82e8f86-60dc-49f2-b048-d2fdbd6b5225
+
 Or you can even run it straight in the browser: [talk.wasm](examples/talk.wasm)
 
 ## Implementation details
@@ -109,30 +113,37 @@ options:
   -d  N,     --duration N        [0      ] duration of audio to process in milliseconds
   -mc N,     --max-context N     [-1     ] maximum number of text context tokens to store
   -ml N,     --max-len N         [0      ] maximum segment length in characters
-  -bo N,     --best-of N         [5      ] number of best candidates to keep
+  -sow,      --split-on-word     [false  ] split on word rather than on token
+  -bo N,     --best-of N         [2      ] number of best candidates to keep
   -bs N,     --beam-size N       [-1     ] beam size for beam search
   -wt N,     --word-thold N      [0.01   ] word timestamp probability threshold
   -et N,     --entropy-thold N   [2.40   ] entropy threshold for decoder fail
   -lpt N,    --logprob-thold N   [-1.00  ] log probability threshold for decoder fail
-  -su,       --speed-up          [false  ] speed up audio by x2 (reduced accuracy)
+  -debug,    --debug-mode        [false  ] enable debug mode (eg. dump log_mel)
   -tr,       --translate         [false  ] translate from source language to english
-  -tdrz,     --tinydiarize       [false  ] enable tinydiarize (requires a tdrz model)
   -di,       --diarize           [false  ] stereo audio diarization
+  -tdrz,     --tinydiarize       [false  ] enable tinydiarize (requires a tdrz model)
   -nf,       --no-fallback       [false  ] do not use temperature fallback while decoding
   -otxt,     --output-txt        [false  ] output result in a text file
   -ovtt,     --output-vtt        [false  ] output result in a vtt file
   -osrt,     --output-srt        [false  ] output result in a srt file
+  -olrc,     --output-lrc        [false  ] output result in a lrc file
   -owts,     --output-words      [false  ] output script for generating karaoke video
+  -fp,       --font-path         [/System/Library/Fonts/Supplemental/Courier New Bold.ttf] path to a monospace font for karaoke video
   -ocsv,     --output-csv        [false  ] output result in a CSV file
+  -oj,       --output-json       [false  ] output result in a JSON file
   -of FNAME, --output-file FNAME [       ] output file path (without file extension)
   -ps,       --print-special     [false  ] print special tokens
   -pc,       --print-colors      [false  ] print colors
   -pp,       --print-progress    [false  ] print progress
-  -nt,       --no-timestamps     [true   ] do not print timestamps
+  -nt,       --no-timestamps     [false  ] do not print timestamps
   -l LANG,   --language LANG     [en     ] spoken language ('auto' for auto-detect)
+  -dl,       --detect-language   [false  ] exit after automatically detecting language
              --prompt PROMPT     [       ] initial prompt
   -m FNAME,  --model FNAME       [models/ggml-base.en.bin] model path
   -f FNAME,  --file FNAME        [       ] input WAV file path
+  -oved D,   --ov-e-device DNAME [CPU    ] the OpenVINO device used for encode inference
+  -ls,       --log-score         [false  ] log best decoder scores of token
 
 
 bash ./models/download-ggml-model.sh base.en
@@ -698,6 +709,19 @@ took to execute it. The results are summarized in the following Github issue:
 
 [Benchmark results](https://github.com/ggerganov/whisper.cpp/issues/89)
 
+Additionally a script to run whisper.cpp with different models and audio files is provided [bench.py](bench.py).
+
+You can run it with the following command, by default it will run against any standard model in the models folder.
+
+```bash
+python3 extra/bench.py -f samples/jfk.wav -t 2,4,8 -p 1,2
+```
+
+It is written in python with the intention of being easy to modify and extend for your benchmarking use case.
+
+It outputs a csv file with the results of the benchmarking.
+
+
 ## ggml format
 
 The original models are converted to a custom binary format. This allows to pack everything needed into a single file:
@@ -719,7 +743,7 @@ in [models](models).
 ## [Bindings](https://github.com/ggerganov/whisper.cpp/discussions/categories/bindings)
 
 - [X] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) | [#310](https://github.com/ggerganov/whisper.cpp/discussions/310)
-- [X] Javascript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309)
+- [X] JavaScript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309)
   - React Native (iOS / Android): [whisper.rn](https://github.com/mybigday/whisper.rn)
 - [X] Go: [bindings/go](bindings/go) | [#312](https://github.com/ggerganov/whisper.cpp/discussions/312)
 - [X] Java:

bindings/go/params.go

@@ -118,6 +118,11 @@ func (p *Params) SetMaxTokensPerSegment(n int) {
 	p.max_tokens = C.int(n)
 }
 
+// Set audio encoder context
+func (p *Params) SetAudioCtx(n int) {
+	p.audio_ctx = C.int(n)
+}
+
 ///////////////////////////////////////////////////////////////////////////////
 // PRIVATE METHODS
 
@@ -141,6 +146,7 @@ func (p *Params) String() string {
 	str += fmt.Sprintf(" n_max_text_ctx=%d", p.n_max_text_ctx)
 	str += fmt.Sprintf(" offset_ms=%d", p.offset_ms)
 	str += fmt.Sprintf(" duration_ms=%d", p.duration_ms)
+	str += fmt.Sprintf(" audio_ctx=%d", p.audio_ctx)
 	if p.translate {
 		str += " translate"
 	}

bindings/go/pkg/whisper/context.go

@@ -125,6 +125,11 @@ func (context *context) SetMaxTokensPerSegment(n uint) {
 	context.params.SetMaxTokensPerSegment(int(n))
 }
 
+// Set audio encoder context
+func (context *context) SetAudioCtx(n uint) {
+	context.params.SetAudioCtx(int(n))
+}
+
 // ResetTimings resets the mode timings. Should be called before processing
 func (context *context) ResetTimings() {
 	context.model.ctx.Whisper_reset_timings()

bindings/go/pkg/whisper/interface.go

@@ -48,6 +48,7 @@ type Context interface {
 	SetMaxSegmentLength(uint)     // Set max segment length in characters
 	SetTokenTimestamps(bool)      // Set token timestamps flag
 	SetMaxTokensPerSegment(uint)  // Set max tokens per segment (0 = no limit)
+	SetAudioCtx(uint)             // Set audio encoder context
 
 	// Process mono audio data and return any errors.
 	// If defined, newly generated segments are passed to the

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
 }

bindings/ruby/ext/.gitignore

@@ -1,6 +1,8 @@
 Makefile
 ggml.c
 ggml.h
+ggml-alloc.c
+ggml-alloc.h
 whisper.bundle
 whisper.cpp
 whisper.h

bindings/ruby/ext/extconf.rb

@@ -3,6 +3,8 @@ system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.cpp')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.h')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.h')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.c')} .")
+system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-alloc.h')} .")
+system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-alloc.c')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','examples','dr_wav.h')} .")
 
 

coreml/whisper-encoder.mm

@@ -22,7 +22,13 @@ struct whisper_coreml_context * whisper_coreml_init(const char * path_model) {
 
     NSURL * url_model = [NSURL fileURLWithPath: path_model_str];
 
-    const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model error:nil]);
+    // select which device to run the Core ML model on
+    MLModelConfiguration *config = [[MLModelConfiguration alloc] init];
+    config.computeUnits = MLComputeUnitsCPUAndGPU;
+    //config.computeUnits = MLComputeUnitsCPUAndNeuralEngine;
+    //config.computeUnits = MLComputeUnitsAll;
+
+    const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model configuration:config error:nil]);
 
     if (data == NULL) {
         return NULL;

examples/CMakeLists.txt

@@ -23,7 +23,6 @@ add_library(${TARGET} STATIC
     common.cpp
     common-ggml.h
     common-ggml.cpp
-    grammar-parser.cpp
     )
 
 include(DefaultTargetOptions)

examples/bench/bench.cpp

@@ -1,6 +1,7 @@
 #include "whisper.h"
 
 #include <cstdio>
+#include <cstring>
 #include <string>
 #include <thread>
 
@@ -44,13 +45,13 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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  0 - whisper\n",                                 "");
     fprintf(stderr, "                           %-7s  1 - memcpy\n",                                  "");
     fprintf(stderr, "                           %-7s  2 - ggml_mul_mat\n",                            "");
     fprintf(stderr, "\n");
 }
 
-int whisper_bench_encoder(const whisper_params & params) {
+int whisper_bench_full(const whisper_params & params) {
     // whisper init
 
     struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
@@ -69,12 +70,49 @@ int whisper_bench_encoder(const whisper_params & params) {
         fprintf(stderr, "error: failed to set mel: %d\n", ret);
         return 3;
     }
-
+    // heat encoder
     if (int ret = whisper_encode(ctx, 0, params.n_threads) != 0) {
         fprintf(stderr, "error: failed to encode model: %d\n", ret);
         return 4;
     }
 
+    whisper_token tokens[512];
+    memset(tokens, 0, sizeof(tokens));
+
+    // prompt heat
+    if (int ret = whisper_decode(ctx, tokens, 256, 0, params.n_threads) != 0) {
+        fprintf(stderr, "error: failed to encode model: %d\n", ret);
+        return 4;
+    }
+
+    // text-generation heat
+    if (int ret = whisper_decode(ctx, tokens, 1, 256, params.n_threads) != 0) {
+        fprintf(stderr, "error: failed to encode model: %d\n", ret);
+        return 4;
+    }
+
+    whisper_reset_timings(ctx);
+
+    // actual run
+    if (int ret = whisper_encode(ctx, 0, params.n_threads) != 0) {
+        fprintf(stderr, "error: failed to encode model: %d\n", ret);
+        return 4;
+    }
+
+    for (int i = 0; i < 16; i++) {
+        if (int ret = whisper_decode(ctx, tokens, 256, 0, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to encode model: %d\n", ret);
+            return 4;
+        }
+    }
+
+    for (int i = 0; i < 256; i++) {
+        if (int ret = whisper_decode(ctx, tokens, 1, i, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to encode model: %d\n", ret);
+            return 4;
+        }
+    }
+
     whisper_print_timings(ctx);
     whisper_free(ctx);
 
@@ -103,7 +141,7 @@ int main(int argc, char ** argv) {
     int ret = -1;
 
     switch (params.what) {
-        case 0: ret = whisper_bench_encoder(params);                break;
+        case 0: ret = whisper_bench_full(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;

examples/command/command.cpp

@@ -6,10 +6,9 @@
 // ref: https://github.com/ggerganov/whisper.cpp/issues/171
 //
 
-#include "common.h"
 #include "common-sdl.h"
+#include "common.h"
 #include "whisper.h"
-#include "grammar-parser.h"
 
 #include <sstream>
 #include <cassert>
@@ -22,11 +21,6 @@
 #include <vector>
 #include <map>
 
-bool file_exists(const std::string & fname) {
-    std::ifstream f(fname.c_str());
-    return f.good();
-}
-
 // command-line parameters
 struct whisper_params {
     int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
@@ -39,10 +33,6 @@ struct whisper_params {
     float vad_thold    = 0.6f;
     float freq_thold   = 100.0f;
 
-    float grammar_penalty = 100.0f;
-
-    grammar_parser::parse_state grammar_parsed;
-
     bool speed_up      = false;
     bool translate     = false;
     bool print_special = false;
@@ -54,8 +44,6 @@ struct whisper_params {
     std::string fname_out;
     std::string commands;
     std::string prompt;
-    std::string context;
-    std::string grammar;
 };
 
 void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
@@ -85,9 +73,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         else if (arg == "-f"   || arg == "--file")          { params.fname_out     = argv[++i]; }
         else if (arg == "-cmd" || arg == "--commands")      { params.commands      = argv[++i]; }
         else if (arg == "-p"   || arg == "--prompt")        { params.prompt        = argv[++i]; }
-        else if (arg == "-ctx" || arg == "--context")       { params.context       = argv[++i]; }
-        else if (                 arg == "--grammar")       { params.grammar       = argv[++i]; }
-        else if (                 arg == "--grammar-penalty") { params.grammar_penalty = std::stof(argv[++i]); }
         else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             whisper_print_usage(argc, argv, params);
@@ -121,30 +106,16 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -f FNAME,   --file FNAME     [%-7s] text output file name\n",                       params.fname_out.c_str());
     fprintf(stderr, "  -cmd FNAME, --commands FNAME [%-7s] text file with allowed commands\n",             params.commands.c_str());
     fprintf(stderr, "  -p,         --prompt         [%-7s] the required activation prompt\n",              params.prompt.c_str());
-    fprintf(stderr, "  -ctx,       --context        [%-7s] sample text to help the transcription\n",       params.context.c_str());
-    fprintf(stderr, "  --grammar GRAMMAR            [%-7s] GBNF grammar to guide decoding\n",              params.grammar.c_str());
-    fprintf(stderr, "  --grammar-penalty N          [%-7.1f] scales down logits of nongrammar tokens\n",   params.grammar_penalty);
     fprintf(stderr, "\n");
 }
 
-std::string transcribe(
+std::string transcribe(whisper_context * ctx, const whisper_params & params, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
-                 whisper_context * ctx,
-            const whisper_params & params,
-        const std::vector<float> & pcmf32,
-               const std::string & grammar_rule,
-                           float & logprob_min,
-                           float & logprob_sum,
-                             int & n_tokens,
-                         int64_t & t_ms) {
     const auto t_start = std::chrono::high_resolution_clock::now();
 
-    logprob_min = 0.0f;
+    prob = 0.0f;
-    logprob_sum = 0.0f;
-    n_tokens    = 0;
     t_ms = 0;
 
-    //whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+    whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
-    whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_BEAM_SEARCH);
 
     wparams.print_progress   = false;
     wparams.print_special    = params.print_special;
@@ -152,7 +123,6 @@ std::string transcribe(
     wparams.print_timestamps = !params.no_timestamps;
     wparams.translate        = params.translate;
     wparams.no_context       = true;
-    wparams.no_timestamps    = params.no_timestamps;
     wparams.single_segment   = true;
     wparams.max_tokens       = params.max_tokens;
     wparams.language         = params.language.c_str();
@@ -161,28 +131,11 @@ std::string transcribe(
     wparams.audio_ctx        = params.audio_ctx;
     wparams.speed_up         = params.speed_up;
 
-    wparams.temperature     = 0.4f;
-    wparams.temperature_inc = 1.0f;
-    wparams.greedy.best_of  = 5;
-
-    wparams.beam_search.beam_size = 5;
-
-    wparams.initial_prompt = params.context.data();
-
-    const auto & grammar_parsed = params.grammar_parsed;
-    auto grammar_rules = grammar_parsed.c_rules();
-
-    if (!params.grammar_parsed.rules.empty() && !grammar_rule.empty()) {
-        wparams.grammar_rules   = grammar_rules.data();
-        wparams.n_grammar_rules = grammar_rules.size();
-        wparams.i_start_rule    = grammar_parsed.symbol_ids.at(grammar_rule);
-        wparams.grammar_penalty = params.grammar_penalty;
-    }
-
     if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
         return "";
     }
 
+    int prob_n = 0;
     std::string result;
 
     const int n_segments = whisper_full_n_segments(ctx);
@@ -191,17 +144,19 @@ std::string transcribe(
 
         result += text;
 
-        const int n = whisper_full_n_tokens(ctx, i);
+        const int n_tokens = whisper_full_n_tokens(ctx, i);
-        for (int j = 0; j < n; ++j) {
+        for (int j = 0; j < n_tokens; ++j) {
             const auto token = whisper_full_get_token_data(ctx, i, j);
 
-            if(token.plog > 0.0f) exit(0);
+            prob += token.p;
-            logprob_min = std::min(logprob_min, token.plog);
+            ++prob_n;
-            logprob_sum += token.plog;
-            ++n_tokens;
         }
     }
 
+    if (prob_n > 0) {
+        prob /= prob_n;
+    }
+
     const auto t_end = std::chrono::high_resolution_clock::now();
     t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
 
@@ -460,9 +415,7 @@ int always_prompt_transcription(struct whisper_context * ctx, audio_async & audi
     bool is_running = true;
     bool ask_prompt = true;
 
-    float logprob_min = 0.0f;
+    float prob = 0.0f;
-    float logprob_sum = 0.0f;
-    int   n_tokens    = 0;
 
     std::vector<float> pcmf32_cur;
 
@@ -500,7 +453,7 @@ int always_prompt_transcription(struct whisper_context * ctx, audio_async & audi
                 // detect the commands
                 audio.get(params.command_ms, pcmf32_cur);
 
-                const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, "", logprob_min, logprob_sum, n_tokens, t_ms));
+                const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, prob, t_ms));
 
                 const auto words = get_words(txt);
 
@@ -541,22 +494,13 @@ int process_general_transcription(struct whisper_context * ctx, audio_async & au
     bool have_prompt = false;
     bool ask_prompt  = true;
 
-    float logprob_min0 = 0.0f;
+    float prob0 = 0.0f;
-    float logprob_min  = 0.0f;
+    float prob  = 0.0f;
-
-    float logprob_sum0 = 0.0f;
-    float logprob_sum  = 0.0f;
-
-    int n_tokens0 = 0;
-    int n_tokens  = 0;
 
     std::vector<float> pcmf32_cur;
     std::vector<float> pcmf32_prompt;
 
-    std::string k_prompt = "Ok Whisper, start listening for commands.";
+    const std::string k_prompt = "Ok Whisper, start listening for commands.";
-    if (!params.prompt.empty()) {
-        k_prompt = params.prompt;
-    }
 
     fprintf(stderr, "\n");
     fprintf(stderr, "%s: general-purpose mode\n", __func__);
@@ -589,11 +533,9 @@ int process_general_transcription(struct whisper_context * ctx, audio_async & au
                     // wait for activation phrase
                     audio.get(params.prompt_ms, pcmf32_cur);
 
-                    const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, "prompt", logprob_min0, logprob_sum0, n_tokens0, t_ms));
+                    const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, prob0, t_ms));
 
-                    const float p = 100.0f * std::exp(logprob_min0);
+                    fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", txt.c_str(), "\033[0m", (int) t_ms);
-
-                    fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms, p = %.2f%%)\n", __func__, "\033[1m", txt.c_str(), "\033[0m", (int) t_ms, p);
 
                     const float sim = similarity(txt, k_prompt);
 
@@ -614,30 +556,19 @@ int process_general_transcription(struct whisper_context * ctx, audio_async & au
                     // we have heard the activation phrase, now detect the commands
                     audio.get(params.command_ms, pcmf32_cur);
 
-                    //printf("len prompt:  %.4f\n", pcmf32_prompt.size() / (float) WHISPER_SAMPLE_RATE);
-                    //printf("len command: %.4f\n", pcmf32_cur.size() / (float) WHISPER_SAMPLE_RATE);
-
-                    // prepend 3 second of silence
-                    pcmf32_cur.insert(pcmf32_cur.begin(), 3.0f*WHISPER_SAMPLE_RATE, 0.0f);
-
                     // prepend the prompt audio
                     pcmf32_cur.insert(pcmf32_cur.begin(), pcmf32_prompt.begin(), pcmf32_prompt.end());
 
-                    const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, "root", logprob_min, logprob_sum, n_tokens, t_ms));
+                    const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, prob, t_ms));
 
-                    //const float p = 100.0f * std::exp((logprob - logprob0) / (n_tokens - n_tokens0));
+                    prob = 100.0f*(prob - prob0);
-                    const float p = 100.0f * std::exp(logprob_min);
 
                     //fprintf(stdout, "%s: heard '%s'\n", __func__, txt.c_str());
 
                     // find the prompt in the text
                     float best_sim = 0.0f;
                     size_t best_len = 0;
-                    for (size_t n = 0.8*k_prompt.size(); n <= 1.2*k_prompt.size(); ++n) {
+                    for (int n = 0.8*k_prompt.size(); n <= 1.2*k_prompt.size(); ++n) {
-                        if (n >= txt.size()) {
-                            break;
-                        }
-
                         const auto prompt = txt.substr(0, n);
 
                         const float sim = similarity(prompt, k_prompt);
@@ -650,16 +581,9 @@ int process_general_transcription(struct whisper_context * ctx, audio_async & au
                         }
                     }
 
-                    fprintf(stdout, "%s:   DEBUG: txt = '%s', prob = %.2f%%\n", __func__, txt.c_str(), p);
-                    if (best_len == 0) {
-                        fprintf(stdout, "%s: WARNING: command not recognized, try again\n", __func__);
-                    } else {
-                        // cut the prompt from the decoded text
                     const std::string command = ::trim(txt.substr(best_len));
 
                     fprintf(stdout, "%s: Command '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", command.c_str(), "\033[0m", (int) t_ms);
-                    }
-
                     fprintf(stdout, "\n");
                 }
 
@@ -724,37 +648,13 @@ int main(int argc, char ** argv) {
 
     int  ret_val = 0;
 
-    if (!params.grammar.empty()) {
-        auto & grammar = params.grammar_parsed;
-        if (file_exists(params.grammar.c_str())) {
-            // read grammar from file
-            std::ifstream ifs(params.grammar.c_str());
-            const std::string txt = std::string((std::istreambuf_iterator<char>(ifs)), std::istreambuf_iterator<char>());
-            grammar = grammar_parser::parse(txt.c_str());
-        } else {
-            // read grammar from string
-            grammar = grammar_parser::parse(params.grammar.c_str());
-        }
-
-        // will be empty (default) if there are parse errors
-        if (grammar.rules.empty()) {
-            ret_val = 1;
-        } else {
-            fprintf(stderr, "%s: grammar:\n", __func__);
-            grammar_parser::print_grammar(stderr, grammar);
-            fprintf(stderr, "\n");
-        }
-    }
-
-    if (ret_val == 0) {
     if (!params.commands.empty()) {
         ret_val = process_command_list(ctx, audio, params);
-        } else if (!params.prompt.empty() && params.grammar_parsed.rules.empty()) {
+    } else if (!params.prompt.empty()) {
         ret_val = always_prompt_transcription(ctx, audio, params);
     } else {
         ret_val = process_general_transcription(ctx, audio, params);
     }
-    }
 
     audio.pause();
 

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/common.h

@@ -7,6 +7,8 @@
 #include <vector>
 #include <random>
 #include <thread>
+#include <ctime>
+#include <fstream>
 
 #define COMMON_SAMPLE_RATE 16000
 
@@ -139,6 +141,104 @@ bool read_wav(
         std::vector<std::vector<float>> & pcmf32s,
         bool stereo);
 
+// Write PCM data into WAV audio file
+class wav_writer {
+private:
+    std::ofstream file;
+    uint32_t dataSize = 0;
+    std::string wav_filename;
+
+    bool write_header(const uint32_t sample_rate,
+                      const uint16_t bits_per_sample,
+                      const uint16_t channels) {
+
+        file.write("RIFF", 4);
+        file.write("\0\0\0\0", 4);    // Placeholder for file size
+        file.write("WAVE", 4);
+        file.write("fmt ", 4);
+
+        const uint32_t sub_chunk_size = 16;
+        const uint16_t audio_format = 1;      // PCM format
+        const uint32_t byte_rate = sample_rate * channels * bits_per_sample / 8;
+        const uint16_t block_align = channels * bits_per_sample / 8;
+
+        file.write(reinterpret_cast<const char *>(&sub_chunk_size), 4);
+        file.write(reinterpret_cast<const char *>(&audio_format), 2);
+        file.write(reinterpret_cast<const char *>(&channels), 2);
+        file.write(reinterpret_cast<const char *>(&sample_rate), 4);
+        file.write(reinterpret_cast<const char *>(&byte_rate), 4);
+        file.write(reinterpret_cast<const char *>(&block_align), 2);
+        file.write(reinterpret_cast<const char *>(&bits_per_sample), 2);
+        file.write("data", 4);
+        file.write("\0\0\0\0", 4);    // Placeholder for data size
+
+        return true;
+    }
+
+    // It is assumed that PCM data is normalized to a range from -1 to 1
+    bool write_audio(const float * data, size_t length) {
+        for (size_t i = 0; i < length; ++i) {
+            const auto intSample = static_cast<const int16_t>(data[i] * 32767);
+            file.write(reinterpret_cast<const char *>(&intSample), sizeof(int16_t));
+            dataSize += sizeof(int16_t);
+        }
+        if (file.is_open()) {
+            file.seekp(4, std::ios::beg);
+            uint32_t fileSize = 36 + dataSize;
+            file.write(reinterpret_cast<char *>(&fileSize), 4);
+            file.seekp(40, std::ios::beg);
+            file.write(reinterpret_cast<char *>(&dataSize), 4);
+            file.seekp(0, std::ios::end);
+        }
+        return true;
+    }
+
+    bool open_wav(const std::string & filename) {
+        if (filename != wav_filename) {
+            if (file.is_open()) {
+                file.close();
+            }
+        }
+        if (!file.is_open()) {
+            file.open(filename, std::ios::binary);
+            wav_filename = filename;
+            dataSize = 0;
+        }
+        return file.is_open();
+    }
+
+public:
+    bool open(const std::string & filename,
+              const    uint32_t   sample_rate,
+              const    uint16_t   bits_per_sample,
+              const    uint16_t   channels) {
+
+        if (open_wav(filename)) {
+            write_header(sample_rate, bits_per_sample, channels);
+        } else {
+            return false;
+        }
+
+        return true;
+    }
+
+    bool close() {
+        file.close();
+        return true;
+    }
+
+    bool write(const float * data, size_t length) {
+        return write_audio(data, length);
+    }
+
+    ~wav_writer() {
+        if (file.is_open()) {
+            file.close();
+        }
+    }
+};
+
+
 // Apply a high-pass frequency filter to PCM audio
 // Suppresses frequencies below cutoff Hz
 void high_pass_filter(

examples/grammar-parser.cpp

@@ -1,423 +0,0 @@
-#include "grammar-parser.h"
-#include <cstdint>
-#include <cwchar>
-#include <string>
-#include <utility>
-#include <stdexcept>
-#include <exception>
-
-namespace grammar_parser {
-    // NOTE: assumes valid utf8 (but checks for overrun)
-    // copied from whisper.cpp
-    std::pair<uint32_t, const char *> decode_utf8(const char * src) {
-        static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
-        uint8_t  first_byte = static_cast<uint8_t>(*src);
-        uint8_t  highbits   = first_byte >> 4;
-        int      len        = lookup[highbits];
-        uint8_t  mask       = (1 << (8 - len)) - 1;
-        uint32_t value      = first_byte & mask;
-        const char * end    = src + len; // may overrun!
-        const char * pos    = src + 1;
-        for ( ; pos < end && *pos; pos++) {
-            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
-        }
-        return std::make_pair(value, pos);
-    }
-
-    uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) {
-        uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
-        auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id));
-        return result.first->second;
-    }
-
-    uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) {
-        uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
-        state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id;
-        return next_id;
-    }
-
-    void add_rule(
-            parse_state & state,
-            uint32_t      rule_id,
-            const std::vector<whisper_grammar_element> & rule) {
-        if (state.rules.size() <= rule_id) {
-            state.rules.resize(rule_id + 1);
-        }
-        state.rules[rule_id] = rule;
-    }
-
-    bool is_word_char(char c) {
-        return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9');
-    }
-
-    std::pair<uint32_t, const char *> parse_hex(const char * src, int size) {
-        const char * pos   = src;
-        const char * end   = src + size;
-        uint32_t     value = 0;
-        for ( ; pos < end && *pos; pos++) {
-            value <<= 4;
-            char c = *pos;
-            if ('a' <= c && c <= 'f') {
-                value += c - 'a' + 10;
-            } else if ('A' <= c && c <= 'F') {
-                value += c - 'A' + 10;
-            } else if ('0' <= c && c <= '9') {
-                value += c - '0';
-            } else {
-                break;
-            }
-        }
-        if (pos != end) {
-            throw std::runtime_error("expecting " + std::to_string(size) + " hex chars at " + src);
-        }
-        return std::make_pair(value, pos);
-    }
-
-    const char * parse_space(const char * src, bool newline_ok) {
-        const char * pos = src;
-        while (*pos == ' ' || *pos == '\t' || *pos == '#' ||
-                (newline_ok && (*pos == '\r' || *pos == '\n'))) {
-            if (*pos == '#') {
-                while (*pos && *pos != '\r' && *pos != '\n') {
-                    pos++;
-                }
-            } else {
-                pos++;
-            }
-        }
-        return pos;
-    }
-
-    const char * parse_name(const char * src) {
-        const char * pos = src;
-        while (is_word_char(*pos)) {
-            pos++;
-        }
-        if (pos == src) {
-            throw std::runtime_error(std::string("expecting name at ") + src);
-        }
-        return pos;
-    }
-
-    std::pair<uint32_t, const char *> parse_char(const char * src) {
-        if (*src == '\\') {
-            switch (src[1]) {
-                case 'x': return parse_hex(src + 2, 2);
-                case 'u': return parse_hex(src + 2, 4);
-                case 'U': return parse_hex(src + 2, 8);
-                case 't': return std::make_pair('\t', src + 2);
-                case 'r': return std::make_pair('\r', src + 2);
-                case 'n': return std::make_pair('\n', src + 2);
-                case '\\':
-                case '"':
-                case '[':
-                case ']':
-                    return std::make_pair(src[1], src + 2);
-                default:
-                    throw std::runtime_error(std::string("unknown escape at ") + src);
-            }
-        } else if (*src) {
-            return decode_utf8(src);
-        }
-        throw std::runtime_error("unexpected end of input");
-    }
-
-    const char * parse_alternates(
-            parse_state       & state,
-            const char        * src,
-            const std::string & rule_name,
-            uint32_t            rule_id,
-            bool                is_nested);
-
-    const char * parse_sequence(
-            parse_state                        & state,
-            const char                         * src,
-            const std::string                  & rule_name,
-            std::vector<whisper_grammar_element> & out_elements,
-            bool                                 is_nested) {
-        size_t last_sym_start = out_elements.size();
-        const char * pos = src;
-        while (*pos) {
-            if (*pos == '"') { // literal string
-                pos++;
-                last_sym_start = out_elements.size();
-                while (*pos != '"') {
-                    auto char_pair = parse_char(pos);
-                         pos       = char_pair.second;
-                    out_elements.push_back({WHISPER_GRETYPE_CHAR, char_pair.first});
-                }
-                pos = parse_space(pos + 1, is_nested);
-            } else if (*pos == '[') { // char range(s)
-                pos++;
-                enum whisper_gretype start_type = WHISPER_GRETYPE_CHAR;
-                if (*pos == '^') {
-                    pos++;
-                    start_type = WHISPER_GRETYPE_CHAR_NOT;
-                }
-                last_sym_start = out_elements.size();
-                while (*pos != ']') {
-                    auto char_pair = parse_char(pos);
-                         pos       = char_pair.second;
-                    enum whisper_gretype type = last_sym_start < out_elements.size()
-                        ? WHISPER_GRETYPE_CHAR_ALT
-                        : start_type;
-
-                    out_elements.push_back({type, char_pair.first});
-                    if (pos[0] == '-' && pos[1] != ']') {
-                        auto endchar_pair = parse_char(pos + 1);
-                             pos          = endchar_pair.second;
-                        out_elements.push_back({WHISPER_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first});
-                    }
-                }
-                pos = parse_space(pos + 1, is_nested);
-            } else if (is_word_char(*pos)) { // rule reference
-                const char * name_end    = parse_name(pos);
-                uint32_t     ref_rule_id = get_symbol_id(state, pos, name_end - pos);
-                pos = parse_space(name_end, is_nested);
-                last_sym_start = out_elements.size();
-                out_elements.push_back({WHISPER_GRETYPE_RULE_REF, ref_rule_id});
-            } else if (*pos == '(') { // grouping
-                // parse nested alternates into synthesized rule
-                pos = parse_space(pos + 1, true);
-                uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
-                pos = parse_alternates(state, pos, rule_name, sub_rule_id, true);
-                last_sym_start = out_elements.size();
-                // output reference to synthesized rule
-                out_elements.push_back({WHISPER_GRETYPE_RULE_REF, sub_rule_id});
-                if (*pos != ')') {
-                    throw std::runtime_error(std::string("expecting ')' at ") + pos);
-                }
-                pos = parse_space(pos + 1, is_nested);
-            } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
-                if (last_sym_start == out_elements.size()) {
-                    throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos);
-                }
-
-                // apply transformation to previous symbol (last_sym_start to end) according to
-                // rewrite rules:
-                // S* --> S' ::= S S' |
-                // S+ --> S' ::= S S' | S
-                // S? --> S' ::= S |
-                uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
-                std::vector<whisper_grammar_element> sub_rule;
-                // add preceding symbol to generated rule
-                sub_rule.insert(
-                    sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
-                if (*pos == '*' || *pos == '+') {
-                    // cause generated rule to recurse
-                    sub_rule.push_back({WHISPER_GRETYPE_RULE_REF, sub_rule_id});
-                }
-                // mark start of alternate def
-                sub_rule.push_back({WHISPER_GRETYPE_ALT, 0});
-                if (*pos == '+') {
-                    // add preceding symbol as alternate only for '+' (otherwise empty)
-                    sub_rule.insert(
-                        sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
-                }
-                sub_rule.push_back({WHISPER_GRETYPE_END, 0});
-                add_rule(state, sub_rule_id, sub_rule);
-
-                // in original rule, replace previous symbol with reference to generated rule
-                out_elements.resize(last_sym_start);
-                out_elements.push_back({WHISPER_GRETYPE_RULE_REF, sub_rule_id});
-
-                pos = parse_space(pos + 1, is_nested);
-            } else {
-                break;
-            }
-        }
-        return pos;
-    }
-
-    const char * parse_alternates(
-            parse_state       & state,
-            const char        * src,
-            const std::string & rule_name,
-            uint32_t            rule_id,
-            bool                is_nested) {
-        std::vector<whisper_grammar_element> rule;
-        const char * pos = parse_sequence(state, src, rule_name, rule, is_nested);
-        while (*pos == '|') {
-            rule.push_back({WHISPER_GRETYPE_ALT, 0});
-            pos = parse_space(pos + 1, true);
-            pos = parse_sequence(state, pos, rule_name, rule, is_nested);
-        }
-        rule.push_back({WHISPER_GRETYPE_END, 0});
-        add_rule(state, rule_id, rule);
-        return pos;
-    }
-
-    const char * parse_rule(parse_state & state, const char * src) {
-        const char * name_end = parse_name(src);
-        const char * pos      = parse_space(name_end, false);
-        size_t       name_len = name_end - src;
-        uint32_t     rule_id  = get_symbol_id(state, src, name_len);
-        const std::string name(src, name_len);
-
-        if (!(pos[0] == ':' && pos[1] == ':' && pos[2] == '=')) {
-            throw std::runtime_error(std::string("expecting ::= at ") + pos);
-        }
-        pos = parse_space(pos + 3, true);
-
-        pos = parse_alternates(state, pos, name, rule_id, false);
-
-        if (*pos == '\r') {
-            pos += pos[1] == '\n' ? 2 : 1;
-        } else if (*pos == '\n') {
-            pos++;
-        } else if (*pos) {
-            throw std::runtime_error(std::string("expecting newline or end at ") + pos);
-        }
-        return parse_space(pos, true);
-    }
-
-    parse_state parse(const char * src) {
-        try {
-            parse_state state;
-            const char * pos = parse_space(src, true);
-            while (*pos) {
-                pos = parse_rule(state, pos);
-            }
-            return state;
-        } catch (const std::exception & err) {
-            fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());
-            return parse_state();
-        }
-    }
-
-    void print_grammar_char(FILE * file, uint32_t c) {
-        if (0x20 <= c && c <= 0x7f) {
-            fprintf(file, "%c", static_cast<char>(c));
-        } else {
-            // cop out of encoding UTF-8
-            fprintf(file, "<U+%04X>", c);
-        }
-    }
-
-    bool is_char_element(whisper_grammar_element elem) {
-        switch (elem.type) {
-            case WHISPER_GRETYPE_CHAR:           return true;
-            case WHISPER_GRETYPE_CHAR_NOT:       return true;
-            case WHISPER_GRETYPE_CHAR_ALT:       return true;
-            case WHISPER_GRETYPE_CHAR_RNG_UPPER: return true;
-            default:                           return false;
-        }
-    }
-
-    void print_rule_binary(FILE * file, const std::vector<whisper_grammar_element> & rule) {
-        for (auto elem : rule) {
-            switch (elem.type) {
-                case WHISPER_GRETYPE_END:            fprintf(file, "END");            break;
-                case WHISPER_GRETYPE_ALT:            fprintf(file, "ALT");            break;
-                case WHISPER_GRETYPE_RULE_REF:       fprintf(file, "RULE_REF");       break;
-                case WHISPER_GRETYPE_CHAR:           fprintf(file, "CHAR");           break;
-                case WHISPER_GRETYPE_CHAR_NOT:       fprintf(file, "CHAR_NOT");       break;
-                case WHISPER_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
-                case WHISPER_GRETYPE_CHAR_ALT:       fprintf(file, "CHAR_ALT");       break;
-            }
-            switch (elem.type) {
-                case WHISPER_GRETYPE_END:
-                case WHISPER_GRETYPE_ALT:
-                case WHISPER_GRETYPE_RULE_REF:
-                    fprintf(file, "(%u) ", elem.value);
-                    break;
-                case WHISPER_GRETYPE_CHAR:
-                case WHISPER_GRETYPE_CHAR_NOT:
-                case WHISPER_GRETYPE_CHAR_RNG_UPPER:
-                case WHISPER_GRETYPE_CHAR_ALT:
-                    fprintf(file, "(\"");
-                    print_grammar_char(file, elem.value);
-                    fprintf(file, "\") ");
-                    break;
-            }
-        }
-        fprintf(file, "\n");
-    }
-
-    void print_rule(
-            FILE     * file,
-            uint32_t   rule_id,
-            const std::vector<whisper_grammar_element> & rule,
-            const std::map<uint32_t, std::string>    & symbol_id_names) {
-        if (rule.empty() || rule.back().type != WHISPER_GRETYPE_END) {
-            throw std::runtime_error(
-                "malformed rule, does not end with WHISPER_GRETYPE_END: " + std::to_string(rule_id));
-        }
-        fprintf(file, "%s ::= ", symbol_id_names.at(rule_id).c_str());
-        for (size_t i = 0, end = rule.size() - 1; i < end; i++) {
-            whisper_grammar_element elem = rule[i];
-            switch (elem.type) {
-                case WHISPER_GRETYPE_END:
-                    throw std::runtime_error(
-                        "unexpected end of rule: " + std::to_string(rule_id) + "," +
-                        std::to_string(i));
-                case WHISPER_GRETYPE_ALT:
-                    fprintf(file, "| ");
-                    break;
-                case WHISPER_GRETYPE_RULE_REF:
-                    fprintf(file, "%s ", symbol_id_names.at(elem.value).c_str());
-                    break;
-                case WHISPER_GRETYPE_CHAR:
-                    fprintf(file, "[");
-                    print_grammar_char(file, elem.value);
-                    break;
-                case WHISPER_GRETYPE_CHAR_NOT:
-                    fprintf(file, "[^");
-                    print_grammar_char(file, elem.value);
-                    break;
-                case WHISPER_GRETYPE_CHAR_RNG_UPPER:
-                    if (i == 0 || !is_char_element(rule[i - 1])) {
-                        throw std::runtime_error(
-                            "WHISPER_GRETYPE_CHAR_RNG_UPPER without preceding char: " +
-                            std::to_string(rule_id) + "," + std::to_string(i));
-                    }
-                    fprintf(file, "-");
-                    print_grammar_char(file, elem.value);
-                    break;
-                case WHISPER_GRETYPE_CHAR_ALT:
-                    if (i == 0 || !is_char_element(rule[i - 1])) {
-                        throw std::runtime_error(
-                            "WHISPER_GRETYPE_CHAR_ALT without preceding char: " +
-                            std::to_string(rule_id) + "," + std::to_string(i));
-                    }
-                    print_grammar_char(file, elem.value);
-                    break;
-            }
-            if (is_char_element(elem)) {
-                switch (rule[i + 1].type) {
-                    case WHISPER_GRETYPE_CHAR_ALT:
-                    case WHISPER_GRETYPE_CHAR_RNG_UPPER:
-                        break;
-                    default:
-                        fprintf(file, "] ");
-                }
-            }
-        }
-        fprintf(file, "\n");
-    }
-
-    void print_grammar(FILE * file, const parse_state & state) {
-        try {
-            std::map<uint32_t, std::string> symbol_id_names;
-            for (auto kv : state.symbol_ids) {
-                symbol_id_names[kv.second] = kv.first;
-            }
-            for (size_t i = 0, end = state.rules.size(); i < end; i++) {
-                // fprintf(file, "%zu: ", i);
-                // print_rule_binary(file, state.rules[i]);
-                print_rule(file, uint32_t(i), state.rules[i], symbol_id_names);
-                // fprintf(file, "\n");
-            }
-        } catch (const std::exception & err) {
-            fprintf(stderr, "\n%s: error printing grammar: %s\n", __func__, err.what());
-        }
-    }
-
-    std::vector<const whisper_grammar_element *> parse_state::c_rules() const{
-        std::vector<const whisper_grammar_element *> ret;
-        for (const auto & rule : rules) {
-            ret.push_back(rule.data());
-        }
-        return ret;
-    }
-}

examples/grammar-parser.h

@@ -1,29 +0,0 @@
-// Implements a parser for an extended Backus-Naur form (BNF), producing the
-// binary context-free grammar format specified by whisper.h. Supports character
-// ranges, grouping, and repetition operators. As an example, a grammar for
-// arithmetic might look like:
-//
-// root  ::= expr
-// expr  ::= term ([-+*/] term)*
-// term  ::= num | "(" space expr ")" space
-// num   ::= [0-9]+ space
-// space ::= [ \t\n]*
-
-#pragma once
-#include "whisper.h"
-#include <vector>
-#include <map>
-#include <cstdint>
-#include <string>
-
-namespace grammar_parser {
-    struct parse_state {
-        std::map<std::string, uint32_t>                   symbol_ids;
-        std::vector<std::vector<whisper_grammar_element>> rules;
-
-        std::vector<const whisper_grammar_element *>      c_rules() const;
-    };
-
-    parse_state parse(const char * src);
-    void print_grammar(FILE * file, const parse_state & state);
-}

examples/lsp/lsp.cpp

@@ -324,7 +324,7 @@ 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,
@@ -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

@@ -83,6 +83,7 @@ struct whisper_params {
     bool output_wts      = false;
     bool output_csv      = false;
     bool output_jsn      = false;
+    bool output_jsn_full = false;
     bool output_lrc      = false;
     bool print_special   = false;
     bool print_colors    = false;
@@ -151,6 +152,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         else if (arg == "-fp"   || arg == "--font-path")       { params.font_path       = argv[++i]; }
         else if (arg == "-ocsv" || arg == "--output-csv")      { params.output_csv      = true; }
         else if (arg == "-oj"   || arg == "--output-json")     { params.output_jsn      = true; }
+        else if (arg == "-ojf"  || arg == "--output-json-full"){ params.output_jsn_full = params.output_jsn = true; }
         else if (arg == "-of"   || arg == "--output-file")     { params.fname_out.emplace_back(argv[++i]); }
         else if (arg == "-ps"   || arg == "--print-special")   { params.print_special   = true; }
         else if (arg == "-pc"   || arg == "--print-colors")    { params.print_colors    = true; }
@@ -206,6 +208,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -fp,       --font-path         [%-7s] path to a monospace font for karaoke video\n",     params.font_path.c_str());
     fprintf(stderr, "  -ocsv,     --output-csv        [%-7s] output result in a CSV file\n",                    params.output_csv ? "true" : "false");
     fprintf(stderr, "  -oj,       --output-json       [%-7s] output result in a JSON file\n",                   params.output_jsn ? "true" : "false");
+    fprintf(stderr, "  -ojf,      --output-json-full  [%-7s] include more information in the JSON file\n",      params.output_jsn_full ? "true" : "false");
     fprintf(stderr, "  -of FNAME, --output-file FNAME [%-7s] output file path (without file extension)\n",      "");
     fprintf(stderr, "  -ps,       --print-special     [%-7s] print special tokens\n",                           params.print_special ? "true" : "false");
     fprintf(stderr, "  -pc,       --print-colors      [%-7s] print colors\n",                                   params.print_colors ? "true" : "false");
@@ -260,7 +263,7 @@ 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 +495,7 @@ 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);
 
@@ -511,7 +514,12 @@ bool output_score(struct whisper_context * ctx, const char * fname, const whispe
     return true;
 }
 
-bool output_json(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
+bool output_json(
+             struct whisper_context * ctx,
+                         const char * fname,
+               const whisper_params & params,
+    std::vector<std::vector<float>>   pcmf32s,
+                               bool   full) {
     std::ofstream fout(fname);
     int indent = 0;
 
@@ -528,7 +536,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
     auto end_arr = [&](bool end) {
         indent--;
         doindent();
-        fout << (end ? "]\n" : "},\n");
+        fout << (end ? "]\n" : "],\n");
     };
 
     auto start_obj = [&](const char *name) {
@@ -569,12 +577,29 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
         end_value(end);
     };
 
+    auto value_f = [&](const char *name, const float val, bool end) {
+        start_value(name);
+        fout << val;
+        end_value(end);
+    };
+
     auto value_b = [&](const char *name, const bool val, bool end) {
         start_value(name);
         fout << (val ? "true" : "false");
         end_value(end);
     };
 
+    auto times_o = [&](int64_t t0, int64_t t1, bool end) {
+        start_obj("timestamps");
+        value_s("from", to_timestamp(t0, true).c_str(), false);
+        value_s("to", to_timestamp(t1, true).c_str(), true);
+        end_obj(false);
+        start_obj("offsets");
+        value_i("from", t0 * 10, false);
+        value_i("to", t1 * 10, true);
+        end_obj(end);
+    };
+
     if (!fout.is_open()) {
         fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
         return false;
@@ -620,15 +645,26 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
                 const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
 
                 start_obj(nullptr);
-                    start_obj("timestamps");
+                    times_o(t0, t1, false);
-                        value_s("from", to_timestamp(t0, true).c_str(), false);
+                    value_s("text", text, !params.diarize && !params.tinydiarize && !full);
-                        value_s("to", to_timestamp(t1, true).c_str(), true);
+
-                    end_obj(false);
+                    if (full) {
-                    start_obj("offsets");
+                        start_arr("tokens");
-                        value_i("from", t0 * 10, false);
+                        const int n = whisper_full_n_tokens(ctx, i);
-                        value_i("to", t1 * 10, true);
+                        for (int j = 0; j < n; ++j) {
-                    end_obj(false);
+                            auto token = whisper_full_get_token_data(ctx, i, j);
-                    value_s("text", text, !params.diarize && !params.tinydiarize);
+                            start_obj(nullptr);
+                                value_s("text", whisper_token_to_str(ctx, token.id), false);
+                                if(token.t0 > -1 && token.t1 > -1) {
+                                    // If we have per-token timestamps, write them out
+                                    times_o(token.t0, token.t1, false);
+                                }
+                                value_i("id", token.id, false);
+                                value_f("p", token.p, true);
+                            end_obj(j == (n - 1));
+                        }
+                        end_arr(!params.diarize && !params.tinydiarize);
+                    }
 
                     if (params.diarize && pcmf32s.size() == 2) {
                         value_s("speaker", estimate_diarization_speaker(pcmf32s, t0, t1, true).c_str(), true);
@@ -912,7 +948,7 @@ int main(int argc, char ** argv) {
             wparams.offset_ms        = params.offset_t_ms;
             wparams.duration_ms      = params.duration_ms;
 
-            wparams.token_timestamps = params.output_wts || params.max_len > 0;
+            wparams.token_timestamps = params.output_wts || params.output_jsn_full || params.max_len > 0;
             wparams.thold_pt         = params.word_thold;
             wparams.max_len          = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
             wparams.split_on_word    = params.split_on_word;
@@ -944,8 +980,9 @@ int main(int argc, char ** argv) {
                 wparams.progress_callback_user_data = &user_data;
             }
 
-            // example for abort mechanism
+            // examples for abort mechanism
-            // in this example, we do not abort the processing, but we could if the flag is set to true
+            // in examples below, we do not abort the processing, but we could if the flag is set to true
+
             // the callback is called before every encoder run - if it returns false, the processing is aborted
             {
                 static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
@@ -957,6 +994,17 @@ int main(int argc, char ** argv) {
                 wparams.encoder_begin_callback_user_data = &is_aborted;
             }
 
+            // the callback is called before every computation - if it returns true, the computation is aborted
+            {
+                static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
+
+                wparams.abort_callback = [](void * user_data) {
+                    bool is_aborted = *(bool*)user_data;
+                    return is_aborted;
+                };
+                wparams.abort_callback_user_data = &is_aborted;
+            }
+
             if (whisper_full_parallel(ctx, wparams, pcmf32.data(), pcmf32.size(), params.n_processors) != 0) {
                 fprintf(stderr, "%s: failed to process audio\n", argv[0]);
                 return 10;
@@ -1000,7 +1048,7 @@ int main(int argc, char ** argv) {
             // output to JSON file
             if (params.output_jsn) {
                 const auto fname_jsn = fname_out + ".json";
-                output_json(ctx, fname_jsn.c_str(), params, pcmf32s);
+                output_json(ctx, fname_jsn.c_str(), params, pcmf32s, params.output_jsn_full);
             }
 
             // output to LRC file

examples/stream/README.md

@@ -39,6 +39,20 @@ brew install sdl2
 make stream
 ```
 
+Ensure you are at the root of the repo when running `make stream`.  Not within the `examples/stream` dir
+as the libraries needed like `common-sdl.h` are located within `examples`.  Attempting to compile within
+`examples/steam` means your compiler cannot find them and it gives an error it cannot find the file.
+
+```bash
+whisper.cpp/examples/stream$ make stream
+g++     stream.cpp   -o stream
+stream.cpp:6:10: fatal error: common/sdl.h: No such file or directory
+    6 | #include "common/sdl.h"
+      |          ^~~~~~~~~~~~~~
+compilation terminated.
+make: *** [<builtin>: stream] Error 1
+```
+
 ## Web version
 
 This tool can also run in the browser: [examples/stream.wasm](/examples/stream.wasm)

examples/stream/stream.cpp

@@ -2,9 +2,8 @@
 //
 // 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>
@@ -14,6 +13,7 @@
 #include <vector>
 #include <fstream>
 
+
 //  500 -> 00:05.000
 // 6000 -> 01:00.000
 std::string to_timestamp(int64_t t) {
@@ -52,6 +52,7 @@ struct whisper_params {
     std::string language  = "en";
     std::string model     = "models/ggml-base.en.bin";
     std::string fname_out;
+    bool save_audio = false; // save audio to wav file
 };
 
 void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
@@ -82,6 +83,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         else if (arg == "-m"   || arg == "--model")         { params.model         = argv[++i]; }
         else if (arg == "-f"   || arg == "--file")          { params.fname_out     = argv[++i]; }
         else if (arg == "-tdrz" || arg == "--tinydiarize")  { params.tinydiarize   = true; }
+        else if (arg == "-sa"  || arg == "--save-audio")    { params.save_audio    = true; }
 
         else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
@@ -117,6 +119,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -m FNAME, --model FNAME   [%-7s] model path\n",                                     params.model.c_str());
     fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                          params.fname_out.c_str());
     fprintf(stderr, "  -tdrz,     --tinydiarize  [%-7s] enable tinydiarize (requires a tdrz model)\n",     params.tinydiarize ? "true" : "false");
+    fprintf(stderr, "  -sa,      --save-audio    [%-7s] save the recorded audio to a file\n",              params.save_audio ? "true" : "false");
     fprintf(stderr, "\n");
 }
 
@@ -154,7 +157,6 @@ int main(int argc, char ** argv) {
     audio.resume();
 
     // whisper init
-
     if (params.language != "auto" && whisper_lang_id(params.language.c_str()) == -1){
         fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
         whisper_print_usage(argc, argv, params);
@@ -212,7 +214,18 @@ int main(int argc, char ** argv) {
         }
     }
 
-    printf("[Start speaking]");
+    wav_writer wavWriter;
+    // save wav file
+    if (params.save_audio) {
+        // Get current date/time for filename
+        time_t now = time(0);
+        char buffer[80];
+        strftime(buffer, sizeof(buffer), "%Y%m%d%H%M%S", localtime(&now));
+        std::string filename = std::string(buffer) + ".wav";
+
+        wavWriter.open(filename, WHISPER_SAMPLE_RATE, 16, 1);
+    }
+    printf("[Start speaking]\n");
     fflush(stdout);
 
     auto t_last  = std::chrono::high_resolution_clock::now();
@@ -220,6 +233,9 @@ int main(int argc, char ** argv) {
 
     // main audio loop
     while (is_running) {
+        if (params.save_audio) {
+            wavWriter.write(pcmf32_new.data(), pcmf32_new.size());
+        }
         // handle Ctrl + C
         is_running = sdl_poll_events();
 

examples/talk-llama/CMakeLists.txt

@@ -7,7 +7,7 @@ if (WHISPER_SDL2)
 
     # TODO: this is temporary
     #       need to export ggml symbols for MSVC, but too lazy ..
-    add_executable(${TARGET} talk-llama.cpp llama.cpp ../common.cpp ../common-sdl.cpp ../../ggml.c ../../whisper.cpp)
+    add_executable(${TARGET} talk-llama.cpp llama.cpp ../common.cpp ../common-sdl.cpp ../../ggml.c ../../ggml-alloc.c ../../whisper.cpp)
 
     target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS} ../../)
     target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})

examples/talk-llama/README.md

@@ -2,6 +2,12 @@
 
 Talk with an LLaMA AI in your terminal
 
+*Latest perf as of 2 Nov 2023 using Whisper Medium + LLaMA v2 13B Q8_0 on M2 Ultra:*
+
+https://github.com/ggerganov/whisper.cpp/assets/1991296/d97a3788-bf2a-4756-9a43-60c6b391649e
+
+*Previous demo running on CPUs*
+
 [Demo Talk](https://user-images.githubusercontent.com/1991296/228024237-848f998c-c334-46a6-bef8-3271590da83b.mp4)
 
 ## Building
@@ -19,7 +25,7 @@ brew install sdl2
 make talk-llama
 
 # Run it
-./talk-llama -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/13B/ggml-model-q4_0.bin -p "Georgi" -t 8
+./talk-llama -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/llama-13b/ggml-model-q4_0.gguf -p "Georgi" -t 8
 ```
 
 - The `-mw` argument specifies the Whisper model that you would like to use. Recommended `base` or `small` for real-time experience
@@ -36,7 +42,7 @@ This feature is especially helpful for maintaining context in long conversations
 Example usage:
 
 ```bash
-./talk-llama --session ./my-session-file -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/13B/ggml-model-q4_0.bin -p "Georgi" -t 8
+./talk-llama --session ./my-session-file -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/llama-13b/ggml-model-q4_0.gguf -p "Georgi" -t 8
 ```
 
 ## TTS

examples/talk-llama/llama-util.h

@@ -1,474 +0,0 @@
-// Internal header to be included only by llama.cpp.
-// Contains wrappers around OS interfaces.
-
-#ifndef LLAMA_UTIL_H
-#define LLAMA_UTIL_H
-
-#include <cstdio>
-#include <cstdint>
-#include <cerrno>
-#include <cstring>
-#include <cstdarg>
-#include <cstdlib>
-#include <climits>
-
-#include <string>
-#include <vector>
-#include <stdexcept>
-
-#ifdef __has_include
-    #if __has_include(<unistd.h>)
-        #include <unistd.h>
-        #if defined(_POSIX_MAPPED_FILES)
-            #include <sys/mman.h>
-        #endif
-        #if defined(_POSIX_MEMLOCK_RANGE)
-            #include <sys/resource.h>
-        #endif
-    #endif
-#endif
-
-#if defined(_WIN32)
-    #define WIN32_LEAN_AND_MEAN
-    #ifndef NOMINMAX
-        #define NOMINMAX
-    #endif
-    #include <windows.h>
-    #include <io.h>
-    #include <stdio.h> // for _fseeki64
-#endif
-
-#define LLAMA_ASSERT(x) \
-    do { \
-        if (!(x)) { \
-            fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
-            abort(); \
-        } \
-    } while (0)
-
-#ifdef __GNUC__
-#ifdef __MINGW32__
-__attribute__((format(gnu_printf, 1, 2)))
-#else
-__attribute__((format(printf, 1, 2)))
-#endif
-#endif
-static std::string format(const char * fmt, ...) {
-    va_list ap, ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
-    LLAMA_ASSERT(size >= 0 && size < INT_MAX);
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    LLAMA_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-    return std::string(buf.data(), size);
-}
-
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
-
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
-        }
-        seek(0, SEEK_END);
-        size = tell();
-        seek(0, SEEK_SET);
-    }
-
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        LLAMA_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
-    }
-
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        LLAMA_ASSERT(ret == 0); // same
-    }
-
-    void read_raw(void * ptr, size_t len) const {
-        if (len == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, len, 1, fp);
-        if (ferror(fp)) {
-            throw std::runtime_error(format("read error: %s", strerror(errno)));
-        }
-        if (ret != 1) {
-            throw std::runtime_error(std::string("unexpectedly reached end of file"));
-        }
-    }
-
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
-    }
-
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
-    }
-
-    void write_raw(const void * ptr, size_t len) const {
-        if (len == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, len, 1, fp);
-        if (ret != 1) {
-            throw std::runtime_error(format("write error: %s", strerror(errno)));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-};
-
-#if defined(_WIN32)
-static std::string llama_format_win_err(DWORD err) {
-    LPSTR buf;
-    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
-                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
-    if (!size) {
-        return "FormatMessageA failed";
-    }
-    std::string ret(buf, size);
-    LocalFree(buf);
-    return ret;
-}
-#endif
-
-struct llama_mmap {
-    void * addr;
-    size_t size;
-
-    llama_mmap(const llama_mmap &) = delete;
-
-#ifdef _POSIX_MAPPED_FILES
-    static constexpr bool SUPPORTED = true;
-
-    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */) {
-        size = file->size;
-        int fd = fileno(file->fp);
-        int flags = MAP_SHARED;
-#ifdef __linux__
-        flags |= MAP_POPULATE;
-#endif
-        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
-        if (addr == MAP_FAILED) {
-            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
-        }
-
-        if (prefetch > 0) {
-            // Advise the kernel to preload the mapped memory
-            if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
-                fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
-                        strerror(errno));
-            }
-        }
-    }
-
-    ~llama_mmap() {
-        munmap(addr, size);
-    }
-#elif defined(_WIN32)
-    static constexpr bool SUPPORTED = true;
-
-    llama_mmap(struct llama_file * file, bool prefetch = true) {
-        size = file->size;
-
-        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
-
-        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
-        DWORD error = GetLastError();
-
-        if (hMapping == NULL) {
-            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
-        }
-
-        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
-        error = GetLastError();
-        CloseHandle(hMapping);
-
-        if (addr == NULL) {
-            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
-        }
-
-        #if _WIN32_WINNT >= _WIN32_WINNT_WIN8
-        if (prefetch) {
-            // Advise the kernel to preload the mapped memory
-            WIN32_MEMORY_RANGE_ENTRY range;
-            range.VirtualAddress = addr;
-            range.NumberOfBytes = (SIZE_T)size;
-            if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
-                fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-            }
-        }
-        #else
-        #pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
-        #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
-    }
-
-    ~llama_mmap() {
-        if (!UnmapViewOfFile(addr)) {
-            fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
-                    llama_format_win_err(GetLastError()).c_str());
-        }
-    }
-#else
-    static constexpr bool SUPPORTED = false;
-
-    llama_mmap(struct llama_file *, bool prefetch = true) {
-        (void)prefetch;
-        throw std::runtime_error(std::string("mmap not supported"));
-    }
-#endif
-};
-
-// Represents some region of memory being locked using mlock or VirtualLock;
-// will automatically unlock on destruction.
-struct llama_mlock {
-    void * addr = NULL;
-    size_t size = 0;
-    bool failed_already = false;
-
-    llama_mlock() {}
-    llama_mlock(const llama_mlock &) = delete;
-
-    ~llama_mlock() {
-        if (size) {
-            raw_unlock(addr, size);
-        }
-    }
-
-    void init(void * ptr) {
-        LLAMA_ASSERT(addr == NULL && size == 0);
-        addr = ptr;
-    }
-
-    void grow_to(size_t target_size) {
-        LLAMA_ASSERT(addr);
-        if (failed_already) {
-            return;
-        }
-        size_t granularity = lock_granularity();
-        target_size = (target_size + granularity - 1) & ~(granularity - 1);
-        if (target_size > size) {
-            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
-                size = target_size;
-            } else {
-                failed_already = true;
-            }
-        }
-    }
-
-#ifdef _POSIX_MEMLOCK_RANGE
-    static constexpr bool SUPPORTED = true;
-
-    size_t lock_granularity() {
-        return (size_t) sysconf(_SC_PAGESIZE);
-    }
-
-    #ifdef __APPLE__
-        #define MLOCK_SUGGESTION \
-            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
-            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
-    #else
-        #define MLOCK_SUGGESTION \
-            "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
-    #endif
-
-    bool raw_lock(const void * addr, size_t size) {
-        if (!mlock(addr, size)) {
-            return true;
-        } else {
-            char* errmsg = std::strerror(errno);
-            bool suggest = (errno == ENOMEM);
-
-            // Check if the resource limit is fine after all
-            struct rlimit lock_limit;
-            if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit))
-                suggest = false;
-            if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size))
-                suggest = false;
-
-            fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
-                    size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
-            return false;
-        }
-    }
-
-    #undef MLOCK_SUGGESTION
-
-    void raw_unlock(void * addr, size_t size) {
-        if (munlock(addr, size)) {
-            fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
-        }
-    }
-#elif defined(_WIN32)
-    static constexpr bool SUPPORTED = true;
-
-    size_t lock_granularity() {
-        SYSTEM_INFO si;
-        GetSystemInfo(&si);
-        return (size_t) si.dwPageSize;
-    }
-
-    bool raw_lock(void * ptr, size_t len) {
-        for (int tries = 1; ; tries++) {
-            if (VirtualLock(ptr, len)) {
-                return true;
-            }
-            if (tries == 2) {
-                fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
-                    len, size, llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-
-            // It failed but this was only the first try; increase the working
-            // set size and try again.
-            SIZE_T min_ws_size, max_ws_size;
-            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
-                fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-            // Per MSDN: "The maximum number of pages that a process can lock
-            // is equal to the number of pages in its minimum working set minus
-            // a small overhead."
-            // Hopefully a megabyte is enough overhead:
-            size_t increment = len + 1048576;
-            // The minimum must be <= the maximum, so we need to increase both:
-            min_ws_size += increment;
-            max_ws_size += increment;
-            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
-                fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-        }
-    }
-
-    void raw_unlock(void * ptr, size_t len) {
-        if (!VirtualUnlock(ptr, len)) {
-            fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
-                    llama_format_win_err(GetLastError()).c_str());
-        }
-    }
-#else
-    static constexpr bool SUPPORTED = false;
-
-    size_t lock_granularity() {
-        return (size_t) 65536;
-    }
-
-    bool raw_lock(const void * addr, size_t len) {
-        fprintf(stderr, "warning: mlock not supported on this system\n");
-        return false;
-    }
-
-    void raw_unlock(const void * addr, size_t len) {}
-#endif
-};
-
-// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
-struct llama_buffer {
-    uint8_t * addr = NULL;
-    size_t size = 0;
-
-    llama_buffer() = default;
-
-    void resize(size_t len) {
-        delete[] addr;
-        addr = new uint8_t[len];
-        size = len;
-    }
-
-    ~llama_buffer() {
-        delete[] addr;
-    }
-
-    // disable copy and move
-    llama_buffer(const llama_buffer&) = delete;
-    llama_buffer(llama_buffer&&) = delete;
-    llama_buffer& operator=(const llama_buffer&) = delete;
-    llama_buffer& operator=(llama_buffer&&) = delete;
-};
-
-#ifdef GGML_USE_CUBLAS
-#include "ggml-cuda.h"
-struct llama_ctx_buffer {
-    uint8_t * addr = NULL;
-    bool is_cuda;
-    size_t size = 0;
-
-    llama_ctx_buffer() = default;
-
-    void resize(size_t size) {
-        free();
-
-        addr = (uint8_t *) ggml_cuda_host_malloc(size);
-        if (addr) {
-            is_cuda = true;
-        }
-        else {
-            // fall back to pageable memory
-            addr = new uint8_t[size];
-            is_cuda = false;
-        }
-        this->size = size;
-    }
-
-    void free() {
-        if (addr) {
-            if (is_cuda) {
-                ggml_cuda_host_free(addr);
-            }
-            else {
-                delete[] addr;
-            }
-        }
-        addr = NULL;
-    }
-
-    ~llama_ctx_buffer() {
-        free();
-    }
-
-    // disable copy and move
-    llama_ctx_buffer(const llama_ctx_buffer&) = delete;
-    llama_ctx_buffer(llama_ctx_buffer&&) = delete;
-    llama_ctx_buffer& operator=(const llama_ctx_buffer&) = delete;
-    llama_ctx_buffer& operator=(llama_ctx_buffer&&) = delete;
-};
-#else
-typedef llama_buffer llama_ctx_buffer;
-#endif
-
-#endif

examples/talk-llama/llama.h

@@ -1,8 +1,16 @@
 #ifndef LLAMA_H
 #define LLAMA_H
 
+#include "ggml.h"
+#ifdef GGML_USE_CUBLAS
+#include "ggml-cuda.h"
+#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
+#else
+#define LLAMA_MAX_DEVICES 1
+#endif // GGML_USE_CUBLAS
 #include <stddef.h>
 #include <stdint.h>
+#include <stdio.h>
 #include <stdbool.h>
 
 #ifdef LLAMA_SHARED
@@ -19,18 +27,26 @@
 #    define LLAMA_API
 #endif
 
-#define LLAMA_FILE_MAGIC_GGJT        0x67676a74u // 'ggjt'
+#ifdef __GNUC__
-#define LLAMA_FILE_MAGIC_GGLA        0x67676c61u // 'ggla'
+#    define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
-#define LLAMA_FILE_MAGIC_GGMF        0x67676d66u // 'ggmf'
+#elif defined(_MSC_VER)
-#define LLAMA_FILE_MAGIC_GGML        0x67676d6cu // 'ggml'
+#    define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define DEPRECATED(func, hint) func
+#endif
+
+#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
+
 #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
 
-#define LLAMA_FILE_VERSION           3
-#define LLAMA_FILE_MAGIC             LLAMA_FILE_MAGIC_GGJT
-#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
 #define LLAMA_SESSION_VERSION 1
 
+#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
+// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
+#define LLAMA_SUPPORTS_GPU_OFFLOAD
+#endif
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -41,10 +57,57 @@ extern "C" {
     // TODO: show sample usage
     //
 
+    struct llama_model;
     struct llama_context;
 
     typedef int llama_token;
 
+    enum llama_log_level {
+        LLAMA_LOG_LEVEL_ERROR = 2,
+        LLAMA_LOG_LEVEL_WARN  = 3,
+        LLAMA_LOG_LEVEL_INFO  = 4
+    };
+
+    enum llama_vocab_type {
+        LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
+        LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
+    };
+
+    enum llama_token_type {
+        LLAMA_TOKEN_TYPE_UNDEFINED    = 0,
+        LLAMA_TOKEN_TYPE_NORMAL       = 1,
+        LLAMA_TOKEN_TYPE_UNKNOWN      = 2,
+        LLAMA_TOKEN_TYPE_CONTROL      = 3,
+        LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
+        LLAMA_TOKEN_TYPE_UNUSED       = 5,
+        LLAMA_TOKEN_TYPE_BYTE         = 6,
+    };
+
+    // model file types
+    enum llama_ftype {
+        LLAMA_FTYPE_ALL_F32              = 0,
+        LLAMA_FTYPE_MOSTLY_F16           = 1, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0          = 2, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1          = 3, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
+        // LLAMA_FTYPE_MOSTLY_Q4_2       = 5, // support has been removed
+        // LLAMA_FTYPE_MOSTLY_Q4_3       = 6, // support has been removed
+        LLAMA_FTYPE_MOSTLY_Q8_0          = 7, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_0          = 8, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_1          = 9, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q2_K          = 10,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_S        = 11,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_M        = 12,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_L        = 13,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_K_S        = 14,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_K_M        = 15,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_K_S        = 16,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_K_M        = 17,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q6_K          = 18,// except 1d tensors
+
+        LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
+    };
+
     typedef struct llama_token_data {
         llama_token id; // token id
         float logit;    // log-odds of the token
@@ -60,67 +123,152 @@ extern "C" {
     typedef void (*llama_progress_callback)(float progress, void *ctx);
 
     struct llama_context_params {
-        int n_ctx;        // text context
+        uint32_t seed;         // RNG seed, -1 for random
-        int n_gpu_layers; // number of layers to store in VRAM
+        int32_t  n_ctx;        // text context
-        int seed;         // RNG seed, -1 for random
+        int32_t  n_batch;      // prompt processing batch size
+        int32_t  n_gpu_layers; // number of layers to store in VRAM
+        int32_t  main_gpu;     // the GPU that is used for scratch and small tensors
 
+        const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
+
+        // ref: https://github.com/ggerganov/llama.cpp/pull/2054
+        float    rope_freq_base;  // RoPE base frequency
+        float    rope_freq_scale; // RoPE frequency scaling factor
+
+        // called with a progress value between 0 and 1, pass NULL to disable
+        llama_progress_callback progress_callback;
+        // context pointer passed to the progress callback
+        void * progress_callback_user_data;
+
+        // Keep the booleans together to avoid misalignment during copy-by-value.
+        bool low_vram;   // if true, reduce VRAM usage at the cost of performance
+        bool mul_mat_q;  // if true, use experimental mul_mat_q kernels
         bool f16_kv;     // use fp16 for KV cache
         bool logits_all; // the llama_eval() call computes all logits, not just the last one
         bool vocab_only; // only load the vocabulary, no weights
         bool use_mmap;   // use mmap if possible
         bool use_mlock;  // force system to keep model in RAM
         bool embedding;  // embedding mode only
-
-        // called with a progress value between 0 and 1, pass NULL to disable
-        llama_progress_callback progress_callback;
-        // context pointer passed to the progress callback
-        void * progress_callback_user_data;
     };
 
-    // model file types
+    // Signature for logging events
-    enum llama_ftype {
+    // Note that text includes the new line character at the end for most events.
-        LLAMA_FTYPE_ALL_F32              = 0,
+    // If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
-        LLAMA_FTYPE_MOSTLY_F16           = 1, // except 1d tensors
+    // if it exists.
-        LLAMA_FTYPE_MOSTLY_Q4_0          = 2, // except 1d tensors
+    // It might not exist for progress report where '.' is output repeatedly.
-        LLAMA_FTYPE_MOSTLY_Q4_1          = 3, // except 1d tensors
+    typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
-        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
+
-        // LLAMA_FTYPE_MOSTLY_Q4_2       = 5, // support has been removed
+    // model quantization parameters
-        // LLAMA_FTYPE_MOSTLY_Q4_3       = 6, // support has been removed
+    typedef struct llama_model_quantize_params {
-        LLAMA_FTYPE_MOSTLY_Q8_0          = 7, // except 1d tensors
+        int nthread;                 // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
-        LLAMA_FTYPE_MOSTLY_Q5_0          = 8, // except 1d tensors
+        enum llama_ftype ftype;      // quantize to this llama_ftype
-        LLAMA_FTYPE_MOSTLY_Q5_1          = 9, // except 1d tensors
+        bool allow_requantize;       // allow quantizing non-f32/f16 tensors
+        bool quantize_output_tensor; // quantize output.weight
+        bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
+    } llama_model_quantize_params;
+
+    // grammar types
+    struct llama_grammar;
+
+    // grammar element type
+    enum llama_gretype {
+        // end of rule definition
+        LLAMA_GRETYPE_END            = 0,
+
+        // start of alternate definition for rule
+        LLAMA_GRETYPE_ALT            = 1,
+
+        // non-terminal element: reference to rule
+        LLAMA_GRETYPE_RULE_REF       = 2,
+
+        // terminal element: character (code point)
+        LLAMA_GRETYPE_CHAR           = 3,
+
+        // inverse char(s) ([^a], [^a-b] [^abc])
+        LLAMA_GRETYPE_CHAR_NOT       = 4,
+
+        // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
+        // be an inclusive range ([a-z])
+        LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
+
+        // modifies a preceding LLAMA_GRETYPE_CHAR or
+        // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
+        LLAMA_GRETYPE_CHAR_ALT       = 6,
     };
 
-    LLAMA_API struct llama_context_params llama_context_default_params();
+    typedef struct llama_grammar_element {
+        enum llama_gretype type;
+        uint32_t           value; // Unicode code point or rule ID
+    } llama_grammar_element;
 
-    LLAMA_API bool llama_mmap_supported();
+    // performance timing information
-    LLAMA_API bool llama_mlock_supported();
+    struct llama_timings {
+        double t_start_ms;
+        double t_end_ms;
+        double t_load_ms;
+        double t_sample_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_sample;
+        int32_t n_p_eval;
+        int32_t n_eval;
+    };
+
+    LLAMA_API struct llama_context_params llama_context_default_params(void);
+    LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
 
-    // TODO: not great API - very likely to change
     // Initialize the llama + ggml backend
+    // If numa is true, use NUMA optimizations
     // Call once at the start of the program
-    LLAMA_API void llama_init_backend();
+    LLAMA_API void llama_backend_init(bool numa);
 
-    LLAMA_API int64_t llama_time_us();
+    // Call once at the end of the program - currently only used for MPI
+    LLAMA_API void llama_backend_free(void);
 
-    // Various functions for loading a ggml llama model.
+    LLAMA_API struct llama_model * llama_load_model_from_file(
-    // Allocate (almost) all memory needed for the model.
-    // Return NULL on failure
-    LLAMA_API struct llama_context * llama_init_from_file(
                              const char * path_model,
             struct llama_context_params   params);
 
+    LLAMA_API void llama_free_model(struct llama_model * model);
+
+    LLAMA_API struct llama_context * llama_new_context_with_model(
+                     struct llama_model * model,
+            struct llama_context_params   params);
+
     // Frees all allocated memory
     LLAMA_API void llama_free(struct llama_context * ctx);
 
-    // TODO: not great API - very likely to change
+    LLAMA_API int64_t llama_time_us(void);
+
+    LLAMA_API int  llama_max_devices    (void);
+    LLAMA_API bool llama_mmap_supported (void);
+    LLAMA_API bool llama_mlock_supported(void);
+
+    LLAMA_API int llama_n_vocab    (const struct llama_context * ctx);
+    LLAMA_API int llama_n_ctx      (const struct llama_context * ctx);
+    LLAMA_API int llama_n_ctx_train(const struct llama_context * ctx);
+    LLAMA_API int llama_n_embd     (const struct llama_context * ctx);
+
+    LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_context * ctx);
+
+    LLAMA_API int llama_model_n_vocab    (const struct llama_model * model);
+    LLAMA_API int llama_model_n_ctx      (const struct llama_model * model);
+    LLAMA_API int llama_model_n_ctx_train(const struct llama_model * model);
+    LLAMA_API int llama_model_n_embd     (const struct llama_model * model);
+
+    // Get a string describing the model type
+    LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
+    // Returns the total size of all the tensors in the model in bytes
+    LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
+    // Returns the total number of parameters in the model
+    LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
+
     // Returns 0 on success
-    // nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
     LLAMA_API int llama_model_quantize(
             const char * fname_inp,
             const char * fname_out,
-      enum llama_ftype   ftype,
+            const llama_model_quantize_params * params);
-            int          nthread);
 
     // Apply a LoRA adapter to a loaded model
     // path_base_model is the path to a higher quality model to use as a base for
@@ -128,8 +276,15 @@ extern "C" {
     // The model needs to be reloaded before applying a new adapter, otherwise the adapter
     // will be applied on top of the previous one
     // Returns 0 on success
-    LLAMA_API int llama_apply_lora_from_file(
+    LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
             struct llama_context * ctx,
+                      const char * path_lora,
+                      const char * path_base_model,
+                             int   n_threads),
+            "please use llama_model_apply_lora_from_file instead");
+
+    LLAMA_API int llama_model_apply_lora_from_file(
+            const struct llama_model * model,
                           const char * path_lora,
                           const char * path_base_model,
                                  int   n_threads);
@@ -138,7 +293,7 @@ extern "C" {
     LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
 
     // Sets the current rng seed.
-    LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
+    LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
 
     // Returns the maximum size in bytes of the state (rng, logits, embedding
     // and kv_cache) - will often be smaller after compacting tokens
@@ -168,21 +323,19 @@ extern "C" {
                              int   n_past,
                              int   n_threads);
 
-    // Convert the provided text into tokens.
+    // Same as llama_eval, but use float matrix input directly.
-    // The tokens pointer must be large enough to hold the resulting tokens.
+    LLAMA_API int llama_eval_embd(
-    // Returns the number of tokens on success, no more than n_max_tokens
-    // Returns a negative number on failure - the number of tokens that would have been returned
-    // TODO: not sure if correct
-    LLAMA_API int llama_tokenize(
             struct llama_context * ctx,
-                      const char * text,
+                     const float * embd,
-                     llama_token * tokens,
+                             int   n_tokens,
-                             int   n_max_tokens,
+                             int   n_past,
-                            bool   add_bos);
+                             int   n_threads);
 
-    LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
+    // Export a static computation graph for context of 511 and batch size of 1
-    LLAMA_API int llama_n_ctx  (const struct llama_context * ctx);
+    // NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
-    LLAMA_API int llama_n_embd (const struct llama_context * ctx);
+    //       parameters here to keep things simple
+    // IMPORTANT: do not use for anything else other than debugging and testing!
+    LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
 
     // Token logits obtained from the last call to llama_eval()
     // The logits for the last token are stored in the last row
@@ -195,15 +348,75 @@ extern "C" {
     // shape: [n_embd] (1-dimensional)
     LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
 
-    // Token Id -> String. Uses the vocabulary in the provided context
+    //
-    LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
+    // Vocab
+    //
+
+    LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
+
+    LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
+
+    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
 
     // Special tokens
-    LLAMA_API llama_token llama_token_bos();
+    LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx);  // beginning-of-sentence
-    LLAMA_API llama_token llama_token_eos();
+    LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx);  // end-of-sentence
-    LLAMA_API llama_token llama_token_nl();
+    LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx);  // next-line
 
+    //
+    // Tokenization
+    //
+
+    // 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, no more than n_max_tokens
+    // Returns a negative number on failure - the number of tokens that would have been returned
+    LLAMA_API int llama_tokenize(
+            struct llama_context * ctx,
+                      const char * text,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos);
+
+    LLAMA_API int llama_tokenize_with_model(
+        const struct llama_model * model,
+                      const char * text,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos);
+
+    // Token Id -> Piece.
+    // Uses the vocabulary in the provided context.
+    // Does not write null terminator to the buffer.
+    // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    LLAMA_API int llama_token_to_piece(
+            const struct llama_context * ctx,
+                           llama_token   token,
+                                  char * buf,
+                                  int    length);
+
+    LLAMA_API int llama_token_to_piece_with_model(
+              const struct llama_model * model,
+                           llama_token   token,
+                                  char * buf,
+                                  int    length);
+
+    //
+    // Grammar
+    //
+
+    LLAMA_API struct llama_grammar * llama_grammar_init(
+            const llama_grammar_element ** rules,
+                                 size_t    n_rules,
+                                 size_t    start_rule_index);
+
+    LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
+
+    LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
+
+    //
     // Sampling functions
+    //
 
     /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
     LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
@@ -211,6 +424,16 @@ extern "C" {
     /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
     LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
 
+    /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
+    /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
+    /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
+    LLAMA_API void llama_sample_classifier_free_guidance(
+              struct llama_context * ctx,
+            llama_token_data_array * candidates,
+              struct llama_context * guidance_ctx,
+                             float   scale);
+
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
     LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
 
@@ -227,6 +450,9 @@ extern "C" {
     LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
     LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
 
+    /// @details Apply constraints from grammar
+    LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);
+
     /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
     /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
@@ -248,13 +474,60 @@ extern "C" {
     /// @details Randomly selects a token from the candidates based on their probabilities.
     LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
 
+    /// @details Accepts the sampled token into the grammar
+    LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
+
+    //
+    // Beam search
+    //
+
+    struct llama_beam_view {
+        const llama_token * tokens;
+        size_t n_tokens;
+        float p;   // Cumulative beam probability (renormalized relative to all beams)
+        bool eob;  // Callback should set this to true when a beam is at end-of-beam.
+    };
+
+    // Passed to beam_search_callback function.
+    // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
+    // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
+    // These pointers are valid only during the synchronous callback, so should not be saved.
+    struct llama_beams_state {
+        struct llama_beam_view * beam_views;
+        size_t n_beams;               // Number of elements in beam_views[].
+        size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
+        bool last_call;               // True iff this is the last callback invocation.
+    };
+
+    // Type of pointer to the beam_search_callback function.
+    // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
+    // passed back to beam_search_callback. This avoids having to use global variables in the callback.
+    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
+
+    /// @details Deterministically returns entire sentence constructed by a beam search.
+    /// @param ctx Pointer to the llama_context.
+    /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
+    /// @param callback_data A pointer that is simply passed back to callback.
+    /// @param n_beams Number of beams to use.
+    /// @param n_past Number of tokens already evaluated.
+    /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
+    /// @param n_threads Number of threads as passed to llama_eval().
+    LLAMA_API void llama_beam_search(struct llama_context * ctx, llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, int n_past, int n_predict, int n_threads);
+
     // Performance information
+    LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
     LLAMA_API void llama_print_timings(struct llama_context * ctx);
     LLAMA_API void llama_reset_timings(struct llama_context * ctx);
 
     // Print system information
     LLAMA_API const char * llama_print_system_info(void);
 
+    // Set callback for all future logging events.
+    // If this is not called, or NULL is supplied, everything is output on stderr.
+    LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
+
+    LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
+
 #ifdef __cplusplus
 }
 #endif
@@ -264,10 +537,11 @@ extern "C" {
 
 #include <vector>
 #include <string>
+
 struct ggml_tensor;
 
-std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
+const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
 
-#endif
+#endif // LLAMA_API_INTERNAL
 
 #endif // LLAMA_H

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"
 
@@ -25,6 +25,20 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
     return res;
 }
 
+std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
+    std::vector<char> result(8, 0);
+    const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
+    if (n_tokens < 0) {
+        result.resize(-n_tokens);
+        int check = llama_token_to_piece(ctx, token, result.data(), result.size());
+        GGML_ASSERT(check == -n_tokens);
+    } else {
+        result.resize(n_tokens);
+    }
+
+    return std::string(result.data(), result.size());
+}
+
 // command-line parameters
 struct whisper_params {
     int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
@@ -235,7 +249,7 @@ int main(int argc, char ** argv) {
 
     // llama init
 
-    llama_init_backend();
+    llama_backend_init(true);
 
     auto lparams = llama_context_default_params();
 
@@ -244,7 +258,9 @@ int main(int argc, char ** argv) {
     lparams.seed       = 1;
     lparams.f16_kv     = true;
 
-    struct llama_context * ctx_llama = llama_init_from_file(params.model_llama.c_str(), lparams);
+    struct llama_model * model_llama = llama_load_model_from_file(params.model_llama.c_str(), lparams);
+
+    struct llama_context * ctx_llama = llama_new_context_with_model(model_llama, lparams);
 
     // print some info about the processing
     {
@@ -267,7 +283,6 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "\n");
     }
 
-
     // init audio
 
     audio_async audio(30*1000);
@@ -278,8 +293,6 @@ int main(int argc, char ** argv) {
 
     audio.resume();
 
-    int n_iter = 0;
-
     bool is_running  = true;
     bool force_speak = false;
 
@@ -514,7 +527,7 @@ int main(int argc, char ** argv) {
                             //printf("\n---\n");
                             //printf("resetting: '");
                             //for (int i = 0; i < (int) embd.size(); i++) {
-                            //    printf("%s", llama_token_to_str(ctx_llama, embd[i]));
+                            //    printf("%s", llama_token_to_piece(ctx_llama, embd[i]));
                             //}
                             //printf("'\n");
                             //printf("\n---\n");
@@ -582,7 +595,7 @@ int main(int argc, char ** argv) {
                             auto logits = llama_get_logits(ctx_llama);
                             auto n_vocab = llama_n_vocab(ctx_llama);
 
-                            logits[llama_token_eos()] = 0;
+                            logits[llama_token_eos(ctx_llama)] = 0;
 
                             std::vector<llama_token_data> candidates;
                             candidates.reserve(n_vocab);
@@ -593,13 +606,13 @@ int main(int argc, char ** argv) {
                             llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
 
                             // apply repeat penalty
-                            const float nl_logit = logits[llama_token_nl()];
+                            const float nl_logit = logits[llama_token_nl(ctx_llama)];
 
                             llama_sample_repetition_penalty(ctx_llama, &candidates_p,
                                     embd_inp.data() + std::max(0, n_past - repeat_last_n),
                                     repeat_last_n, repeat_penalty);
 
-                            logits[llama_token_nl()] = nl_logit;
+                            logits[llama_token_nl(ctx_llama)] = nl_logit;
 
                             if (temp <= 0) {
                                 // Greedy sampling
@@ -613,22 +626,22 @@ int main(int argc, char ** argv) {
                             }
                         }
 
-                        if (id != llama_token_eos()) {
+                        if (id != llama_token_eos(ctx_llama)) {
                             // add it to the context
                             embd.push_back(id);
 
-                            text_to_speak += llama_token_to_str(ctx_llama, id);
+                            text_to_speak += llama_token_to_piece(ctx_llama, id);
 
-                            printf("%s", llama_token_to_str(ctx_llama, id));
+                            printf("%s", llama_token_to_piece(ctx_llama, id).c_str());
                         }
                     }
 
                     {
                         std::string last_output;
                         for (int i = embd_inp.size() - 16; i < (int) embd_inp.size(); i++) {
-                            last_output += llama_token_to_str(ctx_llama, embd_inp[i]);
+                            last_output += llama_token_to_piece(ctx_llama, embd_inp[i]);
                         }
-                        last_output += llama_token_to_str(ctx_llama, embd[0]);
+                        last_output += llama_token_to_piece(ctx_llama, embd[0]);
 
                         for (std::string & antiprompt : antiprompts) {
                             if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
@@ -649,11 +662,12 @@ 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();
-
-                ++n_iter;
             }
         }
     }

examples/talk/README.md

@@ -31,7 +31,7 @@ To run this, you will need a ggml GPT-2 model: [instructions](https://github.com
 Alternatively, you can simply download the smallest ggml GPT-2 117M model (240 MB) like this:
 
 ```
-wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/ggerganov/ggml/raw/main/ggml-model-gpt-2-117M.bin
+wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/ggerganov/ggml/resolve/main/ggml-model-gpt-2-117M.bin
 ```
 
 ## TTS

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_size   =*/ ctx_size,
-            .mem_buffer = NULL,
+            /*.mem_buffer =*/ NULL,
-            .no_alloc   = false,
+            /*.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
+        sourceCompatibility JavaVersion.VERSION_17
-        targetCompatibility JavaVersion.VERSION_1_8
+        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.activity:activity-compose:1.7.2'
-    implementation 'androidx.compose.material:material-icons-core:1.3.1'
+    implementation 'androidx.compose.material:material-icons-core:1.5.0'
-    implementation 'androidx.compose.material3:material3:1.0.1'
+    implementation 'androidx.compose.material3:material3:1.1.1'
-    implementation "androidx.compose.ui:ui:1.3.2"
+    implementation "androidx.compose.ui:ui:1.5.0"
-    implementation "androidx.compose.ui:ui-tooling-preview:1.3.2"
+    implementation "androidx.compose.ui:ui-tooling-preview:1.5.0"
-    implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.5.1'
+    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.ext:junit:1.1.5'
-    androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.0'
+    androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
-    androidTestImplementation "androidx.compose.ui:ui-test-junit4:1.3.2"
+    androidTestImplementation "androidx.compose.ui:ui-test-junit4:1.5.0"
-    debugImplementation "androidx.compose.ui:ui-tooling:1.3.2"
+    debugImplementation "androidx.compose.ui:ui-tooling:1.5.0"
-    debugImplementation "androidx.compose.ui:ui-test-manifest:1.3.2"
+    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()
+            val window = (view.context as Activity).window
-            ViewCompat.getWindowInsetsController(view)?.isAppearanceLightStatusBars = darkTheme
+            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/CMakeLists.txt

@@ -8,6 +8,7 @@ set(WHISPER_LIB_DIR ${CMAKE_SOURCE_DIR}/../../../../../../../)
 set(
         SOURCE_FILES
         ${WHISPER_LIB_DIR}/ggml.c
+        ${WHISPER_LIB_DIR}/ggml-alloc.c
         ${WHISPER_LIB_DIR}/whisper.cpp
         ${CMAKE_SOURCE_DIR}/jni.c
 )

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.application' version '8.1.1' apply false
-    id 'com.android.library' version '7.3.1' apply false
+    id 'com.android.library' version '8.1.1' apply false
-    id 'org.jetbrains.kotlin.android' version '1.7.10' 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

examples/whisper.objc/README.md

@@ -28,6 +28,8 @@ This can significantly improve the performance of the transcription:
 
 <img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">
 
+## Core ML
+
 If you want to enable Core ML support, you can add the `-DWHISPER_USE_COREML -DWHISPER_COREML_ALLOW_FALLBACK` compiler flag for `whisper.cpp` in Build Phases:
 
 <img width="1072" alt="image" src="https://github.com/ggerganov/whisper.cpp/assets/3001525/103e8f57-6eb6-490d-a60c-f6cf6c319324">
@@ -35,3 +37,13 @@ If you want to enable Core ML support, you can add the `-DWHISPER_USE_COREML -DW
 Then follow the [`Core ML support` section of readme](../../README.md#core-ml-support) for convert the model.
 
 In this project, it also added `-O3 -DNDEBUG` to `Other C Flags`, but adding flags to app proj is not ideal in real world (applies to all C/C++ files), consider splitting xcodeproj in workspace in your own project.
+
+## Metal
+
+You can also enable Metal to make the inference run on the GPU of your device. This might or might not be more efficient
+compared to Core ML depending on the model and device that you use.
+
+To enable Metal, just add `-DGGML_USE_METAL` instead off the `-DWHISPER_USE_COREML` flag and you are ready.
+This will make both the Encoder and the Decoder run on the GPU.
+
+If you want to run the Encoder with Core ML and the Decoder with Metal then simply add both `-DWHISPER_USE_COREML -DGGML_USE_METAL` flags. That's all!

examples/whisper.objc/whisper.objc.xcodeproj/project.pbxproj

@@ -7,6 +7,9 @@
 	objects = {
 
 /* Begin PBXBuildFile section */
+		1844471A2AB211A2007D6BFE /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 184447182AB211A2007D6BFE /* ggml-alloc.c */; };
+		1844471C2AB21655007D6BFE /* ggml-metal.m in Sources */ = {isa = PBXBuildFile; fileRef = 1844471B2AB21655007D6BFE /* ggml-metal.m */; settings = {COMPILER_FLAGS = "-framework Foundation -framework Metal -framework MetalKit -fno-objc-arc"; }; };
+		184447212AB21B43007D6BFE /* ggml-metal.metal in CopyFiles */ = {isa = PBXBuildFile; fileRef = 1844471D2AB2195F007D6BFE /* ggml-metal.metal */; };
 		18627C7B29052BDF00BD2A04 /* AppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 18627C7A29052BDF00BD2A04 /* AppDelegate.m */; };
 		18627C7E29052BDF00BD2A04 /* SceneDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 18627C7D29052BDF00BD2A04 /* SceneDelegate.m */; };
 		18627C8129052BDF00BD2A04 /* ViewController.m in Sources */ = {isa = PBXBuildFile; fileRef = 18627C8029052BDF00BD2A04 /* ViewController.m */; };
@@ -14,7 +17,7 @@
 		18627C8629052BE000BD2A04 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 18627C8529052BE000BD2A04 /* Assets.xcassets */; };
 		18627C8929052BE000BD2A04 /* LaunchScreen.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = 18627C8729052BE000BD2A04 /* LaunchScreen.storyboard */; };
 		18627C8C29052BE000BD2A04 /* main.m in Sources */ = {isa = PBXBuildFile; fileRef = 18627C8B29052BE000BD2A04 /* main.m */; };
-		18627C9429052C4900BD2A04 /* whisper.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 18627C9329052C4900BD2A04 /* whisper.cpp */; settings = {COMPILER_FLAGS = "-DWHISPER_USE_COREML -DWHISPER_COREML_ALLOW_FALLBACK"; }; };
+		18627C9429052C4900BD2A04 /* whisper.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 18627C9329052C4900BD2A04 /* whisper.cpp */; settings = {COMPILER_FLAGS = "-DWHISPER_USE_COREML"; }; };
 		18627C9629052C5800BD2A04 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 18627C9529052C5800BD2A04 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE"; }; };
 		18627C9B29052CFF00BD2A04 /* ggml-base.en.bin in Resources */ = {isa = PBXBuildFile; fileRef = 18627C9A29052CFF00BD2A04 /* ggml-base.en.bin */; };
 		7FE3424B2A0C3FA20015A058 /* whisper-encoder-impl.m in Sources */ = {isa = PBXBuildFile; fileRef = 7FE342452A0C3FA20015A058 /* whisper-encoder-impl.m */; };
@@ -23,7 +26,24 @@
 		7FE3424F2A0C418A0015A058 /* ggml-base.en-encoder.mlmodelc in Resources */ = {isa = PBXBuildFile; fileRef = 7FE3424E2A0C418A0015A058 /* ggml-base.en-encoder.mlmodelc */; };
 /* End PBXBuildFile section */
 
+/* Begin PBXCopyFilesBuildPhase section */
+		184447202AB21B25007D6BFE /* CopyFiles */ = {
+			isa = PBXCopyFilesBuildPhase;
+			buildActionMask = 2147483647;
+			dstPath = "";
+			dstSubfolderSpec = 7;
+			files = (
+				184447212AB21B43007D6BFE /* ggml-metal.metal in CopyFiles */,
+			);
+			runOnlyForDeploymentPostprocessing = 0;
+		};
+/* End PBXCopyFilesBuildPhase section */
+
 /* Begin PBXFileReference section */
+		184447182AB211A2007D6BFE /* ggml-alloc.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-alloc.c"; path = "../../../ggml-alloc.c"; sourceTree = "<group>"; };
+		184447192AB211A2007D6BFE /* ggml-alloc.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-alloc.h"; path = "../../../ggml-alloc.h"; sourceTree = "<group>"; };
+		1844471B2AB21655007D6BFE /* ggml-metal.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; name = "ggml-metal.m"; path = "../../../ggml-metal.m"; sourceTree = "<group>"; };
+		1844471D2AB2195F007D6BFE /* ggml-metal.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; name = "ggml-metal.metal"; path = "../../../ggml-metal.metal"; sourceTree = "<group>"; };
 		18627C7629052BDF00BD2A04 /* whisper.objc.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = whisper.objc.app; sourceTree = BUILT_PRODUCTS_DIR; };
 		18627C7929052BDF00BD2A04 /* AppDelegate.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = "<group>"; };
 		18627C7A29052BDF00BD2A04 /* AppDelegate.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = AppDelegate.m; sourceTree = "<group>"; };
@@ -80,6 +100,10 @@
 		18627C7829052BDF00BD2A04 /* whisper.objc */ = {
 			isa = PBXGroup;
 			children = (
+				1844471D2AB2195F007D6BFE /* ggml-metal.metal */,
+				1844471B2AB21655007D6BFE /* ggml-metal.m */,
+				184447182AB211A2007D6BFE /* ggml-alloc.c */,
+				184447192AB211A2007D6BFE /* ggml-alloc.h */,
 				7FE3424E2A0C418A0015A058 /* ggml-base.en-encoder.mlmodelc */,
 				7FE342442A0C3FA20015A058 /* coreml */,
 				18627C9A29052CFF00BD2A04 /* ggml-base.en.bin */,
@@ -126,6 +150,7 @@
 				18627C7229052BDF00BD2A04 /* Sources */,
 				18627C7329052BDF00BD2A04 /* Frameworks */,
 				18627C7429052BDF00BD2A04 /* Resources */,
+				184447202AB21B25007D6BFE /* CopyFiles */,
 			);
 			buildRules = (
 			);
@@ -194,8 +219,10 @@
 				18627C9629052C5800BD2A04 /* ggml.c in Sources */,
 				18627C7B29052BDF00BD2A04 /* AppDelegate.m in Sources */,
 				7FE3424D2A0C3FA20015A058 /* whisper-decoder-impl.m in Sources */,
+				1844471A2AB211A2007D6BFE /* ggml-alloc.c in Sources */,
 				18627C8C29052BE000BD2A04 /* main.m in Sources */,
 				18627C7E29052BDF00BD2A04 /* SceneDelegate.m in Sources */,
+				1844471C2AB21655007D6BFE /* ggml-metal.m in Sources */,
 				7FE3424B2A0C3FA20015A058 /* whisper-encoder-impl.m in Sources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;

examples/whisper.swiftui/whisper.swiftui.xcodeproj/project.pbxproj

@@ -20,6 +20,7 @@
 		0AAC5DCC29539EB1003032C3 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 0AAC5DC929539EB0003032C3 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -Wno-shorten-64-to-32"; }; };
 		0AAC5DCE2953A05C003032C3 /* WhisperState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 0AAC5DCD2953A05C003032C3 /* WhisperState.swift */; };
 		0AAC5DD12953A394003032C3 /* LibWhisper.swift in Sources */ = {isa = PBXBuildFile; fileRef = 0AAC5DD02953A394003032C3 /* LibWhisper.swift */; };
+		18AED4812AB21F2B009D854F /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 18AED47F2AB21F2B009D854F /* ggml-alloc.c */; };
 /* End PBXBuildFile section */
 
 /* Begin PBXFileReference section */
@@ -41,6 +42,8 @@
 		0AAC5DCA29539EB0003032C3 /* ggml.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = ggml.h; sourceTree = "<group>"; };
 		0AAC5DCD2953A05C003032C3 /* WhisperState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = WhisperState.swift; sourceTree = "<group>"; };
 		0AAC5DD02953A394003032C3 /* LibWhisper.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibWhisper.swift; sourceTree = "<group>"; };
+		18AED47F2AB21F2B009D854F /* ggml-alloc.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; path = "ggml-alloc.c"; sourceTree = "<group>"; };
+		18AED4802AB21F2B009D854F /* ggml-alloc.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = "ggml-alloc.h"; sourceTree = "<group>"; };
 /* End PBXFileReference section */
 
 /* Begin PBXFrameworksBuildPhase section */
@@ -124,6 +127,8 @@
 		0AAC5DC529539E89003032C3 /* whisper.cpp */ = {
 			isa = PBXGroup;
 			children = (
+				18AED47F2AB21F2B009D854F /* ggml-alloc.c */,
+				18AED4802AB21F2B009D854F /* ggml-alloc.h */,
 				0AAC5DC929539EB0003032C3 /* ggml.c */,
 				0AAC5DCA29539EB0003032C3 /* ggml.h */,
 				0AAC5DC729539EB0003032C3 /* whisper.cpp */,
@@ -242,6 +247,7 @@
 				0AA7514C2953B569001EE061 /* RiffWaveUtils.swift in Sources */,
 				0AAC5DCB29539EB1003032C3 /* whisper.cpp in Sources */,
 				0AA7514E2953D958001EE061 /* Recorder.swift in Sources */,
+				18AED4812AB21F2B009D854F /* ggml-alloc.c in Sources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 		};
@@ -369,7 +375,7 @@
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
 				DEVELOPMENT_ASSET_PATHS = "\"whisper.swiftui.demo/Supporting files/Preview Content\"";
-				DEVELOPMENT_TEAM = 3TZ9BM962G;
+				DEVELOPMENT_TEAM = P8JZH34X63;
 				ENABLE_HARDENED_RUNTIME = YES;
 				ENABLE_PREVIEWS = YES;
 				GENERATE_INFOPLIST_FILE = YES;
@@ -410,7 +416,7 @@
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
 				DEVELOPMENT_ASSET_PATHS = "\"whisper.swiftui.demo/Supporting files/Preview Content\"";
-				DEVELOPMENT_TEAM = 3TZ9BM962G;
+				DEVELOPMENT_TEAM = P8JZH34X63;
 				ENABLE_HARDENED_RUNTIME = YES;
 				ENABLE_PREVIEWS = YES;
 				GENERATE_INFOPLIST_FILE = YES;

extra/bench-all.sh

@@ -44,27 +44,26 @@ if [ "$encoder_only" -eq 0 ]; then
     printf "\n"
 fi
 
-printf "| CPU | OS | Config | Model | Th | Load | Enc. | Commit |\n"
+printf "| %6s | %6s | %16s | %11s | %3s | %7s | %7s | %7s | %7s |\n" "CPU" "OS" "Config" "Model" "Th" "Enc." "Dec." "PP" "Commit"
-printf "| --- | -- | ------ | ----- | -- | ---- | ---- | ------ |\n"
+printf "| %6s | %6s | %16s | %11s | %3s | %7s | %7s | %7s | %7s |\n" "---" "---" "---" "---" "---" "---" "---" "---" "---"
 
 for model in "${models[@]}"; do
-    # run once to heat-up the cache
-    ./bench -m ./models/ggml-$model.bin -t $n_threads 2>/dev/null 1>/dev/null
-
     # actual run
     # store stderr output in a variable in order to parse it later
     output=$(./bench -m ./models/ggml-$model.bin -t $n_threads 2>&1)
     ret=$?
 
     # parse the output:
-    load_time=$(echo "$output" | grep "load time" | awk '{print $5}')
+    encode_time=$(echo "$output" | grep "encode time" | awk '{print $11}')
-    encode_time=$(echo "$output" | grep "encode time" | awk '{print $5}')
+    decode_time=$(echo "$output" | grep "decode time" | awk '{print $11}')
+    prompt_time=$(echo "$output" | grep "prompt time" | awk '{print $11}')
     system_info=$(echo "$output" | grep "system_info")
     n_threads=$(echo "$output" | grep "system_info" | awk '{print $4}')
 
     # floor to milliseconds
-    load_time=${load_time%.*}
+    #encode_time=${encode_time%.*}
-    encode_time=${encode_time%.*}
+    #decode_time=${decode_time%.*}
+    #prompt_time=${prompt_time%.*}
 
     config=""
 
@@ -84,9 +83,13 @@ for model in "${models[@]}"; do
         config="$config COREML"
     fi
 
+    if [[ $system_info == *"METAL = 1"* ]]; then
+        config="$config METAL"
+    fi
+
     commit=$(git rev-parse --short HEAD)
 
     if [ $ret -eq 0 ]; then
-        printf "| <todo> | <todo> | $config | $model | $n_threads | $load_time | $encode_time | $commit |\n"
+        printf "| <todo> | <todo> | %16s | %11s | %3s | %7s | %7s | %7s | %7s |\n" "$config" "$model" "$n_threads" "$encode_time" "$decode_time" "$prompt_time" "$commit"
     fi
 done

extra/bench.py

@@ -0,0 +1,222 @@
+import os
+import subprocess
+import re
+import csv
+import wave
+import contextlib
+import argparse
+
+
+# Custom action to handle comma-separated list
+class ListAction(argparse.Action):
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, [int(val) for val in values.split(",")])
+
+
+parser = argparse.ArgumentParser(description="Benchmark the speech recognition model")
+
+# Define the argument to accept a list
+parser.add_argument(
+    "-t",
+    "--threads",
+    dest="threads",
+    action=ListAction,
+    default=[4],
+    help="List of thread counts to benchmark (comma-separated, default: 4)",
+)
+
+parser.add_argument(
+    "-p",
+    "--processors",
+    dest="processors",
+    action=ListAction,
+    default=[1],
+    help="List of processor counts to benchmark (comma-separated, default: 1)",
+)
+
+
+parser.add_argument(
+    "-f",
+    "--filename",
+    type=str,
+    default="./samples/jfk.wav",
+    help="Relative path of the file to transcribe (default: ./samples/jfk.wav)",
+)
+
+# Parse the command line arguments
+args = parser.parse_args()
+
+sample_file = args.filename
+
+threads = args.threads
+processors = args.processors
+
+# Define the models, threads, and processor counts to benchmark
+models = [
+    "ggml-tiny.en.bin",
+    "ggml-tiny.bin",
+    "ggml-base.en.bin",
+    "ggml-base.bin",
+    "ggml-small.en.bin",
+    "ggml-small.bin",
+    "ggml-medium.en.bin",
+    "ggml-medium.bin",
+    "ggml-large.bin",
+]
+
+
+metal_device = ""
+
+# Initialize a dictionary to hold the results
+results = {}
+
+gitHashHeader = "Commit"
+modelHeader = "Model"
+hardwareHeader = "Hardware"
+recordingLengthHeader = "Recording Length (seconds)"
+threadHeader = "Thread"
+processorCountHeader = "Processor Count"
+loadTimeHeader = "Load Time (ms)"
+sampleTimeHeader = "Sample Time (ms)"
+encodeTimeHeader = "Encode Time (ms)"
+decodeTimeHeader = "Decode Time (ms)"
+sampleTimePerRunHeader = "Sample Time per Run (ms)"
+encodeTimePerRunHeader = "Encode Time per Run (ms)"
+decodeTimePerRunHeader = "Decode Time per Run (ms)"
+totalTimeHeader = "Total Time (ms)"
+
+
+def check_file_exists(file: str) -> bool:
+    return os.path.isfile(file)
+
+
+def get_git_short_hash() -> str:
+    try:
+        return (
+            subprocess.check_output(["git", "rev-parse", "--short", "HEAD"])
+            .decode()
+            .strip()
+        )
+    except subprocess.CalledProcessError as e:
+        return ""
+
+
+def wav_file_length(file: str = sample_file) -> float:
+    with contextlib.closing(wave.open(file, "r")) as f:
+        frames = f.getnframes()
+        rate = f.getframerate()
+        duration = frames / float(rate)
+        return duration
+
+
+def extract_metrics(output: str, label: str) -> tuple[float, float]:
+    match = re.search(rf"{label} \s*=\s*(\d+\.\d+)\s*ms\s*/\s*(\d+)\s*runs", output)
+    time = float(match.group(1)) if match else None
+    runs = float(match.group(2)) if match else None
+    return time, runs
+
+
+def extract_device(output: str) -> str:
+    match = re.search(r"picking default device: (.*)", output)
+    device = match.group(1) if match else "Not found"
+    return device
+
+
+# Check if the sample file exists
+if not check_file_exists(sample_file):
+    raise FileNotFoundError(f"Sample file {sample_file} not found")
+
+recording_length = wav_file_length()
+
+
+# Check that all models exist
+# Filter out models from list that are not downloaded
+filtered_models = []
+for model in models:
+    if check_file_exists(f"models/{model}"):
+        filtered_models.append(model)
+    else:
+        print(f"Model {model} not found, removing from list")
+
+models = filtered_models
+
+# Loop over each combination of parameters
+for model in filtered_models:
+    for thread in threads:
+        for processor_count in processors:
+            # Construct the command to run
+            cmd = f"./main -m models/{model} -t {thread} -p {processor_count} -f {sample_file}"
+            # Run the command and get the output
+            process = subprocess.Popen(
+                cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
+            )
+
+            output = ""
+            while process.poll() is None:
+                output += process.stdout.read().decode()
+
+            # Parse the output
+            load_time_match = re.search(r"load time\s*=\s*(\d+\.\d+)\s*ms", output)
+            load_time = float(load_time_match.group(1)) if load_time_match else None
+
+            metal_device = extract_device(output)
+            sample_time, sample_runs = extract_metrics(output, "sample time")
+            encode_time, encode_runs = extract_metrics(output, "encode time")
+            decode_time, decode_runs = extract_metrics(output, "decode time")
+
+            total_time_match = re.search(r"total time\s*=\s*(\d+\.\d+)\s*ms", output)
+            total_time = float(total_time_match.group(1)) if total_time_match else None
+
+            model_name = model.replace("ggml-", "").replace(".bin", "")
+
+            print(
+                f"Ran model={model_name} threads={thread} processor_count={processor_count}, took {total_time}ms"
+            )
+            # Store the times in the results dictionary
+            results[(model_name, thread, processor_count)] = {
+                loadTimeHeader: load_time,
+                sampleTimeHeader: sample_time,
+                encodeTimeHeader: encode_time,
+                decodeTimeHeader: decode_time,
+                sampleTimePerRunHeader: round(sample_time / sample_runs, 2),
+                encodeTimePerRunHeader: round(encode_time / encode_runs, 2),
+                decodeTimePerRunHeader: round(decode_time / decode_runs, 2),
+                totalTimeHeader: total_time,
+            }
+
+# Write the results to a CSV file
+with open("benchmark_results.csv", "w", newline="") as csvfile:
+    fieldnames = [
+        gitHashHeader,
+        modelHeader,
+        hardwareHeader,
+        recordingLengthHeader,
+        threadHeader,
+        processorCountHeader,
+        loadTimeHeader,
+        sampleTimeHeader,
+        encodeTimeHeader,
+        decodeTimeHeader,
+        sampleTimePerRunHeader,
+        encodeTimePerRunHeader,
+        decodeTimePerRunHeader,
+        totalTimeHeader,
+    ]
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+
+    writer.writeheader()
+
+    shortHash = get_git_short_hash()
+    # Sort the results by total time in ascending order
+    sorted_results = sorted(results.items(), key=lambda x: x[1].get(totalTimeHeader, 0))
+    for params, times in sorted_results:
+        row = {
+            gitHashHeader: shortHash,
+            modelHeader: params[0],
+            hardwareHeader: metal_device,
+            recordingLengthHeader: recording_length,
+            threadHeader: params[1],
+            processorCountHeader: params[2],
+        }
+        row.update(times)
+        writer.writerow(row)

extra/sync-ggml.sh

@@ -1,6 +1,7 @@
 #!/bin/bash
 
 cp -rpv ../ggml/src/ggml.c                ./ggml.c
+cp -rpv ../ggml/src/ggml-alloc.c          ./ggml-alloc.c
 cp -rpv ../ggml/src/ggml-cuda.h           ./ggml-cuda.h
 cp -rpv ../ggml/src/ggml-cuda.cu          ./ggml-cuda.cu
 cp -rpv ../ggml/src/ggml-opencl.h         ./ggml-opencl.h
@@ -9,6 +10,7 @@ cp -rpv ../ggml/src/ggml-metal.h         ./ggml-metal.h
 cp -rpv ../ggml/src/ggml-metal.m          ./ggml-metal.m
 cp -rpv ../ggml/src/ggml-metal.metal      ./ggml-metal.metal
 cp -rpv ../ggml/include/ggml/ggml.h       ./ggml.h
+cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h
 cp -rpv ../ggml/examples/common.h         ./examples/common.h
 cp -rpv ../ggml/examples/common.cpp       ./examples/common.cpp
 cp -rpv ../ggml/examples/common-ggml.h    ./examples/common-ggml.h

ggml-alloc.c

@@ -6,6 +6,26 @@
 #include <stdlib.h>
 #include <string.h>
 
+#ifdef __has_include
+    #if __has_include(<unistd.h>)
+        #include <unistd.h>
+        #if defined(_POSIX_MAPPED_FILES)
+            #include <sys/types.h>
+            #include <sys/mman.h>
+        #endif
+    #endif
+#endif
+
+#if defined(_WIN32)
+    #define WIN32_LEAN_AND_MEAN
+    #ifndef NOMINMAX
+        #define NOMINMAX
+    #endif
+    #include <windows.h>
+    #include <memoryapi.h>
+#endif
+
+
 #define UNUSED(x) (void)(x)
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
@@ -99,15 +119,28 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens
 }
 #endif
 
-
+static size_t ggml_allocr_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
-static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
     return ggml_nbytes(tensor);
 
     UNUSED(alloc);
 }
 
+// check if a tensor is allocated by this buffer
+static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) {
+    void * ptr = tensor->data;
+    return ptr >= alloc->data && (char *)ptr < (char *)alloc->data + alloc->max_size;
+}
+
+static bool ggml_is_view(struct ggml_tensor * t) {
+    return t->view_src != NULL;
+}
+
 void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
-    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
+#ifdef GGML_ALLOCATOR_DEBUG
+    GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources
+    GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
+#endif
+    size_t size = ggml_allocr_get_alloc_size(alloc, tensor);
     size = aligned_offset(NULL, size, alloc->alignment);
 
     AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
@@ -131,9 +164,9 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
     if (best_fit_block == -1) {
         // the last block is our last resort
         struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
+        max_avail = MAX(max_avail, block->size);
         if (block->size >= size) {
             best_fit_block = alloc->n_free_blocks - 1;
-            max_avail = MAX(max_avail, block->size);
         } else {
             fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
                     __func__, size, max_avail);
@@ -173,17 +206,17 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
 }
 
 // this is a very naive implementation, but for our case the number of free blocks should be very small
-static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
     void * ptr = tensor->data;
 
-    if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
+    if (ggml_allocr_is_own(alloc, tensor) == false) {
         // the tensor was not allocated in this buffer
         // this can happen because the graph allocator will try to free weights and other tensors from different buffers
         // the easiest way to deal with this is just to ignore it
         return;
     }
 
-    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
+    size_t size = ggml_allocr_get_alloc_size(alloc, tensor);
     size = aligned_offset(NULL, size, alloc->alignment);
     AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
 
@@ -277,17 +310,68 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
     return alloc;
 }
 
-// address and size of the buffer when measuring
+// OS specific functions to allocate and free uncommitted virtual memory
-// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
+static void * alloc_vmem(size_t size) {
-static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
+#if defined(_WIN32)
-static const size_t MEASURE_MAX_SIZE  = 1ULL<<40; // 1 TB
+    return VirtualAlloc(NULL, size, MEM_RESERVE, PAGE_NOACCESS);
+#elif defined(_POSIX_MAPPED_FILES)
+    void * ptr = mmap(NULL, size, PROT_NONE, MAP_PRIVATE | MAP_ANON, -1, 0);
+    if (ptr == MAP_FAILED) {
+        return NULL;
+    }
+    return ptr;
+#else
+    // use a fixed address for other platforms
+    uintptr_t base_addr = (uintptr_t)-size - 0x100;
+    return (void *)base_addr;
+#endif
+}
+
+static void free_vmem(void * base_addr, size_t size) {
+#if defined(_WIN32)
+    VirtualFree(base_addr, 0, MEM_RELEASE);
+    UNUSED(size);
+#elif defined(_POSIX_MAPPED_FILES)
+    munmap(base_addr, size);
+#else
+    // nothing to do
+    UNUSED(base_addr);
+    UNUSED(size);
+#endif
+}
+
+// allocate uncommitted virtual memory to measure the size of the graph
+static void alloc_measure_vmem(void ** base_addr, size_t * size) {
+    // 128GB for 64-bit, 1GB for 32-bit
+    *size = sizeof(void *) == 4 ? 1ULL<<30 : 1ULL<<37;
+    do {
+        *base_addr = alloc_vmem(*size);
+        if (*base_addr != NULL) {
+            AT_PRINTF("allocated %.2f GB of virtual memory for measure buffer at %p\n", *size / 1024.0 / 1024.0 / 1024.0, *base_addr);
+            return;
+        }
+        // try again with half the size
+        *size /= 2;
+    } while (*size > 0);
+
+    GGML_ASSERT(!"failed to allocate virtual memory for measure buffer");
+}
+
+static void free_measure_vmem(void * base_addr, size_t size) {
+    free_vmem(base_addr, size);
+}
 
 struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
     struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
 
+    void * base_addr;
+    size_t size;
+
+    alloc_measure_vmem(&base_addr, &size);
+
     *alloc = (struct ggml_allocr){
-        /*.data          = */ MEASURE_BASE_ADDR,
+        /*.data          = */ base_addr,
-        /*.size          = */ MEASURE_MAX_SIZE,
+        /*.size          = */ size,
         /*.alignment     = */ alignment,
         /*.n_free_blocks = */ 0,
         /*.free_blocks   = */ {{0}},
@@ -307,6 +391,9 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
 }
 
 void ggml_allocr_free(struct ggml_allocr * alloc) {
+    if (alloc->measure) {
+        free_measure_vmem(alloc->data, alloc->size);
+    }
     free(alloc);
 }
 
@@ -316,11 +403,6 @@ bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
 
 //////////// compute graph allocator
 
-static bool ggml_is_view(struct ggml_tensor * t) {
-    return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
-           t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
-}
-
 static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
     if (a->type != b->type) {
         return false;
@@ -336,28 +418,6 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml
     return true;
 }
 
-static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
-    switch (t->op) {
-        case GGML_OP_PERMUTE:
-        case GGML_OP_RESHAPE:
-        case GGML_OP_TRANSPOSE:
-        case GGML_OP_VIEW:
-            return t->src[0];
-        case GGML_OP_CPY:
-            return t->src[1];
-        default:
-            return NULL;
-    }
-}
-
-static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
-    struct ggml_tensor * parent = t;
-    do {
-        parent = get_view_parent(parent);
-    } while (ggml_is_view(parent));
-    return parent;
-}
-
 static bool ggml_op_can_inplace(enum ggml_op op) {
     switch (op) {
         case GGML_OP_SCALE:
@@ -365,7 +425,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
-        case GGML_OP_ACC:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
@@ -375,10 +434,8 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_UNARY:
         case GGML_OP_ROPE:
         case GGML_OP_RMS_NORM:
-        case GGML_OP_SET:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_CONT:
-        case GGML_OP_ADD_REL_POS:
             return true;
 
         default:
@@ -390,24 +447,8 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
     struct hash_node * ht = alloc->hash_table;
     if (node->data == NULL) {
         if (ggml_is_view(node)) {
-            size_t offset;
+            assert(node->view_src->data != NULL);
-            switch(node->op) {
+            node->data = (char *)node->view_src->data + node->view_offs;
-                case GGML_OP_VIEW:
-                    memcpy(&offset, node->op_params, sizeof(size_t));
-                    node->data = (char *) node->src[0]->data + offset;
-                    break;
-                case GGML_OP_PERMUTE:
-                case GGML_OP_RESHAPE:
-                case GGML_OP_TRANSPOSE:
-                    node->data = node->src[0]->data;
-                    break;
-                case GGML_OP_CPY:
-                    node->data = node->src[1]->data;
-                    break;
-                default:
-                    GGML_ASSERT(!"unknown view op");
-                    break;
-            }
         } else {
             // see if we can reuse a parent's buffer (inplace)
             if (ggml_op_can_inplace(node->op)) {
@@ -418,8 +459,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
                     }
 
                     // if the node's data is external, then we cannot re-use it
-                    if ((char *) parent->data < (char *) alloc->data ||
+                    if (ggml_allocr_is_own(alloc, parent) == false) {
-                        (char *) parent->data >= ((char *) alloc->data + alloc->size)) {
                         AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
                         continue;
                     }
@@ -427,7 +467,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
                     struct hash_node * p_hn = hash_get(ht, parent);
                     if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
                         if (ggml_is_view(parent)) {
-                            struct ggml_tensor * view_src = get_view_source(parent);
+                            struct ggml_tensor * view_src = parent->view_src;
                             struct hash_node * view_src_hn = hash_get(ht, view_src);
                             if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
                                 // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
@@ -453,7 +493,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
     }
 }
 
-static size_t ggml_allocator_alloc_graph_tensors_n(
+static size_t ggml_allocr_alloc_graph_tensors_n(
     struct ggml_allocr * alloc,
     struct ggml_cgraph ** graphs, int n_graphs,
     struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
@@ -469,7 +509,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
             struct ggml_tensor * node = gf->nodes[i];
 
             if (ggml_is_view(node)) {
-                struct ggml_tensor * view_src = get_view_source(node);
+                struct ggml_tensor * view_src = node->view_src;
                 hash_get(ht, view_src)->n_views += 1;
             }
 
@@ -531,7 +571,6 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
                 AT_PRINTF("\n");
             }
 
-
             // update parents
             // update immediately if there is no parse_seq
             // update only at barriers if there is parse_seq
@@ -554,17 +593,17 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
 
                         if (p_hn->n_children == 0 && p_hn->n_views == 0) {
                             if (ggml_is_view(parent)) {
-                                struct ggml_tensor * view_src = get_view_source(parent);
+                                struct ggml_tensor * view_src = parent->view_src;
                                 struct hash_node * view_src_hn = hash_get(ht, view_src);
                                 view_src_hn->n_views -= 1;
                                 AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
                                 if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
-                                    ggml_allocator_free_tensor(alloc, view_src);
+                                    ggml_allocr_free_tensor(alloc, view_src);
                                 }
                             }
                             else {
                                 if (parent->data != node->data) {
-                                    ggml_allocator_free_tensor(alloc, parent);
+                                    ggml_allocr_free_tensor(alloc, parent);
                                 }
                             }
                         }
@@ -581,7 +620,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
             for (int i = 0; outputs[g][i] != NULL; i++) {
                 struct ggml_tensor * output = outputs[g][i];
                 AT_PRINTF("output: %s\n", output->name);
-                ggml_allocator_free_tensor(alloc, output);
+                ggml_allocr_free_tensor(alloc, output);
             }
         }
     }
@@ -590,5 +629,5 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
 }
 
 size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
-    return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
+    return ggml_allocr_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
 }

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) {
+static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
-    GGML_ASSERT(nrows % 4 == 0);
+                              const float p_delta, const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
-    const dim3 block_dims(4*CUDA_ROPE_BLOCK_SIZE, 1, 1);
+    GGML_ASSERT(ncols % 4 == 0);
-    const int num_blocks_x = (ncols + 4*CUDA_ROPE_BLOCK_SIZE - 1) / (4*CUDA_ROPE_BLOCK_SIZE);
+    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;
+        rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, n_ctx, cudaStream_main);
-        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);
     } 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);
     }
 
@@ -5746,6 +5745,7 @@ inline void ggml_cuda_op_rope(
     (void) dst;
     (void) src0_ddq_i;
     (void) src1_ddf_i;
+    (void) i02;
     (void) i1;
 }
 
@@ -5781,6 +5781,7 @@ inline void ggml_cuda_op_alibi(
     (void) src1;
     (void) src0_ddq_i;
     (void) src1_ddf_i;
+    (void) i02;
     (void) i1;
 }
 
@@ -6400,10 +6401,7 @@ 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];
+    ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, true);
-    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
 }
 
 void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {

ggml-metal.m

@@ -63,7 +63,10 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(relu);
     GGML_METAL_DECL_KERNEL(gelu);
     GGML_METAL_DECL_KERNEL(soft_max);
+    GGML_METAL_DECL_KERNEL(soft_max_4);
     GGML_METAL_DECL_KERNEL(diag_mask_inf);
+    GGML_METAL_DECL_KERNEL(diag_mask_inf_8);
+    GGML_METAL_DECL_KERNEL(get_rows_f32);
     GGML_METAL_DECL_KERNEL(get_rows_f16);
     GGML_METAL_DECL_KERNEL(get_rows_q4_0);
     GGML_METAL_DECL_KERNEL(get_rows_q4_1);
@@ -75,8 +78,10 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(get_rows_q6_K);
     GGML_METAL_DECL_KERNEL(rms_norm);
     GGML_METAL_DECL_KERNEL(norm);
+    GGML_METAL_DECL_KERNEL(mul_mat_f32_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_1row);
+    GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_l4);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32);
@@ -85,6 +90,7 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(mul_mat_q4_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q5_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q6_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_f32_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
@@ -117,14 +123,17 @@ static NSString * const msl_library_source = @"see metal.metal";
 struct ggml_metal_context * ggml_metal_init(int n_cb) {
     metal_printf("%s: allocating\n", __func__);
 
-    // Show all the Metal device instances in the system
-    NSArray * devices = MTLCopyAllDevices();
     id <MTLDevice> device;
     NSString * s;
+
+#if TARGET_OS_OSX
+    // Show all the Metal device instances in the system
+    NSArray * devices = MTLCopyAllDevices();
     for (device in devices) {
         s = [device name];
         metal_printf("%s: found device: %s\n", __func__, [s UTF8String]);
     }
+#endif
 
     // Pick and show default Metal device
     device = MTLCreateSystemDefaultDevice();
@@ -139,14 +148,22 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
     ctx->n_buffers = 0;
     ctx->concur_list_len = 0;
 
-    ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
+    ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
 
-#if 0
+#ifdef GGML_SWIFT
-    // compile from source string and show compile log
+    // load the default.metallib file
     {
         NSError * error = nil;
 
-        ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
+        NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
+        NSString * llamaBundlePath = [bundle pathForResource:@"llama_llama" ofType:@"bundle"];
+        NSBundle * llamaBundle = [NSBundle bundleWithPath:llamaBundlePath];
+        NSString * libPath = [llamaBundle pathForResource:@"default" ofType:@"metallib"];
+        NSURL * libURL = [NSURL fileURLWithPath:libPath];
+
+        // Load the metallib file into a Metal library
+        ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
+
         if (error) {
             metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
             return NULL;
@@ -207,7 +224,10 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(relu);
         GGML_METAL_ADD_KERNEL(gelu);
         GGML_METAL_ADD_KERNEL(soft_max);
+        GGML_METAL_ADD_KERNEL(soft_max_4);
         GGML_METAL_ADD_KERNEL(diag_mask_inf);
+        GGML_METAL_ADD_KERNEL(diag_mask_inf_8);
+        GGML_METAL_ADD_KERNEL(get_rows_f32);
         GGML_METAL_ADD_KERNEL(get_rows_f16);
         GGML_METAL_ADD_KERNEL(get_rows_q4_0);
         GGML_METAL_ADD_KERNEL(get_rows_q4_1);
@@ -219,8 +239,10 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(get_rows_q6_K);
         GGML_METAL_ADD_KERNEL(rms_norm);
         GGML_METAL_ADD_KERNEL(norm);
+        GGML_METAL_ADD_KERNEL(mul_mat_f32_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_1row);
+        GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_l4);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32);
@@ -229,6 +251,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(mul_mat_q4_K_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q5_K_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q6_K_f32);
+        GGML_METAL_ADD_KERNEL(mul_mm_f32_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32);
@@ -247,13 +270,15 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #undef GGML_METAL_ADD_KERNEL
     }
 
-    metal_printf("%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
     metal_printf("%s: hasUnifiedMemory              = %s\n",       __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
+#if TARGET_OS_OSX
+    metal_printf("%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
     if (ctx->device.maxTransferRate != 0) {
         metal_printf("%s: maxTransferRate               = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
     } else {
         metal_printf("%s: maxTransferRate               = built-in GPU\n", __func__);
     }
+#endif
 
     return ctx;
 }
@@ -273,7 +298,10 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(relu);
     GGML_METAL_DEL_KERNEL(gelu);
     GGML_METAL_DEL_KERNEL(soft_max);
+    GGML_METAL_DEL_KERNEL(soft_max_4);
     GGML_METAL_DEL_KERNEL(diag_mask_inf);
+    GGML_METAL_DEL_KERNEL(diag_mask_inf_8);
+    GGML_METAL_DEL_KERNEL(get_rows_f32);
     GGML_METAL_DEL_KERNEL(get_rows_f16);
     GGML_METAL_DEL_KERNEL(get_rows_q4_0);
     GGML_METAL_DEL_KERNEL(get_rows_q4_1);
@@ -285,8 +313,10 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(get_rows_q6_K);
     GGML_METAL_DEL_KERNEL(rms_norm);
     GGML_METAL_DEL_KERNEL(norm);
+    GGML_METAL_DEL_KERNEL(mul_mat_f32_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_f16_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_1row);
+    GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_l4);
     GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32);
@@ -295,6 +325,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(mul_mat_q4_K_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_q5_K_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_q6_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_f32_f32);
     GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
     GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
     GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
@@ -327,7 +358,7 @@ 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;
@@ -365,6 +396,7 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
     for (int i = 0; i < ctx->n_buffers; ++i) {
         const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
 
+        //metal_printf("ioffs = %10ld, tsize = %10ld, sum = %10ld, ctx->buffers[%d].size = %10ld, name = %s\n", ioffs, tsize, ioffs + tsize, i, ctx->buffers[i].size, ctx->buffers[i].name);
         if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
             *offs = (size_t) ioffs;
 
@@ -401,7 +433,7 @@ 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) {
@@ -454,6 +486,7 @@ bool ggml_metal_add_buffer(
             }
         }
 
+#if TARGET_OS_OSX
         metal_printf(", (%8.2f / %8.2f)",
                 ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
                 ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
@@ -463,6 +496,9 @@ bool ggml_metal_add_buffer(
         } else {
             metal_printf("\n");
         }
+#else
+        metal_printf(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0);
+#endif
     }
 
     return true;
@@ -698,6 +734,7 @@ void ggml_metal_graph_compute(
                     case GGML_OP_ADD:
                         {
                             GGML_ASSERT(ggml_is_contiguous(src0));
+                            GGML_ASSERT(ggml_is_contiguous(src1));
 
                             // utilize float4
                             GGML_ASSERT(ne00 % 4 == 0);
@@ -705,6 +742,7 @@ void ggml_metal_graph_compute(
 
                             if (ggml_nelements(src1) == ne10) {
                                 // src1 is a row
+                                GGML_ASSERT(ne11 == 1);
                                 [encoder setComputePipelineState:ctx->pipeline_add_row];
                             } else {
                                 [encoder setComputePipelineState:ctx->pipeline_add];
@@ -721,6 +759,7 @@ void ggml_metal_graph_compute(
                     case GGML_OP_MUL:
                         {
                             GGML_ASSERT(ggml_is_contiguous(src0));
+                            GGML_ASSERT(ggml_is_contiguous(src1));
 
                             // utilize float4
                             GGML_ASSERT(ne00 % 4 == 0);
@@ -728,6 +767,7 @@ void ggml_metal_graph_compute(
 
                             if (ggml_nelements(src1) == ne10) {
                                 // src1 is a row
+                                GGML_ASSERT(ne11 == 1);
                                 [encoder setComputePipelineState:ctx->pipeline_mul_row];
                             } else {
                                 [encoder setComputePipelineState:ctx->pipeline_mul];
@@ -743,6 +783,8 @@ void ggml_metal_graph_compute(
                         } break;
                     case GGML_OP_SCALE:
                         {
+                            GGML_ASSERT(ggml_is_contiguous(src0));
+
                             const float scale = *(const float *) src1->data;
 
                             [encoder setComputePipelineState:ctx->pipeline_scale];
@@ -750,7 +792,7 @@ void ggml_metal_graph_compute(
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                             [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
 
-                            const int64_t n = ggml_nelements(dst);
+                            const int64_t n = ggml_nelements(dst)/4;
 
                             [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                         } break;
@@ -762,7 +804,7 @@ void ggml_metal_graph_compute(
                                     [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                     [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
 
-                                    const int64_t n = ggml_nelements(dst);
+                                    const int64_t n = ggml_nelements(dst)/4;
 
                                     [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                                 } break;
@@ -782,7 +824,7 @@ void ggml_metal_graph_compute(
                                     [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                     [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
 
-                                    const int64_t n = ggml_nelements(dst);
+                                    const int64_t n = ggml_nelements(dst)/4;
 
                                     [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                                 } break;
@@ -796,13 +838,16 @@ void ggml_metal_graph_compute(
                         {
                             const int nth = 32;
 
+                            if (ne00%4 == 0) {
+                                [encoder setComputePipelineState:ctx->pipeline_soft_max_4];
+                            } else {
                                 [encoder setComputePipelineState:ctx->pipeline_soft_max];
+                            }
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                             [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
                             [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
                             [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
-                            [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
@@ -810,14 +855,23 @@ void ggml_metal_graph_compute(
                         {
                             const int n_past = ((int32_t *)(dst->op_params))[0];
 
+                            if (ne00%8 == 0) {
+                                [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf_8];
+                            } else {
                                 [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
+                            }
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                             [encoder setBytes:&ne00   length:sizeof(ne00) atIndex:2];
                             [encoder setBytes:&ne01   length:sizeof(ne01) atIndex:3];
                             [encoder setBytes:&n_past length:sizeof(int)  atIndex:4];
 
+                            if (ne00%8 == 0) {
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            }
+                            else {
                                 [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            }
                         } break;
                     case GGML_OP_MUL_MAT:
                         {
@@ -830,13 +884,14 @@ void ggml_metal_graph_compute(
 
                             // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                             // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
-                            if (ggml_is_contiguous(src0) &&
+                            if (!ggml_is_transposed(src0) &&
-                                ggml_is_contiguous(src1) &&
+                                !ggml_is_transposed(src1) &&
                                 src1t == GGML_TYPE_F32 &&
                                 [ctx->device supportsFamily:MTLGPUFamilyApple7] &&
                                 ne00%32 == 0 &&
                                 ne11 > 1) {
                                 switch (src0->type) {
+                                    case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32];  break;
                                     case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32];  break;
                                     case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
                                     case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
@@ -856,25 +911,38 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:5];
                                 [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:6];
                                 [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:7];
-                                [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:8];
+                                [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:8];
-                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:9];
+                                [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:9];
-                                [encoder setBytes:&gqa     length:sizeof(gqa)  atIndex:10];
+                                [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:10];
+                                [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:11];
+                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:12];
+                                [encoder setBytes:&gqa     length:sizeof(gqa)  atIndex:13];
                                 [encoder setThreadgroupMemoryLength:8192 atIndex:0];
                                 [encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                             } else {
                                 int nth0 = 32;
                                 int nth1 = 1;
+                                int nrows = 1;
 
                                 // use custom matrix x vector kernel
                                 switch (src0t) {
+                                    case GGML_TYPE_F32:
+                                        {
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_f32_f32];
+                                            nrows = 4;
+                                        } break;
                                     case GGML_TYPE_F16:
                                         {
                                             nth0 = 32;
                                             nth1 = 1;
                                             if (ne11 * ne12 < 4) {
                                                 [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_1row];
+                                            } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
+                                                [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_l4];
+                                                nrows = ne11;
                                             } else {
                                                 [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
+                                                nrows = 4;
                                             }
                                         } break;
                                     case GGML_TYPE_Q4_0:
@@ -995,7 +1063,7 @@ void ggml_metal_graph_compute(
                                 else if (src0t == GGML_TYPE_Q6_K) {
                                     [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 } else {
-                                    int64_t ny = (ne11 + 3)/4;
+                                    int64_t ny = (ne11 + nrows - 1)/nrows;
                                     [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
                             }
@@ -1003,6 +1071,7 @@ void ggml_metal_graph_compute(
                     case GGML_OP_GET_ROWS:
                         {
                             switch (src0->type) {
+                                case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f32];  break;
                                 case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16];  break;
                                 case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
                                 case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
@@ -1018,9 +1087,9 @@ void ggml_metal_graph_compute(
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                            [encoder setBytes:&(src0->ne[0]) length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&(src0->nb[1]) length:sizeof(uint64_t) atIndex:4];
+                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4];
-                            [encoder setBytes:&(dst->nb[1])  length:sizeof(uint64_t) atIndex:5];
+                            [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:5];
 
                             const int64_t n = ggml_nelements(src1);
 
@@ -1141,7 +1210,7 @@ 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

@@ -63,18 +63,18 @@ kernel void kernel_mul_row(
 }
 
 kernel void kernel_scale(
-        device const float * src0,
+        device const float4 * src0,
-        device       float * dst,
+        device       float4 * dst,
         constant     float & scale,
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] * scale;
 }
 
 kernel void kernel_silu(
-        device const float * src0,
+        device const float4 * src0,
-        device       float * dst,
+        device       float4 * dst,
         uint tpig[[thread_position_in_grid]]) {
-    float x = src0[tpig];
+    device const float4 & x = src0[tpig];
     dst[tpig] = x / (1.0f + exp(-x));
 }
 
@@ -89,10 +89,10 @@ constant float GELU_COEF_A    = 0.044715f;
 constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
 
 kernel void kernel_gelu(
-    device const float * src0,
+    device const float4 * src0,
-    device       float * dst,
+    device       float4 * dst,
     uint tpig[[thread_position_in_grid]]) {
-    float x = src0[tpig];
+    device const float4 & x = src0[tpig];
 
     // BEWARE !!!
     // Simply using "tanh" instead of "precise::tanh" will sometimes results in NaNs!
@@ -107,7 +107,6 @@ kernel void kernel_soft_max(
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
-        threadgroup float  * buf [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]],
         uint3   ntg[[threads_per_threadgroup]]) {
@@ -119,64 +118,70 @@ kernel void kernel_soft_max(
     device       float * pdst  = dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
 
     // parallel max
-    buf[tpitg[0]] = -INFINITY;
+    float lmax = psrc0[tpitg[0]];
-    for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
+    for (int i00 = tpitg[0] + ntg[0]; i00 < ne00; i00 += ntg[0]) {
-        buf[tpitg[0]] = MAX(buf[tpitg[0]], psrc0[i00]);
+        lmax = MAX(lmax, psrc0[i00]);
     }
-
+    const float max = simd_max(lmax);
-    // reduce
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-    for (uint i = ntg[0]/2; i > 0; i /= 2) {
-        if (tpitg[0] < i) {
-            buf[tpitg[0]] = MAX(buf[tpitg[0]], buf[tpitg[0] + i]);
-        }
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-    }
-
-    //// broadcast - not needed. There is a threadgroup barrier above in the last iteration of
-    //               the loop, and when that is done, buf[0] has the correct (synchronized) value
-    //if (tpitg[0] == 0) {
-    //    buf[0] = buf[0];
-    //}
-
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-
-    const float max = buf[0];
 
     // parallel sum
-    buf[tpitg[0]] = 0.0f;
+    float lsum = 0.0f;
     for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
         const float exp_psrc0 = exp(psrc0[i00] - max);
-        buf[tpitg[0]] += exp_psrc0;
+        lsum += exp_psrc0;
         // Remember the result of exp here. exp is expensive, so we really do not
         // whish to compute it twice.
         pdst[i00] = exp_psrc0;
     }
 
-    // reduce
+    const float sum = simd_sum(lsum);
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-    for (uint i = ntg[0]/2; i > 0; i /= 2) {
-        if (tpitg[0] < i) {
-            buf[tpitg[0]] += buf[tpitg[0] + i];
-        }
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-    }
-
-    // broadcast - not needed, see above
-    //// broadcast
-    //if (tpitg[0] == 0) {
-    //    buf[0] = buf[0];
-    //}
-
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-
-    const float sum = buf[0];
 
     for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
         pdst[i00] /= sum;
     }
 }
 
+kernel void kernel_soft_max_4(
+        device const float * src0,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = tgpig[2];
+    const int64_t i02 = tgpig[1];
+    const int64_t i01 = tgpig[0];
+
+    device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    device       float4 * pdst4 = (device       float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+
+    // parallel max
+    float4 lmax4 = psrc4[tpitg[0]];
+    for (int i00 = tpitg[0] + ntg[0]; i00 < ne00/4; i00 += ntg[0]) {
+        lmax4 = fmax(lmax4, psrc4[i00]);
+    }
+    float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
+
+    const float max = simd_max(lmax);
+
+    // parallel sum
+    float4 lsum4 = 0.0f;
+    for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) {
+        const float4 exp_psrc4 = exp(psrc4[i00] - max);
+        lsum4 += exp_psrc4;
+        pdst4[i00] = exp_psrc4;
+    }
+    float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
+
+    const float sum = simd_sum(lsum);
+
+    for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) {
+        pdst4[i00] /= sum;
+    }
+}
+
 kernel void kernel_diag_mask_inf(
         device const float * src0,
         device       float * dst,
@@ -195,6 +200,33 @@ kernel void kernel_diag_mask_inf(
      }
 }
 
+kernel void kernel_diag_mask_inf_8(
+        device const float4 * src0,
+        device       float4 * dst,
+        constant    int64_t & ne00,
+        constant    int64_t & ne01,
+        constant        int & n_past,
+        uint3 tpig[[thread_position_in_grid]]) {
+
+    const int64_t i = 2*tpig[0];
+
+    dst[i+0] = src0[i+0];
+    dst[i+1] = src0[i+1];
+    int64_t i4 = 4*i;
+    const int64_t i02 = i4/(ne00*ne01); i4 -= i02*ne00*ne01;
+    const int64_t i01 = i4/(ne00);      i4 -= i01*ne00;
+    const int64_t i00 = i4;
+    for (int k = 3; k >= 0; --k) {
+        if (i00 + 4 + k <= n_past + i01) {
+            break;
+        }
+        dst[i+1][k] = -INFINITY;
+        if (i00 + k > n_past + i01) {
+            dst[i][k] = -INFINITY;
+        }
+    }
+}
+
 kernel void kernel_norm(
         device const  void * src0,
         device       float * dst,
@@ -220,14 +252,10 @@ kernel void kernel_norm(
         }
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
-    //// broadcast
+    const float mean  = sum[0] / ne00;
-    //if (tpitg == 0) {
-    //    sum[0] /= ne00;
-    //}
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    const float mean  = sum[0];
 
     // 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 +263,6 @@ 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 +271,7 @@ kernel void kernel_norm(
         }
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
-    //// broadcast
+    const float variance = sum[0] / ne00;
-    //if (tpitg == 0) {
-    //    sum[0] /= ne00;
-    //}
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    const float variance = sum[0];
 
     const float scale = 1.0f/sqrt(variance + eps);
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
@@ -262,7 +279,6 @@ kernel void kernel_norm(
     }
 }
 
-
 kernel void kernel_rms_norm(
         device const  void * src0,
         device       float * dst,
@@ -507,6 +523,79 @@ kernel void kernel_mul_mat_q8_0_f32(
     }
 }
 
+#define N_F32_F32 4
+
+kernel void kernel_mul_mat_f32_f32(
+        device const  char * src0,
+        device const  char * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]]) {
+
+    const int64_t r0 = tgpig.x;
+    const int64_t rb = tgpig.y*N_F32_F32;
+    const int64_t im = tgpig.z;
+
+    device const float * x = (device const float *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
+
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F32_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
+
+            float sumf = 0;
+            for (int i = tiisg; i < ne00; i += 32) {
+                sumf += (float) x[i] * (float) y[i];
+            }
+
+            float all_sum = simd_sum(sumf);
+            if (tiisg == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        device const float4 * x4 = (device const float4 *)x;
+        for (int row = 0; row < N_F32_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            device const float  * y  = (device const float  *) (src1 + r1*nb11 + im*nb12);
+            device const float4 * y4 = (device const float4 *) y;
+
+            float sumf = 0;
+            for (int i = tiisg; i < ne00/4; i += 32) {
+                for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
+            }
+
+            float all_sum = simd_sum(sumf);
+            if (tiisg == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    }
+}
+
 kernel void kernel_mul_mat_f16_f32_1row(
         device const  char * src0,
         device const  char * src1,
@@ -630,7 +719,49 @@ kernel void kernel_mul_mat_f16_f32(
             }
         }
     }
+}
 
+// Assumes row size (ne00) is a multiple of 4
+kernel void kernel_mul_mat_f16_f32_l4(
+        device const  char * src0,
+        device const  char * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]]) {
+
+    const int nrows = ne11;
+    const int64_t r0 = tgpig.x;
+    const int64_t im = tgpig.z;
+
+    device const half4 * x4 = (device const half4 *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
+
+    for (int r1 = 0; r1 < nrows; ++r1) {
+        device const float4 * y4 = (device const float4 *) (src1 + r1*nb11 + im*nb12);
+
+        float sumf = 0;
+        for (int i = tiisg; i < ne00/4; i += 32) {
+            for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
+        }
+
+        float all_sum = simd_sum(sumf);
+        if (tiisg == 0) {
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    }
 }
 
 kernel void kernel_alibi_f32(
@@ -699,25 +830,27 @@ kernel void kernel_rope(
         constant       int & mode,
         constant     float & freq_base,
         constant     float & freq_scale,
-        uint3 tpig[[thread_position_in_grid]]) {
+        uint  tiitg[[thread_index_in_threadgroup]],
-    const int64_t i3 = tpig[2];
+        uint3 tptg[[threads_per_threadgroup]],
-    const int64_t i2 = tpig[1];
+        uint3 tgpig[[threadgroup_position_in_grid]]) {
-    const int64_t i1 = tpig[0];
+    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 +862,12 @@ 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);
@@ -1138,31 +1271,40 @@ kernel void kernel_mul_mat_q3_K_f32(
     device const block_q3_K * x = (device const block_q3_K *) src0 + first_row*nb + offset0;
     device const float     * yy = (device const float      *) src1 + r1*ne10 + r2*ne00*ne1;
 
-    float yl[16];
+    float yl[32];
 
-    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask1 = 0x3030;
     const uint16_t kmask2 = 0x0f0f;
 
-    const int tid = tiisg/2;
+    const int tid = tiisg/4;
-    const int ix  = tiisg%2;
+    const int ix  = tiisg%4;
-    const int ip  = tid/8;          // 0 or 1
+    const int ip  = tid/4;          // 0 or 1
-    const int il  = tid/2 - 4*ip;   // 0...3
+    const int il  = 2*((tid%4)/2);  // 0 or 2
     const int ir  = tid%2;
     const int n   = 8;
     const int l0  = n*ir;
 
-    const uint16_t m1 = 1 << (4*ip + il);
+    // One would think that the Metal compiler would figure out that ip and il can only have
-    const uint16_t m2 = m1 << 8;
+    // 4 possible states, and optimize accordingly. Well, no. It needs help, and we do it
+    // with these two tales.
+    //
+    // Possible masks for the high bit
+    const ushort4 mm[4] = {{0x0001, 0x0100, 0x0002, 0x0200},  // ip = 0, il = 0
+                           {0x0004, 0x0400, 0x0008, 0x0800},  // ip = 0, il = 2
+                           {0x0010, 0x1000, 0x0020, 0x2000},  // ip = 1, il = 0
+                           {0x0040, 0x4000, 0x0080, 0x8000}}; // ip = 1, il = 2
+
+    // Possible masks for the low 2 bits
+    const int4 qm[2] = {{0x0003, 0x0300, 0x000c, 0x0c00}, {0x0030, 0x3000, 0x00c0, 0xc000}};
+
+    const ushort4 hm = mm[2*ip + il/2];
 
     const int shift = 2*il;
-    const uint16_t qm1 = 0x0003 << shift;
+    const float    v1 = il == 0 ? 4.f : 64.f;
-    const uint16_t qm2 = 0x0300 << shift;
+    const float    v2 = 4.f * v1;
-    const int32_t v1 = 4 << shift;
-    const int32_t v2 = 1024 << shift;
 
     const uint16_t s_shift1 = 4*ip;
-    const uint16_t s_shift2 = s_shift1 + 2*(il/2);
+    const uint16_t s_shift2 = s_shift1 + il;
-    const int ik = 4 + (il%2);
 
     const int q_offset = 32*ip + l0;
     const int y_offset = 128*ip + 32*il + l0;
@@ -1171,12 +1313,19 @@ kernel void kernel_mul_mat_q3_K_f32(
 
     device const float * y1 = yy + ix*QK_K + y_offset;
 
-    float sumf1[2] = {0.f}, sumf2[2] = {0.f};
+    uint32_t scales32, aux32;
-    for (int i = ix; i < nb; i += 2) {
+    thread uint16_t * scales16 = (thread uint16_t *)&scales32;
+    thread const int8_t * scales = (thread const int8_t *)&scales32;
+
+    float sumf1[2] = {0.f};
+    float sumf2[2] = {0.f};
+    for (int i = ix; i < nb; i += 4) {
 
         for (int l = 0; l < 8; ++l) {
             yl[l+ 0] = y1[l+ 0];
             yl[l+ 8] = y1[l+16];
+            yl[l+16] = y1[l+32];
+            yl[l+24] = y1[l+48];
         }
 
         device const uint16_t * q = (device const uint16_t *)(x[i].qs + q_offset);
@@ -1187,27 +1336,43 @@ kernel void kernel_mul_mat_q3_K_f32(
         for (int row = 0; row < 2; ++row) {
 
             const float d_all = (float)dh[0];
-            const char2 scales = as_type<char2>((uint16_t)(((a[il] >> s_shift1) & kmask2) | (((a[ik] >> s_shift2) & kmask1) << 4)));
 
-            float s1 = 0, s2 = 0;
+            scales16[0] = a[4];
-            for (int l = 0; l < n; l += 2) {
+            scales16[1] = a[5];
-                const uint16_t qs = q[l/2];
+            aux32 = ((scales32 >> s_shift2) << 4) & 0x30303030;
-                s1 += yl[l+0] * ((int32_t)(qs & qm1) - ((h[l/2] & m1) ? 0 : v1));
+            scales16[0] = a[il+0];
-                s2 += yl[l+1] * ((int32_t)(qs & qm2) - ((h[l/2] & m2) ? 0 : v2));
+            scales16[1] = a[il+1];
-            }
+            scales32 = ((scales32 >> s_shift1) & 0x0f0f0f0f) | aux32;
-            float d = d_all * (s1 + 1.f/256.f * s2);
-            sumf1[row] += d * scales[0];
-            sumf2[row] += d;
 
-            s1 = s2 = 0;
+            float s1 = 0, s2 = 0, s3 = 0, s4 = 0, s5 = 0, s6 = 0;
             for (int l = 0; l < n; l += 2) {
-                const uint16_t qs = q[l/2+8];
+                const int32_t qs = q[l/2];
-                s1 += yl[l+8] * ((int32_t)(qs & qm1) - ((h[l/2+8] & m1) ? 0 : v1));
+                s1 += yl[l+0] * (qs & qm[il/2][0]);
-                s2 += yl[l+9] * ((int32_t)(qs & qm2) - ((h[l/2+8] & m2) ? 0 : v2));
+                s2 += yl[l+1] * (qs & qm[il/2][1]);
+                s3 += ((h[l/2] & hm[0]) ? 0.f : yl[l+0]) + ((h[l/2] & hm[1]) ? 0.f : yl[l+1]);
+                s4 += yl[l+16] * (qs & qm[il/2][2]);
+                s5 += yl[l+17] * (qs & qm[il/2][3]);
+                s6 += ((h[l/2] & hm[2]) ? 0.f : yl[l+16]) + ((h[l/2] & hm[3]) ? 0.f : yl[l+17]);
             }
-            d = d_all * (s1 + 1.f/256.f * s2);
+            float d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1);
-            sumf1[row] += d * scales[1];
+            float d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2);
-            sumf2[row] += d;
+            sumf1[row] += d1 * (scales[0] - 32);
+            sumf2[row] += d2 * (scales[2] - 32);
+
+            s1 = s2 = s3 = s4 = s5 = s6 = 0;
+            for (int l = 0; l < n; l += 2) {
+                const int32_t qs = q[l/2+8];
+                s1 += yl[l+8] * (qs & qm[il/2][0]);
+                s2 += yl[l+9] * (qs & qm[il/2][1]);
+                s3 += ((h[l/2+8] & hm[0]) ? 0.f : yl[l+8]) + ((h[l/2+8] & hm[1]) ? 0.f : yl[l+9]);
+                s4 += yl[l+24] * (qs & qm[il/2][2]);
+                s5 += yl[l+25] * (qs & qm[il/2][3]);
+                s6 += ((h[l/2+8] & hm[2]) ? 0.f : yl[l+24]) + ((h[l/2+8] & hm[3]) ? 0.f : yl[l+25]);
+            }
+            d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1);
+            d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2);
+            sumf1[row] += d1 * (scales[1] - 32);
+            sumf2[row] += d2 * (scales[3] - 32);
 
             q  += step;
             h  += step;
@@ -1216,15 +1381,17 @@ kernel void kernel_mul_mat_q3_K_f32(
 
         }
 
-        y1 += 2 * QK_K;
+        y1 += 4 * QK_K;
 
     }
 
     for (int row = 0; row < 2; ++row) {
-        const float sumf = (sumf1[row] - 32.f*sumf2[row]) / (1 << shift);
+        const float sumf = (sumf1[row] + 0.25f * sumf2[row]) / (1 << shift);
-        const float tot = simd_sum(sumf);
+        sumf1[row] = simd_sum(sumf);
+    }
     if (tiisg == 0) {
-            dst[r1*ne0 + r2*ne0*ne1 + first_row + row] = tot;
+        for (int row = 0; row < 2; ++row) {
+            dst[r1*ne0 + r2*ne0*ne1 + first_row + row] = sumf1[row];
         }
     }
 }
@@ -1579,17 +1746,25 @@ kernel void kernel_mul_mat_q5_K_f32(
             sc16[2] = ((a[4] >> 0) & kmask2) | ((a[0] & kmask3) >> 2);
             sc16[3] = ((a[4] >> 4) & kmask2) | ((a[2] & kmask3) >> 2);
 
-            float4 acc = {0.f, 0.f, 0.f, 0.f};
+            float4 acc1 = {0.f};
+            float4 acc2 = {0.f};
             for (int l = 0; l < n; ++l) {
                 uint8_t h = qh[l];
-                acc[0] += yl[l+0] * ((uint16_t)(q1[l] & 0x0F) + (h & hm1 ? 16 : 0));
+                acc1[0] += yl[l+0] * (q1[l] & 0x0F);
-                acc[1] += yl[l+8] * ((uint16_t)(q1[l] & 0xF0) + (h & hm2 ? 256 : 0));
+                acc1[1] += yl[l+8] * (q1[l] & 0xF0);
-                acc[2] += yh[l+0] * ((uint16_t)(q2[l] & 0x0F) + (h & hm3 ? 16 : 0));
+                acc1[2] += yh[l+0] * (q2[l] & 0x0F);
-                acc[3] += yh[l+8] * ((uint16_t)(q2[l] & 0xF0) + (h & hm4 ? 256 : 0));
+                acc1[3] += yh[l+8] * (q2[l] & 0xF0);
+                acc2[0] += h & hm1 ? yl[l+0] : 0.f;
+                acc2[1] += h & hm2 ? yl[l+8] : 0.f;
+                acc2[2] += h & hm3 ? yh[l+0] : 0.f;
+                acc2[3] += h & hm4 ? yh[l+8] : 0.f;
             }
             const float dall = dh[0];
             const float dmin = dh[1];
-            sumf[row] += dall * (acc[0] * sc8[0] + acc[1] * sc8[1] * 1.f/16.f + acc[2] * sc8[4] + acc[3] * sc8[5] * 1.f/16.f) -
+            sumf[row] += dall * (sc8[0] * (acc1[0] +  16.f*acc2[0]) +
+                                 sc8[1] * (acc1[1]/16.f + 16.f*acc2[1]) +
+                                 sc8[4] * (acc1[2] +  16.f*acc2[2]) +
+                                 sc8[5] * (acc1[3]/16.f + 16.f*acc2[3])) -
                          dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]);
 
             q1 += step;
@@ -1762,6 +1937,15 @@ kernel void kernel_mul_mat_q6_K_f32(
 
 //============================= templates and their specializations =============================
 
+// NOTE: this is not dequantizing - we are simply fitting the template
+template <typename type4x4>
+void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
+    float4x4 temp = *(((device float4x4 *)src));
+    for (int i = 0; i < 16; i++){
+        reg[i/4][i%4] = temp[i/4][i%4];
+    }
+}
+
 template <typename type4x4>
 void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) {
     half4x4 temp = *(((device half4x4 *)src));
@@ -1773,28 +1957,30 @@ void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg)
 template <typename type4x4>
 void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) {
     device const uint16_t * qs = ((device const uint16_t *)xb + 1);
-    const half d = il ? (xb->d / 16.h) : xb->d;
+    const float d1 = il ? (xb->d / 16.h) : xb->d;
-    const half m = il ? ( -8.h * 16.h) : -8.h;
+    const float d2 = d1 / 256.f;
+    const float md = -8.h * xb->d;
     const ushort mask0 = il ? 0x00F0 : 0x000F;
-    const ushort mask1 = il ? 0xF000 : 0x0F00;
+    const ushort mask1 = mask0 << 8;
 
     for (int i=0;i<8;i++) {
-        reg[i/2][2*(i%2)]   = (((qs[i] & mask0)     ) + m) * d;
+        reg[i/2][2*(i%2)+0] = d1 * (qs[i] & mask0) + md;
-        reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) + m) * d;
+        reg[i/2][2*(i%2)+1] = d2 * (qs[i] & mask1) + md;
     }
 }
 
 template <typename type4x4>
 void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) {
     device const uint16_t * qs = ((device const uint16_t *)xb + 2);
-    const half d = il ? (xb->d / 16.h) : xb->d;
+    const float d1 = il ? (xb->d / 16.h) : xb->d;
-    const half m = xb->m;
+    const float d2 = d1 / 256.f;
+    const float  m = xb->m;
     const ushort mask0 = il ? 0x00F0 : 0x000F;
-    const ushort mask1 = il ? 0xF000 : 0x0F00;
+    const ushort mask1 = mask0 << 8;
 
     for (int i=0;i<8;i++) {
-        reg[i/2][2*(i%2)]   = (((qs[i] & mask0)     ) * d) + m;
+        reg[i/2][2*(i%2)+0] = ((qs[i] & mask0) * d1) + m;
-        reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) * d) + m;
+        reg[i/2][2*(i%2)+1] = ((qs[i] & mask1) * d2) + m;
     }
 }
 
@@ -1830,7 +2016,7 @@ void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg
 
 template <typename type4x4>
 void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) {
-    const float d_all = (float)(xb->d);
+    const half d_all = xb->d;
     device const uint8_t * q = (device const uint8_t *)xb->qs;
     device const uint8_t * h = (device const uint8_t *)xb->hmask;
     device const int8_t * scales = (device const int8_t *)xb->scales;
@@ -1843,16 +2029,18 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg
                                  ((il/4)>0 ? 12  : 3);
     uint16_t kmask2 = il/8 ? 0xF0 : 0x0F;
     uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4];
-    int16_t  dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) : \
+    int16_t  dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2)
-                                 (scale_2&kmask2) | ((scale_1&kmask1) << 4);
+                               : (scale_2&kmask2) | ((scale_1&kmask1) << 4);
-    float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f);
+    half dl = il<8 ? d_all * (dl_int - 32.h) : d_all * (dl_int / 16.h - 32.h);
+    const half ml = 4.h * dl;
 
-    il = (il/2)%4;
+    il = (il/2) & 3;
-    float   coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
+    const half    coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
-    uint8_t mask = il>1 ? (il>2 ? 192    : 48)     : (il>0 ? 12    : 3);
+    const uint8_t mask = il>1 ? (il>2 ? 192    : 48)     : (il>0 ? 12    : 3);
+    dl *= coef;
 
     for (int i = 0; i < 16; ++i) {
-        reg[i/4][i%4] = coef * dl * ((q[i] & mask) - ((h[i] & m) ? 0 : 4.f/coef));
+        reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml);
     }
 #else
     float    kcoef = il&1 ? 1.f/16.f : 1.f;
@@ -1867,19 +2055,24 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg
 #endif
 }
 
+static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) {
+    return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)}
+                 : uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))};
+}
+
 template <typename type4x4>
 void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) {
-    device const uint8_t * q = xb->qs;
+    device const uchar * q = xb->qs;
 
 #if QK_K == 256
-    const float d = (float)(xb->d);
-    const float min = (float)(xb->dmin);
     short is = (il/4) * 2;
     q = q + (il/4) * 32 + 16 * (il&1);
-    il = il%4;
+    il = il & 3;
-    const uchar4 sc = get_scale_min_k4(is, xb->scales);
+    const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
-    const float dl = il<2 ? d * sc[0]   : d * sc[2]/16.h;
+    const half d   = il < 2 ? xb->d : xb->d / 16.h;
-    const float ml = il<2 ? min * sc[1] : min * sc[3];
+    const half min = xb->dmin;
+    const half dl = d * sc[0];
+    const half ml = min * sc[1];
 #else
     q = q + 16 * (il&1);
     device const uint8_t * s = xb->scales;
@@ -1900,19 +2093,19 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg
     device const uint8_t * qh = xb->qh;
 
 #if QK_K == 256
-    const float d = (float)(xb->d);
-    const float min = (float)(xb->dmin);
     short is = (il/4) * 2;
     q  = q + 32 * (il/4) + 16 * (il&1);
     qh = qh + 16 * (il&1);
     uint8_t ul = 1 << (il/2);
-    il = il%4;
+    il = il & 3;
-    const uchar4 sc = get_scale_min_k4(is, xb->scales);
+    const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
-    const float dl = il<2 ? d * sc[0]   : d * sc[2]/16.h;
+    const half d = il < 2 ? xb->d : xb->d / 16.h;
-    const float ml = il<2 ? min * sc[1] : min * sc[3];
+    const half min = xb->dmin;
+    const half dl = d * sc[0];
+    const half ml = min * sc[1];
 
     const ushort mask = il<2 ? 0x0F : 0xF0;
-    const float  qh_val = il<2 ? 16.f : 256.f;
+    const half qh_val = il<2 ? 16.h : 256.h;
     for (int i = 0; i < 16; ++i) {
         reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml;
     }
@@ -1931,7 +2124,7 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg
 
 template <typename type4x4>
 void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) {
-    const float d_all = (float)(xb->d);
+    const half d_all = xb->d;
     device const uint8_t * ql = (device const uint8_t *)xb->ql;
     device const uint8_t * qh = (device const uint8_t *)xb->qh;
     device const int8_t * scales = (device const int8_t *)xb->scales;
@@ -1939,19 +2132,21 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg
 #if QK_K == 256
     ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1);
     qh = qh + 32*(il/8) + 16*(il&1);
-    float sc = scales[(il%2) + 2 * ((il/2))];
+    half sc = scales[(il%2) + 2 * ((il/2))];
-    il = (il/2)%4;
+    il = (il/2) & 3;
 #else
     ql = ql + 16 * (il&1);
-    float sc = scales[il];
+    half sc = scales[il];
 #endif
+    const uint16_t  kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
+    const uint16_t  kmask2 = il>1 ? 0xF0              : 0x0F;
+    const half        coef = il>1 ? 1.f/16.h          : 1.h;
+    const half ml = d_all * sc * 32.h;
+    const half dl = d_all * sc * coef;
     for (int i = 0; i < 16; ++i) {
-        uint16_t  kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
+        const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2))
-        uint16_t  kmask2 = il>1 ? 0xF0              : 0x0F;
+                            : ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4));
-        const float coef = il>1 ? 1.f/16.f          : 1.f;
+        reg[i/4][i%4] = dl * q - ml;
-        float q = il&1 ? ((ql[i]&kmask2)|((qh[i]&kmask1)<<2)) - 32.f/coef : \
-                         ((ql[i]&kmask2)|((qh[i]&kmask1)<<4)) - 32.f/coef;
-        reg[i/4][i%4] = d_all * sc * q * coef;
     }
 }
 
@@ -1991,13 +2186,16 @@ kernel void kernel_get_rows(
 // each block_q contains 16*nl weights
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 kernel void kernel_mul_mm(device const  uchar * src0,
-                           device const  float * src1,
+                          device const  uchar * src1,
                           device        float * dst,
                           constant    int64_t & ne00,
                           constant    int64_t & ne02,
                           constant    int64_t & nb01,
                           constant    int64_t & nb02,
                           constant    int64_t & ne12,
+                          constant    int64_t & nb10,
+                          constant    int64_t & nb11,
+                          constant    int64_t & nb12,
                           constant    int64_t & ne0,
                           constant    int64_t & ne1,
                           constant       uint & gqa,
@@ -2006,7 +2204,7 @@ kernel void kernel_mul_mm(device const  uchar * src0,
                           uint                  tiitg[[thread_index_in_threadgroup]],
                           uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    threadgroup half * sa = ((threadgroup half *)shared_memory);
+    threadgroup half  * sa = (threadgroup half  *)(shared_memory);
     threadgroup float * sb = (threadgroup float *)(shared_memory + 4096);
 
     const uint r0 = tgpig.y;
@@ -2027,10 +2225,15 @@ kernel void kernel_mul_mm(device const  uchar * src0,
     }
 
     short il = (tiitg % THREAD_PER_ROW);
-    uint offset0 = im/gqa*nb02; ushort offset1 = il/nl;
+
+    uint   offset0 = im/gqa*nb02;
+    ushort offset1 = il/nl;
+
     device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1;
-    device const float * y = src1 + (r1 * BLOCK_SIZE_N + thread_col) * ne00 \
+    device const float   * y = (device const float   *)(src1
-                             + BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL) + im * ne00 * ne1;
+        + nb12 * im
+        + nb11 * (r1 * BLOCK_SIZE_N + thread_col)
+        + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
 
     for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
         //load data and store to threadgroup memory
@@ -2110,6 +2313,7 @@ kernel void kernel_mul_mm(device const  uchar * src0,
 typedef void (get_rows_t)(device const void *, device const int *, device float *, constant int64_t &, \
                           constant uint64_t &, constant uint64_t &, uint, uint, uint);
 
+template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_t kernel_get_rows<float4x4,   1, dequantize_f32>;
 template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_t kernel_get_rows<half4x4,    1, dequantize_f16>;
 template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows<block_q4_0, 2, dequantize_q4_0>;
 template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows<block_q4_1, 2, dequantize_q4_1>;
@@ -2120,10 +2324,24 @@ template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows
 template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows<block_q5_K, QK_NL, dequantize_q5_K>;
 template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows<block_q6_K, QK_NL, dequantize_q6_K>;
 
-typedef void (mat_mm_t)(device const uchar *, device const float *, device float *, constant int64_t &,\
+typedef void (mat_mm_t)(
-                             constant int64_t &, constant int64_t &, constant int64_t &, constant int64_t &, \
+        device const  uchar * src0,
-                             constant int64_t &, constant int64_t &, constant uint &, threadgroup uchar *, uint3, uint, uint);
+        device const  uchar * src1,
+        device        float * dst,
+        constant    int64_t & ne00,
+        constant    int64_t & ne02,
+        constant    int64_t & nb01,
+        constant    int64_t & nb02,
+        constant    int64_t & ne12,
+        constant    int64_t & nb10,
+        constant    int64_t & nb11,
+        constant    int64_t & nb12,
+        constant    int64_t & ne0,
+        constant    int64_t & ne1,
+        constant       uint & gqa,
+        threadgroup uchar *, uint3, uint, uint);
 
+template [[host_name("kernel_mul_mm_f32_f32")]]  kernel mat_mm_t kernel_mul_mm<float4x4,   1,     dequantize_f32>;
 template [[host_name("kernel_mul_mm_f16_f32")]]  kernel mat_mm_t kernel_mul_mm<half4x4,    1,     dequantize_f16>;
 template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_0, 2,     dequantize_q4_0>;
 template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_1, 2,     dequantize_q4_1>;

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
+#ifdef GGML_USE_CPU_HBM
-    int result = posix_memalign(&aligned_memory, getpagesize(), size);
+    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)
@@ -4291,10 +4303,21 @@ int64_t ggml_nrows(const struct ggml_tensor * tensor) {
 }
 
 size_t ggml_nbytes(const struct ggml_tensor * tensor) {
-    size_t nbytes = tensor->ne[0]*tensor->nb[0]/ggml_blck_size(tensor->type);
+    size_t nbytes;
+    size_t blck_size = ggml_blck_size(tensor->type);
+    if (blck_size == 1) {
+        nbytes = ggml_type_size(tensor->type);
+        for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+            nbytes += (tensor->ne[i] - 1)*tensor->nb[i];
+        }
+    }
+    else {
+        nbytes = tensor->ne[0]*tensor->nb[0]/blck_size;
         for (int i = 1; i < GGML_MAX_DIMS; ++i) {
             nbytes += (tensor->ne[i] - 1)*tensor->nb[i];
         }
+    }
+
     return nbytes;
 }
 
@@ -4572,6 +4595,11 @@ 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 +4802,7 @@ 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 +5503,7 @@ 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 +5546,7 @@ 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 +17294,18 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         } else {
             // wait for other threads to finish
             const int last = node_n;
-            do {
+            while (true) {
-                //sched_yield();
+                // 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
@@ -18320,10 +18356,11 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
     for (int i = 0; i < cgraph->n_leafs; i++) {
         struct ggml_tensor * node = cgraph->leafs[i];
 
-        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s\n",
+        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s %16s\n",
                 i,
                 node->ne[0], node->ne[1],
-                ggml_op_name(node->op));
+                ggml_op_name(node->op),
+                ggml_get_name(node));
     }
 
     for (int i = 0; i < GGML_OP_COUNT; i++) {
@@ -19962,7 +19999,7 @@ 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 +20040,7 @@ 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
             }
@@ -20082,27 +20119,27 @@ const char * gguf_type_name(enum gguf_type type) {
     return GGUF_TYPE_NAME[type];
 }
 
-int gguf_get_version(struct gguf_context * ctx) {
+int gguf_get_version(const struct gguf_context * ctx) {
     return ctx->header.version;
 }
 
-size_t gguf_get_alignment(struct gguf_context * ctx) {
+size_t gguf_get_alignment(const struct gguf_context * ctx) {
     return ctx->alignment;
 }
 
-size_t gguf_get_data_offset(struct gguf_context * ctx) {
+size_t gguf_get_data_offset(const struct gguf_context * ctx) {
     return ctx->offset;
 }
 
-void * gguf_get_data(struct gguf_context * ctx) {
+void * gguf_get_data(const struct gguf_context * ctx) {
     return ctx->data;
 }
 
-int gguf_get_n_kv(struct gguf_context * ctx) {
+int gguf_get_n_kv(const struct gguf_context * ctx) {
     return ctx->header.n_kv;
 }
 
-int gguf_find_key(struct gguf_context * ctx, const char * key) {
+int gguf_find_key(const struct gguf_context * ctx, const char * key) {
     // return -1 if key not found
     int keyfound = -1;
 
@@ -20118,85 +20155,85 @@ int gguf_find_key(struct gguf_context * ctx, const char * key) {
     return keyfound;
 }
 
-const char * gguf_get_key(struct gguf_context * ctx, int i) {
+const char * gguf_get_key(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].key.data;
 }
 
-enum gguf_type gguf_get_kv_type(struct gguf_context * ctx, int i) {
+enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].type;
 }
 
-enum gguf_type gguf_get_arr_type(struct gguf_context * ctx, int i) {
+enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.arr.type;
 }
 
-const void * gguf_get_arr_data(struct gguf_context * ctx, int i) {
+const void * gguf_get_arr_data(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.arr.data;
 }
 
-const char * gguf_get_arr_str(struct gguf_context * ctx, int key_id, int i) {
+const char * gguf_get_arr_str(const struct gguf_context * ctx, int key_id, int i) {
     struct gguf_kv * kv = &ctx->kv[key_id];
     struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[i];
     return str->data;
 }
 
-int gguf_get_arr_n(struct gguf_context * ctx, int i) {
+int gguf_get_arr_n(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.arr.n;
 }
 
-uint8_t gguf_get_val_u8(struct gguf_context * ctx, int i) {
+uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.uint8;
 }
 
-int8_t gguf_get_val_i8(struct gguf_context * ctx, int i) {
+int8_t gguf_get_val_i8(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.int8;
 }
 
-uint16_t gguf_get_val_u16(struct gguf_context * ctx, int i) {
+uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.uint16;
 }
 
-int16_t gguf_get_val_i16(struct gguf_context * ctx, int i) {
+int16_t gguf_get_val_i16(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.int16;
 }
 
-uint32_t gguf_get_val_u32(struct gguf_context * ctx, int i) {
+uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.uint32;
 }
 
-int32_t gguf_get_val_i32(struct gguf_context * ctx, int i) {
+int32_t gguf_get_val_i32(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.int32;
 }
 
-float gguf_get_val_f32(struct gguf_context * ctx, int i) {
+float gguf_get_val_f32(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.float32;
 }
 
-uint64_t gguf_get_val_u64(struct gguf_context * ctx, int i) {
+uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.uint64;
 }
 
-int64_t gguf_get_val_i64(struct gguf_context * ctx, int i) {
+int64_t gguf_get_val_i64(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.int64;
 }
 
-double gguf_get_val_f64(struct gguf_context * ctx, int i) {
+double gguf_get_val_f64(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.float64;
 }
 
-bool gguf_get_val_bool(struct gguf_context * ctx, int i) {
+bool gguf_get_val_bool(const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.bool_;
 }
 
-const char * gguf_get_val_str (struct gguf_context * ctx, int i) {
+const char * gguf_get_val_str (const struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.str.data;
 }
 
-int gguf_get_n_tensors(struct gguf_context * ctx) {
+int gguf_get_n_tensors(const struct gguf_context * ctx) {
     return ctx->header.n_tensors;
 }
 
-int gguf_find_tensor(struct gguf_context * ctx, const char * name) {
+int gguf_find_tensor(const struct gguf_context * ctx, const char * name) {
     // return -1 if tensor not found
     int tensorfound = -1;
 
@@ -20212,11 +20249,11 @@ int gguf_find_tensor(struct gguf_context * ctx, const char * name) {
     return tensorfound;
 }
 
-size_t gguf_get_tensor_offset(struct gguf_context * ctx, int i) {
+size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i) {
     return ctx->infos[i].offset;
 }
 
-char * gguf_get_tensor_name(struct gguf_context * ctx, int i) {
+char * gguf_get_tensor_name(const struct gguf_context * ctx, int i) {
     return ctx->infos[i].name.data;
 }
 
@@ -20499,7 +20536,7 @@ static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_si
     buf->offset += el_size;
 }
 
-static void gguf_write_to_buf(struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
+static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
     // write header
     gguf_bwrite_el(buf, &ctx->header.magic,     sizeof(ctx->header.magic));
     gguf_bwrite_el(buf, &ctx->header.version,   sizeof(ctx->header.version));
@@ -20614,7 +20651,7 @@ static void gguf_write_to_buf(struct gguf_context * ctx, struct gguf_buf * buf,
     }
 }
 
-void gguf_write_to_file(struct gguf_context * ctx, const char * fname, bool only_meta) {
+void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
     FILE * file = fopen(fname, "wb");
     if (!file) {
         GGML_ASSERT(false && "failed to open file for writing");
@@ -20631,7 +20668,7 @@ void gguf_write_to_file(struct gguf_context * ctx, const char * fname, bool only
     fclose(file);
 }
 
-size_t gguf_get_meta_size(struct gguf_context * ctx) {
+size_t gguf_get_meta_size(const struct gguf_context * ctx) {
     // no allocs - only compute size
     struct gguf_buf buf = gguf_buf_init(0);
 
@@ -20640,7 +20677,7 @@ size_t gguf_get_meta_size(struct gguf_context * ctx) {
     return buf.offset;
 }
 
-void gguf_get_meta_data(struct gguf_context * ctx, void * data) {
+void gguf_get_meta_data(const struct gguf_context * ctx, void * data) {
     struct gguf_buf buf = gguf_buf_init(16*1024);
 
     gguf_write_to_buf(ctx, &buf, true);
@@ -20716,6 +20753,14 @@ int ggml_cpu_has_arm_fma(void) {
 #endif
 }
 
+int ggml_cpu_has_metal(void) {
+#if defined(GGML_USE_METAL)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_f16c(void) {
 #if defined(__F16C__)
     return 1;

ggml.h

@@ -195,6 +195,14 @@
 #    define GGML_DEPRECATED(func, hint) func
 #endif
 
+#ifndef __GNUC__
+#    define GGML_ATTRIBUTE_FORMAT(...)
+#elif defined(__MINGW32__)
+#    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+
 #include <stdint.h>
 #include <stddef.h>
 #include <stdbool.h>
@@ -685,6 +693,7 @@ extern "C" {
 
     GGML_API const char *         ggml_get_name   (const struct ggml_tensor * tensor);
     GGML_API struct ggml_tensor * ggml_set_name   (      struct ggml_tensor * tensor, const char * name);
+    GGML_ATTRIBUTE_FORMAT(2, 3)
     GGML_API struct ggml_tensor * ggml_format_name(      struct ggml_tensor * tensor, const char * fmt, ...);
 
     //
@@ -1866,39 +1875,39 @@ extern "C" {
 
     GGML_API const char * gguf_type_name(enum gguf_type type);
 
-    GGML_API int    gguf_get_version    (struct gguf_context * ctx);
+    GGML_API int    gguf_get_version    (const struct gguf_context * ctx);
-    GGML_API size_t gguf_get_alignment  (struct gguf_context * ctx);
+    GGML_API size_t gguf_get_alignment  (const struct gguf_context * ctx);
-    GGML_API size_t gguf_get_data_offset(struct gguf_context * ctx);
+    GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
-    GGML_API void * gguf_get_data       (struct gguf_context * ctx);
+    GGML_API void * gguf_get_data       (const struct gguf_context * ctx);
 
-    GGML_API int          gguf_get_n_kv(struct gguf_context * ctx);
+    GGML_API int          gguf_get_n_kv(const struct gguf_context * ctx);
-    GGML_API int          gguf_find_key(struct gguf_context * ctx, const char * key);
+    GGML_API int          gguf_find_key(const struct gguf_context * ctx, const char * key);
-    GGML_API const char * gguf_get_key (struct gguf_context * ctx, int i);
+    GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int i);
 
-    GGML_API enum gguf_type gguf_get_kv_type (struct gguf_context * ctx, int i);
+    GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int i);
-    GGML_API enum gguf_type gguf_get_arr_type(struct gguf_context * ctx, int i);
+    GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int i);
 
     // results are undefined if the wrong type is used for the key
-    GGML_API uint8_t      gguf_get_val_u8  (struct gguf_context * ctx, int i);
+    GGML_API uint8_t      gguf_get_val_u8  (const struct gguf_context * ctx, int i);
-    GGML_API int8_t       gguf_get_val_i8  (struct gguf_context * ctx, int i);
+    GGML_API int8_t       gguf_get_val_i8  (const struct gguf_context * ctx, int i);
-    GGML_API uint16_t     gguf_get_val_u16 (struct gguf_context * ctx, int i);
+    GGML_API uint16_t     gguf_get_val_u16 (const struct gguf_context * ctx, int i);
-    GGML_API int16_t      gguf_get_val_i16 (struct gguf_context * ctx, int i);
+    GGML_API int16_t      gguf_get_val_i16 (const struct gguf_context * ctx, int i);
-    GGML_API uint32_t     gguf_get_val_u32 (struct gguf_context * ctx, int i);
+    GGML_API uint32_t     gguf_get_val_u32 (const struct gguf_context * ctx, int i);
-    GGML_API int32_t      gguf_get_val_i32 (struct gguf_context * ctx, int i);
+    GGML_API int32_t      gguf_get_val_i32 (const struct gguf_context * ctx, int i);
-    GGML_API float        gguf_get_val_f32 (struct gguf_context * ctx, int i);
+    GGML_API float        gguf_get_val_f32 (const struct gguf_context * ctx, int i);
-    GGML_API uint64_t     gguf_get_val_u64 (struct gguf_context * ctx, int i);
+    GGML_API uint64_t     gguf_get_val_u64 (const struct gguf_context * ctx, int i);
-    GGML_API int64_t      gguf_get_val_i64 (struct gguf_context * ctx, int i);
+    GGML_API int64_t      gguf_get_val_i64 (const struct gguf_context * ctx, int i);
-    GGML_API double       gguf_get_val_f64 (struct gguf_context * ctx, int i);
+    GGML_API double       gguf_get_val_f64 (const struct gguf_context * ctx, int i);
-    GGML_API bool         gguf_get_val_bool(struct gguf_context * ctx, int i);
+    GGML_API bool         gguf_get_val_bool(const struct gguf_context * ctx, int i);
-    GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);
+    GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int i);
-    GGML_API int          gguf_get_arr_n   (struct gguf_context * ctx, int i);
+    GGML_API int          gguf_get_arr_n   (const struct gguf_context * ctx, int i);
-    GGML_API const void * gguf_get_arr_data(struct gguf_context * ctx, int i);
+    GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int i);
-    GGML_API const char * gguf_get_arr_str (struct gguf_context * ctx, int key_id, int i);
+    GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
 
-    GGML_API int    gguf_get_n_tensors    (struct gguf_context * ctx);
+    GGML_API int    gguf_get_n_tensors    (const struct gguf_context * ctx);
-    GGML_API int    gguf_find_tensor      (struct gguf_context * ctx, const char * name);
+    GGML_API int    gguf_find_tensor      (const struct gguf_context * ctx, const char * name);
-    GGML_API size_t gguf_get_tensor_offset(struct gguf_context * ctx, int i);
+    GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
-    GGML_API char * gguf_get_tensor_name  (struct gguf_context * ctx, int i);
+    GGML_API char * gguf_get_tensor_name  (const struct gguf_context * ctx, int i);
 
     // overrides existing values or adds a new one
     GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t  val);
@@ -1943,11 +1952,11 @@ extern "C" {
     //
 
     // write the entire context to a binary file
-    GGML_API void gguf_write_to_file(struct gguf_context * ctx, const char * fname, bool only_meta);
+    GGML_API void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
 
     // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
-    GGML_API size_t gguf_get_meta_size(struct gguf_context * ctx);
+    GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
-    GGML_API void   gguf_get_meta_data(struct gguf_context * ctx, void * data);
+    GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);
 
     //
     // system info
@@ -1961,6 +1970,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_fma        (void);
     GGML_API int ggml_cpu_has_neon       (void);
     GGML_API int ggml_cpu_has_arm_fma    (void);
+    GGML_API int ggml_cpu_has_metal      (void);
     GGML_API int ggml_cpu_has_f16c       (void);
     GGML_API int ggml_cpu_has_fp16_va    (void);
     GGML_API int ggml_cpu_has_wasm_simd  (void);

grammars/assistant.gbnf

@@ -1,57 +0,0 @@
-# - "turn on lights."
-# - "set thermostat to 22."
-# - "increase TV by 10."
-# - "decrease oven by 50."
-# - "play music."
-# - "stop podcast."
-# - "schedule cleaning at 3pm."
-# - "cancel cleaning."
-# - "remind me to buy milk at 5pm."
-# - "show me security system."
-# - "hide washing machine."
-# - "what is the lights status?"
-# - "what is the current thermostat value?"
-# - "what is the security system status?"
-# - "what is the door lock status?"
-# - "what is the camera battery level?"
-# - "what is the weather like today?"
-# - "what is the forecast for tomorrow?"
-# - "what is the time?"
-# - "what is my schedule for today?"
-# - "what tasks do I have?"
-# - "what reminders do I have?"
-#
-# example:
-#
-#   ./command -m ./models/ggml-tiny.en.bin -t 8 --grammar ./grammars/assistant.gbnf --prompt "Ok Whisper, start listening for commands." --context "Whisper is a home assistant. It recognizes voice commands. Time is 11pm." --grammar-penalty 10
-#
-
-root   ::= init " " (command | question) "."
-prompt ::= init
-
-# leading space is very important!
-init ::= " Ok Whisper, start listening for commands."
-
-command ::= "Turn " ("on" | "off") " " device | "Set " device " to " value |
-            "Increase " device " by " value | "Decrease " device " by " value |
-            "Play " media | "Stop " media | "Schedule " task " at " time | "Cancel " task |
-            "Remind me to " task " at " time | "Show me " device | "Hide " device
-
-question ::= "What is the " device " status?" | "What is the current " device " value?" |
-             "What is the " device " temperature?" | "What is the " device " humidity?" |
-             "What is the " device " power consumption?" | "What is the " device " battery level?" |
-             "What is the weather like today?" | "What is the forecast for tomorrow?" |
-             "What is the time?" | "What is my schedule for today?" | "What tasks do I have?" |
-             "What reminders do I have?"
-
-device ::= "lights" | "thermostat" | "security system" | "door lock" | "camera" | "speaker" | "TV" |
-           "music player" | "coffee machine" | "oven" | "refrigerator" | "washing machine" |
-           "vacuum cleaner"
-
-value ::= [0-9]+
-
-media ::= "music" | "radio" | "podcast" | "audiobook" | "TV show" | "movie"
-
-task ::= [a-zA-Z]+ (" " [a-zA-Z]+)?
-
-time ::= [0-9] [0-9]? ("am" | "pm")?

grammars/chess.gbnf

@@ -1,29 +0,0 @@
-# - bishop to c3
-# - rook to d4
-# - knight to e5
-# - d4 d5 knight to c3
-# - c3 queen to d4 king b1
-# - pawn to a1 bishop to b2 knight to c3
-#
-# The prompt (--prompt) is the initial phrase that the user has to say.
-# This is used to prime Whisper with how the user is expected to speak.
-#
-# Provide long context (--context) with sample moves to help Whisper decode the correct sequence.
-# Longer context is better, but it slightly increases the processing time.
-#
-# example:
-#
-#   ./command -m ./models/ggml-tiny.en.bin -t 8 --grammar ./grammars/chess.gbnf --prompt "rook to b4, f3," --context "d4 d5 knight to c3, pawn to a1, bishop to b2 king e8," --grammar-penalty 100
-#
-
-root   ::= init move move? move? "."
-prompt ::= init "."
-
-# leading space is very important!
-init ::= " rook to b4, f3"
-
-move ::= ", " ((piece | pawn | king) " " "to "?)? [a-h] [1-8]
-
-piece ::= "bishop" | "rook" | "knight" | "queen"
-king  ::= "king"
-pawn  ::= "pawn"

grammars/colors.gbnf

@@ -1,16 +0,0 @@
-# - red
-# - green
-# - blue
-#
-# example:
-#
-#   ./command -m ./models/ggml-tiny.en.bin -t 8 --grammar ./grammars/colors.gbnf --prompt "red, green, blue," --context "green, red, blue,"
-#
-
-root   ::= init color "."
-prompt ::= init "."
-
-# leading space is very important!
-init ::= " red, green, blue"
-
-color ::= ", " ("red" | "green" | "blue")

models/convert-h5-to-coreml.py

@@ -0,0 +1,117 @@
+import argparse
+import importlib.util
+
+spec = importlib.util.spec_from_file_location('whisper_to_coreml', 'models/convert-whisper-to-coreml.py')
+whisper_to_coreml = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(whisper_to_coreml)
+
+from whisper import load_model
+
+from copy import deepcopy
+import torch
+from transformers import WhisperForConditionalGeneration
+from huggingface_hub import metadata_update
+
+# https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets/blob/main/src/multiple_datasets/hub_default_utils.py
+WHISPER_MAPPING = {
+    "layers": "blocks",
+    "fc1": "mlp.0",
+    "fc2": "mlp.2",
+    "final_layer_norm": "mlp_ln",
+    "layers": "blocks",
+    ".self_attn.q_proj": ".attn.query",
+    ".self_attn.k_proj": ".attn.key",
+    ".self_attn.v_proj": ".attn.value",
+    ".self_attn_layer_norm": ".attn_ln",
+    ".self_attn.out_proj": ".attn.out",
+    ".encoder_attn.q_proj": ".cross_attn.query",
+    ".encoder_attn.k_proj": ".cross_attn.key",
+    ".encoder_attn.v_proj": ".cross_attn.value",
+    ".encoder_attn_layer_norm": ".cross_attn_ln",
+    ".encoder_attn.out_proj": ".cross_attn.out",
+    "decoder.layer_norm.": "decoder.ln.",
+    "encoder.layer_norm.": "encoder.ln_post.",
+    "embed_tokens": "token_embedding",
+    "encoder.embed_positions.weight": "encoder.positional_embedding",
+    "decoder.embed_positions.weight": "decoder.positional_embedding",
+    "layer_norm": "ln_post",
+}
+
+# https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets/blob/main/src/multiple_datasets/hub_default_utils.py
+def rename_keys(s_dict):
+    keys = list(s_dict.keys())
+    for key in keys:
+        new_key = key
+        for k, v in WHISPER_MAPPING.items():
+            if k in key:
+                new_key = new_key.replace(k, v)
+
+        print(f"{key} -> {new_key}")
+
+        s_dict[new_key] = s_dict.pop(key)
+    return s_dict
+
+# https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets/blob/main/src/multiple_datasets/hub_default_utils.py
+def convert_hf_whisper(hf_model_name_or_path: str, whisper_state_path: str):
+    transformer_model = WhisperForConditionalGeneration.from_pretrained(hf_model_name_or_path)
+    config = transformer_model.config
+
+    # first build dims
+    dims = {
+        'n_mels': config.num_mel_bins,
+        'n_vocab': config.vocab_size,
+        'n_audio_ctx': config.max_source_positions,
+        'n_audio_state': config.d_model,
+        'n_audio_head': config.encoder_attention_heads,
+        'n_audio_layer': config.encoder_layers,
+        'n_text_ctx': config.max_target_positions,
+        'n_text_state': config.d_model,
+        'n_text_head': config.decoder_attention_heads,
+        'n_text_layer': config.decoder_layers
+    }
+
+    state_dict = deepcopy(transformer_model.model.state_dict())
+    state_dict = rename_keys(state_dict)
+
+    torch.save({"dims": dims, "model_state_dict": state_dict}, whisper_state_path)
+
+# Ported from models/convert-whisper-to-coreml.py
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-name", type=str, help="name of model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1)", required=True)
+    parser.add_argument("--model-path", type=str, help="path to the model (e.g. if published on HuggingFace: Oblivion208/whisper-tiny-cantonese)", required=True)
+    parser.add_argument("--encoder-only", type=bool, help="only convert encoder", default=False)
+    parser.add_argument("--quantize",     type=bool, help="quantize weights to F16", default=False)
+    parser.add_argument("--optimize-ane", type=bool, help="optimize for ANE execution (currently broken)", default=False)
+    args = parser.parse_args()
+
+    if args.model_name not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1"]:
+        raise ValueError("Invalid model name")
+
+    pt_target_path = f"models/hf-{args.model_name}.pt"
+    convert_hf_whisper(args.model_path, pt_target_path)
+
+    whisper = load_model(pt_target_path).cpu()
+    hparams = whisper.dims
+    print(hparams)
+
+    if args.optimize_ane:
+        whisperANE = whisper_to_coreml.WhisperANE(hparams).eval()
+        whisperANE.load_state_dict(whisper.state_dict())
+
+        encoder = whisperANE.encoder
+        decoder = whisperANE.decoder
+    else:
+        encoder = whisper.encoder
+        decoder = whisper.decoder
+
+    # Convert encoder
+    encoder = whisper_to_coreml.convert_encoder(hparams, encoder, quantize=args.quantize)
+    encoder.save(f"models/coreml-encoder-{args.model_name}.mlpackage")
+
+    if args.encoder_only is False:
+        # Convert decoder
+        decoder = whisper_to_coreml.convert_decoder(hparams, decoder, quantize=args.quantize)
+        decoder.save(f"models/coreml-decoder-{args.model_name}.mlpackage")
+
+    print("done converting")

models/convert-whisper-to-openvino.py

@@ -29,7 +29,7 @@ def convert_encoder(hparams, encoder, mname):
 
     # use model optimizer to convert onnx to OpenVINO IR format
     encoder_model = mo.convert_model(onnx_path, compress_to_fp16=True)
-    serialize(encoder_model, xml_path='ggml-' + mname + '-encoder-openvino.xml')
+    serialize(encoder_model, xml_path=os.path.join(os.path.dirname(__file__),"ggml-" + mname + "-encoder-openvino.xml"))
 
     #cleanup
     if os.path.isdir(onnx_folder):

models/download-ggml-model.cmd

@@ -40,7 +40,7 @@ if exist "ggml-%model%.bin" (
   goto :eof
 )
 
-PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-%model%.bin -OutFile ggml-%model%.bin"
+PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Start-BitsTransfer -Source https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-%model%.bin -Destination ggml-%model%.bin"
 
 if %ERRORLEVEL% neq 0 (
   echo Failed to download ggml model %model%

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 {

models/generate-coreml-model.sh

@@ -1,10 +1,14 @@
 #!/bin/bash
 
 # Usage: ./generate-coreml-model.sh <model-name>
-if [ $# -eq 0 ]
+if [ $# -eq 0 ]; then
-  then
   echo "No model name supplied"
-    echo "Usage: ./generate-coreml-model.sh <model-name>"
+  echo "Usage for Whisper models: ./generate-coreml-model.sh <model-name>"
+  echo "Usage for HuggingFace models: ./generate-coreml-model.sh -h5 <model-name> <model-path>"
+  exit 1
+elif [[ "$1" == "-h5" && $# != 3 ]]; then
+  echo "No model name and model path supplied for a HuggingFace model"
+  echo "Usage for HuggingFace models: ./generate-coreml-model.sh -h5 <model-name> <model-path>"
   exit 1
 fi
 
@@ -13,7 +17,14 @@ mname="$1"
 wd=$(dirname "$0")
 cd "$wd/../"
 
+if [[ $mname == "-h5" ]]; then
+  mname="$2"
+  mpath="$3"
+  echo $mpath
+  python3 models/convert-h5-to-coreml.py --model-name $mname --model-path $mpath --encoder-only True
+else
   python3 models/convert-whisper-to-coreml.py --model $mname --encoder-only True
+fi
 
 xcrun coremlc compile models/coreml-encoder-${mname}.mlpackage models/
 rm -rf models/ggml-${mname}-encoder.mlmodelc

whisper.h

@@ -96,37 +96,6 @@ extern "C" {
         void  (*close)(void * ctx);
     } whisper_model_loader;
 
-    // grammar element type
-    enum whisper_gretype {
-        // end of rule definition
-        WHISPER_GRETYPE_END            = 0,
-
-        // start of alternate definition for rule
-        WHISPER_GRETYPE_ALT            = 1,
-
-        // non-terminal element: reference to rule
-        WHISPER_GRETYPE_RULE_REF       = 2,
-
-        // terminal element: character (code point)
-        WHISPER_GRETYPE_CHAR           = 3,
-
-        // inverse char(s) ([^a], [^a-b] [^abc])
-        WHISPER_GRETYPE_CHAR_NOT       = 4,
-
-        // modifies a preceding WHISPER_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
-        // be an inclusive range ([a-z])
-        WHISPER_GRETYPE_CHAR_RNG_UPPER = 5,
-
-        // modifies a preceding WHISPER_GRETYPE_CHAR or
-        // WHISPER_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
-        WHISPER_GRETYPE_CHAR_ALT       = 6,
-    };
-
-    typedef struct whisper_grammar_element {
-        enum whisper_gretype type;
-        uint32_t             value; // Unicode code point or rule ID
-    } whisper_grammar_element;
-
     // Various functions for loading a ggml whisper model.
     // Allocate (almost) all memory needed for the model.
     // Return NULL on failure
@@ -365,6 +334,11 @@ extern "C" {
     // If it returns false, the computation is aborted
     typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, struct whisper_state * state, void * user_data);
 
+    // Abort callback
+    // If not NULL, called before ggml computation
+    // If it returns true, the computation is aborted
+    typedef bool (*whisper_abort_callback)(void * user_data);
+
     // Logits filter callback
     // Can be used to modify the logits before sampling
     // If not NULL, called after applying temperature to logits
@@ -389,7 +363,6 @@ extern "C" {
 
         bool translate;
         bool no_context;        // do not use past transcription (if any) as initial prompt for the decoder
-        bool no_timestamps;     // do not generate timestamps
         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
@@ -460,14 +433,13 @@ extern "C" {
         whisper_encoder_begin_callback encoder_begin_callback;
         void * encoder_begin_callback_user_data;
 
+        // called each time before ggml computation starts
+        whisper_abort_callback abort_callback;
+        void * abort_callback_user_data;
+
         // called by each decoder to filter obtained logits
         whisper_logits_filter_callback logits_filter_callback;
         void * logits_filter_callback_user_data;
-
-        const whisper_grammar_element ** grammar_rules;
-        size_t                           n_grammar_rules;
-        size_t                           i_start_rule;
-        float                            grammar_penalty;
     };
 
     // NOTE: this function allocates memory, and it is the responsibility of the caller to free the pointer - see whisper_free_params()
@@ -522,6 +494,7 @@ extern "C" {
 
     // Get whether the next segment is predicted as a speaker turn
     WHISPER_API bool whisper_full_get_segment_speaker_turn_next(struct whisper_context * ctx, int i_segment);
+    WHISPER_API bool whisper_full_get_segment_speaker_turn_next_from_state(struct whisper_state * state, int i_segment);
 
     // Get the text of the specified segment
     WHISPER_API const char * whisper_full_get_segment_text           (struct whisper_context * ctx, int i_segment);