whisper : add GPU support via cuBLAS (#834)

* make : add WHISPER_CUBLAS

* make : fix CUBLAS build

* whisper : disable Flash Attention + adjust memory buffers

* whisper : remove old commented code

* readme : add cuBLAS instructions

* cmake : add WHISPER_CUBLAS option

* gitignore : ignore build-cublas

.gitignore

@@ -12,6 +12,7 @@ build-em/
 build-debug/
 build-release/
 build-static/
+build-cublas/
 build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/

CMakeLists.txt

@@ -51,7 +51,7 @@ option(WHISPER_SANITIZE_UNDEFINED     "whisper: enable undefined sanitizer" OFF)
 option(WHISPER_BUILD_TESTS            "whisper: build tests"    ${WHISPER_STANDALONE})
 option(WHISPER_BUILD_EXAMPLES         "whisper: build examples" ${WHISPER_STANDALONE})
 
-option(WHISPER_SUPPORT_SDL2           "whisper: support for libSDL2" OFF)
+option(WHISPER_SDL2                   "whisper: support for libSDL2" OFF)
 
 if (APPLE)
     option(WHISPER_NO_ACCELERATE         "whisper: disable Accelerate framework" OFF)
@@ -62,7 +62,8 @@ if (APPLE)
     option(WHISPER_COREML                "whisper: enable Core ML framework" OFF)
     option(WHISPER_COREML_ALLOW_FALLBACK "whisper: allow non-CoreML fallback" OFF)
 else()
-    option(WHISPER_SUPPORT_OPENBLAS      "whisper: support for OpenBLAS" OFF)
+    option(WHISPER_OPENBLAS              "whisper: support for OpenBLAS" OFF)
+    option(WHISPER_CUBLAS                "whisper: support for cuBLAS" OFF)
 endif()
 
 option(WHISPER_PERF "whisper: enable perf timings" OFF)
@@ -127,7 +128,7 @@ if (APPLE)
     endif()
 endif()
 
-if (WHISPER_SUPPORT_OPENBLAS)
+if (WHISPER_OPENBLAS)
     find_library(OPENBLAS_LIB
         NAMES openblas libopenblas
         )
@@ -141,6 +142,31 @@ if (WHISPER_SUPPORT_OPENBLAS)
     endif()
 endif()
 
+if (WHISPER_CUBLAS)
+    cmake_minimum_required(VERSION 3.17)
+
+    find_package(CUDAToolkit)
+
+    if (CUDAToolkit_FOUND)
+        message(STATUS "cuBLAS found")
+
+        enable_language(CUDA)
+
+        set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
+
+        add_compile_definitions(GGML_USE_CUBLAS)
+
+        if (WHISPER_STATIC)
+            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+        else()
+            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
+        endif()
+
+    else()
+        message(WARNING "cuBLAS not found")
+    endif()
+endif()
+
 # compiler flags
 
 if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
@@ -247,6 +273,7 @@ set(TARGET whisper)
 add_library(${TARGET}
     ggml.h
     ggml.c
+    ${GGML_CUDA_SOURCES}
     whisper.h
     whisper.cpp
     )
@@ -279,6 +306,12 @@ if (BUILD_SHARED_LIBS)
         )
 endif()
 
+if (GGML_CUDA_SOURCES)
+    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")
+endif()
+
 if (EMSCRIPTEN)
     set_target_properties(${TARGET} PROPERTIES COMPILE_FLAGS "-msimd128")
 endif()

Makefile

@@ -1,3 +1,5 @@
+default: main bench
+
 ifndef UNAME_S
 UNAME_S := $(shell uname -s)
 endif
@@ -157,6 +159,18 @@ ifdef WHISPER_OPENBLAS
 	LDFLAGS += -lopenblas
 endif
 
+ifdef WHISPER_CUBLAS
+	CFLAGS      += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
+	CXXFLAGS    += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
+	LDFLAGS     += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
+	WHISPER_OBJ += ggml-cuda.o
+	NVCC        = nvcc
+	NVCCFLAGS   = --forward-unknown-to-host-compiler -arch=native
+
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
+endif
+
 ifdef WHISPER_GPROF
 	CFLAGS   += -pg
 	CXXFLAGS += -pg
@@ -200,20 +214,18 @@ $(info I CC:       $(CCV))
 $(info I CXX:      $(CXXV))
 $(info )
 
-default: main bench
-
 #
 # Build library
 #
 
-ggml.o: ggml.c ggml.h
-	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o
+ggml.o: ggml.c ggml.h ggml-cuda.h
+	$(CC)  $(CFLAGS)   -c $< -o $@
 
-whisper.o: whisper.cpp whisper.h ggml.h
-	$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
+whisper.o: whisper.cpp whisper.h ggml.h ggml-cuda.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 ifndef WHISPER_COREML
-WHISPER_OBJ = whisper.o
+WHISPER_OBJ += whisper.o
 else
 whisper-encoder.o: coreml/whisper-encoder.mm coreml/whisper-encoder.h
 	$(CXX) -O3 -I . -c coreml/whisper-encoder.mm -o whisper-encoder.o
@@ -221,7 +233,7 @@ whisper-encoder.o: coreml/whisper-encoder.mm coreml/whisper-encoder.h
 whisper-encoder-impl.o: coreml/whisper-encoder-impl.m coreml/whisper-encoder-impl.h
 	$(CXX) -O3 -I . -fobjc-arc -c coreml/whisper-encoder-impl.m -o whisper-encoder-impl.o
 
-WHISPER_OBJ = whisper.o whisper-encoder.o whisper-encoder-impl.o
+WHISPER_OBJ += whisper.o whisper-encoder.o whisper-encoder-impl.o
 endif
 
 libwhisper.a: ggml.o $(WHISPER_OBJ)

README.md

@@ -18,6 +18,7 @@ High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisp
 - Low memory usage (Flash Attention)
 - Zero memory allocations at runtime
 - Runs on the CPU
+- [Partial GPU support for NVIDIA via cuBLAS](https://github.com/ggerganov/whisper.cpp#nvidia-gpu-support-via-cublas)
 - [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
 
 Supported platforms:
@@ -254,7 +255,7 @@ speed-up - more than x3 faster compared with CPU-only execution. Here are the in
   # using Makefile
   make clean
   WHISPER_COREML=1 make -j
-  
+
   # using CMake
   cd build
   cmake -DWHISPER_COREML=1 ..
@@ -271,20 +272,33 @@ speed-up - more than x3 faster compared with CPU-only execution. Here are the in
   whisper_init_state: first run on a device may take a while ...
   whisper_init_state: Core ML model loaded
 
-  system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | COREML = 1 | 
+  system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | COREML = 1 |
 
   ...
   ```
 
   The first run on a device is slow, since the ANE service compiles the Core ML model to some device-specific format.
   Next runs are faster.
-  
+
 For more information about the Core ML implementation please refer to PR [#566](https://github.com/ggerganov/whisper.cpp/pull/566).
-  
+
+## NVIDIA GPU support via cuBLAS
+
+With NVIDIA cards, the Encoder processing can be offloaded to the GPU to a large extend through cuBLAS.
+First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-downloads
+
+Now build `whisper.cpp` with cuBLAS support:
+
+```
+make clean
+WHISPER_CUBLAS=1 make -j
+```
+
+Run all the examples as usual.
+
 ## Limitations
 
 - Inference only
-- No GPU support (yet)
 
 ## Another example
 
@@ -429,7 +443,7 @@ system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1
 
 main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
 
-[00:00:00.000 --> 00:00:00.320]  
+[00:00:00.000 --> 00:00:00.320]
 [00:00:00.320 --> 00:00:00.370]   And
 [00:00:00.370 --> 00:00:00.690]   so
 [00:00:00.690 --> 00:00:00.850]   my

examples/CMakeLists.txt

@@ -4,7 +4,7 @@ find_package(Threads REQUIRED)
 
 # third-party
 
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # SDL2
     find_package(SDL2 REQUIRED)
 
@@ -27,7 +27,7 @@ include(DefaultTargetOptions)
 
 set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # common-sdl
 
     set(TARGET common-sdl)

examples/command/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # command
     set(TARGET command)
     add_executable(${TARGET} command.cpp)

examples/stream/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # stream
     set(TARGET stream)
     add_executable(${TARGET} stream.cpp)

examples/talk-llama/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # talk-llama
     set(TARGET talk-llama)
     #add_executable(${TARGET} talk-llama.cpp llama.cpp)

examples/talk/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SUPPORT_SDL2)
+if (WHISPER_SDL2)
     # talk
     set(TARGET talk)
     #add_executable(${TARGET} talk.cpp gpt-2.cpp)

whisper.cpp

@@ -102,7 +102,7 @@ static void byteswap_tensor(ggml_tensor * tensor) {
 #define WHISPER_PRINT_DEBUG(...)
 #endif
 
-#define WHISPER_USE_FLASH_ATTN
+//#define WHISPER_USE_FLASH_ATTN
 //#define WHISPER_USE_FLASH_FF
 #define WHISPER_MAX_DECODERS 16
 
@@ -224,11 +224,11 @@ static const std::map<std::string, std::pair<int, std::string>> g_lang = {
 static const size_t MB = 1ull*1024*1024;
 
 static const std::map<e_model, size_t> MEM_REQ_SCRATCH0 = {
-    { MODEL_TINY,     14ull*MB },
-    { MODEL_BASE,     18ull*MB },
-    { MODEL_SMALL,    28ull*MB },
-    { MODEL_MEDIUM,   36ull*MB },
-    { MODEL_LARGE,    44ull*MB },
+    { MODEL_TINY,     62ull*MB },
+    { MODEL_BASE,     80ull*MB },
+    { MODEL_SMALL,   120ull*MB },
+    { MODEL_MEDIUM,  158ull*MB },
+    { MODEL_LARGE,   198ull*MB },
 };
 
 static const std::map<e_model, size_t> MEM_REQ_SCRATCH1 = {
@@ -280,11 +280,11 @@ static const std::map<e_model, size_t> MEM_REQ_KV_CROSS = {
 };
 
 static const std::map<e_model, size_t> MEM_REQ_ENCODE = {
-    { MODEL_TINY,      6ull*MB },
-    { MODEL_BASE,      8ull*MB },
-    { MODEL_SMALL,    13ull*MB },
-    { MODEL_MEDIUM,   22ull*MB },
-    { MODEL_LARGE,    33ull*MB },
+    { MODEL_TINY,     30ull*MB },
+    { MODEL_BASE,     38ull*MB },
+    { MODEL_SMALL,    56ull*MB },
+    { MODEL_MEDIUM,   74ull*MB },
+    { MODEL_LARGE,    94ull*MB },
 };
 
 static const std::map<e_model, size_t> MEM_REQ_DECODE = {
@@ -1554,26 +1554,17 @@ static bool whisper_encode_internal(
 
                 struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_scaled);
 
-                //struct ggml_tensor * V_trans =
-                //    ggml_permute(ctx0,
-                //            ggml_cpy(ctx0,
-                //                Vcur,
-                //                ggml_new_tensor_3d(ctx0, wctx.wtype, n_state/n_head, n_head, n_ctx)),
-                //            1, 2, 0, 3);
-
-                //struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
-
                 struct ggml_tensor * V =
                     ggml_cpy(ctx0,
                             ggml_permute(ctx0,
                                 ggml_reshape_3d(ctx0,
                                     Vcur,
                                     n_state/n_head, n_head, n_ctx),
-                                0, 2, 1, 3),
-                            ggml_new_tensor_3d(ctx0, wctx.wtype, n_state/n_head, n_ctx, n_head)
+                                1, 2, 0, 3),
+                            ggml_new_tensor_3d(ctx0, wctx.wtype, n_ctx, n_state/n_head, n_head)
                             );
 
-                struct ggml_tensor * KQV = ggml_mul_mat(ctx0, ggml_transpose(ctx0, V), KQ_soft_max);
+                struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
 #endif
                 struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);