convert : update for baichuan (#2081)

1. guess n_layers;
2. relax warnings on context size;
3. add a note that its derivations are also supported.

Co-authored-by: Judd <foldl@boxvest.com>

.github/workflows/build.yml

@@ -111,6 +111,7 @@ jobs:
 
       - name: Dependencies
         id: depends
+        continue-on-error: true
         run: |
           brew update
 
@@ -129,15 +130,17 @@ jobs:
 
       - name: Dependencies
         id: depends
+        continue-on-error: true
         run: |
           brew update
 
       - name: Build
         id: cmake_build
         run: |
+          sysctl -a
           mkdir build
           cd build
-          cmake -DLLAMA_AVX2=OFF ..
+          cmake -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF ..
           cmake --build . --config Release
 
       - name: Test

.gitignore

@@ -1,5 +1,6 @@
 *.o
 *.a
+*.so
 .DS_Store
 .build/
 .cache/
@@ -39,8 +40,8 @@ models/*
 /vdot
 /server
 /Pipfile
+/embd-input-test
 /libllama.so
-
 build-info.h
 arm_neon.h
 compile_commands.json

CMakeLists.txt

@@ -68,17 +68,19 @@ option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
 option(LLAMA_CUBLAS                          "llama: use cuBLAS"                                OFF)
+option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
-set(LLAMA_CUDA_DMMV_Y       "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
+set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
 option(LLAMA_CUDA_DMMV_F16                   "llama: use 16 bit floats for dmmv CUDA kernels"   OFF)
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
 option(LLAMA_METAL                           "llama: use Metal"                                 OFF)
 option(LLAMA_K_QUANTS                        "llama: use k-quants"                              ON)
+option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
 
 option(LLAMA_BUILD_TESTS                "llama: build tests"    ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES             "llama: build examples" ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_SERVER               "llama: build server example"                           OFF)
+option(LLAMA_BUILD_SERVER               "llama: build server example"                           ON)
 
 #
 # Build info header
@@ -225,6 +227,14 @@ if (LLAMA_BLAS)
     endif()
 endif()
 
+if (LLAMA_K_QUANTS)
+    set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h)
+    add_compile_definitions(GGML_USE_K_QUANTS)
+    if (LLAMA_QKK_64)
+        add_compile_definitions(GGML_QKK_64)
+    endif()
+endif()
+
 if (LLAMA_CUBLAS)
     cmake_minimum_required(VERSION 3.17)
 
@@ -237,8 +247,14 @@ if (LLAMA_CUBLAS)
         set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
 
         add_compile_definitions(GGML_USE_CUBLAS)
+        if (LLAMA_CUDA_FORCE_DMMV)
+            add_compile_definitions(GGML_CUDA_FORCE_DMMV)
+        endif()
         add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
-        add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
+        add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
+        if (DEFINED LLAMA_CUDA_DMMV_Y)
+            add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
+        endif()
         if (LLAMA_CUDA_DMMV_F16)
             add_compile_definitions(GGML_CUDA_DMMV_F16)
         endif()
@@ -254,7 +270,7 @@ if (LLAMA_CUBLAS)
         if (LLAMA_CUDA_DMMV_F16)
             set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics
         else()
-            set(CMAKE_CUDA_ARCHITECTURES "52") # lowest CUDA 12 standard
+            set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
         endif()
     endif()
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
@@ -289,11 +305,6 @@ if (LLAMA_METAL)
         )
 endif()
 
-if (LLAMA_K_QUANTS)
-    set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h)
-    add_compile_definitions(GGML_USE_K_QUANTS)
-endif()
-
 if (LLAMA_CLBLAST)
     find_package(CLBlast)
     if (CLBlast_FOUND)
@@ -382,11 +393,6 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES
     if (MSVC)
         # TODO: arm msvc?
     else()
-        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
-            # Apple M1, M2, etc.
-            # Raspberry Pi 3, 4, Zero 2 (64-bit)
-            add_compile_options(-mcpu=native)
-        endif()
         if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
             # Raspberry Pi 1, Zero
             add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access)

Makefile

@@ -1,11 +1,5 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple
-
-ifdef LLAMA_BUILD_SERVER
-	BUILD_TARGETS += server
-	LLAMA_SERVER_VERBOSE ?= 1
-server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
-endif
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test
 
 default: $(BUILD_TARGETS)
 
@@ -43,8 +37,11 @@ endif
 
 # keep standard at C11 and C++11
 # -Ofast tends to produce faster code, but may not be available for some compilers.
-#OPT = -Ofast
+ifdef LLAMA_FAST
+OPT = -Ofast
+else
 OPT = -O3
+endif
 CFLAGS   = -I.              $(OPT) -std=c11   -fPIC
 CXXFLAGS = -I. -I./examples $(OPT) -std=c++11 -fPIC
 LDFLAGS  =
@@ -58,6 +55,10 @@ else
 	CXXFLAGS += -DNDEBUG
 endif
 
+ifdef LLAMA_SERVER_VERBOSE
+	CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
+endif
+
 # warnings
 CFLAGS   += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
 CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
@@ -131,6 +132,10 @@ ifndef LLAMA_NO_K_QUANTS
 	CFLAGS   += -DGGML_USE_K_QUANTS
 	CXXFLAGS += -DGGML_USE_K_QUANTS
 	OBJS     += k_quants.o
+ifdef LLAMA_QKK_64
+	CFLAGS   += -DGGML_QKK_64
+	CXXFLAGS += -DGGML_QKK_64
+endif
 endif
 
 ifndef LLAMA_NO_ACCELERATE
@@ -159,16 +164,21 @@ ifdef LLAMA_CUBLAS
 	OBJS      += ggml-cuda.o
 	NVCC      = nvcc
 	NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
+ifdef LLAMA_CUDA_FORCE_DMMV
+	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
+endif # LLAMA_CUDA_FORCE_DMMV
 ifdef LLAMA_CUDA_DMMV_X
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
 else
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
 endif # LLAMA_CUDA_DMMV_X
-ifdef LLAMA_CUDA_DMMV_Y
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
+ifdef LLAMA_CUDA_MMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
+else ifdef LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
 else
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
-endif # LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
+endif # LLAMA_CUDA_MMV_Y
 ifdef LLAMA_CUDA_DMMV_F16
 	NVCCFLAGS += -DGGML_CUDA_DMMV_F16
 endif # LLAMA_CUDA_DMMV_F16
@@ -265,7 +275,7 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch build-info.h
+	rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h
 
 #
 # Examples
@@ -298,6 +308,13 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.
 server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
 
+libembdinput.so: examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) --shared $(CXXFLAGS) $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
+
+
+embd-input-test: libembdinput.so examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.so,$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
+
 train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp    build-info.h ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 

README.md

@@ -5,12 +5,17 @@
 [![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions)
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 
+[Roadmap](https://github.com/users/ggerganov/projects/7) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
+
 Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 **Hot topics:**
 
+- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998
+- k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001
+- New roadmap: https://github.com/users/ggerganov/projects/7
+- Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985
 - p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1
-- Roadmap June 2023: https://github.com/ggerganov/llama.cpp/discussions/1729
 
 <details>
   <summary>Table of Contents</summary>
@@ -81,6 +86,7 @@ as the main playground for developing new features for the [ggml](https://github
 - [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
 - [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
 - [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
+- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) and its derivations (such as [baichuan-7b-sft](https://huggingface.co/hiyouga/baichuan-7b-sft))
 
 **Bindings:**
 
@@ -89,6 +95,7 @@ as the main playground for developing new features for the [ggml](https://github
 - Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
+- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
 
 **UI:**
 
@@ -338,8 +345,9 @@ Building the program with BLAS support may lead to some performance improvements
 
   | Option                  | Legal values           | Default | Description |
   |-------------------------|------------------------|---------|-------------|
+  | LLAMA_CUDA_FORCE_DMMV   | Boolean                |   false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 |      32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_DMMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_MMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_F16     | Boolean                |   false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
   | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
 
@@ -683,6 +691,8 @@ GGML_OPENCL_DEVICE=0
 export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
 ```
 
+(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )
+
 For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
 
 Place your desired model into the `/llama.cpp/models/` directory and execute the `./main (...)` script.

build.zig

@@ -1,61 +1,58 @@
 const std = @import("std");
 
+// Zig Version: 0.11.0-dev.3379+629f0d23b
 pub fn build(b: *std.build.Builder) void {
     const target = b.standardTargetOptions(.{});
-    const optimize = b.standardReleaseOptions();
-    const want_lto = b.option(bool, "lto", "Want -fLTO");
-
-    const lib = b.addStaticLibrary("llama", null);
-    lib.want_lto = want_lto;
-    lib.setTarget(target);
-    lib.setBuildMode(optimize);
+    const optimize = b.standardOptimizeOption(.{});
+    const lib = b.addStaticLibrary(.{
+        .name = "llama",
+        .target = target,
+        .optimize = optimize,
+    });
+    lib.linkLibC();
     lib.linkLibCpp();
     lib.addIncludePath(".");
-    lib.addIncludePath("examples");
+    lib.addIncludePath("./examples");
     lib.addCSourceFiles(&.{
         "ggml.c",
     }, &.{"-std=c11"});
     lib.addCSourceFiles(&.{
         "llama.cpp",
     }, &.{"-std=c++11"});
-    lib.install();
+    b.installArtifact(lib);
 
-    const build_args = .{ .b = b, .lib = lib, .target = target, .optimize = optimize, .want_lto = want_lto };
+    const examples = .{
+        "main",
+        "baby-llama",
+        "embedding",
+        // "metal",
+        "perplexity",
+        "quantize",
+        "quantize-stats",
+        "save-load-state",
+        // "server",
+        "simple",
+        "train-text-from-scratch",
+    };
 
-    const exe = build_example("main", build_args);
-    _ = build_example("quantize", build_args);
-    _ = build_example("perplexity", build_args);
-    _ = build_example("embedding", build_args);
-
-    // create "zig build run" command for ./main
-
-    const run_cmd = exe.run();
-    run_cmd.step.dependOn(b.getInstallStep());
-    if (b.args) |args| {
-        run_cmd.addArgs(args);
+    inline for (examples) |example_name| {
+        const exe = b.addExecutable(.{
+            .name = example_name,
+            .target = target,
+            .optimize = optimize,
+        });
+        exe.addIncludePath(".");
+        exe.addIncludePath("./examples");
+        exe.addCSourceFiles(&.{
+            std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{example_name, example_name}),
+            "examples/common.cpp",
+        }, &.{"-std=c++11"});
+        exe.linkLibrary(lib);
+        b.installArtifact(exe);
+        const run_cmd = b.addRunArtifact(exe);
+        run_cmd.step.dependOn(b.getInstallStep());
+        if (b.args) |args| run_cmd.addArgs(args);
+        const run_step = b.step("run_" ++ example_name, "Run the app");
+        run_step.dependOn(&run_cmd.step);
     }
-
-    const run_step = b.step("run", "Run the app");
-    run_step.dependOn(&run_cmd.step);
-}
-
-fn build_example(comptime name: []const u8, args: anytype) *std.build.LibExeObjStep {
-    const b = args.b;
-    const lib = args.lib;
-    const want_lto = args.want_lto;
-
-    const exe = b.addExecutable(name, null);
-    exe.want_lto = want_lto;
-    lib.setTarget(args.target);
-    lib.setBuildMode(args.optimize);
-    exe.addIncludePath(".");
-    exe.addIncludePath("examples");
-    exe.addCSourceFiles(&.{
-        std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{name, name}),
-        "examples/common.cpp",
-    }, &.{"-std=c++11"});
-    exe.linkLibrary(lib);
-    exe.install();
-
-    return exe;
 }

convert-lora-to-ggml.py

@@ -113,6 +113,10 @@ with open(output_path, "wb") as fout:
 
     write_file_header(fout, params)
     for k, v in model.items():
+        if k.endswith(".default.weight"):
+            k = k.replace(".default.weight", ".weight")
+        if k in ["llama_proj.weight", "llama_proj.bias"]:
+            continue
         if k.endswith("lora_A.weight"):
             if v.dtype != torch.float16 and v.dtype != torch.float32:
                 v = v.float()
@@ -120,7 +124,7 @@ with open(output_path, "wb") as fout:
         else:
             v = v.float()
 
-        t = v.numpy()
+        t = v.detach().numpy()
         tname = translate_tensor_name(k)
         print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
         write_tensor_header(fout, tname, t.shape, t.dtype)

convert.py

@@ -136,7 +136,7 @@ def find_n_mult(n_ff: int, n_embd: int) -> int:
         calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
         if calc_ff == n_ff:
             return n_mult
-    return 1
+    raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
 
 @dataclass
 class Params:
@@ -154,9 +154,15 @@ class Params:
         # try transformer naming first
         if "model.layers.0.self_attn.q_proj.weight" in model:
             n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
+        elif "model.layers.0.self_attn.W_pack.weight" in model:   # next: try baichuan naming
+            n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
         else:
             n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
 
+        if n_layer < 1:
+            raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
+                            "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
+
         n_head=n_embd // 128 # guessed
 
         return Params(
@@ -321,6 +327,10 @@ class Tensor(metaclass=ABCMeta):
     @abstractmethod
     def permute(self, n_head: int) -> 'Tensor': ...
     @abstractmethod
+    def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
+    @abstractmethod
+    def part(self, n_part: int) -> 'UnquantizedTensor': ...
+    @abstractmethod
     def to_ggml(self) -> 'GGMLCompatibleTensor': ...
 
 
@@ -345,6 +355,14 @@ class UnquantizedTensor(Tensor):
     def to_ggml(self) -> 'UnquantizedTensor':
         return self
 
+    def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
+        r = self.ndarray.shape[0] // 3
+        return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
+
+    def part(self, n_part: int) -> 'UnquantizedTensor':
+        r = self.ndarray.shape[0] // 3
+        return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
+
     def permute(self, n_head: int) -> 'UnquantizedTensor':
         return UnquantizedTensor(permute(self.ndarray, n_head))
 
@@ -642,6 +660,19 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor:
         return lazy_tensor.load().permute(n_head)
     return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
 
+def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
+    def load() -> Tensor:
+        return lazy_tensor.load().permute_part(n_part, n_head)
+    s = lazy_tensor.shape.copy()
+    s[0] = s[0] // 3
+    return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
+
+def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
+    def load() -> Tensor:
+        return lazy_tensor.load().part(n_part)
+    s = lazy_tensor.shape.copy()
+    s[0] = s[0] // 3
+    return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)
 
 def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
     out: LazyModel = {}
@@ -650,11 +681,17 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
     out["output.weight"] = model["lm_head.weight"]
 
     for i in itertools.count():
-        if f"model.layers.{i}.self_attn.q_proj.weight" not in model:
+        if f"model.layers.{i}.self_attn.q_proj.weight" in model:
+            out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
+            out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head)
+            out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
+        elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
+            out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
+            out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head)
+            out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
+        else:
             break
-        out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
-        out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head)
-        out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
+
         out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"]
 
         out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"]

examples/CMakeLists.txt

@@ -39,6 +39,7 @@ else()
     add_subdirectory(baby-llama)
     add_subdirectory(train-text-from-scratch)
     add_subdirectory(simple)
+    add_subdirectory(embd-input)
     if (LLAMA_METAL)
         add_subdirectory(metal)
     endif()

examples/alpaca.sh

@@ -7,7 +7,7 @@
 cd `dirname $0`
 cd ..
 
-./main -m ./models/ggml-alpaca-7b-q4.bin \
+./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \
        --color \
        -f ./prompts/alpaca.txt \
        --ctx_size 2048 \

examples/baby-llama/baby-llama.cpp

@@ -566,8 +566,8 @@ struct ggml_tensor * forward(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, 1]
             // Kcur shape [n_embd/n_head, n_head, N, 1]
-            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
 
             // store key and value to memory
             {
@@ -823,8 +823,8 @@ struct ggml_tensor * forward_batch(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, n_batch]
             // Kcur shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
             assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
             assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
 
@@ -1116,7 +1116,7 @@ struct ggml_tensor * forward_lora(
                                                         model->layers[il].wqb,
                                                         cur)),
                                                 n_embd/n_head, n_head, N),
-                                            n_past, n_rot, 0);
+                                            n_past, n_rot, 0, 0);
             struct ggml_tensor * Kcur = ggml_rope(ctx0,
                                             ggml_reshape_3d(ctx0,
                                                 ggml_mul_mat(ctx0,
@@ -1125,7 +1125,7 @@ struct ggml_tensor * forward_lora(
                                                         model->layers[il].wkb,
                                                         cur)),
                                                 n_embd/n_head, n_head, N),
-                                            n_past, n_rot, 0);
+                                            n_past, n_rot, 0, 0);
 
             // store key and value to memory
             {

examples/common.cpp

@@ -110,7 +110,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            params.seed = std::stoi(argv[i]);
+            params.seed = std::stoul(argv[i]);
         } else if (arg == "-t" || arg == "--threads") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -343,6 +343,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.use_mmap = false;
         } else if (arg == "--mtest") {
             params.mem_test = true;
+        } else if (arg == "--numa") {
+            params.numa = true;
         } else if (arg == "--export") {
             params.export_cgraph = true;
         } else if (arg == "--verbose-prompt") {
@@ -414,13 +416,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         exit(1);
     }
 
-#ifdef GGML_USE_CUBLAS
-    if (!params.lora_adapter.empty() && params.n_gpu_layers > 0) {
-        fprintf(stderr, "%s: error: the simultaneous use of LoRAs and GPU acceleration is not supported", __func__);
-        exit(1);
-    }
-#endif // GGML_USE_CUBLAS
-
     if (escape_prompt) {
         process_escapes(params.prompt);
     }
@@ -488,6 +483,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     if (llama_mmap_supported()) {
         fprintf(stderr, "  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
     }
+    fprintf(stderr, "  --numa                attempt optimizations that help on some NUMA systems\n");
+    fprintf(stderr, "                        if run without this previously, it is recommended to drop the system page cache before using this\n");
+    fprintf(stderr, "                        see https://github.com/ggerganov/llama.cpp/issues/1437\n");
 #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
     fprintf(stderr, "  -ngl N, --n-gpu-layers N\n");
     fprintf(stderr, "                        number of layers to store in VRAM\n");

examples/common.h

@@ -22,7 +22,7 @@
 int32_t get_num_physical_cores();
 
 struct gpt_params {
-    int32_t seed                            = -1;  // RNG seed
+    uint32_t seed                           = -1;  // RNG seed
     int32_t n_threads                       = get_num_physical_cores();
     int32_t n_predict                       = -1;  // new tokens to predict
     int32_t n_ctx                           = 512; // context size
@@ -31,7 +31,7 @@ struct gpt_params {
     int32_t n_gpu_layers                    = 0;   // number of layers to store in VRAM
     int32_t main_gpu                        = 0;   // the GPU that is used for scratch and small tensors
     float   tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
-    bool    low_vram                        = 0;   // if true, reduce VRAM usage at the cost of performance
+    int32_t n_probs                         = 0;   // if greater than 0, output the probabilities of top n_probs tokens.
 
     // sampling parameters
     std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
@@ -59,6 +59,7 @@ struct gpt_params {
     std::string lora_adapter = "";  // lora adapter path
     std::string lora_base    = "";  // base model path for the lora adapter
 
+    bool low_vram          = false;   // if true, reduce VRAM usage at the cost of performance
     bool memory_f16        = true;  // use f16 instead of f32 for memory kv
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
@@ -76,6 +77,7 @@ struct gpt_params {
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool mem_test          = false; // compute maximum memory usage
+    bool numa              = false; // attempt optimizations that help on some NUMA systems
     bool export_cgraph     = false; // export the computation graph
     bool verbose_prompt    = false; // print prompt tokens before generation
 };

examples/embd-input/.gitignore

@@ -0,0 +1,4 @@
+PandaGPT
+MiniGPT-4
+*.pth
+

examples/embd-input/CMakeLists.txt

@@ -0,0 +1,15 @@
+set(TARGET embdinput)
+add_library(${TARGET} embd-input-lib.cpp embd-input.h)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+  add_dependencies(${TARGET} BUILD_INFO)
+endif()
+
+set(TARGET embd-input-test)
+add_executable(${TARGET} embd-input-test.cpp)
+target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+  add_dependencies(${TARGET} BUILD_INFO)
+endif()

examples/embd-input/README.md

@@ -0,0 +1,63 @@
+### Examples for input embedding directly
+
+## Requirement
+build  `libembdinput.so`
+run the following comman in main dir (../../).
+```
+make
+```
+
+## [LLaVA](https://github.com/haotian-liu/LLaVA/) example  (llava.py)
+
+1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/).
+2. Convert it to ggml format.
+3. `llava_projection.pth` is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin).
+
+```
+import torch
+
+bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin"
+pth_path = "./examples/embd_input/llava_projection.pth"
+
+dic = torch.load(bin_path)
+used_key = ["model.mm_projector.weight","model.mm_projector.bias"]
+torch.save({k: dic[k] for k in used_key}, pth_path)
+```
+4. Check the path of LLaVA model and `llava_projection.pth` in `llava.py`.
+
+
+## [PandaGPT](https://github.com/yxuansu/PandaGPT) example (panda_gpt.py)
+
+1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format.
+The `adapter_config.json` is
+```
+{
+  "peft_type": "LORA",
+  "fan_in_fan_out": false,
+  "bias": null,
+  "modules_to_save": null,
+  "r": 32,
+  "lora_alpha": 32,
+  "lora_dropout": 0.1,
+  "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"]
+}
+```
+2. Papare the `vicuna` v0 model.
+3. Obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model.
+4. Clone the PandaGPT source.
+```
+git clone https://github.com/yxuansu/PandaGPT
+```
+5. Install the requirement of PandaGPT.
+6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.
+
+## [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4/) example (minigpt4.py)
+
+1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in `embd-input`.
+2. Clone the MiniGPT-4 source.
+```
+git clone https://github.com/Vision-CAIR/MiniGPT-4/
+```
+3. Install the requirement of PandaGPT.
+4. Papare the `vicuna` v0 model.
+5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in `minigpt4.py`.

examples/embd-input/embd-input-lib.cpp

@@ -0,0 +1,223 @@
+// Defines sigaction on msys:
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
+#include "embd-input.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <vector>
+
+static llama_context ** g_ctx;
+
+extern "C" {
+
+struct MyModel* create_mymodel(int argc, char ** argv) {
+    gpt_params params;
+
+    if (gpt_params_parse(argc, argv, params) == false) {
+        return nullptr;
+    }
+
+    fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+    fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
+
+    llama_init_backend(params.numa);
+
+    llama_model * model;
+    llama_context * ctx;
+
+    g_ctx = &ctx;
+
+    // load the model and apply lora adapter, if any
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: unable to load model\n", __func__);
+        return nullptr;
+    }
+
+    // print system information
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
+                params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
+    }
+    struct MyModel * ret = new MyModel();
+    ret->ctx = ctx;
+    ret->params = params;
+    ret->n_past = 0;
+    // printf("ctx: %d\n", ret->ctx);
+    return ret;
+}
+
+void free_mymodel(struct MyModel * mymodel) {
+    llama_context * ctx = mymodel->ctx;
+    llama_print_timings(ctx);
+    llama_free(ctx);
+    delete mymodel;
+}
+
+
+bool eval_float(void * model, float * input, int N){
+    MyModel * mymodel = (MyModel*)model;
+    llama_context * ctx = mymodel->ctx;
+    gpt_params params = mymodel->params;
+    int n_emb = llama_n_embd(ctx);
+    int n_past = mymodel->n_past;
+    int n_batch = N; // params.n_batch;
+
+    for (int i = 0; i < (int) N; i += n_batch) {
+        int n_eval = (int) N - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return false;
+        }
+        n_past += n_eval;
+    }
+    mymodel->n_past = n_past;
+    return true;
+}
+
+bool eval_tokens(void * model, std::vector<llama_token> tokens) {
+    MyModel * mymodel = (MyModel* )model;
+    llama_context * ctx;
+    ctx = mymodel->ctx;
+    gpt_params params = mymodel->params;
+    int n_past = mymodel->n_past;
+    for (int i = 0; i < (int) tokens.size(); i += params.n_batch) {
+        int n_eval = (int) tokens.size() - i;
+        if (n_eval > params.n_batch) {
+            n_eval = params.n_batch;
+        }
+        if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return false;
+        }
+        n_past += n_eval;
+    }
+    mymodel->n_past = n_past;
+    return true;
+}
+
+bool eval_id(struct MyModel* mymodel, int id) {
+    std::vector<llama_token> tokens;
+    tokens.push_back(id);
+    return eval_tokens(mymodel, tokens);
+}
+
+bool eval_string(struct MyModel * mymodel,const char* str){
+    llama_context * ctx = mymodel->ctx;
+    std::string str2 = str;
+    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx, str2, true);
+    eval_tokens(mymodel, embd_inp);
+    return true;
+}
+
+llama_token sampling_id(struct MyModel* mymodel) {
+    llama_context* ctx = mymodel->ctx;
+    gpt_params params = mymodel->params;
+    // int n_ctx = llama_n_ctx(ctx);
+
+    // out of user input, sample next token
+    const float   temp            = params.temp;
+    const int32_t top_k           = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
+    const float   top_p           = params.top_p;
+    const float   tfs_z           = params.tfs_z;
+    const float   typical_p       = params.typical_p;
+    // const int32_t repeat_last_n   = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
+    // const float   repeat_penalty  = params.repeat_penalty;
+    // const float   alpha_presence  = params.presence_penalty;
+    // const float   alpha_frequency = params.frequency_penalty;
+    const int     mirostat        = params.mirostat;
+    const float   mirostat_tau    = params.mirostat_tau;
+    const float   mirostat_eta    = params.mirostat_eta;
+    // const bool    penalize_nl     = params.penalize_nl;
+
+    llama_token id = 0;
+    {
+        auto logits  = llama_get_logits(ctx);
+        auto n_vocab = llama_n_vocab(ctx);
+
+        // Apply params.logit_bias map
+        for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
+            logits[it->first] += it->second;
+        }
+
+        std::vector<llama_token_data> candidates;
+        candidates.reserve(n_vocab);
+        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+            candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        }
+
+        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+        // TODO: Apply penalties
+        // float nl_logit = logits[llama_token_nl()];
+        // auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
+        // llama_sample_repetition_penalty(ctx, &candidates_p,
+        //      last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+        //      last_n_repeat, repeat_penalty);
+        // llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
+        // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+        // last_n_repeat, alpha_frequency, alpha_presence);
+        // if (!penalize_nl) {
+        //     logits[llama_token_nl()] = nl_logit;
+        // }
+
+        if (temp <= 0) {
+            // Greedy sampling
+            id = llama_sample_token_greedy(ctx, &candidates_p);
+        } else {
+            if (mirostat == 1) {
+                static float mirostat_mu = 2.0f * mirostat_tau;
+                const int mirostat_m = 100;
+                llama_sample_temperature(ctx, &candidates_p, temp);
+                id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+            } else if (mirostat == 2) {
+                static float mirostat_mu = 2.0f * mirostat_tau;
+                llama_sample_temperature(ctx, &candidates_p, temp);
+                id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
+            } else {
+                // Temperature sampling
+                llama_sample_top_k(ctx, &candidates_p, top_k, 1);
+                llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
+                llama_sample_typical(ctx, &candidates_p, typical_p, 1);
+                llama_sample_top_p(ctx, &candidates_p, top_p, 1);
+                llama_sample_temperature(ctx, &candidates_p, temp);
+                id = llama_sample_token(ctx, &candidates_p);
+            }
+        }
+    }
+
+    return id;
+}
+
+const char * sampling(struct MyModel * mymodel) {
+    llama_context * ctx = mymodel->ctx;
+    int id = sampling_id(mymodel);
+    static std::string ret;
+    if (id == llama_token_eos()) {
+        ret = "</s>";
+    } else {
+        ret = llama_token_to_str(ctx, id);
+    }
+    eval_id(mymodel, id);
+    return ret.c_str();
+}
+
+}

examples/embd-input/embd-input-test.cpp

@@ -0,0 +1,35 @@
+#include "embd-input.h"
+#include <stdlib.h>
+#include <random>
+#include <string.h>
+
+int main(int argc, char** argv) {
+
+    auto mymodel = create_mymodel(argc, argv);
+    int N = 10;
+    int max_tgt_len = 500;
+    int n_embd = llama_n_embd(mymodel->ctx);
+
+    // add random float embd to test evaluation
+    float * data = new float[N*n_embd];
+    std::default_random_engine e;
+    std::uniform_real_distribution<float>  u(0,1);
+    for (int i=0;i<N*n_embd;i++) {
+        data[i] = u(e);
+    }
+
+    eval_string(mymodel, "user: what is the color of the flag of UN?");
+    eval_float(mymodel, data, N);
+    eval_string(mymodel, "assistant:");
+    eval_string(mymodel, mymodel->params.prompt.c_str());
+    const char* tmp;
+    for (int i=0; i<max_tgt_len; i++) {
+        tmp = sampling(mymodel);
+        if (strcmp(tmp, "</s>")==0) break;
+        printf("%s", tmp);
+        fflush(stdout);
+    }
+    printf("\n");
+    free_mymodel(mymodel);
+    return 0;
+}

examples/embd-input/embd-input.h

@@ -0,0 +1,28 @@
+#ifndef _EMBD_INPUT_H_
+#define _EMBD_INPUT_H_ 1
+
+#include "common.h"
+#include "llama.h"
+#include "build-info.h"
+
+extern "C" {
+
+typedef struct MyModel {
+    llama_context* ctx;
+    gpt_params params;
+    int n_past = 0;
+} MyModel;
+
+struct MyModel* create_mymodel(int argc, char ** argv);
+
+bool eval_float(void* model, float* input, int N);
+bool eval_tokens(void* model, std::vector<llama_token> tokens);
+bool eval_id(struct MyModel* mymodel, int id);
+bool eval_string(struct MyModel* mymodel, const char* str);
+const char * sampling(struct MyModel* mymodel);
+llama_token sampling_id(struct MyModel* mymodel);
+void free_mymodel(struct MyModel* mymodel);
+
+}
+
+#endif

examples/embd-input/embd_input.py

@@ -0,0 +1,71 @@
+import ctypes
+from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
+import numpy as np
+import os
+
+libc = cdll.LoadLibrary("./libembdinput.so")
+libc.sampling.restype=c_char_p
+libc.create_mymodel.restype=c_void_p
+libc.eval_string.argtypes=[c_void_p, c_char_p]
+libc.sampling.argtypes=[c_void_p]
+libc.eval_float.argtypes=[c_void_p, POINTER(c_float), c_int]
+
+
+class MyModel:
+    def __init__(self, args):
+        argc = len(args)
+        c_str = [c_char_p(i.encode()) for i in args]
+        args_c = (c_char_p * argc)(*c_str)
+        self.model = c_void_p(libc.create_mymodel(argc, args_c))
+        self.max_tgt_len = 512
+        self.print_string_eval = True
+
+    def __del__(self):
+        libc.free_mymodel(self.model)
+
+    def eval_float(self, x):
+        libc.eval_float(self.model, x.astype(np.float32).ctypes.data_as(POINTER(c_float)), x.shape[1])
+
+    def eval_string(self, x):
+        libc.eval_string(self.model, x.encode()) # c_char_p(x.encode()))
+        if self.print_string_eval:
+            print(x)
+
+    def eval_token(self, x):
+        libc.eval_id(self.model, x)
+
+    def sampling(self):
+        s = libc.sampling(self.model)
+        return s
+
+    def stream_generate(self, end="</s>"):
+        ret = b""
+        end = end.encode()
+        for _ in range(self.max_tgt_len):
+            tmp = self.sampling()
+            ret += tmp
+            yield tmp
+            if ret.endswith(end):
+                break
+
+    def generate_with_print(self, end="</s>"):
+        ret = b""
+        for i in self.stream_generate(end=end):
+            ret += i
+            print(i.decode(errors="replace"), end="", flush=True)
+        print("")
+        return ret.decode(errors="replace")
+
+
+    def generate(self, end="</s>"):
+        text = b"".join(self.stream_generate(end=end))
+        return text.decode(errors="replace")
+
+if __name__ == "__main__":
+    model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"])
+    model.eval_string("""user: what is the color of the flag of UN?""")
+    x = np.random.random((5120,10))# , dtype=np.float32)
+    model.eval_float(x)
+    model.eval_string("""assistant:""")
+    for i in model.generate():
+        print(i.decode(errors="replace"), end="", flush=True)

examples/embd-input/llava.py

@@ -0,0 +1,70 @@
+import sys
+import os
+sys.path.insert(0, os.path.dirname(__file__))
+from embd_input import MyModel
+import numpy as np
+from torch import nn
+import torch
+from transformers import CLIPVisionModel,  CLIPImageProcessor
+from PIL import Image
+
+# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1'
+vision_tower = "openai/clip-vit-large-patch14"
+select_hidden_state_layer = -2
+# (vision_config.image_size // vision_config.patch_size) ** 2
+image_token_len = (224//14)**2
+
+class Llava:
+    def __init__(self, args):
+        self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
+        self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower)
+        self.mm_projector = nn.Linear(1024, 5120)
+        self.model = MyModel(["main", *args])
+
+    def load_projection(self, path):
+        state = torch.load(path)
+        self.mm_projector.load_state_dict({
+            "weight": state["model.mm_projector.weight"],
+            "bias": state["model.mm_projector.bias"]})
+
+    def chat(self, question):
+        self.model.eval_string("user: ")
+        self.model.eval_string(question)
+        self.model.eval_string("\nassistant: ")
+        return self.model.generate_with_print()
+
+    def chat_with_image(self, image, question):
+        with torch.no_grad():
+            embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+            image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True)
+            select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer]
+            image_feature = select_hidden_state[:, 1:]
+            embd_image = self.mm_projector(image_feature)
+            embd_image = embd_image.cpu().numpy()[0]
+        self.model.eval_string("user: ")
+        self.model.eval_token(32003-2) # im_start
+        self.model.eval_float(embd_image.T)
+        for i in range(image_token_len-embd_image.shape[0]):
+            self.model.eval_token(32003-3) # im_patch
+        self.model.eval_token(32003-1) # im_end
+        self.model.eval_string(question)
+        self.model.eval_string("\nassistant: ")
+        return self.model.generate_with_print()
+
+
+if __name__=="__main__":
+    # model form liuhaotian/LLaVA-13b-delta-v1-1
+    a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"])
+    # Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin.
+    # Also here can use pytorch_model-00003-of-00003.bin directly.
+    a.load_projection(os.path.join(
+        os.path.dirname(__file__) ,
+        "llava_projetion.pth"))
+    respose = a.chat_with_image(
+        Image.open("./media/llama1-logo.png").convert('RGB'),
+        "what is the text in the picture?")
+    respose
+    a.chat("what is the color of it?")
+
+
+

examples/embd-input/minigpt4.py

@@ -0,0 +1,128 @@
+import sys
+import os
+sys.path.insert(0, os.path.dirname(__file__))
+from embd_input import MyModel
+import numpy as np
+from torch import nn
+import torch
+from PIL import Image
+
+minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4")
+sys.path.insert(0, minigpt4_path)
+from minigpt4.models.blip2 import Blip2Base
+from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
+
+
+class MiniGPT4(Blip2Base):
+    """
+    MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4
+    """
+    def __init__(self,
+        args,
+        vit_model="eva_clip_g",
+        q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
+        img_size=224,
+        drop_path_rate=0,
+        use_grad_checkpoint=False,
+        vit_precision="fp32",
+        freeze_vit=True,
+        freeze_qformer=True,
+        num_query_token=32,
+        llama_model="",
+        prompt_path="",
+        prompt_template="",
+        max_txt_len=32,
+        end_sym='\n',
+        low_resource=False,  # use 8 bit and put vit in cpu
+        device_8bit=0
+    ):
+        super().__init__()
+        self.img_size = img_size
+        self.low_resource = low_resource
+        self.preprocessor = Blip2ImageEvalProcessor(img_size)
+
+        print('Loading VIT')
+        self.visual_encoder, self.ln_vision = self.init_vision_encoder(
+            vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
+        )
+        print('Loading VIT Done')
+        print('Loading Q-Former')
+        self.Qformer, self.query_tokens = self.init_Qformer(
+            num_query_token, self.visual_encoder.num_features
+        )
+        self.Qformer.cls = None
+        self.Qformer.bert.embeddings.word_embeddings = None
+        self.Qformer.bert.embeddings.position_embeddings = None
+        for layer in self.Qformer.bert.encoder.layer:
+            layer.output = None
+            layer.intermediate = None
+        self.load_from_pretrained(url_or_filename=q_former_model)
+        print('Loading Q-Former Done')
+        self.llama_proj = nn.Linear(
+            self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size
+        )
+        self.max_txt_len = max_txt_len
+        self.end_sym = end_sym
+        self.model = MyModel(["main", *args])
+        # system promt
+        self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
+           "You will be able to see the image once I provide it to you. Please answer my questions."
+           "###")
+
+    def encode_img(self, image):
+        image = self.preprocessor(image)
+        image = image.unsqueeze(0)
+        device = image.device
+        if self.low_resource:
+            self.vit_to_cpu()
+            image = image.to("cpu")
+
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
+            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
+
+            query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+            query_output = self.Qformer.bert(
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+
+            inputs_llama = self.llama_proj(query_output.last_hidden_state)
+            # atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
+        return inputs_llama
+
+    def load_projection(self, path):
+        state = torch.load(path)["model"]
+        self.llama_proj.load_state_dict({
+            "weight": state["llama_proj.weight"],
+            "bias": state["llama_proj.bias"]})
+
+    def chat(self, question):
+        self.model.eval_string("Human: ")
+        self.model.eval_string(question)
+        self.model.eval_string("\n### Assistant:")
+        return self.model.generate_with_print(end="###")
+
+    def chat_with_image(self, image, question):
+        with torch.no_grad():
+            embd_image = self.encode_img(image)
+        embd_image = embd_image.cpu().numpy()[0]
+        self.model.eval_string("Human: <Img>")
+        self.model.eval_float(embd_image.T)
+        self.model.eval_string("</Img> ")
+        self.model.eval_string(question)
+        self.model.eval_string("\n### Assistant:")
+        return self.model.generate_with_print(end="###")
+
+
+if __name__=="__main__":
+    a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"])
+    a.load_projection(os.path.join(
+        os.path.dirname(__file__) ,
+        "pretrained_minigpt4.pth"))
+    respose = a.chat_with_image(
+        Image.open("./media/llama1-logo.png").convert('RGB'),
+        "what is the text in the picture?")
+    a.chat("what is the color of it?")

examples/embd-input/panda_gpt.py

@@ -0,0 +1,98 @@
+import sys
+import os
+sys.path.insert(0, os.path.dirname(__file__))
+from embd_input import MyModel
+import numpy as np
+from torch import nn
+import torch
+
+# use PandaGPT path
+panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT")
+imagebind_ckpt_path = "./models/panda_gpt/"
+
+sys.path.insert(0, os.path.join(panda_gpt_path,"code","model"))
+from ImageBind.models import imagebind_model
+from ImageBind import data
+
+ModalityType = imagebind_model.ModalityType
+max_tgt_len = 400
+
+class PandaGPT:
+    def __init__(self, args):
+        self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
+        self.visual_encoder.eval()
+        self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120)
+        self.max_tgt_len = max_tgt_len
+        self.model = MyModel(["main", *args])
+        self.generated_text = ""
+        self.device = "cpu"
+
+    def load_projection(self, path):
+        state = torch.load(path, map_location="cpu")
+        self.llama_proj.load_state_dict({
+            "weight": state["llama_proj.weight"],
+            "bias": state["llama_proj.bias"]})
+
+    def eval_inputs(self, inputs):
+        self.model.eval_string("<Img>")
+        embds = self.extract_multimoal_feature(inputs)
+        for i in embds:
+            self.model.eval_float(i.T)
+        self.model.eval_string("</Img> ")
+
+    def chat(self, question):
+        return self.chat_with_image(None, question)
+
+    def chat_with_image(self, inputs, question):
+        if self.generated_text == "":
+            self.model.eval_string("###")
+        self.model.eval_string(" Human: ")
+        if inputs:
+            self.eval_inputs(inputs)
+        self.model.eval_string(question)
+        self.model.eval_string("\n### Assistant:")
+        ret = self.model.generate_with_print(end="###")
+        self.generated_text += ret
+        return ret
+
+    def extract_multimoal_feature(self, inputs):
+        features = []
+        for key in ["image", "audio", "video", "thermal"]:
+            if key + "_paths" in inputs:
+                embeds = self.encode_data(key, inputs[key+"_paths"])
+                features.append(embeds)
+        return features
+
+    def encode_data(self, data_type, data_paths):
+
+        type_map = {
+            "image": ModalityType.VISION,
+            "audio": ModalityType.AUDIO,
+            "video": ModalityType.VISION,
+            "thermal": ModalityType.THERMAL,
+        }
+        load_map = {
+            "image": data.load_and_transform_vision_data,
+            "audio": data.load_and_transform_audio_data,
+            "video": data.load_and_transform_video_data,
+            "thermal": data.load_and_transform_thermal_data
+        }
+
+        load_function = load_map[data_type]
+        key = type_map[data_type]
+
+        inputs = {key: load_function(data_paths, self.device)}
+        with torch.no_grad():
+            embeddings = self.visual_encoder(inputs)
+            embeds = embeddings[key]
+            embeds = self.llama_proj(embeds).cpu().numpy()
+        return embeds
+
+
+if __name__=="__main__":
+    a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"])
+    a.load_projection("./models/panda_gpt/adapter_model.bin")
+    a.chat_with_image(
+        {"image_paths": ["./media/llama1-logo.png"]},
+        "what is the text in the picture? 'llama' or 'lambda'?")
+    a.chat("what is the color of it?")

examples/embedding/embedding.cpp

@@ -18,24 +18,24 @@ int main(int argc, char ** argv) {
     params.embedding = true;
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     }
 
     fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
 
-    if (params.seed < 0) {
+    if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
 
-    fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
     if (params.random_prompt) {
         params.prompt = gpt_random_prompt(rng);
     }
 
-    llama_init_backend();
+    llama_init_backend(params.numa);
 
     llama_model * model;
     llama_context * ctx;

examples/main/README.md

@@ -242,7 +242,7 @@ Example usage: `--logit-bias 29905-inf`
 
 ### RNG Seed
 
--   `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
+-   `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
 
 The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.
 
@@ -262,6 +262,10 @@ These options help improve the performance and memory usage of the LLaMA models.
 
 -   `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance. Disabling mmap results in slower load times but may reduce pageouts if you're not using `--mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
 
+### NUMA support
+
+-   `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop\_caches' as root.
+
 ### Memory Float 32
 
 -   `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended.

examples/main/main.cpp

@@ -85,7 +85,7 @@ int main(int argc, char ** argv) {
     }
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     } else if (params.n_ctx < 8) {
         fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
@@ -94,18 +94,18 @@ int main(int argc, char ** argv) {
 
     fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
 
-    if (params.seed < 0) {
+    if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
 
-    fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
     if (params.random_prompt) {
         params.prompt = gpt_random_prompt(rng);
     }
 
-    llama_init_backend();
+    llama_init_backend(params.numa);
 
     llama_model * model;
     llama_context * ctx;

examples/perplexity/perplexity.cpp

@@ -130,24 +130,24 @@ int main(int argc, char ** argv) {
     params.n_batch = std::min(params.n_batch, params.n_ctx);
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     }
 
     fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
 
-    if (params.seed < 0) {
+    if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
 
-    fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
     if (params.random_prompt) {
         params.prompt = gpt_random_prompt(rng);
     }
 
-    llama_init_backend();
+    llama_init_backend(params.numa);
 
     llama_model * model;
     llama_context * ctx;

examples/quantize-stats/quantize-stats.cpp

@@ -147,7 +147,7 @@ void test_roundtrip_on_chunk(
         const ggml_tensor * layer,
         int64_t offset,
         int64_t chunk_size,
-        const quantize_fns_t & qfns,
+        const ggml_type_traits_t & qfns,
         bool use_reference,
         float * input_scratch,
         char * quantized_scratch,
@@ -163,11 +163,11 @@ void test_roundtrip_on_chunk(
     }
 
     if (use_reference) {
-        qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
     } else {
-        qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float(input_scratch, quantized_scratch, chunk_size);
     }
-    qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
+    qfns.to_float(quantized_scratch, output_scratch, chunk_size);
 
     update_error_stats(chunk_size, input_scratch, output_scratch, stats);
 }
@@ -177,7 +177,7 @@ void test_roundtrip_on_chunk(
 void test_roundtrip_on_layer(
         std::string & name,
         bool print_layer_stats,
-        const quantize_fns_t & qfns,
+        const ggml_type_traits_t & qfns,
         bool use_reference,
         const ggml_tensor * layer,
         std::vector<float> & input_scratch,
@@ -388,8 +388,8 @@ int main(int argc, char ** argv) {
         if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
             continue;
         }
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
+        if (qfns.from_float && qfns.to_float) {
             if (params.verbose) {
                 printf("testing %s ...\n",  ggml_type_name(type));
             }

examples/quantize/quantize.cpp

@@ -180,7 +180,7 @@ int main(int argc, char ** argv) {
         usage(argv[0]);
     }
 
-    llama_init_backend();
+    llama_init_backend(false);
 
     // parse command line arguments
     const std::string fname_inp = argv[arg_idx];

examples/server/README.md

@@ -1,13 +1,13 @@
 # llama.cpp/example/server
 
-This example demonstrates a simple HTTP API server to interact with llama.cpp.
+This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
 
 Command line options:
 
 -   `--threads N`, `-t N`: Set the number of threads to use during computation.
 -   `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
 -   `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
--   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
 -   `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
 -   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
@@ -21,24 +21,22 @@ Command line options:
 -   `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
 -   `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
 -   `--port`: Set the port to listen. Default: `8080`.
+-   `--path`: path from which to serve static files (default examples/server/public)
 -   `--embedding`: Enable embedding extraction, Default: disabled.
 
 ## Build
 
-Build llama.cpp with server from repository root with either make or CMake.
+server is build alongside everything else from the root of the project
 
 - Using `make`:
 
   ```bash
-  LLAMA_BUILD_SERVER=1 make
+  make
   ```
 
 - Using `CMake`:
 
   ```bash
-  mkdir build-server
-  cd build-server
-  cmake -DLLAMA_BUILD_SERVER=ON ..
   cmake --build . --config Release
   ```
 
@@ -59,7 +57,7 @@ server.exe -m models\7B\ggml-model.bin -c 2048
 ```
 
 The above command will start a server that by default listens on `127.0.0.1:8080`.
-You can consume the endpoints with Postman or NodeJS with axios library.
+You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.
 
 ## Testing with CURL
 
@@ -152,7 +150,7 @@ node .
 
     `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
 
-    `seed`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
+    `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
 
     `ignore_eos`: Ignore end of stream token and continue generating (default: false).
 
@@ -190,3 +188,49 @@ Run with bash:
 ```sh
 bash chat.sh
 ```
+
+### API like OAI
+
+API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
+This example must be used with server.cpp
+
+```sh
+python api_like_OAI.py
+```
+
+After running the API server, you can use it in Python by setting the API base URL.
+```python
+openai.api_base = "http://<Your api-server IP>:port"
+```
+
+Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
+
+### Extending or building alternative Web Front End
+
+The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
+
+Read the documentation in `/completion.js` to see convenient ways to access llama.
+
+A simple example is below:
+
+```html
+<html>
+  <body>
+    <pre>
+      <script type="module">
+        import { llama } from '/completion.js'
+
+        const prompt = `### Instruction:
+Write dad jokes, each one paragraph.
+You can use html formatting if needed.
+
+### Response:`
+
+        for await (const chunk of llama(prompt)) {
+          document.write(chunk.data.content)
+        }
+      </script>
+    </pre>
+  </body>
+</html>
+```

examples/server/api_like_OAI.py

@@ -0,0 +1,219 @@
+import argparse
+from flask import Flask, jsonify, request, Response
+import urllib.parse
+import requests
+import time
+import json
+
+
+app = Flask(__name__)
+
+parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
+parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
+parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
+parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
+parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
+parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
+parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
+parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
+parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
+parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
+
+args = parser.parse_args()
+
+def is_present(json, key):
+    try:
+        buf = json[key]
+    except KeyError:
+        return False
+    return True
+
+
+
+#convert chat to prompt
+def convert_chat(messages):
+    prompt = "" + args.chat_prompt.replace("\\n", "\n")
+
+    system_n = args.system_name.replace("\\n", "\n")
+    user_n = args.user_name.replace("\\n", "\n")
+    ai_n = args.ai_name.replace("\\n", "\n")
+    stop = args.stop.replace("\\n", "\n")
+
+
+    for line in messages:
+        if (line["role"] == "system"):
+            prompt += f"{system_n}{line['content']}"
+        if (line["role"] == "user"):
+            prompt += f"{user_n}{line['content']}"
+        if (line["role"] == "assistant"):
+            prompt += f"{ai_n}{line['content']}{stop}"
+    prompt += ai_n.rstrip()
+
+    return prompt
+
+def make_postData(body, chat=False, stream=False):
+    postData = {}
+    if (chat):
+        postData["prompt"] = convert_chat(body["messages"])
+    else:
+        postData["prompt"] = body["prompt"]
+    if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
+    if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
+    if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
+    if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
+    if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
+    if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
+    if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
+    if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
+    if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
+    if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
+    if(is_present(body, "seed")): postData["seed"] = body["seed"]
+    if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
+    if (args.stop != ""):
+        postData["stop"] = [args.stop]
+    else:
+        postData["stop"] = []
+    if(is_present(body, "stop")): postData["stop"] += body["stop"]
+    postData["n_keep"] = -1
+    postData["stream"] = stream
+
+    return postData
+
+def make_resData(data, chat=False, promptToken=[]):
+    resData = {
+        "id": "chatcmpl" if (chat) else "cmpl",
+        "object": "chat.completion" if (chat) else "text_completion",
+        "created": int(time.time()),
+        "truncated": data["truncated"],
+        "model": "LLaMA_CPP",
+        "usage": {
+            "prompt_tokens": data["tokens_evaluated"],
+            "completion_tokens": data["tokens_predicted"],
+            "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
+        }
+    }
+    if (len(promptToken) != 0):
+        resData["promptToken"] = promptToken
+    if (chat):
+        #only one choice is supported
+        resData["choices"] = [{
+            "index": 0,
+            "message": {
+                "role": "assistant",
+                "content": data["content"],
+            },
+            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+        }]
+    else:
+        #only one choice is supported
+        resData["choices"] = [{
+            "text": data["content"],
+            "index": 0,
+            "logprobs": None,
+            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+        }]
+    return resData
+
+def make_resData_stream(data, chat=False, time_now = 0, start=False):
+    resData = {
+        "id": "chatcmpl" if (chat) else "cmpl",
+        "object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
+        "created": time_now,
+        "model": "LLaMA_CPP",
+        "choices": [
+            {
+                "finish_reason": None,
+                "index": 0
+            }
+        ]
+    }
+    if (chat):
+        if (start):
+            resData["choices"][0]["delta"] =  {
+                "role": "assistant"
+            }
+        else:
+            resData["choices"][0]["delta"] =  {
+                "content": data["content"]
+            }
+            if (data["stop"]):
+                resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+    else:
+        resData["choices"][0]["text"] = data["content"]
+        if (data["stop"]):
+            resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+
+    return resData
+
+
+@app.route('/chat/completions', methods=['POST'])
+@app.route('/v1/chat/completions', methods=['POST'])
+def chat_completions():
+    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
+        return Response(status=403)
+    body = request.get_json()
+    stream = False
+    tokenize = False
+    if(is_present(body, "stream")): stream = body["stream"]
+    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
+    postData = make_postData(body, chat=True, stream=stream)
+
+    promptToken = []
+    if (tokenize):
+        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
+        promptToken = tokenData["tokens"]
+
+    if (not stream):
+        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
+        print(data.json())
+        resData = make_resData(data.json(), chat=True, promptToken=promptToken)
+        return jsonify(resData)
+    else:
+        def generate():
+            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
+            time_now = int(time.time())
+            resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
+            yield 'data: {}\n'.format(json.dumps(resData))
+            for line in data.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')
+                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
+                    yield 'data: {}\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream')
+
+
+@app.route('/completions', methods=['POST'])
+@app.route('/v1/completions', methods=['POST'])
+def completion():
+    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
+        return Response(status=403)
+    body = request.get_json()
+    stream = False
+    tokenize = False
+    if(is_present(body, "stream")): stream = body["stream"]
+    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
+    postData = make_postData(body, chat=False, stream=stream)
+
+    promptToken = []
+    if (tokenize):
+        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
+        promptToken = tokenData["tokens"]
+
+    if (not stream):
+        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
+        print(data.json())
+        resData = make_resData(data.json(), chat=False, promptToken=promptToken)
+        return jsonify(resData)
+    else:
+        def generate():
+            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
+            time_now = int(time.time())
+            for line in data.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')
+                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
+                    yield 'data: {}\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream')
+
+if __name__ == '__main__':
+    app.run(args.host, port=args.port)

examples/server/completion.js.hpp

@@ -0,0 +1,375 @@
+unsigned char completion_js[] = {
+  0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44,
+  0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a,
+  0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72,
+  0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64,
+  0x69, 0x63, 0x74, 0x3a, 0x20, 0x35, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20,
+  0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a,
+  0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70,
+  0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x5d, 0x0a, 0x7d,
+  0x3b, 0x0a, 0x0a, 0x6c, 0x65, 0x74, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72,
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+};
+unsigned int completion_js_len = 4462;

examples/server/deps.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+# Download and update deps for binary
+
+# get the directory of this script file
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+PUBLIC=$DIR/public
+
+echo "download js bundle files"
+curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
+echo >> $PUBLIC/index.js # add newline
+
+FILES=$(ls $PUBLIC)
+
+for FILE in $FILES; do
+  func=$(echo $FILE | tr '.' '_')
+  echo "generate $FILE.hpp ($func)"
+  xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp
+done

examples/server/index.html.hpp

@@ -0,0 +1,899 @@
+unsigned char index_html[] = {
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+};
+unsigned int index_html_len = 10752;

examples/server/public/completion.js

@@ -0,0 +1,168 @@
+const paramDefaults = {
+  stream: true,
+  n_predict: 500,
+  temperature: 0.2,
+  stop: ["</s>"]
+};
+
+let generation_settings = null;
+
+
+// Completes the prompt as a generator. Recommended for most use cases.
+//
+// Example:
+//
+//    import { llama } from '/completion.js'
+//
+//    const request = llama("Tell me a joke", {n_predict: 800})
+//    for await (const chunk of request) {
+//      document.write(chunk.data.content)
+//    }
+//
+export async function* llama(prompt, params = {}, config = {}) {
+  let controller = config.controller;
+
+  if (!controller) {
+    controller = new AbortController();
+  }
+
+  const completionParams = { ...paramDefaults, ...params, prompt };
+
+  const response = await fetch("/completion", {
+    method: 'POST',
+    body: JSON.stringify(completionParams),
+    headers: {
+      'Connection': 'keep-alive',
+      'Content-Type': 'application/json',
+      'Accept': 'text/event-stream'
+    },
+    signal: controller.signal,
+  });
+
+  const reader = response.body.getReader();
+  const decoder = new TextDecoder();
+
+  let content = "";
+
+  try {
+    let cont = true;
+
+    while (cont) {
+      const result = await reader.read();
+      if (result.done) {
+        break;
+      }
+
+      // sse answers in the form multiple lines of: value\n with data always present as a key. in our case we
+      // mainly care about the data: key here, which we expect as json
+      const text = decoder.decode(result.value);
+
+      // parse all sse events and add them to result
+      const regex = /^(\S+):\s(.*)$/gm;
+      for (const match of text.matchAll(regex)) {
+        result[match[1]] = match[2]
+      }
+
+      // since we know this is llama.cpp, let's just decode the json in data
+      result.data = JSON.parse(result.data);
+      content += result.data.content;
+
+      // yield
+      yield result;
+
+      // if we got a stop token from server, we will break here
+      if (result.data.stop) {
+        if (result.data.generation_settings) {
+          generation_settings = result.data.generation_settings;
+        }
+        break;
+      }
+    }
+  } catch (e) {
+    if (e.name !== 'AbortError') {
+      console.error("llama error: ", e);
+    }
+    throw e;
+  }
+  finally {
+    controller.abort();
+  }
+
+  return content;
+}
+
+// Call llama, return an event target that you can subcribe to
+//
+// Example:
+//
+//    import { llamaEventTarget } from '/completion.js'
+//
+//    const conn = llamaEventTarget(prompt)
+//    conn.addEventListener("message", (chunk) => {
+//      document.write(chunk.detail.content)
+//    })
+//
+export const llamaEventTarget = (prompt, params = {}, config = {}) => {
+  const eventTarget = new EventTarget();
+  (async () => {
+    let content = "";
+    for await (const chunk of llama(prompt, params, config)) {
+      if (chunk.data) {
+        content += chunk.data.content;
+        eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
+      }
+      if (chunk.data.generation_settings) {
+        eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
+      }
+      if (chunk.data.timings) {
+        eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
+      }
+    }
+    eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
+  })();
+  return eventTarget;
+}
+
+// Call llama, return a promise that resolves to the completed text. This does not support streaming
+//
+// Example:
+//
+//     llamaPromise(prompt).then((content) => {
+//       document.write(content)
+//     })
+//
+//     or
+//
+//     const content = await llamaPromise(prompt)
+//     document.write(content)
+//
+export const llamaPromise = (prompt, params = {}, config = {}) => {
+  return new Promise(async (resolve, reject) => {
+    let content = "";
+    try {
+      for await (const chunk of llama(prompt, params, config)) {
+        content += chunk.data.content;
+      }
+      resolve(content);
+    } catch (error) {
+      reject(error);
+    }
+  });
+};
+
+/**
+ * (deprecated)
+ */
+export const llamaComplete = async (params, controller, callback) => {
+  for await (const chunk of llama(params.prompt, params, { controller })) {
+    callback(chunk);
+  }
+}
+
+// Get the model info from the server. This is useful for getting the context window and so on.
+export const llamaModelInfo = async () => {
+  if (!generation_settings) {
+    generation_settings = await fetch("/model.json").then(r => r.json());
+  }
+  return generation_settings;
+}

examples/server/public/index.html

@@ -0,0 +1,380 @@
+<html>
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
+  <title>llama.cpp - chat</title>
+
+  <style>
+    body {
+      background-color: #fff;
+      color: #000;
+      font-family: system-ui;
+      font-size: 90%;
+    }
+
+    #container {
+      margin: 0em auto;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      height: 100%;
+    }
+
+    main {
+      margin: 3px;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      gap: 1em;
+
+      flex-grow: 1;
+      overflow-y: auto;
+
+      border: 1px solid #ccc;
+      border-radius: 5px;
+      padding: 0.5em;
+    }
+
+    body {
+      max-width: 600px;
+      min-width: 300px;
+      line-height: 1.2;
+      margin: 0 auto;
+      padding: 0 0.5em;
+    }
+
+    p {
+      overflow-wrap: break-word;
+      word-wrap: break-word;
+      hyphens: auto;
+      margin-top: 0.5em;
+      margin-bottom: 0.5em;
+    }
+
+    #write form {
+      margin: 1em 0 0 0;
+      display: flex;
+      flex-direction: column;
+      gap: 0.5em;
+      align-items: stretch;
+    }
+
+    .right {
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+    }
+
+    fieldset {
+      border: none;
+      padding: 0;
+      margin: 0;
+    }
+
+    textarea {
+      padding: 5px;
+      flex-grow: 1;
+      width: 100%;
+    }
+
+    pre code {
+      display: block;
+      background-color: #222;
+      color: #ddd;
+    }
+    code {
+      font-family: monospace;
+      padding: 0.1em 0.3em;
+      border-radius: 3px;
+    }
+
+    fieldset label {
+      margin: 0.5em 0;
+      display: block;
+    }
+
+    header, footer {
+      text-align: center;
+    }
+
+    footer {
+      font-size: 80%;
+      color: #888;
+    }
+  </style>
+
+  <script type="module">
+    import {
+      html, h, signal, effect, computed, render, useSignal, useEffect, useRef
+    } from '/index.js';
+
+    import { llama } from '/completion.js';
+
+    const session = signal({
+      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
+      template: "{{prompt}}\n\n{{history}}\n{{char}}:",
+      historyTemplate: "{{name}}: {{message}}",
+      transcript: [],
+      type: "chat",
+      char: "llama",
+      user: "User",
+    })
+
+    const params = signal({
+      n_predict: 400,
+      temperature: 0.7,
+      repeat_last_n: 256,
+      repeat_penalty: 1.18,
+      top_k: 40,
+      top_p: 0.5,
+    })
+
+    const llamaStats = signal(null)
+    const controller = signal(null)
+
+    const generating = computed(() => controller.value == null )
+    const chatStarted = computed(() => session.value.transcript.length > 0)
+
+    const transcriptUpdate = (transcript) => {
+      session.value = {
+        ...session.value,
+        transcript
+      }
+    }
+
+    // simple template replace
+    const template = (str, extraSettings) => {
+      let settings = session.value;
+      if (extraSettings) {
+        settings = { ...settings, ...extraSettings };
+      }
+      return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
+    }
+
+    // send message to server
+    const chat = async (msg) => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+      controller.value = new AbortController();
+
+      transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
+
+      const prompt = template(session.value.template, {
+        message: msg,
+        history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
+      });
+
+      let currentMessage = '';
+      const history = session.value.transcript
+
+      const llamaParams = {
+        ...params.value,
+        stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
+      }
+
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+        const data = chunk.data;
+        currentMessage += data.content;
+
+        // remove leading whitespace
+        currentMessage = currentMessage.replace(/^\s+/, "")
+
+        transcriptUpdate([...history, ["{{char}}", currentMessage]])
+
+        if (data.stop) {
+          console.log("Completion finished: '", currentMessage, "', summary: ", data);
+        }
+
+        if (data.timings) {
+          llamaStats.value = data.timings;
+        }
+      }
+
+      controller.value = null;
+    }
+
+    function MessageInput() {
+      const message = useSignal("")
+
+      const stop = (e) => {
+        e.preventDefault();
+        if (controller.value) {
+          controller.value.abort();
+          controller.value = null;
+        }
+      }
+
+      const reset = (e) => {
+        stop(e);
+        transcriptUpdate([]);
+      }
+
+      const submit = (e) => {
+        stop(e);
+        chat(message.value);
+        message.value = "";
+      }
+
+      const enterSubmits = (event) => {
+        if (event.which === 13 && !event.shiftKey) {
+          submit(event);
+        }
+      }
+
+      return html`
+        <form onsubmit=${submit}>
+          <div>
+            <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
+          </div>
+          <div class="right">
+            <button type="submit" disabled=${!generating.value} >Send</button>
+            <button onclick=${stop} disabled=${generating}>Stop</button>
+            <button onclick=${reset}>Reset</button>
+          </div>
+        </form>
+      `
+    }
+
+    const ChatLog = (props) => {
+      const messages = session.value.transcript;
+      const container = useRef(null)
+
+      useEffect(() => {
+        // scroll to bottom (if needed)
+        if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
+          container.current.scrollTo(0, container.current.scrollHeight)
+        }
+      }, [messages])
+
+      const chatLine = ([user, msg]) => {
+        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
+      };
+
+      return html`
+        <section id="chat" ref=${container}>
+          ${messages.flatMap(chatLine)}
+        </section>`;
+    };
+
+    const ConfigForm = (props) => {
+      const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
+      const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
+      const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
+
+      return html`
+        <form>
+          <fieldset>
+            <div>
+              <label for="prompt">Prompt</label>
+              <textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="user">User name</label>
+              <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
+            </div>
+
+            <div>
+              <label for="bot">Bot name</label>
+              <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
+            </div>
+
+            <div>
+              <label for="template">Prompt template</label>
+              <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="template">Chat history template</label>
+              <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="temperature">Temperature</label>
+              <input type="range" id="temperature" min="0.0" max="1.0" step="0.01" name="temperature" value="${params.value.temperature}" oninput=${updateParamsFloat} />
+              <span>${params.value.temperature}</span>
+            </div>
+
+            <div>
+              <label for="nPredict">Predictions</label>
+              <input type="range" id="nPredict" min="1" max="2048" step="1" name="n_predict" value="${params.value.n_predict}" oninput=${updateParamsFloat} />
+              <span>${params.value.n_predict}</span>
+            </div>
+
+            <div>
+              <label for="repeat_penalty">Penalize repeat sequence</label>
+              <input type="range" id="repeat_penalty" min="0.0" max="2.0" step="0.01" name="repeat_penalty" value="${params.value.repeat_penalty}" oninput=${updateParamsFloat} />
+              <span>${params.value.repeat_penalty}</span>
+            </div>
+
+            <div>
+              <label for="repeat_last_n">Consider N tokens for penalize</label>
+              <input type="range" id="repeat_last_n" min="0.0" max="2048" name="repeat_last_n" value="${params.value.repeat_last_n}" oninput=${updateParamsFloat} />
+              <span>${params.value.repeat_last_n}</span>
+            </div>
+
+          </fieldset>
+        </form>
+      `
+    }
+    // poor mans markdown replacement
+    const Markdownish = (params) => {
+      const md = params.text
+        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
+        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*/g, '<em>$1</em>')
+        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+        .replace(/`(.*?)`/g, '<code>$1</code>')
+        .replace(/\n/gim, '<br />');
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+    };
+
+    const ModelGenerationInfo = (params) => {
+      if (!llamaStats.value) {
+        return html`<span/>`
+      }
+      return html`
+        <span>
+          ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
+        </span>
+      `
+    }
+
+    function App(props) {
+
+      return html`
+        <div id="container">
+          <header>
+            <h1>llama.cpp</h1>
+          </header>
+
+          <main id="content">
+            <${chatStarted.value ? ChatLog : ConfigForm} />
+          </main>
+
+          <section id="write">
+            <${MessageInput} />
+          </section>
+
+          <footer>
+            <p><${ModelGenerationInfo} /></p>
+            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+          </footer>
+        </div>
+      `;
+    }
+
+    render(h(App), document.body);
+  </script>
+</head>
+
+<body>
+</body>
+
+</html>

examples/simple/simple.cpp

@@ -66,7 +66,7 @@ int main(int argc, char ** argv)
     // Init LLM :
     //---------------------------------
 
-    llama_init_backend();
+    llama_init_backend(params.numa);
 
     llama_model * model;
     llama_context * ctx;

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -294,20 +294,9 @@ void init_model(struct my_llama_model * model) {
 
         ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str());
 
-        // 'layers.10.feed_forward.w1.weight' has length of 32.
-        // ggml_tensor->name only has 32 characters, but we need one more for the '\0' terminator.
-        // ggml_set_name will set the last character to '\0', so we can only store 'layers.10.feed_forward.w1.weigh'.
-        // when saving llama compatible model the tensors names will miss a character.
-        // ggml_set_name(layer.w1, (layers_i + ".feed_forward.w1.weight").c_str());
-        // ggml_set_name(layer.w2, (layers_i + ".feed_forward.w2.weight").c_str());
-        // ggml_set_name(layer.w3, (layers_i + ".feed_forward.w3.weight").c_str());
-
-        strncpy(layer.w1->name, (layers_i + ".feed_forward.w1.weight").c_str(), sizeof(layer.w1->name));
-        strncpy(layer.w2->name, (layers_i + ".feed_forward.w2.weight").c_str(), sizeof(layer.w2->name));
-        strncpy(layer.w3->name, (layers_i + ".feed_forward.w3.weight").c_str(), sizeof(layer.w3->name));
-        layer.w1->padding[0] = 0;
-        layer.w2->padding[0] = 0;
-        layer.w3->padding[0] = 0;
+        ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str());
+        ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
+        ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
     }
 }
 
@@ -454,8 +443,8 @@ struct ggml_tensor * forward(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, 1]
             // Kcur shape [n_embd/n_head, n_head, N, 1]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
 
             // store key and value to memory
             {
@@ -711,8 +700,8 @@ struct ggml_tensor * forward_batch(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, n_batch]
             // Kcur shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
             assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
             assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
 
@@ -996,8 +985,8 @@ struct ggml_tensor * forward_batch_wo_cache(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, n_batch]
             // Kcur shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
             assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
             assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
 
@@ -1218,8 +1207,8 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn(
             // compute Q and K and RoPE them
             // wq   shape [n_embd, n_embd, 1, 1]
             // wk   shape [n_embd, n_embd, 1, 1]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
             assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
             assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
 
@@ -1618,10 +1607,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
         use_buf(-1); struct ggml_tensor * t04 = expand(gf, ggml_mul          (ctx0, t02, t03));                               assert_shape_2d(t04, n_embd, N*n_batch);
         use_buf(-1); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat      (ctx0, layer.wq, t04));                          assert_shape_2d(t05, n_embd, N*n_batch);
         use_buf(-1); struct ggml_tensor * t06 = expand(gf, ggml_reshape_4d   (ctx0, t05, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode));          assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
+        use_buf(-1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode, 0));       assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
         use_buf(-1); struct ggml_tensor * t08 = expand(gf, ggml_mul_mat      (ctx0, layer.wk, t04));                          assert_shape_2d(t08, n_embd, N*n_batch);
         use_buf(-1); struct ggml_tensor * t09 = expand(gf, ggml_reshape_4d   (ctx0, t08, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode));          assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
+        use_buf(-1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode, 0));       assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
         use_buf(-1); struct ggml_tensor * t11 = expand(gf, ggml_mul_mat      (ctx0, t04, layer.wv));                          assert_shape_2d(t11, N*n_batch, n_embd);
         use_buf(-1); struct ggml_tensor * t12 = expand(gf, ggml_reshape_4d   (ctx0, t11, N, n_batch, n_embd/n_head, n_head)); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
         use_buf(-1); struct ggml_tensor * t13 = expand(gf, ggml_permute      (ctx0, t07, 0, 2, 1, 3));                        assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
@@ -2368,7 +2357,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
         file->write_u32(0);
         file->write_u32(0);
         file->write_u32(GGML_TYPE_F32);
-        file->seek(0-file->tell() & 31, SEEK_CUR);
+        file->seek((0-file->tell()) & 31, SEEK_CUR);
         return;
     }
     const char * name = ggml_get_name(tensor);
@@ -2383,7 +2372,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
     file->write_u32(tensor->type);
     file->write_raw(ne, sizeof(ne[0]) * nd);
     file->write_raw(name, name_len);
-    file->seek(0-file->tell() & 31, SEEK_CUR);
+    file->seek((0-file->tell()) & 31, SEEK_CUR);
     file->write_raw(tensor->data, ggml_nbytes(tensor));
 }
 
@@ -2404,7 +2393,7 @@ void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
     std::string name = file->read_string(name_len);
     GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0);
 
-    file->seek(0-file->tell() & 31, SEEK_CUR);
+    file->seek((0-file->tell()) & 31, SEEK_CUR);
     file->read_raw(tensor->data, ggml_nbytes(tensor));
 }
 
@@ -2682,7 +2671,8 @@ struct train_params {
     const char * fn_checkpoint_out;
     const char * fn_model_out;
 
-    int seed;
+    uint32_t seed;
+
     int n_ctx;
     int n_embd;
     int n_mult;
@@ -2779,7 +2769,7 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p
     fprintf(stderr, "  --checkpoint-in FNAME      path from which to load training checkpoint (default '%s')\n", params->fn_checkpoint_in);
     fprintf(stderr, "  --checkpoint-out FNAME     path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out);
     fprintf(stderr, "  --model-out FNAME          path to save ggml model (default '%s')\n", params->fn_model_out);
-    fprintf(stderr, "  -s SEED, --seed SEED       RNG seed (default: -1, use random seed for < 0)\n");
+    fprintf(stderr, "  -s SEED, --seed SEED       RNG seed (default: -1, use random seed for -1)\n");
     fprintf(stderr, "  -c N, --ctx N              Context size used during training (default %d)\n", params->n_ctx);
     fprintf(stderr, "  --embd N                   Embedding size used for new models (default %d)\n", params->n_embd);
     fprintf(stderr, "  --mult N                   Mult size used for new models, influences feedforward size. (default %d)\n", params->n_mult);
@@ -3045,10 +3035,10 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (params.seed < 0) {
+    if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
-    printf("%s: seed: %d\n", __func__, params.seed);
+    printf("%s: seed: %u\n", __func__, params.seed);
     srand(params.seed);
 
     struct llama_context_params llama_params = llama_context_default_params();

ggml-cuda.h

@@ -8,10 +8,6 @@ extern "C" {
 
 #define GGML_CUDA_MAX_DEVICES       16
 
-struct ggml_tensor_extra_gpu {
-    void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
-};
-
 void   ggml_init_cublas(void);
 void   ggml_cuda_set_tensor_split(const float * tensor_split);
 
@@ -29,6 +25,7 @@ void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
 void   ggml_cuda_free_data(struct ggml_tensor * tensor);
 void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
 void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
+void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
 void   ggml_cuda_set_main_device(int main_device);
 void   ggml_cuda_set_scratch_size(size_t scratch_size);
 void   ggml_cuda_free_scratch(void);

ggml-metal.m

@@ -51,21 +51,21 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(get_rows_f16);
     GGML_METAL_DECL_KERNEL(get_rows_q4_0);
     GGML_METAL_DECL_KERNEL(get_rows_q4_1);
-    GGML_METAL_DECL_KERNEL(get_rows_q2_k);
-    GGML_METAL_DECL_KERNEL(get_rows_q3_k);
-    GGML_METAL_DECL_KERNEL(get_rows_q4_k);
-    GGML_METAL_DECL_KERNEL(get_rows_q5_k);
-    GGML_METAL_DECL_KERNEL(get_rows_q6_k);
+    GGML_METAL_DECL_KERNEL(get_rows_q2_K);
+    GGML_METAL_DECL_KERNEL(get_rows_q3_K);
+    GGML_METAL_DECL_KERNEL(get_rows_q4_K);
+    GGML_METAL_DECL_KERNEL(get_rows_q5_K);
+    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_f16_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32);
-    GGML_METAL_DECL_KERNEL(mul_mat_q3_k_f32);
-    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_mat_q2_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mat_q3_K_f32);
+    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(rope);
     GGML_METAL_DECL_KERNEL(alibi_f32);
     GGML_METAL_DECL_KERNEL(cpy_f32_f16);
@@ -132,7 +132,13 @@ struct ggml_metal_context * ggml_metal_init(void) {
             exit(1);
         }
 
+#ifdef GGML_QKK_64
+        MTLCompileOptions* options = [MTLCompileOptions new];
+        options.preprocessorMacros = @{ @"QK_K" : @(64) };
+        ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error];
+#else
         ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error];
+#endif
         if (error) {
             fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
             exit(1);
@@ -159,21 +165,21 @@ struct ggml_metal_context * ggml_metal_init(void) {
         GGML_METAL_ADD_KERNEL(get_rows_f16);
         GGML_METAL_ADD_KERNEL(get_rows_q4_0);
         GGML_METAL_ADD_KERNEL(get_rows_q4_1);
-        GGML_METAL_ADD_KERNEL(get_rows_q2_k);
-        GGML_METAL_ADD_KERNEL(get_rows_q3_k);
-        GGML_METAL_ADD_KERNEL(get_rows_q4_k);
-        GGML_METAL_ADD_KERNEL(get_rows_q5_k);
-        GGML_METAL_ADD_KERNEL(get_rows_q6_k);
+        GGML_METAL_ADD_KERNEL(get_rows_q2_K);
+        GGML_METAL_ADD_KERNEL(get_rows_q3_K);
+        GGML_METAL_ADD_KERNEL(get_rows_q4_K);
+        GGML_METAL_ADD_KERNEL(get_rows_q5_K);
+        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_f16_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
-        GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32);
-        GGML_METAL_ADD_KERNEL(mul_mat_q3_k_f32);
-        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_mat_q2_K_f32);
+        GGML_METAL_ADD_KERNEL(mul_mat_q3_K_f32);
+        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(rope);
         GGML_METAL_ADD_KERNEL(alibi_f32);
         GGML_METAL_ADD_KERNEL(cpy_f32_f16);
@@ -196,7 +202,9 @@ struct ggml_metal_context * ggml_metal_init(void) {
 
 void ggml_metal_free(struct ggml_metal_context * ctx) {
     fprintf(stderr, "%s: deallocating\n", __func__);
-
+    for (int i = 0; i < ctx->n_buffers; ++i) {
+        [ctx->buffers[i].metal release];
+    }
     free(ctx);
 }
 
@@ -662,7 +670,7 @@ void ggml_metal_graph_compute(
 
                                             nth0 = 4;
                                             nth1 = 16;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32];
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_K_f32];
                                         } break;
                                     case GGML_TYPE_Q3_K:
                                         {
@@ -671,7 +679,7 @@ void ggml_metal_graph_compute(
 
                                             nth0 = 4;
                                             nth1 = 16;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_k_f32];
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_K_f32];
                                         } break;
                                     case GGML_TYPE_Q4_K:
                                         {
@@ -680,7 +688,7 @@ void ggml_metal_graph_compute(
 
                                             nth0 = 4;
                                             nth1 = 16;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32];
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32];
                                         } break;
                                     case GGML_TYPE_Q5_K:
                                         {
@@ -689,7 +697,7 @@ void ggml_metal_graph_compute(
 
                                             nth0 = 4;
                                             nth1 = 16;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_k_f32];
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_K_f32];
                                         } break;
                                     case GGML_TYPE_Q6_K:
                                         {
@@ -698,7 +706,7 @@ void ggml_metal_graph_compute(
 
                                             nth0 = 4;
                                             nth1 = 16;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_k_f32];
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_K_f32];
                                         } break;
                                     default:
                                         {
@@ -750,11 +758,11 @@ void ggml_metal_graph_compute(
                                 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;
-                                case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break;
-                                case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_k]; break;
-                                case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break;
-                                case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_k]; break;
-                                case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break;
+                                case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break;
+                                case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break;
+                                case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
+                                case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break;
+                                case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break;
                                 default: GGML_ASSERT(false && "not implemented");
                             }
 

ggml-metal.metal

@@ -428,7 +428,7 @@ kernel void kernel_mul_mat_q4_0_f32(
     }
     threadgroup_barrier(mem_flags::mem_threadgroup);
     if (ith == 0) {
-        for (uint i = 16; i < nth; i += 16) sum[0] += sum[i];
+        for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
         dst[r1*ne0 + r0] = sum[0];
     }
 }
@@ -497,7 +497,7 @@ kernel void kernel_mul_mat_q4_1_f32(
     }
     threadgroup_barrier(mem_flags::mem_threadgroup);
     if (ith == 0) {
-        for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
+        for (uint i = 16; i < nth; i += 16) sum[0] += sum[i];
         dst[r1*ne0 + r0] = sum[0];
     }
 }
@@ -775,47 +775,76 @@ kernel void kernel_cpy_f32_f32(
 
 //============================================ k-quants ======================================================
 
+#ifndef QK_K
 #define QK_K 256
+#else
+static_assert(QK_K == 256 || QK_K == 64, "QK_K must be 256 or 64");
+#endif
+
+#if QK_K == 256
+#define K_SCALE_SIZE 12
+#else
+#define K_SCALE_SIZE 4
+#endif
 
 typedef struct {
     uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
     uint8_t qs[QK_K/4];      // quants
     half d;           // super-block scale for quantized scales
     half dmin;        // super-block scale for quantized mins
-} block_q2_k;
+} block_q2_K;
 // 84 bytes / block
 
 typedef struct {
     uint8_t hmask[QK_K/8];     // quants - high bit
     uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
-    half d;                    // super-block scale
-} block_q3_k;
-// 110 bytes / block
+#if QK_K == 64
+    uint8_t scales[2];
+#else
+    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
+#endif
+    half d;             // super-block scale
+} block_q3_K;
 
+#if QK_K == 64
+typedef struct {
+    half    d[2];          // super-block scales/mins
+    uint8_t scales[2];
+    uint8_t qs[QK_K/2];    // 4-bit quants
+} block_q4_K;
+#else
 typedef struct {
     half d;             // super-block scale for quantized scales
     half dmin;          // super-block scale for quantized mins
-    uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
     uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_k;
-// 144 bytes / block
+} block_q4_K;
+#endif
 
+#if QK_K == 64
+typedef struct {
+    half  d;                     // super-block scales/mins
+    int8_t  scales[QK_K/16];     // 8-bit block scales
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+#else
 typedef struct {
     half d;                      // super-block scale for quantized scales
     half dmin;                   // super-block scale for quantized mins
     uint8_t scales[3*QK_K/64];   // scales and mins, quantized with 6 bits
     uint8_t qh[QK_K/8];          // quants, high bit
     uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_k;
+} block_q5_K;
 // 176 bytes / block
+#endif
 
 typedef struct {
     uint8_t ql[QK_K/2];      // quants, lower 4 bits
     uint8_t qh[QK_K/4];      // quants, upper 2 bits
     int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
     half d;                  // super-block scale
-} block_q6_k;
+} block_q6_K;
 // 210 bytes / block
 
 static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
@@ -836,7 +865,7 @@ static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
 
 //========================================== dequantization =============================
 
-static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, int k) {
+static void dequantize_row_q2_K(device const block_q2_K * x, device float * y, int k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -847,6 +876,7 @@ static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, i
 
         device const uint8_t * q = x[i].qs;
 
+#if QK_K == 256
         int is = 0;
         float dl, ml;
         for (int n = 0; n < QK_K; n += 128) {
@@ -865,14 +895,29 @@ static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, i
             }
             q += 32;
         }
+#else
+        float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4);
+        float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4);
+        float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4);
+        float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4);
+        for (int l = 0; l < 16; ++l) {
+            y[l+ 0] = dl1 * ((q[l] >> 0) & 3) - ml1;
+            y[l+16] = dl2 * ((q[l] >> 2) & 3) - ml2;
+            y[l+32] = dl3 * ((q[l] >> 4) & 3) - ml3;
+            y[l+48] = dl4 * ((q[l] >> 6) & 3) - ml4;
+        }
+        y += QK_K;
+#endif
 
     }
 }
 
-static void dequantize_row_q3_k(device const block_q3_k * x, device float * y, int k) {
+static void dequantize_row_q3_K(device const block_q3_K * x, device float * y, int k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
+#if QK_K == 256
+
     const uint16_t kmask1 = 0x0303;
     const uint16_t kmask2 = 0x0f0f;
 
@@ -918,22 +963,49 @@ static void dequantize_row_q3_k(device const block_q3_k * x, device float * y, i
             }
             q += 32;
         }
-
     }
+#else
+    for (int i = 0; i < nb; i++) {
+
+        const float d_all = (float)(x[i].d);
+
+        device const uint8_t * q = x[i].qs;
+        device const uint8_t * hm = x[i].hmask;
+
+        const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8);
+        const float d2 = d_all * ((x[i].scales[0] >>  4) - 8);
+        const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8);
+        const float d4 = d_all * ((x[i].scales[1] >>  4) - 8);
+
+        for (int l = 0; l < 8; ++l) {
+            uint8_t h = hm[l];
+            y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4));
+            y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4));
+            y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4));
+            y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4));
+            y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4));
+            y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4));
+            y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4));
+            y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4));
+        }
+        y += QK_K;
+    }
+#endif
 
 }
 
-static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, int k) {
+static void dequantize_row_q4_K(device const block_q4_K * x, device float * y, int k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
-
     for (int i = 0; i < nb; i++) {
 
+        device const uint8_t * q = x[i].qs;
+
+#if QK_K == 256
         const float d = x[i].d;
         const float min = x[i].dmin;
 
-        device const uint8_t * q = x[i].qs;
         device const uint8_t * scales = x[i].scales;
 
         int is = 0;
@@ -945,14 +1017,29 @@ static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, i
             for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l]  >> 4) - m2;
             q += 32; is += 2;
         }
+#else
+        device const uint8_t * s = x[i].scales;
+        device const half2 * dh = (device const half2 *)x[i].d;
+        const float2 d = (float2)dh[0];
+        const float d1 = d[0] * (s[0] & 0xF);
+        const float d2 = d[0] * (s[1] & 0xF);
+        const float m1 = d[1] * (s[0] >>  4);
+        const float m2 = d[1] * (s[1] >>  4);
+        for (int l = 0; l < 32; ++l) {
+            y[l+ 0] = d1 * (q[l] & 0xF) - m1;
+            y[l+32] = d2 * (q[l] >>  4) - m2;
+        }
+        y += QK_K;
+#endif
 
     }
 }
 
-static void dequantize_row_q5_k(device const block_q5_k * x, device float * y, int k) {
+static void dequantize_row_q5_K(device const block_q5_K * x, device float * y, int k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
+#if QK_K == 256
    for (int i = 0; i < nb; i++) {
 
         const float d = (float)(x[i].d);
@@ -973,10 +1060,32 @@ static void dequantize_row_q5_k(device const block_q5_k * x, device float * y, i
             u1 <<= 2; u2 <<= 2;
         }
     }
+#else
+    for (int i = 0; i < nb; i++) {
+
+        const float d = (float)x[i].d;
+
+        device const uint8_t * ql = x[i].qs;
+        device const uint8_t * qh = x[i].qh;
+        device const int8_t  * sc = x[i].scales;
+
+        for (int l = 0; l < 8; ++l) {
+            y[l+ 0] = d * sc[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16));
+            y[l+ 8] = d * sc[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16));
+            y[l+16] = d * sc[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16));
+            y[l+24] = d * sc[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16));
+            y[l+32] = d * sc[2] * ((ql[l+ 0] >>  4) - (qh[l] & 0x10 ? 0 : 16));
+            y[l+40] = d * sc[2] * ((ql[l+ 8] >>  4) - (qh[l] & 0x20 ? 0 : 16));
+            y[l+48] = d * sc[3] * ((ql[l+16] >>  4) - (qh[l] & 0x40 ? 0 : 16));
+            y[l+56] = d * sc[3] * ((ql[l+24] >>  4) - (qh[l] & 0x80 ? 0 : 16));
+        }
+        y += QK_K;
+    }
+#endif
 
 }
 
-static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, int k) {
+static void dequantize_row_q6_K(device const block_q6_K * x, device float * y, int k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -988,6 +1097,7 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i
 
         const float d = x[i].d;
 
+#if QK_K == 256
         for (int n = 0; n < QK_K; n += 128) {
             for (int l = 0; l < 32; ++l) {
                 int is = l/16;
@@ -1005,10 +1115,23 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i
             qh += 32;
             sc += 8;
         }
+#else
+        for (int l = 0; l < 16; ++l) {
+            const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+            const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+            const int8_t q3 = (int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+            const int8_t q4 = (int8_t)((ql[l+16]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            y[l+ 0] = d * sc[0] * q1;
+            y[l+16] = d * sc[1] * q2;
+            y[l+32] = d * sc[2] * q3;
+            y[l+48] = d * sc[3] * q4;
+        }
+        y  += 64;
+#endif
     }
 }
 
-kernel void kernel_get_rows_q2_k(
+kernel void kernel_get_rows_q2_K(
         device const  void * src0,
         device const   int * src1,
         device       float * dst,
@@ -1019,12 +1142,12 @@ kernel void kernel_get_rows_q2_k(
     const int i = tpig;
     const int r = ((device int32_t *) src1)[i];
 
-    dequantize_row_q2_k(
-            (device const block_q2_k *) ((device char *) src0 + r*nb01),
+    dequantize_row_q2_K(
+            (device const block_q2_K *) ((device char *) src0 + r*nb01),
                        (device float *) ((device char *)  dst + i*nb1), ne00);
 }
 
-kernel void kernel_get_rows_q3_k(
+kernel void kernel_get_rows_q3_K(
         device const  void * src0,
         device const   int * src1,
         device       float * dst,
@@ -1035,12 +1158,12 @@ kernel void kernel_get_rows_q3_k(
     const int i = tpig;
     const int r = ((device int32_t *) src1)[i];
 
-    dequantize_row_q3_k(
-            (device const block_q3_k *) ((device char *) src0 + r*nb01),
+    dequantize_row_q3_K(
+            (device const block_q3_K *) ((device char *) src0 + r*nb01),
                        (device float *) ((device char *)  dst + i*nb1), ne00);
 }
 
-kernel void kernel_get_rows_q4_k(
+kernel void kernel_get_rows_q4_K(
         device const  void * src0,
         device const   int * src1,
         device       float * dst,
@@ -1051,12 +1174,12 @@ kernel void kernel_get_rows_q4_k(
     const int i = tpig;
     const int r = ((device int32_t *) src1)[i];
 
-    dequantize_row_q4_k(
-            (device const block_q4_k *) ((device char *) src0 + r*nb01),
+    dequantize_row_q4_K(
+            (device const block_q4_K *) ((device char *) src0 + r*nb01),
                        (device float *) ((device char *)  dst + i*nb1), ne00);
 }
 
-kernel void kernel_get_rows_q5_k(
+kernel void kernel_get_rows_q5_K(
         device const  void * src0,
         device const   int * src1,
         device       float * dst,
@@ -1067,12 +1190,12 @@ kernel void kernel_get_rows_q5_k(
     const int i = tpig;
     const int r = ((device int32_t *) src1)[i];
 
-    dequantize_row_q5_k(
-            (device const block_q5_k *) ((device char *) src0 + r*nb01),
+    dequantize_row_q5_K(
+            (device const block_q5_K *) ((device char *) src0 + r*nb01),
                        (device float *) ((device char *)  dst + i*nb1), ne00);
 }
 
-kernel void kernel_get_rows_q6_k(
+kernel void kernel_get_rows_q6_K(
         device const  void * src0,
         device const   int * src1,
         device       float * dst,
@@ -1083,14 +1206,14 @@ kernel void kernel_get_rows_q6_k(
     const int i = tpig;
     const int r = ((device int32_t *) src1)[i];
 
-    dequantize_row_q6_k(
-            (device const block_q6_k *) ((device char *) src0 + r*nb01),
+    dequantize_row_q6_K(
+            (device const block_q6_K *) ((device char *) src0 + r*nb01),
                        (device float *) ((device char *)  dst + i*nb1), ne00);
 }
 
 //====================================== dot products =========================
 
-kernel void kernel_mul_mat_q2_k_f32(
+kernel void kernel_mul_mat_q2_K_f32(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
@@ -1107,12 +1230,15 @@ kernel void kernel_mul_mat_q2_k_f32(
     const int64_t r0 = tgpig.x;
     const int64_t r1 = tgpig.y;
 
-    device const block_q2_k * x = (device const block_q2_k *) src0 + r0*nb;
+    device const block_q2_K * x = (device const block_q2_K *) src0 + r0*nb;
     device const float     * yy = (device const float      *) src1 + r1*ne10;
 
     const int nth = tptg.x*tptg.y;
     const int ith = tptg.y*tpitg.x + tpitg.y;
 
+    float sumf = 0;
+
+#if QK_K == 256
     const int tid = tpitg.y;    // 0...16
     const int il  = tid/4;      // 0...3
     const int ir  = tid%4;      // 0...3
@@ -1125,9 +1251,6 @@ kernel void kernel_mul_mat_q2_k_f32(
     const int y_offset = 64*il + n*ir;
     const int q_offset = 32*ip + n*ir;
 
-    sum[ith] = 0.0f;
-
-    float sumf = 0;
     for (int i = tpitg.x; i < nb; i += tptg.x) {
 
         device const uint8_t * q = x[i].qs + q_offset;
@@ -1140,7 +1263,6 @@ kernel void kernel_mul_mat_q2_k_f32(
 
         device const float   * y = yy + i*QK_K + y_offset;
 
-        //float4 s = {0.f, 0.f, 0.f, 0.f};
         float2 s = {0.f, 0.f};
         float smin = 0;
         for (int l = 0; l < n; ++l) {
@@ -1155,25 +1277,38 @@ kernel void kernel_mul_mat_q2_k_f32(
         sumf += dall * (s[0] * d1 + s[1] * d2) - dmin * smin;
 
     }
+#else
+    const int il = 4 * tpitg.x;
+
+    uint32_t aux[2];
+    thread const uint8_t * d = (thread const uint8_t *)aux;
+    thread const uint8_t * m = (thread const uint8_t *)aux + 4;
+
+    for (int i = tpitg.y; i < nb; i += tptg.y) {
+
+        device const uint8_t * q = x[i].qs + il;
+        device const float   * y = yy + i*QK_K + il;
+
+        const float dall = (float)x[i].d;
+        const float dmin = (float)x[i].dmin;
+
+        device const uint32_t * a = (device const uint32_t *)x[i].scales;
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = (a[0] >> 4) & 0x0f0f0f0f;
+
+        for (int l = 0; l < 4; ++l) {
+            sumf += y[l+ 0] * (dall * d[0] * ((q[l] >> 0) & 3) - dmin * m[0])
+                  + y[l+16] * (dall * d[1] * ((q[l] >> 2) & 3) - dmin * m[1])
+                  + y[l+32] * (dall * d[2] * ((q[l] >> 4) & 3) - dmin * m[2])
+                  + y[l+48] * (dall * d[3] * ((q[l] >> 6) & 3) - dmin * m[3]);
+        }
+    }
+#endif
+
     sum[ith] = sumf;
 
-    //int mask1 = (ith%4 == 0);
-    //int mask2 = (ith%16 == 0);
-
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    //for (int i = 1; i < 4; ++i) sum[ith] += mask1 * sum[ith + i];
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    //for (int i = 4; i < 16; i += 4) sum[ith] += mask2 * sum[ith + i];
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    //if (ith == 0) {
-    //    for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
-    //    dst[r1*ne0 + r0] = sum[0];
-    //}
-
     //
     // Accumulate the sum from all threads in the threadgroup
-    // This version is slightly faster than the commented out one below,
-    // which I copy-pasted from ggerganov's q4_0 dot product for metal.
     //
     threadgroup_barrier(mem_flags::mem_threadgroup);
     if (ith%4 == 0) {
@@ -1190,7 +1325,7 @@ kernel void kernel_mul_mat_q2_k_f32(
     }
 }
 
-kernel void kernel_mul_mat_q3_k_f32(
+kernel void kernel_mul_mat_q3_K_f32(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
@@ -1203,23 +1338,25 @@ kernel void kernel_mul_mat_q3_k_f32(
         uint2 tpitg[[thread_position_in_threadgroup]],
         uint2  tptg[[threads_per_threadgroup]]) {
 
-    const uint16_t kmask1 = 0x0303;
-    const uint16_t kmask2 = 0x0f0f;
-
-    const uint8_t m3 = 3;
-    const int8_t  m4 = 4;
-
     const int nb = ne00/QK_K;
 
     const int64_t r0 = tgpig.x;
     const int64_t r1 = tgpig.y;
 
-    device const block_q3_k * x = (device const block_q3_k *) src0 + r0*nb;
+    device const block_q3_K * x = (device const block_q3_K *) src0 + r0*nb;
     device const float     * yy = (device const float      *) src1 + r1*ne10;
 
     const int nth = tptg.x*tptg.y;
     const int ith = tptg.y*tpitg.x + tpitg.y;
 
+#if QK_K == 256
+
+    const uint8_t m3 = 3;
+    const int8_t  m4 = 4;
+
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
+
     const int tid = tpitg.y;        // expecting 16
     const int ip  = tid/8;          // 0 or 1
     const int il  = tid/2 - 4*ip;   // 0...3
@@ -1273,6 +1410,39 @@ kernel void kernel_mul_mat_q3_k_f32(
 
     //sum[ith] = sumf;
     sum[ith] = sumf1 - 32.f*sumf2;
+#else
+    const int il = 4 * tpitg.x;  // 0, 4, 8, 12
+    const int im = il/8;         // 0, 0, 1, 1
+    const int in = il%8;         // 0, 4, 0, 4
+
+    float sumf = 0;
+
+    for (int i = tpitg.y; i < nb; i += tptg.y) {
+
+        const float d_all = (float)(x[i].d);
+
+        device const uint8_t * q = x[i].qs + il;
+        device const uint8_t * h = x[i].hmask + in;
+        device const float   * y = yy + i * QK_K + il;
+
+        const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8);
+        const float d2 = d_all * ((x[i].scales[0] >>  4) - 8);
+        const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8);
+        const float d4 = d_all * ((x[i].scales[1] >>  4) - 8);
+
+        for (int l = 0; l < 4; ++l) {
+            const uint8_t hm = h[l] >> im;
+            sumf += y[l+ 0] * d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((hm & 0x01) ? 0 : 4))
+                  + y[l+16] * d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((hm & 0x04) ? 0 : 4))
+                  + y[l+32] * d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((hm & 0x10) ? 0 : 4))
+                  + y[l+48] * d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((hm & 0x40) ? 0 : 4));
+        }
+
+    }
+
+    sum[ith] = sumf;
+
+#endif
 
     //
     // Accumulate the sum from all threads in the threadgroup
@@ -1293,7 +1463,7 @@ kernel void kernel_mul_mat_q3_k_f32(
 
 }
 
-kernel void kernel_mul_mat_q4_k_f32(
+kernel void kernel_mul_mat_q4_K_f32(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
@@ -1305,21 +1475,25 @@ kernel void kernel_mul_mat_q4_k_f32(
         uint2 tpitg[[thread_position_in_threadgroup]],
         uint2  tptg[[threads_per_threadgroup]]) {
 
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
     const int nb = ne00/QK_K;
 
     const int64_t r0 = tgpig.x;
     const int64_t r1 = tgpig.y;
 
-    device const block_q4_k * x = (device const block_q4_k *) src0 + r0*nb;
-    device const float     * yy = (device const float      *) src1 + r1*ne10;
-
     const int nth = tptg.x*tptg.y;
     const int ith = tptg.y*tpitg.x + tpitg.y;
 
+    device const block_q4_K * x = (device const block_q4_K *) src0 + r0*nb;
+    device const float     * yy = (device const float      *) src1 + r1*ne10;
+
+    float sumf = 0;
+
+#if QK_K == 256
+
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
     const int tid = tpitg.y;   // 0...16
     const int il  = tid/4;     // 0...3
     const int ir  = tid - 4*il;// 0...3
@@ -1332,11 +1506,8 @@ kernel void kernel_mul_mat_q4_k_f32(
     const int q_offset = 32*im + l0;
     const int y_offset = 64*im + l0;
 
-    sum[ith] = 0.0f;
-
     uchar2 sc1, sc2, sc3, sc4;
 
-    float sumf = 0;
     for (int i = tpitg.x; i < nb; i += tptg.x) {
 
         device const uint8_t * q1 = (x + i)->qs + q_offset;
@@ -1365,6 +1536,30 @@ kernel void kernel_mul_mat_q4_k_f32(
         sumf += dall * (s[0] * sc1[0] + s[1] * sc1[1] + s[2] * sc3[0] + s[3] * sc3[1]) - dmin * smin;
 
     }
+#else
+    uint16_t aux16[2];
+    thread const uint8_t * scales = (thread const uint8_t *)aux16;
+
+    const int il  = 4*tpitg.x;
+
+    for (int i = tpitg.y; i < nb; i += tptg.y) {
+
+        device const uint8_t * q = x[i].qs + il;
+        device const float   * y = yy + i * QK_K + il;
+
+        const float d = (float)x[i].d[0];
+        const float m = (float)x[i].d[1];
+
+        device const uint16_t * a = (device const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+        for (int l = 0; l < 4; ++l) {
+            sumf += d * scales[0] * (y[l+ 0] * (q[l] & 0xF) + y[l+16] * (q[l+16] & 0xF)) - m * scales[2] * (y[l+ 0] + y[l+16])
+                  + d * scales[1] * (y[l+32] * (q[l] >>  4) + y[l+48] * (q[l+16] >>  4)) - m * scales[3] * (y[l+32] + y[l+48]);
+        }
+    }
+#endif
 
     sum[ith] = sumf;
 
@@ -1401,7 +1596,7 @@ kernel void kernel_mul_mat_q4_k_f32(
     //}
 }
 
-kernel void kernel_mul_mat_q5_k_f32(
+kernel void kernel_mul_mat_q5_K_f32(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
@@ -1413,21 +1608,25 @@ kernel void kernel_mul_mat_q5_k_f32(
         uint2 tpitg[[thread_position_in_threadgroup]],
         uint2  tptg[[threads_per_threadgroup]]) {
 
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
     const int nb = ne00/QK_K;
 
     const int64_t r0 = tgpig.x;
     const int64_t r1 = tgpig.y;
 
-    device const block_q5_k * x = (device const block_q5_k *) src0 + r0*nb;
+    device const block_q5_K * x = (device const block_q5_K *) src0 + r0*nb;
     device const float     * yy = (device const float      *) src1 + r1*ne10;
 
     const int nth = tptg.x*tptg.y;
     const int ith = tptg.y*tpitg.x + tpitg.y;
 
+    float sumf = 0;
+
+#if QK_K == 256
+
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
     const int tid = tpitg.y;   // 0...16
     const int il  = tid/4;     // 0...3
     const int ir  = tid - 4*il;// 0...3
@@ -1447,7 +1646,6 @@ kernel void kernel_mul_mat_q5_k_f32(
 
     uchar2 sc1, sc2, sc3, sc4;
 
-    float sumf = 0;
     for (int i = tpitg.x; i < nb; i += tptg.x) {
 
         device const uint8_t * q1 = (x + i)->qs + q_offset;
@@ -1479,6 +1677,28 @@ kernel void kernel_mul_mat_q5_k_f32(
         sumf += dall * (s[0] * sc1[0] + s[1] * sc1[1] + s[2] * sc3[0] + s[3] * sc3[1]) - dmin * smin;
 
     }
+#else
+    const int il  = 4 * tpitg.x;  // 0, 4, 8, 12
+    const int im  = il/8;         // 0, 0, 1, 1
+    const int in  = il%8;         // 0, 4, 0, 4
+
+    for (int i = tpitg.y; i < nb; i += tptg.y) {
+
+        const float d = (float)x[i].d;
+        device const uint8_t * q = x[i].qs + il;
+        device const uint8_t * h = x[i].qh + in;
+        device const int8_t  * s = x[i].scales;
+        device const float   * y = yy + i*QK_K + il;
+
+        for (int l = 0; l < 4; ++l) {
+            const uint8_t hl = h[l] >> im;
+            sumf += y[l+ 0] * d * s[0] * ((q[l+ 0] & 0xF) - (hl & 0x01 ? 0 : 16))
+                  + y[l+16] * d * s[1] * ((q[l+16] & 0xF) - (hl & 0x04 ? 0 : 16))
+                  + y[l+32] * d * s[2] * ((q[l+ 0] >>  4) - (hl & 0x10 ? 0 : 16))
+                  + y[l+48] * d * s[3] * ((q[l+16] >>  4) - (hl & 0x40 ? 0 : 16));
+        }
+    }
+#endif
     sum[ith] = sumf;
 
     //
@@ -1500,7 +1720,7 @@ kernel void kernel_mul_mat_q5_k_f32(
 
 }
 
-kernel void kernel_mul_mat_q6_k_f32(
+kernel void kernel_mul_mat_q6_K_f32(
         device const  void * src0,
         device const float * src1,
         device       float * dst,
@@ -1522,12 +1742,15 @@ kernel void kernel_mul_mat_q6_k_f32(
     const int64_t r0 = tgpig.x;
     const int64_t r1 = tgpig.y;
 
-    device const block_q6_k * x = (device const block_q6_k *) src0 + r0*nb;
+    device const block_q6_K * x = (device const block_q6_K *) src0 + r0*nb;
     device const float     * yy = (device const float      *) src1 + r1*ne10;
 
     const int nth = tptg.x*tptg.y;
     const int ith = tptg.y*tpitg.x + tpitg.y;
 
+    float sumf = 0;
+
+#if QK_K == 256
     // Note: we absolutely assume that tptg.y = 16 and QK_K = 256!
     const int iqs  = 16 * tpitg.y;
     const int ip   = iqs / 128;         // 0 or 1
@@ -1540,7 +1763,6 @@ kernel void kernel_mul_mat_q6_k_f32(
     const int q_offset_l = 64*ip + l0;
     const int q_offset_h = 32*ip + l0;
 
-    float sumf = 0;
     for (int i = tpitg.x; i < nb; i += tptg.x) {
 
         device const uint8_t * ql = x[i].ql + q_offset_l;
@@ -1562,6 +1784,28 @@ kernel void kernel_mul_mat_q6_k_f32(
         sumf += dall * (sums[0] * sc[0] + sums[1] * sc[2] + sums[2] * sc[4] + sums[3] * sc[6]);
 
     }
+#else
+    const int il  = 4*tpitg.x;    // 0, 4, 8, 12
+
+    for (int i = tpitg.y; i < nb; i += tptg.y) {
+        device const float * y = yy + i * QK_K + il;
+        device const uint8_t * ql = x[i].ql + il;
+        device const uint8_t * qh = x[i].qh + il;
+        device const int8_t  * s  = x[i].scales;
+
+        const float d = x[i].d;
+
+        float4 sums = {0.f, 0.f, 0.f, 0.f};
+        for (int l = 0; l < 4; ++l) {
+            sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32);
+            sums[1] += y[l+16] * ((int8_t)((ql[l+16] & 0xF) | ((qh[l] & kmask2) << 2)) - 32);
+            sums[2] += y[l+32] * ((int8_t)((ql[l+ 0] >>  4) | ((qh[l] & kmask3) >> 0)) - 32);
+            sums[3] += y[l+48] * ((int8_t)((ql[l+16] >>  4) | ((qh[l] & kmask4) >> 2)) - 32);
+        }
+        sumf += d * (sums[0] * s[0] + sums[1] * s[1] + sums[2] * s[2] + sums[3] * s[3]);
+    }
+
+#endif
 
     sum[ith] = sumf;
 

ggml-opencl.cpp

@@ -21,11 +21,19 @@
 
 #define CL_DMMV_BLOCK_SIZE 32
 
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 1
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
 #define MULTILINE_QUOTE(...) #__VA_ARGS__
 static std::string program_source = MULTILINE_QUOTE(
 
 typedef char int8_t;
 typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
 typedef int int32_t;
 typedef uint uint32_t;
 
@@ -175,7 +183,9 @@ void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float
     *v0 = vload_half(0, &x[ib + 0]);
     *v1 = vload_half(0, &x[ib + 1]);
 }
+);
 
+static std::string k_quants_source = MULTILINE_QUOTE(
 inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m)
 {
     if (j < 4)
@@ -199,7 +209,7 @@ __kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __globa
     const int is = 8 * n + l / 16;
 
     const uint8_t q = x[i].qs[32 * n + l];
-    __global float *y = yy + i * 256 + 128 * n;
+    __global float *y = yy + i * QK_K + 128 * n;
 
     const float dall = vload_half(0, &x[i].d);
     const float dmin = vload_half(0, &x[i].dmin);
@@ -231,7 +241,7 @@ __kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __globa
     float d_all = vload_half(0, &x[i].d);
     float dl = d_all * (us - 32);
 
-    __global float *y = yy + i * 256 + 128 * n + 32 * j;
+    __global float *y = yy + i * QK_K + 128 * n + 32 * j;
     const __global uint8_t *q = x[i].qs + 32 * n;
     const __global uint8_t *hm = x[i].hmask;
 
@@ -248,7 +258,7 @@ __kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __globa
     const int is = 2 * il;
     const int n = 4;
 
-    __global float *y = yy + i * 256 + 64 * il + n * ir;
+    __global float *y = yy + i * QK_K + 64 * il + n * ir;
 
     const float dall = vload_half(0, &x[i].d);
     const float dmin = vload_half(0, &x[i].dmin);
@@ -277,7 +287,7 @@ __kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __globa
     const int ir = tid % 16;
     const int is = 2 * il;
 
-    __global float *y = yy + i * 256 + 64 * il + 2 * ir;
+    __global float *y = yy + i * QK_K + 64 * il + 2 * ir;
 
     const float dall = vload_half(0, &x[i].d);
     const float dmin = vload_half(0, &x[i].dmin);
@@ -309,7 +319,7 @@ __kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __globa
     const int il = tid - 32 * ip;
     const int is = 8 * ip + il / 16;
 
-    __global float *y = yy + i * 256 + 128 * ip + il;
+    __global float *y = yy + i * QK_K + 128 * ip + il;
 
     const float d = vload_half(0, &x[i].d);
 
@@ -323,161 +333,383 @@ __kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __globa
     y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
 }
 
+__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
 
-void vec_dot_q2_K(__global const struct block_q2_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
+    const int row = get_group_id(0);
 
-    int n = iqs / 128;
-    int r = iqs - 128 * n;
-    int l = r / 8;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
 
-    __global const float *y = yy + 128 * n + l;
-    __global const uint8_t *q = x[ib].qs + 32 * n + l;
-    __global const uint8_t *s = x[ib].scales + 8 * n;
+    __global const struct block_q2_K * x = xx + ib0;
 
-    const float dall = vload_half(0, &x[ib].d);
-    const float dmin = vload_half(0, &x[ib].dmin);
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0,1
 
-    float sum = y[  0] * (dall * ((s[0] & 0xF) * ((q[ 0] >> 0) & 3)) - dmin * (s[0] >> 4))
-              + y[ 32] * (dall * ((s[2] & 0xF) * ((q[ 0] >> 2) & 3)) - dmin * (s[2] >> 4))
-              + y[ 64] * (dall * ((s[4] & 0xF) * ((q[ 0] >> 4) & 3)) - dmin * (s[4] >> 4))
-              + y[ 96] * (dall * ((s[6] & 0xF) * ((q[ 0] >> 6) & 3)) - dmin * (s[6] >> 4))
-              + y[ 16] * (dall * ((s[1] & 0xF) * ((q[16] >> 0) & 3)) - dmin * (s[1] >> 4))
-              + y[ 48] * (dall * ((s[3] & 0xF) * ((q[16] >> 2) & 3)) - dmin * (s[3] >> 4))
-              + y[ 80] * (dall * ((s[5] & 0xF) * ((q[16] >> 4) & 3)) - dmin * (s[5] >> 4))
-              + y[112] * (dall * ((s[7] & 0xF) * ((q[16] >> 6) & 3)) - dmin * (s[7] >> 4));
+    const int step = 16/K_QUANTS_PER_ITERATION;
 
-    *result = sum;
-}
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
 
-void vec_dot_q3_K(__global const struct block_q3_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
+    const int q_offset = 32*im + l0;
+    const int s_offset = 8*im;
+    const int y_offset = 128*im + l0;
 
-    const uint32_t kmask1 = 0x03030303;
-    const uint32_t kmask2 = 0x0f0f0f0f;
+    tmp[16 * ix + tid] = 0;
 
-    uint32_t aux[3];
-    uint32_t utmp[4];
+    uint32_t aux[4];
+    const uint8_t * d = (const uint8_t *)aux;
+    const uint8_t * m = (const uint8_t *)(aux + 2);
 
-    int n = iqs/128;
-    int r = iqs - 128*n;
-    int l = r/8;
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
 
-    __global const float   * y = yy + 128*n + l;
-    __global const uint8_t * q = x[ib].qs + 32*n + l;
-    __global const uint8_t * hm = x[ib].hmask + l;
-    const int8_t * s = (const int8_t *)utmp + 8*n;
+        __global const float   * y = yy + i * QK_K + y_offset;
+        __global const uint8_t * q = x[i].qs + q_offset;
 
-    aux[0] = x[ib].scales[0] | x[ib].scales[1] << 8 | x[ib].scales[2] << 16 | x[ib].scales[3] << 24;
-    aux[1] = x[ib].scales[4] | x[ib].scales[5] << 8 | x[ib].scales[6] << 16 | x[ib].scales[7] << 24;
-    aux[2] = x[ib].scales[8] | x[ib].scales[9] << 8 | x[ib].scales[10] << 16 | x[ib].scales[11] << 24;
+        const float dall = vload_half(0, &x[i].d);
+        const float dmin = vload_half(0, &x[i].dmin);
 
-    utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
-    utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
-    utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
-    utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
+        __global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset);
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = a[1] & 0x0f0f0f0f;
+        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
+        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
 
-    const float dall = vload_half(0, &x[ib].d);
-    const uint8_t m = 1 << (4*n);
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
+            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
 
-    float sum = y[  0] * (s[0] - 32) * (((q[ 0] >> 0) & 3) - (hm[ 0] & (m << 0) ? 0 : 4))
-              + y[ 32] * (s[2] - 32) * (((q[ 0] >> 2) & 3) - (hm[ 0] & (m << 1) ? 0 : 4))
-              + y[ 64] * (s[4] - 32) * (((q[ 0] >> 4) & 3) - (hm[ 0] & (m << 2) ? 0 : 4))
-              + y[ 96] * (s[6] - 32) * (((q[ 0] >> 6) & 3) - (hm[ 0] & (m << 3) ? 0 : 4))
-              + y[ 16] * (s[1] - 32) * (((q[16] >> 0) & 3) - (hm[16] & (m << 0) ? 0 : 4))
-              + y[ 48] * (s[3] - 32) * (((q[16] >> 2) & 3) - (hm[16] & (m << 1) ? 0 : 4))
-              + y[ 80] * (s[5] - 32) * (((q[16] >> 4) & 3) - (hm[16] & (m << 2) ? 0 : 4))
-              + y[112] * (s[7] - 32) * (((q[16] >> 6) & 3) - (hm[16] & (m << 3) ? 0 : 4));
+        }
+        tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
 
-    *result = sum * dall;
-
-}
-
-void vec_dot_q4_K(__global const struct block_q4_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
-
-    const int j  = iqs / 64;        // j  is in 0...3
-    const int ir = (iqs - 64*j)/2;  // ir is in 0...28 in steps of 4
-    const int is = 2*j;             // is is in 0...6 in steps of 2
-
-    __global const float   * y = yy + 64*j + ir;
-    __global const uint8_t * q = x[ib].qs + 32*j + ir;
-
-    const float dall = vload_half(0, &x[ib].d);
-    const float dmin = vload_half(0, &x[ib].dmin);
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[ib].scales, &sc, &m);
-    const float d1 = dall * sc;
-    const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[ib].scales, &sc, &m);
-    const float d2 = dall * sc;
-    const float m2 = dmin * m;
-
-    float sum = 0;
-    for (int k = 0; k < 4; ++k) {
-        sum += y[k +  0] * (d1 * (q[k] & 0xF) - m1);
-        sum += y[k + 32] * (d2 * (q[k] >>  4) - m2);
     }
 
-    *result = sum;
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
 }
 
-void vec_dot_q5_K(__global const struct block_q5_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
+__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
 
-    const int j  = iqs / 64;
-    const int ir = (iqs - 64*j)/2;
-    const int is = 2*j;
+    const int row = get_group_id(0);
 
-    __global const float   * y  = yy + 64*j + ir;
-    __global const uint8_t * ql = x[ib].qs + 32*j + ir;
-    __global const uint8_t * qh = x[ib].qh + ir;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
 
-    const float dall = vload_half(0, &x[ib].d);
-    const float dmin = vload_half(0, &x[ib].dmin);
+    __global const struct block_q3_K * x = xx + ib0;
 
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[ib].scales, &sc, &m);
-    const float d1 = dall * sc;
-    const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[ib].scales, &sc, &m);
-    const float d2 = dall * sc;
-    const float m2 = dmin * m;
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
+    const int step = 16/K_QUANTS_PER_ITERATION;
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0....15 or 0...7
+
+    const uint8_t m = 1 << (4*im);
+
+    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
+    const int q_offset =  32*im + l0;
+    const int y_offset = 128*im + l0;
+
+    uint16_t utmp[4];
+    const int8_t * s = (const int8_t *)utmp;
+
+    const uint16_t s_shift = 4*im;
+
+    tmp[16 * ix + tid] = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        __global const float   * y  = yy + i * QK_K + y_offset;
+        __global const uint8_t * q = x[i].qs + q_offset;
+        __global const uint8_t * h = x[i].hmask + l0;
+
+        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
+        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
+        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
+
+        const float d = vload_half(0, &x[i].d);
+
+        float sum = 0;
+        for (int l = 0; l < n; ++l) {
+            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
+            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
+        }
+        tmp[16 * ix + tid] += d * sum;
 
-    uint8_t hm  = 1 << is;
-    float sum = 0;
-    for (int k = 0; k < 4; ++k) {
-        sum += y[k +  0] * (d1 * ((ql[k] & 0xF) + (qh[k] & hm ? 16 : 0)) - m1);
     }
-    hm <<= 1;
-    for (int k = 0; k < 4; ++k) {
-        sum += y[k + 32] * (d2 * ((ql[k] >>  4) + (qh[k] & hm ? 16 : 0)) - m2);
-    }
-    *result = sum;
 
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
 }
 
-void vec_dot_q6_K(__global const struct block_q6_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
+__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
 
+    //to rename it later, just to test now
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
 
-    const int ip = iqs / 128;        // 0 or 1
-    const int il = (iqs - 128*ip)/8; // 0...15
-    const int is = 8*ip;
+    const int row = get_group_id(0);
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
 
-    __global const float * y = yy + 128*ip + il;
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...15
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;
 
-    const float d = vload_half(0, &x[ib].d);
+    const int step = 8/K_QUANTS_PER_ITERATION;
 
-    __global const uint8_t * ql = x[ib].ql + 64*ip + il;
-    __global const uint8_t * qh = x[ib].qh + 32*ip + il;
-    __global const int8_t  * sc = x[ib].scales + is;
+    const int il  = tid/step;     // 0...3
+    const int ir  = tid - step*il;// 0...3
+    const int n   = 2*K_QUANTS_PER_ITERATION;
 
-    *result = y[  0] * d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh[ 0] >> 0) & 3) << 4)) - 32)
-           + y[ 32] * d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh[ 0] >> 2) & 3) << 4)) - 32)
-           + y[ 64] * d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh[ 0] >> 4) & 3) << 4)) - 32)
-           + y[ 96] * d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh[ 0] >> 6) & 3) << 4)) - 32)
-           + y[ 16] * d * sc[1] * ((int8_t)((ql[16] & 0xF) | (((qh[16] >> 0) & 3) << 4)) - 32)
-           + y[ 48] * d * sc[3] * ((int8_t)((ql[48] & 0xF) | (((qh[16] >> 2) & 3) << 4)) - 32)
-           + y[ 80] * d * sc[5] * ((int8_t)((ql[16]  >> 4) | (((qh[16] >> 4) & 3) << 4)) - 32)
-           + y[112] * d * sc[7] * ((int8_t)((ql[48]  >> 4) | (((qh[16] >> 6) & 3) << 4)) - 32);
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
 
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    __global const struct block_q4_K * x = xx + ib0;
+
+    tmp[16 * ix + tid] = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        __global const uint8_t * q1 = x[i].qs + q_offset;
+        __global const uint8_t * q2 = q1 + 64;
+        __global const float   * y1 = yy + i*QK_K + y_offset;
+        __global const float   * y2 = y1 + 128;
+
+        const float dall = vload_half(0, &x[i].d);
+        const float dmin = vload_half(0, &x[i].dmin);
+
+        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        float4 s = (float4)(0.f);
+        float smin = 0;
+        for (int l = 0; l < n; ++l) {
+            s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4);
+            s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4);
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+
+__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
+
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int row = get_group_id(0);
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const int tid = get_local_id(0)/2;  // 0...15
+    const int ix  = get_local_id(0)%2;
+
+    const int il  = tid/4;     // 0...3
+    const int ir  = tid - 4*il;// 0...3
+    const int n   = 2;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    const uint8_t hm1  = 1 << (2*im);
+    const uint8_t hm2  = hm1 << 4;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    __global const struct block_q5_K * x = xx + ib0;
+
+    tmp[16 * ix + tid] = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2) {
+
+        __global const uint8_t * ql1 = x[i].qs + q_offset;
+        __global const uint8_t * ql2 = ql1 + 64;
+        __global const uint8_t * qh  = x[i].qh + l0;
+        __global const float   * y1  = yy + i*QK_K + y_offset;
+        __global const float   * y2  = y1 + 128;
+
+        const float dall = vload_half(0, &x[i].d);
+        const float dmin = vload_half(0, &x[i].dmin);
+
+        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        float4 sum = (float4)(0.f);
+        float smin = 0;
+        for (int l = 0; l < n; ++l) {
+            sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+                   + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0));
+            sum.y += y1[l+32] * ((ql1[l+ 0] >>  4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+                   + y1[l+48] * ((ql1[l+16] >>  4) + (qh[l+16] & (hm1 << 1) ? 16 : 0));
+            sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+                   + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0));
+            sum.w += y2[l+32] * ((ql2[l+ 0] >>  4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+                   + y2[l+48] * ((ql2[l+16] >>  4) + (qh[l+16] & (hm2 << 1) ? 16 : 0));
+            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
+        }
+        tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+
+__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) {
+
+    const int row = get_group_id(0);
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    __global const struct block_q6_K * x = xx + ib0;
+
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
+    const int is = 0;
+#else
+    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
+    const int is = in / 4;
+#endif
+    const int ql_offset = 64*im + l0;
+    const int qh_offset = 32*im + l0;
+    const int s_offset  =  8*im + is;
+    const int y_offset = 128*im + l0;
+
+    tmp[16 * ix + tid] = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        __global const float   * y  = yy + i * QK_K + y_offset;
+        __global const uint8_t * ql = x[i].ql + ql_offset;
+        __global const uint8_t * qh = x[i].qh + qh_offset;
+        __global const int8_t  * s  = x[i].scales + s_offset;
+
+        const float d = vload_half(0, &x[i].d);
+
+#if K_QUANTS_PER_ITERATION == 1
+        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
+        tmp[16 * ix + tid] += sum;
+#else
+        float sum = 0;
+        for (int l = 0; l < 4; ++l) {
+            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp[16 * ix + tid] += sum;
+#endif
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
 }
 
 );
@@ -549,44 +781,6 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
 }
 );
 
-std::string dequant_mul_mat_vec_k_template = MULTILINE_QUOTE(
-__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
-    const int block_size = get_local_size(0);
-    const int row = get_group_id(0);
-    const int tid = get_local_id(0);
-
-    const int iter_stride = 256;
-    const int vals_per_iter = iter_stride / block_size;
-    const int num_blocks_per_row = ncols / 256;
-    const int ib0 = row*num_blocks_per_row;
-
-    tmp[tid] = 0;
-
-    for (int i = 0; i < ncols; i += iter_stride) {
-        const int col = i + vals_per_iter*tid;
-        const int ib = ib0 + col/256; // x block index
-        const int iqs = col%256; // x quant index
-        const int iybs = col - col%256; // y block start index
-
-        // dequantize
-        float v;
-        DOT_KERNEL(x, ib, iqs, y + iybs, &v);
-        tmp[tid] += v;
-    }
-
-    // sum up partial sums and write back result
-    barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=block_size/2; s>0; s>>=1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if (tid == 0) {
-        dst[row] = tmp[0];
-    }
-}
-);
 
 std::string mul_template = MULTILINE_QUOTE(
 __kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) {
@@ -649,18 +843,6 @@ std::array<std::string, 2> mul_str_values = {
     "mul_f32", "float"
 };
 
-std::array<std::string, 3> dmmv_k_str_keys = {
-    "KERNEL_NAME", "X_TYPE", "DOT_KERNEL"
-};
-
-std::array<std::string, 15> dmmv_k_str_values = {
-    "dequantize_mul_mat_vec_q2_K", "struct block_q2_K", "vec_dot_q2_K",
-    "dequantize_mul_mat_vec_q3_K", "struct block_q3_K", "vec_dot_q3_K",
-    "dequantize_mul_mat_vec_q4_K", "struct block_q4_K", "vec_dot_q4_K",
-    "dequantize_mul_mat_vec_q5_K", "struct block_q5_K", "vec_dot_q5_K",
-    "dequantize_mul_mat_vec_q6_K", "struct block_q6_K", "vec_dot_q6_K",
-};
-
 std::string& replace(std::string& s, const std::string& from, const std::string& to) {
     size_t pos = 0;
     while ((pos = s.find(from, pos)) != std::string::npos) {
@@ -673,6 +855,7 @@ std::string& replace(std::string& s, const std::string& from, const std::string&
 std::string generate_kernels() {
     std::stringstream src;
     src << program_source << '\n';
+    src << k_quants_source << '\n';
     for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) {
         std::string dequant_kernel = dequant_template;
         std::string dmmv_kernel = dequant_mul_mat_vec_template;
@@ -690,13 +873,6 @@ std::string generate_kernels() {
         }
         src << mul_kernel << '\n';
     }
-    for (size_t i = 0; i < dmmv_k_str_values.size(); i += dmmv_k_str_keys.size()) {
-        std::string dmmv_k_kernel = dequant_mul_mat_vec_k_template;
-        for (size_t j = 0; j < dmmv_k_str_keys.size(); j++) {
-            replace(dmmv_k_kernel, dmmv_k_str_keys[j], dmmv_k_str_values[i + j]);
-        }
-        src << dmmv_k_kernel << '\n';
-    }
 
     return src.str();
 }
@@ -729,10 +905,11 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co
         exit(1);
     }
 
-    const char* compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math "
-                               "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1";
+    std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math "
+                               "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 "
+                               "-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION);
 
-    err = clBuildProgram(p, 0, NULL, compile_opts, NULL, NULL);
+    err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
     if(err < 0) {
 
         clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
@@ -1199,7 +1376,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
     const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
     const int64_t ne10 = src1->ne[0];
     const int64_t ne11 = src1->ne[1];

ggml.h

@@ -198,9 +198,11 @@
 #define GGML_MAX_PARAMS        256
 #define GGML_MAX_CONTEXTS      64
 #define GGML_MAX_OPT           4
-#define GGML_MAX_NAME          32
+#define GGML_MAX_NAME          48
 #define GGML_DEFAULT_N_THREADS 4
 
+#define GGML_UNUSED(x) (void)(x)
+
 #define GGML_ASSERT(x) \
     do { \
         if (!(x)) { \
@@ -209,6 +211,30 @@
         } \
     } while (0)
 
+// used to copy the number of elements and stride in bytes of tensors into local variables.
+// main purpose is to reduce code duplication and improve readability.
+//
+// example:
+//
+//    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+//    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
+//
+#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
+    const type prefix##0 = (pointer)->array[0]; \
+    GGML_UNUSED(prefix##0);
+#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_1    (type, prefix, pointer, array) \
+    const type prefix##1 = (pointer)->array[1]; \
+    GGML_UNUSED(prefix##1);
+#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_2    (type, prefix, pointer, array) \
+    const type prefix##2 = (pointer)->array[2]; \
+    GGML_UNUSED(prefix##2);
+#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_3  (type, prefix, pointer, array) \
+    const type prefix##3 = (pointer)->array[3]; \
+    GGML_UNUSED(prefix##3);
+
 #ifdef  __cplusplus
 extern "C" {
 #endif
@@ -224,8 +250,8 @@ extern "C" {
     GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t x);
     GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
 
-    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
-    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
+    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
+    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
 
     struct ggml_object;
     struct ggml_context;
@@ -295,12 +321,15 @@ extern "C" {
         GGML_OP_SUM,
         GGML_OP_SUM_ROWS,
         GGML_OP_MEAN,
+        GGML_OP_ARGMAX,
         GGML_OP_REPEAT,
         GGML_OP_REPEAT_BACK,
         GGML_OP_ABS,
         GGML_OP_SGN,
         GGML_OP_NEG,
         GGML_OP_STEP,
+        GGML_OP_TANH,
+        GGML_OP_ELU,
         GGML_OP_RELU,
         GGML_OP_GELU,
         GGML_OP_GELU_QUICK,
@@ -332,9 +361,8 @@ extern "C" {
         GGML_OP_ROPE_BACK,
         GGML_OP_ALIBI,
         GGML_OP_CLAMP,
-        GGML_OP_CONV_1D_S1_PH,
-        GGML_OP_CONV_1D_S2_PH,
-        GGML_OP_CONV_2D_SK_P0,
+        GGML_OP_CONV_1D,
+        GGML_OP_CONV_2D,
 
         GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_FF,
@@ -345,6 +373,10 @@ extern "C" {
         GGML_OP_MAP_UNARY,
         GGML_OP_MAP_BINARY,
 
+        GGML_OP_MAP_CUSTOM1,
+        GGML_OP_MAP_CUSTOM2,
+        GGML_OP_MAP_CUSTOM3,
+
         GGML_OP_CROSS_ENTROPY_LOSS,
         GGML_OP_CROSS_ENTROPY_LOSS_BACK,
 
@@ -440,6 +472,9 @@ extern "C" {
 
 
     // compute types
+
+    // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
+    // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
     enum ggml_task_type {
         GGML_TASK_INIT = 0,
         GGML_TASK_COMPUTE,
@@ -465,6 +500,9 @@ extern "C" {
     GGML_API int64_t ggml_cycles(void);
     GGML_API int64_t ggml_cycles_per_ms(void);
 
+    GGML_API void    ggml_numa_init(void); // call once for better performance on NUMA systems
+    GGML_API bool    ggml_is_numa(void); // true if init detected that system has >1 NUMA node
+
     GGML_API void    ggml_print_object (const struct ggml_object * obj);
     GGML_API void    ggml_print_objects(const struct ggml_context * ctx);
 
@@ -680,6 +718,11 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    // argmax along rows
+    GGML_API struct ggml_tensor * ggml_argmax(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     // if a is the same shape as b, and a is not parameter, return a
     // otherwise, return a new tensor: repeat(a) to fit in b
     GGML_API struct ggml_tensor * ggml_repeat(
@@ -724,6 +767,22 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    GGML_API struct ggml_tensor * ggml_tanh(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_tanh_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     GGML_API struct ggml_tensor * ggml_relu(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
@@ -1029,13 +1088,15 @@ extern "C" {
     // rotary position embedding
     // if mode & 1 == 1, skip n_past elements
     // if mode & 2 == 1, GPT-NeoX style
+    // if mode & 4 == 1, ChatGLM style
     // TODO: avoid creating a new tensor every time
     GGML_API struct ggml_tensor * ggml_rope(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             int                   n_past,
             int                   n_dims,
-            int                   mode);
+            int                   mode,
+            int                   n_ctx);
 
     // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_inplace(
@@ -1043,7 +1104,8 @@ extern "C" {
             struct ggml_tensor  * a,
             int                   n_past,
             int                   n_dims,
-            int                   mode);
+            int                   mode,
+            int                   n_ctx);
 
     // rotary position embedding backward, i.e compute dx from dy
     // a - dy
@@ -1071,58 +1133,33 @@ extern "C" {
             float                 min,
             float                 max);
 
-    // TODO: implement general-purpose convolutions
-    // GGML_API struct ggml_tensor * ggml_conv_1d(
-    //        struct ggml_context * ctx,
-    //        struct ggml_tensor  * a,
-    //        struct ggml_tensor  * b,
-    //        int                   s0
-    //        int                   p0,
-    //        int                   d0);
-    //
-    // GGML_API struct ggml_tensor * ggml_conv_2d(
-    //        struct ggml_context * ctx,
-    //        struct ggml_tensor  * a,
-    //        struct ggml_tensor  * b,
-    //        int                   s0,
-    //        int                   s1,
-    //        int                   p0,
-    //        int                   p1,
-    //        int                   d0,
-    //        int                   d1);
-
-    // padding = half
-    // TODO: we don't support extra parameters for now
-    //       that's why we are hard-coding the stride, padding, and dilation
-    //       not great ..
-    // example:
-    // a:      3   80  768    1
-    // b:   3000   80    1    1
-    // res: 3000  768    1    1
-    // used in whisper
-    GGML_API struct ggml_tensor * ggml_conv_1d_s1_ph(
+    GGML_API struct ggml_tensor * ggml_conv_1d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
 
-    // used in whisper
-    GGML_API struct ggml_tensor * ggml_conv_1d_s2_ph(
+    GGML_API struct ggml_tensor * ggml_conv_2d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            int                   s0,
+            int                   s1,
+            int                   p0,
+            int                   p1,
+            int                   d0,
+            int                   d1);
 
-    // kernel size is a->ne[0] x a->ne[1]
-    // stride is equal to kernel size
-    // padding is zero
-    // example:
-    // a:     16   16    3  768
-    // b:   1024 1024    3    1
-    // res:   64   64  768    1
-    // used in sam
-    GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
+    // conv_1d with padding = half
+    // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
+    GGML_API struct ggml_tensor* ggml_conv_1d_ph(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            int                   s,
+            int                   d);
 
     GGML_API struct ggml_tensor * ggml_flash_attn(
             struct ggml_context * ctx,
@@ -1167,21 +1204,73 @@ extern "C" {
             int                   h0,
             int                   w);
 
-    // Mapping operations
-    typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
+    // custom operators
+
+    typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
     typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
 
+    typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+
     GGML_API struct ggml_tensor * ggml_map_unary_f32(
             struct ggml_context        * ctx,
             struct ggml_tensor         * a,
                    ggml_unary_op_f32_t   fun);
 
+    GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
+            struct ggml_context        * ctx,
+            struct ggml_tensor         * a,
+                   ggml_unary_op_f32_t   fun);
+
     GGML_API struct ggml_tensor * ggml_map_binary_f32(
             struct ggml_context         * ctx,
             struct ggml_tensor          * a,
             struct ggml_tensor          * b,
                    ggml_binary_op_f32_t   fun);
 
+    GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
+            struct ggml_context         * ctx,
+            struct ggml_tensor          * a,
+            struct ggml_tensor          * b,
+                   ggml_binary_op_f32_t   fun);
+
+    GGML_API struct ggml_tensor * ggml_map_custom1_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+                   ggml_custom1_op_f32_t   fun);
+
+    GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+                   ggml_custom1_op_f32_t   fun);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+                   ggml_custom2_op_f32_t   fun);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+                   ggml_custom2_op_f32_t   fun);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+            struct ggml_tensor           * c,
+                   ggml_custom3_op_f32_t   fun);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+            struct ggml_tensor           * c,
+                   ggml_custom3_op_f32_t   fun);
+
     // loss function
 
     GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
@@ -1425,26 +1514,19 @@ extern "C" {
     // Internal types and functions exposed for tests and benchmarks
     //
 
-#ifdef  __cplusplus
-    // restrict not standard in C++
-#define GGML_RESTRICT
-#else
-#define GGML_RESTRICT restrict
-#endif
-    typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-    typedef void (*quantize_row_q_t)  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-    typedef void (*vec_dot_q_t)       (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
+    typedef void (*ggml_to_float_t)(const void * x, float * y, int k);
+    typedef void (*ggml_from_float_t)(const float * x, void * y, int k);
+    typedef void (*ggml_vec_dot_t)(const int n, float * s, const void * x, const void * y);
 
     typedef struct {
-        dequantize_row_q_t dequantize_row_q;
-        quantize_row_q_t   quantize_row_q;
-        quantize_row_q_t   quantize_row_q_reference;
-        quantize_row_q_t   quantize_row_q_dot;
-        vec_dot_q_t        vec_dot_q;
-        enum ggml_type     vec_dot_type;
-    } quantize_fns_t;
+        ggml_to_float_t   to_float;
+        ggml_from_float_t from_float;
+        ggml_from_float_t from_float_reference;
+        ggml_vec_dot_t    vec_dot;
+        enum ggml_type    vec_dot_type;
+    } ggml_type_traits_t;
 
-    quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
+    ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i);
 
 #ifdef  __cplusplus
 }

k_quants.h

@@ -7,7 +7,13 @@
 #include <stddef.h>
 
 // Super-block size
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
 #define QK_K 256
+#define K_SCALE_SIZE 12
+#endif
 
 //
 // Super-block quantization structures
@@ -29,38 +35,67 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "w
 // weight is represented as x = a * q
 // 16 blocks of 16 elemenets each
 // Effectively 3.4375 bits per weight
+#ifdef GGML_QKK_64
 typedef struct {
     uint8_t hmask[QK_K/8];     // quants - high bit
     uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
+    uint8_t scales[2];
     ggml_fp16_t d;             // super-block scale
 } block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_K block size/padding");
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
+#else
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+    uint8_t scales[12];        // scales, quantized with 6 bits
+    ggml_fp16_t d;             // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
+#endif
 
 // 4-bit quantization
 // 16 blocks of 32 elements each
 // weight is represented as x = a * q + b
 // Effectively 4.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_fp16_t d[2];          // super-block scales/mins
+    uint8_t scales[2];         // 4-bit block scales/mins
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
+#else
 typedef struct {
     ggml_fp16_t d;             // super-block scale for quantized scales
     ggml_fp16_t dmin;          // super-block scale for quantized mins
-    uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
     uint8_t qs[QK_K/2];        // 4--bit quants
 } block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
+#endif
 
 // 5-bit quantization
 // 16 blocks of 32 elements each
 // weight is represented as x = a * q + b
 // Effectively 5.5 bits per weight
+#ifdef GGML_QKK_64
 typedef struct {
-    ggml_fp16_t d;               // super-block scale for quantized scales
-    ggml_fp16_t dmin;            // super-block scale for quantized mins
-    uint8_t scales[3*QK_K/64];   // scales and mins, quantized with 6 bits
+    ggml_fp16_t d;               // super-block scale
+    int8_t  scales[QK_K/16];     // 8-bit block scales
     uint8_t qh[QK_K/8];          // quants, high bit
     uint8_t qs[QK_K/2];          // quants, low 4 bits
 } block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
+#else
+typedef struct {
+    ggml_fp16_t d;               // super-block scale for quantized scales
+    ggml_fp16_t dmin;            // super-block scale for quantized mins
+    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+#endif
 
 // 6-bit quantization
 // weight is represented as x = a * q

llama-util.h

@@ -172,12 +172,14 @@ struct llama_mmap {
 #ifdef _POSIX_MAPPED_FILES
     static constexpr bool SUPPORTED = true;
 
-    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */) {
+    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
         size = file->size;
         int fd = fileno(file->fp);
         int flags = MAP_SHARED;
+        // prefetch/readahead impairs performance on NUMA systems
+        if (numa) { prefetch = 0; }
 #ifdef __linux__
-        flags |= MAP_POPULATE;
+        if (prefetch) { flags |= MAP_POPULATE; }
 #endif
         addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
         if (addr == MAP_FAILED) {
@@ -191,6 +193,14 @@ struct llama_mmap {
                         strerror(errno));
             }
         }
+        if (numa) {
+            // advise the kernel not to use readahead
+            // (because the next page might not belong on the same node)
+            if (madvise(addr, file->size, MADV_RANDOM)) {
+                fprintf(stderr, "warning: madvise(.., MADV_RANDOM) failed: %s\n",
+                        strerror(errno));
+            }
+        }
     }
 
     ~llama_mmap() {
@@ -199,7 +209,9 @@ struct llama_mmap {
 #elif defined(_WIN32)
     static constexpr bool SUPPORTED = true;
 
-    llama_mmap(struct llama_file * file, bool prefetch = true) {
+    llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
+        (void) numa;
+
         size = file->size;
 
         HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
@@ -244,8 +256,10 @@ struct llama_mmap {
 #else
     static constexpr bool SUPPORTED = false;
 
-    llama_mmap(struct llama_file *, bool prefetch = true) {
-        (void)prefetch;
+    llama_mmap(struct llama_file *, bool prefetch = true, bool numa = false) {
+        (void) prefetch;
+        (void) numa;
+
         throw std::runtime_error(std::string("mmap not supported"));
     }
 #endif

llama.cpp

@@ -21,9 +21,13 @@
 #endif
 #ifdef GGML_USE_K_QUANTS
 #ifndef QK_K
+#ifdef GGML_QKK_64
+#define QK_K 64
+#else
 #define QK_K 256
 #endif
 #endif
+#endif
 
 #include <array>
 #include <ctime>
@@ -62,6 +66,7 @@ enum e_model {
     MODEL_65B,
 };
 
+static const size_t kB = 1024;
 static const size_t MB = 1024*1024;
 
 // computed for n_ctx == 2048
@@ -125,6 +130,34 @@ static const std::map<e_model, size_t> & MEM_REQ_EVAL()
     return k_sizes;
 }
 
+// amount of VRAM needed per batch size to hold temporary results
+// the values for 3b and 65b are not derived from testing but instead chosen conservatively
+static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
+{
+    static std::map<e_model, size_t> k_sizes = {
+        { MODEL_3B,   512ull * kB },
+        { MODEL_7B,   512ull * kB },
+        { MODEL_13B,  640ull * kB },
+        { MODEL_30B,  768ull * kB },
+        { MODEL_65B, 1536ull * kB },
+    };
+    return k_sizes;
+}
+
+// amount of VRAM needed per batch size and context to hold temporary results
+// the values for 3b and 65b are not derived from testing but instead chosen conservatively
+static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
+{
+    static std::map<e_model, size_t> k_sizes = {
+        { MODEL_3B,  128ull },
+        { MODEL_7B,  128ull },
+        { MODEL_13B, 160ull },
+        { MODEL_30B, 208ull },
+        { MODEL_65B, 416ull },
+    };
+    return k_sizes;
+}
+
 // default hparams (LLaMA 7B)
 struct llama_hparams {
     uint32_t n_vocab = 32000;
@@ -161,8 +194,8 @@ struct llama_layer {
 };
 
 struct llama_kv_cache {
-    struct ggml_tensor * k;
-    struct ggml_tensor * v;
+    struct ggml_tensor * k = NULL;
+    struct ggml_tensor * v = NULL;
 
     struct ggml_context * ctx = NULL;
 
@@ -249,7 +282,13 @@ struct llama_model {
 
 struct llama_context {
     llama_context(const llama_model & model, const llama_vocab & vocab) : model(model), vocab(vocab), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
-
+#ifdef GGML_USE_METAL
+    ~llama_context() {
+        if (ctx_metal) {
+            ggml_metal_free(ctx_metal);
+        }
+    }
+#endif
     std::mt19937 rng;
 
     bool has_evaluated_once = false;
@@ -360,96 +399,14 @@ static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml
     return size / ggml_blck_size(type);
 }
 
-struct llama_load_tensor_shard {
-    std::vector<uint32_t> ne;
-    size_t size;
-    enum ggml_type type;
-    size_t file_idx;
-    size_t file_off;
-
-    void calc_size() {
-        size = llama_calc_tensor_size(ne, type);
-    }
-};
-
-enum llama_split_type {
-    SPLIT_NONE,
-    SPLIT_BY_COLUMNS,
-    SPLIT_BY_ROWS
-};
-
 struct llama_load_tensor {
-    std::vector<llama_load_tensor_shard> shards;
-
     std::string name;
     enum ggml_type type = GGML_TYPE_F32;
-    llama_split_type split_type = SPLIT_NONE;
     std::vector<uint32_t> ne;
+    size_t file_off;
     size_t size;
     struct ggml_tensor * ggml_tensor = NULL;
     uint8_t * data;
-
-    llama_load_tensor(const std::string & name) : name(name) {}
-
-    void calc_all() {
-        calc_type();
-        calc_split_type();
-        calc_ne();
-        calc_size();
-    }
-
-    void calc_type() {
-        const auto & first_shard = shards.at(0);
-        for (const auto & shard : shards) {
-            if (shard.type != first_shard.type) {
-                throw std::runtime_error(format("inconsistent tensor shard type in '%s'", name.c_str()));
-            }
-        }
-        type = first_shard.type;
-    }
-
-    void calc_split_type() {
-        if (shards.at(0).ne.size() == 1 || // 1D tensors are just duplicated in every file
-            shards.size() == 1) { // only one file?
-            split_type = SPLIT_NONE;
-        } else if (name.find("tok_embeddings.") == 0 ||
-            name.find(".attention.wo.weight") != std::string::npos ||
-            name.find(".feed_forward.w2.weight") != std::string::npos) {
-            split_type = SPLIT_BY_COLUMNS;
-        } else {
-            split_type = SPLIT_BY_ROWS;
-        }
-    }
-
-    void calc_ne() {
-        const auto & first_shard = shards.at(0);
-        for (const auto & shard : shards) {
-            if (shard.ne != first_shard.ne) {
-                throw std::runtime_error(format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
-                             name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str()));
-            }
-        }
-        ne = first_shard.ne;
-        LLAMA_ASSERT(shards.size() <= UINT32_MAX);
-        uint32_t n_shards = (uint32_t) shards.size();
-        switch (split_type) {
-            case SPLIT_NONE:
-                ne = first_shard.ne;
-                break;
-            case SPLIT_BY_COLUMNS:
-                ne = {checked_mul<uint32_t>(first_shard.ne[0], n_shards),
-                      first_shard.ne[1]};
-                break;
-            case SPLIT_BY_ROWS:
-                ne = {first_shard.ne[0],
-                      checked_mul<uint32_t>(first_shard.ne[1], n_shards)};
-                break;
-        }
-    }
-
-    void calc_size() {
-        size = llama_calc_tensor_size(ne, type);
-    }
 };
 
 struct llama_load_tensors_map {
@@ -472,13 +429,13 @@ struct llama_file_loader {
     llama_hparams hparams;
     llama_vocab vocab;
 
-    llama_file_loader(const char * fname, size_t file_idx, llama_load_tensors_map & tensors_map)
+    llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map)
         : file(fname, "rb") {
         fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
         read_magic();
         read_hparams();
         read_vocab();
-        read_tensor_metadata(file_idx, tensors_map);
+        read_tensor_metadata(tensors_map);
     }
     void read_magic() {
         uint32_t magic = file.read_u32();
@@ -524,9 +481,7 @@ struct llama_file_loader {
             std::string word = file.read_string(len);
 
             float score = 0.0f;
-            if (file_version >= LLAMA_FILE_VERSION_GGMF_V1) {
-                file.read_raw(&score, sizeof(score));
-            }
+            file.read_raw(&score, sizeof(score));
 
             vocab.token_to_id[word] = i;
 
@@ -535,19 +490,19 @@ struct llama_file_loader {
             tok_score.score = score;
         }
     }
-    void read_tensor_metadata(size_t file_idx, llama_load_tensors_map & tensors_map) {
+    void read_tensor_metadata(llama_load_tensors_map & tensors_map) {
         while (file.tell() < file.size) {
-            llama_load_tensor_shard shard;
+            llama_load_tensor tensor;
             uint32_t n_dims = file.read_u32();
             uint32_t name_len = file.read_u32();
-            shard.type = (enum ggml_type) file.read_u32();
-            shard.ne.resize(n_dims);
-            file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
+            tensor.type = (enum ggml_type) file.read_u32();
+            tensor.ne.resize(n_dims);
+            file.read_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * n_dims);
             std::string name = file.read_string(name_len);
             if (n_dims < 1 || n_dims > 2) {
                 throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
             }
-            switch (shard.type) {
+            switch (tensor.type) {
                 case GGML_TYPE_F32:
                 case GGML_TYPE_F16:
                 case GGML_TYPE_Q4_0:
@@ -562,30 +517,20 @@ struct llama_file_loader {
                 case GGML_TYPE_Q6_K:
                     break;
                 default: {
-                    throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type));
+                    throw std::runtime_error(format("unrecognized tensor type %u\n", tensor.type));
                 }
             }
 
-            if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
-                // skip to the next multiple of 32 bytes
-                file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
-            }
-            shard.file_idx = file_idx;
-            shard.file_off = file.tell();
+            // skip to the next multiple of 32 bytes
+            file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
 
-            shard.calc_size();
-            file.seek(shard.size, SEEK_CUR);
+            tensor.file_off = file.tell();
+            tensor.name = name;
+            tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type);
+            file.seek(tensor.size, SEEK_CUR);
 
-            auto it = tensors_map.name_to_idx.find(name);
-            size_t idx;
-            if (it != tensors_map.name_to_idx.end()) {
-                idx = it->second;
-            } else {
-                tensors_map.tensors.emplace_back(name);
-                idx = tensors_map.tensors.size() - 1;
-                tensors_map.name_to_idx.emplace(name, idx);
-            }
-            tensors_map.tensors.at(idx).shards.push_back(shard);
+            tensors_map.tensors.push_back(tensor);
+            tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
         }
     }
 };
@@ -655,56 +600,19 @@ struct llama_file_saver {
 };
 
 struct llama_model_loader {
-    std::vector<std::unique_ptr<llama_file_loader>> file_loaders;
+    std::unique_ptr<llama_file_loader> file_loader;
     llama_load_tensors_map tensors_map;
     bool use_mmap;
     size_t num_ggml_tensors_created = 0;
     struct ggml_context * ggml_ctx = NULL;
     std::unique_ptr<llama_mmap> mapping;
 
-    llama_model_loader(const std::string & fname_base, bool use_mmap, bool vocab_only) {
-        auto * first_file = new llama_file_loader(fname_base.c_str(), 0, tensors_map);
-        file_loaders.emplace_back(first_file);
-        uint32_t n_parts = vocab_only ? 1 : guess_n_parts();
-        for (uint32_t i = 1; i < n_parts; i++) {
-            std::string fname = fname_base + "." + std::to_string(i);
-            auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
-            file_loaders.emplace_back(ith_file);
-            if (ith_file->hparams != first_file->hparams) {
-                throw std::runtime_error(format("llama.cpp: hparams inconsistent between files"));
-            }
-        }
+    llama_model_loader(const std::string & fname_base, bool use_mmap) {
+        file_loader = std::unique_ptr<llama_file_loader>(new llama_file_loader(fname_base.c_str(), tensors_map));
         if (!llama_mmap::SUPPORTED) {
             use_mmap = false;
         }
-        if (use_mmap && alignment_prevents_mmap()) {
-            fprintf(stderr, "llama.cpp: can't use mmap because tensors are not aligned; convert to new format to avoid this\n");
-            use_mmap = false;
-        }
         this->use_mmap = use_mmap;
-        for (llama_load_tensor & lt : tensors_map.tensors) {
-            lt.calc_all();
-        }
-    }
-
-    bool alignment_prevents_mmap() {
-        for (const llama_load_tensor & lt : tensors_map.tensors) {
-            for (const llama_load_tensor_shard & shard : lt.shards) {
-                if (shard.file_off & 3) {
-                    return true;
-                }
-            }
-        }
-        return false;
-    }
-
-    uint32_t guess_n_parts() const {
-        auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
-        if (it == tensors_map.name_to_idx.end()) {
-            throw std::runtime_error(std::string("missing tok_embeddings.weight"));
-        }
-        const llama_load_tensor & lt = tensors_map.tensors.at(it->second);
-        return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
     }
 
     void calc_sizes(size_t * ctx_size_p, size_t * mmapped_size_p) const {
@@ -770,7 +678,7 @@ struct llama_model_loader {
         }
 
         if (use_mmap) {
-            mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size));
+            mapping.reset(new llama_mmap(&file_loader->file, prefetch_size, ggml_is_numa()));
             if (lmlock) {
                 lmlock->init(mapping->addr);
             }
@@ -826,45 +734,13 @@ struct llama_model_loader {
 
     void load_data_for(llama_load_tensor & lt) {
         if (use_mmap) {
-            LLAMA_ASSERT(lt.shards.size() == 1);
-            lt.data = (uint8_t *) mapping->addr + lt.shards.at(0).file_off;
-        } else if (lt.split_type == SPLIT_NONE) {
-            llama_file & file = file_loaders.at(lt.shards.at(0).file_idx)->file;
-            file.seek(lt.shards.at(0).file_off, SEEK_SET);
+            lt.data = (uint8_t *) mapping->addr + lt.file_off;
+        } else {
+            llama_file & file = file_loader->file;
+            file.seek(lt.file_off, SEEK_SET);
             file.read_raw(lt.data, lt.size);
-        } else if (lt.split_type == SPLIT_BY_ROWS) {
-            size_t offset = 0;
-            for (llama_load_tensor_shard & shard : lt.shards) {
-                llama_file & file = file_loaders.at(shard.file_idx)->file;
-                file.seek(shard.file_off, SEEK_SET);
-                file.read_raw(lt.data + offset, shard.size);
-                offset += shard.size;
-            }
-            LLAMA_ASSERT(offset == lt.size);
-        } else if (lt.split_type == SPLIT_BY_COLUMNS) {
-            // Let's load the data into temporary buffers to ensure the OS performs large loads.
-            std::vector<llama_buffer> tmp_bufs(lt.shards.size());
-            for (size_t i = 0; i < lt.shards.size(); i++) {
-                llama_load_tensor_shard & shard = lt.shards.at(i);
-                llama_file & file = file_loaders.at(shard.file_idx)->file;
-                file.seek(shard.file_off, SEEK_SET);
-                tmp_bufs.at(i).resize(shard.size);
-                file.read_raw(tmp_bufs.at(i).addr, shard.size);
-            }
-            // Then reshape.
-            size_t num_rows = lt.ne.at(1);
-            size_t per_shard_row_size = lt.shards.at(0).size / num_rows;
-            size_t out_offset = 0;
-            for (size_t row = 0; row < num_rows; row++) {
-                for (llama_buffer & tmp_buf : tmp_bufs) {
-                    memcpy(lt.data + out_offset,
-                           tmp_buf.addr + row * per_shard_row_size,
-                           per_shard_row_size);
-                    out_offset += per_shard_row_size;
-                }
-            }
-            LLAMA_ASSERT(out_offset == lt.size);
         }
+
         if (0) {
             print_checksum(lt);
         }
@@ -934,7 +810,7 @@ static bool kv_cache_init(
 
 struct llama_context_params llama_context_default_params() {
     struct llama_context_params result = {
-        /*.seed                        =*/ -1,
+        /*.seed                        =*/ LLAMA_DEFAULT_SEED,
         /*.n_ctx                       =*/ 512,
         /*.n_batch                     =*/ 512,
         /*.gpu_layers                  =*/ 0,
@@ -973,7 +849,7 @@ bool llama_mlock_supported() {
     return llama_mlock::SUPPORTED;
 }
 
-void llama_init_backend() {
+void llama_init_backend(bool numa) {
     ggml_time_init();
 
     // needed to initialize f16 tables
@@ -982,6 +858,10 @@ void llama_init_backend() {
         struct ggml_context * ctx = ggml_init(params);
         ggml_free(ctx);
     }
+
+    if (numa) {
+        ggml_numa_init();
+    }
 }
 
 int64_t llama_time_us() {
@@ -1059,12 +939,12 @@ static void llama_model_load_internal(
 
     model.t_start_us = ggml_time_us();
 
-    std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap, vocab_only));
+    std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap));
 
-    vocab = std::move(ml->file_loaders.at(0)->vocab);
-    model.hparams = ml->file_loaders.at(0)->hparams;
+    vocab = std::move(ml->file_loader->vocab);
+    model.hparams = ml->file_loader->hparams;
     model.n_gpu_layers = n_gpu_layers;
-    llama_file_version file_version = ml->file_loaders.at(0)->file_version;
+    llama_file_version file_version = ml->file_loader->file_version;
     auto & hparams = model.hparams;
 
     {
@@ -1098,7 +978,6 @@ static void llama_model_load_internal(
         fprintf(stderr, "%s: n_rot      = %u\n",  __func__, hparams.n_rot);
         fprintf(stderr, "%s: ftype      = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
         fprintf(stderr, "%s: n_ff       = %u\n",  __func__, n_ff);
-        fprintf(stderr, "%s: n_parts    = %zu\n", __func__, ml->file_loaders.size());
         fprintf(stderr, "%s: model size = %s\n",  __func__, llama_model_type_name(model.type));
     }
 
@@ -1266,14 +1145,18 @@ static void llama_model_load_internal(
             fprintf(stderr, "%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
             ggml_cuda_set_scratch_size(0); // disable scratch
         } else {
-            vram_scratch = n_batch * MB;
+            const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type);
+            const size_t vram_scratch_per_context = VRAM_REQ_SCRATCH_PER_CONTEXT().at(model.type);
+            vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context);
             ggml_cuda_set_scratch_size(vram_scratch);
             if (n_gpu_layers > 0) {
-                fprintf(stderr, "%s: allocating batch_size x 1 MB = %zd MB VRAM for the scratch buffer\n",
-                        __func__, vram_scratch / MB);
+                fprintf(stderr, "%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n",
+                        __func__, vram_scratch_base / kB, vram_scratch_per_context,
+                        (vram_scratch + MB - 1) / MB); // round up
             }
         }
 #endif // GGML_USE_CUBLAS
+
 #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
         const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
 
@@ -1282,6 +1165,10 @@ static void llama_model_load_internal(
             fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__);
         }
         size_t vram_kv_cache = 0;
+
+#ifdef GGML_USE_CUBLAS
+        const int max_backend_supported_layers = hparams.n_layer + 3;
+        const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
         if (n_gpu_layers > (int) hparams.n_layer + 1) {
             if (low_vram) {
                 fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
@@ -1298,14 +1185,18 @@ static void llama_model_load_internal(
                 vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2;
             }
         }
-        const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
+#elif defined(GGML_USE_CLBLAST)
+        const int max_backend_supported_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers = hparams.n_layer + 1;
+#endif // GGML_USE_CUBLAS
+
         fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n",
-                __func__, std::min(n_gpu_layers, max_offloadable_layers), hparams.n_layer + 3);
+                __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
         fprintf(stderr, "%s: total VRAM used: %zu MB\n",
                 __func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
 #else
         (void) n_gpu_layers;
-#endif
+#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
     }
 
     // populate `tensors_by_name`
@@ -1361,22 +1252,26 @@ static bool llama_model_load(
 
 // evaluate the transformer
 //
-//   - lctx:         llama context
-//   - tokens:       new batch of tokens to process
-//   - n_past:       the context size so far
-//   - n_threads:    number of threads to use
-//   - cgraph_fname: filename of the exported computation graph
+//   - lctx:      llama context
+//   - tokens:    new batch of tokens to process
+//   - embd       embeddings input
+//   - n_tokens   number of tokens
+//   - n_past:    the context size so far
+//   - n_threads: number of threads to use
 //
 static bool llama_eval_internal(
-        llama_context &  lctx,
-    const llama_token *  tokens,
-            const int    n_tokens,
-            const int    n_past,
-            const int    n_threads,
+         llama_context & lctx,
+     const llama_token * tokens,
+           const float * embd,
+             const int   n_tokens,
+             const int   n_past,
+             const int   n_threads,
             const char * cgraph_fname) {
 
+    LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
+
     // enforce that the first token is BOS
-    if (n_past == 0 && tokens[0] != llama_token_bos()) {
+    if (tokens && n_past == 0 && tokens[0] != llama_token_bos()) {
         fprintf(stderr, "%s: first token must be BOS\n", __func__);
         return false;
     }
@@ -1416,12 +1311,18 @@ static bool llama_eval_internal(
     ggml_cgraph gf = {};
     gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
 
-    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    ggml_set_name(embd, "embd");
-    memcpy(embd->data, tokens, N*ggml_element_size(embd));
-
     struct ggml_tensor * cur;
-    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
+    struct ggml_tensor * inpL;
+
+    if (tokens) {
+        struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+        ggml_set_name(embd, "embd");
+        memcpy(embd->data, tokens, N*ggml_element_size(embd));
+        inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
+    } else {
+        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
+        memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
+    }
 
     const int i_gpu_start = n_layer - n_gpu_layers;
     (void) i_gpu_start;
@@ -1483,11 +1384,11 @@ static bool llama_eval_internal(
             offload_func_kq(tmpq);
             ggml_set_name(tmpq, "tmpq");
 
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0);
+            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
             offload_func_kq(Kcur);
             ggml_set_name(Kcur, "Kcur");
 
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
             offload_func_kq(Qcur);
             ggml_set_name(Qcur, "Qcur");
 
@@ -2004,10 +1905,10 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     llama_sample_softmax(ctx, candidates);
 
+    const int64_t t_start_sample_us = ggml_time_us();
+
     // Compute the cumulative probabilities
     float cum_sum = 0.0f;
     size_t last_idx = candidates->size;
@@ -2036,9 +1937,8 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     llama_sample_softmax(nullptr, candidates);
+    const int64_t t_start_sample_us = ggml_time_us();
 
     // Compute the first and second derivatives
     std::vector<float> first_derivatives(candidates->size - 1);
@@ -2090,11 +1990,11 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     // Compute the softmax of logits and calculate entropy
     llama_sample_softmax(nullptr, candidates);
 
+    const int64_t t_start_sample_us = ggml_time_us();
+
     float entropy = 0.0f;
     for (size_t i = 0; i < candidates->size; ++i) {
         entropy += -candidates->data[i].p * logf(candidates->data[i].p);
@@ -2263,13 +2163,11 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_
 
     if (ctx) {
         ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-        ctx->n_sample++;
     }
     return X;
 }
 
 llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
-    assert(ctx);
     int64_t t_start_sample_us;
     t_start_sample_us = ggml_time_us();
 
@@ -2284,13 +2182,14 @@ llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_tok
         candidates->size = 1;
     }
 
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+
     // Normalize the probabilities of the remaining words
     llama_sample_softmax(ctx, candidates);
 
     // Sample the next word X from the remaining words
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
     llama_token X = llama_sample_token(ctx, candidates);
     t_start_sample_us = ggml_time_us();
 
@@ -2358,10 +2257,10 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
     }
     float * f32_output = (float *) output.addr;
 
-    quantize_fns_t qtype;
+    ggml_type_traits_t qtype;
     if (ggml_is_quantized(tensor.type)) {
-        qtype = ggml_internal_get_quantize_fn(tensor.type);
-        if (qtype.dequantize_row_q == NULL) {
+        qtype = ggml_internal_get_type_traits(tensor.type);
+        if (qtype.to_float == NULL) {
             throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type)));
         }
     } else if (tensor.type != GGML_TYPE_F16) {
@@ -2372,7 +2271,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
         if (tensor.type == GGML_TYPE_F16) {
             ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor.data, f32_output, nelements);
         } else if (ggml_is_quantized(tensor.type)) {
-            qtype.dequantize_row_q(tensor.data, f32_output, nelements);
+            qtype.to_float(tensor.data, f32_output, nelements);
         } else {
             LLAMA_ASSERT(false); // unreachable
         }
@@ -2397,7 +2296,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
             if (typ == GGML_TYPE_F16) {
                 ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
             } else {
-                qtype.dequantize_row_q(inbuf, outbuf, nels);
+                qtype.to_float(inbuf, outbuf, nels);
             }
         };
         workers.push_back(std::thread(compute, tensor.type, tensor.data + in_buff_offs, f32_output + out_buff_offs, thr_elems));
@@ -2443,9 +2342,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         nthread = std::thread::hardware_concurrency();
     }
 
-    std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false,
-                                                                            /*vocab_only*/ false));
-    llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), params->ftype);
+    std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false));
+    llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loader.get(), params->ftype);
 
 #ifdef GGML_USE_K_QUANTS
     int n_attention_wv    = 0;
@@ -2470,6 +2368,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     std::vector<std::thread> workers;
     std::mutex mutex;
 
+    auto use_more_bits = [] (int i_layer, int num_layers) -> bool {
+        return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
+    };
+
     size_t idx = 0;
     for (llama_load_tensor & tensor : model_loader->tensors_map.tensors) {
         llama_buffer read_data;
@@ -2524,15 +2426,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 if      (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
                 else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
-                         (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8 ||
-                         (i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
+                        use_more_bits(i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
+                else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
+                        (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
                 ++i_attention_wv;
             } else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
                 if      (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
                 else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
-                         (i_feed_forward_w2 < n_feed_forward_w2/8 || i_feed_forward_w2 >= 7*n_feed_forward_w2/8 ||
-                         (i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
+                         use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+                //else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < n_feed_forward_w2/8) new_type = GGML_TYPE_Q6_K;
                 ++i_feed_forward_w2;
             } else if (tensor.name.find("attention.wo.weight") != std::string::npos) {
                 if      (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
@@ -2641,6 +2544,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     }
 }
 
+
+
 //
 // interface implementation
 //
@@ -2679,7 +2584,7 @@ struct llama_context * llama_new_context_with_model(
 
     llama_context * ctx = new llama_context(*model, model->vocab);
 
-    if (params.seed < 0) {
+    if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
 
@@ -2861,7 +2766,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
 
     // create a name -> tensor map of the model to accelerate lookups
     std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
-    for (auto & kv: model.tensors_by_name) {
+    for (const auto & kv: model.tensors_by_name) {
         model_tensors.insert(kv);
     }
 
@@ -2872,7 +2777,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
     llama_buffer base_buf;
     if (path_base_model) {
         fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
-        model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*vocab_only*/ false));
+        model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));
 
         size_t ctx_size;
         size_t mmapped_size;
@@ -2890,7 +2795,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
 
         // maybe this should in llama_model_loader
         if (model_loader->use_mmap) {
-            model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0));
+            model_loader->mapping.reset(new llama_mmap(&model_loader->file_loader->file, /* prefetch */ 0, ggml_is_numa()));
         }
     }
 
@@ -2951,7 +2856,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
                         return false;
                     }
         }
-        ggml_tensor* lora_tensor;
+        ggml_tensor * lora_tensor;
         if (n_dims == 2) {
             lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
         }
@@ -2959,6 +2864,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
             fprintf(stderr, "%s: unsupported tensor dimension %d\n", __func__, n_dims);
             return 1;
         }
+        ggml_set_name(lora_tensor, "lora_tensor");
 
         // load tensor data
         size_t offset = fin.tellg();
@@ -2974,6 +2880,21 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
             lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
 
             ggml_tensor * dest_t = model_tensors[base_name];
+
+            offload_func_t offload_func = llama_nop;
+            offload_func_t offload_func_force_inplace = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+            if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
+                if (dest_t->type != GGML_TYPE_F16) {
+                    throw std::runtime_error(format(
+                        "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
+                }
+                offload_func = ggml_cuda_assign_buffers;
+                offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
+            }
+#endif // GGML_USE_CUBLAS
+
             ggml_tensor * base_t;
             if (model_loader) {
                 // load from base model
@@ -3001,7 +2922,12 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
             }
 
             ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
+            GGML_ASSERT(loraA->type == GGML_TYPE_F32);
+            ggml_set_name(loraA, "loraA");
+
             ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
+            GGML_ASSERT(loraB->type == GGML_TYPE_F32);
+            ggml_set_name(loraB, "loraB");
 
             if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
                 fprintf(stderr, "%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
@@ -3011,19 +2937,32 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
 
             // w = w + BA*s
             ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
+            offload_func(BA);
+            ggml_set_name(BA, "BA");
 
             if (scaling != 1.0f) {
                 ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
+                ggml_set_name(scale_tensor, "scale_tensor");
+
                 BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
+                offload_func(BA);
+                ggml_set_name(BA, "BA_scaled");
             }
 
             ggml_tensor * r;
             if (base_t == dest_t) {
                 r = ggml_add_inplace(lora_ctx, dest_t, BA);
+                offload_func_force_inplace(r);
+                ggml_set_name(r, "r_add_inplace");
             }
             else {
                 r = ggml_add(lora_ctx, base_t, BA);
+                offload_func(r);
+                ggml_set_name(r, "r_add");
+
                 r = ggml_cpy(lora_ctx, r, dest_t);
+                offload_func(r);
+                ggml_set_name(r, "r_cpy");
             }
 
             struct ggml_cgraph gf = ggml_build_forward(r);
@@ -3078,8 +3017,8 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
 
 #define LLAMA_MAX_RNG_STATE (64*1024)
 
-void llama_set_rng_seed(struct llama_context * ctx, int seed) {
-    if (seed < 0) {
+void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
         seed = time(NULL);
     }
     ctx->rng.seed(seed);
@@ -3323,7 +3262,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
     return nread;
 }
 
-bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
     llama_file file(path_session, "rb");
 
     // sanity checks
@@ -3377,6 +3316,15 @@ bool llama_load_session_file(struct llama_context * ctx, const char * path_sessi
     return true;
 }
 
+bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    try {
+        return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+    } catch (const std::exception & err) {
+        fprintf(stderr, "error loading session file: %s\n", err.what());
+        return false;
+    }
+}
+
 bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
     llama_file file(path_session, "wb");
 
@@ -3408,7 +3356,29 @@ int llama_eval(
                          int   n_tokens,
                          int   n_past,
                          int   n_threads) {
-    if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, nullptr)) {
+    if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) {
+        fprintf(stderr, "%s: failed to eval\n", __func__);
+        return 1;
+    }
+
+    // get a more accurate load time, upon first eval
+    // TODO: fix this
+    if (!ctx->has_evaluated_once) {
+        ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
+        ctx->has_evaluated_once = true;
+    }
+
+    return 0;
+}
+
+
+int llama_eval_embd(
+            struct llama_context * ctx,
+                     const float * embd,
+                             int   n_tokens,
+                             int   n_past,
+                             int   n_threads) {
+    if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) {
         fprintf(stderr, "%s: failed to eval\n", __func__);
         return 1;
     }
@@ -3429,7 +3399,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) {
 
     const std::vector<llama_token> tmp(n_batch, llama_token_bos());
 
-    if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), n_ctx, 1, fname)) {
+    if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) {
         fprintf(stderr, "%s: failed to eval\n", __func__);
         return 1;
     }
@@ -3510,23 +3480,35 @@ llama_token llama_token_nl() {
     return 13;
 }
 
+struct llama_timings llama_get_timings(struct llama_context * ctx) {
+    struct llama_timings result = {
+        /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
+        /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
+        /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
+        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
+        /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
+        /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
+
+        /*.n_sample =*/ std::max(1, ctx->n_sample),
+        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
+        /*.n_eval   =*/ std::max(1, ctx->n_eval),
+    };
+
+    return result;
+}
 
 void llama_print_timings(struct llama_context * ctx) {
-    const int64_t t_end_us = ggml_time_us();
-
-    const int32_t n_sample = std::max(1, ctx->n_sample);
-    const int32_t n_eval   = std::max(1, ctx->n_eval);
-    const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
+    const llama_timings timings = llama_get_timings(ctx);
 
     fprintf(stderr, "\n");
-    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
+    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, timings.t_load_ms);
     fprintf(stderr, "%s:      sample time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample, 1e6 / ctx->t_sample_us * n_sample);
+            __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
     fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval, 1e6 / ctx->t_p_eval_us * n_p_eval);
+            __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
     fprintf(stderr, "%s:        eval time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_eval_us,   n_eval,   1e-3 * ctx->t_eval_us   / n_eval,   1e6 / ctx->t_eval_us   * n_eval);
-    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
+            __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
+    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
 }
 
 void llama_reset_timings(struct llama_context * ctx) {

llama.h

@@ -46,6 +46,8 @@
 #define LLAMA_SESSION_MAGIC          LLAMA_FILE_MAGIC_GGSN
 #define LLAMA_SESSION_VERSION        1
 
+#define LLAMA_DEFAULT_SEED           0xFFFFFFFF
+
 #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
@@ -81,11 +83,11 @@ extern "C" {
     typedef void (*llama_progress_callback)(float progress, void *ctx);
 
    struct llama_context_params {
-        int seed;                              // RNG seed, -1 for random
-        int n_ctx;                             // text context
-        int n_batch;                           // prompt processing batch size
-        int n_gpu_layers;                      // number of layers to store in VRAM
-        int main_gpu;                          // the GPU that is used for scratch and small tensors
+        uint32_t seed;                         // RNG seed, -1 for random
+        int32_t  n_ctx;                        // text context
+        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
         float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
         // called with a progress value between 0 and 1, pass NULL to disable
         llama_progress_callback progress_callback;
@@ -132,6 +134,20 @@ extern "C" {
         bool quantize_output_tensor; // quantize output.weight
     } llama_model_quantize_params;
 
+    // performance timing information
+    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();
     LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
 
@@ -140,8 +156,9 @@ extern "C" {
 
     // 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_init_backend(bool numa);
 
     LLAMA_API int64_t llama_time_us();
 
@@ -195,7 +212,7 @@ 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
@@ -225,6 +242,14 @@ extern "C" {
                              int   n_past,
                              int   n_threads);
 
+    // Same as llama_eval, but use float matrix input directly.
+    LLAMA_API int llama_eval_embd(
+            struct llama_context * ctx,
+                     const float * embd,
+                             int   n_tokens,
+                             int   n_past,
+                             int   n_threads);
+
     // Export a static computation graph for context of 511 and batch size of 1
     // NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
     //       parameters here to keep things simple
@@ -320,6 +345,7 @@ extern "C" {
     LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
 
     // 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);
 

pocs/vdot/q8dot.cpp

@@ -136,7 +136,7 @@ int main(int argc, char** argv) {
 
     auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
 
-    auto funcs = ggml_internal_get_quantize_fn(ggml_type);
+    auto funcs = ggml_internal_get_type_traits(ggml_type);
 
     Stat simple, ggml;
 
@@ -156,8 +156,8 @@ int main(int argc, char** argv) {
 
         t1 = std::chrono::high_resolution_clock::now();
         float fs;
-        if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data());
-        else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data());
+        if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data());
+        else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data());
         t2 = std::chrono::high_resolution_clock::now();
         t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
         if (iloop > 3) ggml.addResult(fs, t);

pocs/vdot/vdot.cpp

@@ -235,7 +235,7 @@ int main(int argc, char** argv) {
     int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64);
     int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64);
 
-    auto funcs = useQ4_1 ? ggml_internal_get_quantize_fn(GGML_TYPE_Q4_1) : ggml_internal_get_quantize_fn(GGML_TYPE_Q4_0);
+    auto funcs = useQ4_1 ? ggml_internal_get_type_traits(GGML_TYPE_Q4_1) : ggml_internal_get_type_traits(GGML_TYPE_Q4_0);
 
     std::vector<block_q4_0> q40;
     std::vector<block_q4_1> q41;
@@ -261,9 +261,9 @@ int main(int argc, char** argv) {
         // Note, we do not include this in the timing as in practical application
         // we already have the quantized model weights.
         if (useQ4_1) {
-            funcs.quantize_row_q(x1.data(), q41.data(), kVecSize);
+            funcs.from_float(x1.data(), q41.data(), kVecSize);
         } else {
-            funcs.quantize_row_q(x1.data(), q40.data(), kVecSize);
+            funcs.from_float(x1.data(), q40.data(), kVecSize);
         }
 
         // Now measure time the dot product needs using the "scalar" version above
@@ -282,9 +282,10 @@ int main(int argc, char** argv) {
             dot_q4_q8(kVecSize, &result, q40.data(), q8.data());
         }
         else {
-            funcs.quantize_row_q_dot(y1.data(), q8.data(), kVecSize);
-            if (useQ4_1) funcs.vec_dot_q(kVecSize, &result, q41.data(), q8.data());
-            else funcs.vec_dot_q(kVecSize, &result, q40.data(), q8.data());
+            auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type);
+            vdot.from_float(y1.data(), q8.data(), kVecSize);
+            if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data());
+            else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data());
         }
         sumq += result;
         t2 = std::chrono::high_resolution_clock::now();

scripts/sync-ggml.sh

@@ -1,6 +1,14 @@
 #!/bin/bash
 
-cp -rpv ../ggml/src/ggml.c          ./ggml.c
-cp -rpv ../ggml/src/ggml-cuda.cu    ./ggml-cuda.cu
-cp -rpv ../ggml/src/ggml-cuda.h     ./ggml-cuda.h
-cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
+cp -rpv ../ggml/src/ggml.c           ./ggml.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
+cp -rpv ../ggml/src/ggml-opencl.cpp  ./ggml-opencl.cpp
+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/tests/test-opt.c    ./tests/test-opt.c
+cp -rpv ../ggml/tests/test-grad0.c  ./tests/test-grad0.c

tests/test-grad0.c

@@ -1154,7 +1154,7 @@ int main(int argc, const char ** argv) {
                             continue;
                         }
 
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode));
+                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
 
                         GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
                         check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);

tests/test-quantize-fns.cpp

@@ -40,26 +40,26 @@ float array_rmse(const float * a1, const float * a2, size_t n) {
 }
 
 // Total quantization error on test data
-float total_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
+float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
     std::vector<uint8_t> tmp_q(2*test_size);
     std::vector<float> tmp_out(test_size);
 
-    qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
+    qfns.from_float(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
     return array_rmse(test_data, tmp_out.data(), test_size);
 }
 
 // Total quantization error on test data
-float reference_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
+float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
     std::vector<uint8_t> tmp_q(2*test_size);
     std::vector<float> tmp_out(test_size);
     std::vector<float> tmp_out_ref(test_size);
 
-    qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
+    qfns.from_float(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
 
-    qfns.quantize_row_q_reference(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out_ref.data(), test_size);
+    qfns.from_float_reference(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
 
     return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
 }
@@ -73,15 +73,17 @@ float dot_product(const float * a1, const float * a2, size_t test_size) {
 }
 
 // Total dot product error
-float dot_product_error(quantize_fns_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
+float dot_product_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
     std::vector<uint8_t> tmp_q1(2*test_size);
     std::vector<uint8_t> tmp_q2(2*test_size);
 
-    qfns.quantize_row_q    (test_data1, tmp_q1.data(), test_size);
-    qfns.quantize_row_q_dot(test_data2, tmp_q2.data(), test_size);
+    auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
+
+    qfns.from_float(test_data1, tmp_q1.data(), test_size);
+    vdot.from_float(test_data2, tmp_q2.data(), test_size);
 
     float result = INFINITY;
-    qfns.vec_dot_q(test_size, &result, tmp_q1.data(), tmp_q2.data());
+    qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data());
 
     const float dot_ref = dot_product(test_data1, test_data2, test_size);
 
@@ -123,9 +125,9 @@ int main(int argc, char * argv[]) {
 
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
 
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        if (qfns.from_float && qfns.to_float) {
             const float total_error = total_quantization_error(qfns, test_size, test_data.data());
             const float max_quantization_error =
                 type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :

tests/test-quantize-perf.cpp

@@ -21,6 +21,7 @@
 #define QK 32
 #define WARMUP 5
 #define ITERATIONS 10
+#define MAX_ITERATIONS 100000000
 
 #define L1_SIZE      32*128
 #define L2_SIZE     32*2048
@@ -36,9 +37,9 @@ struct quantize_perf_params {
     bool op_dequantize_row_q = false;
     bool op_quantize_row_q_dot = false;
     bool op_vec_dot_q = false;
+    int64_t iterations = ITERATIONS;
 };
 
-
 #if defined(__x86_64__) || defined(__i386__)
 
 #include <x86intrin.h>
@@ -75,7 +76,7 @@ void * align_with_offset(void * ptr, int offset) {
     return (char *) std::align(MAX_ALIGNMENT, MAX_ALIGNMENT, ptr, dummy_size) + offset;
 }
 
-void benchmark_function(size_t size, size_t q_size, std::function<size_t(void)> function) {
+void benchmark_function(size_t size, size_t q_size, int64_t iterations, std::function<size_t(void)> function) {
     int64_t min_time_us = INT64_MAX;
     int64_t total_time_us = 0;
     int64_t min_time_cycles = INT64_MAX;
@@ -86,7 +87,7 @@ void benchmark_function(size_t size, size_t q_size, std::function<size_t(void)>
     }
 
 
-    for (int i = 0; i < ITERATIONS; i++) {
+    for (int i = 0; i < iterations; i++) {
         const int64_t start_time = ggml_time_us();
         const int64_t start_cycles = cpu_cycles();
 
@@ -102,9 +103,38 @@ void benchmark_function(size_t size, size_t q_size, std::function<size_t(void)>
     }
 
     printf("      min cycles/%d vals   : %9.2f\n",  QK, QK * min_time_cycles / (float) size);
-    printf("      avg cycles/%d vals   : %9.2f\n",  QK, QK * total_time_cycles / (float) (size * ITERATIONS));
-    printf("      float32 throughput   : %9.2f GB/s\n",  gigabytes_per_second(4 * size * ITERATIONS, total_time_us));
-    printf("      quantized throughput : %9.2f GB/s\n",  gigabytes_per_second(q_size * ITERATIONS, total_time_us));
+    printf("      avg cycles/%d vals   : %9.2f\n",  QK, QK * total_time_cycles / (float) (size * iterations));
+    printf("      float32 throughput   : %9.2f GB/s\n",  gigabytes_per_second(4 * size * iterations, total_time_us));
+    printf("      quantized throughput : %9.2f GB/s\n",  gigabytes_per_second(q_size * iterations, total_time_us));
+}
+
+void usage(char * argv[]) {
+    printf("Benchmark quantization specific functions on synthetic data\n");
+    printf("\n");
+    printf("usage: %s [options]\n", argv[0]);
+    printf("\n");
+    printf("options: (default)\n");
+    printf("  -h, --help            show this help message and exit\n");
+    printf("  --size SIZE           set test size, divisible by 32 (L1_SIZE:%d)\n", L1_SIZE);
+    printf("  -3                    use size as L1, L2, L3 sizes (L1:%d L2:%d L3:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE);
+    printf("  -4                    use size as L1, L2, L3, MEM sizes (L1:%d L2:%d L3:%d MEM:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE, MEM_SIZE);
+    printf("  --op OP               set test opration as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n");
+    printf("                        quantize_row_q_dot, vec_dot_q (all)\n");
+    printf("  --type TYPE           set test type as");
+    for (int i = 0; i < GGML_TYPE_COUNT; i++) {
+        ggml_type type = (ggml_type) i;
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
+        if (ggml_type_name(type) != NULL) {
+            if (qfns.from_float && qfns.to_float) {
+                printf(" %s", ggml_type_name(type));
+            }
+        }
+    }
+    printf(" (all)\n");
+    printf("  --alignment-offset OFFSET\n");
+    printf("                        set alignment offset as OFFSET (0)\n");
+    printf("  -i NUM, --iterations NUM\n");
+    printf("                        set test iteration number (%d)\n", ITERATIONS);
 }
 
 int main(int argc, char * argv[]) {
@@ -178,6 +208,21 @@ int main(int argc, char * argv[]) {
                 break;
             }
             params.alignment_offset = alignment;
+        } else if ((arg == "-i") || (arg == "--iterations")) {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            int number = std::stoi(argv[i]);
+            if (number < 0 || number > MAX_ITERATIONS) {
+            fprintf(stderr, "error: iterations must be less than %d\n", MAX_ITERATIONS);
+                invalid_param = true;
+                break;
+            }
+            params.iterations = number;
+        } else if ((arg == "-h") || (arg == "--help")) {
+            usage(argv);
+            return 1;
         } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             return 1;
@@ -213,6 +258,8 @@ int main(int argc, char * argv[]) {
     generate_data(0, largest, test_data1);
     generate_data(1, largest, test_data2);
 
+    int64_t iterations = params.iterations;
+
 
     // Initialize GGML, ensures float conversion tables are initialized
     struct ggml_init_params ggml_params = {
@@ -224,12 +271,12 @@ int main(int argc, char * argv[]) {
 
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
-        if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
+        if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
             continue;
         }
 
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        if (qfns.from_float && qfns.to_float) {
             printf("%s\n", ggml_type_name(type));
 
             if (params.op_quantize_row_q_reference) {
@@ -237,11 +284,11 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q_reference(test_data1, test_q1, size);
+                        qfns.from_float_reference(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
-                    benchmark_function(size, quantized_size, quantize_fn);
+                    benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
             }
@@ -251,26 +298,26 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q(test_data1, test_q1, size);
+                        qfns.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
-                    benchmark_function(size, quantized_size, quantize_fn);
+                    benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
             }
 
             if (params.op_dequantize_row_q) {
                 printf("  dequantize_row_q\n");
-                qfns.quantize_row_q(test_data1, test_q1, largest);
+                qfns.from_float(test_data1, test_q1, largest);
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.dequantize_row_q(test_q1, test_out, size);
+                        qfns.to_float(test_q1, test_out, size);
                         return test_out[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
-                    benchmark_function(size, quantized_size, quantize_fn);
+                    benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
             }
@@ -280,28 +327,29 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q_dot(test_data1, test_q1, size);
+                        auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
+                        vdot.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
-                    benchmark_function(size, quantized_size, quantize_fn);
+                    benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
             }
 
             if (params.op_vec_dot_q) {
                 printf("  vec_dot_q\n");
-                qfns.quantize_row_q(test_data1, test_q1, largest);
-                qfns.quantize_row_q(test_data2, test_q2, largest);
+                qfns.from_float(test_data1, test_q1, largest);
+                qfns.from_float(test_data2, test_q2, largest);
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
                         float result;
-                        qfns.vec_dot_q(size, &result, test_q1, test_q2);
+                        qfns.vec_dot(size, &result, test_q1, test_q2);
                         return result;
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
-                    benchmark_function(size, quantized_size, quantize_fn);
+                    benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
             }