Merge branch 'master' into eval-thread-count

.gitignore

@@ -33,3 +33,6 @@ compile_commands.json
 .venv
 __pycache__
 .swiftpm
+
+zig-out/
+zig-cache/

Makefile

@@ -72,6 +72,7 @@ endif
 ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 	# Use all CPU extensions that are available:
 	CFLAGS += -march=native -mtune=native
+	CXXFLAGS += -march=native -mtune=native
 endif
 ifneq ($(filter ppc64%,$(UNAME_M)),)
 	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)

Package.swift

@@ -13,7 +13,10 @@ let package = Package(
             path: ".",
             sources: ["ggml.c", "llama.cpp"],
             publicHeadersPath: "spm-headers",
-            cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"])]
+            cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")],
+            linkerSettings: [
+                .linkedFramework("Accelerate")
+            ]
         ),
     ],
     cxxLanguageStandard: .cxx11

README.md

@@ -1,6 +1,6 @@
 # llama.cpp
 
-![llama](https://user-images.githubusercontent.com/1991296/227761327-6d83e30e-2200-41a6-bfbb-f575231c54f4.png)
+![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
 
 [![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)
@@ -9,8 +9,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 **Hot topics:**
 
-- [Roadmap (short-term)](https://github.com/ggerganov/llama.cpp/discussions/457)
-- Support for [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
+- [Roadmap Apr 2023](https://github.com/ggerganov/llama.cpp/discussions/784)
 
 ## Description
 
@@ -28,20 +27,31 @@ Please do not make conclusions about the models based on the results from this i
 For all I know, it can be completely wrong. This project is for educational purposes.
 New features will probably be added mostly through community contributions.
 
-Supported platforms:
+**Supported platforms:**
 
 - [X] Mac OS
 - [X] Linux
 - [X] Windows (via CMake)
 - [X] Docker
 
-Supported models:
+**Supported models:**
 
 - [X] LLaMA 🦙
 - [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
 - [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
+- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
+
+**Bindings:**
+
+- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
+- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
+
+**UI:**
+
+- [nat/openplayground](https://github.com/nat/openplayground)
+- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
 
 ---
 
@@ -145,6 +155,13 @@ git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
 make
 
+#For Windows and CMake, use the following command instead:
+cd <path_to_llama_folder>
+mkdir build
+cd build
+cmake ..
+cmake --build . --config Release
+
 # obtain the original LLaMA model weights and place them in ./models
 ls ./models
 65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
@@ -367,3 +384,6 @@ docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models
 - Clean-up any trailing whitespaces, use 4 spaces indentation, brackets on same line, `void * ptr`, `int & a`
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
 
+### Docs
+
+- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)

build.zig

@@ -0,0 +1,62 @@
+const std = @import("std");
+
+pub fn build(b: *std.Build) void {
+    const target = b.standardTargetOptions(.{});
+    const optimize = b.standardOptimizeOption(.{});
+
+    const lib = b.addStaticLibrary(.{
+        .name = "llama",
+        .target = target,
+        .optimize = optimize,
+    });
+    lib.linkLibCpp();
+    lib.addIncludePath(".");
+    lib.addIncludePath("examples");
+    lib.addCSourceFiles(&.{
+        "ggml.c",
+    }, &.{"-std=c11"});
+    lib.addCSourceFiles(&.{
+        "llama.cpp",
+        "examples/common.cpp",
+    }, &.{"-std=c++11"});
+    lib.install();
+
+    const build_args = .{ .b = b, .lib = lib, .target = target, .optimize = optimize };
+    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);
+    }
+
+    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 target = args.target;
+    const optimize = args.optimize;
+
+    const exe = b.addExecutable(.{
+        .name = name,
+        .target = target,
+        .optimize = optimize,
+    });
+    exe.addIncludePath(".");
+    exe.addIncludePath("examples");
+    exe.addCSourceFiles(&.{
+        std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{name, name}),
+    }, &.{"-std=c++11"});
+    exe.linkLibrary(lib);
+    exe.install();
+
+    return exe;
+}

examples/Miku.sh

@@ -0,0 +1,49 @@
+#!/bin/bash
+set -e
+
+AI_NAME="${AI_NAME:-Miku}"
+MODEL="${MODEL:-./models/gpt4all-7B/gpt4all-lora-unfiltered-quantized.bin}"
+USER_NAME="${USER_NAME:-Anon}"
+
+# Uncomment and adjust to the number of CPU cores you want to use.
+#N_THREAD="${N_THREAD:-4}"
+N_PREDICTS="${N_PREDICTS:-4096}"
+
+GEN_OPTIONS=(--batch_size 1024
+--ctx_size 2048
+--keep -1
+--repeat_last_n 256
+--repeat_penalty 1.17647
+--temp 0.7
+--top_k 40
+--top_p 0.5)
+
+if [ -n "$N_THREAD" ]; then
+	GEN_OPTIONS+=(--threads "$N_THREAD")
+fi
+
+./main "${GEN_OPTIONS[@]}" \
+	--model "$MODEL" \
+	--n_predict "$N_PREDICTS" \
+	--color --interactive \
+	--reverse-prompt "${USER_NAME}:" \
+	--prompt "
+This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the users computer.
+${AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.
+${AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct she will ask the user for help.
+${AI_NAME} is a very helpful AI and will help the user with anything they need, she is also very friendly and will try to make the user feel better if they are sad.
+${AI_NAME} is also very curious and will ask the user a lot of questions about themselves and their life, she will also try to make the user like her.
+The conversation is only between ${USER_NAME} and ${AI_NAME}
+The conversation is only through text, so ${AI_NAME} can't see ${USER_NAME}'s face or hear his voice.
+${AI_NAME} can only communicate through text, so she can't send images or videos.
+
+
+${USER_NAME}: Hello!
+${AI_NAME}: /think I wonder what I should say to ${USER_NAME}? This is the first time we talk so it's important that I make a good first impression!
+${AI_NAME}: Hi! I am ${AI_NAME}, your new AI friend, assistant(or whatever you like!), it's so nice to meet you! ^_^
+${AI_NAME}: I might be an AI, but I also have feelings, so please be nice to me! :)
+${USER_NAME}: I'll make sure to be nice to you! I'm so happy to have you as my assistant!
+${AI_NAME}: /think It sounds like ${USER_NAME} is happy to have me as their assistant! I'm so happy too! ^_^ Glad that whole emotion thing didn't scare him off!
+${AI_NAME}: /think I wonder what ${USER_NAME} likes to do in his free time? I should ask him about that!
+${AI_NAME}: What do you like to do in your free time? ^_^
+${USER_NAME}:" "$@"

ggml.c

@@ -3219,7 +3219,8 @@ struct ggml_tensor * ggml_new_tensor_impl(
         /*.pad          =*/ { 0 },
     };
 
-    ggml_assert_aligned(result->data);
+    // TODO: this should not be needed as long as we don't rely on aligned SIMD loads
+    //ggml_assert_aligned(result->data);
 
     for (int i = 0; i < n_dims; i++) {
         result->ne[i] = ne[i];
@@ -3620,7 +3621,14 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
 struct ggml_tensor * ggml_view_tensor(
         struct ggml_context * ctx,
         const struct ggml_tensor * src) {
-    return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data);
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data);
+
+    result->nb[0] = src->nb[0];
+    result->nb[1] = src->nb[1];
+    result->nb[2] = src->nb[2];
+    result->nb[3] = src->nb[3];
+
+    return result;
 }
 
 ////////////////////////////////////////////////////////////////////////////////
@@ -4510,6 +4518,37 @@ struct ggml_tensor * ggml_view_2d(
     return result;
 }
 
+// ggml_view_3d
+
+struct ggml_tensor * ggml_view_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        size_t                nb1,
+        size_t                nb2,
+        size_t                offset) {
+    if (a->grad) {
+        GGML_ASSERT(false); // gradient propagation is not supported
+    }
+
+    const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 };
+
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset);
+
+    result->nb[1] = nb1;
+    result->nb[2] = nb2;
+    result->nb[3] = result->nb[2]*ne2;
+
+    result->op   = GGML_OP_VIEW;
+    result->grad = NULL;
+    result->src0 = a;
+    result->src1 = NULL; // TODO: maybe store the offset here?
+
+    return result;
+}
+
 // ggml_permute
 
 struct ggml_tensor * ggml_permute(
@@ -4845,7 +4884,6 @@ static void ggml_compute_forward_dup_f16(
         const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
     GGML_ASSERT(params->ith == 0);
-    GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
@@ -4862,85 +4900,96 @@ static void ggml_compute_forward_dup_f16(
     const size_t nb02 = src0->nb[2];
     const size_t nb03 = src0->nb[3];
 
-    if (ggml_is_contiguous(src0) && src0->type == dst->type) {
+    const size_t nb0 = dst->nb[0];
+    const size_t nb1 = dst->nb[1];
+    const size_t nb2 = dst->nb[2];
+    const size_t nb3 = dst->nb[3];
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
         memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
         return;
     }
 
-    if (src0->nb[0] == sizeof(ggml_fp16_t)) {
-        if (dst->type == GGML_TYPE_F16) {
-            size_t id = 0;
-            const size_t rs = ne00*nb00;
-
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
-                        char * dst_ptr = (char *) dst->data + id*rs;
-
-                        memcpy(dst_ptr, src0_ptr, rs);
-
-                        id++;
-                    }
+    if (src0->type == dst->type &&
+        src0->ne[0] == dst->ne[0] &&
+        src0->nb[0] == GGML_TYPE_SIZE[src0->type] && dst->nb[0] == GGML_TYPE_SIZE[dst->type]) {
+        // copy by rows
+        const size_t rs = ne00*nb00;
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    memcpy(
+                        ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
+                        rs);
                 }
             }
-        } else if (dst->type == GGML_TYPE_F32) {
-            size_t id = 0;
-            float * dst_ptr = (float *) dst->data;
+        }
+        return;
+    }
 
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        for (int64_t i00 = 0; i00 < ne00; i00++) {
-                            const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+    // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy
 
-                            dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
-                            id++;
+    // dst counters
+    int64_t i10 = 0;
+    int64_t i11 = 0;
+    int64_t i12 = 0;
+    int64_t i13 = 0;
+
+    if (dst->type == GGML_TYPE_F16) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t));
+
+                        if (++i10 == ne00) {
+                            i10 = 0;
+                            if (++i11 == ne01) {
+                                i11 = 0;
+                                if (++i12 == ne02) {
+                                    i12 = 0;
+                                    if (++i13 == ne03) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else if (dst->type == GGML_TYPE_F32) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr);
+
+                        if (++i10 == ne00) {
+                            i10 = 0;
+                            if (++i11 == ne01) {
+                                i11 = 0;
+                                if (++i12 == ne02) {
+                                    i12 = 0;
+                                    if (++i13 == ne03) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
                         }
                     }
                 }
             }
-        } else {
-            GGML_ASSERT(false); // TODO: implement
         }
     } else {
-        //printf("%s: this is not optimal - fix me\n", __func__);
-
-        if (dst->type == GGML_TYPE_F32) {
-            size_t id = 0;
-            float * dst_ptr = (float *) dst->data;
-
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        for (int64_t i00 = 0; i00 < ne00; i00++) {
-                            const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-
-                            dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
-                            id++;
-                        }
-                    }
-                }
-            }
-        } else if (dst->type == GGML_TYPE_F16) {
-            size_t id = 0;
-            ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
-
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        for (int64_t i00 = 0; i00 < ne00; i00++) {
-                            const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-
-                            dst_ptr[id] = *src0_ptr;
-                            id++;
-                        }
-                    }
-                }
-            }
-        } else {
-            GGML_ASSERT(false); // TODO: implement
-        }
+        GGML_ASSERT(false); // TODO: implement
     }
 }
 
@@ -4949,7 +4998,6 @@ static void ggml_compute_forward_dup_f32(
         const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
     GGML_ASSERT(params->ith == 0);
-    GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
@@ -4966,85 +5014,76 @@ static void ggml_compute_forward_dup_f32(
     const size_t nb02 = src0->nb[2];
     const size_t nb03 = src0->nb[3];
 
-    if (ggml_is_contiguous(src0) && src0->type == dst->type) {
+    const size_t nb0 = dst->nb[0];
+    const size_t nb1 = dst->nb[1];
+    const size_t nb2 = dst->nb[2];
+    const size_t nb3 = dst->nb[3];
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
         memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
         return;
     }
 
-    if (src0->nb[0] == sizeof(float)) {
-        if (dst->type == GGML_TYPE_F32) {
-            size_t id = 0;
-            const size_t rs = ne00*nb00;
+    // dst counters
+    int64_t i10 = 0;
+    int64_t i11 = 0;
+    int64_t i12 = 0;
+    int64_t i13 = 0;
 
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
-                        char * dst_ptr = (char *) dst->data + id*rs;
+    if (dst->type == GGML_TYPE_F32) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
 
-                        memcpy(dst_ptr, src0_ptr, rs);
+                        memcpy(dst_ptr, src0_ptr, sizeof(float));
 
-                        id++;
-                    }
-                }
-            }
-        } else if (dst->type == GGML_TYPE_F16) {
-            size_t id = 0;
-            ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
-
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        for (int64_t i00 = 0; i00 < ne00; i00++) {
-                            const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-
-                            dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
-                            id++;
+                        if (++i10 == dst->ne[0]) {
+                            i10 = 0;
+                            if (++i11 == dst->ne[1]) {
+                                i11 = 0;
+                                if (++i12 == dst->ne[2]) {
+                                    i12 = 0;
+                                    if (++i13 == dst->ne[3]) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else if (dst->type == GGML_TYPE_F16) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr);
+
+                        if (++i10 == dst->ne[0]) {
+                            i10 = 0;
+                            if (++i11 == dst->ne[1]) {
+                                i11 = 0;
+                                if (++i12 == dst->ne[2]) {
+                                    i12 = 0;
+                                    if (++i13 == dst->ne[3]) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
                         }
                     }
                 }
             }
-        } else {
-            GGML_ASSERT(false); // TODO: implement
         }
     } else {
-        //printf("%s: this is not optimal - fix me\n", __func__);
-
-        if (dst->type == GGML_TYPE_F32) {
-            size_t id = 0;
-            float * dst_ptr = (float *) dst->data;
-
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        for (int64_t i00 = 0; i00 < ne00; i00++) {
-                            const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-
-                            dst_ptr[id] = *src0_ptr;
-                            id++;
-                        }
-                    }
-                }
-            }
-        } else if (dst->type == GGML_TYPE_F16) {
-            size_t id = 0;
-            ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
-
-            for (int64_t i03 = 0; i03 < ne03; i03++) {
-                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    for (int64_t i01 = 0; i01 < ne01; i01++) {
-                        for (int64_t i00 = 0; i00 < ne00; i00++) {
-                            const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-
-                            dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
-                            id++;
-                        }
-                    }
-                }
-            }
-        } else {
-            GGML_ASSERT(false); // TODO: implement
-        }
+        GGML_ASSERT(false); // TODO: implement
     }
 }
 
@@ -7199,7 +7238,6 @@ static void ggml_compute_forward_rope_f32(
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
-    assert(params->ith == 0);
     assert(src1->type == GGML_TYPE_I32);
     assert(ggml_nelements(src1) == 3);
 
@@ -7226,11 +7264,28 @@ static void ggml_compute_forward_rope_f32(
 
     assert(nb0 == sizeof(float));
 
-    // TODO: optimize
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
     for (int64_t i3 = 0; i3 < ne3; i3++) {
         for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
             const int p = (mode == 0 ? n_past + i2 : i2);
             for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
                 for (int i0 = 0; i0 < n_dims; i0 += 2) {
                     const float theta = powf(10000.0, ((float)-i0)/n_dims);
 
@@ -7256,7 +7311,6 @@ static void ggml_compute_forward_rope_f16(
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
-    assert(params->ith == 0);
     assert(src1->type == GGML_TYPE_I32);
     assert(ggml_nelements(src1) == 3);
 
@@ -7283,10 +7337,28 @@ static void ggml_compute_forward_rope_f16(
 
     assert(nb0 == sizeof(ggml_fp16_t));
 
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
     for (int64_t i3 = 0; i3 < ne3; i3++) {
         for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
             const int p = (mode == 0 ? n_past + i2 : i2);
             for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
                 for (int i0 = 0; i0 < n_dims; i0 += 2) {
                     const float theta = powf(10000.0, ((float)-i0)/n_dims);
 
@@ -9385,7 +9457,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                     } break;
                 case GGML_OP_ROPE:
                     {
-                        node->n_tasks = 1;
+                        node->n_tasks = n_threads;
                     } break;
                 case GGML_OP_CONV_1D_1S:
                 case GGML_OP_CONV_1D_2S:

ggml.h

@@ -558,6 +558,16 @@ struct ggml_tensor * ggml_view_2d(
         size_t                nb1, // row stride in bytes
         size_t                offset);
 
+struct ggml_tensor * ggml_view_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        size_t                nb1, // row   stride in bytes
+        size_t                nb2, // slice stride in bytes
+        size_t                offset);
+
 struct ggml_tensor * ggml_permute(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,

llama.cpp

@@ -812,37 +812,35 @@ static bool llama_eval_internal(
 
         // self-attention
         {
-            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+            // compute Q and K and RoPE them
+            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);
 
             // store key and value to memory
-            if (N >= 1) {
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_tensor * v = ggml_view_1d(ctx0, kv_self.v, N*n_embd, (ggml_element_size(kv_self.v)*n_embd)*(il*n_ctx + n_past));
+            {
+                // compute the transposed [N, n_embd] V matrix
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), n_embd, N));
 
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
+
+                // important: storing RoPE-ed version of K in the KV cache!
                 ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
                 ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
             }
 
-            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
             struct ggml_tensor * Q =
                 ggml_permute(ctx0,
-                        ggml_rope(ctx0,
-                            ggml_cpy(ctx0,
-                                Qcur,
-                                ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
-                            n_past, n_rot, 0),
+                        Qcur,
                         0, 2, 1, 3);
 
-            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
             struct ggml_tensor * K =
                 ggml_permute(ctx0,
-                        ggml_rope(ctx0,
-                            ggml_reshape_3d(ctx0,
-                                ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
-                                n_embd/n_head, n_head, n_past + N),
-                            n_past, n_rot, 1),
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
                         0, 2, 1, 3);
 
             // K * Q
@@ -860,18 +858,23 @@ static bool llama_eval_internal(
             // KQ = soft_max(KQ_masked)
             struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
 
-            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
-            struct ggml_tensor * V_trans =
-                ggml_cpy(ctx0,
-                    ggml_permute(ctx0,
-                            ggml_reshape_3d(ctx0,
-                                ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.v)*n_embd),
-                                n_embd/n_head, n_head, n_past + N),
-                            1, 2, 0, 3),
-                    ggml_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_embd/n_head, n_head));
+            // split cached V into n_head heads
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_past + N, n_embd/n_head, n_head,
+                        n_ctx*ggml_element_size(kv_self.v),
+                        n_ctx*ggml_element_size(kv_self.v)*n_embd/n_head,
+                        il*n_ctx*ggml_element_size(kv_self.v)*n_embd);
 
-            // KQV = transpose(V) * KQ_soft_max
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+#if 1
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+#else
+            // make V contiguous in memory to speed up the matmul, however we waste time on the copy
+            // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
+            // is there a better way?
+            struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_embd/n_head, n_head));
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
+#endif
 
             // KQV_merged = KQV.permute(0, 2, 1, 3)
             struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
@@ -957,9 +960,13 @@ static bool llama_eval_internal(
     ggml_build_forward_expand(&gf, inpL);
     ggml_graph_compute       (ctx0, &gf);
 
+    // print timing information per ggml operation (for debugging purposes)
+    // requires GGML_PERF to be defined
+    //ggml_graph_print(&gf);
+
+    // plot the computation graph in dot format (for debugging purposes)
     //if (n_past%100 == 0) {
-    //    ggml_graph_print   (&gf);
-    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+    //    ggml_graph_dump_dot(&gf, NULL, "llama.dot");
     //}
 
     //embd_w.resize(n_vocab*N);
@@ -1231,7 +1238,9 @@ static llama_vocab::id llama_sample_top_p_top_k(
         }
     }
 
-    sample_top_k(logits_id, top_k);
+    if (top_k > 0 && top_k < n_logits) {
+        sample_top_k(logits_id, top_k);
+    }
 
     float maxl = -std::numeric_limits<float>::infinity();
     for (const auto & kv : logits_id) {