cmake : fix the metal file foder path (#4217 )

readme : fix typo (#4253 )
llama.cpp uses GitHub Actions, not Gitlab Actions.
2026-07-14 08:25:55 +02:00 · 2023-11-30 23:44:11 +02:00 · 2023-11-30 23:43:32 +02:00 · 2023-11-30 23:43:08 +02:00 · 2023-11-30 23:42:23 +02:00 · 2023-11-30 23:40:23 +02:00
38 changed files with 1859 additions and 93 deletions
@@ -498,6 +498,17 @@ jobs:
          path: |
            cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip

+  ios-xcode-build:
+    runs-on: macos-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Build Xcode project
+        run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
+
+
 #  freeBSD-latest:
 #    runs-on: macos-12
 #    steps:
@@ -47,6 +47,7 @@ models-mnt
 /libllama.so
 /llama-bench
 /llava-cli
+/lookahead
 /main
 /metal
 /perplexity
@@ -43,6 +43,7 @@ else()
 endif()

 # general
+option(BUILD_SHARED_LIBS                "build shared libraries"                                OFF)
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
 option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      ON)
 option(LLAMA_LTO                        "llama: enable link time optimization"                  OFF)
@@ -100,6 +101,9 @@ option(LLAMA_BUILD_TESTS                "llama: build tests"    ${LLAMA_STANDALO
 option(LLAMA_BUILD_EXAMPLES             "llama: build examples" ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER               "llama: build server example"                           ON)

+# Required for relocatable CMake package
+include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
+
 #
 # Compile flags
 #
@@ -161,7 +165,7 @@ if (LLAMA_METAL)
    #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")

    # copy ggml-metal.metal to bin directory
-    configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
+    configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)

    set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
        ${FOUNDATION_LIBRARY}
@@ -2,7 +2,7 @@
 BUILD_TARGETS = \
 	main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
 	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
-	speculative infill tokenize benchmark-matmult parallel finetune export-lora tests/test-c.o
+	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o

 # Binaries only useful for tests
 TEST_TARGETS = \
@@ -657,6 +657,9 @@ speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS)
 parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

+lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
 ifdef LLAMA_METAL
 metal: examples/metal/metal.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
@@ -10,6 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++

 ### Hot topics

+- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225
+- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
 - Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167

 ----
@@ -114,6 +116,8 @@ as the main playground for developing new features for the [ggml](https://github
 - [nat/openplayground](https://github.com/nat/openplayground)
 - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
 - [withcatai/catai](https://github.com/withcatai/catai)
+- [semperai/amica](https://github.com/semperai/amica)
+- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)

 ---

@@ -892,7 +896,7 @@ Additionally, there the following images, similar to the above:
 - `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
 - `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)

-The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the Gitlab Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
+The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).

 #### Usage

@@ -26,7 +26,7 @@ add_custom_command(
    COMMENT "Generating build details from Git"
    COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
            -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake"
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
    WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
    DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
    VERBATIM
@@ -59,7 +59,7 @@ class Model:
                from safetensors import safe_open
                ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
            else:
-                ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu"))
+                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))

            with ctx as model_part:
                for name in model_part.keys():
@@ -267,7 +267,7 @@ class Params:
            n_ctx = 2048

        return Params(
-            n_vocab          = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
+            n_vocab          = model["tok_embeddings.weight"].shape[0],
            n_embd           = config["dim"],
            n_layer          = config["n_layers"],
            n_ctx            = n_ctx,
@@ -32,6 +32,7 @@ else()
    add_subdirectory(save-load-state)
    add_subdirectory(simple)
    add_subdirectory(speculative)
+    add_subdirectory(lookahead)
    add_subdirectory(train-text-from-scratch)
    if (LLAMA_METAL)
        add_subdirectory(metal)
@@ -0,0 +1 @@
+xcuserdata
@@ -0,0 +1,7 @@
+# llama.swiftui
+
+Local inference of llama.cpp on an iPhone.
+So far I only tested with starcoder 1B model, but it can most likely handle 7B models as well.
+
+https://github.com/bachittle/llama.cpp/assets/39804642/e290827a-4edb-4093-9642-2a5e399ec545
+
@@ -0,0 +1,176 @@
+import Foundation
+
+// import llama
+
+enum LlamaError: Error {
+    case couldNotInitializeContext
+}
+
+actor LlamaContext {
+    private var model: OpaquePointer
+    private var context: OpaquePointer
+    private var batch: llama_batch
+    private var tokens_list: [llama_token]
+
+    var n_len: Int32 = 512
+    var n_cur: Int32 = 0
+    var n_decode: Int32 = 0
+
+    init(model: OpaquePointer, context: OpaquePointer) {
+        self.model = model
+        self.context = context
+        self.tokens_list = []
+        self.batch = llama_batch_init(512, 0, 1)
+    }
+
+    deinit {
+        llama_free(context)
+        llama_free_model(model)
+        llama_backend_free()
+    }
+
+    static func createContext(path: String) throws -> LlamaContext {
+        llama_backend_init(false)
+        let model_params = llama_model_default_params()
+
+        let model = llama_load_model_from_file(path, model_params)
+        guard let model else {
+            print("Could not load model at \(path)")
+            throw LlamaError.couldNotInitializeContext
+        }
+        var ctx_params = llama_context_default_params()
+        ctx_params.seed = 1234
+        ctx_params.n_ctx = 2048
+        ctx_params.n_threads = 8
+        ctx_params.n_threads_batch = 8
+
+        let context = llama_new_context_with_model(model, ctx_params)
+        guard let context else {
+            print("Could not load context!")
+            throw LlamaError.couldNotInitializeContext
+        }
+
+        return LlamaContext(model: model, context: context)
+    }
+
+    func get_n_tokens() -> Int32 {
+        return batch.n_tokens;
+    }
+
+    func completion_init(text: String) {
+        print("attempting to complete \"\(text)\"")
+
+        tokens_list = tokenize(text: text, add_bos: true)
+
+        let n_ctx = llama_n_ctx(context)
+        let n_kv_req = tokens_list.count + (Int(n_len) - tokens_list.count)
+
+        print("\n n_len = \(n_len), n_ctx = \(n_ctx), n_kv_req = \(n_kv_req)")
+
+        if n_kv_req > n_ctx {
+            print("error: n_kv_req > n_ctx, the required KV cache size is not big enough")
+        }
+
+        for id in tokens_list {
+            print(token_to_piece(token: id))
+        }
+
+        // batch = llama_batch_init(512, 0) // done in init()
+        batch.n_tokens = Int32(tokens_list.count)
+
+        for i1 in 0..<batch.n_tokens {
+            let i = Int(i1)
+            batch.token[i] = tokens_list[i]
+            batch.pos[i] = i1
+            batch.n_seq_id[Int(i)] = 1
+            batch.seq_id[Int(i)]![0] = 0
+            batch.logits[i] = 0
+        }
+        batch.logits[Int(batch.n_tokens) - 1] = 1 // true
+
+        if llama_decode(context, batch) != 0 {
+            print("llama_decode() failed")
+        }
+
+        n_cur = batch.n_tokens
+    }
+
+    func completion_loop() -> String {
+        var new_token_id: llama_token = 0
+
+        let n_vocab = llama_n_vocab(model)
+        let logits = llama_get_logits_ith(context, batch.n_tokens - 1)
+
+        var candidates = Array<llama_token_data>()
+        candidates.reserveCapacity(Int(n_vocab))
+
+        for token_id in 0..<n_vocab {
+            candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
+        }
+        candidates.withUnsafeMutableBufferPointer() { buffer in
+            var candidates_p = llama_token_data_array(data: buffer.baseAddress, size: buffer.count, sorted: false)
+
+            new_token_id = llama_sample_token_greedy(context, &candidates_p)
+        }
+
+        if new_token_id == llama_token_eos(context) || n_cur == n_len {
+            print("\n")
+            return ""
+        }
+
+        let new_token_str = token_to_piece(token: new_token_id)
+        print(new_token_str)
+        // tokens_list.append(new_token_id)
+
+        batch.n_tokens = 0
+
+        batch.token[Int(batch.n_tokens)] = new_token_id
+        batch.pos[Int(batch.n_tokens)] = n_cur
+        batch.n_seq_id[Int(batch.n_tokens)] = 1
+        batch.seq_id[Int(batch.n_tokens)]![0] = 0
+        batch.logits[Int(batch.n_tokens)] = 1 // true
+        batch.n_tokens += 1
+
+        n_decode += 1
+
+        n_cur += 1
+
+        if llama_decode(context, batch) != 0 {
+            print("failed to evaluate llama!")
+        }
+
+        return new_token_str
+    }
+
+    func clear() {
+        tokens_list.removeAll()
+    }
+
+    private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
+        let n_tokens = text.count + (add_bos ? 1 : 0)
+        let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
+        let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, false)
+
+        var swiftTokens: [llama_token] = []
+        for i in 0..<tokenCount {
+            swiftTokens.append(tokens[Int(i)])
+        }
+
+        tokens.deallocate()
+
+        return swiftTokens
+    }
+
+    private func token_to_piece(token: llama_token) -> String {
+        let result = UnsafeMutablePointer<Int8>.allocate(capacity: 8)
+        result.initialize(repeating: Int8(0), count: 8)
+
+        let _ = llama_token_to_piece(model, token, result, 8)
+
+        let resultStr = String(cString: result)
+
+        result.deallocate()
+
+        return resultStr
+    }
+}
@@ -0,0 +1,5 @@
+//
+//  Use this file to import your target's public headers that you would like to expose to Swift.
+//
+
+#import "llama.h"
@@ -0,0 +1,481 @@
+// !$*UTF8*$!
+{
+    archiveVersion = 1;
+    classes = {
+    };
+    objectVersion = 56;
+    objects = {
+
+/* Begin PBXBuildFile section */
+        542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; };
+        5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; };
+        542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
+        542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; };
+        542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; };
+        542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; };
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+
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+            defaultConfigurationIsVisible = 0;
+            defaultConfigurationName = Release;
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+        8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */ = {
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+            buildConfigurations = (
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+            defaultConfigurationName = Release;
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+/* End XCConfigurationList section */
+    };
+    rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
+}
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Workspace
+   version = "1.0">
+   <FileRef
+      location = "self:">
+   </FileRef>
+</Workspace>
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
+<plist version="1.0">
+<dict>
+    <key>IDEDidComputeMac32BitWarning</key>
+    <true/>
+</dict>
+</plist>
@@ -0,0 +1,11 @@
+{
+  "colors" : [
+    {
+      "idiom" : "universal"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
@@ -0,0 +1,13 @@
+{
+  "images" : [
+    {
+      "idiom" : "universal",
+      "platform" : "ios",
+      "size" : "1024x1024"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
@@ -0,0 +1,6 @@
+{
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
@@ -0,0 +1,45 @@
+import Foundation
+
+@MainActor
+class LlamaState: ObservableObject {
+    @Published var messageLog = ""
+
+    private var llamaContext: LlamaContext?
+    private var modelUrl: URL? {
+        Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models")
+        // Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
+    }
+    init() {
+        do {
+            try loadModel()
+        } catch {
+            messageLog += "Error!\n"
+        }
+    }
+
+    private func loadModel() throws {
+        messageLog += "Loading model...\n"
+        if let modelUrl {
+            llamaContext = try LlamaContext.createContext(path: modelUrl.path())
+            messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
+        } else {
+            messageLog += "Could not locate model\n"
+        }
+    }
+
+    func complete(text: String) async {
+        guard let llamaContext else {
+            return
+        }
+        messageLog += "Attempting to complete text...\n"
+        await llamaContext.completion_init(text: text)
+        messageLog += "\(text)"
+
+        while await llamaContext.n_cur <= llamaContext.n_len {
+            let result = await llamaContext.completion_loop()
+            messageLog += "\(result)"
+        }
+        await llamaContext.clear()
+        messageLog += "\n\ndone\n"
+    }
+}
@@ -0,0 +1,6 @@
+{
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
@@ -0,0 +1,42 @@
+import SwiftUI
+
+struct ContentView: View {
+    @StateObject var llamaState = LlamaState()
+
+    @State private var multiLineText = ""
+
+    var body: some View {
+        VStack {
+            ScrollView(.vertical) {
+                Text(llamaState.messageLog)
+            }
+
+            TextEditor(text: $multiLineText)
+                .frame(height: 200)
+                .padding()
+                .border(Color.gray, width: 0.5)
+            Button(action: {
+                sendText()
+            }) {
+                Text("Send")
+                    .padding()
+                    .background(Color.blue)
+                    .foregroundColor(.white)
+                    .cornerRadius(8)
+            }
+        }
+        .padding()
+    }
+
+    func sendText() {
+        Task {
+            await llamaState.complete(text: multiLineText)
+            multiLineText = ""
+        }
+    }
+}
+/*
+#Preview {
+    ContentView()
+}
+*/
@@ -0,0 +1,10 @@
+import SwiftUI
+
+@main
+struct llama_swiftuiApp: App {
+    var body: some Scene {
+        WindowGroup {
+            ContentView()
+        }
+    }
+}
@@ -0,0 +1,5 @@
+set(TARGET lookahead)
+add_executable(${TARGET} lookahead.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
@@ -0,0 +1,7 @@
+# llama.cpp/examples/lookahead
+
+Demonstartion of lookahead decoding technique:
+
+https://lmsys.org/blog/2023-11-21-lookahead-decoding/
+
+More info: https://github.com/ggerganov/llama.cpp/pull/4207
@@ -0,0 +1,487 @@
+#include "common.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+struct ngram_data {
+    bool active = false;
+
+    llama_seq_id seq_id = -1;
+
+    std::vector<int> i_batch;
+
+    std::vector<llama_token> tokens;
+};
+
+// n-gram container
+struct ngram_container {
+    ngram_container(int n_vocab, int N, int G) {
+        cnt.resize(n_vocab);
+        head.resize(n_vocab);
+        tokens.resize(n_vocab * G * (N - 1));
+    }
+
+    int n_total = 0;
+
+    std::vector<int> cnt;
+    std::vector<int> head;
+
+    // [n_vocab][G][N - 1]
+    // for each token of the vocab, keep a ring-buffer of capacity G of n-grams of size N - 1
+    std::vector<llama_token> tokens;
+};
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+
+    if (gpt_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    const int W = 15; // lookahead window
+    const int N = 5;  // n-gram size
+    const int G = 15; // max verification n-grams
+
+    const bool dump_kv_cache = params.dump_kv_cache;
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("lookahead", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+#endif // LOG_DISABLE_LOGS
+
+    // init llama.cpp
+    llama_backend_init(params.numa);
+
+    llama_model * model = NULL;
+    llama_context * ctx = NULL;
+
+    // load the target model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+
+    // Tokenize the prompt
+    const bool add_bos = llama_should_add_bos_token(model);
+    LOG("add_bos tgt: %d\n", add_bos);
+
+    std::vector<llama_token> inp;
+    std::vector<llama_token> all;
+
+    inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+    all = inp;
+
+    const int max_context_size     = llama_n_ctx(ctx);
+    const int max_tokens_list_size = max_context_size - 4;
+
+    if ((int) inp.size() > max_tokens_list_size) {
+        fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
+        return 1;
+    }
+
+    fprintf(stderr, "\n\n");
+
+    for (auto id : inp) {
+        fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
+    }
+
+    fflush(stderr);
+
+    const int n_input = inp.size();
+
+    const auto t_enc_start = ggml_time_us();
+
+    // eval the prompt
+    llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0,           0));
+    llama_decode(ctx, llama_batch_get_one(&inp.back(),           1, n_input - 1, 0));
+
+    for (int s = 1; s < W + G + 1; ++s) {
+        llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
+    }
+
+    const auto t_enc_end = ggml_time_us();
+
+    int n_predict = 0;
+    int n_accept  = 0;
+
+    int n_past = inp.size();
+
+    llama_token id = 0;
+
+    // used to determine end of generation
+    bool has_eos = false;
+
+    // for each decoded batch, we have at most W + G + 1 distinct sequences:
+    // seq_id == 0           : the current input token
+    // seq_id [1, W]         : tokens from the past N - 1 Jacobi iterations
+    // seq_id [W + 1, W + G] : verification n-grams
+    llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
+
+    // target model sampling context
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
+
+    // verification n-grams
+    std::vector<ngram_data> ngrams_cur(G);
+
+    // tokens for the past N - 1 Jacobi iterations
+    std::vector<llama_token> tokens_j_prev(W);
+    std::vector<std::vector<llama_token>> tokens_j(N - 1);
+    for (int j = 0; j < N - 1; j++) {
+        tokens_j[j].resize(W);
+
+        for (int i = 0; i < W; i++) {
+            // there are different ways to init these tokens
+            if (0) {
+                // initialize randomly from the prompt tokens
+                tokens_j[j][i] = all[1 + rand() % (all.size() - 1)];
+            } else {
+                // initialize with a sequence of increasing numbers
+                tokens_j[j][i] = 100 + i;
+            }
+        }
+    }
+
+    std::vector<llama_seq_id> seq_id_look;
+
+    // the input token belongs both to all sequences
+    std::vector<llama_seq_id> seq_id_all(W + G + 1);
+    for (int i = 0; i < W + G + 1; i++) {
+        seq_id_all[i] = i;
+    }
+
+    // here we keep adding new n-grams as we go
+    ngram_container ngrams_observed(llama_n_vocab(model), N, G);
+
+    // debug
+    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
+
+    const auto t_dec_start = ggml_time_us();
+
+    // sample first token
+    {
+        id = llama_sampling_sample(ctx_sampling, ctx, NULL, 0);
+
+        llama_sampling_accept(ctx_sampling, ctx, id, true);
+
+        {
+            const std::string token_str = llama_token_to_piece(ctx, id);
+
+            printf("%s", token_str.c_str());
+            fflush(stdout);
+        }
+    }
+
+    while (true) {
+        // debug
+        if (dump_kv_cache) {
+            llama_kv_cache_view_update(ctx, &kvc_view);
+            dump_kv_cache_view_seqs(kvc_view, 40);
+        }
+
+        // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
+        //
+        // Example for W = 5, N = 4, G = 2:
+        // (I = input, L = lookahead, V = verification)
+        //
+        // Batch:  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
+        // T:        -2 -2 -2 -2 -1 -1 -1 -1 -1  0  0  0  0  0  0
+        // Info:   I  L  L  L  L  L  L  L  L  L  L  L  L  L  L  V  V  V  V  V  V
+        // Pos:    0  1  2  3  4  1  2  3  4  5  2  3  4  5  6  1  2  3  1  2  3   (+ n_past)
+        // Logits: 1  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1
+        // ---------------------------------------------------------------------
+        // Seq:    0
+        //         1              1              1
+        //         2  2              2              2
+        //         3  3  3              3              3
+        //         4  4  4  4              4              4
+        //         5  5  5  5  5              5              5
+        //         6                                            6  6  6
+        //         7                                                     7  7  7
+        // ---------------------------------------------------------------------
+        //                                       |  |  |  |  |  |  |  |  |  |  |
+        //                                       V  V  V  V  V  |  |  |  |  |  |
+        //                                         j_tokens     |  |  |  |  |  |
+        //                                                      V  V  V  V  V  V
+        //                                                             id
+        {
+            llama_batch_clear(batch);
+
+            // current token - first token of the first level
+            llama_batch_add(batch, id, n_past, seq_id_all, true);
+
+            // verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
+            {
+                const int g_cur = ngrams_observed.cnt[id];
+
+                ngrams_cur.resize(g_cur);
+                for (int g = 0; g < g_cur; g++) {
+                    ngrams_cur[g].active = true;
+                    ngrams_cur[g].tokens.resize(N);
+                    ngrams_cur[g].i_batch.resize(N);
+                    ngrams_cur[g].seq_id = W + 1 + g;
+                    ngrams_cur[g].i_batch[0] = 0;
+                    ngrams_cur[g].tokens [0] = id;
+                }
+
+                for (int j = 0; j < N - 1; j++) {
+                    for (int g = 0; g < g_cur; g++) {
+                        const int idx = id*(N - 1)*G + g*(N - 1);
+
+                        const llama_token t = ngrams_observed.tokens[idx + j];
+
+                        ngrams_cur[g].tokens [j + 1] = t;
+                        ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
+
+                        llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
+                    }
+                }
+            }
+
+            // fill the remaining W - 1 tokens for the first level
+            for (int i = 1; i < W; i++) {
+                seq_id_look.resize(W - i);
+                for (int j = 0; j < W - i; j++) {
+                    seq_id_look[j] = i + j + 1;
+                }
+
+                llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
+            }
+
+            // fill the rest of the levels
+            for (int j = 1; j < N - 1; j++) {
+                for (int i = 0; i < W; i++) {
+                    llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
+                }
+            }
+        }
+
+        if (llama_decode(ctx, batch) != 0) {
+            fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__);
+            return 1;
+        }
+
+        int seq_id_best = 0;
+
+        for (int v = 0; v < N; ++v) {
+            int i_batch = 0;
+
+            // if no active ngrams are left, it means the sampled token does not pass the verification
+            if (v > 0) {
+                for (int g = 0; g < (int) ngrams_cur.size(); g++) {
+                    if (ngrams_cur[g].active) {
+                        i_batch = ngrams_cur[g].i_batch[v];
+                        seq_id_best = ngrams_cur[g].seq_id;
+
+                        ++n_accept;
+                        break;
+                    }
+                }
+
+                // no more matches -> create a new batch
+                if (i_batch == 0) {
+                    break;
+                }
+            }
+
+            // sample the next token
+            id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_batch);
+
+            llama_sampling_accept(ctx_sampling, ctx, id, true);
+
+            // print
+            {
+                const std::string token_str = llama_token_to_piece(ctx, id);
+
+                if (v == 0) {
+                    printf("%s", token_str.c_str());
+                } else {
+                    // print light cyan
+                    printf("\033[0;96m%s\033[0m", token_str.c_str());
+                }
+                fflush(stdout);
+
+                if (id == llama_token_eos(model)) {
+                    has_eos = true;
+                }
+
+                all.push_back(id);
+            }
+
+            ++n_predict;
+            ++n_past;
+
+            if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
+                break;
+            }
+
+            // verify across active n-grams
+            for (int g = 0; g < (int) ngrams_cur.size(); g++) {
+                if (ngrams_cur[g].active) {
+                    if (v == N - 1) {
+                        ngrams_cur[g].active = false;
+                    } else {
+                        if (id != ngrams_cur[g].tokens[v + 1]) {
+                            ngrams_cur[g].active = false;
+                        }
+                    }
+                }
+            }
+
+            // print known n-grams starting with token id (debug)
+            if (0 && v == 0) {
+                if (ngrams_observed.cnt[id] > 0) {
+                    printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
+                }
+
+                for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
+                    printf("   - ngram %2d: ", i);
+
+                    const int idx = id*(N - 1)*G + i*(N - 1);
+
+                    for (int j = 0; j < N - 1; j++) {
+                        const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
+
+                        printf("%s", token_str.c_str());
+                    }
+
+                    printf("\n");
+                }
+            }
+
+            // update lookahead tokens
+            {
+                for (int i = 0; i < W; i++) {
+                    tokens_j_prev[i] = tokens_j[0][i];
+                }
+
+                for (int j = 0; j < N - 2; j++) {
+                    tokens_j[j] = tokens_j[j + 1];
+                }
+
+                if (v == 0) {
+                    // sample from the last level
+                    for (int i = 0; i < W; i++) {
+                        tokens_j[N - 2][i] = llama_sampling_sample(ctx_sampling, ctx, NULL, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
+                    }
+                } else {
+                    for (int i = 0; i < W; i++) {
+                        // there are different ways to init these tokens
+                        if (0) {
+                            // random init
+                            tokens_j[N - 2][i] = all[1 + rand() % (all.size() - 1)];
+                        } else {
+                            // init from the previous level
+                            tokens_j[N - 2][i] = tokens_j[0][i];
+                        }
+                    }
+                }
+            }
+
+            // update observed ngrams
+            if (v == 0) {
+                // the first token of the n-gram is determined by the index in the container so it is not stored
+                std::vector<llama_token> ngram(N - 1);
+
+                // n-gram generation
+                // ref: https://github.com/hao-ai-lab/LookaheadDecoding/issues/14#issuecomment-1826198518
+                for (int f = 0; f < W; ++f) {
+                    const int ft = tokens_j_prev[f]; // first token of the n-gram
+
+                    for (int j = 0; j < N - 1; ++j) {
+                        ngram[j] = tokens_j[j][f];
+                    }
+
+                    // filter-out repeating n-grams
+                    {
+                        bool is_unique = true;
+
+                        for (int k = 0; k < ngrams_observed.cnt[ft]; ++k) {
+                            const int idx = ft*(N - 1)*G + k*(N - 1);
+
+                            bool is_match = true;
+                            for (int j = 0; j < N - 1; ++j) {
+                                if (ngrams_observed.tokens[idx + j] != ngram[j]) {
+                                    is_match = false;
+                                    break;
+                                }
+                            }
+
+                            if (is_match) {
+                                is_unique = false;
+                                break;
+                            }
+                        }
+
+                        if (!is_unique) {
+                            continue;
+                        }
+                    }
+
+                    const int head = ngrams_observed.head[ft];
+                    const int idx  = ft*(N - 1)*G + head*(N - 1);
+
+                    for (int i = 0; i < N - 1; i++) {
+                        ngrams_observed.tokens[idx + i] = ngram[i];
+                    }
+
+                    ngrams_observed.cnt[ft]  = std::min(G, ngrams_observed.cnt[ft] + 1);
+                    ngrams_observed.head[ft] = (head + 1) % G;
+
+                    ngrams_observed.n_total++;
+                }
+            }
+        }
+
+        if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
+            break;
+        }
+
+        // KV cache management
+        // if no verification token matched, we simply remove all cells from this batch -> no fragmentation
+        llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
+
+        if (seq_id_best != 0) {
+            // if a verification token matched, we keep the best sequence and remove the rest
+            // this leads to some KV cache fragmentation
+            llama_kv_cache_seq_keep(ctx, seq_id_best);
+            llama_kv_cache_seq_cp  (ctx, seq_id_best, 0, -1, -1);
+            llama_kv_cache_seq_rm  (ctx, seq_id_best,    -1, -1);
+
+            for (int s = 1; s < W + G + 1; ++s) {
+                llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
+            }
+        }
+    }
+
+    auto t_dec_end = ggml_time_us();
+
+    LOG_TEE("\n\n");
+
+    LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
+    LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
+
+    LOG_TEE("\n");
+    LOG_TEE("W = %2d\n", W);
+    LOG_TEE("N = %2d\n", N);
+    LOG_TEE("G = %2d\n", G);
+    LOG_TEE("\n");
+    LOG_TEE("n_predict = %d\n", n_predict);
+    LOG_TEE("n_accept  = %d\n", n_accept);
+
+    llama_print_timings(ctx);
+
+    llama_kv_cache_view_free(&kvc_view);
+    llama_sampling_free(ctx_sampling);
+
+    llama_batch_free(batch);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    fprintf(stderr, "\n\n");
+
+    return 0;
+}
@@ -234,6 +234,55 @@ node index.js

 -   **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.

+-   **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
+
+    *Options:*
+
+    See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
+
+    *Examples:*
+
+    You can use either Python `openai` library with appropriate checkpoints:
+
+    ```python
+    import openai
+
+    client = openai.OpenAI(
+        base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
+        api_key = "sk-no-key-required"
+    )
+
+    completion = client.chat.completions.create(
+    model="gpt-3.5-turbo",
+    messages=[
+        {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
+        {"role": "user", "content": "Write a limerick about python exceptions"}
+    ]
+    )
+
+    print(completion.choices[0].message)
+    ```
+    ... or raw HTTP requests:
+
+    ```shell
+    curl http://localhost:8080/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer no-key" \
+    -d '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+    {
+        "role": "system",
+        "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
+    },
+    {
+        "role": "user",
+        "content": "Write a limerick about python exceptions"
+    }
+    ]
+    }'
+    ```
+
 ## More examples

 ### Change system prompt on runtime
@@ -11,10 +11,10 @@ app = Flask(__name__)
 slot_id = -1

 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("--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.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
+parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
+parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
+parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT'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="")
@@ -34,19 +34,19 @@ def is_present(json, key):

 #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")
+    system_n = args.system_name
+    user_n = args.user_name
+    ai_n = args.ai_name
+    stop = args.stop

+    prompt = "" + args.chat_prompt + stop

    for line in messages:
        if (line["role"] == "system"):
-            prompt += f"{system_n}{line['content']}"
+            prompt += f"{system_n}{line['content']}{stop}"
        if (line["role"] == "user"):
-            prompt += f"{user_n}{line['content']}"
+            prompt += f"{user_n}{line['content']}{stop}"
        if (line["role"] == "assistant"):
            prompt += f"{ai_n}{line['content']}{stop}"
    prompt += ai_n.rstrip()
@@ -130,7 +130,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
            }
        ]
    }
-    slot_id = data["slot_id"]
+    slot_id = data.get("slot_id")
    if (chat):
        if (start):
            resData["choices"][0]["delta"] =  {
@@ -150,11 +150,13 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
    return resData


-@app.route('/chat/completions', methods=['POST'])
-@app.route('/v1/chat/completions', methods=['POST'])
+@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
+@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
 def chat_completions():
    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
        return Response(status=403)
+    if request.method == 'OPTIONS':
+        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
    body = request.get_json()
    stream = False
    tokenize = False
@@ -177,20 +179,22 @@ def chat_completions():
            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))
+            yield 'data: {}\n\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')
+                    yield 'data: {}\n\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})


-@app.route('/completions', methods=['POST'])
-@app.route('/v1/completions', methods=['POST'])
+@app.route('/completions', methods=['POST', 'OPTIONS'])
+@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
 def completion():
    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
        return Response(status=403)
+    if request.method == 'OPTIONS':
+        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
    body = request.get_json()
    stream = False
    tokenize = False
@@ -216,8 +220,8 @@ def completion():
                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')
+                    yield 'data: {}\n\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})

 if __name__ == '__main__':
    app.run(args.host, port=args.port)
@@ -29,6 +29,8 @@
 #define SERVER_VERBOSE 1
 #endif

+#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+
 using json = nlohmann::json;

 struct server_params
@@ -59,6 +61,10 @@ static bool server_verbose = false;
 #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_INFO(   MSG, ...) server_log("INFO",    __func__, __LINE__, MSG, __VA_ARGS__)

+json oaicompat_completion_params_parse(const json &body);
+std::string format_chatml(std::vector<json> messages);
+
+
 //
 // base64 utils (TODO: move to common in the future)
 //
@@ -378,6 +384,9 @@ struct llama_client_slot
    bool stopped_word = false;
    bool stopped_limit = false;

+    bool oaicompat = false;
+    std::string oaicompat_model;
+
    std::string stopping_word;

    // sampling
@@ -477,7 +486,7 @@ struct llama_client_slot
        };
    }

-    void print_timings() {
+    void print_timings() const {
        LOG_TEE("\n");
        LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
@@ -609,6 +618,11 @@ struct llama_server_context

    std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
    {
+        // TODO: currently, we tokenize using special tokens by default
+        //       this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
+        //       but it's better compared to completely ignoring ChatML and other chat templates
+        const bool TMP_FORCE_SPECIAL = true;
+
        // If `add_bos` is true, we only add BOS, when json_prompt is a string,
        // or the first element of the json_prompt array is a string.
        std::vector<llama_token> prompt_tokens;
@@ -624,12 +638,12 @@ struct llama_server_context
                    std::vector<llama_token> p;
                    if (first)
                    {
-                        p = ::llama_tokenize(ctx, s, add_bos);
+                        p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
                        first = false;
                    }
                    else
                    {
-                        p = ::llama_tokenize(ctx, s, false);
+                        p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
                    }
                    prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
                }
@@ -646,7 +660,7 @@ struct llama_server_context
        else
        {
            auto s = json_prompt.template get<std::string>();
-            prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
+            prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
        }

        return prompt_tokens;
@@ -677,6 +691,14 @@ struct llama_server_context
        slot_params default_params;
        llama_sampling_params default_sparams;

+        if (data.count("__oaicompat") != 0) {
+            slot->oaicompat = true;
+            slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+        } else {
+            slot->oaicompat = false;
+            slot->oaicompat_model = "";
+        }
+
        slot->params.stream           = json_value(data, "stream",            false);
        slot->params.cache_prompt     = json_value(data, "cache_prompt",      false);
        slot->params.n_predict        = json_value(data, "n_predict",         default_params.n_predict);
@@ -1170,6 +1192,12 @@ struct llama_server_context
            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
        }

+        if (slot.oaicompat)
+        {
+            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
+            res.result_json["model"] = slot.oaicompat_model;
+        }
+
        queue_results.push_back(res);
    }

@@ -1217,6 +1245,12 @@ struct llama_server_context
            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
        }

+        if (slot.oaicompat)
+        {
+            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
+            res.result_json["model"] = slot.oaicompat_model;
+        }
+
        queue_results.push_back(res);
    }

@@ -1257,7 +1291,7 @@ struct llama_server_context
        task_server task;
        task.id = id_gen++;
        task.target_id = 0;
-        task.data = data;
+        task.data = std::move(data);
        task.infill_mode = infill;
        task.embedding_mode = embedding;
        task.type = COMPLETION_TASK;
@@ -2180,6 +2214,233 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
    }
 }

+
+static std::string random_string()
+{
+    static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
+
+    std::random_device rd;
+    std::mt19937 generator(rd());
+
+    std::string result(32, ' ');
+
+    for (int i = 0; i < 32; ++i) {
+        result[i] = str[generator() % str.size()];
+    }
+
+    return result;
+}
+
+static std::string gen_chatcmplid()
+{
+    std::stringstream chatcmplid;
+    chatcmplid << "chatcmpl-" << random_string();
+    return chatcmplid.str();
+}
+
+std::string format_chatml(std::vector<json> messages)
+{
+    std::ostringstream chatml_msgs;
+
+    for (auto it = messages.begin(); it != messages.end(); ++it) {
+        chatml_msgs << "<|im_start|>"
+                    << json_value(*it, "role",    std::string("user")) << '\n';
+        chatml_msgs << json_value(*it, "content", std::string(""))
+                    << "<|im_end|>\n";
+    }
+
+    chatml_msgs << "<|im_start|>assistant" << '\n';
+
+    return chatml_msgs.str();
+}
+
+/* llama.cpp completion api semantics */
+json oaicompat_completion_params_parse(
+    const json &body /* openai api json semantics */)
+{
+    json llama_params;
+
+    llama_params["__oaicompat"] = true;
+
+    // Map OpenAI parameters to llama.cpp parameters
+    llama_params["prompt"]            = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
+    llama_params["temperature"]       = json_value(body, "temperature", 0.8);
+    llama_params["top_k"]             = json_value(body, "top_k", 40);
+    llama_params["top_p"]             = json_value(body, "top_p", 0.95);
+    llama_params["n_predict"]         = json_value(body, "max_tokens", -1);
+    llama_params["logit_bias"]        = json_value(body, "logit_bias",json::object());
+    llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
+    llama_params["presence_penalty"]  = json_value(body, "presence_penalty", 0.0);
+    llama_params["seed"]              = json_value(body, "seed", 0);
+    llama_params["stream"]            = json_value(body, "stream", false);
+    llama_params["mirostat"]          = json_value(body, "mirostat", false);
+    llama_params["mirostat_tau"]      = json_value(body, "mirostat_tau", 0.0);
+    llama_params["mirostat_eta"]      = json_value(body, "mirostat_eta", 0.0);
+    llama_params["penalize_nl"]       = json_value(body, "penalize_nl", false);
+    llama_params["typical_p"]         = json_value(body, "typical_p", 0.0);
+    llama_params["repeat_last_n"]     = json_value(body, "repeat_last_n", 0);
+    llama_params["ignore_eos"]        = json_value(body, "ignore_eos", false);
+    llama_params["tfs_z"]             = json_value(body, "tfs_z", 0.0);
+
+    if (llama_params.count("grammar") != 0) {
+        llama_params["grammar"] = json_value(body, "grammar", json::object());
+    }
+
+    // Handle 'stop' field
+    if (body["stop"].is_null()) {
+        llama_params["stop"] = json::array({});
+    } else if (body["stop"].is_string()) {
+        llama_params["stop"] = json::array({body["stop"].get<std::string>()});
+    } else {
+        llama_params["stop"] = json_value(body, "stop", json::array());
+    }
+
+    // Ensure there is ChatML-specific end sequence among stop words
+    llama_params["stop"].push_back("<|im_end|>");
+
+    return llama_params;
+}
+
+static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
+{
+    json result = response.result_json;
+
+    bool stopped_word        = result.count("stopped_word") != 0;
+    bool stopped_eos         = json_value(result, "stopped_eos", false);
+    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
+    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
+    std::string content      = json_value(result, "content", std::string(""));
+
+    std::string finish_reason = "length";
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+
+    json choices =
+        streaming ? json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"delta", json::object()}}})
+                  : json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"message", json{{"content", content},
+                                                         {"role", "assistant"}}}}});
+
+    std::time_t t = std::time(0);
+
+    json res =
+        json{{"choices", choices},
+            {"created", t},
+            {"model",
+                json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+            {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
+            {"usage",
+                json{{"completion_tokens", num_tokens_predicted},
+                    {"prompt_tokens", num_prompt_tokens},
+                    {"total_tokens", num_tokens_predicted + num_prompt_tokens}}},
+            {"id", gen_chatcmplid()}};
+
+    if (server_verbose) {
+        res["__verbose"] = result;
+    }
+
+    if (result.contains("completion_probabilities")) {
+        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
+    }
+
+    return res;
+}
+
+// return value is vector as there is one case where we might need to generate two responses
+static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
+    json result = response.result_json;
+
+    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
+        return std::vector<json>({response.result_json});
+    }
+
+    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
+    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+
+    bool stopped_word   = json_value(result, "stopped_word", false);
+    bool stopped_eos    = json_value(result, "stopped_eos", false);
+    bool stopped_limit  = json_value(result, "stopped_limit", false);
+    std::string content = json_value(result, "content", std::string(""));
+
+    std::string finish_reason;
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+    if (stopped_limit) {
+        finish_reason = "length";
+    }
+
+    std::time_t t = std::time(0);
+
+    json choices;
+
+    if (!finish_reason.empty()) {
+        choices = json::array({json{{"finish_reason", finish_reason},
+                                    {"index", 0},
+                                    {"delta", json::object()}}});
+    } else {
+        if (first) {
+            if (content.empty()) {
+                choices = json::array({json{{"finish_reason", nullptr},
+                                            {"index", 0},
+                                            {"delta", json{{"role", "assistant"}}}}});
+            } else {
+                // We have to send this as two updates to conform to openai behavior
+                json initial_ret = json{{"choices", json::array({json{
+                                        {"finish_reason", nullptr},
+                                        {"index", 0},
+                                        {"delta", json{
+                                            {"role", "assistant"}
+                                        }}}})},
+                            {"created", t},
+                            {"id", gen_chatcmplid()},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                json second_ret = json{
+                            {"choices", json::array({json{{"finish_reason", nullptr},
+                                                            {"index", 0},
+                                                            {"delta", json{
+                                                            {"content", content}}}
+                                                            }})},
+                            {"created", t},
+                            {"id", gen_chatcmplid()},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                return std::vector<json>({initial_ret, second_ret});
+            }
+        } else {
+            // Some idiosyncrasy in task processing logic makes several trailing calls
+            // with empty content, we ignore these at the calee site.
+            if (content.empty()) {
+                return std::vector<json>({json::object()});
+            }
+
+            choices = json::array({json{
+                {"finish_reason", nullptr},
+                {"index", 0},
+                {"delta",
+                json{
+                    {"content", content},
+                }},
+            }});
+        }
+    }
+
+    json ret = json{{"choices", choices},
+                    {"created", t},
+                    {"id", gen_chatcmplid()},
+                    {"model", modelname},
+                    {"object", "chat.completion.chunk"}};
+
+    return std::vector<json>({ret});
+}
+
 static json format_partial_response(
    llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
 ) {
@@ -2356,9 +2617,9 @@ int main(int argc, char **argv)
                            task_result result = llama.next_result(task_id);
                            if (!result.error) {
                                const std::string str =
-                                "data: " +
-                                result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                "\n\n";
+                                    "data: " +
+                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
+                                    "\n\n";
                                LOG_VERBOSE("data stream", {
                                    { "to_send", str }
                                });
@@ -2371,9 +2632,9 @@ int main(int argc, char **argv)
                                }
                            } else {
                                const std::string str =
-                                "error: " +
-                                result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                "\n\n";
+                                    "error: " +
+                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
+                                    "\n\n";
                                LOG_VERBOSE("data stream", {
                                    { "to_send", str }
                                });
@@ -2398,6 +2659,98 @@ int main(int argc, char **argv)
                }
            });

+
+
+    svr.Get("/v1/models", [&params](const httplib::Request&, httplib::Response& res)
+            {
+                std::time_t t = std::time(0);
+
+                json models = {
+                    {"object", "list"},
+                    {"data", {
+                        {
+                            {"id", params.model_alias},
+                            {"object", "model"},
+                            {"created", t},
+                            {"owned_by", "llamacpp"}
+                        },
+                    }}
+                };
+
+                res.set_content(models.dump(), "application/json");
+            });
+
+    // TODO: add mount point without "/v1" prefix -- how?
+    svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
+            {
+                json data = oaicompat_completion_params_parse(json::parse(req.body));
+
+                const int task_id = llama.request_completion(data, false, false);
+
+                if (!json_value(data, "stream", false)) {
+                    std::string completion_text;
+                    task_result result = llama.next_result(task_id);
+
+                    if (!result.error && result.stop) {
+                        json oaicompat_result = format_final_response_oaicompat(data, result);
+
+                        res.set_content(oaicompat_result.dump(-1, ' ', false,
+                                            json::error_handler_t::replace),
+                                            "application/json");
+                    } else {
+                        res.status = 500;
+                        res.set_content(result.result_json["content"], "text/plain");
+                        return;
+                    }
+                } else {
+                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
+                        while (true) {
+                            task_result llama_result = llama.next_result(task_id);
+                            if (!llama_result.error) {
+                                std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
+
+                                for (auto it = result_array.begin(); it != result_array.end(); ++it)
+                                {
+                                    if (!it->empty()) {
+                                        const std::string str =
+                                            "data: " +
+                                            it->dump(-1, ' ', false, json::error_handler_t::replace) +
+                                            "\n\n";
+                                        LOG_VERBOSE("data stream", {{"to_send", str}});
+                                        if (!sink.write(str.c_str(), str.size())) {
+                                            return false;
+                                        }
+                                    }
+                                }
+                                if (llama_result.stop) {
+                                    break;
+                                }
+                            } else {
+                                const std::string str =
+                                    "error: " +
+                                    llama_result.result_json.dump(-1, ' ', false,
+                                            json::error_handler_t::replace) +
+                                    "\n\n";
+                                LOG_VERBOSE("data stream", {{"to_send", str}});
+                                if (!sink.write(str.c_str(), str.size())) {
+                                    return false;
+                                }
+                                break;
+                            }
+                        }
+                        sink.done();
+                        return true;
+                    };
+
+                    auto on_complete = [task_id, &llama](bool) {
+                        // cancel request
+                        llama.request_cancel(task_id);
+                    };
+
+                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+                }
+            });
+
    svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
            {
                json data = json::parse(req.body);
@@ -0,0 +1,8 @@
+# llama.cpp/examples/speculative
+
+Demonstartion of speculative decoding and tree-based speculative decoding techniques
+
+More info:
+
+- https://github.com/ggerganov/llama.cpp/pull/2926
+- https://github.com/ggerganov/llama.cpp/pull/3624
@@ -1433,7 +1433,8 @@ void ggml_metal_graph_compute(
                            const int n_past     = ((int32_t *) dst->op_params)[0];
                            const int n_dims     = ((int32_t *) dst->op_params)[1];
                            const int mode       = ((int32_t *) dst->op_params)[2];
-                            const int n_orig_ctx = ((int32_t *) dst->op_params)[3];
+                            // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
+                            const int n_orig_ctx = ((int32_t *) dst->op_params)[4];

                            float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
                            memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
@@ -9373,7 +9373,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
    // TODO: find the optimal values for these
    if (ggml_is_contiguous(src0) &&
        ggml_is_contiguous(src1) &&
-        src0->type == GGML_TYPE_F32 &&
+      //src0->type == GGML_TYPE_F32 &&
        src1->type == GGML_TYPE_F32 &&
        (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {

@@ -15689,13 +15689,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
            {
                n_tasks = 1;
            } break;
-        case GGML_OP_COUNT:
-            {
-                GGML_ASSERT(false);
-            } break;
        default:
            {
-                printf("%s: op %s not implemented\n", __func__, ggml_op_name(node->op));
+                fprintf(stderr, "%s: op not implemented: ", __func__);
+                if (node->op < GGML_OP_COUNT) {
+                    fprintf(stderr, "%s\n", ggml_op_name(node->op));
+                } else {
+                    fprintf(stderr, "%d\n", node->op);
+                }
                GGML_ASSERT(false);
            } break;
    }
@@ -244,11 +244,10 @@
 #define GGML_ASSERT(x) \
    do { \
        if (!(x)) { \
-            fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
-            fflush(stderr); \
            fflush(stdout); \
+            fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
            ggml_print_backtrace(); \
-            exit(1); \
+            abort(); \
        } \
    } while (0)

@@ -1118,6 +1118,12 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_
 //

 struct llama_state {
+    llama_state() {
+#ifdef GGML_USE_METAL
+        ggml_metal_log_set_callback(log_callback, log_callback_user_data);
+#endif
+    }
+
    // We save the log callback globally
    ggml_log_callback log_callback = llama_log_callback_default;
    void * log_callback_user_data = nullptr;
@@ -2639,15 +2645,15 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
    }

    // general kv
-    LLAMA_LOG_INFO("%s: general.name   = %s\n",    __func__, model.name.c_str());
+    LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());

    // special tokens
-    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
-    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
-    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
-    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
-    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
-    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token  = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
+    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
+    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
+    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
+    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
+    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
+    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
 }

 static void llm_load_tensors(
@@ -5544,18 +5550,8 @@ static int llama_decode_internal(
        n_threads = std::min(4, n_threads);
    }

-    // If all tensors can be run on the GPU then using more than 1 thread is detrimental.
-    const bool full_offload_supported =
-        model.arch == LLM_ARCH_LLAMA      ||
-        model.arch == LLM_ARCH_BAICHUAN   ||
-        model.arch == LLM_ARCH_FALCON     ||
-        model.arch == LLM_ARCH_REFACT     ||
-        model.arch == LLM_ARCH_MPT        ||
-        model.arch == LLM_ARCH_STARCODER  ||
-        model.arch == LLM_ARCH_STABLELM;
-
    const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
-    if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
+    if (ggml_cpu_has_cublas() && fully_offloaded) {
        n_threads = 1;
    }

@@ -6414,10 +6410,13 @@ struct llama_grammar_candidate {
 // pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
 static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
        const char         * src,
+        size_t               n_src,
        llama_partial_utf8   partial_start) {
    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
    const char          * pos      = src;
    std::vector<uint32_t> code_points;
+    // common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
+    code_points.reserve(n_src + 1);
    uint32_t              value    = partial_start.value;
    int                   n_remain = partial_start.n_remain;

@@ -6468,6 +6467,13 @@ static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
 }

+static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+        std::string src,
+        llama_partial_utf8 partial_start
+) {
+    return decode_utf8(src.c_str(), src.size(), partial_start);
+}
+
 // returns true iff pos points to the end of one of the definitions of a rule
 static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
    switch (pos->type) {
@@ -7021,6 +7027,7 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
    // Replace the data in candidates with the new_candidates data
    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
    candidates->size = new_candidates.size();
+    candidates->sorted = false;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
@@ -7117,7 +7124,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
        } else if (piece.empty() || piece[0] == 0) {
            candidates->data[i].logit = -INFINITY;
        } else {
-            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
+            candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
        }
    }
@@ -7324,7 +7331,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
    const std::string piece = llama_token_to_piece(ctx, token);

    // Note terminating 0 in decoded string
-    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
+    const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
    const auto & code_points = decoded.first;
    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
@@ -8569,8 +8576,6 @@ struct llama_context * llama_new_context_with_model(

 #ifdef GGML_USE_METAL
            if (model->n_gpu_layers > 0) {
-                ggml_metal_log_set_callback(llama_log_callback_default, NULL);
-
                ctx->ctx_metal = ggml_metal_init(1);
                if (!ctx->ctx_metal) {
                    LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
@@ -9706,6 +9711,9 @@ const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal
 void llama_log_set(ggml_log_callback log_callback, void * user_data) {
    g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
    g_state.log_callback_user_data = user_data;
+#ifdef GGML_USE_METAL
+    ggml_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+#endif
 }

 static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
@@ -185,7 +185,7 @@ extern "C" {
        // ref: https://github.com/ggerganov/llama.cpp/pull/2054
        float    rope_freq_base;   // RoPE base frequency, 0 = from model
        float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
-        float    yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
+        float    yarn_ext_factor;  // YaRN extrapolation mix factor, negative = from model
        float    yarn_attn_factor; // YaRN magnitude scaling factor
        float    yarn_beta_fast;   // YaRN low correction dim
        float    yarn_beta_slow;   // YaRN high correction dim
@@ -1,5 +1,3 @@
-set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
-set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
 set(BUILD_NUMBER 0)
 set(BUILD_COMMIT "unknown")
 set(BUILD_COMPILER "unknown")
@@ -58,23 +56,3 @@ else()
    )
    set(BUILD_TARGET ${OUT})
 endif()
-
-# Only write the build info if it changed
-if(EXISTS ${OUTPUT_FILE})
-    file(READ ${OUTPUT_FILE} CONTENTS)
-    string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_COMMIT ${CMAKE_MATCH_1})
-    string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_COMPILER ${CMAKE_MATCH_1})
-    string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
-    set(OLD_TARGET ${CMAKE_MATCH_1})
-    if (
-        NOT OLD_COMMIT   STREQUAL BUILD_COMMIT   OR
-        NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
-        NOT OLD_TARGET   STREQUAL BUILD_TARGET
-    )
-        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
-    endif()
-else()
-    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
-endif()
@@ -0,0 +1,24 @@
+include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
+
+set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
+set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
+
+# Only write the build info if it changed
+if(EXISTS ${OUTPUT_FILE})
+    file(READ ${OUTPUT_FILE} CONTENTS)
+    string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_COMMIT ${CMAKE_MATCH_1})
+    string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_COMPILER ${CMAKE_MATCH_1})
+    string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
+    set(OLD_TARGET ${CMAKE_MATCH_1})
+    if (
+        NOT OLD_COMMIT   STREQUAL BUILD_COMMIT   OR
+        NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
+        NOT OLD_TARGET   STREQUAL BUILD_TARGET
+    )
+        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+    endif()
+else()
+    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+endif()
Author	SHA1	Message	Date
Li Tan	f7f9e06212	cmake : fix the metal file foder path (#4217 )	2023-11-30 23:44:11 +02:00
Dawid Wysocki	74daabae69	readme : fix typo (#4253 ) llama.cpp uses GitHub Actions, not Gitlab Actions.	2023-11-30 23:43:32 +02:00
Daniel Bevenius	b18c66ca6e	llama : fix alignment of general.name in print meta (#4254 ) * llama: fix alignment of general.name in print meta This commit fixes the alignment of the general.name field in the llm_load_print_meta function. Currently the output looks like this: ```console llm_load_print_meta: model ftype = mostly Q4_0 llm_load_print_meta: model params = 13.02 B llm_load_print_meta: model size = 6.86 GiB (4.53 BPW) llm_load_print_meta: general.name = LLaMA v2 ``` And with this commit it looks like this: ```console llm_load_print_meta: model ftype = mostly Q4_0 llm_load_print_meta: model params = 13.02 B llm_load_print_meta: model size = 6.86 GiB (4.53 BPW) llm_load_print_meta: general.name = LLaMA v2 ``` Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com> * llama: fix alignment of special tokens Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com> --------- Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>	2023-11-30 23:43:08 +02:00
slaren	f4d973cecb	convert.py : fix llama/llama2 conversion due to vocab_size=-1 (#4258 )	2023-11-30 23:42:23 +02:00
tarcey	954e22858c	llama : fix typical sampling (#4261 ) Typical sampling was broken because after copying new_candidates into canditates, the "sorted" bool is left at "true", but the new data is no longer sorted according to probability. Patch to set "sorted" to false. Test: Generating with temp=0.0001 (approx. argmax) should generate the same sequence at typical>=1.0 and typical=0.9999 (approx. disabled, but enters the typical sampling codepath).	2023-11-30 23:40:23 +02:00
rhjdvsgsgks	e2bd725f4b	py : fix oai proxy (#3972 ) * fix oai proxy fix generation not stoped while bot stop talking in chat mode fix possible `slot_id` not exist response for cors (and pre flight) * oai proxy: workaround for some client (such as Chatbox) * use stop as separator to replace hardcoded `\n`	2023-11-30 22:50:40 +02:00
Georgi Gerganov	1f5cd83275	examples : add readme files	2023-11-29 11:00:17 +02:00
Peter Sugihara	4fea3420ee	readme : add FreeChat (#4248 )	2023-11-29 09:16:34 +02:00
Jared Van Bortel	64e64aa255	ggml : restore abort() in GGML_ASSERT (#4242 )	2023-11-28 11:51:11 +02:00
Georgi Gerganov	8406b0924b	ggml : re-enable BLAS for CPU when src0 != F32 + remove redundant full offload checks in llama.cpp (#4240 ) * ggml : use blas even if src0 is not F32 * llama : use n_threads_batch only when n_tokens >= 32 ggml-ci * llama : revert n_threads_batch logic ggml-ci	2023-11-28 10:32:03 +02:00
bandoti	b38a16dfcf	cmake : fix issue with version info not getting baked into LlamaConfig.cmake (#3970 ) * Split CPP generation from build-info query * Remove blank lines * Add BUILD_SHARED_LIBS option	2023-11-27 21:25:42 +02:00
Kasumi	0dab8cd7cc	readme : add Amica to UI list (#4230 )	2023-11-27 19:39:42 +02:00
Bailey Chittle	bb03290c17	examples : iOS example with swift ui (#4159 ) * copy to llama.cpp as subdir * attempt enabling metal, fails * ggml metal compiles! * Update README.md * initial conversion to new format, utf8 errors? * bug fixes, but now has an invalid memory access :( * added O3, now has insufficient memory access * begin sync with master * update to match latest code, new errors * fixed it! * fix for loop conditionals, increase result size * fix current workflow errors * attempt a llama.swiftui workflow * Update .github/workflows/build.yml Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2023-11-27 16:56:52 +02:00
Jared Van Bortel	f3b269813f	ggml : fix -Warray-bounds warning with gcc (#4231 )	2023-11-26 22:58:43 -05:00
Georgi Gerganov	3e73d31d9c	lookahead : support `-n -1` infinite generation	2023-11-26 21:52:23 +02:00
Georgi Gerganov	9656026b53	readme : update hot topics	2023-11-26 20:42:51 +02:00
Georgi Gerganov	922754a8d6	lookahead : add example for lookahead decoding (#4207 ) * lookahead : init * lookahead : generate and store n-grams * lookahead : use loop instead recursion to generate n-grams * lookahead : initial working implementation * lookahead : filter repeating n-grams * lookahead : use deterministic init * lookahead : add to Makefile * lookahead : fix a bug in the seq_id of the lookahead tokens * lookahead : add comments --------- Co-authored-by: slaren <slarengh@gmail.com>	2023-11-26 20:33:07 +02:00
Xiao-Yong Jin	22da05536f	metal : fix yarn (#4220 ) get the correct n_orig_ctx in metal	2023-11-26 10:30:02 +02:00
Galunid	1ddb52ec38	scripts : Use mmap in torch load (#4202 ) * Use mmap in torch load, prefer .bin files when loading * Revert .bin > .safetensors preference	2023-11-25 22:45:02 +01:00
Marcus Dunn	f837c3a992	llama : grammar `reserve` space in `decode_utf8` (#4210 ) * reserve space for codepoints * improvement for the appended 0	2023-11-25 18:58:23 +02:00
crasm	3014b5415d	Update docs for yarn_ext_factor <0.0 as unspecified instead of NaN (#4189 )	2023-11-25 10:47:07 -05:00
Georgi Gerganov	04814e718e	readme : update hot topics	2023-11-25 12:02:13 +02:00
Georgi Gerganov	af19d35734	server : OAI API compatibility (#4198 ) * Add openai-compatible POST /v1/chat/completions API endpoint to server example * fix code style * Update server README.md * Improve server README.md * Fix server.cpp code style according to review * server : some style changes * server : indentation * server : enable special tokens during tokenization by default * server : minor code style * server : change random string generator * straightforward /v1/models endpoint --------- Co-authored-by: kir-gadjello <111190790+kir-gadjello@users.noreply.github.com> Co-authored-by: Tobi Lütke <tobi@Tobis-MacBook-Pro.local>	2023-11-25 11:29:06 +02:00
slaren	e9c13ff781	llama : set metal log callback correctly (#4204 )	2023-11-24 18:10:01 +01:00