main : pass LOG_TEE callback to llama.cpp log (#4033)

* main : Call llama_log_set to use LOG_TEE

* tabs to spaces

.devops/tools.sh

@@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
     ./quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
     ./main "$@"
+elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
+    ./finetune "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -34,6 +36,8 @@ else
     echo "              ex: --outtype f16 \"/models/7B/\" "
     echo "  --quantize (-q): Optimize with quantization process ggml"
     echo "              ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
+    echo "  --finetune (-f): Run finetune command to create a lora finetune of the model"
+    echo "              See documentation for finetune for command-line parameters"
     echo "  --all-in-one (-a): Execute --convert & --quantize"
     echo "              ex: \"/models/\" 7B"
     echo "  --server (-s): Run a model on the server"

.github/workflows/build.yml

@@ -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:

CMakeLists.txt

@@ -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}

README.md

@@ -116,6 +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)
 
 ---
 
@@ -322,7 +324,7 @@ mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
 
 ### BLAS Build
 
-Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
+Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
 
 - #### Accelerate Framework:
 
@@ -894,7 +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
 

common/CMakeLists.txt

@@ -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

convert.py

@@ -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,

examples/batched.swift/README.md

@@ -1,4 +1,4 @@
 This is a swift clone of `examples/batched`.
 
 $ `make`
-$ `./swift MODEL_PATH [PROMPT] [PARALLEL]`
+$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]`

examples/llama.swiftui/.gitignore

@@ -0,0 +1 @@
+xcuserdata

examples/llama.swiftui/README.md

@@ -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
+

examples/llama.swiftui/llama.cpp.swift/LibLlama.swift

@@ -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
+    }
+}

examples/llama.swiftui/llama.cpp.swift/bridging-header.h

@@ -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"

examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj

@@ -0,0 +1,481 @@
+// !$*UTF8*$!
+{
+    archiveVersion = 1;
+    classes = {
+    };
+    objectVersion = 56;
+    objects = {
+
+/* Begin PBXBuildFile section */
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+        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"; }; };
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+/* End PBXBuildFile section */
+
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+/* End PBXFileReference section */
+
+/* Begin PBXFrameworksBuildPhase section */
+        8A1C83702AC328BD0096AF73 /* Frameworks */ = {
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+            buildActionMask = 2147483647;
+            files = (
+                549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
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+            );
+            runOnlyForDeploymentPostprocessing = 0;
+        };
+/* End PBXFrameworksBuildPhase section */
+
+/* Begin PBXGroup section */
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+            isa = PBXGroup;
+            children = (
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examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/contents.xcworkspacedata

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Workspace
+   version = "1.0">
+   <FileRef
+      location = "self:">
+   </FileRef>
+</Workspace>

examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist

@@ -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>

examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json

@@ -0,0 +1,11 @@
+{
+  "colors" : [
+    {
+      "idiom" : "universal"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}

examples/llama.swiftui/llama.swiftui/Assets.xcassets/AppIcon.appiconset/Contents.json

@@ -0,0 +1,13 @@
+{
+  "images" : [
+    {
+      "idiom" : "universal",
+      "platform" : "ios",
+      "size" : "1024x1024"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}

examples/llama.swiftui/llama.swiftui/Assets.xcassets/Contents.json

@@ -0,0 +1,6 @@
+{
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}

examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift

@@ -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"
+    }
+}

examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json

@@ -0,0 +1,6 @@
+{
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}

examples/llama.swiftui/llama.swiftui/UI/ContentView.swift

@@ -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()
+}
+*/

examples/llama.swiftui/llama.swiftui/llama_swiftuiApp.swift

@@ -0,0 +1,10 @@
+import SwiftUI
+
+@main
+struct llama_swiftuiApp: App {
+    var body: some Scene {
+        WindowGroup {
+            ContentView()
+        }
+    }
+}

examples/lookahead/README.md

@@ -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

examples/main/main.cpp

@@ -100,6 +100,12 @@ static void sigint_handler(int signo) {
 }
 #endif
 
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
 int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
@@ -113,6 +119,7 @@ int main(int argc, char ** argv) {
     log_set_target(log_filename_generator("main", "log"));
     LOG_TEE("Log start\n");
     log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
 #endif // LOG_DISABLE_LOGS
 
     // TODO: Dump params ?

examples/server/api_like_OAI.py

@@ -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)

examples/speculative/README.md

@@ -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

ggml.c

@@ -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;
     }

ggml.h

@@ -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)
 

llama.cpp

@@ -2645,15 +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(
@@ -5550,18 +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;
     }
 
@@ -7037,6 +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;

scripts/build-info.cmake

@@ -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()

scripts/gen-build-info-cpp.cmake

@@ -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()