llama : fix typo LAMMAFILE -> LLAMAFILE (#6974)

* Fix more int overflow during quant.

* Fix some more int overflow in softmax.

* Revert back to int64_t.

.github/workflows/bench.yml

@@ -32,7 +32,7 @@ on:
     -  cron: '04 2 * * *'
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}-${{ github.event.inputs.sha }}
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }}
   cancel-in-progress: true
 
 jobs:

.github/workflows/server.yml

@@ -23,7 +23,7 @@ on:
     -  cron: '2 4 * * *'
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
   cancel-in-progress: true
 
 jobs:
@@ -41,23 +41,16 @@ jobs:
             sanitizer: ""
       fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
 
-    container:
-      image: ubuntu:latest
-      ports:
-        - 8888
-      options: --cpus 4
-
     steps:
       - name: Dependencies
         id: depends
         run: |
-          apt-get update
-          apt-get -y install \
+          sudo apt-get update
+          sudo apt-get -y install \
             build-essential \
             xxd \
             git \
             cmake \
-            python3-pip \
             curl \
             wget \
             language-pack-en \
@@ -70,6 +63,17 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r examples/server/tests/requirements.txt
+
       - name: Verify server deps
         id: verify_server_deps
         run: |
@@ -100,10 +104,6 @@ jobs:
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
           cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
 
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r examples/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests
@@ -129,6 +129,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
       - name: libCURL
         id: get_libcurl

.gitignore

@@ -34,6 +34,7 @@ lcov-report/
 gcovr-report/
 
 build*
+!build.zig
 cmake-build-*
 out/
 tmp/
@@ -100,6 +101,9 @@ qnt-*.txt
 perf-*.txt
 
 examples/jeopardy/results.txt
+examples/server/*.html.hpp
+examples/server/*.js.hpp
+examples/server/*.mjs.hpp
 
 poetry.lock
 poetry.toml

CMakeLists.txt

@@ -43,17 +43,7 @@ else()
     set(LLAMA_METAL_DEFAULT OFF)
 endif()
 
-# TODO: fix this for Android CI
-#       https://github.com/ggerganov/llama.cpp/pull/6716#issuecomment-2061509191
-#if (CMAKE_SYSTEM_NAME MATCHES "ANDROID")
-#    set(LLAMA_LLAMAFILE_DEFAULT OFF)
-#else()
-#    set(LLAMA_LLAMAFILE_DEFAULT ON)
-#endif()
-
-# TODO: temporary disable until MoE is fixed
-#       https://github.com/ggerganov/llama.cpp/pull/6716
-set(LLAMA_LLAMAFILE_DEFAULT OFF)
+set(LLAMA_LLAMAFILE_DEFAULT ON)
 
 # general
 option(BUILD_SHARED_LIBS                "build shared libraries"                                OFF)

Makefile

@@ -384,10 +384,6 @@ ifdef LLAMA_OPENBLAS
 	MK_LDFLAGS  += $(shell pkg-config --libs openblas)
 endif # LLAMA_OPENBLAS
 
-# TODO: temporary disable until MoE is fixed
-#       https://github.com/ggerganov/llama.cpp/pull/6716
-LLAMA_NO_LLAMAFILE := 1
-
 ifndef LLAMA_NO_LLAMAFILE
 	MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
 	OBJS        += sgemm.o
@@ -699,7 +695,7 @@ OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
 llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
+COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h
 COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o
 
 common.o: common/common.cpp $(COMMON_H_DEPS)
@@ -772,7 +768,7 @@ batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-quantize: examples/quantize/quantize.cpp                      build-info.o ggml.o llama.o $(OBJS)
+quantize: examples/quantize/quantize.cpp                      ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
@@ -800,10 +796,19 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
+# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
+examples/server/%.hpp: examples/server/public/% Makefile
+	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
+		echo "unsigned char $${NAME}[] = {" && \
+		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
+		echo "};" && \
+		echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
+	) > $@
+
 gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

README-sycl.md

@@ -229,12 +229,12 @@ source /opt/intel/oneapi/setvars.sh
 # Build LLAMA with MKL BLAS acceleration for intel GPU
 mkdir -p build && cd build
 
-# Option 1: Use FP16 for better performance in long-prompt inference
-#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
-
-# Option 2: Use FP32 by default
+# Option 1: Use FP32 (recommended for better performance in most cases)
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
+# Option 2: Use FP16
+cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+
 #build all binary
 cmake --build . --config Release -j -v
 ```
@@ -250,12 +250,12 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
 # Build LLAMA with Nvidia BLAS acceleration through SYCL
 mkdir -p build && cd build
 
-# Option 1: Use FP16 for better performance in long-prompt  inference
-cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
-
-# Option 2: Use FP32 by default
+# Option 1: Use FP32 (recommended for better performance in most cases)
 cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
+# Option 2: Use FP16
+cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+
 #build all binary
 cmake --build . --config Release -j -v
 
@@ -416,6 +416,10 @@ mkdir -p build
 cd build
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+
+# Option 2: Or FP16
 cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
 
 make -j

README.md

@@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ### Recent API changes
 
+- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
 - [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
 - [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
 - [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
@@ -92,10 +93,11 @@ Typically finetunes of the base models below are supported as well.
 
 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
+- [x] LLaMA 3 🦙🦙🦙
 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
 - [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
-- [X] Falcon
+- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
@@ -118,8 +120,9 @@ Typically finetunes of the base models below are supported as well.
 - [x] [CodeShell](https://github.com/WisdomShell/codeshell)
 - [x] [Gemma](https://ai.google.dev/gemma)
 - [x] [Mamba](https://github.com/state-spaces/mamba)
+- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf)
 - [x] [Xverse](https://huggingface.co/models?search=xverse)
-- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
+- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
 - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
 - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
 - [x] [OLMo](https://allenai.org/olmo)
@@ -134,6 +137,8 @@ Typically finetunes of the base models below are supported as well.
 - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
 - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
 - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
+- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
+- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 
 **HTTP server**
 
@@ -549,7 +554,7 @@ Building the program with BLAS support may lead to some performance improvements
   OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
 
   You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
-    - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed.
+    - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed.
 
     - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
 
@@ -574,6 +579,12 @@ Building the program with BLAS support may lead to some performance improvements
 
   Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
 
+  Linux packaging:
+  Fedora Linux:
+  ```bash
+  sudo dnf install clblast
+  ```
+
   Alternatively, they may be built from source.
 
   - <details>
@@ -1110,7 +1121,9 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m
 - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
 - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
-- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT`
+- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
+
+![matmul](media/matmul.png)
 
 ### Docs
 

build.zig

@@ -140,4 +140,33 @@ pub fn build(b: *std.build.Builder) !void {
     if (server.target.isWindows()) {
         server.linkSystemLibrary("ws2_32");
     }
+
+    const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" };
+    for (server_assets) |asset| {
+        const input_path = b.fmt("examples/server/public/{s}", .{asset});
+        const output_path = b.fmt("examples/server/{s}.hpp", .{asset});
+
+        // Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`:
+
+        const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize));
+        defer b.allocator.free(input);
+
+        var buf = std.ArrayList(u8).init(b.allocator);
+        defer buf.deinit();
+
+        for (input) |byte| {
+            try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte});
+        }
+
+        var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_");
+        defer b.allocator.free(name);
+        std.mem.replaceScalar(u8, name, '.', '_');
+
+        try std.fs.cwd().writeFile(output_path, b.fmt(
+            "unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n",
+            .{ name, buf.items, name, input.len },
+        ));
+
+        std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path });
+    }
 }

ci/run.sh

@@ -160,7 +160,9 @@ function gg_run_test_scripts_debug {
 
     set -e
 
+    # TODO: too slow, run on dedicated node
     (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+   #(cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
 
     set +e
 }
@@ -184,6 +186,7 @@ function gg_run_test_scripts_release {
     set -e
 
     (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
 
     set +e
 }

common/common.cpp

@@ -234,15 +234,63 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     return result;
 }
 
+bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
+    const char * sep = strchr(data, '=');
+    if (sep == nullptr || sep - data >= 128) {
+        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
+        return false;
+    }
+    llama_model_kv_override kvo;
+    std::strncpy(kvo.key, data, sep - data);
+    kvo.key[sep - data] = 0;
+    sep++;
+    if (strncmp(sep, "int:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+        kvo.val_i64 = std::atol(sep);
+    } else if (strncmp(sep, "float:", 6) == 0) {
+        sep += 6;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
+        kvo.val_f64 = std::atof(sep);
+    } else if (strncmp(sep, "bool:", 5) == 0) {
+        sep += 5;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
+        if (std::strcmp(sep, "true") == 0) {
+            kvo.val_bool = true;
+        } else if (std::strcmp(sep, "false") == 0) {
+            kvo.val_bool = false;
+        } else {
+            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
+            return false;
+        }
+    } else if (strncmp(sep, "str:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+        if (strlen(sep) > 127) {
+            fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+            return false;
+        }
+        strncpy(kvo.val_str, sep, 127);
+        kvo.val_str[127] = '\0';
+    } else {
+        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
+        return false;
+    }
+    overrides.emplace_back(std::move(kvo));
+    return true;
+}
+
 bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
-    llama_sampling_params& sparams = params.sparams;
+    llama_sampling_params & sparams = params.sparams;
 
     if (arg == "-s" || arg == "--seed") {
         if (++i >= argc) {
             invalid_param = true;
             return true;
         }
+        // This is temporary, in the future the samplign state will be moved fully to llama_sampling_context.
         params.seed = std::stoul(argv[i]);
+        sparams.seed = std::stoul(argv[i]);
         return true;
     }
     if (arg == "-t" || arg == "--threads") {
@@ -1087,6 +1135,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.n_print = std::stoi(argv[i]);
         return true;
     }
+    if (arg == "--check-tensors") {
+        params.check_tensors = true;
+        return true;
+    }
     if (arg == "--ppl-output-type") {
         if (++i >= argc) {
             invalid_param = true;
@@ -1238,47 +1290,11 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
             invalid_param = true;
             return true;
         }
-        char* sep = strchr(argv[i], '=');
-        if (sep == nullptr || sep - argv[i] >= 128) {
-            fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
-            invalid_param = true;
-            return true;
-        }
-        struct llama_model_kv_override kvo;
-        std::strncpy(kvo.key, argv[i], sep - argv[i]);
-        kvo.key[sep - argv[i]] = 0;
-        sep++;
-        if (strncmp(sep, "int:", 4) == 0) {
-            sep += 4;
-            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
-            kvo.int_value = std::atol(sep);
-        }
-        else if (strncmp(sep, "float:", 6) == 0) {
-            sep += 6;
-            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
-            kvo.float_value = std::atof(sep);
-        }
-        else if (strncmp(sep, "bool:", 5) == 0) {
-            sep += 5;
-            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
-            if (std::strcmp(sep, "true") == 0) {
-                kvo.bool_value = true;
-            }
-            else if (std::strcmp(sep, "false") == 0) {
-                kvo.bool_value = false;
-            }
-            else {
-                fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
-                invalid_param = true;
-                return true;
-            }
-        }
-        else {
+        if (!parse_kv_override(argv[i], params.kv_overrides)) {
             fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
             invalid_param = true;
             return true;
         }
-        params.kv_overrides.push_back(kvo);
         return true;
     }
 #ifndef LOG_DISABLE_LOGS
@@ -1549,9 +1565,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("                        path to dynamic lookup cache to use for lookup decoding (updated by generation)\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("                        types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
     printf("  -ptc N, --print-token-count N\n");
     printf("                        print token count every N tokens (default: %d)\n", params.n_print);
+    printf("  --check-tensors       check model tensor data for invalid values\n");
     printf("\n");
 #ifndef LOG_DISABLE_LOGS
     log_print_usage();
@@ -1772,6 +1789,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
     mparams.tensor_split    = params.tensor_split;
     mparams.use_mmap        = params.use_mmap;
     mparams.use_mlock       = params.use_mlock;
+    mparams.check_tensors   = params.check_tensors;
     if (params.kv_overrides.empty()) {
         mparams.kv_overrides = NULL;
     } else {
@@ -2326,12 +2344,12 @@ std::vector<llama_token> llama_tokenize(
     return result;
 }
 
-std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
+std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);

common/common.h

@@ -86,8 +86,8 @@ struct gpt_params {
 
     ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
 
-    llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
-    llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
+    enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
+    enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
 
     // // sampling parameters
     struct llama_sampling_params sparams;
@@ -161,6 +161,7 @@ struct gpt_params {
     bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
     bool no_kv_offload     = false; // disable KV offloading
     bool warmup            = true;  // warmup run
+    bool check_tensors     = false; // validate tensor data
 
     std::string cache_type_k = "f16"; // KV cache data type for the K
     std::string cache_type_v = "f16"; // KV cache data type for the V
@@ -170,6 +171,8 @@ struct gpt_params {
     std::string image  = ""; // path to an image file
 };
 
+bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
+
 bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
 
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
@@ -237,11 +240,12 @@ std::vector<llama_token> llama_tokenize(
                         bool   add_special,
                         bool   parse_special = false);
 
-// tokenizes a token into a piece
+// tokenizes a token into a piece, optionally renders special/control tokens
 // should work similar to Python's `tokenizer.id_to_piece`
 std::string llama_token_to_piece(
         const struct llama_context * ctx,
-                       llama_token   token);
+                       llama_token   token,
+                       bool          special = true);
 
 // TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
 //       that takes into account the tokenizer type and decides how to handle the leading space

common/log.h

@@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG() INSTEAD
 //
-#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
+#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
     #define LOG_IMPL(str, ...)                                                                                      \
     do {                                                                                                            \
         if (LOG_TARGET != nullptr)                                                                                  \
@@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG_TEE() INSTEAD
 //
-#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
+#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
     #define LOG_TEE_IMPL(str, ...)                                                                                                      \
     do {                                                                                                                                \
         if (LOG_TARGET != nullptr)                                                                                                      \

common/sampling.cpp

@@ -1,4 +1,6 @@
+#define LLAMA_API_INTERNAL
 #include "sampling.h"
+#include <random>
 
 struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
     struct llama_sampling_context * result = new llama_sampling_context();
@@ -33,6 +35,8 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
 
     result->prev.resize(params.n_prev);
 
+    llama_sampling_set_rng_seed(result, params.seed);
+
     return result;
 }
 
@@ -62,6 +66,13 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
     ctx->cur.clear();
 }
 
+void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        seed = time(NULL);
+    }
+    ctx->rng.seed(seed);
+}
+
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
     if (dst->grammar) {
         llama_grammar_free(dst->grammar);
@@ -203,7 +214,7 @@ static llama_token llama_sampling_sample_impl(
 
             sampler_queue(ctx_main, params, cur_p, min_keep);
 
-            id = llama_sample_token(ctx_main, &cur_p);
+            id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);
 
             //{
             //    const int n_top = 10;

common/sampling.h

@@ -4,9 +4,10 @@
 
 #include "grammar-parser.h"
 
+#include <random>
 #include <string>
-#include <vector>
 #include <unordered_map>
+#include <vector>
 
 // sampler types
 enum class llama_sampler_type : char {
@@ -20,25 +21,26 @@ enum class llama_sampler_type : char {
 
 // sampling parameters
 typedef struct llama_sampling_params {
-    int32_t     n_prev                = 64;       // number of previous tokens to remember
-    int32_t     n_probs               = 0;        // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t     min_keep              = 0;        // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t     top_k                 = 40;       // <= 0 to use vocab size
-    float       top_p                 = 0.95f;    // 1.0 = disabled
-    float       min_p                 = 0.05f;    // 0.0 = disabled
-    float       tfs_z                 = 1.00f;    // 1.0 = disabled
-    float       typical_p             = 1.00f;    // 1.0 = disabled
-    float       temp                  = 0.80f;    // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float       dynatemp_range        = 0.00f;    // 0.0 = disabled
-    float       dynatemp_exponent     = 1.00f;    // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t     penalty_last_n        = 64;       // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float       penalty_repeat        = 1.00f;    // 1.0 = disabled
-    float       penalty_freq          = 0.00f;    // 0.0 = disabled
-    float       penalty_present       = 0.00f;    // 0.0 = disabled
-    int32_t     mirostat              = 0;        // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float       mirostat_tau          = 5.00f;    // target entropy
-    float       mirostat_eta          = 0.10f;    // learning rate
-    bool        penalize_nl           = false;     // consider newlines as a repeatable token
+    int32_t     n_prev                = 64;                 // number of previous tokens to remember
+    int32_t     n_probs               = 0;                  // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t     min_keep              = 0;                  // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t     top_k                 = 40;                 // <= 0 to use vocab size
+    float       top_p                 = 0.95f;              // 1.0 = disabled
+    float       min_p                 = 0.05f;              // 0.0 = disabled
+    float       tfs_z                 = 1.00f;              // 1.0 = disabled
+    float       typical_p             = 1.00f;              // 1.0 = disabled
+    float       temp                  = 0.80f;              // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float       dynatemp_range        = 0.00f;              // 0.0 = disabled
+    float       dynatemp_exponent     = 1.00f;              // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t     penalty_last_n        = 64;                 // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float       penalty_repeat        = 1.00f;              // 1.0 = disabled
+    float       penalty_freq          = 0.00f;              // 0.0 = disabled
+    float       penalty_present       = 0.00f;              // 0.0 = disabled
+    int32_t     mirostat              = 0;                  // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float       mirostat_tau          = 5.00f;              // target entropy
+    float       mirostat_eta          = 0.10f;              // learning rate
+    bool        penalize_nl           = false;              // consider newlines as a repeatable token
+    uint32_t    seed                  = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context
 
     std::vector<llama_sampler_type> samplers_sequence = {
         llama_sampler_type::TOP_K,
@@ -79,6 +81,8 @@ struct llama_sampling_context {
     // TODO: replace with ring-buffer
     std::vector<llama_token>      prev;
     std::vector<llama_token_data> cur;
+
+    std::mt19937 rng;
 };
 
 #include "common.h"
@@ -93,6 +97,9 @@ void llama_sampling_free(struct llama_sampling_context * ctx);
 // - reset grammar
 void llama_sampling_reset(llama_sampling_context * ctx);
 
+// Set the sampler seed
+void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed);
+
 // Copy the sampler context
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);
 

convert-hf-to-gguf.py

@@ -363,6 +363,16 @@ class Model(ABC):
                         scores.append(-1000.0)
                         toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
 
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            print(
+                f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]"
+            )
+            for i in range(1, pad_count + 1):
+                tokens.append(f"[PAD{i}]")
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
         assert len(tokens) == vocab_size
 
         self.gguf_writer.add_tokenizer_model("llama")
@@ -1789,6 +1799,12 @@ class QwenModel(Model):
 class Qwen2Model(Model):
     model_arch = gguf.MODEL_ARCH.QWEN2
 
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
 
 @Model.register("Qwen2MoeForCausalLM")
 class Qwen2MoeModel(Model):
@@ -1979,6 +1995,91 @@ class Phi2Model(Model):
         self.gguf_writer.add_add_bos_token(False)
 
 
+@Model.register("Phi3ForCausalLM")
+class Phi3MiniModel(Model):
+    model_arch = gguf.MODEL_ARCH.PHI3
+
+    def set_vocab(self):
+        from sentencepiece import SentencePieceProcessor
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        if not tokenizer_path.is_file():
+            print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
+            sys.exit(1)
+
+        tokenizer = SentencePieceProcessor(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+
+            piece = tokenizer.id_to_piece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.get_score(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.is_unknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.is_control(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.is_unused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.is_byte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if (token_id >= vocab_size):
+                        print(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
+
+        rot_pct = 1.0
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        rms_eps = self.find_hparam(["rms_norm_eps"])
+
+        self.gguf_writer.add_name("Phi3")
+        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
+
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(8192)
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
+        self.gguf_writer.add_file_type(self.ftype)
+
+
 @Model.register("PlamoForCausalLM")
 class PlamoModel(Model):
     model_arch = gguf.MODEL_ARCH.PLAMO

examples/batched.swift/Sources/main.swift

@@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
 
 private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
     var result = [CChar](repeating: 0, count: 8)
-    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
+    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
     if nTokens < 0 {
         let actualTokensCount = -Int(nTokens)
         result = .init(repeating: 0, count: actualTokensCount)
@@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
             model,
             token,
             &result,
-            Int32(result.count)
+            Int32(result.count),
+            false
         )
         assert(check == actualTokensCount)
     } else {

examples/gguf-split/tests.sh

@@ -4,16 +4,16 @@ set -eu
 
 if [ $# -lt 1 ]
 then
-  echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
-  echo "example: $0 ../../build/bin ../../tmp"
-  exit 1
+    echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
+    echo "example: $0 ../../build/bin ../../tmp"
+    exit 1
 fi
 
 if [ $# -gt 1 ]
 then
-  TMP_DIR=$2
+    TMP_DIR=$2
 else
-  TMP_DIR=/tmp
+    TMP_DIR=/tmp
 fi
 
 set -x
@@ -21,7 +21,7 @@ set -x
 SPLIT=$1/gguf-split
 MAIN=$1/main
 WORK_PATH=$TMP_DIR/gguf-split
-CUR_DIR=$(pwd)
+ROOT_DIR=$(realpath $(dirname $0)/../../)
 
 mkdir -p "$WORK_PATH"
 
@@ -30,8 +30,8 @@ rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf
 
 # 1. Get a model
 (
-  cd $WORK_PATH
-  "$CUR_DIR"/../../scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
+cd $WORK_PATH
+"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
 )
 echo PASS
 

examples/imatrix/imatrix.cpp

@@ -23,6 +23,7 @@ struct Stats {
 };
 
 struct StatParams {
+    std::string dataset;
     std::string ofile = "imatrix.dat";
     int         n_output_frequency = 10;
     int         verbosity = 1;
@@ -46,7 +47,7 @@ private:
     std::vector<float>                     m_src1_data;
     std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
                                                   //
-    void save_imatrix(const char * file_name) const;
+    void save_imatrix(const char * file_name, const char * dataset) const;
     void keep_imatrix(int ncall) const;
 };
 
@@ -199,7 +200,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
 }
 
 void IMatrixCollector::save_imatrix() const {
-    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
+    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
 }
 
 void IMatrixCollector::keep_imatrix(int ncall) const {
@@ -207,24 +208,33 @@ void IMatrixCollector::keep_imatrix(int ncall) const {
     if (file_name.empty()) file_name = "imatrix.dat";
     file_name += ".at_";
     file_name += std::to_string(ncall);
-    save_imatrix(file_name.c_str());
+    save_imatrix(file_name.c_str(), m_params.dataset.c_str());
 }
 
-void IMatrixCollector::save_imatrix(const char * fname) const {
+void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
     std::ofstream out(fname, std::ios::binary);
     int n_entries = m_stats.size();
-    out.write((const char*)&n_entries, sizeof(n_entries));
-    for (auto& p : m_stats) {
+    out.write((const char *) &n_entries, sizeof(n_entries));
+    for (const auto & p : m_stats) {
         int len = p.first.size();
-        out.write((const char*)&len, sizeof(len));
+        out.write((const char *) &len, sizeof(len));
         out.write(p.first.c_str(), len);
-        out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
+        out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
         int nval = p.second.values.size();
-        out.write((const char*)&nval, sizeof(nval));
-        if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
+        out.write((const char *) &nval, sizeof(nval));
+        if (nval > 0) out.write((const char *) p.second.values.data(), nval * sizeof(float));
     }
+
+    // Write the number of call the matrix was computed with
+    out.write((const char *) &m_last_call, sizeof(m_last_call));
+
+    // Write the dataset name at the end of the file to later on specify it in quantize
+    int n_dataset = strlen(dataset);
+    out.write((const char *) &n_dataset, sizeof(n_dataset));
+    out.write(dataset, n_dataset);
+
     if (m_params.verbosity > 0) {
-        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
+        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
     }
 }
 
@@ -547,6 +557,29 @@ int main(int argc, char ** argv) {
         }
     }
 
+    gpt_params params;
+    params.n_batch = 512;
+    if (!gpt_params_parse(args.size(), args.data(), params)) {
+        return 1;
+    }
+
+    params.logits_all = true;
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+    print_build_info();
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    sparams.dataset = params.prompt_file;
     g_collector.set_parameters(std::move(sparams));
 
     if (!combine_files.empty()) {
@@ -585,28 +618,6 @@ int main(int argc, char ** argv) {
         }
     }
 
-    gpt_params params;
-    params.n_batch = 512;
-    if (!gpt_params_parse(args.size(), args.data(), params)) {
-        return 1;
-    }
-
-    params.logits_all = true;
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
-
-    print_build_info();
-
-    if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = time(NULL);
-    }
-
-    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
-
-    std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
-
     llama_backend_init();
     llama_numa_init(params.numa);
 

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

@@ -322,7 +322,7 @@ actor LlamaContext {
         defer {
             result.deallocate()
         }
-        let nTokens = llama_token_to_piece(model, token, result, 8)
+        let nTokens = llama_token_to_piece(model, token, result, 8, false)
 
         if nTokens < 0 {
             let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@@ -330,7 +330,7 @@ actor LlamaContext {
             defer {
                 newResult.deallocate()
             }
-            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
+            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
             let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
             return Array(bufferPointer)
         } else {

examples/llava/clip.cpp

@@ -3,6 +3,7 @@
 // I'll gradually clean and extend it
 // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
 #include "clip.h"
+#include "log.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
@@ -23,7 +24,6 @@
 #include <cstdlib>
 #include <cstring>
 #include <fstream>
-#include <iostream>
 #include <map>
 #include <regex>
 #include <stdexcept>
@@ -104,6 +104,7 @@ static std::string format(const char * fmt, ...) {
 #define TN_POS_EMBD        "%s.position_embd.weight"
 #define TN_CLASS_EMBD      "v.class_embd"
 #define TN_PATCH_EMBD      "v.patch_embd.weight"
+#define TN_PATCH_BIAS      "v.patch_embd.bias"
 #define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
 #define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
@@ -145,7 +146,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
 static int get_key_idx(const gguf_context * ctx, const char * key) {
     int i = gguf_find_key(ctx, key);
     if (i == -1) {
-        fprintf(stderr, "key %s not found in file\n", key);
+        LOG_TEE("key %s not found in file\n", key);
         throw std::runtime_error(format("Missing required key: %s", key));
     }
 
@@ -247,7 +248,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
 
 static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
     size_t tensor_size = ggml_nbytes(tensor);
-    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
+    LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
             prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
             tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
 }
@@ -265,7 +266,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
 static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
     std::ofstream file(filename, std::ios::binary);
     if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
         return;
     }
 
@@ -284,7 +285,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s
 static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
     std::ofstream file(filename, std::ios::binary);
     if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
         return;
     }
 
@@ -425,6 +426,7 @@ struct clip_vision_model {
     // embeddings
     struct ggml_tensor * class_embedding;
     struct ggml_tensor * patch_embeddings;
+    struct ggml_tensor * patch_bias;
     struct ggml_tensor * position_embeddings;
 
     struct ggml_tensor * pre_ln_w;
@@ -501,6 +503,11 @@ struct clip_ctx {
     bool use_gelu = false;
     int32_t ftype = 1;
 
+    bool has_class_embedding = true;
+    bool has_pre_norm = true;
+    bool has_post_norm = false;
+    bool has_patch_bias = false;
+
     struct gguf_context * ctx_gguf;
     struct ggml_context * ctx_data;
 
@@ -515,7 +522,7 @@ struct clip_ctx {
 
 static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return nullptr;
     }
 
@@ -526,7 +533,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     const int patch_size           = hparams.patch_size;
     const int num_patches          = ((image_size / patch_size) * (image_size / patch_size));
     const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side);
-    const int num_positions        = num_patches + 1;
+    const int num_positions        = num_patches + (ctx->has_class_embedding ? 1 : 0);
     const int hidden_size          = hparams.hidden_size;
     const int n_head               = hparams.n_head;
     const int d_head               = hidden_size / n_head;
@@ -557,16 +564,23 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
     inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
 
+    if (ctx->has_patch_bias) {
+        // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+    }
+
     // concat class_embeddings and patch_embeddings
-    struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
+    struct ggml_tensor * embeddings = inp;
+    if (ctx->has_class_embedding) {
+        embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
+        embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
+                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
+        embeddings = ggml_acc(ctx0, embeddings, inp,
+                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
+    }
     ggml_set_name(embeddings, "embeddings");
     ggml_set_input(embeddings);
 
-    embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
-            embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
-
-    embeddings = ggml_acc(ctx0, embeddings, inp,
-            embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
 
     struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
     ggml_set_name(positions, "positions");
@@ -576,7 +590,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
 
     // pre-layernorm
-    {
+    if (ctx->has_pre_norm) {
         embeddings = ggml_norm(ctx0, embeddings, eps);
         ggml_set_name(embeddings, "pre_ln");
 
@@ -664,6 +678,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         embeddings = cur;
     }
 
+    // post-layernorm
+    if (ctx->has_post_norm) {
+        embeddings = ggml_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "post_ln");
+
+        embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
+    }
+
     // llava projector
     {
         embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
@@ -879,21 +901,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         const int idx_name = gguf_find_key(ctx, KEY_NAME);
         if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
             const std::string name = gguf_get_val_str(ctx, idx_name);
-            printf("%s: model name:   %s\n", __func__, name.c_str());
+            LOG_TEE("%s: model name:   %s\n", __func__, name.c_str());
         }
-        printf("%s: description:  %s\n", __func__, description.c_str());
-        printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
-        printf("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
-        printf("%s: n_tensors:    %d\n", __func__, n_tensors);
-        printf("%s: n_kv:         %d\n", __func__, n_kv);
-        printf("%s: ftype:        %s\n", __func__, ftype_str.c_str());
-        printf("\n");
+        LOG_TEE("%s: description:  %s\n", __func__, description.c_str());
+        LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
+        LOG_TEE("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
+        LOG_TEE("%s: n_tensors:    %d\n", __func__, n_tensors);
+        LOG_TEE("%s: n_kv:         %d\n", __func__, n_kv);
+        LOG_TEE("%s: ftype:        %s\n", __func__, ftype_str.c_str());
+        LOG_TEE("\n");
     }
     const int n_tensors = gguf_get_n_tensors(ctx);
 
     // kv
     const int n_kv = gguf_get_n_kv(ctx);
-    printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
+    LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
         __func__, n_kv, n_tensors, fname);
     {
         std::map<enum ggml_type, uint32_t> n_type;
@@ -904,7 +926,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             n_type[type]++;
         }
 
-        printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+        LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
         for (int i = 0; i < n_kv; i++) {
             const char * name           = gguf_get_key(ctx, i);
             const enum gguf_type type   = gguf_get_kv_type(ctx, i);
@@ -920,7 +942,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             }
             replace_all(value, "\n", "\\n");
 
-            printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+            LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
         }
 
         // print type counts
@@ -929,7 +951,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                 continue;
             }
 
-            printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
+            LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
         }
     }
 
@@ -944,7 +966,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             size_t tensor_size = ggml_nbytes(cur);
             model_size += tensor_size;
             if (verbosity >= 3) {
-                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
+                LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
                        __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
             }
         }
@@ -971,18 +993,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
 #ifdef GGML_USE_CUDA
     new_clip->backend = ggml_backend_cuda_init(0);
-    printf("%s: CLIP using CUDA backend\n", __func__);
+    LOG_TEE("%s: CLIP using CUDA backend\n", __func__);
 #endif
 
 #ifdef GGML_USE_METAL
     new_clip->backend = ggml_backend_metal_init();
-    printf("%s: CLIP using Metal backend\n", __func__);
+    LOG_TEE("%s: CLIP using Metal backend\n", __func__);
 #endif
 
 
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();
-        printf("%s: CLIP using CPU backend\n", __func__);
+        LOG_TEE("%s: CLIP using CPU backend\n", __func__);
     }
 
     // model size and capabilities
@@ -1006,15 +1028,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
 
         if (verbosity >= 1) {
-            printf("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
-            printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
-            printf("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
-            printf("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
-            printf("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
+            LOG_TEE("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
+            LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
+            LOG_TEE("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
+            LOG_TEE("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
+            LOG_TEE("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
         }
     }
 
-    printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
+    LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
 
     // load tensors
     {
@@ -1027,7 +1049,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
         new_clip->ctx_data = ggml_init(params);
         if (!new_clip->ctx_data) {
-            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            LOG_TEE("%s: ggml_init() failed\n", __func__);
             clip_free(new_clip);
             gguf_free(ctx);
             return nullptr;
@@ -1035,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
         auto fin = std::ifstream(fname, std::ios::binary);
         if (!fin) {
-            printf("cannot open model file for loading tensors\n");
+            LOG_TEE("cannot open model file for loading tensors\n");
             clip_free(new_clip);
             gguf_free(ctx);
             return nullptr;
@@ -1057,7 +1079,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
             fin.seekg(offset, std::ios::beg);
             if (!fin) {
-                printf("%s: failed to seek for tensor %s\n", __func__, name);
+                LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name);
                 clip_free(new_clip);
                 gguf_free(ctx);
                 return nullptr;
@@ -1128,34 +1150,61 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
 
         if (verbosity >= 2) {
-            printf("\n%s: vision model hparams\n", __func__);
-            printf("image_size         %d\n", hparams.image_size);
-            printf("patch_size         %d\n", hparams.patch_size);
-            printf("v_hidden_size      %d\n", hparams.hidden_size);
-            printf("v_n_intermediate   %d\n", hparams.n_intermediate);
-            printf("v_projection_dim   %d\n", hparams.projection_dim);
-            printf("v_n_head           %d\n", hparams.n_head);
-            printf("v_n_layer          %d\n", hparams.n_layer);
-            printf("v_eps              %f\n", hparams.eps);
-            printf("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
-            printf("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
-            printf("v_image_grid_pinpoints: ");
+            LOG_TEE("\n%s: vision model hparams\n", __func__);
+            LOG_TEE("image_size         %d\n", hparams.image_size);
+            LOG_TEE("patch_size         %d\n", hparams.patch_size);
+            LOG_TEE("v_hidden_size      %d\n", hparams.hidden_size);
+            LOG_TEE("v_n_intermediate   %d\n", hparams.n_intermediate);
+            LOG_TEE("v_projection_dim   %d\n", hparams.projection_dim);
+            LOG_TEE("v_n_head           %d\n", hparams.n_head);
+            LOG_TEE("v_n_layer          %d\n", hparams.n_layer);
+            LOG_TEE("v_eps              %f\n", hparams.eps);
+            LOG_TEE("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
+            LOG_TEE("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
+            LOG_TEE("v_image_grid_pinpoints: ");
             for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
-                printf("%d ", hparams.image_grid_pinpoints[i]);
+                LOG_TEE("%d ", hparams.image_grid_pinpoints[i]);
             }
-            printf("\n");
-            printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
+            LOG_TEE("\n");
+            LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
 
         }
 
+        try {
+            vision_model.class_embedding  = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
+            new_clip->has_class_embedding = true;
+        } catch (const std::exception& e) {
+            new_clip->has_class_embedding = false;
+        }
+
+        try {
+            vision_model.pre_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
+            vision_model.pre_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
+            new_clip->has_pre_norm = true;
+        } catch (std::exception & e) {
+            new_clip->has_pre_norm = false;
+        }
+
+        try {
+            vision_model.post_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight"));
+            vision_model.post_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
+            new_clip->has_post_norm = true;
+        } catch (std::exception & e) {
+            new_clip->has_post_norm = false;
+        }
+
+        try {
+            vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
+            new_clip->has_patch_bias = true;
+        } catch (std::exception & e) {
+            new_clip->has_patch_bias = false;
+        }
+
         try {
             vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
-            vision_model.class_embedding     = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
             vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
-            vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
-            vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
         } catch(const std::exception& e) {
-            fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
+            LOG_TEE("%s: failed to load vision model tensors\n", __func__);
         }
 
         // LLaVA projection
@@ -1184,7 +1233,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             } catch (std::runtime_error & e) {  }
             try {
                 vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
-                // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
+                // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
             } catch (std::runtime_error & e) {  }
         } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
             // MobileVLM projection
@@ -1264,7 +1313,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
         ggml_gallocr_reserve(new_clip->compute_alloc, gf);
         size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
-        printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
+        LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
     }
 
     return new_clip;
@@ -1304,7 +1353,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
     int nx, ny, nc;
     auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
     if (!data) {
-        fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
+        LOG_TEE("%s: failed to load image '%s'\n", __func__, fname);
         return false;
     }
     build_clip_img_from_data(data, nx, ny, img);
@@ -1316,7 +1365,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
     int nx, ny, nc;
     auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
     if (!data) {
-        fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
+        LOG_TEE("%s: failed to decode image bytes\n", __func__);
         return false;
     }
     build_clip_img_from_data(data, nx, ny, img);
@@ -1325,7 +1374,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
 }
 
 // Linear interpolation between two points
-inline float lerp(float s, float e, float t) {
+inline float clip_lerp(float s, float e, float t) {
     return s + (e - s) * t;
 }
 // Bilinear resize function
@@ -1347,17 +1396,17 @@ static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int ta
             float y_lerp = py - y_floor;
 
             for (int c = 0; c < 3; c++) {
-                float top = lerp(
+                float top = clip_lerp(
                     static_cast<float>(src.buf[3 * (y_floor * src.nx + x_floor) + c]),
                     static_cast<float>(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]),
                     x_lerp
                 );
-                float bottom = lerp(
+                float bottom = clip_lerp(
                     static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]),
                     static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]),
                     x_lerp
                 );
-                dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(lerp(top, bottom, y_lerp));
+                dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(clip_lerp(top, bottom, y_lerp));
             }
         }
     }
@@ -1506,7 +1555,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
         int downscaled_height = static_cast<int>(original_height * scale);
         int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
         int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
         if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
             max_effective_resolution = effective_resolution;
             min_wasted_resolution = wasted_resolution;
@@ -1545,7 +1594,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
 bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
     auto & params = ctx->vision_model.hparams;
@@ -1622,7 +1671,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             }
 
             for (size_t i = 0; i < patches.size(); i++) {
-                // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
+                // LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
                 clip_image_u8_free(patches[i]);
             }
 
@@ -1765,7 +1814,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
 
 bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
 
@@ -1777,7 +1826,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3
 
 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
 
@@ -1939,7 +1988,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             new_type = type;
             if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
                 new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
-                // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
+                // LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
             }
             const size_t n_elms = ggml_nelements(cur);
             float * f32_data;
@@ -1958,7 +2007,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
                 f32_data = (float *)conv_buf.data();
                 break;
             default:
-                printf("Please use an input file in f32 or f16\n");
+                LOG_TEE("Please use an input file in f32 or f16\n");
                 gguf_free(ctx_out);
                 return false;
             }
@@ -1985,7 +2034,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             fout.put(0);
         }
 
-        printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
+        LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
                orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
     }
 
@@ -2001,8 +2050,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     gguf_free(ctx_out);
 
     {
-        printf("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
-        printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
+        LOG_TEE("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
+        LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
     }
 
     return true;

examples/llava/llava-cli.cpp

@@ -1,4 +1,5 @@
 #include "ggml.h"
+#include "log.h"
 #include "common.h"
 #include "clip.h"
 #include "llava.h"
@@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
             n_eval = n_batch;
         }
         if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
             return false;
         }
         *n_past += n_eval;
@@ -73,7 +74,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
     size_t img_base64_str_start, img_base64_str_end;
     find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
     if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
+        LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
         return NULL;
     }
 
@@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
 
     auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
     if (!embed) {
-        fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
+        LOG_TEE("%s: could not load image from base64 string.\n", __func__);
         return NULL;
     }
 
@@ -112,8 +113,8 @@ struct llava_context {
 };
 
 static void show_additional_info(int /*argc*/, char ** argv) {
-    fprintf(stderr, "\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    fprintf(stderr, "  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
 static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
@@ -123,18 +124,18 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
     auto prompt = params->prompt;
     if (prompt_contains_image(prompt)) {
         if (!params->image.empty()) {
-            fprintf(stderr, "using base64 encoded image instead of command line image path\n");
+            LOG_TEE("using base64 encoded image instead of command line image path\n");
         }
         embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
         if (!embed) {
-            fprintf(stderr, "%s: can't load image from prompt\n", __func__);
+            LOG_TEE("%s: can't load image from prompt\n", __func__);
             return NULL;
         }
         params->prompt = remove_image_from_prompt(prompt);
     } else {
         embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
         if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
+            LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str());
             return NULL;
         }
     }
@@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
         // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
         system_prompt = prompt.substr(0, image_pos);
         user_prompt = prompt.substr(image_pos + std::string("<image>").length());
-        printf("system_prompt: %s\n", system_prompt.c_str());
+        LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
-        printf("user_prompt: %s\n", user_prompt.c_str());
+        LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
     } else {
@@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
     }
@@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
 
     // generate the response
 
-    fprintf(stderr, "\n");
+    LOG_TEE("\n");
 
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
     std::string response = "";
@@ -224,7 +225,7 @@ static struct llava_context * llava_init(gpt_params * params) {
 
     llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
     if (model == NULL) {
-        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        LOG_TEE("%s: error: unable to load model\n" , __func__);
         return NULL;
     }
 
@@ -234,7 +235,7 @@ static struct llava_context * llava_init(gpt_params * params) {
     llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
 
     if (ctx_llama == NULL) {
-        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
         return NULL;
     }
 
@@ -257,6 +258,12 @@ static void llava_free(struct llava_context * ctx_llava) {
     llama_backend_free();
 }
 
+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) {
     ggml_time_init();
 
@@ -266,6 +273,14 @@ int main(int argc, char ** argv) {
         show_additional_info(argc, argv);
         return 1;
     }
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("llava", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
     if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
         gpt_print_usage(argc, argv, params);
         show_additional_info(argc, argv);
@@ -274,7 +289,7 @@ int main(int argc, char ** argv) {
 
     auto ctx_llava = llava_init(&params);
     if (ctx_llava == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava\n", __func__);
+        LOG_TEE("%s: error: failed to init llava\n", __func__);
         return 1;
     }
 

examples/llava/llava.cpp

@@ -54,7 +54,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int>& ori
         int downscaled_height = static_cast<int>(original_height * scale);
         int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
         int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
         if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
             max_effective_resolution = effective_resolution;
             min_wasted_resolution = wasted_resolution;
@@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
     model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
     if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
         if (newline_tmp->buffer == NULL) {
-            printf("newline_tmp tensor buffer is NULL\n");
+            LOG_TEE("newline_tmp tensor buffer is NULL\n");
         }
         ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
     } else {
         model.newline->data = newline_tmp->data;
         if (model.newline->data == NULL) {
-            printf("newline_tmp tensor data is NULL\n");
+            LOG_TEE("newline_tmp tensor data is NULL\n");
         }
     }
 
@@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
     img_res_v.size = 0;
     img_res_v.data = nullptr;
     if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
-        fprintf(stderr, "%s: unable to preprocess image\n", __func__);
+        LOG_TEE("%s: unable to preprocess image\n", __func__);
         delete[] img_res_v.data;
         return false;
     }
@@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
         delete[] img_res_v.data;
         if (!encoded) {
-            fprintf(stderr, "Unable to encode image\n");
+            LOG_TEE("Unable to encode image\n");
 
             return false;
         }
@@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
             image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
             const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
             if (!encoded) {
-                fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
                 return false;
             }
         }
         const int64_t t_img_enc_batch_us = ggml_time_us();
-        printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+        LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
 
         const int32_t * image_grid = clip_image_grid(ctx_clip);
 
@@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
     }
 
-    printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
+    LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
 
     const int64_t t_img_enc_end_us = ggml_time_us();
     float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
 
-    printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
+    LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
 
     return true;
 }
@@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
     int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
     auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
     if (n_image_embd != n_llama_embd) {
-        printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
+        LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
         return false;
     }
     return true;
@@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
 bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
     float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
     if (!image_embd) {
-        fprintf(stderr, "Unable to allocate memory for image embeddings\n");
+        LOG_TEE("Unable to allocate memory for image embeddings\n");
         return false;
     }
 
     int n_img_pos;
     if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
-        fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
+        LOG_TEE("%s: cannot encode image, aborting\n", __func__);
         free(image_embd);
         return false;
     }
@@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
         }
         llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
         if (llama_decode(ctx_llama, batch)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
+            LOG_TEE("%s : failed to eval\n", __func__);
             return false;
         }
         *n_past += n_eval;
@@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
     clip_image_u8 * img = clip_image_u8_init();
     if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
         clip_image_u8_free(img);
-        fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
+        LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__);
         return NULL;
     }
 
@@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
     bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
     if (!image_embed_result) {
         clip_image_u8_free(img);
-        fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
+        LOG_TEE("%s: coulnd't embed the image\n", __func__);
         return NULL;
     }
 
@@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
 static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
     auto file = fopen(path, "rb");
     if (file == NULL) {
-        fprintf(stderr, "%s: can't read file %s\n", __func__, path);
+        LOG_TEE("%s: can't read file %s\n", __func__, path);
         return false;
     }
 
@@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
 
     auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
     if (buffer == NULL) {
-        fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
+        LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
         perror("Memory allocation error");
         fclose(file);
         return false;
@@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx
     long image_bytes_length;
     auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
     if (!loaded) {
-        fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
+        LOG_TEE("%s: failed to load %s\n", __func__, image_path);
         return NULL;
     }
 

examples/lookup/lookup-stats.cpp

@@ -30,7 +30,6 @@ int main(int argc, char ** argv){
 
     // load the model
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    llama_set_rng_seed(ctx, params.seed);
     GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
 
     // tokenize the prompt

examples/lookup/lookup.cpp

@@ -38,7 +38,6 @@ int main(int argc, char ** argv){
 
     // load the model
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    llama_set_rng_seed(ctx, params.seed);
     GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
 
     // tokenize the prompt

examples/main/main.cpp

@@ -240,7 +240,6 @@ int main(int argc, char ** argv) {
                 return 1;
             }
             session_tokens.resize(n_token_count_out);
-            llama_set_rng_seed(ctx, params.seed);
             LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
         }
     }

examples/quantize/CMakeLists.txt

@@ -1,6 +1,6 @@
 set(TARGET quantize)
 add_executable(${TARGET} quantize.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(${TARGET} PRIVATE ../../common)
 target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/quantize/quantize.cpp

@@ -8,7 +8,6 @@
 #include <unordered_map>
 #include <fstream>
 #include <cmath>
-#include <algorithm>
 
 struct quant_option {
     std::string name;
@@ -53,6 +52,10 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "COPY",   LLAMA_FTYPE_ALL_F32,       "only copy tensors, no quantizing", },
 };
 
+static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE       = "quantize.imatrix.file";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET    = "quantize.imatrix.dataset";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES  = "quantize.imatrix.entries_count";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS   = "quantize.imatrix.chunks_count";
 
 static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
     std::string ftype_str;
@@ -97,6 +100,7 @@ static void usage(const char * executable) {
     printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
     printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
     printf("  --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
+    printf("  --keep-split: will generate quatized model in the same shards as input");
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
     printf("Note: --include-weights and --exclude-weights cannot be used together\n");
@@ -112,7 +116,7 @@ static void usage(const char * executable) {
     exit(1);
 }
 
-static void load_imatrix(const std::string & imatrix_file, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
     std::ifstream in(imatrix_file.c_str(), std::ios::binary);
     if (!in) {
         printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
@@ -159,18 +163,33 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
             printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
         }
     }
-    printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
+
+    // latest imatrix version contains the dataset filename at the end of the file
+    int m_last_call = 0;
+    if (in.peek() != EOF) {
+        in.read((char *)&m_last_call, sizeof(m_last_call));
+        int dataset_len;
+        in.read((char *)&dataset_len, sizeof(dataset_len));
+        std::vector<char> dataset_as_vec(dataset_len);
+        in.read(dataset_as_vec.data(), dataset_len);
+        imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
+        printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
+    }
+    printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
+    return m_last_call;
 }
 
-static void prepare_imatrix(const std::string & imatrix_file,
+static int prepare_imatrix(const std::string & imatrix_file,
+        std::string & imatrix_dataset,
         const std::vector<std::string> & included_weights,
         const std::vector<std::string> & excluded_weights,
         std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+    int m_last_call = -1;
     if (!imatrix_file.empty()) {
-        load_imatrix(imatrix_file, imatrix_data);
+        m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data);
     }
     if (imatrix_data.empty()) {
-        return;
+        return m_last_call;
     }
     if (!excluded_weights.empty()) {
         for (auto& name : excluded_weights) {
@@ -196,6 +215,7 @@ static void prepare_imatrix(const std::string & imatrix_file,
     if (!imatrix_data.empty()) {
         printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
     }
+    return m_last_call;
 }
 
 static ggml_type parse_ggml_type(const char * arg) {
@@ -210,43 +230,6 @@ static ggml_type parse_ggml_type(const char * arg) {
     return result;
 }
 
-static bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
-    const char* sep = strchr(data, '=');
-    if (sep == nullptr || sep - data >= 128) {
-        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
-        return false;
-    }
-    llama_model_kv_override kvo;
-    std::strncpy(kvo.key, data, sep - data);
-    kvo.key[sep - data] = 0;
-    sep++;
-    if (strncmp(sep, "int:", 4) == 0) {
-        sep += 4;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
-        kvo.int_value = std::atol(sep);
-    } else if (strncmp(sep, "float:", 6) == 0) {
-        sep += 6;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
-        kvo.float_value = std::atof(sep);
-    } else if (strncmp(sep, "bool:", 5) == 0) {
-        sep += 5;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
-        if (std::strcmp(sep, "true") == 0) {
-            kvo.bool_value = true;
-        } else if (std::strcmp(sep, "false") == 0) {
-            kvo.bool_value = false;
-        } else {
-            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
-            return false;
-        }
-    } else {
-        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
-        return false;
-    }
-    overrides.emplace_back(std::move(kvo));
-    return true;
-}
-
 int main(int argc, char ** argv) {
     if (argc < 3) {
         usage(argv[0]);
@@ -300,6 +283,8 @@ int main(int argc, char ** argv) {
             } else {
                 usage(argv[0]);
             }
+        } else if (strcmp(argv[arg_idx], "--keep-split")) {
+            params.keep_split = true;
         } else {
             usage(argv[0]);
         }
@@ -313,10 +298,43 @@ int main(int argc, char ** argv) {
         usage(argv[0]);
     }
 
+    std::string imatrix_dataset;
     std::unordered_map<std::string, std::vector<float>> imatrix_data;
-    prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data);
+    int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
     if (!imatrix_data.empty()) {
         params.imatrix = &imatrix_data;
+        {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+            strncpy(kvo.val_str, imatrix_file.c_str(), 127);
+            kvo.val_str[127] = '\0';
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+        if (!imatrix_dataset.empty()) {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+            strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
+            kvo.val_str[127] = '\0';
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+
+        {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+            kvo.val_i64 = imatrix_data.size();
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+
+        if (m_last_call > 0) {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+            kvo.val_i64 = m_last_call;
+            kv_overrides.emplace_back(std::move(kvo));
+        }
     }
     if (!kv_overrides.empty()) {
         kv_overrides.emplace_back();
@@ -332,20 +350,28 @@ int main(int argc, char ** argv) {
     std::string fname_out;
 
     std::string ftype_str;
+    std::string suffix = ".gguf";
     if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
         std::string fpath;
         const size_t pos = fname_inp.find_last_of("/\\");
         if (pos != std::string::npos) {
             fpath = fname_inp.substr(0, pos + 1);
         }
-        // export as [inp path]/ggml-model-[ftype].gguf
-        fname_out = fpath + "ggml-model-" + ftype_str + ".gguf";
+
+        // export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting
+        fname_out = fpath + "ggml-model-" + ftype_str;
+        if (!params.keep_split) {
+            fname_out += suffix;
+        }
         arg_idx++;
         if (ftype_str == "COPY") {
             params.only_copy = true;
         }
     } else {
         fname_out = argv[arg_idx];
+        if (params.keep_split && fname_out.find(suffix) != std::string::npos) {
+            fname_out = fname_out.substr(0, fname_out.length() - suffix.length());
+        }
         arg_idx++;
 
         if (argc <= arg_idx) {

examples/quantize/tests.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+
+set -eu
+
+if [ $# -lt 1 ]
+then
+    echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
+    echo "example: $0 ../../build/bin ../../tmp"
+    exit 1
+fi
+
+if [ $# -gt 1 ]
+then
+    TMP_DIR=$2
+else
+    TMP_DIR=/tmp
+fi
+
+set -x
+
+SPLIT=$1/gguf-split
+QUANTIZE=$1/quantize
+MAIN=$1/main
+WORK_PATH=$TMP_DIR/quantize
+ROOT_DIR=$(realpath $(dirname $0)/../../)
+
+mkdir -p "$WORK_PATH"
+
+# Clean up in case of previously failed test
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf
+
+# 1. Get a model
+(
+cd $WORK_PATH
+"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
+)
+echo PASS
+
+# 2. Split model
+$SPLIT --split-max-tensors 28  $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
+echo PASS
+echo
+
+# 3. Requant model with '--keep_split'
+$QUANTIZE --allow-requantize --keep_split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K
+echo PASS
+echo
+
+# 3a. Test the requanted model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# 4. Requant mode without '--keep_split'
+$QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K
+echo PASS
+echo
+
+# 4b. Test the requanted model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# Clean up
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf

examples/server/CMakeLists.txt

@@ -1,12 +1,29 @@
 set(TARGET server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
 option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET}
+include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
+set(TARGET_SRCS
     server.cpp
     utils.hpp
     httplib.h
 )
+set(PUBLIC_ASSETS
+    index.html
+    index.js
+    completion.js
+    json-schema-to-grammar.mjs
+)
+foreach(asset ${PUBLIC_ASSETS})
+    set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
+    set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
+    list(APPEND TARGET_SRCS ${output})
+    add_custom_command(
+        DEPENDS "${input}"
+        OUTPUT "${output}"
+        COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
+    )
+endforeach()
+add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>

examples/server/bench/script.js

@@ -90,7 +90,8 @@ export default function () {
         "model": model,
         "stream": true,
         "seed": 42,
-        "max_tokens": max_tokens
+        "max_tokens": max_tokens,
+        "stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS
     }
 
     const params = {method: 'POST', body: JSON.stringify(payload)};

examples/server/completion.js.hpp

@@ -1,496 +0,0 @@
-unsigned char completion_js[] = {
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-  0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a,
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-  0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x5f, 0x67, 0x65, 0x6e, 0x65,
-  0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69,
-  0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72,
-  0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61,
-  0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67,
-  0x73, 0x3b, 0x0a, 0x7d, 0x0a
-};
-unsigned int completion_js_len = 5909;

examples/server/deps.sh

@@ -8,13 +8,3 @@ PUBLIC=$DIR/public
 echo "download js bundle files"
 curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
 echo >> $PUBLIC/index.js # add newline
-
-FILES=$(ls $PUBLIC)
-
-cd $PUBLIC
-for FILE in $FILES; do
-  echo "generate $FILE.hpp"
-
-  # use simple flag for old version of xxd
-  xxd -i $FILE > $DIR/$FILE.hpp
-done

examples/server/public/index.html

@@ -881,11 +881,11 @@
         .replace(/&/g, '&')
         .replace(/</g, '&lt;')
         .replace(/>/g, '&gt;')
-        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
-        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
-        .replace(/__(.*?)__/g, '<strong>$1</strong>')
-        .replace(/\*(.*?)\*/g, '<em>$1</em>')
-        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
+        .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
+        .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
         .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
         .replace(/`(.*?)`/g, '<code>$1</code>')
         .replace(/\n/gim, '<br />');

examples/server/server.cpp

@@ -854,7 +854,7 @@ struct server_context {
         slot.sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
         slot.params.n_keep             = json_value(data, "n_keep",            slot.params.n_keep);
         slot.params.n_discard          = json_value(data, "n_discard",         default_params.n_discard);
-        slot.params.seed               = json_value(data, "seed",              default_params.seed);
+        slot.sparams.seed              = json_value(data, "seed",              default_sparams.seed);
         slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
         slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
 
@@ -1028,7 +1028,6 @@ struct server_context {
                 send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
-            llama_set_rng_seed(ctx, slot.params.seed);
         }
 
         slot.command = SLOT_COMMAND_LOAD_PROMPT;
@@ -1118,7 +1117,7 @@ struct server_context {
 
     bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
-        const std::string token_str = llama_token_to_piece(ctx, result.tok);
+        const std::string token_str = llama_token_to_piece(ctx, result.tok, false);
         slot.sampled = result.tok;
 
         // search stop word and delete it
@@ -1208,6 +1207,27 @@ struct server_context {
             LOG_VERBOSE("eos token found", {});
         }
 
+        auto n_ctx_train = llama_n_ctx_train(model);
+        if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1
+                    && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
+            LOG_WARNING("n_predict is not set and self-context extend is disabled."
+                        " Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", {
+                    { "id_slot",              slot.id },
+                    { "params.n_predict",     slot.params.n_predict },
+                    { "slot.n_prompt_tokens", slot.n_prompt_tokens },
+                    { "slot.n_decoded",       slot.n_decoded },
+                    { "slot.n_predict",       slot.n_predict },
+                    { "n_slots",              params.n_parallel },
+                    { "slot.n_ctx",           slot.n_ctx },
+                    { "n_ctx",                n_ctx },
+                    { "n_ctx_train",          n_ctx_train },
+                    { "ga_n",                 slot.ga_n },
+                });
+            slot.truncated      = true;
+            slot.stopped_limit  = true;
+            slot.has_next_token = false; // stop prediction
+        }
+
         LOG_VERBOSE("next token", {
             {"id_slot",        slot.id},
             {"id_task",        slot.id_task},
@@ -2142,7 +2162,7 @@ struct server_context {
         });
 
         // process the created batch of tokens
-        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
+        for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
             const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
 
             for (auto & slot : slots) {
@@ -2372,7 +2392,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
     printf("  -n, --n-predict           maximum tokens to predict (default: %d)\n", params.n_predict);
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("                            advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                            types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("                            types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
     printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
     printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
     printf("  --chat-template JINJA_TEMPLATE\n");
@@ -2803,43 +2823,11 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
                 invalid_param = true;
                 break;
             }
-            char * sep = strchr(argv[i], '=');
-            if (sep == nullptr || sep - argv[i] >= 128) {
-                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-
-            struct llama_model_kv_override kvo;
-            std::strncpy(kvo.key, argv[i], sep - argv[i]);
-            kvo.key[sep - argv[i]] = 0;
-            sep++;
-            if (strncmp(sep, "int:", 4) == 0) {
-                sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
-                kvo.int_value = std::atol(sep);
-            } else if (strncmp(sep, "float:", 6) == 0) {
-                sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
-                kvo.float_value = std::atof(sep);
-            } else if (strncmp(sep, "bool:", 5) == 0) {
-                sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
-                if (std::strcmp(sep, "true") == 0) {
-                    kvo.bool_value = true;
-                } else if (std::strcmp(sep, "false") == 0) {
-                    kvo.bool_value = false;
-                } else {
-                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
-                    invalid_param = true;
-                    break;
-                }
-            } else {
+            if (!parse_kv_override(argv[i], params.kv_overrides)) {
                 fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
                 invalid_param = true;
                 break;
             }
-            params.kv_overrides.push_back(kvo);
         } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             server_print_usage(argv[0], default_params, default_sparams);

examples/server/tests/features/results.feature

@@ -0,0 +1,57 @@
+@llama.cpp
+@results
+Feature: Results
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
+    And   a model file test-model-00001-of-00003.gguf
+    And   128 as batch size
+    And   256 KV cache size
+    And   128 max tokens to predict
+
+  Scenario Outline: Multi users completion
+    Given <n_slots> slots
+    And   continuous batching
+    Then  the server is starting
+    Then  the server is healthy
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    And  all slots are idle
+    Then all predictions are equal
+    Examples:
+      | n_slots |
+      | 1       |
+      | 2       |

examples/server/tests/features/steps/steps.py

@@ -61,6 +61,7 @@ def step_server_config(context, server_fqdn, server_port):
     context.server_metrics = False
     context.server_process = None
     context.seed = None
+    context.draft = None
     context.server_seed = None
     context.user_api_key = None
     context.response_format = None
@@ -107,6 +108,11 @@ def step_n_gpu_layer(context, ngl):
     context.n_gpu_layer = ngl
 
 
+@step('{draft:d} as draft')
+def step_draft(context, draft):
+    context.draft = draft
+
+
 @step('{n_ctx:d} KV cache size')
 def step_n_ctx(context, n_ctx):
     context.n_ctx = n_ctx
@@ -254,6 +260,15 @@ def step_n_tokens_predicted(context, predicted_n):
     assert_n_tokens_predicted(context.completion, predicted_n)
 
 
+@step('all predictions are equal')
+@async_run_until_complete
+async def step_predictions_equal(context):
+    n_completions = await gather_tasks_results(context)
+    assert n_completions >= 2, "need at least 2 completions"
+    assert_all_predictions_equal(context.tasks_result)
+    context.tasks_result = []
+
+
 @step('the completion is  truncated')
 def step_assert_completion_truncated(context):
     step_assert_completion_truncated(context, '')
@@ -1020,6 +1035,23 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
         assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
                                                      f' {n_predicted} <> {expected_predicted_n}')
 
+def assert_all_predictions_equal(completion_responses):
+    content_0 = completion_responses[0]['content']
+
+    if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+        print(f"content 0: {content_0}")
+
+    i = 1
+    for response in completion_responses[1:]:
+        content = response['content']
+
+        if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+            print(f"content {i}: {content}")
+
+        assert content == content_0, "contents not equal"
+
+        i += 1
+
 
 async def gather_tasks_results(context):
     n_tasks = len(context.concurrent_tasks)
@@ -1148,6 +1180,8 @@ def start_server_background(context):
         server_args.extend(['--ubatch-size', context.n_ubatch])
     if context.n_gpu_layer:
         server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
+    if context.draft is not None:
+        server_args.extend(['--draft', context.draft])
     if context.server_continuous_batching:
         server_args.append('--cont-batching')
     if context.server_embeddings:

examples/server/tests/tests.sh

@@ -4,9 +4,8 @@ set -eu
 
 if [ $# -lt 1 ]
 then
-  # Start @llama.cpp scenario
-  behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+    # Start @llama.cpp scenario
+    behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
 else
-  behave "$@"
+    behave "$@"
 fi
-

flake.lock

@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1712791164,
-        "narHash": "sha256-3sbWO1mbpWsLepZGbWaMovSO7ndZeFqDSdX0hZ9nVyw=",
+        "lastModified": 1714076141,
+        "narHash": "sha256-Drmja/f5MRHZCskS6mvzFqxEaZMeciScCTFxWVLqWEY=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "1042fd8b148a9105f3c0aca3a6177fd1d9360ba5",
+        "rev": "7bb2ccd8cdc44c91edba16c48d2c8f331fb3d856",
         "type": "github"
       },
       "original": {

ggml-alloc.c

@@ -371,16 +371,16 @@ struct ggml_gallocr {
 };
 
 ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) {
-    ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1);
+    ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr));
     GGML_ASSERT(galloc != NULL);
 
-    galloc->bufts = calloc(sizeof(ggml_backend_buffer_type_t) * n_bufs, 1);
+    galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
     GGML_ASSERT(galloc->bufts != NULL);
 
-    galloc->buffers = calloc(sizeof(ggml_backend_buffer_t) * n_bufs, 1);
+    galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs);
     GGML_ASSERT(galloc->buffers != NULL);
 
-    galloc->buf_tallocs = calloc(sizeof(struct ggml_dyn_tallocr *) * n_bufs, 1);
+    galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
     GGML_ASSERT(galloc->buf_tallocs != NULL);
 
     for (int i = 0; i < n_bufs; i++) {
@@ -646,8 +646,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         free(galloc->hash_set.keys);
         free(galloc->hash_values);
         galloc->hash_set.size = hash_size;
-        galloc->hash_set.keys = calloc(sizeof(struct ggml_tensor *), hash_size);
-        galloc->hash_values   = calloc(sizeof(struct hash_node), hash_size);
+        galloc->hash_set.keys = calloc(hash_size, sizeof(struct ggml_tensor *));
+        galloc->hash_values   = calloc(hash_size, sizeof(struct hash_node));
         GGML_ASSERT(galloc->hash_set.keys != NULL);
         GGML_ASSERT(galloc->hash_values != NULL);
     } else {
@@ -667,7 +667,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     // set the node_allocs from the hash table
     if (galloc->n_nodes < graph->n_nodes) {
         free(galloc->node_allocs);
-        galloc->node_allocs = calloc(sizeof(struct node_alloc), graph->n_nodes);
+        galloc->node_allocs = calloc(graph->n_nodes, sizeof(struct node_alloc));
         GGML_ASSERT(galloc->node_allocs != NULL);
     }
     galloc->n_nodes = graph->n_nodes;
@@ -697,7 +697,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     }
     if (galloc->n_leafs < graph->n_leafs) {
         free(galloc->leaf_allocs);
-        galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
+        galloc->leaf_allocs = calloc(graph->n_leafs, sizeof(galloc->leaf_allocs[0]));
         GGML_ASSERT(galloc->leaf_allocs != NULL);
     }
     galloc->n_leafs = graph->n_leafs;

ggml-backend.c

@@ -822,7 +822,11 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t
 GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_CPY:
-            return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float
+            return
+                op->type != GGML_TYPE_IQ2_XXS &&
+                op->type != GGML_TYPE_IQ2_XS  &&
+                op->type != GGML_TYPE_IQ1_S   &&
+                op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
             return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
         default:
@@ -1721,23 +1725,23 @@ ggml_backend_sched_t ggml_backend_sched_new(
     GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
     GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU
 
-    struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
+    struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched));
 
     // initialize hash table
     sched->hash_set          = ggml_hash_set_new(graph_size);
-    sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size);
-    sched->tensor_copies     = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size);
+    sched->tensor_backend_id = calloc(sched->hash_set.size, sizeof(sched->tensor_backend_id[0]));
+    sched->tensor_copies     = calloc(sched->hash_set.size, sizeof(sched->tensor_copies[0]));
 
     const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2;
-    sched->node_backend_ids  = calloc(sizeof(sched->node_backend_ids[0]), nodes_size);
-    sched->leaf_backend_ids  = calloc(sizeof(sched->leaf_backend_ids[0]), nodes_size);
+    sched->node_backend_ids  = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
+    sched->leaf_backend_ids  = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
 
     sched->n_backends = n_backends;
 
     sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
 
     const int initial_splits_capacity = 16;
-    sched->splits = calloc(sizeof(sched->splits[0]), initial_splits_capacity);
+    sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0]));
     sched->splits_capacity = initial_splits_capacity;
 
     for (int b = 0; b < n_backends; b++) {
@@ -1780,12 +1784,14 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
 
 void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
     // reset state for the next run
-    size_t hash_size = sched->hash_set.size;
-    memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
-    memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
-    memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
+    if (!sched->is_reset) {
+        size_t hash_size = sched->hash_set.size;
+        memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
+        memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
+        memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
 
-    sched->is_reset = true;
+        sched->is_reset = true;
+    }
     sched->is_alloc = false;
 }
 
@@ -1968,10 +1974,10 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
     struct ggml_hash_set hash_set = {
         /* .size = */ graph->visited_hash_table.size,
-        /* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT
+        /* .keys = */ calloc(graph->visited_hash_table.size, sizeof(hash_set.keys[0])) // NOLINT
     };
-    struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT
-    bool * node_init = calloc(sizeof(node_init[0]), hash_set.size);
+    struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
+    bool * node_init = calloc(hash_set.size, sizeof(node_init[0]));
 
     struct ggml_init_params params = {
         /* .mem_size   = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),

ggml-cuda/convert.cu

@@ -5,16 +5,16 @@
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) {
-    const int64_t i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
+    const int64_t i = (int64_t)2*(blockDim.x*blockIdx.x + threadIdx.x);
 
     if (i >= k) {
         return;
     }
 
     const int64_t ib = i/qk; // block index
-    const int iqs = (i%qk)/qr; // quant index
-    const int iybs = i - i%qk; // y block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
+    const int64_t iqs = (i%qk)/qr; // quant index
+    const int64_t iybs = i - i%qk; // y block start index
+    const int64_t y_offset = qr == 1 ? 1 : qk/2;
 
     // dequantize
     dfloat2 v;
@@ -29,7 +29,7 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h
 #if __CUDA_ARCH__ >= CC_PASCAL
     constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE;
 
-    const int   i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x;
+    const int64_t   i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x;
     const int * x0 = ((int *) vx) + blockIdx.x * nint;
     half2 * y2 = (half2 *) (y + i0);
 
@@ -73,9 +73,9 @@ static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t
     const int64_t i = blockIdx.x;
 
     // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
     const int64_t ib = 8*i + ir;
     if (ib >= nb32) {
         return;
@@ -101,9 +101,9 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t
     const int64_t i = blockIdx.x;
 
     // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
     const int64_t ib = 8*i + ir;
     if (ib >= nb32) {
         return;
@@ -127,14 +127,14 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t
 template<typename dst_t>
 static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_q2_K * x = (const block_q2_K *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int n   = tid/32;
-    const int l   = tid - 32*n;
-    const int is  = 8*n + l/16;
+    const int64_t n   = tid/32;
+    const int64_t l   = tid - 32*n;
+    const int64_t is  = 8*n + l/16;
 
     const uint8_t q = x[i].qs[32*n + l];
     dst_t * y = yy + i*QK_K + 128*n;
@@ -146,8 +146,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
     y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
     y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
 #else
-    const int is = tid/16;  // 0 or 1
-    const int il = tid%16;  // 0...15
+    const int64_t is = tid/16;  // 0 or 1
+    const int64_t il = tid%16;  // 0...15
     const uint8_t q = x[i].qs[il] >> (2*is);
     dst_t * y = yy + i*QK_K + 16*is + il;
     float dall = __low2half(x[i].dm);
@@ -161,19 +161,19 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
 template<typename dst_t>
 static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i = blockIdx.x;
+    const int64_t i = blockIdx.x;
     const block_q3_K * x = (const block_q3_K *) vx;
 
 #if QK_K == 256
-    const int r = threadIdx.x/4;
-    const int tid = r/2;
-    const int is0 = r%2;
-    const int l0 = 16*is0 + 4*(threadIdx.x%4);
-    const int n = tid / 4;
-    const int j = tid - 4*n;
+    const int64_t r = threadIdx.x/4;
+    const int64_t tid = r/2;
+    const int64_t is0 = r%2;
+    const int64_t l0 = 16*is0 + 4*(threadIdx.x%4);
+    const int64_t n = tid / 4;
+    const int64_t j = tid - 4*n;
 
     uint8_t m = 1 << (4*n + j);
-    int is = 8*n + 2*j + is0;
+    int64_t is = 8*n + 2*j + is0;
     int shift = 2*j;
 
     int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
@@ -189,11 +189,11 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t
 
     for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
 #else
-    const int tid = threadIdx.x;
-    const int is  = tid/16;  // 0 or 1
-    const int il  = tid%16;  // 0...15
-    const int im  = il/8;    // 0...1
-    const int in  = il%8;    // 0...7
+    const int64_t tid = threadIdx.x;
+    const int64_t is  = tid/16;  // 0 or 1
+    const int64_t il  = tid%16;  // 0...15
+    const int64_t im  = il/8;    // 0...1
+    const int64_t in  = il%8;    // 0...7
 
     dst_t * y = yy + i*QK_K + 16*is + il;
 
@@ -227,15 +227,15 @@ template<typename dst_t>
 static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
     const block_q4_K * x = (const block_q4_K *) vx;
 
-    const int i = blockIdx.x;
+    const int64_t i = blockIdx.x;
 
 #if QK_K == 256
     // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int is  = 2*il;
-    const int n   = 4;
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
+    const int64_t is  = 2*il;
+    const int64_t n   = 4;
 
     dst_t * y = yy + i*QK_K + 64*il + n*ir;
 
@@ -254,7 +254,7 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t
         y[l +32] = d2 * (q[l] >>  4) - m2;
     }
 #else
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
     const uint8_t * q = x[i].qs;
     dst_t * y = yy + i*QK_K;
     const float d = (float)x[i].dm[0];
@@ -268,14 +268,14 @@ template<typename dst_t>
 static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
     const block_q5_K * x = (const block_q5_K *) vx;
 
-    const int i = blockIdx.x;
+    const int64_t i = blockIdx.x;
 
 #if QK_K == 256
     // assume 64 threads - this is very slightly better than the one below
-    const int tid = threadIdx.x;
-    const int il  = tid/16;   // il is in 0...3
-    const int ir  = tid%16;   // ir is in 0...15
-    const int is  = 2*il;     // is is in 0...6
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/16;   // il is in 0...3
+    const int64_t ir  = tid%16;   // ir is in 0...15
+    const int64_t is  = 2*il;     // is is in 0...6
 
     dst_t * y = yy + i*QK_K + 64*il + 2*ir;
 
@@ -298,11 +298,11 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t
     y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
     y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
 #else
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
     const uint8_t q = x[i].qs[tid];
-    const int im = tid/8;  // 0...3
-    const int in = tid%8;  // 0...7
-    const int is = tid/16; // 0 or 1
+    const int64_t im = tid/8;  // 0...3
+    const int64_t in = tid%8;  // 0...7
+    const int64_t is = tid/16; // 0 or 1
     const uint8_t h = x[i].qh[in] >> im;
     const float d = x[i].d;
     dst_t * y = yy + i*QK_K + tid;
@@ -359,13 +359,13 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t
 template<typename dst_t>
 static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
     const uint16_t * q2 = x[i].qs + 4*ib;
     const uint8_t  * aux8 = (const uint8_t *)q2;
@@ -383,13 +383,13 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
 template<typename dst_t>
 static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq2_xs * x = (const block_iq2_xs *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
     const uint16_t * q2 = x[i].qs + 4*ib;
     const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
@@ -405,13 +405,13 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
 template<typename dst_t>
 static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq2_s * x = (const block_iq2_s *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
     const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
     const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
@@ -426,13 +426,13 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
     const uint8_t  * q3 = x[i].qs + 8*ib;
     const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
@@ -454,13 +454,13 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
 template<typename dst_t>
 static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq3_s * x = (const block_iq3_s *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
     const uint8_t * qs = x[i].qs + 8*ib;
     const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
@@ -480,13 +480,13 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq1_s * x = (const block_iq1_s  *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
     const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
     const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
@@ -506,18 +506,18 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq1_m * x = (const block_iq1_m  *) vx;
 
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
     const uint16_t * sc = (const uint16_t *)x[i].scales;
     iq1m_scale_t scale;
     scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-    const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
     const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
     const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
     uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
@@ -537,12 +537,12 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);
 
-    const int tid = threadIdx.x;
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 4*il;
     const uint8_t  * q4 = x[ib].qs + 4*il;
     const float d = (float)x[ib].d;
@@ -556,12 +556,12 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst
 #if QK_K != 64
 template<typename dst_t>
 static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq4_xs * x = (const block_iq4_xs *)vx;
 
-    const int tid = threadIdx.x;
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 4*il;
     const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
     const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);

ggml-cuda/softmax.cu

@@ -28,7 +28,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
     extern __shared__ float data_soft_max_f32[];
     float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
     // shared memory buffer to cache values between iterations:
-    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols;
+    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + (int64_t)rowx*ncols;
 
     float max_val = -INFINITY;
 
@@ -40,8 +40,8 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
             break;
         }
 
-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
+        const int64_t ix = (int64_t)rowx*ncols + col;
+        const int64_t iy = (int64_t)rowy*ncols + col;
 
         const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
 
@@ -109,7 +109,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
             return;
         }
 
-        const int idst = rowx*ncols + col;
+        const int64_t idst = (int64_t)rowx*ncols + col;
         dst[idst] = vals[col] * inv_sum;
     }
 }

ggml-impl.h

@@ -11,6 +11,12 @@
 #include <string.h> // memcpy
 #include <math.h>   // fabsf
 
+#undef MIN
+#undef MAX
+
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -45,7 +51,7 @@ extern "C" {
 // 16-bit float
 // on Arm, we use __fp16
 // on x86, we use uint16_t
-#if defined(__ARM_NEON) && !defined(_MSC_VER)
+#if defined(__ARM_NEON)
 
 // if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
 //
@@ -53,8 +59,262 @@ extern "C" {
 //
 #include <arm_neon.h>
 
+#ifdef _MSC_VER
+
+typedef uint16_t ggml_fp16_internal_t;
+
+#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) }
+
+#else
+
 typedef __fp16 ggml_fp16_internal_t;
 
+#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) }
+
+#endif // _MSC_VER
+
+#if !defined(__aarch64__)
+
+// 32-bit ARM compatibility
+
+// vaddvq_s16
+// vpaddq_s16
+// vpaddq_s32
+// vaddvq_s32
+// vaddvq_f32
+// vmaxvq_f32
+// vcvtnq_s32_f32
+// vzip1_u8
+// vzip2_u8
+
+inline static int32_t vaddvq_s16(int16x8_t v) {
+    return
+        (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
+        (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
+        (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
+        (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
+}
+
+inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
+    int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
+    int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
+    return vcombine_s16(a0, b0);
+}
+
+inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) {
+    int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a));
+    int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b));
+    return vcombine_s32(a0, b0);
+}
+
+inline static int32_t vaddvq_s32(int32x4_t v) {
+    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
+}
+
+inline static float vaddvq_f32(float32x4_t v) {
+    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
+}
+
+inline static float vmaxvq_f32(float32x4_t v) {
+    return
+        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
+            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
+}
+
+inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
+    int32x4_t res;
+
+    res[0] = roundf(vgetq_lane_f32(v, 0));
+    res[1] = roundf(vgetq_lane_f32(v, 1));
+    res[2] = roundf(vgetq_lane_f32(v, 2));
+    res[3] = roundf(vgetq_lane_f32(v, 3));
+
+    return res;
+}
+
+inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[0]; res[1] = b[0];
+    res[2] = a[1]; res[3] = b[1];
+    res[4] = a[2]; res[5] = b[2];
+    res[6] = a[3]; res[7] = b[3];
+
+    return res;
+}
+
+inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[4]; res[1] = b[4];
+    res[2] = a[5]; res[3] = b[5];
+    res[4] = a[6]; res[5] = b[6];
+    res[6] = a[7]; res[7] = b[7];
+
+    return res;
+}
+
+// vld1q_s16_x2
+// vld1q_u8_x2
+// vld1q_u8_x4
+// vld1q_s8_x2
+// vld1q_s8_x4
+// TODO: double-check these work correctly
+
+typedef struct ggml_int16x8x2_t {
+    int16x8_t val[2];
+} ggml_int16x8x2_t;
+
+inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) {
+    ggml_int16x8x2_t res;
+
+    res.val[0] = vld1q_s16(ptr + 0);
+    res.val[1] = vld1q_s16(ptr + 8);
+
+    return res;
+}
+
+typedef struct ggml_uint8x16x2_t {
+    uint8x16_t val[2];
+} ggml_uint8x16x2_t;
+
+inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) {
+    ggml_uint8x16x2_t res;
+
+    res.val[0] = vld1q_u8(ptr + 0);
+    res.val[1] = vld1q_u8(ptr + 16);
+
+    return res;
+}
+
+typedef struct ggml_uint8x16x4_t {
+    uint8x16_t val[4];
+} ggml_uint8x16x4_t;
+
+inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
+    ggml_uint8x16x4_t res;
+
+    res.val[0] = vld1q_u8(ptr + 0);
+    res.val[1] = vld1q_u8(ptr + 16);
+    res.val[2] = vld1q_u8(ptr + 32);
+    res.val[3] = vld1q_u8(ptr + 48);
+
+    return res;
+}
+
+typedef struct ggml_int8x16x2_t {
+    int8x16_t val[2];
+} ggml_int8x16x2_t;
+
+inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) {
+    ggml_int8x16x2_t res;
+
+    res.val[0] = vld1q_s8(ptr + 0);
+    res.val[1] = vld1q_s8(ptr + 16);
+
+    return res;
+}
+
+typedef struct ggml_int8x16x4_t {
+    int8x16_t val[4];
+} ggml_int8x16x4_t;
+
+inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
+    ggml_int8x16x4_t res;
+
+    res.val[0] = vld1q_s8(ptr + 0);
+    res.val[1] = vld1q_s8(ptr + 16);
+    res.val[2] = vld1q_s8(ptr + 32);
+    res.val[3] = vld1q_s8(ptr + 48);
+
+    return res;
+}
+
+// NOTE: not tested
+inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
+    int8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
+// NOTE: not tested
+inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
+    uint8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
+#else
+
+#define ggml_int16x8x2_t  int16x8x2_t
+#define ggml_uint8x16x2_t uint8x16x2_t
+#define ggml_uint8x16x4_t uint8x16x4_t
+#define ggml_int8x16x2_t  int8x16x2_t
+#define ggml_int8x16x4_t  int8x16x4_t
+
+#define ggml_vld1q_s16_x2 vld1q_s16_x2
+#define ggml_vld1q_u8_x2  vld1q_u8_x2
+#define ggml_vld1q_u8_x4  vld1q_u8_x4
+#define ggml_vld1q_s8_x2  vld1q_s8_x2
+#define ggml_vld1q_s8_x4  vld1q_s8_x4
+#define ggml_vqtbl1q_s8   vqtbl1q_s8
+#define ggml_vqtbl1q_u8   vqtbl1q_u8
+
+#endif // !defined(__aarch64__)
+
+#if !defined(__ARM_FEATURE_DOTPROD)
+
+inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
+    const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
+    const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
+
+    return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
+}
+
+#else
+
+#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c)
+
+#endif // !defined(__ARM_FEATURE_DOTPROD)
+
+#endif // defined(__ARM_NEON)
+
+#if defined(__ARM_NEON) && !defined(__MSC_VER)
+
 #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
 #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
 
@@ -75,8 +335,6 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 
 #else
 
-typedef uint16_t ggml_fp16_internal_t;
-
 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
 #else
@@ -221,7 +479,7 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 
 #endif // __F16C__
 
-#endif // __ARM_NEON
+#endif // defined(__ARM_NEON) && (!defined(__MSC_VER)
 
 // precomputed f32 table for f16 (256 KB)
 // defined in ggml.c, initialized in ggml_init()

ggml-quants.c

@@ -14,47 +14,6 @@
 #include <stdlib.h> // for qsort
 #include <stdio.h>  // for GGML_ASSERT
 
-#ifdef __ARM_NEON
-
-// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
-//
-//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
-//
-#include <arm_neon.h>
-
-#else
-
-#ifdef __wasm_simd128__
-#include <wasm_simd128.h>
-#else
-#if defined(__POWER9_VECTOR__) || defined(__powerpc64__)
-#include <altivec.h>
-#undef bool
-#define bool _Bool
-#else
-#if defined(_MSC_VER) || defined(__MINGW32__)
-#include <intrin.h>
-#else
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
-#if !defined(__riscv)
-#include <immintrin.h>
-#endif
-#endif
-#endif
-#endif
-#endif
-#endif
-
-#ifdef __riscv_v_intrinsic
-#include <riscv_vector.h>
-#endif
-
-#undef MIN
-#undef MAX
-
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-
 #define UNUSED GGML_UNUSED
 
 // some compilers don't provide _mm256_set_m128i, e.g. gcc 7
@@ -276,258 +235,6 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
 #endif // __AVX__ || __AVX2__ || __AVX512F__
 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
 
-#if defined(__ARM_NEON)
-
-#ifdef _MSC_VER
-
-#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) }
-
-#else
-
-#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) }
-
-#endif
-
-#if !defined(__aarch64__)
-
-// 64-bit compatibility
-
-// vaddvq_s16
-// vpaddq_s16
-// vpaddq_s32
-// vaddvq_s32
-// vaddvq_f32
-// vmaxvq_f32
-// vcvtnq_s32_f32
-// vzip1_u8
-// vzip2_u8
-
-inline static int32_t vaddvq_s16(int16x8_t v) {
-    return
-        (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
-        (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
-        (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
-        (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
-}
-
-inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
-    int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
-    int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
-    return vcombine_s16(a0, b0);
-}
-
-inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) {
-    int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a));
-    int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b));
-    return vcombine_s32(a0, b0);
-}
-
-inline static int32_t vaddvq_s32(int32x4_t v) {
-    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
-}
-
-inline static float vaddvq_f32(float32x4_t v) {
-    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
-}
-
-inline static float vmaxvq_f32(float32x4_t v) {
-    return
-        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
-            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
-}
-
-inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
-    int32x4_t res;
-
-    res[0] = roundf(vgetq_lane_f32(v, 0));
-    res[1] = roundf(vgetq_lane_f32(v, 1));
-    res[2] = roundf(vgetq_lane_f32(v, 2));
-    res[3] = roundf(vgetq_lane_f32(v, 3));
-
-    return res;
-}
-
-inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) {
-    uint8x8_t res;
-
-    res[0] = a[0]; res[1] = b[0];
-    res[2] = a[1]; res[3] = b[1];
-    res[4] = a[2]; res[5] = b[2];
-    res[6] = a[3]; res[7] = b[3];
-
-    return res;
-}
-
-inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) {
-    uint8x8_t res;
-
-    res[0] = a[4]; res[1] = b[4];
-    res[2] = a[5]; res[3] = b[5];
-    res[4] = a[6]; res[5] = b[6];
-    res[6] = a[7]; res[7] = b[7];
-
-    return res;
-}
-
-// vld1q_s16_x2
-// vld1q_u8_x2
-// vld1q_u8_x4
-// vld1q_s8_x2
-// vld1q_s8_x4
-// TODO: double-check these work correctly
-
-typedef struct ggml_int16x8x2_t {
-    int16x8_t val[2];
-} ggml_int16x8x2_t;
-
-inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) {
-    ggml_int16x8x2_t res;
-
-    res.val[0] = vld1q_s16(ptr + 0);
-    res.val[1] = vld1q_s16(ptr + 8);
-
-    return res;
-}
-
-typedef struct ggml_uint8x16x2_t {
-    uint8x16_t val[2];
-} ggml_uint8x16x2_t;
-
-inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) {
-    ggml_uint8x16x2_t res;
-
-    res.val[0] = vld1q_u8(ptr + 0);
-    res.val[1] = vld1q_u8(ptr + 16);
-
-    return res;
-}
-
-typedef struct ggml_uint8x16x4_t {
-    uint8x16_t val[4];
-} ggml_uint8x16x4_t;
-
-inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
-    ggml_uint8x16x4_t res;
-
-    res.val[0] = vld1q_u8(ptr + 0);
-    res.val[1] = vld1q_u8(ptr + 16);
-    res.val[2] = vld1q_u8(ptr + 32);
-    res.val[3] = vld1q_u8(ptr + 48);
-
-    return res;
-}
-
-typedef struct ggml_int8x16x2_t {
-    int8x16_t val[2];
-} ggml_int8x16x2_t;
-
-inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) {
-    ggml_int8x16x2_t res;
-
-    res.val[0] = vld1q_s8(ptr + 0);
-    res.val[1] = vld1q_s8(ptr + 16);
-
-    return res;
-}
-
-typedef struct ggml_int8x16x4_t {
-    int8x16_t val[4];
-} ggml_int8x16x4_t;
-
-inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
-    ggml_int8x16x4_t res;
-
-    res.val[0] = vld1q_s8(ptr + 0);
-    res.val[1] = vld1q_s8(ptr + 16);
-    res.val[2] = vld1q_s8(ptr + 32);
-    res.val[3] = vld1q_s8(ptr + 48);
-
-    return res;
-}
-
-// NOTE: not tested
-inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
-    int8x16_t res;
-
-    res[ 0] = a[b[ 0]];
-    res[ 1] = a[b[ 1]];
-    res[ 2] = a[b[ 2]];
-    res[ 3] = a[b[ 3]];
-    res[ 4] = a[b[ 4]];
-    res[ 5] = a[b[ 5]];
-    res[ 6] = a[b[ 6]];
-    res[ 7] = a[b[ 7]];
-    res[ 8] = a[b[ 8]];
-    res[ 9] = a[b[ 9]];
-    res[10] = a[b[10]];
-    res[11] = a[b[11]];
-    res[12] = a[b[12]];
-    res[13] = a[b[13]];
-    res[14] = a[b[14]];
-    res[15] = a[b[15]];
-
-    return res;
-}
-
-// NOTE: not tested
-inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
-    uint8x16_t res;
-
-    res[ 0] = a[b[ 0]];
-    res[ 1] = a[b[ 1]];
-    res[ 2] = a[b[ 2]];
-    res[ 3] = a[b[ 3]];
-    res[ 4] = a[b[ 4]];
-    res[ 5] = a[b[ 5]];
-    res[ 6] = a[b[ 6]];
-    res[ 7] = a[b[ 7]];
-    res[ 8] = a[b[ 8]];
-    res[ 9] = a[b[ 9]];
-    res[10] = a[b[10]];
-    res[11] = a[b[11]];
-    res[12] = a[b[12]];
-    res[13] = a[b[13]];
-    res[14] = a[b[14]];
-    res[15] = a[b[15]];
-
-    return res;
-}
-
-#else
-
-#define ggml_int16x8x2_t  int16x8x2_t
-#define ggml_uint8x16x2_t uint8x16x2_t
-#define ggml_uint8x16x4_t uint8x16x4_t
-#define ggml_int8x16x2_t  int8x16x2_t
-#define ggml_int8x16x4_t  int8x16x4_t
-
-#define ggml_vld1q_s16_x2 vld1q_s16_x2
-#define ggml_vld1q_u8_x2  vld1q_u8_x2
-#define ggml_vld1q_u8_x4  vld1q_u8_x4
-#define ggml_vld1q_s8_x2  vld1q_s8_x2
-#define ggml_vld1q_s8_x4  vld1q_s8_x4
-#define ggml_vqtbl1q_s8   vqtbl1q_s8
-#define ggml_vqtbl1q_u8   vqtbl1q_u8
-
-#endif
-
-#if !defined(__ARM_FEATURE_DOTPROD)
-
-inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
-    const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
-    const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
-
-    return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
-}
-
-#else
-
-#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c)
-
-#endif
-
-#endif
-
 #if defined(__ARM_NEON) || defined(__wasm_simd128__)
 #define B1(c,s,n)  0x ## n ## c ,  0x ## n ## s
 #define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
@@ -12676,3 +12383,287 @@ void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k)
     block_iq2_s * restrict y = vy;
     quantize_row_iq2_s_reference(x, y, k);
 }
+
+static bool validate_float(float f, size_t i) {
+    if (isinf(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
+        return false;
+    }
+
+    if (isnan(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
+        return false;
+    }
+
+    return true;
+}
+
+static bool isinf_fp16(ggml_fp16_t f) {
+    return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) == 0;
+}
+
+static bool isnan_fp16(ggml_fp16_t f) {
+    return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) != 0;
+}
+
+static bool validate_fp16(ggml_fp16_t f, size_t i) {
+    if (isinf_fp16(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
+        return false;
+    }
+
+    if (isnan_fp16(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
+        return false;
+    }
+
+    return true;
+}
+
+#define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        if (!validate_fp16(q[i].d, i)) { \
+            return false; \
+        } \
+    }
+
+#define VALIDATE_ROW_DATA_DM_F16_IMPL(type, data, nb, d, m) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        if (!validate_fp16(q[i].d, i) || !validate_fp16(q[i].m, i)) { \
+            return false; \
+        } \
+    }
+
+bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
+    if (type < 0 || type >= GGML_TYPE_COUNT) {
+        fprintf(stderr, "%s: invalid type %d\n", __func__, type);
+        return false;
+    }
+
+    if (nbytes % ggml_type_size(type) != 0) {
+        fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type);
+        return false;
+    }
+
+    const size_t nb = nbytes/ggml_type_size(type);
+
+    switch (type) {
+        case GGML_TYPE_F16:
+            {
+                const ggml_fp16_t * f = (const ggml_fp16_t *) data;
+                size_t i = 0;
+#if defined(__AVX2__)
+                for (; i + 15 < nb; i += 16) {
+                    __m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
+                    __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi16(0x7c00));
+                    __m256i cmp = _mm256_cmpeq_epi16(vexp, _mm256_set1_epi16(0x7c00));
+                    int mask = _mm256_movemask_epi8(cmp);
+                    if (mask) {
+                        for (size_t j = 0; j < 16; ++j) {
+                            if (!validate_fp16(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#elif defined(__ARM_NEON)
+                for (; i + 7 < nb; i += 8) {
+                    uint16x8_t v = vld1q_u16(f + i);
+                    uint16x8_t vexp = vandq_u16(v, vdupq_n_u16(0x7c00));
+                    uint16x8_t cmp = vceqq_u16(vexp, vdupq_n_u16(0x7c00));
+                    uint64_t mask = vget_lane_u64(vreinterpret_u64_u8(vshrn_n_u16(cmp, 4)), 0);
+                    if (mask) {
+                        for (size_t j = 0; j < 8; ++j) {
+                            if (!validate_fp16(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#endif
+                for (; i < nb; ++i) {
+                    if (!validate_fp16(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_F32:
+            {
+                const float * f = (const float *) data;
+                size_t i = 0;
+#if defined(__AVX2__)
+                for (; i + 7 < nb; i += 8) {
+                    __m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
+                    __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi32(0x7f800000));
+                    __m256i cmp = _mm256_cmpeq_epi32(vexp, _mm256_set1_epi32(0x7f800000));
+                    int mask = _mm256_movemask_epi8(cmp);
+                    if (mask) {
+                        for (size_t j = 0; j < 8; ++j) {
+                            if (!validate_float(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#elif defined(__ARM_NEON)
+                for (; i + 3 < nb; i += 4) {
+                    uint32x4_t v = vld1q_u32((const uint32_t *)f + i);
+                    uint32x4_t vexp = vandq_u32(v, vdupq_n_u32(0x7f800000));
+                    uint32x4_t cmp = vceqq_u32(vexp, vdupq_n_u32(0x7f800000));
+                    uint64_t mask = vget_lane_u64(vreinterpret_u64_u16(vshrn_n_u32(cmp, 8)), 0);
+                    if (mask) {
+                        for (size_t j = 0; j < 4; ++j) {
+                            if (!validate_float(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#endif
+                for (; i < nb; ++i) {
+                    if (!validate_float(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_F64:
+            {
+                const double * f = (const double *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                    if (!validate_float(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_Q4_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb);
+            } break;
+        case GGML_TYPE_Q4_1:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_1, data, nb, d, m);
+            } break;
+        case GGML_TYPE_Q5_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_0, data, nb);
+            } break;
+        case GGML_TYPE_Q5_1:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_1, data, nb, d, m);
+            } break;
+        case GGML_TYPE_Q8_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
+            } break;
+        case GGML_TYPE_Q2_K:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);
+            } break;
+        case GGML_TYPE_Q3_K:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q3_K, data, nb);
+            } break;
+        case GGML_TYPE_Q4_K:
+            {
+            #ifdef GGML_QKK_64
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]);
+            #else
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin);
+            #endif
+            } break;
+        case GGML_TYPE_Q5_K:
+            {
+            #ifdef GGML_QKK_64
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb);
+            #else
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin);
+            #endif
+            } break;
+        case GGML_TYPE_Q6_K:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q6_K, data, nb);
+            } break;
+        case GGML_TYPE_Q8_K:
+            {
+                const block_q8_K * q = (const block_q8_K *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                    if (!validate_float(q[i].d, i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_IQ1_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ1_M:
+            {
+                const block_iq1_m * q = (const block_iq1_m *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                #if QK_K == 64
+                    if (!validate_fp16(q[i].d, i)) {
+                        return false;
+                    }
+                #else
+                    iq1m_scale_t scale;
+                    const uint16_t * sc = (const uint16_t *)q[i].scales;
+                    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+                    if (!validate_fp16(scale.f16, i)) {
+                        return false;
+                    }
+                #endif
+                }
+            } break;
+        case GGML_TYPE_IQ2_XXS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xxs, data, nb);
+            } break;
+        case GGML_TYPE_IQ2_XS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xs, data, nb);
+            } break;
+        case GGML_TYPE_IQ2_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ3_XXS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_xxs, data, nb);
+            } break;
+
+        case GGML_TYPE_IQ3_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ4_XS:
+        #if QK_K != 64
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb);
+            } break;
+        #endif
+        // with QK_K == 64, iq4_xs is iq4_nl
+        case GGML_TYPE_IQ4_NL:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
+            } break;
+        case GGML_TYPE_I8:
+        case GGML_TYPE_I16:
+        case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+            // nothing to validate
+            break;
+        default:
+            {
+                fprintf(stderr, "%s: invalid type %d\n", __func__, type);
+                return false;
+            }
+    }
+
+    return true;
+}

ggml-sycl.cpp

@@ -13416,11 +13416,16 @@ void print_device_detail(int id, sycl::device &device, std::string device_type)
     version += std::to_string(prop.get_minor_version());
 
     device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), "");
+    std::string name = std::string(prop.get_name());
+    name = std::regex_replace(name, std::regex("\\(R\\)"), "");
+    name = std::regex_replace(name, std::regex("\\(TM\\)"), "");
 
-    fprintf(stderr, "|%2d|%18s|%45s|%10s|%11d|%8d|%7d|%15lu|\n", id, device_type.c_str(),
-            prop.get_name(), version.c_str(), prop.get_max_compute_units(),
+    auto global_mem_size = prop.get_global_mem_size()/1000000;
+
+    fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(),
+            name.c_str(), version.c_str(), prop.get_max_compute_units(),
             prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
-            prop.get_global_mem_size());
+            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
 }
 
 void ggml_backend_sycl_print_sycl_devices() {
@@ -13428,9 +13433,10 @@ void ggml_backend_sycl_print_sycl_devices() {
     int device_count = dpct::dev_mgr::instance().device_count();
     std::map<std::string, size_t> DeviceNums;
     fprintf(stderr, "found %d SYCL devices:\n", device_count);
-    fprintf(stderr, "|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |\n");
-    fprintf(stderr, "|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|\n");
-    fprintf(stderr, "|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|\n");
+    fprintf(stderr, "|  |                   |                                       |       |Max    |        |Max  |Global |                     |\n");
+    fprintf(stderr, "|  |                   |                                       |       |compute|Max work|sub  |mem    |                     |\n");
+    fprintf(stderr, "|ID|        Device Type|                                   Name|Version|units  |group   |group|size   |       Driver version|\n");
+    fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n");
     for (int id = 0; id < device_count; ++id) {
         sycl::device device = dpct::dev_mgr::instance().get_device(id);
         sycl::backend backend = device.get_backend();

ggml.c

@@ -858,18 +858,6 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
 // simd mappings
 //
 
-#if defined(__ARM_NEON)
-#if !defined(__aarch64__)
-
-// 64-bit compatibility
-
-inline static float vaddvq_f32(float32x4_t v) {
-    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
-}
-
-#endif
-#endif
-
 // we define a common set of C macros which map to specific intrinsics based on the current architecture
 // we then implement the fundamental computation operations below using only these macros
 // adding support for new architectures requires to define the corresponding SIMD macros
@@ -10825,7 +10813,7 @@ static void ggml_compute_forward_mul_mat(
 #endif
 
 #if GGML_USE_LLAMAFILE
-    if (nb10 == ggml_type_size(src1->type)) {
+    if (src1_cont) {
         for (int64_t i13 = 0; i13 < ne13; i13++)
             for (int64_t i12 = 0; i12 < ne12; i12++)
                 if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
@@ -10878,15 +10866,13 @@ UseGgmlGemm1:;
     const size_t row_size = ggml_row_size(vec_dot_type, ne10);
 
 #if GGML_USE_LLAMAFILE
-    if (nb10 == ggml_type_size(src1->type) || src1->type != vec_dot_type) {
+    if (src1->type != vec_dot_type) {
         for (int64_t i13 = 0; i13 < ne13; i13++)
             for (int64_t i12 = 0; i12 < ne12; i12++)
                 if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
                                      (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
                                      nb01/ggml_type_size(src0->type),
-                                     (const char *)wdata + ggml_row_size(vec_dot_type,
-                                         nb12/ggml_type_size(src1->type)*i12 +
-                                         nb13/ggml_type_size(src1->type)*i13),
+                                     (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size,
                                      row_size/ggml_type_size(vec_dot_type),
                                      (char *)dst->data + i12*nb2 + i13*nb3,
                                      nb1/ggml_type_size(dst->type),
@@ -20628,7 +20614,7 @@ static void gguf_free_kv(struct gguf_kv * kv) {
 }
 
 struct gguf_context * gguf_init_empty(void) {
-    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+    struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));
 
     memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
     ctx->header.version   = GGUF_VERSION;
@@ -20673,7 +20659,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     bool ok = true;
 
-    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+    struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));
 
     // read the header
     {
@@ -20710,9 +20696,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the kv pairs
     {
-        ctx->kv = GGML_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv));
+        const uint64_t n_kv = ctx->header.n_kv;
 
-        for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
+        // header.n_kv will hold the actual value of pairs that were successfully read in the loop below
+        ctx->header.n_kv = 0;
+        ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv));
+
+        for (uint64_t i = 0; i < n_kv; ++i) {
             struct gguf_kv * kv = &ctx->kv[i];
 
             //fprintf(stderr, "%s: reading kv %d\n", __func__, i);
@@ -20761,7 +20751,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                         return NULL;
                                     }
 
-                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * gguf_type_size(kv->value.arr.type));
+                                    kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
 
                                     ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset);
                                 } break;
@@ -20775,7 +20765,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                         return NULL;
                                     }
 
-                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * sizeof(struct gguf_str));
+                                    kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str));
 
                                     for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
                                         ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset);
@@ -20791,6 +20781,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             if (!ok) {
                 break;
             }
+
+            ctx->header.n_kv++;
         }
 
         if (!ok) {
@@ -20803,7 +20795,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the tensor infos
     {
-        ctx->infos = GGML_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
+        ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
 
         for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
             struct gguf_tensor_info * info = &ctx->infos[i];
@@ -20824,8 +20816,17 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
             ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);
 
+            // TODO: return an error instead of crashing with GGML_ASSERT
             gguf_tensor_info_sanitize(info);
 
+            // make sure there is no duplicated tensor names
+            for (uint64_t j = 0; j < i; ++j) {
+                if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) {
+                    fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data);
+                    ok = false;
+                }
+            }
+
             if (!ok) {
                 fprintf(stderr, "%s: failed to read tensor info\n", __func__);
                 fclose(file);
@@ -20994,7 +20995,7 @@ void gguf_free(struct gguf_context * ctx) {
         GGML_FREE(ctx->infos);
     }
 
-    GGML_ALIGNED_FREE(ctx);
+    GGML_FREE(ctx);
 }
 
 const char * gguf_type_name(enum gguf_type type) {
@@ -21305,7 +21306,7 @@ void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_ty
     ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
     ctx->kv[idx].value.arr.type = type;
     ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*gguf_type_size(type));
+    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, gguf_type_size(type));
     memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type));
 }
 
@@ -21315,7 +21316,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char **
     ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
     ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING;
     ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*sizeof(struct gguf_str));
+    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, sizeof(struct gguf_str));
     for (int i = 0; i < n; i++) {
         struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
         str->n    = strlen(data[i]);
@@ -21342,7 +21343,7 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
             case GGUF_TYPE_ARRAY:
                 {
                     if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) {
-                        const char ** data = GGML_MALLOC(src->kv[i].value.arr.n*sizeof(char *));
+                        const char ** data = GGML_CALLOC(src->kv[i].value.arr.n, sizeof(char *));
                         for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) {
                             data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
                         }
@@ -21362,6 +21363,10 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
 void gguf_add_tensor(
              struct gguf_context * ctx,
         const struct ggml_tensor * tensor) {
+    if (gguf_find_tensor(ctx, tensor->name) != -1) {
+        GGML_ASSERT(false && "duplicated tensor name");
+    }
+
     const int idx = ctx->header.n_tensors;
     ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info));
 
@@ -21430,7 +21435,7 @@ struct gguf_buf {
 
 static struct gguf_buf gguf_buf_init(size_t size) {
     struct gguf_buf buf = {
-        /*buf.data   =*/ size == 0 ? NULL : GGML_MALLOC(size),
+        /*buf.data   =*/ size == 0 ? NULL : GGML_CALLOC(1, size),
         /*buf.size   =*/ size,
         /*buf.offset =*/ 0,
     };

ggml.h

@@ -762,6 +762,8 @@ extern "C" {
     // use this to compute the memory overhead of a tensor
     GGML_API size_t ggml_tensor_overhead(void);
 
+    GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);
+
     // main
 
     GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);

gguf-py/gguf/constants.py

@@ -124,6 +124,7 @@ class MODEL_ARCH(IntEnum):
     QWEN2      = auto()
     QWEN2MOE   = auto()
     PHI2       = auto()
+    PHI3       = auto()
     PLAMO      = auto()
     CODESHELL  = auto()
     ORION      = auto()
@@ -200,6 +201,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN2:          "qwen2",
     MODEL_ARCH.QWEN2MOE:       "qwen2moe",
     MODEL_ARCH.PHI2:           "phi2",
+    MODEL_ARCH.PHI3:           "phi3",
     MODEL_ARCH.PLAMO:          "plamo",
     MODEL_ARCH.CODESHELL:      "codeshell",
     MODEL_ARCH.ORION:          "orion",
@@ -550,6 +552,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.PHI3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.CODESHELL: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.POS_EMBD,
@@ -872,6 +888,7 @@ GGML_QUANT_SIZES = {
     GGMLQuantizationType.I32:     (1, 4),
     GGMLQuantizationType.I64:     (1, 8),
     GGMLQuantizationType.F64:     (1, 8),
+    GGMLQuantizationType.IQ1_M:   (256, QK_K // 8 + QK_K // 16  + QK_K // 32),
 }
 
 

gguf-py/gguf/gguf_reader.py

@@ -234,8 +234,14 @@ class GGUFReader:
 
     def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
         tensors = []
+        tensor_names = set() # keep track of name to prevent duplicated tensors
         for field in fields:
             _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts
+            # check if there's any tensor having same name already in the list
+            tensor_name = str(bytes(name_data), encoding = 'utf-8')
+            if tensor_name in tensor_names:
+                raise ValueError(f'Found duplicated tensor with name {tensor_name}')
+            tensor_names.add(tensor_name)
             ggml_type = GGMLQuantizationType(raw_dtype[0])
             n_elems = np.prod(dims)
             block_size, type_size = GGML_QUANT_SIZES[ggml_type]
@@ -267,7 +273,7 @@ class GGUFReader:
                 item_count = n_bytes
                 item_type = np.uint8
             tensors.append(ReaderTensor(
-                name = str(bytes(name_data), encoding = 'utf-8'),
+                name = tensor_name,
                 tensor_type = ggml_type,
                 shape = dims,
                 n_elements = n_elems,

gguf-py/gguf/gguf_writer.py

@@ -63,6 +63,7 @@ class GGUFWriter:
         self.kv_data_count = 0
         self.ti_data = bytearray()
         self.ti_data_count = 0
+        self.ti_names = set()
         self.use_temp_file = use_temp_file
         self.temp_file = None
         self.tensors = []
@@ -197,6 +198,10 @@ class GGUFWriter:
         if self.state is not WriterState.EMPTY:
             raise ValueError(f'Expected output file to be empty, got {self.state}')
 
+        if name in self.ti_names:
+            raise ValueError(f'Duplicated tensor name {name}')
+        self.ti_names.add(name)
+
         encoded_name = name.encode("utf8")
         self.ti_data += self._pack("Q", len(encoded_name))
         self.ti_data += encoded_name

gguf-py/gguf/tensor_mapping.py

@@ -117,6 +117,7 @@ class TensorNameMap:
             "h.{bid}.attn.c_attn",                                                 # gpt2
             "transformer.h.{bid}.mixer.Wqkv",                                      # phi2
             "encoder.layers.{bid}.attn.Wqkv",                                      # nomic-bert
+            "model.layers.{bid}.self_attn.qkv_proj"                                # phi3
         ),
 
         # Attention query
@@ -234,6 +235,7 @@ class TensorNameMap:
             "h.{bid}.mlp.c_fc",                                       # gpt2
             "transformer.h.{bid}.mlp.fc1",                            # phi2
             "model.layers.{bid}.mlp.fc1",                             # phi2
+            "model.layers.{bid}.mlp.gate_up_proj",                    # phi3
             "model.layers.layers.{bid}.mlp.up_proj",                  # plamo
             "model.layers.{bid}.feed_forward.w3",                     # internlm2
             "encoder.layers.{bid}.mlp.fc11",                          # nomic-bert

llama.cpp

@@ -75,6 +75,7 @@
 #include <forward_list>
 #include <fstream>
 #include <functional>
+#include <future>
 #include <initializer_list>
 #include <locale>
 #include <map>
@@ -211,6 +212,7 @@ enum llm_arch {
     LLM_ARCH_QWEN2,
     LLM_ARCH_QWEN2MOE,
     LLM_ARCH_PHI2,
+    LLM_ARCH_PHI3,
     LLM_ARCH_PLAMO,
     LLM_ARCH_CODESHELL,
     LLM_ARCH_ORION,
@@ -246,6 +248,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN2,           "qwen2"      },
     { LLM_ARCH_QWEN2MOE,        "qwen2moe"   },
     { LLM_ARCH_PHI2,            "phi2"       },
+    { LLM_ARCH_PHI3,            "phi3"       },
     { LLM_ARCH_PLAMO,           "plamo"      },
     { LLM_ARCH_CODESHELL,       "codeshell"  },
     { LLM_ARCH_ORION,           "orion"      },
@@ -793,6 +796,23 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_PHI3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_PLAMO,
         {
@@ -1600,12 +1620,12 @@ struct llama_mlock {
 };
 using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
-static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
+static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
         GGML_ASSERT(check == -n_tokens);
     }
     else {
@@ -2863,6 +2883,7 @@ namespace GGUFMeta {
                 case LLAMA_KV_OVERRIDE_TYPE_BOOL:  return "bool";
                 case LLAMA_KV_OVERRIDE_TYPE_INT:   return "int";
                 case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
+                case LLAMA_KV_OVERRIDE_TYPE_STR:   return "str";
             }
             return "unknown";
         }
@@ -2874,13 +2895,16 @@ namespace GGUFMeta {
                     __func__, override_type_to_str(ovrd->tag), ovrd->key);
                 switch (ovrd->tag) {
                     case LLAMA_KV_OVERRIDE_TYPE_BOOL:  {
-                        LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false");
+                        LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false");
                     } break;
                     case LLAMA_KV_OVERRIDE_TYPE_INT:   {
-                        LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value);
+                        LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64);
                     } break;
                     case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
-                        LLAMA_LOG_INFO("%.6f\n", ovrd->float_value);
+                        LLAMA_LOG_INFO("%.6f\n", ovrd->val_f64);
+                    } break;
+                    case LLAMA_KV_OVERRIDE_TYPE_STR: {
+                        LLAMA_LOG_INFO("%s\n", ovrd->val_str);
                     } break;
                     default:
                         // Shouldn't be possible to end up here, but just in case...
@@ -2899,7 +2923,7 @@ namespace GGUFMeta {
         static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
         try_override(OT & target, const struct llama_model_kv_override * ovrd) {
             if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
-                target = ovrd->bool_value;
+                target = ovrd->val_bool;
                 return true;
             }
             return false;
@@ -2909,7 +2933,7 @@ namespace GGUFMeta {
         static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
         try_override(OT & target, const struct llama_model_kv_override * ovrd) {
             if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
-                target = ovrd->int_value;
+                target = ovrd->val_i64;
                 return true;
             }
             return false;
@@ -2919,7 +2943,7 @@ namespace GGUFMeta {
         static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
         try_override(T & target, const struct llama_model_kv_override * ovrd) {
             if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
-                target = ovrd->float_value;
+                target = ovrd->val_f64;
                 return true;
             }
             return false;
@@ -2928,12 +2952,11 @@ namespace GGUFMeta {
         template<typename OT>
         static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
         try_override(T & target, const struct llama_model_kv_override * ovrd) {
-            (void)target;
-            (void)ovrd;
-            if (!ovrd) { return false; }
-            // Currently, we should never end up here so it would be a bug if we do.
-            throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n",
-                ovrd ? ovrd->key : "NULL"));
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_STR, ovrd)) {
+                target = ovrd->val_str;
+                return true;
+            }
+            return false;
         }
 
         static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
@@ -2966,6 +2989,7 @@ struct llama_model_loader {
     size_t  n_bytes    = 0;
 
     bool use_mmap = false;
+    bool check_tensors;
 
     llama_files files;
     llama_ftype ftype;
@@ -2980,9 +3004,13 @@ struct llama_model_loader {
 
         ggml_tensor * tensor;
 
-        llama_tensor_weight(uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
+        llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
             const int tensor_idx = gguf_find_tensor(gguf_ctx, name);
             offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
+
+            if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) {
+                throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name));
+            }
         }
     };
     std::vector<llama_tensor_weight> weights;
@@ -2995,7 +3023,7 @@ struct llama_model_loader {
     std::string arch_name;
     LLM_KV      llm_kv    = LLM_KV(LLM_ARCH_UNKNOWN);
 
-    llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) {
+    llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) {
         int trace = 0;
         if (getenv("LLAMA_TRACE")) {
             trace = atoi(getenv("LLAMA_TRACE"));
@@ -3021,15 +3049,15 @@ struct llama_model_loader {
         get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
         llm_kv = LLM_KV(llm_arch_from_string(arch_name));
 
+        files.emplace_back(new llama_file(fname.c_str(), "rb"));
+        contexts.emplace_back(ctx);
+
         // Save tensors data offset of the main file.
         // For subsidiary files, `meta` tensor data offset must not be used,
         // so we build a unified tensors index for weights.
         for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
-            weights.emplace_back(0, cur->name, meta, cur);
+            weights.emplace_back(files.back().get(), 0, cur->name, meta, cur);
         }
-        files.emplace_back(new llama_file(fname.c_str(), "rb"));
-        contexts.emplace_back(ctx);
-
         uint16_t n_split = 0;
         get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
 
@@ -3063,13 +3091,14 @@ struct llama_model_loader {
                     throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
                 }
 
-                // Save tensors data offset info of the shard.
-                for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
-                    weights.emplace_back(idx, cur->name, ctx_gguf, cur);
-                }
                 files.emplace_back(new llama_file(split_path, "rb"));
                 contexts.emplace_back(ctx);
 
+                // Save tensors data offset info of the shard.
+                for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+                    weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur);
+                }
+
                 gguf_free(ctx_gguf);
             }
 
@@ -3091,9 +3120,17 @@ struct llama_model_loader {
 
         fver = (enum llama_fver) gguf_get_version(meta);
 
+        std::set<std::string> tensor_names;
         for (auto & w : weights) {
             n_elements += ggml_nelements(w.tensor);
             n_bytes    += ggml_nbytes(w.tensor);
+            // make sure there is no duplicated tensor names
+            const std::string name(w.tensor->name);
+            auto found = tensor_names.find(name);
+            if (found != tensor_names.end()) {
+                throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name));
+            }
+            tensor_names.insert(name);
         }
 
         LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
@@ -3199,6 +3236,7 @@ struct llama_model_loader {
         }
 
         this->use_mmap = use_mmap;
+        this->check_tensors = check_tensors;
     }
 
     ~llama_model_loader() {
@@ -3278,6 +3316,10 @@ struct llama_model_loader {
         return nullptr;
     }
 
+    const llama_tensor_weight * get_weight(int i) const {
+        return get_weight(get_tensor_name(i));
+    }
+
     const llama_tensor_weight & require_weight(const char * name) const {
         const llama_tensor_weight * weight = get_weight(name);
         if (!weight) {
@@ -3453,6 +3495,10 @@ struct llama_model_loader {
             file->seek(w.offs, SEEK_SET);
             file->read_raw(cur->data, ggml_nbytes(cur));
         }
+
+        if (check_tensors && !ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) {
+            throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+        }
     }
 
     size_t size_done = 0;
@@ -3469,6 +3515,8 @@ struct llama_model_loader {
         GGML_ASSERT(size_data != 0 && "call init_mappings() first");
 
         std::vector<no_init<uint8_t>> read_buf;
+        std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
+
         for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
             const auto * weight = get_weight(ggml_get_name(cur));
             if (weight == nullptr) {
@@ -3490,37 +3538,66 @@ struct llama_model_loader {
                 if (bufs_mmap.count(weight->idx)) {
                     buf_mmap = bufs_mmap.at(weight->idx);
                 }
+                uint8_t * data = (uint8_t *) mapping->addr + weight->offs;
+
+                if (check_tensors) {
+                    validation_result.emplace_back(std::async(std::launch::async, [cur, data, n_size] {
+                        return std::make_pair(cur, ggml_validate_row_data(cur->type, data, n_size));
+                    }));
+                }
+
                 GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
                 if (buf_mmap && cur->data == nullptr) {
-                    ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + weight->offs);
+                    ggml_backend_tensor_alloc(buf_mmap, cur, data);
                     if (lmlocks) {
                         const auto & lmlock = lmlocks->at(weight->idx);
-                        lmlock->grow_to(weight->offs + ggml_nbytes(cur));
+                        lmlock->grow_to(weight->offs + n_size);
                     }
 
                     auto & mmap_used = mmaps_used[weight->idx];
                     mmap_used.first  = std::min(mmap_used.first,  weight->offs);
                     mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
                 } else {
-                    ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + weight->offs, 0, n_size);
+                    ggml_backend_tensor_set(cur, data, 0, n_size);
                 }
             } else {
                 GGML_ASSERT(weight->idx < files.size());
                 const auto & file = files.at(weight->idx);
                 if (ggml_backend_buffer_is_host(cur->buffer)) {
                     file->seek(weight->offs, SEEK_SET);
-                    file->read_raw(cur->data, ggml_nbytes(cur));
+                    file->read_raw(cur->data, n_size);
+                    if (check_tensors) {
+                        validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
+                            return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
+                        }));
+                    }
                 } else {
-                    read_buf.resize(ggml_nbytes(cur));
+                    read_buf.resize(n_size);
                     file->seek(weight->offs, SEEK_SET);
-                    file->read_raw(read_buf.data(), ggml_nbytes(cur));
+                    file->read_raw(read_buf.data(), n_size);
                     ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
+                    if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
+                        throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+                    }
                 }
             }
 
             size_done += n_size;
         }
 
+        // check validation results
+        bool validation_failed = false;
+        for (auto & future : validation_result) {
+            auto result = future.get();
+            if (!result.second) {
+                LLAMA_LOG_ERROR("%s: tensor '%s' has invalid data\n", __func__, ggml_get_name(result.first));
+                validation_failed = true;
+            }
+        }
+        if (validation_failed) {
+            throw std::runtime_error("found tensors with invalid data");
+        }
+
         // check if this is the last call and do final cleanup
         if (size_done >= size_data) {
             // unmap offloaded tensors and metadata
@@ -3955,6 +4032,16 @@ static void llm_load_hparams(
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_3B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_PHI3:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_3B; break;
@@ -4340,7 +4427,7 @@ static void llm_load_vocab(
             }
         }
 
-        // find EOT token: "<|eot_id|>", "<|im_emd|>", "<end_of_turn>", etc.
+        // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
         //
         // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
         //       for now, we apply this workaround to find the EOT token based on its text
@@ -4351,7 +4438,8 @@ static void llm_load_vocab(
                         //       need to fix convert script
                         //vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
                         (t.first == "<|eot_id|>" ||
-                         t.first == "<|im_emd|>" ||
+                         t.first == "<|im_end|>" ||
+                         t.first == "<|end|>" ||
                          t.first == "<end_of_turn>"
                         )
                    ) {
@@ -5375,6 +5463,33 @@ static bool llm_load_tensors(
                         layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
                     }
                 } break;
+            case LLM_ARCH_PHI3:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd });
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab });
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context* ctx_layer = ctx_for_layer(i);
+                        ggml_context* ctx_split = ctx_for_layer_split(i);
+
+                        auto& layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd });
+
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false);
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd });
+
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd });
+                        layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff });
+                    }
+                } break;
             case LLM_ARCH_PLAMO:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@@ -5909,7 +6024,7 @@ static bool llm_load_tensors(
 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
 static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
     try {
-        llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);
+        llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
 
         model.hparams.vocab_only = params.vocab_only;
 
@@ -6326,7 +6441,7 @@ static struct ggml_tensor * llm_build_kqv(
     struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
     cb(kq, "kq", il);
 
-    if (model.arch == LLM_ARCH_PHI2) {
+    if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
         // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
         // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
         ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
@@ -8967,12 +9082,140 @@ struct llm_build_context {
 
         cur = ggml_add(ctx0, cur, model.output_b);
         cb(cur, "result_output", -1);
+        ggml_build_forward_expand(gf, cur);
+        return gf;
+    }
+
+    struct ggml_cgraph * build_phi3() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            auto residual = inpL;
+
+            // self-attention
+            {
+                struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    NULL,
+                    LLM_NORM_RMS, cb, il);
+                cb(attn_norm_output, "attn_norm", il);
+
+                struct ggml_tensor * Qcur = nullptr;
+                struct ggml_tensor * Kcur = nullptr;
+                struct ggml_tensor * Vcur = nullptr;
+
+                if (model.layers[il].wqkv) {
+                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
+                    cb(cur, "wqkv", il);
+
+                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
+                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
+                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
+                }
+                else {
+                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+                }
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                    model.layers[il].wo, NULL,
+                    Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor* inp_out_ids = build_inp_out_ids();
+                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+            }
+
+            cur = ggml_add(ctx0, cur, residual);
+            residual = cur;
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            // FF
+            // special-case: the up and gate tensors are merged into a single tensor
+            // TOOD: support into llm_build_ffn
+            {
+                struct ggml_tensor* up = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
+                cb(up, "ffn_up", il);
+
+                auto g = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), 0));
+                auto y = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), up->nb[1] / 2));
+
+                y = ggml_mul(ctx0, y, ggml_silu(ctx0, g));
+                cb(y, "ffn_gate", il);
+
+                auto down = ggml_mul_mat(ctx0, model.layers[il].ffn_down, y);
+                cb(down, "ffn_down", il);
+
+                cur = down;
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, residual, cur);
+            cb(cur, "l_out", il);
+
+            inpL = cur;
+        }
+
+        cur = llm_build_norm(ctx0, inpL, hparams,
+            model.output_norm,
+            NULL,
+            LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
 
         return gf;
     }
 
+
     struct ggml_cgraph * build_plamo() {
         struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
@@ -10474,6 +10717,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_phi2();
             } break;
+        case LLM_ARCH_PHI3:
+            {
+                result = llm.build_phi3();
+            } break;
         case LLM_ARCH_PLAMO:
             {
                 result = llm.build_plamo();
@@ -11234,6 +11481,10 @@ static int llama_decode_internal(
         }
     }
 
+    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+    // overlap with device computation.
+    ggml_backend_sched_reset(lctx.sched);
+
     return 0;
 }
 
@@ -13312,7 +13563,8 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
 
     for (size_t i = 0; i < candidates->size; ++i) {
         const llama_token id    = candidates->data[i].id;
-        const std::string piece = llama_token_to_piece(ctx, id);
+        const std::string piece = llama_token_to_piece(ctx, id, false);
+
         if (llama_token_is_eog(&ctx->model, id)) {
             if (!allow_eog) {
                 candidates->data[i].logit = -INFINITY;
@@ -13477,7 +13729,7 @@ llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_da
     return result;
 }
 
-llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
     GGML_ASSERT(ctx);
 
     const int64_t t_start_sample_us = ggml_time_us();
@@ -13490,7 +13742,6 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
     }
 
     std::discrete_distribution<> dist(probs.begin(), probs.end());
-    auto & rng = ctx->rng;
     int idx = dist(rng);
 
     llama_token result = candidates->data[idx].id;
@@ -13500,6 +13751,10 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
     return result;
 }
 
+llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+    return llama_sample_token_with_rng(ctx, candidates, ctx->rng);
+}
+
 void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
     const int64_t t_start_sample_us = ggml_time_us();
 
@@ -13512,7 +13767,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
         GGML_ASSERT(false);
     }
 
-    const std::string piece = llama_token_to_piece(ctx, token);
+    const std::string piece = llama_token_to_piece(ctx, token, false);
 
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
@@ -14158,14 +14413,20 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
 }
 
 static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
+    if (nthread < 2) {
+        // single-thread
+        size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
+        if (!ggml_validate_row_data(new_type, new_data, new_size)) {
+            throw std::runtime_error("quantized data validation failed");
+        }
+        return new_size;
+    }
+
     std::mutex mutex;
     int64_t counter = 0;
     size_t new_size = 0;
-    if (nthread < 2) {
-        // single-thread
-        return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
-    }
-    auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size,
+    bool valid = true;
+    auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
             nrows, n_per_row, imatrix]() {
         const int64_t nrows_per_chunk = chunk_size / n_per_row;
         size_t local_size = 0;
@@ -14180,7 +14441,17 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
             }
             lock.unlock();
             const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
-            local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
+            size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
+            local_size += this_size;
+
+            // validate the quantized data
+            const size_t row_size  = ggml_row_size(new_type, n_per_row);
+            void * this_data = (char *) new_data + first_row * row_size;
+            if (!ggml_validate_row_data(new_type, this_data, this_size)) {
+                std::unique_lock<std::mutex> lock(mutex);
+                valid = false;
+                break;
+            }
         }
     };
     for (int it = 0; it < nthread - 1; ++it) {
@@ -14189,6 +14460,9 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
     compute();
     for (auto & w : workers) { w.join(); }
     workers.clear();
+    if (!valid) {
+        throw std::runtime_error("quantized data validation failed");
+    }
     return new_size;
 }
 
@@ -14251,7 +14525,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
         kv_overrides = v->data();
     }
-    llama_model_loader ml(fname_inp, use_mmap, kv_overrides);
+    llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
     ml.init_mappings(false); // no prefetching
 
     llama_model model;
@@ -14289,11 +14563,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         for (auto & o : overrides) {
             if (o.key[0] == 0) break;
             if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
-                gguf_set_val_f32(ctx_out, o.key, o.float_value);
+                gguf_set_val_f32(ctx_out, o.key, o.val_f64);
             } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
-                gguf_set_val_i32(ctx_out, o.key, o.int_value);
+                gguf_set_val_i32(ctx_out, o.key, o.val_i64);
             } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
-                gguf_set_val_bool(ctx_out, o.key, o.bool_value);
+                gguf_set_val_bool(ctx_out, o.key, o.val_bool);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
+                gguf_set_val_str(ctx_out, o.key, o.val_str);
             } else {
                 LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
             }
@@ -14335,26 +14611,74 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     std::vector<no_init<uint8_t>> work;
     std::vector<no_init<float>> f32_conv_buf;
 
+    uint16_t n_split = 1;
+    // Assume split index is continuous
+    if (params->keep_split) {
+        for (int i = 0; i < ml.n_tensors; ++i) {
+            n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split);
+        }
+    }
+    std::vector<gguf_context*> ctx_outs(n_split, NULL);
+    ctx_outs[0] = ctx_out;
+
     // populate the original tensors so we get an initial meta data
     for (int i = 0; i < ml.n_tensors; ++i) {
-        const struct ggml_tensor * meta = ml.get_tensor_meta(i);
-        gguf_add_tensor(ctx_out, meta);
+        auto weight = ml.get_weight(i);
+        uint16_t i_split = params->keep_split ? weight->idx : 0;
+        struct ggml_tensor * tensor = weight->tensor;
+        if (ctx_outs[i_split] == NULL) {
+            ctx_outs[i_split] = gguf_init_empty();
+        }
+        gguf_add_tensor(ctx_outs[i_split], tensor);
     }
 
-    std::ofstream fout(fname_out, std::ios::binary);
-    fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+    // Set split info if needed
+    if (n_split > 1) {
+        for (size_t i = 0; i < ctx_outs.size(); ++i) {
+            gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
+            gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
+            gguf_set_val_i32(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
+        }
+    }
 
-    const size_t meta_size = gguf_get_meta_size(ctx_out);
+    int cur_split = -1;
+    std::ofstream fout;
+    auto close_ofstream = [&]() {
+        // Write metadata and close file handler
+        if (fout.is_open()) {
+            fout.seekp(0);
+            std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split]));
+            gguf_get_meta_data(ctx_outs[cur_split], data.data());
+            fout.write((const char *) data.data(), data.size());
+            fout.close();
+        }
+    };
+    auto new_ofstream = [&](int index) {
+        cur_split = index;
+        GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
+        std::string fname = fname_out;
+        if (params->keep_split) {
+            char split_path[PATH_MAX] = {0};
+            llama_split_path(split_path, sizeof(split_path), fname_out.c_str(), cur_split, n_split);
+            fname = std::string(split_path);
+        }
 
-    LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);
-
-    // placeholder for the meta data
-    ::zeros(fout, meta_size);
+        fout = std::ofstream(fname, std::ios::binary);
+        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+        const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split]);
+        // placeholder for the meta data
+        ::zeros(fout, meta_size);
+    };
 
     const auto tn = LLM_TN(model.arch);
-
+    new_ofstream(0);
     for (int i = 0; i < ml.n_tensors; ++i) {
-        struct ggml_tensor * tensor = ml.get_tensor_meta(i);
+        auto weight = ml.get_weight(i);
+        struct ggml_tensor * tensor = weight->tensor;
+        if (weight->idx != cur_split && params->keep_split) {
+            close_ofstream();
+            new_ofstream(weight->idx);
+        }
 
         const std::string name = ggml_get_name(tensor);
 
@@ -14509,26 +14833,18 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         total_size_new += new_size;
 
         // update the gguf meta data as we go
-        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
-        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
+        gguf_set_tensor_type(ctx_outs[cur_split], name.c_str(), new_type);
+        gguf_set_tensor_data(ctx_outs[cur_split], name.c_str(), new_data, new_size);
 
         // write tensor data + padding
         fout.write((const char *) new_data, new_size);
         zeros(fout, GGML_PAD(new_size, align) - new_size);
     }
-
-    // go back to beginning of file and write the updated meta data
-    {
-        fout.seekp(0);
-        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
-        gguf_get_meta_data(ctx_out, data.data());
-        fout.write((const char *) data.data(), data.size());
+    close_ofstream();
+    for (auto & c:ctx_outs) {
+        gguf_free(c);
     }
 
-    fout.close();
-
-    gguf_free(ctx_out);
-
     LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
     LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
 
@@ -14572,7 +14888,7 @@ static int llama_apply_lora_from_file_internal(
     std::unique_ptr<llama_model_loader> ml;
     if (path_base_model) {
         LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
-        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr));
+        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr));
         ml->init_mappings(/*prefetch*/ false); // no prefetching
     }
 
@@ -14831,6 +15147,7 @@ struct llama_model_params llama_model_default_params() {
         /*.vocab_only                  =*/ false,
         /*.use_mmap                    =*/ true,
         /*.use_mlock                   =*/ false,
+        /*.check_tensors               =*/ false,
     };
 
 #ifdef GGML_USE_METAL
@@ -14884,6 +15201,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
         /*.quantize_output_tensor      =*/ true,
         /*.only_copy                   =*/ false,
         /*.pure                        =*/ false,
+        /*.keep_split                  =*/ false,
         /*.imatrix                     =*/ nullptr,
         /*.kv_overrides                =*/ nullptr,
     };
@@ -15392,6 +15710,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_QWEN2:
         case LLM_ARCH_QWEN2MOE:
         case LLM_ARCH_PHI2:
+        case LLM_ARCH_PHI3:
         case LLM_ARCH_GEMMA:
         case LLM_ARCH_STARCODER2:
             return LLAMA_ROPE_TYPE_NEOX;
@@ -15405,6 +15724,10 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
     return LLAMA_ROPE_TYPE_NONE;
 }
 
+enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) {
+    return ctx->cparams.pooling_type;
+}
+
 int32_t llama_n_vocab(const struct llama_model * model) {
     return model->hparams.n_vocab;
 }
@@ -15883,6 +16206,8 @@ struct llama_data_file_context : llama_data_context {
  *
 */
 static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
+    llama_synchronize(ctx);
+
     // copy rng
     {
         std::ostringstream rng_ss;
@@ -16035,6 +16360,8 @@ size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) {
 
 // Sets the state reading from the specified source address
 size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
+    llama_synchronize(ctx);
+
     const uint8_t * inp = src;
 
     // set rng
@@ -16339,6 +16666,8 @@ size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id)
 }
 
 static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) {
+    llama_synchronize(ctx);
+
     const auto & kv_self = ctx->kv_self;
     GGML_ASSERT(!kv_self.recurrent); // not implemented
 
@@ -16456,6 +16785,8 @@ size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_s
 }
 
 size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) {
+    llama_synchronize(ctx);
+
     auto & kv_self = ctx->kv_self;
     GGML_ASSERT(!kv_self.recurrent); // not implemented
 
@@ -16991,7 +17322,7 @@ static std::string llama_decode_text(const std::string & text) {
 }
 
 // does not write null-terminator to buf
-int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
+int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
     if (0 <= token && token < llama_n_vocab(model)) {
         switch (llama_vocab_get_type(model->vocab)) {
         case LLAMA_VOCAB_TYPE_WPM:
@@ -17006,7 +17337,9 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_user_defined_token(model->vocab, token)) {
+            } else if (
+                    (llama_is_user_defined_token(model->vocab, token)) ||
+                    (llama_is_control_token     (model->vocab, token) && special)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 if (length < (int) result.length()) {
                     return -(int) result.length();
@@ -17019,8 +17352,6 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, "\xe2\x96\x85", 3);
                 return 3;
-            } else if (llama_is_control_token(model->vocab, token)) {
-                ;
             } else if (llama_is_byte_token(model->vocab, token)) {
                 if (length < 1) {
                     return -1;
@@ -17041,15 +17372,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_user_defined_token(model->vocab, token)) {
+            } else if (
+                    (llama_is_user_defined_token(model->vocab, token)) ||
+                    (llama_is_control_token     (model->vocab, token) && special)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 if (length < (int) result.length()) {
                     return -(int) result.length();
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_control_token(model->vocab, token)) {
-                ;
             }
             break;
         }
@@ -17247,6 +17578,24 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
         }
+    } else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
+        // Llama 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
+        }
+        if (add_ass) {
+            ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
+        }
+    } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) {
+        // Phi 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
     } else {
         // template not supported
         return -1;
@@ -17379,6 +17728,11 @@ const char * llama_print_system_info(void) {
     s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
     s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
     s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
+#ifdef GGML_USE_LLAMAFILE
+    s += "LLAMAFILE = 1 | ";
+#else
+    s += "LLAMAFILE = 0 | ";
+#endif
 
     return s.c_str();
 }

llama.h

@@ -195,15 +195,19 @@ extern "C" {
         LLAMA_KV_OVERRIDE_TYPE_INT,
         LLAMA_KV_OVERRIDE_TYPE_FLOAT,
         LLAMA_KV_OVERRIDE_TYPE_BOOL,
+        LLAMA_KV_OVERRIDE_TYPE_STR,
     };
 
     struct llama_model_kv_override {
-        char key[128];
         enum llama_model_kv_override_type tag;
+
+        char key[128];
+
         union {
-            int64_t int_value;
-            double float_value;
-            bool bool_value;
+            int64_t val_i64;
+            double  val_f64;
+            bool    val_bool;
+            char    val_str[128];
         };
     };
 
@@ -232,9 +236,10 @@ extern "C" {
         const struct llama_model_kv_override * kv_overrides;
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
-        bool vocab_only; // only load the vocabulary, no weights
-        bool use_mmap;   // use mmap if possible
-        bool use_mlock;  // force system to keep model in RAM
+        bool vocab_only;    // only load the vocabulary, no weights
+        bool use_mmap;      // use mmap if possible
+        bool use_mlock;     // force system to keep model in RAM
+        bool check_tensors; // validate model tensor data
     };
 
     struct llama_context_params {
@@ -288,6 +293,7 @@ extern "C" {
         bool quantize_output_tensor;         // quantize output.weight
         bool only_copy;                      // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
         bool pure;                           // quantize all tensors to the default type
+        bool keep_split;                     // quantize to the same number of shards
         void * imatrix;                      // pointer to importance matrix data
         void * kv_overrides;                 // pointer to vector containing overrides
     } llama_model_quantize_params;
@@ -390,8 +396,10 @@ extern "C" {
     LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
 
-    LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
-    LLAMA_API enum llama_rope_type  llama_rope_type (const struct llama_model * model);
+    LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
+
+    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model   * model);
+    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model   * model);
 
     LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
     LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
@@ -828,11 +836,13 @@ extern "C" {
     // Uses the vocabulary in the provided context.
     // Does not write null terminator to the buffer.
     // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    // @param special If true, special tokens are rendered in the output.
     LLAMA_API int32_t llama_token_to_piece(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
-                               int32_t   length);
+                               int32_t   length,
+                                  bool   special);
 
     /// Apply chat template. Inspired by hf apply_chat_template() on python.
     /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
@@ -985,7 +995,7 @@ extern "C" {
             struct llama_context * ctx,
           llama_token_data_array * candidates);
 
-    /// @details Randomly selects a token from the candidates based on their probabilities.
+    /// @details Randomly selects a token from the candidates based on their probabilities using the RNG of ctx.
     LLAMA_API llama_token llama_sample_token(
             struct llama_context * ctx,
           llama_token_data_array * candidates);
@@ -1072,8 +1082,9 @@ extern "C" {
 // Internal API to be implemented by llama.cpp and used by tests/benchmarks only
 #ifdef LLAMA_API_INTERNAL
 
-#include <vector>
+#include <random>
 #include <string>
+#include <vector>
 
 struct ggml_tensor;
 
@@ -1110,6 +1121,10 @@ std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
         const std::string & src,
         llama_partial_utf8   partial_start);
 
+// Randomly selects a token from the candidates based on their probabilities using given std::mt19937.
+// This is a temporary workaround in order to fix race conditions when sampling with multiple sequences.
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng);
+
 #endif // LLAMA_API_INTERNAL
 
 #endif // LLAMA_H

scripts/xxd.cmake

@@ -0,0 +1,16 @@
+# CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}`
+# Usage: cmake -DINPUT=examples/server/public/index.html -DOUTPUT=examples/server/index.html.hpp -P scripts/xxd.cmake
+
+SET(INPUT "" CACHE STRING "Input File")
+SET(OUTPUT "" CACHE STRING "Output File")
+
+get_filename_component(filename "${INPUT}" NAME)
+string(REGEX REPLACE "\\.|-" "_" name "${filename}")
+
+file(READ "${INPUT}" hex_data HEX)
+string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}")
+
+string(LENGTH ${hex_data} hex_len)
+math(EXPR len "${hex_len} / 2")
+
+file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n")

sgemm.h

@@ -1,11 +1,13 @@
 #pragma once
+#include <stdint.h>
 #include <stdbool.h>
 #ifdef __cplusplus
 extern "C" {
 #endif
 
-bool llamafile_sgemm(int, int, int, const void *, int, const void *, int,
-                     void *, int, int, int, int, int, int, int);
+bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t,
+                     const void *, int64_t, void *, int64_t, int, int,
+                     int, int, int, int);
 
 #ifdef __cplusplus
 }

tests/test-chat-template.cpp

@@ -46,7 +46,11 @@ int main(void) {
         // No template included in tokenizer_config.json, so this template likely needs to be manually set.
         "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
         // CohereForAI/c4ai-command-r-plus
-        "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}"
+        "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
+        // Llama-3
+        "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
+        // Phi-3
+        "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + ' ' + message['content'] + '<|end|> ' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> ' }}{% else %}{{ eos_token }}{% endif %}"
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -73,6 +77,10 @@ int main(void) {
         "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
         // CohereForAI/c4ai-command-r-plus
         "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
+        // Llama 3
+        "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
+        // Phi 3
+        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\nI am an assistant<|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;