llama : allow pooled embeddings on any model (#7477 )

* create append_pooling operation; allow to specify attention_type; add last token pooling; update examples * find result_norm/result_embd tensors properly; update output allocation logic * only use embd output for pooling_type NONE * get rid of old causal_attn accessor * take out attention_type; add in llama_set_embeddings * bypass logits when doing non-NONE pooling
swiftui : enable stream updating (#7754 )
2026-08-10 21:45:46 +02:00 · 2024-06-21 08:38:22 +03:00 · 2024-06-21 08:30:58 +03:00 · 2024-06-20 22:01:15 +02:00 · 2024-06-20 21:59:59 +02:00 · 2024-06-20 16:40:13 +02:00
23 changed files with 693 additions and 449 deletions
@@ -665,6 +665,7 @@ if (LLAMA_SYCL)
    #todo: AOT

    find_package(IntelSYCL REQUIRED)
+    find_package(MKL REQUIRED)

    message(STATUS "SYCL found")

@@ -679,11 +680,9 @@ if (LLAMA_SYCL)
    endif()

    add_compile_options(-I./) #include DPCT
-    add_compile_options(-I/${SYCL_INCLUDE_DIR})

    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
    if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
    endif()
@@ -693,8 +692,10 @@ if (LLAMA_SYCL)
    list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp")

    if (WIN32)
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib)
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
    else()
+        add_compile_options(-I/${SYCL_INCLUDE_DIR})
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
        if (LLAMA_SYCL_TARGET STREQUAL "INTEL")
            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
        elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
@@ -11,9 +11,21 @@
            "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
        }
    },
-
+    {
+        "name": "sycl-base",
+        "hidden": true,
+        "generator": "Ninja",
+        "binaryDir": "${sourceDir}/build-${presetName}",
+        "cacheVariables": {
+            "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
+            "CMAKE_CXX_COMPILER": "icx",
+            "LLAMA_SYCL": "ON",
+            "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
+        }
+    },
    { "name": "debug",   "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
-    { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
+    { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
+    { "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
    { "name": "static",  "hidden": true, "cacheVariables": { "LLAMA_STATIC": "ON" } },

    {
@@ -35,15 +47,18 @@
    },

    { "name": "arm64-windows-llvm-debug"  , "inherits": [ "base", "arm64-windows-llvm",  "debug"   ] },
-    { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm",  "release" ] },
-    { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm",  "release", "static" ] },
+    { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm",  "reldbg" ] },
+    { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm",  "reldbg", "static" ] },

    { "name": "arm64-windows-msvc-debug"  , "inherits": [ "base", "arm64-windows-msvc",  "debug"   ] },
-    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "release" ] },
-    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "release", "static" ] },
+    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg" ] },
+    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg", "static" ] },

    { "name": "x64-windows-msvc-debug"  , "inherits": [ "base", "debug"   ] },
-    { "name": "x64-windows-msvc-release", "inherits": [ "base", "release" ] },
-    { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "release", "static" ] }
+    { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
+    { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
+
+    { "name": "x64-windows-sycl-debug"  , "inherits": [ "sycl-base", "debug"   ] },
+    { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }
  ]
 }
@@ -410,15 +410,9 @@ Output (example):

 4. Install build tools

-a. Download & install cmake for Windows: https://cmake.org/download/
+a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer)
+b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/)

-b. Download & install mingw-w64 make for Windows provided by w64devkit
-
- Download the 1.19.0 version of [w64devkit](https://github.com/skeeto/w64devkit/releases/download/v1.19.0/w64devkit-1.19.0.zip).
-
- Extract `w64devkit` on your pc.
-
- Add the **bin** folder path in the Windows system PATH environment (for e.g. `C:\xxx\w64devkit\bin\`).

 ### II. Build llama.cpp

@@ -428,10 +422,10 @@ On the oneAPI command line window, step into the llama.cpp main directory and ru
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force

 # Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+cmake -B build -G "Ninja" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release

 # Option 2: Or FP16
-cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+cmake -B build -G "Ninja" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON

 cmake --build build --config Release -j
 ```
@@ -441,9 +435,23 @@ Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former in
 .\examples\sycl\win-build-sycl.bat
 ```

+Or, use CMake presets to build:
+```sh
+cmake --preset x64-windows-sycl-release
+cmake --build build-x64-windows-sycl-release -j --target llama-cli
+
+cmake -DLLAMA_SYCL_F16=ON --preset x64-windows-sycl-release
+cmake --build build-x64-windows-sycl-release -j --target llama-cli
+
+cmake --preset x64-windows-sycl-debug
+cmake --build build-x64-windows-sycl-debug -j --target llama-cli
+```
+
+Or, you can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project.
+
 *Notes:*

- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make llama-cli`.
+- In case of a minimal experimental setup, the user can build the inference executable only through `cmake --build build --config Release -j --target llama-cli`.

 ### III. Run the inference

@@ -6,7 +6,6 @@
 #include "llama.h"

 #include <algorithm>
-#include <cassert>
 #include <cinttypes>
 #include <cmath>
 #include <codecvt>
@@ -542,6 +541,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
        /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
        else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
        else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+        else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
        else { invalid_param = true; }
        return true;
    }
@@ -1870,6 +1870,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param

    options.push_back({ "backend" });
    options.push_back({ "*",           "       --rpc SERVERS",          "comma separated list of RPC servers" });
+
    if (llama_supports_mlock()) {
        options.push_back({ "*",           "       --mlock",                "force system to keep model in RAM rather than swapping or compressing" });
    }
@@ -2657,7 +2658,14 @@ static bool llama_download_file(const std::string & url, const std::string & pat
        }

        // Set the output file
-        std::unique_ptr<FILE, decltype(&fclose)> outfile(fopen(path_temporary.c_str(), "wb"), fclose);
+
+        struct FILE_deleter {
+            void operator()(FILE * f) const {
+                fclose(f);
+            }
+        };
+
+        std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
        if (!outfile) {
            fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
            return false;
@@ -214,7 +214,7 @@ src_func = f"""
 """

 convert_py_pth = pathlib.Path("convert-hf-to-gguf.py")
-convert_py = convert_py_pth.read_text()
+convert_py = convert_py_pth.read_text(encoding="utf-8")
 convert_py = re.sub(
    r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
    lambda m: m.group(1) + src_func + m.group(3),
@@ -222,7 +222,7 @@ convert_py = re.sub(
    flags=re.DOTALL | re.MULTILINE,
 )

-convert_py_pth.write_text(convert_py)
+convert_py_pth.write_text(convert_py, encoding="utf-8")

 logger.info("+++ convert-hf-to-gguf.py was updated")

@@ -17,9 +17,10 @@ static std::vector<std::string> split_lines(const std::string & s) {
    return lines;
 }

-static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
-    for (size_t i = 0; i < tokens.size(); i++) {
-        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
+    size_t n_tokens = tokens.size();
+    for (size_t i = 0; i < n_tokens; i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, true);
    }
 }

@@ -40,13 +41,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu

        // try to get sequence embeddings - supported only when pooling_type is not NONE
        const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
-        if (embd == NULL) {
-            embd = llama_get_embeddings_ith(ctx, i);
-            if (embd == NULL) {
-                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
-                continue;
-            }
-        }
+        GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");

        float * out = output + batch.seq_id[i][0] * n_embd;
        //TODO: I would also add a parameter here to enable normalization or not.
@@ -97,6 +92,12 @@ int main(int argc, char ** argv) {
    const int n_ctx_train = llama_n_ctx_train(model);
    const int n_ctx = llama_n_ctx(ctx);

+    const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
+    if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
+        return 1;
+    }
+
    if (n_ctx > n_ctx_train) {
        fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
                __func__, n_ctx_train, n_ctx);
@@ -44,6 +44,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve

        // clear previous kv_cache values (irrelevant for embeddings)
        llama_kv_cache_clear(ctx);
+        llama_set_embeddings(ctx, true);
        llama_set_causal_attn(ctx, false);

        // run model
@@ -98,7 +99,9 @@ static std::string generate(llama_context * ctx, const std::string & prompt, boo
    llama_token eos_token = llama_token_eos(mdl);

    llama_kv_cache_clear(ctx);
+    llama_set_embeddings(ctx, false);
    llama_set_causal_attn(ctx, true);
+
    llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);

    std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
@@ -166,8 +169,7 @@ int main(int argc, char * argv[]) {

    llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);

-    // create new context - set to embedding mode
-    cparams.embeddings = true;
+    // create generation context
    llama_context * ctx = llama_new_context_with_model(mdl, cparams);

    // ### Embedding/Representation ###
@@ -131,22 +131,29 @@ class LlamaState: ObservableObject {

        messageLog += "\(text)"

-        while await llamaContext.n_cur < llamaContext.n_len {
-            let result = await llamaContext.completion_loop()
-            messageLog += "\(result)"
+        Task.detached {
+            while await llamaContext.n_cur < llamaContext.n_len {
+                let result = await llamaContext.completion_loop()
+                await MainActor.run {
+                    self.messageLog += "\(result)"
+                }
+            }
+
+            let t_end = DispatchTime.now().uptimeNanoseconds
+            let t_generation = Double(t_end - t_heat_end) / self.NS_PER_S
+            let tokens_per_second = Double(await llamaContext.n_len) / t_generation
+
+            await llamaContext.clear()
+
+            await MainActor.run {
+                self.messageLog += """
+                    \n
+                    Done
+                    Heat up took \(t_heat)s
+                    Generated \(tokens_per_second) t/s\n
+                    """
+            }
        }
-
-        let t_end = DispatchTime.now().uptimeNanoseconds
-        let t_generation = Double(t_end - t_heat_end) / NS_PER_S
-        let tokens_per_second = Double(await llamaContext.n_len) / t_generation
-
-        await llamaContext.clear()
-        messageLog += """
-            \n
-            Done
-            Heat up took \(t_heat)s
-            Generated \(tokens_per_second) t/s\n
-            """
    }

    func bench() async {
@@ -73,9 +73,10 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
    return chunks;
 }

-static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
-    for (size_t i = 0; i < tokens.size(); i++) {
-        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
+    size_t n_tokens = tokens.size();
+    for (size_t i = 0; i < n_tokens; i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, true);
    }
 }

@@ -160,6 +161,12 @@ int main(int argc, char ** argv) {
    const int n_ctx_train = llama_n_ctx_train(model);
    const int n_ctx = llama_n_ctx(ctx);

+    const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
+    if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
+        return 1;
+    }
+
    if (n_ctx > n_ctx_train) {
        fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
                __func__, n_ctx_train, n_ctx);
@@ -13,16 +13,16 @@ if %errorlevel% neq 0 goto ERROR

 ::  for FP16
 ::  faster for long-prompt inference
-::  cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+::  cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON

 ::  for FP32
-cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
+cmake -G "Ninja" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
 if %errorlevel% neq 0 goto ERROR
 ::  build example/main only
 ::  make main

 ::  build all binary
-make -j
+cmake --build . -j
 if %errorlevel% neq 0 goto ERROR

 cd ..
@@ -635,7 +635,7 @@ static int64_t get_row_rounding(const std::array<float, GGML_CUDA_MAX_DEVICES> &
        }

        const int cc = ggml_cuda_info().devices[id].cc;
-        row_rounding = std::max(row_rounding, (int64_t)get_mmq_y_host(cc, get_mmq_x_max_host(cc)));
+        row_rounding = std::max(row_rounding, (int64_t)get_mmq_y_host(cc));
    }
    return row_rounding;
 }
@@ -652,8 +652,8 @@ static int get_mmq_x_max_host(const int cc) {
 }

 // Round rows to this value for --split-mode row:
-static int get_mmq_y_host(const int cc, const int mmq_x) {
-    return cc >= CC_VOLTA && mmq_x >= 32 ? 128 : 64;
+static int get_mmq_y_host(const int cc) {
+    return cc >= CC_VOLTA ? 128 : 64;
 }

 //////////////////////
@@ -30,34 +30,34 @@ void ggml_cuda_op_mul_mat_q(

    switch (src0->type) {
        case GGML_TYPE_Q4_0:
-            mul_mat_q_case<GGML_TYPE_Q4_0>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q4_0>(ctx, args, stream);
            break;
        case GGML_TYPE_Q4_1:
-            mul_mat_q_case<GGML_TYPE_Q4_1>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q4_1>(ctx, args, stream);
            break;
        case GGML_TYPE_Q5_0:
-            mul_mat_q_case<GGML_TYPE_Q5_0>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q5_0>(ctx, args, stream);
            break;
        case GGML_TYPE_Q5_1:
-            mul_mat_q_case<GGML_TYPE_Q5_1>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q5_1>(ctx, args, stream);
            break;
        case GGML_TYPE_Q8_0:
-            mul_mat_q_case<GGML_TYPE_Q8_0>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q8_0>(ctx, args, stream);
            break;
        case GGML_TYPE_Q2_K:
-            mul_mat_q_case<GGML_TYPE_Q2_K>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
            break;
        case GGML_TYPE_Q3_K:
-            mul_mat_q_case<GGML_TYPE_Q3_K>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q3_K>(ctx, args, stream);
            break;
        case GGML_TYPE_Q4_K:
-            mul_mat_q_case<GGML_TYPE_Q4_K>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q4_K>(ctx, args, stream);
            break;
        case GGML_TYPE_Q5_K:
-            mul_mat_q_case<GGML_TYPE_Q5_K>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q5_K>(ctx, args, stream);
            break;
        case GGML_TYPE_Q6_K:
-            mul_mat_q_case<GGML_TYPE_Q6_K>(args, stream);
+            mul_mat_q_case<GGML_TYPE_Q6_K>(ctx, args, stream);
            break;
        default:
            GGML_ASSERT(false);
@@ -8,6 +8,7 @@
 #include <cstdint>

 #define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1)
+#define MMQ_NWARPS 8

 typedef void (*load_tiles_mmq_t)(
    const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm,
@@ -15,7 +16,7 @@ typedef void (*load_tiles_mmq_t)(
 typedef void (*vec_dot_mmq_t)(
    const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc,
    const int * __restrict__ y, float * __restrict__ sum, const int & k0);
-typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1);
+typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max);

 struct block_q8_1_mmq {
    half2  ds[4];
@@ -50,21 +51,17 @@ static constexpr __device__ int get_mmq_x_max_device() {

 // get_mmq_y_host is in common.cuh so that it can be used to determine the correct way to round for --split-mode row

+static constexpr __device__ int get_mmq_y_device() {
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-static constexpr __device__ int get_mmq_y_device(int mmq_x) {
-    return mmq_x >= 32 ? 128 : 64;
-}
+    return 128;
 #else
 #if __CUDA_ARCH__ >= CC_VOLTA
-static constexpr __device__ int get_mmq_y_device(int mmq_x) {
-    return mmq_x >= 32 ? 128 : 64;
-}
+    return 128;
 #else
-static constexpr __device__ int get_mmq_y_device(int /*mmq_x*/) {
    return 64;
-}
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+}

 #define TILE_X_SIZES_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_0 + mmq_y/QI4_0, 0}
 #define TILE_X_SIZES_Q4_1 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_1 + mmq_y/QI4_1, 0}
@@ -1734,30 +1731,34 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
 }

 template<int mmq_x, int mmq_y, int nwarps, bool need_check>
-static __device__ __forceinline__ void mmq_write_back_dp4a(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) {
+static __device__ __forceinline__ void mmq_write_back_dp4a(
+    const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) {
+
 #pragma unroll
    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = blockIdx.y*mmq_x + j0 + threadIdx.y;
+        const int j = j0 + threadIdx.y;

-        if (j >= ne1) {
+        if (j > j_max) {
            return;
        }

 #pragma unroll
        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = blockIdx.x*mmq_y + i0 + threadIdx.x;
+            const int i = i0 + threadIdx.x;

-            if (need_check && i >= ne0) {
+            if (need_check && i > i_max) {
                continue;
            }

-            dst[j*ne0 + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
+            dst[j*stride + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
        }
    }
 }

 template<int mmq_x, int mmq_y, int nwarps, bool need_check>
-static __device__ __forceinline__ void mmq_write_back_mma(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) {
+static __device__ __forceinline__ void mmq_write_back_mma(
+    const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) {
+
    typedef mma_int_C_I16J8 mma_C;

    const int i0 = threadIdx.y*mma_C::I;
@@ -1769,19 +1770,19 @@ static __device__ __forceinline__ void mmq_write_back_mma(const float * __restri
    for (int j0 = 0; j0 < mmq_x; j0 += mma_C::J) {
 #pragma unroll
        for (int l = 0; l < mma_C::ne; ++l) {
-            const int j = blockIdx.y*mmq_x + j0 + mma_C::get_j(l);
+            const int j = j0 + mma_C::get_j(l);

-            if (j >= ne1) {
+            if (j > j_max) {
                continue;
            }

-            const int i = blockIdx.x*mmq_y + i0 + mma_C::get_i(l);
+            const int i = i0 + mma_C::get_i(l);

-            if (need_check && i >= ne0) {
+            if (need_check && i > i_max) {
                continue;
            }

-            dst[j*ne0 + i] = sum[(j0/mma_C::J)*mma_C::ne + l];
+            dst[j*stride + i] = sum[(j0/mma_C::J)*mma_C::ne + l];
        }
    }
 }
@@ -1896,32 +1897,16 @@ static bool mmq_need_sum(const ggml_type type_x) {
    return false;
 }

-template <ggml_type type, int mmq_x, int nwarps, bool need_check>
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*nwarps, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#else
-#if __CUDA_ARCH__ >= CC_VOLTA
-    __launch_bounds__(WARP_SIZE*nwarps, 1)
-#else
-    __launch_bounds__(WARP_SIZE*nwarps, 2)
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-static __global__ void mul_mat_q(
-    const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst,
-    const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) {
-
-    // Skip unused template specializations for faster compilation:
-    if (mmq_x > get_mmq_x_max_device()) {
-        NO_DEVICE_CODE;
-        return;
-    }
+template <ggml_type type, int mmq_x, int nwarps, bool need_check, bool fixup>
+static __device__ void mul_mat_q_process_tile(
+    const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+    const int & ne00, const int & ne01, const int & stride01, const int & ne10, const int & ne11, const int & stride11, const int & ne0,
+    const int & it, const int & jt, const int & kb0_start, const int & kb0_stop) {

    constexpr int              qk         = ggml_cuda_type_traits<type>::qk;
    constexpr int              qr         = ggml_cuda_type_traits<type>::qr;
    constexpr int              qi         = ggml_cuda_type_traits<type>::qi;
-    constexpr int              mmq_y      = get_mmq_y_device(mmq_x);
+    constexpr int              mmq_y      = get_mmq_y_device();
    constexpr int              vdr        = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vdr;
    constexpr load_tiles_mmq_t load_tiles = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::load_tiles;

@@ -1941,20 +1926,18 @@ static __global__ void mul_mat_q(
    int   * tile_x_sc = (int   *) (tile_x_dm + txs.dm);
    int   * tile_y    = (int   *) (tile_x_sc + txs.sc); // [mmq_x * (WARP_SIZE + WARP_SIZE/QI8_1)]

-    const int blocks_per_row_x = ne00 / qk;
-    const int blocks_per_warp = WARP_SIZE / qi;
-
-    const int & ne1 = ne11;
-
-    const int tile_x_max_i = ne01 - blockIdx.x*mmq_y - 1;
-
-    const int * y = (const int *) yc + blockIdx.y*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int));
+    constexpr int blocks_per_warp = WARP_SIZE / qi;

    float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};

-    for (int kb0 = 0; kb0 < blocks_per_row_x; kb0 += blocks_per_warp) {
+    const int tile_x_max_i = ne01 - it*mmq_y - 1;
+    const int tile_y_max_j = ne11 - jt*mmq_x - 1;

-        load_tiles(x, tile_x_qs, tile_x_dm, tile_x_sc, stride01*blockIdx.x*mmq_y + kb0, tile_x_max_i, stride01);
+    const int * y = (const int *) yc + jt*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int));
+
+    for (int kb0 = kb0_start; kb0 < kb0_stop; kb0 += blocks_per_warp) {
+
+        load_tiles(x, tile_x_qs, tile_x_dm, tile_x_sc, stride01*it*mmq_y + kb0, tile_x_max_i, stride01);

 #pragma unroll
        for (int kr = 0; kr < qr; ++kr) {
@@ -1977,7 +1960,176 @@ static __global__ void mul_mat_q(
        }
    }

-    write_back(sum, dst, ne0, ne1);
+    if (fixup) {
+        write_back(sum, tmp_fixup + blockIdx.x*(mmq_x*mmq_y), mmq_y, mmq_y, mmq_x);
+    } else {
+        write_back(sum, dst + jt*mmq_x*ne0 + it*mmq_y, ne0, tile_x_max_i, tile_y_max_j);
+    }
+}
+
+
+// The mul_mat_q kernel implements "stream-k" work partitioning as described in https://arxiv.org/abs/2301.03598
+
+template <ggml_type type, int mmq_x, int nwarps, bool need_check>
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*nwarps, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#else
+#if __CUDA_ARCH__ >= CC_VOLTA
+    __launch_bounds__(WARP_SIZE*nwarps, 1)
+#else
+    __launch_bounds__(WARP_SIZE*nwarps, 2)
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+static __global__ void mul_mat_q(
+    const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+    const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) {
+
+    // Skip unused template specializations for faster compilation:
+    if (mmq_x > get_mmq_x_max_device()) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
+    constexpr int qk    = ggml_cuda_type_traits<type>::qk;
+    constexpr int qi    = ggml_cuda_type_traits<type>::qi;
+    constexpr int mmq_y = get_mmq_y_device();
+
+    // On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead:
+#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA
+    {
+        constexpr bool fixup = false;
+        mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
+            (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
+                blockIdx.x, blockIdx.y, 0, ne00/qk);
+        return;
+    }
+#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA
+
+    const     int64_t blocks_per_ne00 = ne00 / qk;
+    constexpr int     blocks_per_warp = WARP_SIZE / qi;
+
+    const int ntx = (ne11 + mmq_x - 1) / mmq_x; // Number of tiles x
+    const int nty = (ne01 + mmq_y - 1) / mmq_y; // Number of tiles y
+
+    // kbc == k block continuous, current index in continuous ijk space.
+    int64_t       kbc      = GGML_PAD((int64_t) blockIdx.x     *blocks_per_ne00*ntx*nty / gridDim.x, blocks_per_warp);
+    const int64_t kbc_stop = GGML_PAD((int64_t)(blockIdx.x + 1)*blocks_per_ne00*ntx*nty / gridDim.x, blocks_per_warp);
+
+    // kb0 == k index when doing the matrix multiplication for an output tile.
+    int kb0_start = kbc % blocks_per_ne00;
+    int kb0_stop  = min(blocks_per_ne00, kb0_start + kbc_stop - kbc);
+    while (kbc < kbc_stop && kb0_stop == blocks_per_ne00) {
+        const int jt =  kbc /    (blocks_per_ne00*nty);                    // j index of current tile.
+        const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00; // i index of current tile.
+
+        constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
+        mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
+            (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
+             it, jt, kb0_start, kb0_stop);
+
+        kbc += blocks_per_ne00;
+        kbc -= kbc % blocks_per_ne00;
+
+        kb0_start = 0;
+        kb0_stop  = min(blocks_per_ne00, kbc_stop - kbc);
+    }
+
+    if (kbc >= kbc_stop) {
+        return;
+    }
+
+    const int jt =  kbc /    (blocks_per_ne00*nty);
+    const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00;
+
+    constexpr bool fixup = true; // Last index writes it data to fixup buffer to avoid data races with other blocks.
+    mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
+        (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
+            it, jt, kb0_start, kb0_stop);
+}
+
+
+template <ggml_type type, int mmq_x, int nwarps, bool need_check>
+static __global__ void mul_mat_q_stream_k_fixup(
+    float * __restrict__ dst, const float * __restrict__ tmp_last_tile, const int ne00, const int ne01, const int ne11, const int ne0, const int block_num_mmq) {
+
+    constexpr int     mmq_y           = get_mmq_y_device();
+    constexpr int     qk              = ggml_cuda_type_traits<type>::qk;
+    constexpr int     qi              = ggml_cuda_type_traits<type>::qi;
+    constexpr int     blocks_per_warp = WARP_SIZE / qi;
+    const     int64_t blocks_per_ne00 = ne00 / qk;
+
+    float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};
+
+    const int ntx = (ne11 + mmq_x - 1) / mmq_x;
+    const int nty = (ne01 + mmq_y - 1) / mmq_y;
+
+    bool any_fixup = false;
+
+    const int bidx_start = (blockIdx.y*nty + blockIdx.x)     * block_num_mmq / (gridDim.y*gridDim.x);
+    const int bidx_stop  = (blockIdx.y*nty + blockIdx.x + 1) * block_num_mmq / (gridDim.y*gridDim.x) + 1;
+
+    for (int bidx = bidx_start; bidx < bidx_stop; ++bidx) {
+        const int64_t kbc      = GGML_PAD((int64_t) bidx     *blocks_per_ne00*ntx*nty / block_num_mmq, blocks_per_warp);
+        const int64_t kbc_stop = GGML_PAD((int64_t)(bidx + 1)*blocks_per_ne00*ntx*nty / block_num_mmq, blocks_per_warp);
+
+        // Skip fixup tile if the MMQ CUDA block never wrote anything to it:
+        if (kbc == kbc_stop || kbc_stop % blocks_per_ne00 == 0) {
+            continue;
+        }
+
+        const int jt =  kbc_stop /    (blocks_per_ne00*nty);
+        const int it = (kbc_stop - jt*(blocks_per_ne00*nty)) / blocks_per_ne00;
+
+        // Skip fixup tile if it's unrelated to the output tile assigned to this CUDA block:
+        if (it != blockIdx.x || jt != blockIdx.y) {
+            continue;
+        }
+
+        any_fixup = true;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE] += tmp_last_tile[bidx*(mmq_x*mmq_y) + j*mmq_y + i];
+            }
+        }
+    }
+
+    if (!any_fixup) {
+        return;
+    }
+
+    dst += blockIdx.y*mmq_x*ne0 + blockIdx.x*mmq_y;
+
+    const int i_max = ne01 - blockIdx.x*mmq_y - 1;
+    const int j_max = ne11 - blockIdx.y*mmq_x - 1;
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+
+        if (j > j_max) {
+            return;
+        }
+
+#pragma unroll
+        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+
+            if (need_check && i > i_max) {
+                continue;
+            }
+
+            dst[j*ne0 + i] += sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
+        }
+    }
 }

 struct mmq_args {
@@ -1987,124 +2139,151 @@ struct mmq_args {
    int64_t ne0;
 };

-constexpr int mmq_get_nwarps(int mmq_x) {
-    return mmq_x >= 32 ? 8 : 4;
-}
-
 static int mmq_get_shmem(const ggml_type type, const int mmq_x, const int mmq_y) {
    const tile_x_sizes txs = get_tile_x_sizes_host(type, mmq_y);
-    const int nwarps = mmq_get_nwarps(mmq_x);

    const int shmem_x = txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
    const int shmem_y = mmq_x*WARP_SIZE*sizeof(int) + mmq_x*(WARP_SIZE/QI8_1)*sizeof(half2);
-    return shmem_x + GGML_PAD(shmem_y, nwarps*WARP_SIZE*sizeof(int));
+    return shmem_x + GGML_PAD(shmem_y, MMQ_NWARPS*WARP_SIZE*sizeof(int));
 }

-template <ggml_type type, int mmq_x, int nwarps>
-static void launch_mul_mat_q(const mmq_args & args, cudaStream_t stream) {
+template <ggml_type type, int mmq_x>
+static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
    const int id = ggml_cuda_get_device();
    const int cc = ggml_cuda_info().devices[id].cc;
-    const int mmq_y = get_mmq_y_host(cc, mmq_x);
+    const int nsm = ggml_cuda_info().devices[id].nsm;
+    const int mmq_y = get_mmq_y_host(cc);

-    const int block_num_x = (args.ne01 + mmq_y - 1) / mmq_y;
-    const int block_num_y = (args.ne11 + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+    const dim3 block_dims(WARP_SIZE, MMQ_NWARPS, 1);

    const int shmem = mmq_get_shmem(type, mmq_x, mmq_y);

 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
    static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
    if (!shmem_limit_raised[id]) {
-        CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, nwarps, false>, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
-        CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, nwarps, true>,  cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
+        CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, MMQ_NWARPS, false>, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
+        CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, MMQ_NWARPS, true>,  cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
        shmem_limit_raised[id] = true;
    }
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))

+    const int nty = (args.ne01 + mmq_y - 1) / mmq_y;
+    const int ntx = (args.ne11 + mmq_x - 1) / mmq_x;
+    const dim3 block_nums_xy_tiling(nty, ntx, 1);
+
+    const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;
+    if (!use_stream_k) {
+        if (args.ne01 % mmq_y == 0) {
+            constexpr bool need_check = false;
+            mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>
+                (args.x, args.y, args.dst, nullptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+        } else {
+            constexpr bool need_check = true;
+            mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>
+                (args.x, args.y, args.dst, nullptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+        }
+        return;
+    }
+
+    const dim3 block_nums_mmq(nsm, 1, 1);
+
+    ggml_cuda_pool & pool = ctx.pool();
+    ggml_cuda_pool_alloc<float> tmp_fixup(pool, block_nums_mmq.x * mmq_x*mmq_y);
+
    if (args.ne01 % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q<type, mmq_x, nwarps, need_check><<<block_nums, block_dims, shmem, stream>>>
-            (args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+        constexpr bool need_check = false;
+
+        mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_mmq, block_dims, shmem, stream>>>
+            (args.x, args.y, args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+
+        mul_mat_q_stream_k_fixup<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, 0, stream>>>
+            (args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.ne11, args.ne0, block_nums_mmq.x);
    } else {
-        const bool need_check = true;
-        mul_mat_q<type, mmq_x, nwarps, need_check><<<block_nums, block_dims, shmem, stream>>>
-            (args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+        constexpr bool need_check = true;
+
+        mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_mmq, block_dims, shmem, stream>>>
+            (args.x, args.y, args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+
+        mul_mat_q_stream_k_fixup<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, 0, stream>>>
+            (args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.ne11, args.ne0, block_nums_mmq.x);
    }
 }

 template <ggml_type type>
-void mul_mat_q_case(const mmq_args & args, cudaStream_t stream) {
+void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
    const int id    = ggml_cuda_get_device();
    const int nsm   = ggml_cuda_info().devices[id].nsm;
    const int cc    = ggml_cuda_info().devices[id].cc;
    const int smpbo = ggml_cuda_info().devices[id].smpbo;

    const int mmq_x_max = get_mmq_x_max_host(cc);
-    const int mmq_y = get_mmq_y_host(cc, mmq_x_max);
+    const int mmq_y = get_mmq_y_host(cc);
    const int block_num_y = (args.ne01 + mmq_y - 1) / mmq_y;
+    const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;

    int mmq_x_best  = 0;
-    int nwaves_best = INT_MAX;
+    int nparts_best = INT_MAX;

-    for (int mmq_x = 8; mmq_x <= mmq_x_max && nwaves_best > 1; mmq_x += 8) {
-        const int block_num_x = (args.ne11 + mmq_x - 1) / mmq_x;
-        const int nwaves = (block_num_x*block_num_y + nsm - 1) / nsm;
+    for (int mmq_x = 8; mmq_x <= mmq_x_max && nparts_best > 1; mmq_x += 8) {
+        const int ntiles_x = (args.ne11 + mmq_x - 1) / mmq_x;
+        const int nwaves_xy_tiling = ntiles_x*block_num_y;

-        if (nwaves < nwaves_best && mmq_get_shmem(type, mmq_x, mmq_y) <= smpbo) {
+        const int nparts = use_stream_k ? ntiles_x : nwaves_xy_tiling;
+
+        if (nparts < nparts_best && mmq_get_shmem(type, mmq_x, mmq_y) <= smpbo) {
            mmq_x_best  = mmq_x;
-            nwaves_best = nwaves;
+            nparts_best = nparts;
        }
    }

    switch (mmq_x_best) {
        case   8:
-            launch_mul_mat_q<type,   8, mmq_get_nwarps(  8)>(args, stream);
+            launch_mul_mat_q<type,   8>(ctx, args, stream);
            break;
        case  16:
-            launch_mul_mat_q<type,  16, mmq_get_nwarps( 16)>(args, stream);
+            launch_mul_mat_q<type,  16>(ctx, args, stream);
            break;
        case  24:
-            launch_mul_mat_q<type,  24, mmq_get_nwarps( 24)>(args, stream);
+            launch_mul_mat_q<type,  24>(ctx, args, stream);
            break;
        case  32:
-            launch_mul_mat_q<type,  32, mmq_get_nwarps( 32)>(args, stream);
+            launch_mul_mat_q<type,  32>(ctx, args, stream);
            break;
        case  40:
-            launch_mul_mat_q<type,  40, mmq_get_nwarps( 40)>(args, stream);
+            launch_mul_mat_q<type,  40>(ctx, args, stream);
            break;
        case  48:
-            launch_mul_mat_q<type,  48, mmq_get_nwarps( 48)>(args, stream);
+            launch_mul_mat_q<type,  48>(ctx, args, stream);
            break;
        case  56:
-            launch_mul_mat_q<type,  56, mmq_get_nwarps( 56)>(args, stream);
+            launch_mul_mat_q<type,  56>(ctx, args, stream);
            break;
        case  64:
-            launch_mul_mat_q<type,  64, mmq_get_nwarps( 64)>(args, stream);
+            launch_mul_mat_q<type,  64>(ctx, args, stream);
            break;
        case  72:
-            launch_mul_mat_q<type,  72, mmq_get_nwarps( 72)>(args, stream);
+            launch_mul_mat_q<type,  72>(ctx, args, stream);
            break;
        case  80:
-            launch_mul_mat_q<type,  80, mmq_get_nwarps( 80)>(args, stream);
+            launch_mul_mat_q<type,  80>(ctx, args, stream);
            break;
        case  88:
-            launch_mul_mat_q<type,  88, mmq_get_nwarps( 88)>(args, stream);
+            launch_mul_mat_q<type,  88>(ctx, args, stream);
            break;
        case  96:
-            launch_mul_mat_q<type,  96, mmq_get_nwarps( 96)>(args, stream);
+            launch_mul_mat_q<type,  96>(ctx, args, stream);
            break;
        case 104:
-            launch_mul_mat_q<type, 104, mmq_get_nwarps(104)>(args, stream);
+            launch_mul_mat_q<type, 104>(ctx, args, stream);
            break;
        case 112:
-            launch_mul_mat_q<type, 112, mmq_get_nwarps(112)>(args, stream);
+            launch_mul_mat_q<type, 112>(ctx, args, stream);
            break;
        case 120:
-            launch_mul_mat_q<type, 120, mmq_get_nwarps(120)>(args, stream);
+            launch_mul_mat_q<type, 120>(ctx, args, stream);
            break;
        case 128:
-            launch_mul_mat_q<type, 128, mmq_get_nwarps(128)>(args, stream);
+            launch_mul_mat_q<type, 128>(ctx, args, stream);
            break;
        default:
            fprintf(stderr, "mmq_x_best=%d\n", mmq_x_best);
@@ -2114,7 +2293,7 @@ void mul_mat_q_case(const mmq_args & args, cudaStream_t stream) {
 }

 #define DECL_MMQ_CASE(type)                                                        \
-    template void mul_mat_q_case<type>(const mmq_args & args, cudaStream_t stream) \
+    template void mul_mat_q_case<type>(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) \

 extern DECL_MMQ_CASE(GGML_TYPE_Q4_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_1);
@@ -735,6 +735,12 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs
 }

 static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) {
+    for (size_t i = 0, n = 3; i < n; ++i) {
+        if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) {
+            return false;
+        }
+    }
+
    switch (op->op) {
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(op)) {
@@ -4911,7 +4911,7 @@ static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);

-    GGML_TENSOR_BINARY_OP_LOCALS;
+    GGML_TENSOR_BINARY_OP_LOCALS01;

    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
    queue_ptr main_stream = ctx.stream();
@@ -588,266 +588,222 @@ namespace dpct
        out = prop;
    }

-    /// dpct device extension
-    class device_ext : public sycl::device
-    {
-        typedef std::mutex mutex_type;
+   /// dpct device extension
+    class device_ext : public sycl::device {
+      typedef std::mutex mutex_type;

-    public:
-        device_ext() : sycl::device(), _ctx(*this) {}
-        ~device_ext()
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            clear_queues();
-        }
-        device_ext(const sycl::device &base) : sycl::device(base), _ctx(*this)
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            init_queues();
-        }
+     public:
+      device_ext() : sycl::device() {}
+      ~device_ext() {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        clear_queues();
+      }
+      device_ext(const sycl::device &base) : sycl::device(base) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        init_queues();
+      }

-        int is_native_atomic_supported() { return 0; }
-        int get_major_version() const
-        {
-            return dpct::get_major_version(*this);
-        }
+      int is_native_atomic_supported() { return 0; }
+      int get_major_version() const { return dpct::get_major_version(*this); }

-        int get_minor_version() const
-        {
-            return dpct::get_minor_version(*this);
-        }
+      int get_minor_version() const { return dpct::get_minor_version(*this); }

-        int get_max_compute_units() const
-        {
-            return get_device_info().get_max_compute_units();
-        }
+      int get_max_compute_units() const {
+        return get_device_info().get_max_compute_units();
+      }

-        /// Return the maximum clock frequency of this device in KHz.
-        int get_max_clock_frequency() const
-        {
-            return get_device_info().get_max_clock_frequency();
-        }
+      /// Return the maximum clock frequency of this device in KHz.
+      int get_max_clock_frequency() const {
+        return get_device_info().get_max_clock_frequency();
+      }

-        int get_integrated() const { return get_device_info().get_integrated(); }
+      int get_integrated() const { return get_device_info().get_integrated(); }

-        int get_max_sub_group_size() const
-        {
-            return get_device_info().get_max_sub_group_size();
-        }
+      int get_max_sub_group_size() const {
+        return get_device_info().get_max_sub_group_size();
+      }

-        int get_max_register_size_per_work_group() const
-        {
-            return get_device_info().get_max_register_size_per_work_group();
-        }
+      int get_max_register_size_per_work_group() const {
+        return get_device_info().get_max_register_size_per_work_group();
+      }

-        int get_max_work_group_size() const
-        {
-            return get_device_info().get_max_work_group_size();
-        }
+      int get_max_work_group_size() const {
+        return get_device_info().get_max_work_group_size();
+      }

-        int get_mem_base_addr_align() const
-        {
-            return get_info<sycl::info::device::mem_base_addr_align>();
-        }
+      int get_mem_base_addr_align() const {
+        return get_info<sycl::info::device::mem_base_addr_align>();
+      }

-        size_t get_global_mem_size() const
-        {
-            return get_device_info().get_global_mem_size();
-        }
+      size_t get_global_mem_size() const {
+        return get_device_info().get_global_mem_size();
+      }

-        size_t get_max_mem_alloc_size() const
-        {
-            return get_device_info().get_max_mem_alloc_size();
-        }
+      size_t get_max_mem_alloc_size() const {
+        return get_device_info().get_max_mem_alloc_size();
+      }

-        /// Get the number of bytes of free and total memory on the SYCL device.
-        /// \param [out] free_memory The number of bytes of free memory on the SYCL device.
-        /// \param [out] total_memory The number of bytes of total memory on the SYCL device.
-        void get_memory_info(size_t &free_memory, size_t &total_memory)
-        {
-            total_memory = get_device_info().get_global_mem_size();
-            const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not "
-                                 "supported (export/set ZES_ENABLE_SYSMAN=1 to support), "
-                                 "use total memory as free memory";
+      /// Get the number of bytes of free and total memory on the SYCL device.
+      /// \param [out] free_memory The number of bytes of free memory on the
+      /// SYCL device. \param [out] total_memory The number of bytes of total
+      /// memory on the SYCL device.
+      void get_memory_info(size_t &free_memory, size_t &total_memory) {
+        total_memory = get_device_info().get_global_mem_size();
+        const char *warning_info =
+            "get_memory_info: [warning] ext_intel_free_memory is not "
+            "supported (export/set ZES_ENABLE_SYSMAN=1 to support), "
+            "use total memory as free memory";
 #if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105)
-            if (!has(sycl::aspect::ext_intel_free_memory))
-            {
-                std::cerr << warning_info << std::endl;
-                free_memory = total_memory;
-            }
-            else
-            {
-                free_memory = get_info<sycl::ext::intel::info::device::free_memory>();
-            }
+        if (!has(sycl::aspect::ext_intel_free_memory)) {
+          std::cerr << warning_info << std::endl;
+          free_memory = total_memory;
+        } else {
+          free_memory = get_info<sycl::ext::intel::info::device::free_memory>();
+        }
 #else
-            std::cerr << warning_info << std::endl;
-            free_memory = total_memory;
+        std::cerr << warning_info << std::endl;
+        free_memory = total_memory;
 #if defined(_MSC_VER) && !defined(__clang__)
 #pragma message("Querying the number of bytes of free memory is not supported")
 #else
 #warning "Querying the number of bytes of free memory is not supported"
 #endif
 #endif
+      }
+
+      void get_device_info(device_info &out) const {
+        dpct::get_device_info(out, *this);
+      }
+
+      device_info get_device_info() const {
+        device_info prop;
+        dpct::get_device_info(prop, *this);
+        return prop;
+      }
+
+      void reset() {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        clear_queues();
+        init_queues();
+      }
+
+      sycl::queue &in_order_queue() { return _q_in_order; }
+
+      sycl::queue &out_of_order_queue() { return _q_out_of_order; }
+
+      sycl::queue &default_queue() { return in_order_queue(); }
+
+      void queues_wait_and_throw() {
+        std::unique_lock<mutex_type> lock(m_mutex);
+        lock.unlock();
+        for (auto &q : _queues) {
+          q.wait_and_throw();
        }
+        // Guard the destruct of current_queues to make sure the ref count is
+        // safe.
+        lock.lock();
+      }

-        void get_device_info(device_info &out) const
-        {
-            dpct::get_device_info(out, *this);
-        }
+      sycl::queue create_queue(bool enable_exception_handler = false) {
+        return create_in_order_queue(enable_exception_handler);
+      }

-        device_info get_device_info() const
-        {
-            device_info prop;
-            dpct::get_device_info(prop, *this);
-            return prop;
-        }
+      sycl::queue create_queue(sycl::device device,
+                               bool enable_exception_handler = false) {
+        return create_in_order_queue(device, enable_exception_handler);
+      }

-        void reset()
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            clear_queues();
-            init_queues();
-        }
+      sycl::queue create_in_order_queue(bool enable_exception_handler = false) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return create_queue_impl(enable_exception_handler,
+                                 sycl::property::queue::in_order());
+      }

-        sycl::queue &in_order_queue() { return *_q_in_order; }
-
-        sycl::queue &out_of_order_queue() { return *_q_out_of_order; }
-
-        sycl::queue &default_queue()
-        {
-            return in_order_queue();
-        }
-
-        void queues_wait_and_throw()
-        {
-            std::unique_lock<mutex_type> lock(m_mutex);
-            std::vector<std::shared_ptr<sycl::queue>> current_queues(
-                _queues);
-            lock.unlock();
-            for (const auto &q : current_queues)
-            {
-                q->wait_and_throw();
-            }
-            // Guard the destruct of current_queues to make sure the ref count is safe.
-            lock.lock();
-        }
-
-        sycl::queue *create_queue(bool enable_exception_handler = false)
-        {
-            return create_in_order_queue(enable_exception_handler);
-        }
-
-        sycl::queue *create_queue(sycl::context context, sycl::device device,
-                                bool enable_exception_handler = false) {
-            return create_in_order_queue(context, device, enable_exception_handler);
-        }
-
-        sycl::queue *create_in_order_queue(bool enable_exception_handler = false) {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return create_queue_impl(enable_exception_handler,
-                                    sycl::property::queue::in_order());
-        }
-
-        sycl::queue *create_in_order_queue(sycl::context context, sycl::device device,
+      sycl::queue create_in_order_queue(sycl::device device,
                                        bool enable_exception_handler = false) {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return create_queue_impl(context, device, enable_exception_handler,
-                                    sycl::property::queue::in_order());
-        }
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return create_queue_impl(device, enable_exception_handler,
+                                 sycl::property::queue::in_order());
+      }

-        sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return create_queue_impl(enable_exception_handler);
-        }
+      sycl::queue create_out_of_order_queue(
+          bool enable_exception_handler = false) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return create_queue_impl(enable_exception_handler);
+      }

-        void destroy_queue(sycl::queue *&queue)
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            _queues.erase(std::remove_if(_queues.begin(), _queues.end(),
-                                         [=](const std::shared_ptr<sycl::queue> &q) -> bool
-                                         {
-                                             return q.get() == queue;
-                                         }),
-                          _queues.end());
-            queue = nullptr;
-        }
-        void set_saved_queue(sycl::queue *q)
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            _saved_queue = q;
-        }
-        sycl::queue *get_saved_queue() const
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return _saved_queue;
-        }
-        sycl::context get_context() const { return _ctx; }
+      void destroy_queue(sycl::queue queue) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        _queues.clear();
+      }
+      void set_saved_queue(sycl::queue q) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        _saved_queue = q;
+      }
+      sycl::queue get_saved_queue() const {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return _saved_queue;
+      }

-    private:
-        void clear_queues()
-        {
-            _queues.clear();
-            _q_in_order = _q_out_of_order = _saved_queue = nullptr;
-        }
+     private:
+      void clear_queues() { _queues.clear(); }

-        void init_queues()
-        {
-            _q_in_order = create_queue_impl(true, sycl::property::queue::in_order());
-            _q_out_of_order = create_queue_impl(true);
-            _saved_queue = &default_queue();
-        }
+      void init_queues() {
+        _q_in_order =
+            create_queue_impl(true, sycl::property::queue::in_order());
+        _q_out_of_order = create_queue_impl(true);
+        _saved_queue = default_queue();
+      }

-        /// Caller should acquire resource \p m_mutex before calling this function.
-        template <class... Properties>
-        sycl::queue *create_queue_impl(bool enable_exception_handler,
-                                       Properties... properties)
-        {
-            sycl::async_handler eh = {};
-            if (enable_exception_handler)
-            {
-                eh = exception_handler;
-            }
-            _queues.push_back(std::make_shared<sycl::queue>(
-                _ctx, *this, eh,
-                sycl::property_list(
+      /// Caller should acquire resource \p m_mutex before calling this
+      /// function.
+      template <class... Properties>
+      sycl::queue create_queue_impl(bool enable_exception_handler,
+                                    Properties... properties) {
+        sycl::async_handler eh = {};
+        if (enable_exception_handler) {
+          eh = exception_handler;
+        }
+        auto q = sycl::queue(*this, eh,
+                             sycl::property_list(
 #ifdef DPCT_PROFILING_ENABLED
-                    sycl::property::queue::enable_profiling(),
+                                 sycl::property::queue::enable_profiling(),
 #endif
-                    properties...)));
+                                 properties...));
+        _queues.push_back(q);

-            return _queues.back().get();
-        }
+        return _queues.back();
+      }

-        template <class... Properties>
-        sycl::queue *create_queue_impl(sycl::context context, sycl::device device,
+      template <class... Properties>
+      sycl::queue create_queue_impl(sycl::device device,
                                    bool enable_exception_handler,
                                    Properties... properties) {
-            sycl::async_handler eh = {};
-            if (enable_exception_handler) {
-                eh = exception_handler;
-            }
-            _queues.push_back(std::make_shared<sycl::queue>(
-                context, device, eh,
-                sycl::property_list(
-        #ifdef DPCT_PROFILING_ENABLED
-                    sycl::property::queue::enable_profiling(),
-        #endif
-                    properties...)));
-
-            return _queues.back().get();
+        sycl::async_handler eh = {};
+        if (enable_exception_handler) {
+          eh = exception_handler;
        }
+        _queues.push_back(
+            sycl::queue(device, eh,
+                        sycl::property_list(
+#ifdef DPCT_PROFILING_ENABLED
+                            sycl::property::queue::enable_profiling(),
+#endif
+                            properties...)));

-        void get_version(int &major, int &minor) const
-        {
-            detail::get_version(*this, major, minor);
-        }
-        sycl::queue *_q_in_order, *_q_out_of_order;
-        sycl::queue *_saved_queue;
-        sycl::context _ctx;
-        std::vector<std::shared_ptr<sycl::queue>> _queues;
-        mutable mutex_type m_mutex;
+        return _queues.back();
+      }
+
+      void get_version(int &major, int &minor) const {
+        detail::get_version(*this, major, minor);
+      }
+      sycl::queue _q_in_order, _q_out_of_order;
+      sycl::queue _saved_queue;
+      std::vector<sycl::queue> _queues;
+      mutable mutex_type m_mutex;
    };

+
    /// device manager
    class dev_mgr
    {
@@ -312,6 +312,12 @@
    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)

+#define GGML_TENSOR_BINARY_OP_LOCALS01 \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb)
+
 #ifdef  __cplusplus
 extern "C" {
 #endif
@@ -7649,6 +7649,50 @@ struct llm_build_context {
        return lctx.inp_s_seq;
    }

+    struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) {
+        // find result_norm tensor for input
+        struct ggml_tensor * inp = nullptr;
+        for (int i = gf->n_nodes - 1; i >= 0; --i) {
+            inp = gf->nodes[i];
+            if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
+                break;
+            } else {
+                inp = nullptr;
+            }
+        }
+        GGML_ASSERT(inp != nullptr && "missing result_norm/result_embd tensor");
+
+        struct ggml_tensor * cur;
+
+        switch (pooling_type) {
+            case LLAMA_POOLING_TYPE_MEAN:
+                {
+                    struct ggml_tensor * inp_mean = build_inp_mean();
+                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
+                } break;
+            case LLAMA_POOLING_TYPE_CLS:
+            case LLAMA_POOLING_TYPE_LAST:
+                {
+                    struct ggml_tensor * inp_cls = build_inp_cls();
+                    cur = ggml_get_rows(ctx0, inp, inp_cls);
+                } break;
+            case LLAMA_POOLING_TYPE_NONE:
+                {
+                    cur = inp;
+                } break;
+            default:
+                {
+                    GGML_ASSERT(false && "unknown pooling type");
+                } break;
+        }
+
+        cb(cur, "result_embd_pooled", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
    struct ggml_cgraph * build_llama() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

@@ -8629,8 +8673,6 @@ struct llm_build_context {
        if (model.arch != LLM_ARCH_JINA_BERT_V2) {
            inp_pos = build_inp_pos();
        }
-        struct ggml_tensor * inp_mean = build_inp_mean();
-        struct ggml_tensor * inp_cls  = build_inp_cls();

        // construct input embeddings (token, type, position)
        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
@@ -8805,28 +8847,6 @@ struct llm_build_context {
        cur = inpL;
        cb(cur, "result_embd", -1);

-        // pooling layer
-        switch (pooling_type) {
-            case LLAMA_POOLING_TYPE_NONE:
-                {
-                    // nop
-                } break;
-            case LLAMA_POOLING_TYPE_MEAN:
-                {
-                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
-                    cb(cur, "result_embd_pooled", -1);
-                } break;
-            case LLAMA_POOLING_TYPE_CLS:
-                {
-                    cur = ggml_get_rows(ctx0, cur, inp_cls);
-                    cb(cur, "result_embd_pooled", -1);
-                } break;
-            case LLAMA_POOLING_TYPE_UNSPECIFIED:
-                {
-                    GGML_ASSERT(false && "Invalid pooling type");
-                } break;
-        }
-
        ggml_build_forward_expand(gf, cur);

        return gf;
@@ -11911,6 +11931,11 @@ static struct ggml_cgraph * llama_build_graph(
            GGML_ASSERT(false);
    }

+    // add on pooling layer
+    if (lctx.cparams.embeddings) {
+        result = llm.append_pooling(result);
+    }
+
    llm.free();

    return result;
@@ -12000,7 +12025,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
        // (!a || b) is a logical implication (a -> b)
        // !hparams.causal_attn -> !cparams.causal_attn
        (hparams.causal_attn || !cparams.causal_attn) &&
-        "causal attention with embedding models is not supported"
+        "causal attention is not supported by this model"
    );

    if (lctx.inp_KQ_mask) {
@@ -12132,6 +12157,37 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
        }
    }

+    if (cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
+        const int64_t n_tokens = batch.n_tokens;
+
+        GGML_ASSERT(lctx.inp_cls);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+
+        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
+
+        std::vector<int> last_pos(n_tokens, -1);
+        std::vector<int> last_row(n_tokens, -1);
+
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
+            const llama_pos    pos    = batch.pos[i];
+
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");
+
+            if (pos >= last_pos[seq_id]) {
+                last_pos[seq_id] = pos;
+                last_row[seq_id] = i;
+            }
+        }
+
+        for (int i = 0; i < n_tokens; ++i) {
+            if (last_row[i] >= 0) {
+                data[i] = last_row[i];
+            }
+        }
+    }
+
    if (kv_self.recurrent) {
        const int64_t n_kv = kv_self.n;

@@ -12193,8 +12249,8 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
    const auto n_embd  = hparams.n_embd;

    // TODO: use a per-batch flag for logits presence instead
-    const bool has_logits = cparams.causal_attn;
-    const bool has_embd   = cparams.embeddings && (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
+    const bool has_logits = !cparams.embeddings;
+    const bool has_embd   =  cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);

    const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
    const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
@@ -12324,11 +12380,13 @@ static int llama_decode_internal(
    std::vector<std::vector<llama_seq_id>> seq_id;

    // count outputs
-    if (batch_all.logits) {
+    if (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE) {
+        n_outputs = n_tokens_all;
+    } else if (batch_all.logits) {
        for (uint32_t i = 0; i < n_tokens_all; ++i) {
            n_outputs += batch_all.logits[i] != 0;
        }
-    } else if (lctx.logits_all || (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
+    } else if (lctx.logits_all) {
        n_outputs = n_tokens_all;
    } else {
        // keep last output only
@@ -12459,30 +12517,13 @@ static int llama_decode_internal(
            // no output
            res  = nullptr;
            embd = nullptr;
-        } else if (!hparams.causal_attn) {
-            res = nullptr; // do not extract logits for embedding models such as BERT
-
-            // token or sequence embeddings
-            embd = gf->nodes[gf->n_nodes - 1];
-
-            GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
        } else if (cparams.embeddings) {
-            // the embeddings could be in the second to last tensor, or any of the previous tensors
-            int i_embd = gf->n_nodes - 2;
-            for (int i = 3; strcmp(embd->name, "result_norm") != 0; ++i) {
-                i_embd = gf->n_nodes - i;
-                if (i_embd < 0) { break; }
-                embd = gf->nodes[i_embd];
-            }
-            GGML_ASSERT(i_embd >= 0 && "missing result_norm tensor");
-
-            // TODO: use a per-batch flag to know when to skip logits while keeping embeddings
-            if (!cparams.causal_attn) {
-                res = nullptr; // do not extract logits when not needed
-                // skip computing logits
-                // TODO: is this safe?
-                gf->n_nodes = i_embd + 1;
+            res = nullptr; // do not extract logits for embedding case
+            embd = gf->nodes[gf->n_nodes - 1];
+            if (strcmp(embd->name, "result_embd_pooled") != 0) {
+                embd = gf->nodes[gf->n_nodes - 2];
            }
+            GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
        } else {
            embd = nullptr; // do not extract embeddings when not needed
            GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
@@ -12551,11 +12592,10 @@ static int llama_decode_internal(
                            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
                        }
                    } break;
-                case LLAMA_POOLING_TYPE_CLS:
                case LLAMA_POOLING_TYPE_MEAN:
+                case LLAMA_POOLING_TYPE_CLS:
+                case LLAMA_POOLING_TYPE_LAST:
                    {
-                        GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);
-
                        // extract sequence embeddings
                        auto & embd_seq_out = lctx.embd_seq;
                        embd_seq_out.clear();
@@ -18112,6 +18152,10 @@ void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)
    ctx->abort_callback_data = abort_callback_data;
 }

+void llama_set_embeddings(struct llama_context * ctx, bool embeddings) {
+    ctx->cparams.embeddings = embeddings;
+}
+
 void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
    ctx->cparams.causal_attn = causal_attn;
 }
@@ -174,6 +174,7 @@ extern "C" {
        LLAMA_POOLING_TYPE_NONE = 0,
        LLAMA_POOLING_TYPE_MEAN = 1,
        LLAMA_POOLING_TYPE_CLS  = 2,
+        LLAMA_POOLING_TYPE_LAST = 3,
    };

    enum llama_split_mode {
@@ -293,7 +294,6 @@ extern "C" {

        enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
        enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
-                                                        // (ignored if no pooling layer)

        // ref: https://github.com/ggerganov/llama.cpp/pull/2054
        float    rope_freq_base;   // RoPE base frequency, 0 = from model
@@ -786,6 +786,10 @@ extern "C" {
    // Get the number of threads used for prompt and batch processing (multiple token).
    LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);

+    // Set whether the model is in embeddings model or not
+    // If true, embeddings will be returned but logits will not
+    LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
+
    // Set whether to use causal attention or not
    // If set to true, the model will only attend to the past tokens
    LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
@@ -1,2 +1,2 @@
 -r ./requirements-convert-legacy-llama.txt
-torch~=2.1.1
+torch~=2.2.1
@@ -1,2 +1,2 @@
 -r ./requirements-convert-legacy-llama.txt
-torch~=2.1.1
+torch~=2.2.1
@@ -1,4 +1,4 @@
-numpy~=1.24.4
+numpy~=1.26.4
 sentencepiece~=0.2.0
 transformers>=4.40.1,<5.0.0
 gguf>=0.1.0
Author	SHA1	Message	Date
Douglas HanleyandGitHub	80ea089d77	llama : allow pooled embeddings on any model (#7477 ) * create append_pooling operation; allow to specify attention_type; add last token pooling; update examples * find result_norm/result_embd tensors properly; update output allocation logic * only use embd output for pooling_type NONE * get rid of old causal_attn accessor * take out attention_type; add in llama_set_embeddings * bypass logits when doing non-NONE pooling	2024-06-21 08:38:22 +03:00
Shuichi TsutsumiandGitHub	0e64591e82	swiftui : enable stream updating (#7754 )	2024-06-21 08:30:58 +03:00
Hamdoud HakemandGitHub	b1ef562bc1	requirements : Bump torch and numpy for python3.12 (#8041 )	2024-06-20 22:01:15 +02:00
Hamdoud HakemandGitHub	17b291a6a5	convert-hf : Fix the encoding in the convert-hf-to-gguf-update.py (#8040 )	2024-06-20 21:59:59 +02:00
Johannes Gäßler GitHub slaren	abd894ad96	common: fix warning (#8036 ) * common: fix warning * Update common/common.cpp Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com>	2024-06-20 16:40:13 +02:00
luoyu-intelandGitHub	de391e4c80	[SYCL] Fix windows build and inference (#8003 ) * add sycl preset * fix debug link error. fix windows crash * update README	2024-06-20 21:19:05 +08:00
Johannes GäßlerandGitHub	d50f8897a7	CUDA: stream-k decomposition for MMQ (#8018 ) * CUDA: stream-k decomposition for MMQ * fix undefined memory reads for small matrices	2024-06-20 14:39:21 +02:00
Michael de GansandGitHub	2075a66a96	metal : fix `ggml_metal_supports_op` for BF16 (#8021 ) Currently the Metal backend does not support BF16. `ggml_metal_supports_op` was returning true in these cases, leading to a crash with models converted with `--leave-output-tensor`. This commit checks if the first few sources types are BF16 and returns false if that's the case.	2024-06-20 08:32:01 +03:00