metal : another fix for the fa kernel

ci : add VULKAN support to ggml-ci (#9055 )
server : update deps (#9183 )
2026-04-23 16:37:33 +03:00 · 2024-08-26 15:08:38 +03:00 · 2024-08-26 12:19:39 +03:00 · 2024-08-26 12:16:57 +03:00 · 2024-08-26 11:08:59 +02:00 · 2024-08-26 11:03:30 +02:00
29 changed files with 1748 additions and 832 deletions
--- a/CMakePresets.json
+++ b/CMakePresets.json
@@ -28,6 +28,7 @@
    { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
    { "name": "reldbg",  "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
    { "name": "static",  "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
+    { "name": "sycl_f16",  "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } },

    {
        "name": "arm64-windows-msvc", "hidden": true,
@@ -60,6 +61,8 @@
    { "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" ] }
+    { "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] },
+    { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] },
+    { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] }
  ]
 }
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -13,6 +13,9 @@
 # # with SYCL support
 # GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
+# # with VULKAN support
+# GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+#

 if [ -z "$2" ]; then
    echo "usage: $0 <output-dir> <mnt-dir>"
@@ -40,7 +43,7 @@ if [ ! -z ${GG_BUILD_METAL} ]; then
 fi

 if [ ! -z ${GG_BUILD_CUDA} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=1"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native"
 fi

 if [ ! -z ${GG_BUILD_SYCL} ]; then
@@ -52,6 +55,10 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then

    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
 fi
+
+if [ ! -z ${GG_BUILD_VULKAN} ]; then
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
+fi
 ## helpers

 # download a file if it does not exist or if it is outdated
@@ -107,7 +114,7 @@ function gg_run_ctest_debug {
    gg_check_build_requirements

    (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                          ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time make -j$(nproc)                                  ) 2>&1 | tee -a $OUT/${ci}-make.log

    (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log

@@ -138,7 +145,7 @@ function gg_run_ctest_release {
    gg_check_build_requirements

    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log

    if [ -z ${GG_BUILD_LOW_PERF} ]; then
        (time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log
@@ -266,7 +273,6 @@ function gg_sum_ctest_with_model_release {
 }

 # open_llama_7b_v2
-# requires: GG_BUILD_CUDA

 function gg_run_open_llama_7b_v2 {
    cd ${SRC}
@@ -290,8 +296,8 @@ function gg_run_open_llama_7b_v2 {

    set -e

-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log

    python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf

@@ -425,7 +431,7 @@ function gg_run_pythia_1_4b {
    set -e

    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log

    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf

@@ -535,7 +541,6 @@ function gg_sum_pythia_1_4b {
 }

 # pythia_2_8b
-# requires: GG_BUILD_CUDA

 function gg_run_pythia_2_8b {
    cd ${SRC}
@@ -556,8 +561,8 @@ function gg_run_pythia_2_8b {

    set -e

-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log

    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf

@@ -692,7 +697,7 @@ function gg_run_embd_bge_small {
    set -e

    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log

    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf

@@ -761,7 +766,7 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
    fi

    if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
-        if [ -z ${GG_BUILD_CUDA} ]; then
+        if [ -z ${GG_BUILD_CUDA} ] && [ -z ${GG_BUILD_VULKAN} ]; then
            test $ret -eq 0 && gg_run pythia_1_4b
        else
            test $ret -eq 0 && gg_run pythia_2_8b
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -901,7 +901,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
        }
        return true;
    }
-    if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") {
+    if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--n-gpu-layers-draft") {
        CHECK_ARG
        params.n_gpu_layers_draft = std::stoi(argv[i]);
        if (!llama_supports_gpu_offload()) {
@@ -1861,13 +1861,19 @@ std::string string_get_sortable_timestamp() {

 void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
    if (search.empty()) {
-        return; // Avoid infinite loop if 'search' is an empty string
+        return;
    }
+    std::string builder;
+    builder.reserve(s.length());
    size_t pos = 0;
-    while ((pos = s.find(search, pos)) != std::string::npos) {
-        s.replace(pos, search.length(), replace);
-        pos += replace.length();
+    size_t last_pos = 0;
+    while ((pos = s.find(search, last_pos)) != std::string::npos) {
+        builder.append(s, last_pos, pos - last_pos);
+        builder.append(replace);
+        last_pos = pos + search.length();
    }
+    builder.append(s, last_pos, std::string::npos);
+    s = std::move(builder);
 }

 void string_process_escapes(std::string & input) {
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -63,6 +63,7 @@ class Model:
    model_name: str | None
    metadata_override: Path | None
    dir_model_card: Path
+    is_lora: bool

    # subclasses should define this!
    model_arch: gguf.MODEL_ARCH
@@ -70,7 +71,7 @@ class Model:
    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False,
                 use_temp_file: bool = False, eager: bool = False,
                 metadata_override: Path | None = None, model_name: str | None = None,
-                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
+                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False, is_lora: bool = False):
        if type(self) is Model:
            raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")

@@ -92,6 +93,7 @@ class Model:
        self.metadata_override = metadata_override
        self.model_name = model_name
        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
+        self.is_lora = is_lora  # true if model is used inside convert_lora_to_gguf.py

        # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
        if self.ftype == gguf.LlamaFileType.GUESSED:
@@ -1593,7 +1595,8 @@ class LlamaModel(Model):
                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))

-                self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
+                if not self.is_lora:
+                    self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))

        super().prepare_tensors()

@@ -2140,8 +2143,9 @@ class Phi3MiniModel(Model):
        if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
            raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')

-        self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG]  + ".weight", np.array(long_factors, dtype=np.float32))
-        self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32))
+        if not self.is_lora:
+            self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG]  + ".weight", np.array(long_factors, dtype=np.float32))
+            self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32))


@Model.register("PlamoForCausalLM")
@@ -3839,7 +3843,8 @@ class ExaoneModel(Model):
                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))

-                self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
+                if not self.is_lora:
+                    self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))

        super().prepare_tensors()

--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -386,6 +386,7 @@ if __name__ == '__main__':
            dry_run=args.dry_run,
            dir_lora_model=dir_lora,
            lora_alpha=alpha,
+            is_lora=True,
        )

        logger.info("Exporting model...")
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -20,7 +20,7 @@
 **oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:

 - **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
+- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL and oneDNN)*.
 - **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
 - **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.

@@ -28,10 +28,6 @@

 The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).

-When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
-
-It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
-
 ## Recommended Release

 The SYCL backend would be broken by some PRs due to no online CI.
@@ -45,6 +41,10 @@ The following release is verified with good quality:

 ## News

+
+- 2024.8
+  - Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs.
+
 - 2024.5
  - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
  - Arch Linux is verified successfully.
@@ -196,7 +196,7 @@ Please follow the instructions for downloading and installing the Toolkit for Li

 Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.

-Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
+Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs.

 - **Adding support to Nvidia GPUs**

@@ -255,8 +255,6 @@ or
 # Export relevant ENV variables
 source /opt/intel/oneapi/setvars.sh

-# Build LLAMA with MKL BLAS acceleration for intel GPU
-
 # Option 1: Use FP32 (recommended for better performance in most cases)
 cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -216,13 +216,19 @@ static std::string gguf_data_to_str(enum gguf_type type, const void * data, int

 static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
    if (search.empty()) {
-        return; // Avoid infinite loop if 'search' is an empty string
+        return;
    }
+    std::string builder;
+    builder.reserve(s.length());
    size_t pos = 0;
-    while ((pos = s.find(search, pos)) != std::string::npos) {
-        s.replace(pos, search.length(), replace);
-        pos += replace.length();
+    size_t last_pos = 0;
+    while ((pos = s.find(search, last_pos)) != std::string::npos) {
+        builder.append(s, last_pos, pos - last_pos);
+        builder.append(replace);
+        last_pos = pos + search.length();
    }
+    builder.append(s, last_pos, std::string::npos);
+    s = std::move(builder);
 }

 static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -104,7 +104,7 @@ 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("  --keep-split: will generate quantized model in the same shards as input\n");
    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");
--- a/examples/server/public/index.js
+++ b/examples/server/public/index.js
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -1760,7 +1760,8 @@ extern "C" {
            struct ggml_tensor  * v,
            struct ggml_tensor  * mask,
            float                 scale,
-            float                 max_bias);
+            float                 max_bias,
+            float                 logit_softcap);

    GGML_API void ggml_flash_attn_ext_set_prec(
            struct ggml_tensor * a,
@@ -1777,10 +1778,8 @@ extern "C" {

    GGML_API struct ggml_tensor * ggml_ssm_conv(
            struct ggml_context * ctx,
-            struct ggml_tensor  * s,
-            struct ggml_tensor  * x,
-            struct ggml_tensor  * c,
-            struct ggml_tensor  * sq);
+            struct ggml_tensor  * sx,
+            struct ggml_tensor  * c);

    GGML_API struct ggml_tensor * ggml_ssm_scan(
            struct ggml_context * ctx,
@@ -1789,8 +1788,7 @@ extern "C" {
            struct ggml_tensor  * dt,
            struct ggml_tensor  * A,
            struct ggml_tensor  * B,
-            struct ggml_tensor  * C,
-            struct ggml_tensor  * sq);
+            struct ggml_tensor  * C);

    // partition into non-overlapping windows with padding if needed
    // example:
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -549,6 +549,13 @@ if (GGML_SYCL)
    file(GLOB   GGML_SOURCES_SYCL "ggml-sycl/*.cpp")
    list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp")

+    find_package(DNNL)
+    message("-- DNNL found:" ${DNNL_FOUND})
+    if (GGML_SYCL_TARGET STREQUAL "INTEL")
+        add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND})
+    else()
+        add_compile_definitions(GGML_SYCL_DNNL=0)
+    endif()
    if (WIN32)
        find_package(IntelSYCL REQUIRED)
        find_package(MKL REQUIRED)
@@ -561,6 +568,9 @@ if (GGML_SYCL)
            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl pthread m dl onemkl)
        endif()
    endif()
+    if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
+        list(APPEND GGML_EXTRA_LIBS DNNL::dnnl)
+    endif()
 endif()

 if (GGML_RPC)
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -22,6 +22,7 @@ typedef void (* fattn_kernel_t)(
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
+        const float logit_softcap,
        const int ne00,
        const int ne01,
        const int ne02,
@@ -657,11 +658,17 @@ void launch_fattn(
    const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
    const int  shmem = 0;

-    float scale    = 1.0f;
-    float max_bias = 0.0f;
+    float scale         = 1.0f;
+    float max_bias      = 0.0f;
+    float logit_softcap = 0.0f;

-    memcpy(&scale,    (float *) KQV->op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
+    memcpy(&scale,         (float *) KQV->op_params + 0, sizeof(float));
+    memcpy(&max_bias,      (float *) KQV->op_params + 1, sizeof(float));
+    memcpy(&logit_softcap, (float *) KQV->op_params + 2, sizeof(float));
+
+    if (logit_softcap != 0.0f) {
+        scale /= logit_softcap;
+    }

    const uint32_t n_head      = Q->ne[2];
    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
@@ -675,7 +682,7 @@ void launch_fattn(
        V_data,
        mask ? ((const char *) mask->data) : nullptr,
        (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
-        scale, max_bias, m0, m1, n_head_log2,
+        scale, max_bias, m0, m1, n_head_log2, logit_softcap,
        Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
        K->ne[0], K->ne[1], K->ne[2], K->ne[3],
        mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
--- a/ggml/src/ggml-cuda/fattn-tile-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu
@@ -4,7 +4,7 @@

 #define FATTN_KQ_STRIDE_TILE_F16 64

-template<int D, int ncols, int nwarps, int parallel_blocks> // D == head size
+template<int D, int ncols, int nwarps, int parallel_blocks, bool use_logit_softcap> // D == head size
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(nwarps*WARP_SIZE, 1)
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@@ -20,6 +20,7 @@ static __global__ void flash_attn_tile_ext_f16(
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
+        const float logit_softcap,
        const int ne00,
        const int ne01,
        const int ne02,
@@ -44,6 +45,12 @@ static __global__ void flash_attn_tile_ext_f16(
        const int ne2,
        const int ne3) {
 #ifdef FP16_AVAILABLE
+    // Skip unused kernel variants for faster compilation:
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
@@ -154,7 +161,13 @@ static __global__ void flash_attn_tile_ext_f16(
            for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) {
                const int j_KQ = j_KQ_0 + threadIdx.y;

-                half sum = __low2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]) + __high2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
+                half sum;
+                if (use_logit_softcap) {
+                    const float2 tmp = __half22float2(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
+                    sum = logit_softcap * tanhf(tmp.x + tmp.y);
+                } else {
+                    sum = __low2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]) + __high2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
+                }
                sum += mask ? slopeh*maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);

                kqmax_new[j_KQ_0/nwarps] = ggml_cuda_hmax(kqmax_new[j_KQ_0/nwarps], sum);
@@ -270,20 +283,20 @@ static __global__ void flash_attn_tile_ext_f16(
 #endif // FP16_AVAILABLE
 }

-template <int cols_per_block, int parallel_blocks>
+template <int cols_per_block, int parallel_blocks, bool use_logit_softcap>
 void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const ggml_tensor * Q = dst->src[0];
    switch (Q->ne[0]) {
        case  64: {
            constexpr int      D = 64;
            constexpr int nwarps = 8;
-            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
+            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
        } break;
        case 128: {
            constexpr int      D = 128;
            constexpr int nwarps = 8;
-            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
+            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
        } break;
        default: {
@@ -296,24 +309,45 @@ void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_ten
    const ggml_tensor * KQV = dst;
    const ggml_tensor * Q   = dst->src[0];

-    const int32_t precision = KQV->op_params[2];
+    const int32_t precision = KQV->op_params[3];
    GGML_ASSERT(precision == GGML_PREC_DEFAULT);

+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
    if (Q->ne[1] <= 16) {
        constexpr int cols_per_block = 16;
        constexpr int parallel_blocks = 4;
-        launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] <= 32) {
        constexpr int cols_per_block = 32;
        constexpr int parallel_blocks = 4;
-        launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    constexpr int cols_per_block = 32;
    constexpr int parallel_blocks = 1;
-    launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+    } else {
+        constexpr bool use_logit_softcap = true;
+        launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+    }
 }
--- a/ggml/src/ggml-cuda/fattn-tile-f32.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu
@@ -4,7 +4,7 @@

 #define FATTN_KQ_STRIDE_TILE_F32 32

-template<int D, int ncols, int nwarps, int parallel_blocks> // D == head size
+template<int D, int ncols, int nwarps, int parallel_blocks, bool use_logit_softcap> // D == head size
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(nwarps*WARP_SIZE, 1)
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@@ -20,6 +20,7 @@ static __global__ void flash_attn_tile_ext_f32(
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
+        const float logit_softcap,
        const int ne00,
        const int ne01,
        const int ne02,
@@ -43,6 +44,12 @@ static __global__ void flash_attn_tile_ext_f32(
        const int ne1,
        const int ne2,
        const int ne3) {
+    // Skip unused kernel variants for faster compilation:
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
@@ -151,6 +158,10 @@ static __global__ void flash_attn_tile_ext_f32(
            for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) {
                const int j_KQ = j_KQ_0 + threadIdx.y;

+                if (use_logit_softcap) {
+                    sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] = logit_softcap * tanhf(sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
+                }
+
                sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;

                kqmax_new[j_KQ_0/nwarps] = fmaxf(kqmax_new[j_KQ_0/nwarps], sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
@@ -267,20 +278,20 @@ static __global__ void flash_attn_tile_ext_f32(
    }
 }

-template <int cols_per_block, int parallel_blocks>
+template <int cols_per_block, int parallel_blocks, bool use_logit_softcap>
 void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const ggml_tensor * Q = dst->src[0];
    switch (Q->ne[0]) {
        case  64: {
            constexpr int      D = 64;
            constexpr int nwarps = 8;
-            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
+            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
        } break;
        case 128: {
            constexpr int      D = 128;
            constexpr int nwarps = 8;
-            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
+            fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
        } break;
        default: {
@@ -290,23 +301,45 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
 }

 void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * KQV = dst;
    const ggml_tensor * Q = dst->src[0];

+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
    if (Q->ne[1] <= 16) {
        constexpr int cols_per_block = 16;
        constexpr int parallel_blocks = 4;
-        launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] <= 32) {
        constexpr int cols_per_block = 32;
        constexpr int parallel_blocks = 4;
-        launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    constexpr int cols_per_block = 32;
    constexpr int parallel_blocks = 1;
-    launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+    } else {
+        constexpr bool use_logit_softcap = true;
+        launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
+    }
 }
--- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
@@ -1,7 +1,7 @@
 #include "common.cuh"
 #include "fattn-common.cuh"

-template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
+template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@@ -17,6 +17,7 @@ static __global__ void flash_attn_vec_ext_f16(
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
+        const float logit_softcap,
        const int ne00,
        const int ne01,
        const int ne02,
@@ -41,6 +42,12 @@ static __global__ void flash_attn_vec_ext_f16(
        const int ne2,
        const int ne3) {
 #ifdef FP16_AVAILABLE
+    // Skip unused kernel variants for faster compilation:
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

    constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16<D>(type_K);
@@ -190,6 +197,11 @@ static __global__ void flash_attn_vec_ext_f16(
            for (int j = 0; j < ncols; ++j) {
                half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]);
                sum = warp_reduce_sum(sum);
+
+                if (use_logit_softcap) {
+                    sum = logit_softcap*tanhf(sum);
+                }
+
                sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);

                if (ncols == 1) {
@@ -286,10 +298,10 @@ static __global__ void flash_attn_vec_ext_f16(
 #endif // FP16_AVAILABLE
 }

-template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
+template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap>
 void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    constexpr int nwarps = D/WARP_SIZE;
-    fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V>;
+    fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
    constexpr bool need_f16_K = D != 128;
    constexpr bool need_f16_V = D != 128 && D != 64;
    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
@@ -297,48 +309,81 @@ void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx,

 template <int D, ggml_type type_K, ggml_type type_V>
 void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * KQV = dst;
-    ggml_tensor * Q   = dst->src[0];
-    ggml_tensor * K   = dst->src[1];
-    ggml_tensor * V   = dst->src[2];
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];
+    const ggml_tensor * K   = dst->src[1];
+    const ggml_tensor * V   = dst->src[2];

-    const int32_t precision = KQV->op_params[2];
+    const int32_t precision = KQV->op_params[3];
    GGML_ASSERT(precision == GGML_PREC_DEFAULT);

    GGML_ASSERT(K->type == type_K);
    GGML_ASSERT(V->type == type_V);

+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
    if (Q->ne[1] == 1) {
        constexpr int cols_per_block  = 1;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] == 2) {
        constexpr int cols_per_block  = 2;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] <= 4) {
        constexpr int cols_per_block  = 4;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] <= 8) {
        constexpr int cols_per_block  = 8;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    constexpr int cols_per_block  = 8;
    constexpr int parallel_blocks = 1;
-    ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+    } else {
+        constexpr bool use_logit_softcap = true;
+        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+    }
 }

 #define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V)                          \
--- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
@@ -1,7 +1,7 @@
 #include "common.cuh"
 #include "fattn-common.cuh"

-template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
+template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@@ -17,6 +17,7 @@ static __global__ void flash_attn_vec_ext_f32(
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
+        const float logit_softcap,
        const int ne00,
        const int ne01,
        const int ne02,
@@ -40,6 +41,12 @@ static __global__ void flash_attn_vec_ext_f32(
        const int ne1,
        const int ne2,
        const int ne3) {
+    // Skip unused kernel variants for faster compilation:
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

    constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32<D>(type_K);
@@ -180,6 +187,11 @@ static __global__ void flash_attn_vec_ext_f32(
            for (int j = 0; j < ncols; ++j) {
                float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]);
                sum = warp_reduce_sum(sum);
+
+                if (use_logit_softcap) {
+                    sum = logit_softcap*tanhf(sum);
+                }
+
                sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;

                kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum);
@@ -267,10 +279,10 @@ static __global__ void flash_attn_vec_ext_f32(
    }
 }

-template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
+template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap>
 void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    constexpr int nwarps = D/WARP_SIZE;
-    fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V>;
+    fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
    constexpr bool need_f16_K = D != 128;
    constexpr bool need_f16_V = D != 128 && D != 64;
    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
@@ -278,44 +290,78 @@ void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx,

 template <int D, ggml_type type_K, ggml_type type_V>
 void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * Q   = dst->src[0];
-    ggml_tensor * K   = dst->src[1];
-    ggml_tensor * V   = dst->src[2];
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];
+    const ggml_tensor * K   = dst->src[1];
+    const ggml_tensor * V   = dst->src[2];

    GGML_ASSERT(K->type == type_K);
    GGML_ASSERT(V->type == type_V);

+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
    if (Q->ne[1] == 1) {
        constexpr int cols_per_block  = 1;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] == 2) {
        constexpr int cols_per_block  = 2;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] <= 4) {
        constexpr int cols_per_block  = 4;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    if (Q->ne[1] <= 8) {
        constexpr int cols_per_block  = 8;
        constexpr int parallel_blocks = 4;
-        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        } else {
+            constexpr bool use_logit_softcap = true;
+            ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+        }
        return;
    }

    constexpr int cols_per_block  = 8;
    constexpr int parallel_blocks = 1;
-    ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+    } else {
+        constexpr bool use_logit_softcap = true;
+        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
+    }
 }

 #define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V)                          \
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
@@ -6,7 +6,7 @@
 #endif // FP16_MMA_AVAILABLE

 // D == head size, VKQ_stride == num VKQ rows calculated in parallel:
-template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
+template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t, bool use_logit_softcap>
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(nwarps*WARP_SIZE, 1)
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@@ -22,6 +22,7 @@ static __global__ void flash_attn_ext_f16(
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
+        const float logit_softcap,
        const int ne00,
        const int ne01,
        const int ne02,
@@ -46,6 +47,12 @@ static __global__ void flash_attn_ext_f16(
        const int ne2,
        const int ne3) {
 #ifdef FP16_MMA_AVAILABLE
+    // Skip unused kernel variants for faster compilation:
+    if (use_logit_softcap && !(D == 128 || D == 256)) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

    const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
@@ -85,6 +92,8 @@ static __global__ void flash_attn_ext_f16(
    const half  slopeh = __float2half(slopef);
    const half2 slope2 = make_half2(slopef, slopef);

+    const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
+
    frag_b Q_b[D/16][ncols/frag_n];

    // A single buffer for temporarily holding tiles of KQ and VKQ parts:
@@ -194,6 +203,10 @@ static __global__ void flash_attn_ext_f16(
                    const int k = k0 + threadIdx.x;

                    KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
+
+                    if (use_logit_softcap) {
+                        KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
+                    }
                }

                float KQ_max_new = KQ_max_f[j0/nwarps];
@@ -237,6 +250,15 @@ static __global__ void flash_attn_ext_f16(
                    const int k = k0 + threadIdx.x;

                    KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
+
+                    if (use_logit_softcap) {
+                        // There is no dedicated tangens hyperbolicus function for half2.
+                        KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
+                        KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
+                                               /(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
+
+                        KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
+                    }
                }

                half2 KQ_max_new = KQ_max_h2[j0/nwarps];
@@ -427,7 +449,8 @@ static_assert(get_VKQ_stride( 80, 4, 16) ==  16, "Test failed.");

 template <int D, int cols_per_block, typename KQ_acc_t>
 void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * Q = dst->src[0];
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];

    constexpr int nwarps = 4;

@@ -435,20 +458,50 @@ void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggm
    const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
    const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;

+    float logit_softcap;
+    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
    if (4*blocks_num_pb1 < 2*nsm) {
        constexpr int parallel_blocks = 4;
-        fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
+        fattn_kernel_t fattn_kernel;
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        } else {
+            constexpr bool use_logit_softcap = true;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        }
        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
        return;
    }
    if (2*blocks_num_pb1 < 2*nsm) {
        constexpr int parallel_blocks = 2;
-        fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
+        fattn_kernel_t fattn_kernel;
+        if (logit_softcap == 0.0f) {
+            constexpr bool use_logit_softcap = false;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        } else {
+            constexpr bool use_logit_softcap = true;
+            fattn_kernel = flash_attn_ext_f16<
+                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+        }
        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
        return;
    }
    constexpr int parallel_blocks = 1;
-    fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
+    fattn_kernel_t fattn_kernel;
+    if (logit_softcap == 0.0f) {
+        constexpr bool use_logit_softcap = false;
+        fattn_kernel = flash_attn_ext_f16<
+            D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+    } else {
+        constexpr bool use_logit_softcap = true;
+        fattn_kernel = flash_attn_ext_f16<
+            D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+    }
    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
 }

--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -13,7 +13,7 @@ static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, g
    const ggml_tensor * KQV = dst;
    const ggml_tensor * Q   = dst->src[0];

-    const int32_t precision = KQV->op_params[2];
+    const int32_t precision = KQV->op_params[3];

    if (precision != GGML_PREC_DEFAULT) {
        if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
@@ -301,7 +301,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst

    ggml_cuda_set_device(ctx.device);
    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    const int32_t precision = KQV->op_params[2];
+    const int32_t precision = KQV->op_params[3];

    // On AMD the tile kernels perform poorly, use the vec kernel instead:
    if (cc >= CC_OFFSET_AMD) {
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -2624,9 +2624,14 @@ static enum ggml_status ggml_metal_graph_compute(

                        float scale;
                        float max_bias;
+                        float logit_softcap;
+                        memcpy(&scale,         ((int32_t *) dst->op_params) + 0, sizeof(scale));
+                        memcpy(&max_bias,      ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
+                        memcpy(&logit_softcap, ((int32_t *) dst->op_params) + 2, sizeof(logit_softcap));

-                        memcpy(&scale,    ((int32_t *) dst->op_params) + 0, sizeof(scale));
-                        memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
+                        if (logit_softcap != 0.0f) {
+                            scale /= logit_softcap;
+                        }

                        const uint32_t n_head      = src0->ne[2];
                        const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
@@ -2677,30 +2682,31 @@ static enum ggml_status ggml_metal_graph_compute(
                        } else {
                            [encoder setBuffer:id_src0     offset:offs_src0           atIndex:3];
                        }
-                        [encoder setBuffer:id_dst      offset:offs_dst            atIndex:4];
-                        [encoder setBytes:&ne01        length:sizeof( int64_t)    atIndex:5];
-                        [encoder setBytes:&ne02        length:sizeof( int64_t)    atIndex:6];
-                        [encoder setBytes:&ne03        length:sizeof( int64_t)    atIndex:7];
-                        [encoder setBytes:&nb01        length:sizeof(uint64_t)    atIndex:8];
-                        [encoder setBytes:&nb02        length:sizeof(uint64_t)    atIndex:9];
-                        [encoder setBytes:&nb03        length:sizeof(uint64_t)    atIndex:10];
-                        [encoder setBytes:&ne11        length:sizeof( int64_t)    atIndex:11];
-                        [encoder setBytes:&ne12        length:sizeof( int64_t)    atIndex:12];
-                        [encoder setBytes:&ne13        length:sizeof( int64_t)    atIndex:13];
-                        [encoder setBytes:&nb11        length:sizeof(uint64_t)    atIndex:14];
-                        [encoder setBytes:&nb12        length:sizeof(uint64_t)    atIndex:15];
-                        [encoder setBytes:&nb13        length:sizeof(uint64_t)    atIndex:16];
-                        [encoder setBytes:&nb21        length:sizeof(uint64_t)    atIndex:17];
-                        [encoder setBytes:&nb22        length:sizeof(uint64_t)    atIndex:18];
-                        [encoder setBytes:&nb23        length:sizeof(uint64_t)    atIndex:19];
-                        [encoder setBytes:&nb31        length:sizeof(uint64_t)    atIndex:20];
-                        [encoder setBytes:&ne1         length:sizeof( int64_t)    atIndex:21];
-                        [encoder setBytes:&ne2         length:sizeof( int64_t)    atIndex:22];
-                        [encoder setBytes:&scale       length:sizeof(   float)    atIndex:23];
-                        [encoder setBytes:&max_bias    length:sizeof(   float)    atIndex:24];
-                        [encoder setBytes:&m0          length:sizeof(m0)          atIndex:25];
-                        [encoder setBytes:&m1          length:sizeof(m1)          atIndex:26];
-                        [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27];
+                        [encoder setBuffer:id_dst        offset:offs_dst              atIndex:4];
+                        [encoder setBytes:&ne01          length:sizeof( int64_t)      atIndex:5];
+                        [encoder setBytes:&ne02          length:sizeof( int64_t)      atIndex:6];
+                        [encoder setBytes:&ne03          length:sizeof( int64_t)      atIndex:7];
+                        [encoder setBytes:&nb01          length:sizeof(uint64_t)      atIndex:8];
+                        [encoder setBytes:&nb02          length:sizeof(uint64_t)      atIndex:9];
+                        [encoder setBytes:&nb03          length:sizeof(uint64_t)      atIndex:10];
+                        [encoder setBytes:&ne11          length:sizeof( int64_t)      atIndex:11];
+                        [encoder setBytes:&ne12          length:sizeof( int64_t)      atIndex:12];
+                        [encoder setBytes:&ne13          length:sizeof( int64_t)      atIndex:13];
+                        [encoder setBytes:&nb11          length:sizeof(uint64_t)      atIndex:14];
+                        [encoder setBytes:&nb12          length:sizeof(uint64_t)      atIndex:15];
+                        [encoder setBytes:&nb13          length:sizeof(uint64_t)      atIndex:16];
+                        [encoder setBytes:&nb21          length:sizeof(uint64_t)      atIndex:17];
+                        [encoder setBytes:&nb22          length:sizeof(uint64_t)      atIndex:18];
+                        [encoder setBytes:&nb23          length:sizeof(uint64_t)      atIndex:19];
+                        [encoder setBytes:&nb31          length:sizeof(uint64_t)      atIndex:20];
+                        [encoder setBytes:&ne1           length:sizeof( int64_t)      atIndex:21];
+                        [encoder setBytes:&ne2           length:sizeof( int64_t)      atIndex:22];
+                        [encoder setBytes:&scale         length:sizeof(   float)      atIndex:23];
+                        [encoder setBytes:&max_bias      length:sizeof(   float)      atIndex:24];
+                        [encoder setBytes:&m0            length:sizeof(m0)            atIndex:25];
+                        [encoder setBytes:&m1            length:sizeof(m1)            atIndex:26];
+                        [encoder setBytes:&n_head_log2   length:sizeof(n_head_log2)   atIndex:27];
+                        [encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28];

                        if (!use_vec_kernel) {
                            // half8x8 kernel
--- a/ggml/src/ggml-metal.metal
+++ b/ggml/src/ggml-metal.metal
@@ -1976,6 +1976,7 @@ typedef void (flash_attn_ext_f16_t)(
        constant     float & m0,
        constant     float & m1,
        constant  uint32_t & n_head_log2,
+        constant     float & logit_softcap,
        threadgroup   half * shared,
        uint3  tgpig[[threadgroup_position_in_grid]],
        uint3  tpitg[[thread_position_in_threadgroup]],
@@ -2014,6 +2015,7 @@ kernel void kernel_flash_attn_ext_f16(
        constant     float & m0,
        constant     float & m1,
        constant  uint32_t & n_head_log2,
+        constant     float & logit_softcap,
        threadgroup   half * shared [[threadgroup(0)]],
        uint3  tgpig[[threadgroup_position_in_grid]],
        uint3  tpitg[[thread_position_in_threadgroup]],
@@ -2142,14 +2144,21 @@ kernel void kernel_flash_attn_ext_f16(
                    const short tx = tiisg%4;
                    const short ty = tiisg/4;

-                    if (mask != q) {
-                        // mqk = mqk*scale + mask*slope
-                        ss[8*cc + ty*TF + 2*tx + 0] = scale*ss[8*cc + ty*TF + 2*tx + 0] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 0];
-                        ss[8*cc + ty*TF + 2*tx + 1] = scale*ss[8*cc + ty*TF + 2*tx + 1] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 1];
-                    } else {
+                    if (iq1 + ty < ne01) {
                        // mqk = mqk*scale
                        ss[8*cc + ty*TF + 2*tx + 0] *= scale;
                        ss[8*cc + ty*TF + 2*tx + 1] *= scale;
+
+                        if (logit_softcap != 0.0f) {
+                            ss[8*cc + ty*TF + 2*tx + 0] = logit_softcap*precise::tanh(ss[8*cc + ty*TF + 2*tx + 0]);
+                            ss[8*cc + ty*TF + 2*tx + 1] = logit_softcap*precise::tanh(ss[8*cc + ty*TF + 2*tx + 1]);
+                        }
+
+                        if (mask != q) {
+                            // mqk = mqk + mask*slope
+                            ss[8*cc + ty*TF + 2*tx + 0] += slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 0];
+                            ss[8*cc + ty*TF + 2*tx + 1] += slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 1];
+                        }
                    }
                }
            }
@@ -2345,6 +2354,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
        constant     float & m0,
        constant     float & m1,
        constant  uint32_t & n_head_log2,
+        constant     float & logit_softcap,
        threadgroup   half * shared [[threadgroup(0)]],
        uint3  tgpig[[threadgroup_position_in_grid]],
        uint3  tpitg[[thread_position_in_threadgroup]],
@@ -2479,7 +2489,13 @@ kernel void kernel_flash_attn_ext_vec_f16(

                    // mqk = mqk*scale + mask*slope
                    if (tiisg == 0) {
-                        mqk = mqk*scale + ((mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f);
+                        mqk *= scale;
+
+                        if (logit_softcap != 0.0f) {
+                            mqk = logit_softcap*precise::tanh(mqk);
+                        }
+
+                        mqk += (mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f;

                        ss4[cc] = mqk;
                    }
--- a/ggml/src/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl.cpp
@@ -38,6 +38,7 @@

 #include "ggml-sycl/backend.hpp"
 #include "ggml-sycl/presets.hpp"
+#include "ggml-sycl/gemm.hpp"

 bool   ggml_sycl_loaded(void);
 void   ggml_sycl_free_data(struct ggml_tensor * tensor);
@@ -2482,6 +2483,7 @@ inline void ggml_sycl_op_mul_mat_sycl(

        const sycl::half alpha_f16 = 1.0f;
        const sycl::half beta_f16 = 0.0f;
+#if !GGML_SYCL_DNNL
        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
            *stream, oneapi::mkl::transpose::trans,
            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
@@ -2491,6 +2493,13 @@ inline void ggml_sycl_op_mul_mat_sycl(
            dpct::library_data_t::real_half)));
        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
        to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
+#else
+        auto dnnl_stream = ctx.stream_dnnl(stream);
+        DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ptr, DnnlGemmWrapper::to_dt<sycl::half>(),
+            src0_ptr, DnnlGemmWrapper::to_dt<sycl::half>(), dst_f16.get(), DnnlGemmWrapper::to_dt<sycl::half>());
+        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
+        to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff* src1_ncols, stream);
+#endif
    }
    else {
        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
@@ -2513,13 +2522,18 @@ inline void ggml_sycl_op_mul_mat_sycl(

        const float alpha = 1.0f;
        const float beta = 0.0f;
-
+#if !GGML_SYCL_DNNL
        SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
            *stream, oneapi::mkl::transpose::trans,
            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
            dpct::get_value(&alpha, *stream), src0_ddf_i, ne00,
            src1_ddf1_i, ne10, dpct::get_value(&beta, *stream),
            dst_dd_i, ldc)));
+#else
+        auto dnnl_stream = ctx.stream_dnnl(stream);
+         DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ddf1_i, DnnlGemmWrapper::to_dt<float>(),
+            src0_ddf_i, DnnlGemmWrapper::to_dt<float>(), dst_dd_i, DnnlGemmWrapper::to_dt<float>());
+#endif
    }
    (void) dst;
    (void) src1_ddq_i;
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -19,6 +19,10 @@
 #include "dpct/helper.hpp"
 #include "ggml-sycl.h"
 #include "presets.hpp"
+#if GGML_SYCL_DNNL
+#include "dnnl.hpp"
+#include "dnnl_sycl.hpp"
+#endif

 #define GGML_COMMON_DECL_SYCL
 #define GGML_COMMON_IMPL_SYCL
@@ -277,6 +281,52 @@ struct ggml_backend_sycl_context {
        return stream(device, 0);
    }

+#if GGML_SYCL_DNNL
+    dnnl::engine make_engine(sycl::queue* q) {
+        // Get the device associated with the queue
+        sycl::device dev = q->get_device();
+        // Get the context associated with the queue
+        sycl::context ctx = q->get_context();
+        const dnnl::engine eng = dnnl::sycl_interop::make_engine(dev, ctx);
+        return eng;
+    }
+
+    std::unordered_map<sycl::queue*, dnnl::stream> stream_map;
+    std::unordered_map<sycl::queue*, dnnl::engine> engine_map;
+    dnnl::stream stream_dnnl(int device, int _stream) {
+        auto q = stream(device, _stream);
+        return stream_dnnl(q);
+    }
+    dnnl::engine engine_dnnl(sycl::queue* qptr) {
+        auto it = engine_map.find(qptr);
+        if (it == engine_map.end()) {
+            auto eng = make_engine(qptr);
+            engine_map[qptr] = eng;
+            return eng;
+        }
+        else
+        {
+            return it->second;
+        }
+    }
+    dnnl::stream stream_dnnl(sycl::queue* qptr) {
+        auto it = stream_map.find(qptr);
+        if (it == stream_map.end()) {
+            auto eng = engine_dnnl(qptr);
+            auto stream = dnnl::sycl_interop::make_stream(eng, *qptr);
+            stream_map[qptr] = stream;
+            return stream;
+        }
+        else
+        {
+            return it->second;
+        }
+    }
+    dnnl::stream stream_dnnl() {
+        return stream_dnnl(device, 0);
+    }
+#endif
+
    // pool
    std::unique_ptr<ggml_sycl_pool> pools[GGML_SYCL_MAX_DEVICES];

--- a/ggml/src/ggml-sycl/gemm.hpp
+++ b/ggml/src/ggml-sycl/gemm.hpp
@@ -0,0 +1,101 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_GEMM_HPP
+#define GGML_SYCL_GEMM_HPP
+
+#include <fstream>
+#include <iostream>
+
+#include "ggml-sycl.h"
+
+#if GGML_SYCL_DNNL
+
+#include "dnnl.hpp"
+#include "dnnl_sycl.hpp"
+
+class DnnlGemmWrapper {
+public:
+    using dt = dnnl::memory::data_type;
+    using tag = dnnl::memory::format_tag;
+
+    template<typename T>
+    static constexpr dt to_dt() {
+        if constexpr (std::is_same_v<T, float>) return dt::f32;
+        else if constexpr (std::is_same_v<T, sycl::half>) return dt::f16;
+        else static_assert(0);
+    }
+
+    static inline void row_gemm(sycl::queue& q, bool a_trans,
+        bool b_trans, int m, int n, int k,
+        const void* a, dt at, const void* b, dt bt, void* c, dt ct)
+    {
+        // Get the device associated with the queue
+        sycl::device dev = q.get_device();
+        // Get the context associated with the queue
+        sycl::context ctx = q.get_context();
+        const dnnl::engine eng = dnnl::sycl_interop::make_engine(dev, ctx);
+        const dnnl::stream stream = dnnl::sycl_interop::make_stream(eng, q);
+        dnnl::memory::dims a_dims = { m, k };
+        dnnl::memory::dims b_dims = { k, n };
+        dnnl::memory::dims c_dims = { m, n };
+        const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
+        const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
+        const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
+        auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
+        auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
+        auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
+        auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
+
+        // Create the primitive.
+        auto matmul_prim = dnnl::matmul(matmul_pd);
+        // Primitive arguments.
+        std::unordered_map<int, dnnl::memory> matmul_args;
+        matmul_args.insert({ DNNL_ARG_SRC, a_mem });
+        matmul_args.insert({ DNNL_ARG_WEIGHTS, b_mem });
+        matmul_args.insert({ DNNL_ARG_DST, c_mem });
+
+        matmul_prim.execute(stream, matmul_args);
+    }
+
+
+    static inline void row_gemm(const dnnl::stream& stream, bool a_trans,
+        bool b_trans, int m, int n, int k,
+        const void* a, dt at, const void* b, dt bt, void* c, dt ct)
+    {
+        auto const eng = stream.get_engine();
+        dnnl::memory::dims a_dims = { m, k };
+        dnnl::memory::dims b_dims = { k, n };
+        dnnl::memory::dims c_dims = { m, n };
+        const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
+        const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
+        const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
+        auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
+        auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
+        auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
+        auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
+
+        // Create the primitive.
+        auto matmul_prim = dnnl::matmul(matmul_pd);
+        // Primitive arguments.
+        std::unordered_map<int, dnnl::memory> matmul_args;
+        matmul_args.insert({ DNNL_ARG_SRC, a_mem });
+        matmul_args.insert({ DNNL_ARG_WEIGHTS, b_mem });
+        matmul_args.insert({ DNNL_ARG_DST, c_mem });
+
+        matmul_prim.execute(stream, matmul_args);
+    }
+};
+
+#endif
+
+#endif // GGML_SYCL_GEMM_HPP
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -7095,7 +7095,8 @@ struct ggml_tensor * ggml_flash_attn_ext(
        struct ggml_tensor  * v,
        struct ggml_tensor  * mask,
        float                 scale,
-        float                 max_bias) {
+        float                 max_bias,
+        float                 logit_softcap) {
    GGML_ASSERT(ggml_can_mul_mat(k, q));
    // TODO: check if vT can be multiplied by (k*qT)

@@ -7122,7 +7123,7 @@ struct ggml_tensor * ggml_flash_attn_ext(
    int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);

-    float params[] = { scale, max_bias };
+    float params[] = { scale, max_bias, logit_softcap };
    ggml_set_op_params(result, params, sizeof(params));

    result->op   = GGML_OP_FLASH_ATTN_EXT;
@@ -7142,7 +7143,7 @@ void ggml_flash_attn_ext_set_prec(

    const int32_t prec_i32 = (int32_t) prec;

-    ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second
+    ggml_set_op_params_i32(a, 3, prec_i32); // scale is on first pos, max_bias on second
 }

 // ggml_flash_attn_back
@@ -7229,43 +7230,34 @@ struct ggml_tensor * ggml_flash_attn_back(

 struct ggml_tensor * ggml_ssm_conv(
        struct ggml_context * ctx,
-        struct ggml_tensor  * s,
-        struct ggml_tensor  * x,
-        struct ggml_tensor  * c,
-        struct ggml_tensor  * sq) {
-    GGML_ASSERT(ggml_is_3d(s));
-    GGML_ASSERT(ggml_is_matrix(x));
+        struct ggml_tensor  * sx,
+        struct ggml_tensor  * c) {
+    GGML_ASSERT(ggml_is_3d(sx));
    GGML_ASSERT(ggml_is_matrix(c));
-    GGML_ASSERT(ggml_is_matrix(sq));
-    GGML_ASSERT(sq->type == GGML_TYPE_I32);

-    const int64_t d_conv   = c->ne[0];
-    const int64_t d_inner  = c->ne[1];
-    const int64_t n_tokens = x->ne[1];
-    const int64_t n_kv     = s->ne[2];
+    const int64_t d_conv  = c->ne[0];
+    const int64_t d_inner = c->ne[1];
+    const int64_t n_t     = sx->ne[0] - d_conv + 1; // tokens per sequence
+    const int64_t n_s     = sx->ne[2];

-    GGML_ASSERT( s->ne[0] == d_conv - 1);
-    GGML_ASSERT( s->ne[1] == d_inner);
-    GGML_ASSERT( x->ne[0] == d_inner);
-    GGML_ASSERT(sq->ne[0] == n_kv);
-    GGML_ASSERT(sq->ne[1] == n_tokens);
+    // TODO: maybe support other strides than 1?
+    GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t);
+    GGML_ASSERT(sx->ne[1] == d_inner);
+    GGML_ASSERT(n_t >= 0);

    bool is_node = false;

-    if (s->grad || x->grad || c->grad || sq->grad) {
+    if (sx->grad || c->grad) {
        GGML_ABORT("fatal error"); // TODO: implement
        is_node = true;
    }

-    // 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv}
-    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv));
+    struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_t, n_s);

    result->op   = GGML_OP_SSM_CONV;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src[0] = s;
-    result->src[1] = x;
-    result->src[2] = c;
-    result->src[3] = sq;
+    result->src[0] = sx;
+    result->src[1] = c;

    return result;
 }
@@ -7279,39 +7271,42 @@ struct ggml_tensor * ggml_ssm_scan(
        struct ggml_tensor  * dt,
        struct ggml_tensor  * A,
        struct ggml_tensor  * B,
-        struct ggml_tensor  * C,
-        struct ggml_tensor  * sq) {
+        struct ggml_tensor  * C) {
    GGML_ASSERT(ggml_is_contiguous(s));
    GGML_ASSERT(ggml_is_contiguous(x));
    GGML_ASSERT(ggml_is_contiguous(dt));
    GGML_ASSERT(ggml_is_contiguous(A));
-    GGML_ASSERT(sq->type == GGML_TYPE_I32);
+    GGML_ASSERT(ggml_is_matrix(A));
+    GGML_ASSERT(ggml_is_3d(B));
+    GGML_ASSERT(ggml_is_3d(s));
    GGML_ASSERT(B->nb[0] == ggml_type_size(B->type));
    GGML_ASSERT(C->nb[0] == ggml_type_size(C->type));
    GGML_ASSERT(ggml_are_same_shape(x, dt));
+    GGML_ASSERT(ggml_are_same_shape(B, C));

    {
-        const int64_t d_state  = s->ne[0];
-        const int64_t d_inner  = s->ne[1];
-        const int64_t n_tokens = x->ne[1];
+        const int64_t d_state      = s->ne[0];
+        const int64_t d_inner      = s->ne[1];
+        const int64_t n_seq_tokens = x->ne[1];
+        const int64_t n_seqs       = x->ne[2];

+        GGML_ASSERT(s->ne[2] == n_seqs);
        GGML_ASSERT(x->ne[0] == d_inner);
        GGML_ASSERT(A->ne[0] == d_state);
        GGML_ASSERT(A->ne[1] == d_inner);
        GGML_ASSERT(B->ne[0] == d_state);
-        GGML_ASSERT(B->ne[1] == n_tokens);
-        GGML_ASSERT(C->ne[0] == d_state);
-        GGML_ASSERT(C->ne[1] == n_tokens);
+        GGML_ASSERT(B->ne[1] == n_seq_tokens);
+        GGML_ASSERT(B->ne[2] == n_seqs);
    }

    bool is_node = false;

-    if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) {
+    if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad) {
        GGML_ABORT("fatal error"); // TODO: implement
        is_node = true;
    }

-    // 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv}
+    // concatenated y + ssm_states
    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s));

    result->op   = GGML_OP_SSM_SCAN;
@@ -7322,7 +7317,6 @@ struct ggml_tensor * ggml_ssm_scan(
    result->src[3] = A;
    result->src[4] = B;
    result->src[5] = C;
-    result->src[6] = sq;

    return result;
 }
@@ -10995,11 +10989,6 @@ static void ggml_compute_forward_concat_f32(

    GGML_TENSOR_BINARY_OP_LOCALS

-    // TODO: support for transposed / permuted tensors
-    GGML_ASSERT(nb0  == sizeof(float));
-    GGML_ASSERT(nb00 == sizeof(float));
-    GGML_ASSERT(nb10 == sizeof(float));
-
    const int32_t dim = ggml_get_op_params_i32(dst, 0);

    GGML_ASSERT(dim >= 0 && dim < 4);
@@ -15283,11 +15272,17 @@ static void ggml_compute_forward_flash_attn_ext_f16(
    const int ir0 = dr*ith;
    const int ir1 = MIN(ir0 + dr, nr);

-    float scale    = 1.0f;
-    float max_bias = 0.0f;
+    float scale         = 1.0f;
+    float max_bias      = 0.0f;
+    float logit_softcap = 0.0f;

-    memcpy(&scale,    (float *) dst->op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+    memcpy(&scale,         (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias,      (float *) dst->op_params + 1, sizeof(float));
+    memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float));
+
+    if (logit_softcap != 0) {
+        scale /= logit_softcap;
+    }

    const uint32_t n_head      = neq2;
    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
@@ -15351,7 +15346,13 @@ static void ggml_compute_forward_flash_attn_ext_f16(
            const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3);
            kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1);

-            s = s*scale + mv; // scale KQ value and apply mask
+            s = s*scale; // scale KQ value
+
+            if (logit_softcap != 0.0f) {
+                s = logit_softcap*tanhf(s);
+            }
+
+            s += mv; // apply mask

            const float Mold = M;

@@ -15360,7 +15361,7 @@ static void ggml_compute_forward_flash_attn_ext_f16(

            const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3));

-            if (v->type== GGML_TYPE_F16) {
+            if (v->type == GGML_TYPE_F16) {
                if (s > M) {
                    // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f
                    M = s;
@@ -15427,7 +15428,7 @@ static void ggml_compute_forward_flash_attn_ext(
        const struct ggml_tensor * v,
        const struct ggml_tensor * mask,
        struct ggml_tensor * dst) {
-    switch (dst->op_params[2]) {
+    switch (dst->op_params[3]) {
        case GGML_PREC_DEFAULT:
        case GGML_PREC_F32:
            {
@@ -15782,27 +15783,22 @@ static void ggml_compute_forward_flash_attn_back(
 static void ggml_compute_forward_ssm_conv_f32(
        const struct ggml_compute_params * params,
        struct ggml_tensor * dst) {
-    const struct ggml_tensor * src0 = dst->src[0]; // conv_state
-    const struct ggml_tensor * src1 = dst->src[1]; // x
-    const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight
-    const struct ggml_tensor * src3 = dst->src[3]; // state_seq
+    const struct ggml_tensor * src0 = dst->src[0]; // conv_x
+    const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight

    const int ith = params->ith;
    const int nth = params->nth;

-    const int nc   = src2->ne[0]; // d_conv
-    const int nr   = src0->ne[1]; // d_inner
-    const int n_t  = src1->ne[1]; // n_tokens
-    const int n_kv = src0->ne[2]; // max number of sequences in the batch
+    const int nc  = src1->ne[0]; // d_conv
+    const int ncs = src0->ne[0]; // d_conv - 1 + n_t
+    const int nr  = src0->ne[1]; // d_inner
+    const int n_t =  dst->ne[1]; // tokens per sequence
+    const int n_s =  dst->ne[2]; // number of sequences in the batch

-    GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == ggml_nelements(dst));
+    GGML_ASSERT( dst->ne[0] == nr);
    GGML_ASSERT(src0->nb[0] == sizeof(float));
    GGML_ASSERT(src1->nb[0] == sizeof(float));
-    GGML_ASSERT(src2->nb[0] == sizeof(float));
-    GGML_ASSERT(src3->nb[0] == sizeof(int32_t));
    GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
-    // for use with the destination state offset between sequences
-    GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->ne[0]*sizeof(float));

    // rows per thread
    const int dr = (nr + nth - 1)/nth;
@@ -15812,76 +15808,29 @@ static void ggml_compute_forward_ssm_conv_f32(
    const int ir1 = MIN(ir0 + dr, nr);
    const int ir  = ir1 - ir0;

-    if (n_kv > 1) {
-        // multiple sequences means it's hard to know when it's the first time a state is read,
-        // so copy them all over to the destination, just to be sure.
-        for (int i3 = 0; i3 < n_kv; ++i3) {
-            float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
-            float * s  = (float *) ((char *)  dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float));
-            // can't use memcpy because of d_conv vs d_conv - 1
+    for (int i3 = 0; i3 < n_s; ++i3) {
+        for (int i2 = 0; i2 < n_t; ++i2) {
+            // {d_conv - 1 + n_t, d_inner, n_seqs}
+            // sliding window
+            const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s}
+            const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner}
+            float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s}
+
+            // TODO: transpose the output for smaller strides for big batches?
+            // d_inner
            for (int i1 = 0; i1 < ir; ++i1) {
-                for (int i0 = 0; i0 < nc - 1; ++i0) {
-                    // copy s0 to last (d_conv - 1) columns of s
-                    s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)];
+                // rowwise dot product
+                // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision
+                float sumf = 0.0f;
+
+                // d_conv
+                for (int i0 = 0; i0 < nc; ++i0) {
+                    sumf += s[i0 + i1*ncs] * c[i0 + i1*nc];
                }
+                x[i1] = sumf;
            }
        }
    }
-
-    for (int i2 = 0; i2 < n_t; ++i2) {
-        int32_t * sq = (int32_t *) ((char *) src3->data +  i2*(src3->nb[1])); // {n_kv, n_tokens}
-        float *   x  = (float *)   ((char *)  dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens}
-        float *   s  = (float *)   ((char *)  dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv}
-        float *   s0; // {d_conv - 1, d_inner, n_kv}
-        float *   x0 = (float *)   ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
-        float *   c  = (float *)   ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner}
-        int ne0s0;
-
-        GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
-
-        // avoid needing to copy the state for the first token
-        if (i2 == 0) {
-            s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv}
-            ne0s0 = src0->ne[0];
-        } else {
-            // the source is the last (d_conv - 1) columns of the destination
-            s0 = s + 1;
-            ne0s0 = nc;
-        }
-
-        // d_inner
-        for (int i1 = 0; i1 < ir; ++i1) {
-            // shift state left
-            for (int i0 = 0; i0 < nc - 1; ++i0) {
-                s[i0 + i1*nc] = s0[i0 + i1*ne0s0];
-            }
-            // insert x on the last column
-            s[(nc - 1) + i1*nc] = x0[i1];
-        }
-
-        // handle copies when there are multiple output states
-        for (int i3 = 1; i3 < n_kv; ++i3) {
-            int32_t seq = sq[i3];
-            if (0 <= seq && seq < n_kv) {
-                float * s1 = s + (seq - sq[0])*nc*nr;
-                memcpy(s1, s, nc*ir*sizeof(float));
-            } else {
-                // stop at negative or too big seq_ids
-                break;
-            }
-        }
-
-        // it seems a little faster when this is separate from the state shift
-        for (int i1 = 0; i1 < ir; ++i1) {
-            // rowwise dot product
-            float sumf = 0.0f;
-            for (int i0 = 0; i0 < nc; ++i0) {
-                int i = i0 + i1*nc;
-                sumf += s[i] * c[i];
-            }
-            x[i1] = sumf;
-        }
-    }
 }

 static void ggml_compute_forward_ssm_conv(
@@ -15910,15 +15859,14 @@ static void ggml_compute_forward_ssm_scan_f32(
    const struct ggml_tensor * src3 = dst->src[3]; // A
    const struct ggml_tensor * src4 = dst->src[4]; // B
    const struct ggml_tensor * src5 = dst->src[5]; // C
-    const struct ggml_tensor * src6 = dst->src[6]; // sq

    const int ith = params->ith;
    const int nth = params->nth;

-    const int64_t nc   = src0->ne[0]; // d_state
-    const int64_t nr   = src0->ne[1]; // d_inner
-    const int64_t n_t  = src1->ne[1]; // number of tokens in the batch
-    const int64_t n_kv = src0->ne[2]; // max number of sequences in the batch
+    const int64_t nc  = src0->ne[0]; // d_state
+    const int64_t nr  = src0->ne[1]; // d_inner
+    const int64_t n_t = src1->ne[1]; // number of tokens per sequence
+    const int64_t n_s = src0->ne[2]; // number of sequences in the batch

    GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst));
    GGML_ASSERT(src0->nb[0] == sizeof(float));
@@ -15927,12 +15875,12 @@ static void ggml_compute_forward_ssm_scan_f32(
    GGML_ASSERT(src3->nb[0] == sizeof(float));
    GGML_ASSERT(src4->nb[0] == sizeof(float));
    GGML_ASSERT(src5->nb[0] == sizeof(float));
-    // required for the dot product between s and C, and when copying the states
+    // required for the dot product between s and C
    GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
    // required for per-sequence offsets for states
    GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float));
-    // required to get correct offset for state destination (i.e. src1->nb[2])
-    GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*sizeof(float));
+    // required to get correct offset for state destination (i.e. src1->nb[3])
+    GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float));

    // rows per thread
    const int dr = (nr + nth - 1)/nth;
@@ -15942,64 +15890,36 @@ static void ggml_compute_forward_ssm_scan_f32(
    const int ir1 = MIN(ir0 + dr, nr);
    const int ir  = ir1 - ir0;

-    if (n_kv > 1) {
-        // it's hard to know if the source states have already been copied
-        // when there are multiple, so copy them already.
-        for (int i3 = 0; i3 < n_kv; ++i3) {
-            float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
-            float * s  = (float *) ((char *)  dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]);
-            memcpy(s, s0, nc*ir*sizeof(float));
-        }
-    }
+    for (int i3 = 0; i3 < n_s; ++i3) {
+        for (int i2 = 0; i2 < n_t; ++i2) {
+            const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s}
+            const float * x  = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
+            const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s}
+            const float * A  = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
+            const float * B  = (const float *) ((const char *) src4->data +  i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
+            const float * C  = (const float *) ((const char *) src5->data +  i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
+            float * y = (float *) ((char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
+            float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s}

-    for (int i2 = 0; i2 < n_t; ++i2) {
-        int32_t * sq = (int32_t *) ((char *) src6->data +  i2*(src6->nb[1])); // {n_kv, n_tokens}
-        float *   y  = (float *)   ((char *)  dst->data + ir0*(src1->nb[0]) +    i2*(src1->nb[1])); // {d_inner, n_tokens}
-        float *   s  = (float *)   ((char *)  dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv}
-        float *   s0;
-        float *   x  = (float *)   ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
-        float *   dt = (float *)   ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens}
-        float *   A  = (float *)   ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
-        float *   B  = (float *)   ((char *) src4->data +  i2*(src4->nb[1])); // {d_state, n_tokens}
-        float *   C  = (float *)   ((char *) src5->data +  i2*(src5->nb[1])); // {d_state, n_tokens}
+            // use the output as the source for the next token-wise iterations
+            if (i2 > 0) { s0 = s; }

-        GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
-
-        // avoid needing to copy the state for the first token
-        if (i2 == 0) {
-            s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv}
-        } else {
-            // otherwise the source is the same as the destination
-            s0 = s;
-        }
-
-        // d_inner
-        for (int i1 = 0; i1 < ir; ++i1) {
-            // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
-            float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
-            float x_dt = x[i1] * dt_soft_plus;
-            float sumf = 0.0f;
-            // d_state
-            for (int i0 = 0; i0 < nc; ++i0) {
-                int i = i0 + i1*nc;
-                // state = prev_state * dA + dB * x
-                float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
-                // y = rowwise_dotprod(state, C)
-                sumf += state * C[i0];
-                s[i] = state;
-            }
-            y[i1] = sumf;
-        }
-
-        // handle copies when there are multiple output states
-        for (int i3 = 1; i3 < n_kv; ++i3) {
-            int32_t seq = sq[i3];
-            if (0 <= seq && seq < n_kv) {
-                float * s1 = s + (seq - sq[0])*nc*nr;
-                memcpy(s1, s, nc*ir*sizeof(float));
-            } else {
-                // stop at negative or too big seq_ids
-                break;
+            // d_inner
+            for (int i1 = 0; i1 < ir; ++i1) {
+                // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
+                float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
+                float x_dt = x[i1] * dt_soft_plus;
+                float sumf = 0.0f;
+                // d_state
+                for (int i0 = 0; i0 < nc; ++i0) {
+                    int i = i0 + i1*nc;
+                    // state = prev_state * dA + dB * x
+                    float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
+                    // y = rowwise_dotprod(state, C)
+                    sumf += state * C[i0];
+                    s[i] = state;
+                }
+                y[i1] = sumf;
            }
        }
    }
--- a/include/llama.h
+++ b/include/llama.h
@@ -511,6 +511,9 @@ extern "C" {
    // to the decoder to start generating output sequence. For other models, it returns -1.
    LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);

+    // Returns true if the model is recurrent (like Mamba, RWKV, etc.)
+    LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model);
+
    // Returns 0 on success
    LLAMA_API uint32_t llama_model_quantize(
            const char * fname_inp,
--- a/src/llama-impl.h
+++ b/src/llama-impl.h
@@ -31,11 +31,17 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *

 static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
    if (search.empty()) {
-        return; // Avoid infinite loop if 'search' is an empty string
+        return;
    }
+    std::string builder;
+    builder.reserve(s.length());
    size_t pos = 0;
-    while ((pos = s.find(search, pos)) != std::string::npos) {
-        s.replace(pos, search.length(), replace);
-        pos += replace.length();
+    size_t last_pos = 0;
+    while ((pos = s.find(search, last_pos)) != std::string::npos) {
+        builder.append(s, last_pos, pos - last_pos);
+        builder.append(replace);
+        last_pos = pos + search.length();
    }
+    builder.append(s, last_pos, std::string::npos);
+    s = std::move(builder);
 }
--- a/src/llama.cpp
+++ b/src/llama.cpp
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1652,19 +1652,20 @@ struct test_flash_attn_ext : public test_case {
    const bool mask; // use mask

    const float max_bias; // ALiBi
+    const float logit_softcap; // Gemma 2

    const ggml_type type_KV;

    std::string vars() override {
-        return VARS_TO_STR7(hs, nh, kv, nb, mask, max_bias, type_KV);
+        return VARS_TO_STR8(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV);
    }

    double max_nmse_err() override {
        return 5e-4;
    }

-    test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
-        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {}
+    test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
+        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), type_KV(type_KV) {}

    ggml_tensor * build_graph(ggml_context * ctx) override {
        const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV));
@@ -1673,7 +1674,7 @@ struct test_flash_attn_ext : public test_case {
        ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
        ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
        ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr;
-        ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias);
+        ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias, logit_softcap);
        return out;
    }
 };
@@ -2437,11 +2438,14 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
        for (bool mask : { true, false } ) {
            for (float max_bias : { 0.0f, 8.0f }) {
                if (!mask && max_bias > 0.0f) continue;
-                for (int nh : { 32, }) {
-                    for (int kv : { 512, 1024, }) {
-                        for (int nb : { 1, 2, 4, 8, }) {
-                            for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
-                                test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, type_KV));
+                for (float logit_softcap : {0.0f, 10.0f}) {
+                    if (hs != 128 && logit_softcap != 0.0f) continue;
+                    for (int nh : { 32, }) {
+                        for (int kv : { 512, 1024, }) {
+                            for (int nb : { 1, 2, 4, 8, }) {
+                                for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
+                                    test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV));
+                                }
                            }
                        }
                    }
@@ -2483,7 +2487,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
    }

    GGML_ABORT("fatal error");
-    return false;
 }

 static void usage(char ** argv) {
--- a/tests/test-lora-conversion-inference.sh
+++ b/tests/test-lora-conversion-inference.sh
@@ -14,7 +14,7 @@ MODELS_REPO_URL=https://huggingface.co/ggml-org/$MODELS_REPO
 # Clone the Hugging Face repository if the directory does not exist
 if [ ! -d "$MODELS_REPO" ]; then
    echo "Cloning the Hugging Face repository..."
-    git clone $MODELS_REPO_URL
+    git clone $MODELS_REPO_URL --depth 1
 else
    echo "Repository already exists. Skipping clone."
 fi
Author	SHA1	Message	Date
Georgi Gerganov	a95225cdfd	metal : another fix for the fa kernel	2024-08-26 15:08:38 +03:00
Georgi Gerganov	7a3df798fc	ci : add VULKAN support to ggml-ci (#9055 )	2024-08-26 12:19:39 +03:00
Georgi Gerganov	e5edb210cd	server : update deps (#9183 )	2024-08-26 12:16:57 +03:00
slaren	0c41e03ceb	metal : gemma2 flash attention support (#9159 )	2024-08-26 11:08:59 +02:00
slaren	f12ceaca0c	ggml-ci : try to improve build time (#9160 )	2024-08-26 11:03:30 +02:00
Justine Tunney	436787f170	llama : fix time complexity of string replacement (#9163 ) This change fixes a bug where replacing text in a very long string could cause llama.cpp to hang indefinitely. This is because the algorithm used was quadratic, due to memmove() when s.replace() is called in a loop. It seems most search results and LLM responses actually provide the O(n**2) algorithm, which is a great tragedy. Using a builder string fixes things	2024-08-26 09:09:53 +03:00
Herman Semenov	93bc3839f9	common: fixed not working find argument --n-gpu-layers-draft (#9175 ) Some checks are pending Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full-cuda.Dockerfile platforms:linux/amd64 tag:full-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full.Dockerfile platforms:linux/amd64,linux/arm64 tag:full]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-cuda.Dockerfile platforms:linux/amd64 tag:light-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-intel.Dockerfile platforms:linux/amd64 tag:light-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:light-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli.Dockerfile platforms:linux/amd64,linux/arm64 tag:light]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-cuda.Dockerfile platforms:linux/amd64 tag:server-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-intel.Dockerfile platforms:linux/amd64 tag:server-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:server-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server.Dockerfile platforms:linux/amd64,linux/arm64 tag:server]) (push) Waiting to run Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run	2024-08-26 00:54:37 +02:00
Johannes Gäßler	f91fc5639b	CUDA: fix Gemma 2 numerical issues for FA (#9166 )	2024-08-25 22:11:48 +02:00
Johannes Gäßler	e11bd856d5	CPU/CUDA: Gemma 2 FlashAttention support (#8542 ) Some checks failed Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full-cuda.Dockerfile platforms:linux/amd64 tag:full-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full.Dockerfile platforms:linux/amd64,linux/arm64 tag:full]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-cuda.Dockerfile platforms:linux/amd64 tag:light-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-intel.Dockerfile platforms:linux/amd64 tag:light-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:light-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli.Dockerfile platforms:linux/amd64,linux/arm64 tag:light]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-cuda.Dockerfile platforms:linux/amd64 tag:server-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-intel.Dockerfile platforms:linux/amd64 tag:server-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:server-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server.Dockerfile platforms:linux/amd64,linux/arm64 tag:server]) (push) Waiting to run Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run Nix aarch64 builds / nix-build-aarch64 (push) Has been cancelled * CPU/CUDA: Gemma 2 FlashAttention support * apply logit_softcap to scale in kernel * disable logit softcapping tests on Metal * remove metal check	2024-08-24 21:34:59 +02:00
João Dinis Ferreira	8f824ffe8e	quantize : fix typo in usage help of `quantize.cpp` (#9145 ) Some checks failed Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full-cuda.Dockerfile platforms:linux/amd64 tag:full-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full.Dockerfile platforms:linux/amd64,linux/arm64 tag:full]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-cuda.Dockerfile platforms:linux/amd64 tag:light-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-intel.Dockerfile platforms:linux/amd64 tag:light-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:light-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli.Dockerfile platforms:linux/amd64,linux/arm64 tag:light]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-cuda.Dockerfile platforms:linux/amd64 tag:server-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-intel.Dockerfile platforms:linux/amd64 tag:server-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:server-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server.Dockerfile platforms:linux/amd64,linux/arm64 tag:server]) (push) Waiting to run Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run update-flake-lock / lockfile (push) Has been cancelled	2024-08-24 09:22:45 +03:00
Xuan Son Nguyen	3ba780e2a8	lora : fix llama conversion script with ROPE_FREQS (#9117 ) Some checks failed Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run Python check requirements.txt / check-requirements (push) Has been cancelled Python Type-Check / pyright type-check (push) Has been cancelled	2024-08-23 12:58:53 +02:00
piDack	a07c32ea54	llama : use F32 precision in GLM4 attention and no FA (#9130 ) Some checks are pending Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full-cuda.Dockerfile platforms:linux/amd64 tag:full-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full.Dockerfile platforms:linux/amd64,linux/arm64 tag:full]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-cuda.Dockerfile platforms:linux/amd64 tag:light-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-intel.Dockerfile platforms:linux/amd64 tag:light-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:light-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli.Dockerfile platforms:linux/amd64,linux/arm64 tag:light]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-cuda.Dockerfile platforms:linux/amd64 tag:server-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-intel.Dockerfile platforms:linux/amd64 tag:server-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:server-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server.Dockerfile platforms:linux/amd64,linux/arm64 tag:server]) (push) Waiting to run Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run	2024-08-23 10:27:17 +03:00
Akarshan Biswas	11b84eb457	[SYCL] Add a space to supress a cmake warning (#9133 ) Some checks are pending Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full-cuda.Dockerfile platforms:linux/amd64 tag:full-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full.Dockerfile platforms:linux/amd64,linux/arm64 tag:full]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-cuda.Dockerfile platforms:linux/amd64 tag:light-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-intel.Dockerfile platforms:linux/amd64 tag:light-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:light-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli.Dockerfile platforms:linux/amd64,linux/arm64 tag:light]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-cuda.Dockerfile platforms:linux/amd64 tag:server-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-intel.Dockerfile platforms:linux/amd64 tag:server-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:server-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server.Dockerfile platforms:linux/amd64,linux/arm64 tag:server]) (push) Waiting to run Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run	2024-08-22 22:09:47 +08:00
luoyu-intel	1731d4238f	[SYCL] Add oneDNN primitive support (#9091 ) Some checks are pending Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full-cuda.Dockerfile platforms:linux/amd64 tag:full-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full.Dockerfile platforms:linux/amd64,linux/arm64 tag:full]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-cuda.Dockerfile platforms:linux/amd64 tag:light-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-intel.Dockerfile platforms:linux/amd64 tag:light-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:light-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli.Dockerfile platforms:linux/amd64,linux/arm64 tag:light]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-cuda.Dockerfile platforms:linux/amd64 tag:server-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-intel.Dockerfile platforms:linux/amd64 tag:server-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:server-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server.Dockerfile platforms:linux/amd64,linux/arm64 tag:server]) (push) Waiting to run Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run * add onednn * add sycl_f16 * add dnnl stream * add engine map * use dnnl for intel only * use fp16fp16fp16 * update doc	2024-08-22 12:50:10 +08:00
compilade	a1631e53f6	llama : simplify Mamba with advanced batch splits (#8526 ) Some checks are pending Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full-cuda.Dockerfile platforms:linux/amd64 tag:full-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/full.Dockerfile platforms:linux/amd64,linux/arm64 tag:full]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-cuda.Dockerfile platforms:linux/amd64 tag:light-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-intel.Dockerfile platforms:linux/amd64 tag:light-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:light-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-cli.Dockerfile platforms:linux/amd64,linux/arm64 tag:light]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-cuda.Dockerfile platforms:linux/amd64 tag:server-cuda]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-intel.Dockerfile platforms:linux/amd64 tag:server-intel]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server-rocm.Dockerfile platforms:linux/amd64,linux/arm64 tag:server-rocm]) (push) Waiting to run Publish Docker image / Push Docker image to Docker Hub (map[dockerfile:.devops/llama-server.Dockerfile platforms:linux/amd64,linux/arm64 tag:server]) (push) Waiting to run Nix CI / nix-eval (macos-latest) (push) Waiting to run Nix CI / nix-eval (ubuntu-latest) (push) Waiting to run Nix CI / nix-build (macos-latest) (push) Waiting to run Nix CI / nix-build (ubuntu-latest) (push) Waiting to run flake8 Lint / Lint (push) Waiting to run * llama : advanced batch splits This includes equal-sequence-length batch splits which are useful to simplify recurrent model operators. * llama : always make recurrent state slots contiguous * ggml : simplify mamba operators * llama : fix integer signedness mixing * llama : logits_all has priority over batch->logits Otherwise, the server embeddings tests failed. This was likely an existing problem but was only detected here because of an additional assertion. * llama : apply suggestions Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * llama : fix t5 segfault * llama : fix Mamba session save and restore * llama : minor cosmetic changes * llama : rename llama_reorder_outputs to llama_output_reorder Also move it closer to llama_output_reserve. * llama : fix pooled embeddings when using batches with equal_seqs * minor : add struct members for clarity ggml-ci * llama : fix T5 segfault again * llama : fix Mamba pooled embeddings with multiple sequences Until the pooled embeddings are refactored to allow splitting across ubatches for causal embeddings, recurrent models can only process a single sequence per ubatch when calculating pooled embeddings. * llama : add llama_model_is_recurrent to simplify figuring that out This will make it easier to more cleanly support RWKV-v6 and Mamba-2. * llama : fix simple splits when the batch contains embeddings --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2024-08-21 17:58:11 -04:00