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29 files changed, 11509 insertions, 0 deletions

diff --git a/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt b/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt
new file mode 100644
index 0000000..3ccce58
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt
@@ -0,0 +1,80 @@
+cmake_minimum_required(VERSION 3.13)
+
+find_package(Python3 REQUIRED)
+
+# Shader locations
+set(SHADER_DIR "${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders")
+set(SHADER_OUTPUT_DIR "${CMAKE_CURRENT_BINARY_DIR}/generated")
+set(SHADER_HEADER "${SHADER_OUTPUT_DIR}/ggml-wgsl-shaders.hpp")
+file(MAKE_DIRECTORY ${SHADER_OUTPUT_DIR})
+
+message(STATUS "Shader output dir: ${SHADER_OUTPUT_DIR}")
+
+# Find all WGSL files
+file(GLOB WGSL_SHADER_FILES "${SHADER_DIR}/*.wgsl")
+
+# Generate the header using a Python script
+add_custom_command(
+    OUTPUT ${SHADER_HEADER}
+    COMMAND ${CMAKE_COMMAND} -E echo "Embedding WGSL shaders to ggml-wgsl-shaders.hpp"
+    COMMAND ${CMAKE_COMMAND} -E make_directory ${SHADER_OUTPUT_DIR}
+    COMMAND ${CMAKE_COMMAND} -E env PYTHONIOENCODING=utf-8
+        ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders/embed_wgsl.py
+            --input_dir "${SHADER_DIR}"
+            --output_file "${SHADER_HEADER}"
+    DEPENDS ${WGSL_SHADER_FILES} ${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders/embed_wgsl.py
+    VERBATIM
+)
+
+add_custom_target(generate_shaders DEPENDS ${SHADER_HEADER})
+
+ggml_add_backend_library(ggml-webgpu
+    ggml-webgpu.cpp
+    ${SHADER_HEADER}
+    ../../include/ggml-webgpu.h
+)
+
+add_dependencies(ggml-webgpu generate_shaders)
+
+if(EMSCRIPTEN)
+    set(EMDAWNWEBGPU_DIR "" CACHE PATH "Path to emdawnwebgpu_pkg")
+
+    if(NOT EMDAWNWEBGPU_DIR)
+        # default built-in port
+        target_compile_options(ggml-webgpu PRIVATE "--use-port=emdawnwebgpu")
+        target_link_options(ggml-webgpu INTERFACE "--use-port=emdawnwebgpu")
+    else()
+        # custom port
+        target_compile_options(ggml-webgpu PRIVATE "--use-port=${EMDAWNWEBGPU_DIR}/emdawnwebgpu.port.py")
+        target_link_options(ggml-webgpu INTERFACE "--use-port=${EMDAWNWEBGPU_DIR}/emdawnwebgpu.port.py")
+    endif()
+
+    if (GGML_WEBGPU_JSPI)
+        target_compile_options(ggml-webgpu PRIVATE "-fwasm-exceptions")
+        target_link_options(ggml-webgpu INTERFACE "-sJSPI" "-fwasm-exceptions")
+    else()
+        target_compile_options(ggml-webgpu PRIVATE "-fexceptions")
+        target_link_options(ggml-webgpu INTERFACE "-sASYNCIFY" "-exceptions")
+    endif()
+else()
+    find_package(Dawn REQUIRED)
+    set(DawnWebGPU_TARGET dawn::webgpu_dawn)
+endif()
+
+if (GGML_WEBGPU_DEBUG)
+    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_DEBUG=1)
+    if(EMSCRIPTEN)
+        target_link_options(ggml-webgpu INTERFACE "-sASSERTIONS=2")
+    endif()
+endif()
+
+if (GGML_WEBGPU_CPU_PROFILE)
+    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_CPU_PROFILE=1)
+endif()
+
+if (GGML_WEBGPU_GPU_PROFILE)
+    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_GPU_PROFILE=1)
+endif()
+
+target_include_directories(ggml-webgpu PRIVATE ${SHADER_OUTPUT_DIR})
+target_link_libraries(ggml-webgpu PRIVATE ${DawnWebGPU_TARGET})
diff --git a/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
new file mode 100644
index 0000000..63f797f
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
@@ -0,0 +1,538 @@
+#ifndef GGML_WEBGPU_SHADER_LIB_HPP
+#define GGML_WEBGPU_SHADER_LIB_HPP
+
+#include "ggml.h"
+#include "pre_wgsl.hpp"
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#define GGML_WEBGPU_F16_SIZE_BYTES                   2
+#define GGML_WEBGPU_F32_SIZE_BYTES                   4
+#define GGML_WEBGPU_I32_SIZE_BYTES                   4
+#define GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES 8u
+#define GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE     128u
+// Matches GGML_PAD(..., 256) in src/llama-context.cpp for KV cache sizing.
+#define GGML_WEBGPU_KV_SEQ_PAD                       256u
+
+#define GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE 512u
+
+struct ggml_webgpu_processed_shader {
+    std::string           wgsl;
+    std::string           variant;
+    std::shared_ptr<void> decisions;
+};
+
+// Same hash combine function as in boost
+template <typename T> inline void ggml_webgpu_hash_combine(size_t & seed, const T & value) {
+    seed ^= std::hash<T>{}(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
+}
+
+/** FlashAttention */
+
+struct ggml_webgpu_flash_attn_pipeline_key {
+    ggml_type kv_type;
+    uint32_t  head_dim_qk;
+    uint32_t  head_dim_v;
+    bool      kv_direct;
+    bool      has_mask;
+    bool      has_sinks;
+    bool      uses_logit_softcap;
+
+    bool operator==(const ggml_webgpu_flash_attn_pipeline_key & other) const {
+        return kv_type == other.kv_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v &&
+               kv_direct == other.kv_direct && has_mask == other.has_mask && has_sinks == other.has_sinks &&
+               uses_logit_softcap == other.uses_logit_softcap;
+    }
+};
+
+struct ggml_webgpu_flash_attn_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_flash_attn_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.kv_type);
+        ggml_webgpu_hash_combine(seed, key.head_dim_qk);
+        ggml_webgpu_hash_combine(seed, key.head_dim_v);
+        ggml_webgpu_hash_combine(seed, key.kv_direct);
+        ggml_webgpu_hash_combine(seed, key.has_mask);
+        ggml_webgpu_hash_combine(seed, key.has_sinks);
+        ggml_webgpu_hash_combine(seed, key.uses_logit_softcap);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_flash_attn_shader_lib_context {
+    ggml_webgpu_flash_attn_pipeline_key key;
+    uint32_t                            sg_mat_m;
+    uint32_t                            sg_mat_n;
+    uint32_t                            sg_mat_k;
+    size_t                              wg_mem_limit_bytes;
+    uint32_t                            max_subgroup_size;
+};
+
+struct ggml_webgpu_flash_attn_shader_decisions {
+    uint32_t q_tile  = 0;
+    uint32_t kv_tile = 0;
+    uint32_t wg_size = 0;
+};
+
+// This is exposed because it's necessary in supports_op
+inline size_t ggml_webgpu_flash_attn_wg_mem_bytes(uint32_t q_tile,
+                                                  uint32_t kv_tile,
+                                                  uint32_t head_dim_qk,
+                                                  uint32_t head_dim_v,
+                                                  bool     has_mask,
+                                                  bool     kv_direct) {
+    const uint32_t max_head_dim = std::max(head_dim_qk, head_dim_v);
+    size_t         f16_elems    = 0;
+    size_t         f32_elems    = 0;
+    f16_elems += q_tile * head_dim_qk;        // q_shmem
+    if (!kv_direct) {
+        f16_elems += kv_tile * max_head_dim;  // kv_shmem
+    }
+    f16_elems += q_tile * head_dim_v;         // o_shmem
+    if (has_mask) {
+        f16_elems += q_tile * kv_tile;        // mask_shmem
+    }
+    f16_elems += q_tile * kv_tile;            // inter_shmem
+    f32_elems += q_tile;                      // row_max_shmem
+    f32_elems += q_tile;                      // exp_sum_shmem
+    return f16_elems * GGML_WEBGPU_F16_SIZE_BYTES + f32_elems * GGML_WEBGPU_F32_SIZE_BYTES;
+}
+
+static uint32_t ggml_webgpu_flash_attn_max_kv_tile(const ggml_webgpu_flash_attn_shader_lib_context & context) {
+    const size_t limit_bytes = context.wg_mem_limit_bytes;
+    const size_t q_tile      = context.sg_mat_m;
+    const size_t base_q_bytes =
+        (context.key.head_dim_qk + context.key.head_dim_v) * q_tile * GGML_WEBGPU_F16_SIZE_BYTES +
+        2 * q_tile * GGML_WEBGPU_F32_SIZE_BYTES;
+    size_t bytes_per_kv = 0;
+    if (!context.key.kv_direct) {
+        bytes_per_kv += std::max(context.key.head_dim_qk, context.key.head_dim_v);
+    }
+    if (context.key.has_mask) {
+        bytes_per_kv += q_tile;
+    }
+    bytes_per_kv += q_tile;
+    bytes_per_kv *= GGML_WEBGPU_F16_SIZE_BYTES;
+    const uint32_t max_kv_tile = (limit_bytes - base_q_bytes) / bytes_per_kv;
+    return (max_kv_tile / context.sg_mat_n) * context.sg_mat_n;
+}
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_flash_attn_shader(
+    pre_wgsl::Preprocessor &                          preprocessor,
+    const char *                                      shader_src,
+    const ggml_webgpu_flash_attn_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              variant = "flash_attn";
+
+    switch (context.key.kv_type) {
+        case GGML_TYPE_F32:
+            defines.push_back("KV_F32");
+            break;
+        case GGML_TYPE_F16:
+            defines.push_back("KV_F16");
+            break;
+        case GGML_TYPE_Q4_0:
+            defines.push_back("KV_Q4_0");
+            break;
+        case GGML_TYPE_Q8_0:
+            defines.push_back("KV_Q8_0");
+            break;
+        default:
+            GGML_ABORT("Unsupported KV type for flash attention shader");
+    }
+    variant += std::string("_") + ggml_type_name(context.key.kv_type);
+
+    if (context.key.has_mask) {
+        defines.push_back("MASK");
+        variant += "_mask";
+    }
+    if (context.key.has_sinks) {
+        defines.push_back("SINKS");
+        variant += "_sinks";
+    }
+    if (context.key.uses_logit_softcap) {
+        defines.push_back("LOGIT_SOFTCAP");
+        variant += "_lgsc";
+    }
+
+    if (context.key.kv_direct) {
+        defines.push_back("KV_DIRECT");
+        variant += "_kvdirect";
+    }
+
+    defines.push_back(std::string("HEAD_DIM_QK=") + std::to_string(context.key.head_dim_qk));
+    variant += std::string("_hsqk") + std::to_string(context.key.head_dim_qk);
+
+    defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(context.key.head_dim_v));
+    variant += std::string("_hsv") + std::to_string(context.key.head_dim_v);
+    // For now these are not part of the variant name
+    defines.push_back(std::string("SG_MAT_M=") + std::to_string(context.sg_mat_m));
+    defines.push_back(std::string("SG_MAT_N=") + std::to_string(context.sg_mat_n));
+    defines.push_back(std::string("SG_MAT_K=") + std::to_string(context.sg_mat_k));
+
+    // Add chosen Q/KV tile sizes
+    uint32_t q_tile  = context.sg_mat_m;
+    uint32_t kv_tile = std::min(ggml_webgpu_flash_attn_max_kv_tile(context),
+                                context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES);
+    if (context.key.kv_direct) {
+        GGML_ASSERT(kv_tile <= GGML_WEBGPU_KV_SEQ_PAD);
+        // Avoids having to use bounds-checks and decreasing performance for direct KV loads
+        while (GGML_WEBGPU_KV_SEQ_PAD % kv_tile != 0) {
+            kv_tile -= context.sg_mat_n;
+        }
+    }
+
+    defines.push_back(std::string("Q_TILE=") + std::to_string(q_tile));
+    defines.push_back(std::string("KV_TILE=") + std::to_string(kv_tile));
+
+    // workgroup size
+    uint32_t wg_size = std::max(context.max_subgroup_size, GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE);
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
+
+    ggml_webgpu_processed_shader result;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_flash_attn_shader_decisions>();
+    decisions->q_tile  = q_tile;
+    decisions->kv_tile = kv_tile;
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
+    return result;
+}
+
+/** Generic **/
+
+struct ggml_webgpu_generic_shader_lib_context {
+    int      vec4;
+    uint32_t max_wg_size;
+};
+
+struct ggml_webgpu_generic_shader_decisions {
+    uint32_t wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_generic_shader(
+    pre_wgsl::Preprocessor &                       preprocessor,
+    const char *                                   shader_src,
+    const ggml_webgpu_generic_shader_lib_context & context,
+    const std::string &                            base_variant) {
+    std::vector<std::string> defines;
+    std::string              variant = base_variant;
+
+    if (context.vec4) {
+        defines.push_back("VEC4");
+        variant += "_vec";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+    ggml_webgpu_processed_shader result;
+    result.wgsl    = preprocessor.preprocess(shader_src, defines);
+    result.variant = variant;
+    return result;
+}
+
+/** Pad **/
+
+struct ggml_webgpu_pad_pipeline_key {
+    bool circular;
+
+    bool operator==(const ggml_webgpu_pad_pipeline_key & other) const { return circular == other.circular; }
+};
+
+struct ggml_webgpu_pad_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_pad_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.circular);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_pad_shader_lib_context {
+    ggml_webgpu_pad_pipeline_key key;
+    uint32_t                     max_wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_pad_shader(
+    pre_wgsl::Preprocessor &                   preprocessor,
+    const char *                               shader_src,
+    const ggml_webgpu_pad_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              variant = "pad";
+
+    if (context.key.circular) {
+        defines.push_back("CIRCULAR");
+        variant += "_circular";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+    ggml_webgpu_processed_shader result;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
+    return result;
+}
+
+/** Argsort **/
+
+struct ggml_webgpu_argsort_shader_lib_context {
+    uint32_t max_wg_size;
+    size_t   wg_mem_limit_bytes;
+    int32_t  order;
+};
+
+struct ggml_webgpu_argsort_shader_decisions {
+    uint32_t wg_size = 0;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_argsort_shader(
+    pre_wgsl::Preprocessor &                       preprocessor,
+    const char *                                   shader_src,
+    const ggml_webgpu_argsort_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              variant = "argsort";
+    defines.push_back(std::string("ORDER=") + std::to_string(context.order));
+    variant += std::string("_order") + std::to_string(context.order);
+    uint32_t wg_size = 1;
+    while (wg_size * 2 <= context.max_wg_size &&
+           wg_size * GGML_WEBGPU_I32_SIZE_BYTES <= context.wg_mem_limit_bytes / 2) {
+        wg_size *= 2;
+    }
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
+    ggml_webgpu_processed_shader result;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_argsort_shader_decisions>();
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
+    return result;
+}
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_argsort_merge_shader(
+    pre_wgsl::Preprocessor &                       preprocessor,
+    const char *                                   shader_src,
+    const ggml_webgpu_argsort_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              variant = "argsort_merge";
+    defines.push_back(std::string("ORDER=") + std::to_string(context.order));
+    variant += std::string("_order") + std::to_string(context.order);
+    uint32_t wg_size = std::min(GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE, context.max_wg_size);
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
+    ggml_webgpu_processed_shader result;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_argsort_shader_decisions>();
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
+    return result;
+}
+
+/** Set Rows **/
+
+struct ggml_webgpu_set_rows_pipeline_key {
+    int dst_type;
+    int vec4;
+    int i64_idx;
+
+    bool operator==(const ggml_webgpu_set_rows_pipeline_key & other) const {
+        return dst_type == other.dst_type && vec4 == other.vec4 && i64_idx == other.i64_idx;
+    }
+};
+
+struct ggml_webgpu_set_rows_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_set_rows_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.dst_type);
+        ggml_webgpu_hash_combine(seed, key.vec4);
+        ggml_webgpu_hash_combine(seed, key.i64_idx);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_set_rows_shader_lib_context {
+    ggml_webgpu_set_rows_pipeline_key key;
+    uint32_t                          max_wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_set_rows_shader(
+    pre_wgsl::Preprocessor &                        preprocessor,
+    const char *                                    shader_src,
+    const ggml_webgpu_set_rows_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              variant = "set_rows";
+
+    switch (context.key.dst_type) {
+        case GGML_TYPE_F32:
+            defines.push_back("DST_F32");
+            variant += "_dstf32";
+            break;
+        case GGML_TYPE_F16:
+            defines.push_back("DST_F16");
+            variant += "_dstf16";
+            break;
+        default:
+            GGML_ABORT("Unsupported dst type for set_rows shader");
+    }
+
+    if (context.key.vec4) {
+        defines.push_back("VEC4");
+        variant += "_vec";
+    }
+    if (context.key.i64_idx) {
+        defines.push_back("I64_IDX");
+        variant += "_i64idx";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+    ggml_webgpu_processed_shader result;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
+    return result;
+}
+
+struct ggml_webgpu_unary_pipeline_key {
+    int  type;
+    int  op;
+    bool is_unary;  // many unary operators fall under the GGML_OP_UNARY umbrella
+    bool inplace;
+
+    bool operator==(const ggml_webgpu_unary_pipeline_key & other) const {
+        return type == other.type && op == other.op && is_unary == other.is_unary && inplace == other.inplace;
+    }
+};
+
+struct ggml_webgpu_unary_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_unary_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.type);
+        ggml_webgpu_hash_combine(seed, key.op);
+        ggml_webgpu_hash_combine(seed, key.is_unary);
+        ggml_webgpu_hash_combine(seed, key.inplace);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_unary_shader_lib_context {
+    ggml_webgpu_unary_pipeline_key key;
+    uint32_t                       max_wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_unary_shader(
+    pre_wgsl::Preprocessor &                     preprocessor,
+    const char *                                 shader_src,
+    const ggml_webgpu_unary_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              variant = context.key.is_unary ? ggml_unary_op_name((ggml_unary_op) context.key.op) :
+                                                              ggml_op_name((ggml_op) context.key.op);
+    // Operation-specific behavior
+    defines.push_back(variant);
+
+    switch (context.key.type) {
+        case GGML_TYPE_F32:
+            defines.push_back("TYPE_F32");
+            variant += "_f32";
+            break;
+        case GGML_TYPE_F16:
+            defines.push_back("TYPE_F16");
+            variant += "_f16";
+            break;
+        default:
+            GGML_ABORT("Unsupported type for unary shader");
+    }
+
+    if (context.key.inplace) {
+        defines.push_back("INPLACE");
+        variant += "_inplace";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+    ggml_webgpu_processed_shader result;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
+    return result;
+}
+
+/** Binary **/
+
+struct ggml_webgpu_binary_pipeline_key {
+    int  type;
+    int  op;
+    bool inplace;
+    bool overlap;
+
+    bool operator==(const ggml_webgpu_binary_pipeline_key & other) const {
+        return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap;
+    }
+};
+
+struct ggml_webgpu_binary_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_binary_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.type);
+        ggml_webgpu_hash_combine(seed, key.op);
+        ggml_webgpu_hash_combine(seed, key.inplace);
+        ggml_webgpu_hash_combine(seed, key.overlap);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_binary_shader_lib_context {
+    ggml_webgpu_binary_pipeline_key key;
+    uint32_t                        max_wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_binary_shader(
+    pre_wgsl::Preprocessor &                      preprocessor,
+    const char *                                  shader_src,
+    const ggml_webgpu_binary_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              op_name = ggml_op_name((ggml_op) context.key.op);
+    std::string              variant = op_name;
+
+    defines.push_back(std::string("OP_") + op_name);
+
+    switch (context.key.type) {
+        case GGML_TYPE_F32:
+            defines.push_back("TYPE_F32");
+            variant += "_f32";
+            break;
+        case GGML_TYPE_F16:
+            defines.push_back("TYPE_F16");
+            variant += "_f16";
+            break;
+        default:
+            GGML_ABORT("Unsupported type for binary shader");
+    }
+
+    if (context.key.inplace) {
+        defines.push_back("INPLACE");
+        variant += "_inplace";
+    } else if (context.key.overlap) {
+        defines.push_back("OVERLAP");
+        variant += "_overlap";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+    ggml_webgpu_processed_shader result;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
+    return result;
+}
+#endif  // GGML_WEBGPU_SHADER_LIB_HPP
diff --git a/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp
new file mode 100644
index 0000000..32e1202
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -0,0 +1,3469 @@
+/*
+    WebGPU backend implementation.
+    Note: Use ClangFormat to format this file.
+*/
+
+#include "ggml-webgpu.h"
+
+#include "ggml-backend-impl.h"
+#include "ggml-impl.h"
+#include "ggml-webgpu-shader-lib.hpp"
+#include "ggml-wgsl-shaders.hpp"
+#include "pre_wgsl.hpp"
+
+#ifdef __EMSCRIPTEN__
+#    include <emscripten/emscripten.h>
+#endif
+
+#include <webgpu/webgpu_cpp.h>
+
+#include <atomic>
+#include <condition_variable>
+#include <cstdint>
+#include <cstring>
+#include <iostream>
+#include <map>
+#include <mutex>
+#include <optional>
+#include <string>
+#include <vector>
+
+#define ROUNDUP_POW2(x, pow2) (((x) + ((pow2) - 1)) & ~((pow2) - 1))
+#define CEIL_DIV(M, N)        (((M) + (N) - 1) / (N))
+
+#ifdef GGML_WEBGPU_DEBUG
+#    define WEBGPU_LOG_DEBUG(msg)  std::cout << msg << std::endl
+#    define WEBGPU_DEBUG_BUF_ELEMS 512
+#else
+#    define WEBGPU_LOG_DEBUG(msg) ((void) 0)
+#endif  // GGML_WEBGPU_DEBUG
+
+#ifdef GGML_WEBGPU_CPU_PROFILE
+// total timing (aggregated)
+#    define WEBGPU_CPU_PROFILE_TOTAL_START(id) auto cpu_total_start_##id = std::chrono::high_resolution_clock::now();
+
+#    define WEBGPU_CPU_PROFILE_TOTAL_END(id, ctx)                                                         \
+        auto   cpu_total_end_##id = std::chrono::high_resolution_clock::now();                            \
+        double cpu_total_time_##id =                                                                      \
+            std::chrono::duration<double, std::milli>(cpu_total_end_##id - cpu_total_start_##id).count(); \
+        (ctx)->cpu_time_ms[#id] += cpu_total_time_##id;
+// fine-grained timing (not included in totals)
+#    define WEBGPU_CPU_PROFILE_DETAIL_START(id) auto cpu_detail_start_##id = std::chrono::high_resolution_clock::now();
+
+#    define WEBGPU_CPU_PROFILE_DETAIL_END(id, ctx)                                                          \
+        auto   cpu_detail_end_##id = std::chrono::high_resolution_clock::now();                             \
+        double cpu_detail_time_##id =                                                                       \
+            std::chrono::duration<double, std::milli>(cpu_detail_end_##id - cpu_detail_start_##id).count(); \
+        (ctx)->cpu_detail_ms[#id] += cpu_detail_time_##id;
+#else
+#    define WEBGPU_CPU_PROFILE_TOTAL_START(id)
+#    define WEBGPU_CPU_PROFILE_TOTAL_END(id, ctx)
+#    define WEBGPU_CPU_PROFILE_DETAIL_START(id)
+#    define WEBGPU_CPU_PROFILE_DETAIL_END(id, ctx)
+#endif  // GGML_WEBGPU_CPU_PROFILE
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+#    define WEBGPU_NUM_TIMESTAMP_QUERY_BUFS       24
+#    define WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES 16  // e.g. enough for two timestamps
+#endif
+
+/* Constants */
+
+// Track https://github.com/gpuweb/gpuweb/issues/5315 for fixes to implementations so this can be removed.
+#define WEBGPU_MAX_WG_SIZE 288
+
+#define WEBGPU_MUL_MAT_WG_SIZE               256
+#define WEBGPU_NUM_PARAM_BUFS                16u
+#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE     8u
+#define WEBGPU_WAIT_ANY_TIMEOUT_MS           0
+// Maximum number of in-flight submissions per-thread, to avoid exhausting the parameter buffer pool
+#define WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD  WEBGPU_NUM_PARAM_BUFS / WEBGPU_COMMAND_SUBMIT_BATCH_SIZE
+#define WEBGPU_PARAMS_BUF_SIZE_BYTES         128  // enough for 32 parameters
+#define WEBGPU_NUM_SET_ROWS_ERROR_BUFS       16
+#define WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES 4
+#define WEBGPU_STORAGE_BUF_BINDING_MULT      4  // a storage buffer binding size must be a multiple of 4
+
+// For operations which process a row in parallel, this seems like a reasonable default
+#define WEBGPU_ROW_SPLIT_WG_SIZE 64
+
+// Matrix multiplication parameters
+
+// Register tiling parameters
+#define WEBGPU_MUL_MAT_TILE_M    8
+#define WEBGPU_MUL_MAT_TILE_N    8
+#define WEBGPU_MUL_MAT_WG_SIZE_M 8
+#define WEBGPU_MUL_MAT_WG_SIZE_N 8
+#define WEBGPU_MUL_MAT_TILE_K    32
+
+// Subgroup matrix parameters
+// The number of subgroups in the M dimension
+#define WEBGPU_MUL_MAT_SUBGROUP_M        2
+// The number of subgroups in the N dimension
+#define WEBGPU_MUL_MAT_SUBGROUP_N        2
+// The number of subgroup matrices each subgroup accumulates over
+#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M 4
+#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N 2
+
+// Matrix-vector multiplication parameters
+#define WEBGPU_MUL_MAT_VEC_WG_SIZE        256
+// Must be multiple of 4 to work with vectorized paths, and must divide mul_mat_vec wg size
+#define WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG 64
+#define WEBGPU_MUL_MAT_VEC_TILE_K         256
+
+/* End Constants */
+
+// This is a "fake" base pointer, since WebGPU buffers do not have pointers to their locations.
+static void * const webgpu_ptr_base = (void *) (uintptr_t) 0x1000;  // NOLINT
+
+// Always returns the base offset of a tensor, regardless of views.
+static uint64_t webgpu_tensor_offset(const ggml_tensor * tensor) {
+    if (tensor->view_src) {
+        return (uint8_t *) tensor->view_src->data - (uint8_t *) webgpu_ptr_base;
+    }
+    return (uint8_t *) tensor->data - (uint8_t *) webgpu_ptr_base;
+}
+
+/* Struct definitions */
+
+// Forward reference
+static void ggml_webgpu_create_buffer(wgpu::Device &    device,
+                                      wgpu::Buffer &    buffer,
+                                      size_t            size,
+                                      wgpu::BufferUsage usage,
+                                      const char *      label);
+
+struct webgpu_pool_bufs {
+    wgpu::Buffer host_buf;
+    wgpu::Buffer dev_buf;
+};
+
+// The futures to wait on for a single queue submission
+struct webgpu_submission_futures {
+    std::vector<wgpu::FutureWaitInfo> futures;
+};
+
+// Holds a pool of parameter buffers for WebGPU operations
+struct webgpu_buf_pool {
+    std::vector<webgpu_pool_bufs> free;
+
+    // The pool must be synchronized because
+    // 1. The memset pool is shared globally by every ggml buffer,
+    // since allocating a pool per ggml buffer would consume too much memory.
+    // 2. For the per-thread buffer pools in webgpu_context,
+    // buffers are allocated and freed in Dawn callbacks,
+    // which can run on a different thread than the calling thread.
+    std::mutex              mutex;
+    std::condition_variable cv;
+
+    void init(wgpu::Device      device,
+              int               num_bufs,
+              size_t            buf_size,
+              wgpu::BufferUsage dev_buf_usage,
+              wgpu::BufferUsage host_buf_usage) {
+        for (int i = 0; i < num_bufs; i++) {
+            wgpu::Buffer host_buf;
+            wgpu::Buffer dev_buf;
+            ggml_webgpu_create_buffer(device, host_buf, buf_size, host_buf_usage, "ggml_webgpu_host_pool_buf");
+            ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf");
+            free.push_back({ host_buf, dev_buf });
+        }
+    }
+
+    webgpu_pool_bufs alloc_bufs() {
+        std::unique_lock<std::mutex> lock(mutex);
+        cv.wait(lock, [this] { return !free.empty(); });
+        webgpu_pool_bufs bufs = free.back();
+        free.pop_back();
+        return bufs;
+    }
+
+    void free_bufs(std::vector<webgpu_pool_bufs> bufs) {
+        std::lock_guard<std::mutex> lock(mutex);
+        free.insert(free.end(), bufs.begin(), bufs.end());
+        cv.notify_all();
+    }
+
+    void cleanup() {
+        std::lock_guard<std::mutex> lock(mutex);
+        for (auto & bufs : free) {
+            if (bufs.host_buf) {
+                bufs.host_buf.Destroy();
+            }
+            if (bufs.dev_buf) {
+                bufs.dev_buf.Destroy();
+            }
+        }
+        free.clear();
+    }
+
+    ~webgpu_buf_pool() { this->cleanup(); }
+};
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+struct webgpu_gpu_profile_bufs {
+    wgpu::Buffer   host_buf;
+    wgpu::Buffer   dev_buf;
+    wgpu::QuerySet query_set;
+};
+
+// Holds a pool of parameter buffers for WebGPU operations
+struct webgpu_gpu_profile_buf_pool {
+    std::vector<webgpu_gpu_profile_bufs> free;
+
+    std::mutex mutex;
+
+    std::condition_variable cv;
+
+    void init(wgpu::Device      device,
+              int               num_bufs,
+              size_t            buf_size,
+              wgpu::BufferUsage dev_buf_usage,
+              wgpu::BufferUsage host_buf_usage) {
+        for (int i = 0; i < num_bufs; i++) {
+            wgpu::Buffer host_buf;
+            wgpu::Buffer dev_buf;
+            ggml_webgpu_create_buffer(device, host_buf, buf_size, host_buf_usage, "ggml_webgpu_host_profile_buf");
+            ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_profile_buf");
+            // Create a query set for 2 timestamps
+            wgpu::QuerySetDescriptor ts_query_set_desc = {};
+
+            ts_query_set_desc.type      = wgpu::QueryType::Timestamp;
+            ts_query_set_desc.count     = 2;
+            wgpu::QuerySet ts_query_set = device.CreateQuerySet(&ts_query_set_desc);
+
+            free.push_back({ host_buf, dev_buf, ts_query_set });
+        }
+    }
+
+    webgpu_gpu_profile_bufs alloc_bufs() {
+        std::unique_lock<std::mutex> lock(mutex);
+        cv.wait(lock, [this] { return !free.empty(); });
+        webgpu_gpu_profile_bufs bufs = free.back();
+        free.pop_back();
+        return bufs;
+    }
+
+    void free_bufs(std::vector<webgpu_gpu_profile_bufs> bufs) {
+        std::lock_guard<std::mutex> lock(mutex);
+        free.insert(free.end(), bufs.begin(), bufs.end());
+        cv.notify_all();
+    }
+
+    void cleanup() {
+        std::lock_guard<std::mutex> lock(mutex);
+        for (auto & bufs : free) {
+            bufs.host_buf.Destroy();
+            bufs.dev_buf.Destroy();
+            bufs.query_set.Destroy();
+        }
+        free.clear();
+    }
+
+    ~webgpu_gpu_profile_buf_pool() { this->cleanup(); }
+};
+#endif
+
+struct webgpu_pipeline {
+    wgpu::ComputePipeline pipeline;
+    std::string           name;
+    std::shared_ptr<void> context = nullptr;
+};
+
+struct webgpu_command {
+    wgpu::CommandBuffer             commands;
+    std::vector<webgpu_pool_bufs>   params_bufs;
+    std::optional<webgpu_pool_bufs> set_rows_error_bufs;
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    webgpu_gpu_profile_bufs timestamp_query_bufs;
+    std::string             pipeline_name;
+#endif
+};
+
+struct webgpu_capabilities {
+    wgpu::Limits limits;
+    bool         supports_subgroup_matrix = false;
+
+    uint32_t sg_mat_m = 0;
+    uint32_t sg_mat_n = 0;
+    uint32_t sg_mat_k = 0;
+
+    uint32_t subgroup_size     = 0;
+    uint32_t max_subgroup_size = 0;
+    size_t   memset_bytes_per_thread;
+};
+
+// Stores global webgpu members
+struct webgpu_global_context_struct {
+    wgpu::Instance instance;
+    wgpu::Adapter  adapter;
+    wgpu::Device   device;
+    wgpu::Queue    queue;
+
+    webgpu_capabilities  capabilities;
+    // Shared buffer to move data from device to host
+    wgpu::Buffer         get_tensor_staging_buf;
+    // Global mutex for pipeline and staging buffer, will be refactored to exclude pipeline caches.
+    std::recursive_mutex mutex;
+
+    webgpu_buf_pool                memset_buf_pool;
+    std::map<int, webgpu_pipeline> memset_pipelines;  // variant or type index
+    std::atomic_uint               inflight_threads = 0;
+
+#ifdef GGML_WEBGPU_CPU_PROFILE
+    // Profiling: labeled CPU time in ms (total)
+    std::unordered_map<std::string, double> cpu_time_ms;
+    // Profiling: detailed CPU time in ms
+    std::unordered_map<std::string, double> cpu_detail_ms;
+#endif
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    // Profiling: per-shader GPU time in ms
+    std::unordered_map<std::string, double> shader_gpu_time_ms;
+    // Profiling: pool of timestamp query buffers (one per operation)
+    webgpu_gpu_profile_buf_pool             timestamp_query_buf_pool;
+#endif
+
+#ifdef GGML_WEBGPU_DEBUG
+    wgpu::Buffer debug_host_buf;
+    wgpu::Buffer debug_dev_buf;
+#endif
+
+    ~webgpu_global_context_struct() {
+        if (this->get_tensor_staging_buf) {
+            this->get_tensor_staging_buf.Destroy();
+            this->get_tensor_staging_buf = nullptr;
+        }
+#ifdef GGML_WEBGPU_DEBUG
+        if (this->debug_host_buf) {
+            this->debug_host_buf.Destroy();
+            this->debug_host_buf = nullptr;
+        }
+        if (this->debug_dev_buf) {
+            this->debug_dev_buf.Destroy();
+            this->debug_dev_buf = nullptr;
+        }
+#endif
+    }
+};
+
+typedef std::shared_ptr<webgpu_global_context_struct> webgpu_global_context;
+
+// All the base objects needed to run operations on a WebGPU device
+struct webgpu_context_struct {
+    // Points to global instances owned by ggml_backend_webgpu_reg_context
+    webgpu_global_context global_ctx;
+
+    pre_wgsl::Preprocessor p;
+
+    webgpu_buf_pool param_buf_pool;
+    webgpu_buf_pool set_rows_error_buf_pool;
+
+    std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> mul_mat_pipelines;  // src0_type, src1_type, vectorized
+    std::map<int, std::map<int, std::map<int, webgpu_pipeline>>>
+        mul_mat_vec_pipelines;                                                       // src0_type, src1_type, vectorized
+
+    std::unordered_map<ggml_webgpu_flash_attn_pipeline_key, webgpu_pipeline, ggml_webgpu_flash_attn_pipeline_key_hash>
+        flash_attn_pipelines;
+
+    std::unordered_map<int, webgpu_pipeline> argmax_pipelines;         // key is vec4
+    std::unordered_map<int, webgpu_pipeline> argsort_pipelines;        // key is order (asc/desc)
+    std::unordered_map<int, webgpu_pipeline> argsort_merge_pipelines;  // key is order (asc/desc)
+    std::unordered_map<int, webgpu_pipeline> cumsum_pipelines;         // key is fixed, no variants yet
+    std::unordered_map<int, webgpu_pipeline> sum_rows_pipelines;       // key is fixed, no variants yet
+
+    std::unordered_map<ggml_webgpu_set_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_set_rows_pipeline_key_hash>
+                                                  set_rows_pipelines;
+    std::map<int, std::map<int, webgpu_pipeline>> get_rows_pipelines;  // src_type, vectorized
+
+    std::map<int, std::map<int, webgpu_pipeline>> cpy_pipelines;       // src_type, dst_type
+
+    std::unordered_map<ggml_webgpu_binary_pipeline_key, webgpu_pipeline, ggml_webgpu_binary_pipeline_key_hash>
+        binary_pipelines;
+
+    std::map<int, webgpu_pipeline>                               rms_norm_pipelines;  // inplace
+    std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> rope_pipelines;      // type, ff, inplace
+    std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> glu_pipelines;       // glu_op, type, split
+    std::map<int, webgpu_pipeline>                               scale_pipelines;     // inplace
+    std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> soft_max_pipelines;  // mask_type, has_sink, inplace
+    std::unordered_map<ggml_webgpu_unary_pipeline_key, webgpu_pipeline, ggml_webgpu_unary_pipeline_key_hash>
+        unary_pipelines;
+    std::unordered_map<ggml_webgpu_pad_pipeline_key, webgpu_pipeline, ggml_webgpu_pad_pipeline_key_hash> pad_pipelines;
+
+    size_t memset_bytes_per_thread;
+};
+
+typedef std::shared_ptr<webgpu_context_struct> webgpu_context;
+
+// Metadata required for the ggml backend registration/discovery interface
+struct ggml_backend_webgpu_reg_context {
+    // Since the Instance is a global entrypoint into the WebGPU API, it lives here
+    webgpu_global_context webgpu_global_ctx;
+    size_t                device_count;
+    const char *          name;
+};
+
+// Per-device struct for the global logical device interface
+struct ggml_backend_webgpu_device_context {
+    webgpu_global_context webgpu_global_ctx;
+    std::string           device_name;
+    std::string           device_desc;
+};
+
+// Per-thread data required to actually run WebGPU operations in a backend instance
+struct ggml_backend_webgpu_context {
+    webgpu_context webgpu_ctx;
+    std::string    name;
+};
+
+// Per-thread data related to buffers
+struct ggml_backend_webgpu_buffer_context {
+    wgpu::Buffer          buffer;
+    std::string           label;
+    webgpu_global_context global_ctx;
+
+    ggml_backend_webgpu_buffer_context(wgpu::Buffer buf, std::string lbl, webgpu_global_context global_ctx_) :
+        buffer(std::move(buf)),
+        label(std::move(lbl)),
+        global_ctx(std::move(global_ctx_)) {}
+};
+
+/* WebGPU object initializations */
+
+// Process a WGSL shader string, replacing tokens of the form {{KEY}} with
+// the corresponding values provided in `repls`.
+static std::string ggml_webgpu_process_shader_repls(const char *                               src,
+                                                    const std::map<std::string, std::string> & repls) {
+    if (!src) {
+        return std::string();
+    }
+    std::string s = src;
+    for (const auto & kv : repls) {
+        std::string token = "{{" + kv.first + "}}";
+        size_t      pos   = 0;
+        while ((pos = s.find(token, pos)) != std::string::npos) {
+            s.replace(pos, token.length(), kv.second);
+            pos += kv.second.length();
+        }
+    }
+    return s;
+}
+
+static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device &                           device,
+                                                   const char *                             shader_code,
+                                                   const char *                             label,
+                                                   const std::vector<wgpu::ConstantEntry> & constants = {}) {
+    wgpu::ShaderSourceWGSL shader_source;
+    shader_source.code = shader_code;
+
+    wgpu::ShaderModuleDescriptor shader_desc;
+    shader_desc.nextInChain = &shader_source;
+
+    wgpu::ShaderModule shader_module = device.CreateShaderModule(&shader_desc);
+
+    wgpu::ComputePipelineDescriptor pipeline_desc;
+    pipeline_desc.label              = label;
+    pipeline_desc.compute.module     = shader_module;
+    pipeline_desc.compute.entryPoint = "main";   // Entry point in the WGSL code
+    pipeline_desc.layout             = nullptr;  // nullptr means auto layout
+    if (constants.size() > 0) {
+        pipeline_desc.compute.constants     = constants.data();
+        pipeline_desc.compute.constantCount = constants.size();
+    }
+    return { device.CreateComputePipeline(&pipeline_desc), label };
+}
+
+static void ggml_webgpu_create_buffer(wgpu::Device &    device,
+                                      wgpu::Buffer &    buffer,
+                                      size_t            size,
+                                      wgpu::BufferUsage usage,
+                                      const char *      label) {
+    wgpu::BufferDescriptor buffer_desc;
+    buffer_desc.size             = size;
+    buffer_desc.usage            = usage;
+    buffer_desc.label            = label;
+    buffer_desc.mappedAtCreation = false;
+
+    // TODO: error handling
+    buffer = device.CreateBuffer(&buffer_desc);
+}
+
+/** End WebGPU object initializations */
+
+/** WebGPU Actions */
+
+// Wait for the queue to finish processing all submitted work
+static void ggml_backend_webgpu_wait(webgpu_global_context &                  ctx,
+                                     std::vector<webgpu_submission_futures> & futures,
+                                     bool                                     block = true) {
+    // If we have too many in-flight submissions, wait on the oldest one first. If there are many threads,
+    // inflight_max may be 0, meaning that we must wait on all futures.
+    uint64_t timeout_ms       = block ? UINT64_MAX : 0;
+    uint32_t inflight_threads = ctx->inflight_threads;
+    uint32_t inflight_max     = WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD / std::max(inflight_threads, 1u);
+    while (futures.size() >= inflight_max && futures.size() > 0) {
+        ctx->instance.WaitAny(futures[0].futures.size(), futures[0].futures.data(), UINT64_MAX);
+        futures.erase(futures.begin());
+    }
+    size_t i = 0;
+    while (i < futures.size()) {
+        auto waitStatus = ctx->instance.WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms);
+        switch (waitStatus) {
+            case wgpu::WaitStatus::Success:
+                futures.erase(futures.begin() + i);
+                break;
+            case wgpu::WaitStatus::TimedOut:
+                i++;
+                break;
+            case wgpu::WaitStatus::Error:
+                GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an error\n");
+                break;
+            default:
+                GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an unknown status\n");
+                break;
+        }
+    }
+}
+
+static void ggml_backend_webgpu_map_buffer(webgpu_global_context & ctx,
+                                           wgpu::Buffer &          buffer,
+                                           wgpu::MapMode           mode,
+                                           size_t                  offset,
+                                           size_t                  size) {
+    ctx->instance.WaitAny(buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous,
+                                          [](wgpu::MapAsyncStatus status, wgpu::StringView message) {
+                                              if (status != wgpu::MapAsyncStatus::Success) {
+                                                  GGML_LOG_ERROR("ggml_webgpu: Failed to map buffer: %s\n",
+                                                                 message.data);
+                                              }
+                                          }),
+                          UINT64_MAX);
+}
+
+#ifdef GGML_WEBGPU_DEBUG
+// This function adds debugging information to shaders, as WebGPU does not support printing directly.
+// To use, add a bind group entry to the setup for the shader you are debugging, add the buffer and
+// debug statements in the shader, and then call this function after encoding the commands and submitting them.
+static void ggml_backend_webgpu_debug(webgpu_global_context & ctx) {
+    wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
+    encoder.CopyBufferToBuffer(ctx->debug_dev_buf, 0, ctx->debug_host_buf, 0, ctx->debug_host_buf.GetSize());
+    wgpu::CommandBuffer commands = encoder.Finish();
+    ctx->queue.Submit(1, &commands);
+    ggml_backend_webgpu_map_buffer(ctx, ctx->debug_host_buf, wgpu::MapMode::Read, 0, ctx->debug_host_buf.GetSize());
+    const float * debug_data = (const float *) ctx->debug_host_buf.GetConstMappedRange();
+    std::cout << "debug[0]: " << debug_data[0] << "\n";
+    ctx->debug_host_buf.Unmap();
+}
+#endif
+
+static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_global_context       ctx,
+                                                            std::vector<webgpu_command> commands,
+                                                            webgpu_buf_pool &           param_buf_pool,
+                                                            webgpu_buf_pool * set_rows_error_buf_pool = nullptr) {
+    std::vector<wgpu::CommandBuffer> command_buffers;
+    std::vector<webgpu_pool_bufs>    params_bufs;
+    std::vector<webgpu_pool_bufs>    set_rows_error_bufs;
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    std::vector<std::pair<std::string, webgpu_gpu_profile_bufs>> pipeline_name_and_ts_bufs;
+#endif
+
+    for (const auto & command : commands) {
+        command_buffers.push_back(command.commands);
+        params_bufs.insert(params_bufs.end(), command.params_bufs.begin(), command.params_bufs.end());
+        if (command.set_rows_error_bufs) {
+            set_rows_error_bufs.push_back(command.set_rows_error_bufs.value());
+        }
+    }
+    ctx->queue.Submit(command_buffers.size(), command_buffers.data());
+
+    std::vector<wgpu::FutureWaitInfo> futures;
+
+    wgpu::Future p_f = ctx->queue.OnSubmittedWorkDone(
+        wgpu::CallbackMode::AllowSpontaneous,
+        [&param_buf_pool, params_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
+            if (status != wgpu::QueueWorkDoneStatus::Success) {
+                GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", std::string(message).c_str());
+            }
+            // Free the staged buffers
+            param_buf_pool.free_bufs(params_bufs);
+        });
+    futures.push_back({ p_f });
+
+    for (const auto & bufs : set_rows_error_bufs) {
+        wgpu::Future f = bufs.host_buf.MapAsync(
+            wgpu::MapMode::Read, 0, bufs.host_buf.GetSize(), wgpu::CallbackMode::AllowSpontaneous,
+            [set_rows_error_buf_pool, bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) {
+                if (status != wgpu::MapAsyncStatus::Success) {
+                    GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", std::string(message).c_str());
+                } else {
+                    const uint32_t * error_data = (const uint32_t *) bufs.host_buf.GetConstMappedRange();
+                    if (*error_data) {
+                        GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported.");
+                    }
+                    // We can't unmap in here due to WebGPU reentrancy limitations.
+                    if (set_rows_error_buf_pool) {
+                        set_rows_error_buf_pool->free_bufs({ bufs });
+                    }
+                }
+            });
+        futures.push_back({ f });
+    }
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    for (const auto & command : commands) {
+        auto label   = command.pipeline_name;
+        auto ts_bufs = command.timestamp_query_bufs;
+
+        wgpu::Future f = ts_bufs.host_buf.MapAsync(
+            wgpu::MapMode::Read, 0, ts_bufs.host_buf.GetSize(), wgpu::CallbackMode::AllowSpontaneous,
+            [ctx, ts_bufs, label](wgpu::MapAsyncStatus status, wgpu::StringView message) {
+                if (status != wgpu::MapAsyncStatus::Success) {
+                    GGML_LOG_ERROR("ggml_webgpu: Failed to map timestamp buffer: %s\n", std::string(message).c_str());
+                } else {
+                    const uint64_t * ts_data    = (const uint64_t *) ts_bufs.host_buf.GetConstMappedRange();
+                    // WebGPU timestamps are in ns; convert to ms
+                    double           elapsed_ms = double(ts_data[1] - ts_data[0]) * 1e-6;
+                    ctx->shader_gpu_time_ms[label] += elapsed_ms;
+                    // We can't unmap in here due to WebGPU reentrancy limitations.
+                    ctx->timestamp_query_buf_pool.free_bufs({ ts_bufs });
+                }
+            });
+        futures.push_back({ f });
+    }
+#endif
+    return { futures };
+}
+
+static webgpu_command ggml_backend_webgpu_build_multi(
+    webgpu_global_context &                                ctx,
+    webgpu_buf_pool &                                      param_buf_pool,
+    const std::vector<webgpu_pipeline> &                   pipelines,
+    const std::vector<std::vector<uint32_t>> &             params_list,
+    const std::vector<std::vector<wgpu::BindGroupEntry>> & bind_group_entries_list,
+    const std::vector<std::pair<uint32_t, uint32_t>> &     workgroups_list,
+    const std::optional<webgpu_pool_bufs> &                set_rows_error_bufs = std::nullopt) {
+    GGML_ASSERT(pipelines.size() == params_list.size());
+    GGML_ASSERT(pipelines.size() == bind_group_entries_list.size());
+    GGML_ASSERT(pipelines.size() == workgroups_list.size());
+
+    std::vector<webgpu_pool_bufs> params_bufs_list;
+    std::vector<wgpu::BindGroup>  bind_groups;
+
+    for (size_t i = 0; i < pipelines.size(); i++) {
+        webgpu_pool_bufs params_bufs = param_buf_pool.alloc_bufs();
+
+        ggml_backend_webgpu_map_buffer(ctx, params_bufs.host_buf, wgpu::MapMode::Write, 0,
+                                       params_bufs.host_buf.GetSize());
+        uint32_t * _params = (uint32_t *) params_bufs.host_buf.GetMappedRange();
+        for (size_t j = 0; j < params_list[i].size(); j++) {
+            _params[j] = params_list[i][j];
+        }
+        params_bufs.host_buf.Unmap();
+
+        std::vector<wgpu::BindGroupEntry> entries            = bind_group_entries_list[i];
+        uint32_t                          params_binding_num = entries.size();
+        entries.push_back({ .binding = params_binding_num,
+                            .buffer  = params_bufs.dev_buf,
+                            .offset  = 0,
+                            .size    = params_bufs.dev_buf.GetSize() });
+
+        wgpu::BindGroupDescriptor bind_group_desc;
+        bind_group_desc.layout     = pipelines[i].pipeline.GetBindGroupLayout(0);
+        bind_group_desc.entryCount = entries.size();
+        bind_group_desc.entries    = entries.data();
+        bind_group_desc.label      = pipelines[i].name.c_str();
+        bind_groups.push_back(ctx->device.CreateBindGroup(&bind_group_desc));
+
+        params_bufs_list.push_back(params_bufs);
+    }
+
+    wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
+    for (const auto & params_bufs : params_bufs_list) {
+        encoder.CopyBufferToBuffer(params_bufs.host_buf, 0, params_bufs.dev_buf, 0, params_bufs.dev_buf.GetSize());
+    }
+
+    // If there are SET_ROWS operations in this submission, copy their error buffers to the host.
+    if (set_rows_error_bufs) {
+        encoder.CopyBufferToBuffer(set_rows_error_bufs->dev_buf, 0, set_rows_error_bufs->host_buf, 0,
+                                   set_rows_error_bufs->host_buf.GetSize());
+    }
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    webgpu_gpu_profile_bufs ts_bufs = ctx->timestamp_query_buf_pool.alloc_bufs();
+    if (ts_bufs.host_buf.GetMapState() == wgpu::BufferMapState::Mapped) {
+        ts_bufs.host_buf.Unmap();
+    }
+
+    wgpu::PassTimestampWrites   ts_writes = { .querySet                  = ts_bufs.query_set,
+                                              .beginningOfPassWriteIndex = 0,
+                                              .endOfPassWriteIndex       = 1 };
+    wgpu::ComputePassDescriptor pass_desc = { .timestampWrites = &ts_writes };
+    wgpu::ComputePassEncoder    pass      = encoder.BeginComputePass(&pass_desc);
+#else
+    wgpu::ComputePassEncoder pass = encoder.BeginComputePass();
+#endif
+    for (size_t i = 0; i < pipelines.size(); i++) {
+        pass.SetPipeline(pipelines[i].pipeline);
+        pass.SetBindGroup(0, bind_groups[i]);
+        pass.DispatchWorkgroups(workgroups_list[i].first, workgroups_list[i].second, 1);
+    }
+    pass.End();
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    encoder.ResolveQuerySet(ts_bufs.query_set, 0, 2, ts_bufs.dev_buf, 0);
+    encoder.CopyBufferToBuffer(ts_bufs.dev_buf, 0, ts_bufs.host_buf, 0, ts_bufs.host_buf.GetSize());
+#endif
+
+    wgpu::CommandBuffer commands = encoder.Finish();
+    webgpu_command      result   = {};
+    result.commands              = commands;
+    result.params_bufs           = params_bufs_list;
+    result.set_rows_error_bufs   = set_rows_error_bufs;
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    result.timestamp_query_bufs = ts_bufs;
+    // TODO: handle multiple pipeline names
+    result.pipeline_name        = pipelines.front().name;
+#endif
+    return result;
+}
+
+static webgpu_command ggml_backend_webgpu_build(webgpu_global_context &           ctx,
+                                                webgpu_buf_pool &                 param_buf_pool,
+                                                webgpu_pipeline &                 pipeline,
+                                                std::vector<uint32_t>             params,
+                                                std::vector<wgpu::BindGroupEntry> bind_group_entries,
+                                                uint32_t                          wg_x,
+                                                uint32_t                          wg_y                = 1,
+                                                std::optional<webgpu_pool_bufs>   set_rows_error_bufs = std::nullopt) {
+    return ggml_backend_webgpu_build_multi(ctx, param_buf_pool,
+                                           {
+                                               pipeline
+    },
+                                           { params }, { bind_group_entries }, { { wg_x, wg_y } }, set_rows_error_bufs);
+}
+
+static void ggml_backend_webgpu_buffer_memset(webgpu_global_context & ctx,
+                                              wgpu::Buffer &          buf,
+                                              uint32_t                value,
+                                              size_t                  offset,
+                                              size_t                  size) {
+    std::vector<uint32_t>             params  = { (uint32_t) offset, (uint32_t) size, value };
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0, .buffer = buf, .offset = 0, .size = buf.GetSize() }
+    };
+    size_t   bytes_per_wg = WEBGPU_MAX_WG_SIZE * ctx->capabilities.memset_bytes_per_thread;
+    uint32_t wg_x         = CEIL_DIV(size + 3, bytes_per_wg);
+
+    webgpu_command command =
+        ggml_backend_webgpu_build(ctx, ctx->memset_buf_pool, ctx->memset_pipelines[0], params, entries, wg_x);
+    std::vector<webgpu_submission_futures> futures = { ggml_backend_webgpu_submit(ctx, { command },
+                                                                                  ctx->memset_buf_pool) };
+    ggml_backend_webgpu_wait(ctx, futures);
+}
+
+/** End WebGPU Actions */
+
+/** GGML Backend Interface */
+
+static const char * ggml_backend_webgpu_name(ggml_backend_t backend) {
+    ggml_backend_webgpu_context * ctx = (ggml_backend_webgpu_context *) backend->context;
+    return ctx->name.c_str();
+}
+
+static void ggml_backend_webgpu_free(ggml_backend_t backend) {
+    ggml_backend_webgpu_context * ctx = (ggml_backend_webgpu_context *) backend->context;
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_free(" << ctx->name << ")");
+
+#ifdef GGML_WEBGPU_CPU_PROFILE
+    std::cout << "\n[ggml_webgpu cpu profiling summary]\n";
+    double total_cpu = 0.0;
+    for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_time_ms) {
+        total_cpu += kv.second;
+    }
+    std::cout << "ggml_webgpu: total cpu time: " << total_cpu << " ms\n";
+    std::cout << "ggml_webgpu: cpu breakdown:\n";
+    for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_time_ms) {
+        double pct = (total_cpu > 0.0) ? (kv.second / total_cpu * 100.0) : 0.0;
+        std::cout << "ggml_webgpu:  " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n";
+    }
+    if (ctx->webgpu_ctx->global_ctx->cpu_detail_ms.size() > 0) {
+        std::cout << "ggml_webgpu: cpu detailed breakdown:\n";
+    }
+    for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_detail_ms) {
+        double pct = (total_cpu > 0.0) ? (kv.second / total_cpu * 100.0) : 0.0;
+        std::cout << "ggml_webgpu:  " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n";
+    }
+#endif
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    std::cout << "\n[ggml_webgpu gpu profiling summary]\n";
+    double total_gpu = 0.0;
+    for (const auto & kv : ctx->webgpu_ctx->global_ctx->shader_gpu_time_ms) {
+        total_gpu += kv.second;
+    }
+    std::cout << "ggml_webgpu: total gpu time (all shaders): " << total_gpu << " ms\n";
+    std::cout << "\nggml_webgpu: gpu breakdown:\n";
+    for (const auto & kv : ctx->webgpu_ctx->global_ctx->shader_gpu_time_ms) {
+        double pct = (total_gpu > 0.0) ? (kv.second / total_gpu * 100.0) : 0.0;
+        std::cout << "ggml_webgpu:  " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n";
+    }
+#endif
+
+#if defined(GGML_WEBGPU_CPU_PROFILE) && defined(GGML_WEBGPU_GPU_PROFILE)
+    std::cout << "ggml_webgpu: gpu/cpu ratio: " << (total_cpu > 0.0 ? total_gpu / total_cpu : 0.0) << "\n";
+#endif
+
+    delete ctx;
+    delete backend;
+}
+
+static size_t ggml_webgpu_tensor_offset(const ggml_tensor * tensor) {
+    return webgpu_tensor_offset(tensor) + tensor->view_offs;
+}
+
+static wgpu::Buffer ggml_webgpu_tensor_buf(const ggml_tensor * tensor) {
+    ggml_backend_webgpu_buffer_context * ctx = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context;
+    return ctx->buffer;
+}
+
+static size_t ggml_webgpu_tensor_misalignment(webgpu_context & ctx, const ggml_tensor * t) {
+    size_t offset = ggml_webgpu_tensor_offset(t);
+    return offset & (ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1);
+}
+
+static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, const ggml_tensor * t) {
+    size_t offset = ggml_webgpu_tensor_offset(t);
+    return offset & ~(ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1);
+}
+
+static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) {
+    return ROUNDUP_POW2(ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t), WEBGPU_STORAGE_BUF_BINDING_MULT);
+}
+
+// Used to determine if two tensors are the same for in-place operations
+static bool ggml_webgpu_tensor_equal(ggml_tensor * a, ggml_tensor * b) {
+    return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
+           (ggml_webgpu_tensor_offset(a) == ggml_webgpu_tensor_offset(b));
+}
+
+// Used to determine if two tensors share the same buffer and their byte ranges overlap,
+static bool ggml_webgpu_tensor_overlap(ggml_tensor * a, ggml_tensor * b) {
+    return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
+           ggml_webgpu_tensor_offset(a) < (ggml_webgpu_tensor_offset(b) + ggml_nbytes(b)) &&
+           ggml_webgpu_tensor_offset(b) < (ggml_webgpu_tensor_offset(a) + ggml_nbytes(a));
+}
+
+struct binary_overlap_flags {
+    bool inplace;  // src0 == dst
+    bool overlap;  // src1 == dst
+};
+
+static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0,
+                                                              ggml_tensor * src1,
+                                                              ggml_tensor * dst) {
+    binary_overlap_flags flags = {};
+    flags.inplace              = ggml_webgpu_tensor_equal(src0, dst);
+    flags.overlap              = ggml_webgpu_tensor_overlap(src1, dst);
+
+    return flags;
+}
+
+static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    uint32_t ne = (uint32_t) ggml_nelements(dst);
+
+    std::vector<uint32_t> params = {
+        ne, (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        // Convert byte-strides to element-strides
+        (uint32_t) (src->nb[0] / ggml_type_size(src->type)), (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[2] / ggml_type_size(src->type)), (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+        (uint32_t) (dst->nb[0] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        // Logical shapes
+        (uint32_t) src->ne[0], (uint32_t) src->ne[1], (uint32_t) src->ne[2], (uint32_t) dst->ne[0],
+        (uint32_t) dst->ne[1], (uint32_t) dst->ne[2]
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
+    };
+
+    uint32_t wg_x = CEIL_DIV(ne, WEBGPU_MAX_WG_SIZE);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, ctx->cpy_pipelines[src->type][dst->type],
+                                     params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    const bool circular = ggml_get_op_params_i32(dst, 8) != 0;
+
+    ggml_webgpu_pad_pipeline_key       pipeline_key   = { .circular = circular };
+    ggml_webgpu_pad_shader_lib_context shader_lib_ctx = {
+        .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->pad_pipelines.find(pipeline_key);
+    if (it != ctx->pad_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_pad_shader(ctx->p, wgsl_pad, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->pad_pipelines.emplace(pipeline_key, pipeline);
+    }
+
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    const uint32_t ne = (uint32_t) ggml_nelements(dst);
+
+    std::vector<uint32_t> params = {
+        ne,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        // Strides (in elements)
+        (uint32_t) (src->nb[0] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+        // Shapes
+        (uint32_t) src->ne[0],
+        (uint32_t) src->ne[1],
+        (uint32_t) src->ne[2],
+        (uint32_t) src->ne[3],
+        (uint32_t) dst->ne[0],
+        (uint32_t) dst->ne[1],
+        (uint32_t) dst->ne[2],
+        (uint32_t) dst->ne[3],
+        // Pad sizes
+        (uint32_t) ggml_get_op_params_i32(dst, 0),
+        (uint32_t) ggml_get_op_params_i32(dst, 1),
+        (uint32_t) ggml_get_op_params_i32(dst, 2),
+        (uint32_t) ggml_get_op_params_i32(dst, 3),
+        (uint32_t) ggml_get_op_params_i32(dst, 4),
+        (uint32_t) ggml_get_op_params_i32(dst, 5),
+        (uint32_t) ggml_get_op_params_i32(dst, 6),
+        (uint32_t) ggml_get_op_params_i32(dst, 7),
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
+    };
+
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static std::optional<webgpu_command> ggml_webgpu_set_rows(webgpu_context & ctx,
+                                                          ggml_tensor *    src,
+                                                          ggml_tensor *    idx,
+                                                          ggml_tensor *    dst) {
+    // For set rows specifically, we need to check if src and idx are empty tensors.
+    if (ggml_is_empty(src) || ggml_is_empty(idx)) {
+        return std::nullopt;
+    }
+
+    ggml_webgpu_set_rows_pipeline_key key = { .dst_type = dst->type,
+                                              .vec4     = src->ne[0] % 4 == 0,
+                                              .i64_idx  = idx->type == GGML_TYPE_I64 };
+
+    ggml_webgpu_set_rows_shader_lib_context shader_lib_ctx = {
+        .key = key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->set_rows_pipelines.find(key);
+    if (it != ctx->set_rows_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_set_rows_shader(ctx->p, wgsl_set_rows, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->set_rows_pipelines.emplace(key, pipeline);
+    }
+
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    std::optional<webgpu_pool_bufs> error_bufs = std::nullopt;
+    if (key.i64_idx) {
+        error_bufs = ctx->set_rows_error_buf_pool.alloc_bufs();
+        if (error_bufs->host_buf.GetMapState() == wgpu::BufferMapState::Mapped) {
+            error_bufs->host_buf.Unmap();
+        }
+    }
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, idx) / ggml_type_size(idx->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        // Convert byte-strides to element-strides
+        (uint32_t) (src->nb[1] / ggml_type_size(src->type)), (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[3] / ggml_type_size(src->type)), (uint32_t) (idx->nb[0] / ggml_type_size(idx->type)),
+        (uint32_t) (idx->nb[1] / ggml_type_size(idx->type)), (uint32_t) (idx->nb[2] / ggml_type_size(idx->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        // Shape of src
+        (uint32_t) src->ne[0], (uint32_t) src->ne[1], (uint32_t) src->ne[2], (uint32_t) src->ne[3],
+        // Shape of idx
+        (uint32_t) (idx->ne[1]), (uint32_t) (idx->ne[2])
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(idx),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, idx),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, idx) },
+        { .binding = 2,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
+    };
+
+    if (key.i64_idx) {
+        entries.push_back(
+            { .binding = 3, .buffer = error_bufs->dev_buf, .offset = 0, .size = error_bufs->dev_buf.GetSize() });
+    }
+
+    uint32_t threads;
+    if (key.vec4) {
+        threads = (src->ne[1] * src->ne[2] * src->ne[3]) * (src->ne[0] / 4);
+    } else {
+        threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3];
+    }
+    uint32_t wg_x = CEIL_DIV(threads, decisions->wg_size);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, 1,
+                                     error_bufs);
+}
+
+static webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx,
+                                           ggml_tensor *    src,
+                                           ggml_tensor *    idx,
+                                           ggml_tensor *    dst) {
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, idx) / ggml_type_size(idx->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        // Convert byte-strides to element-strides
+        (uint32_t) (src->nb[1] / ggml_type_size(src->type)), (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[3] / ggml_type_size(src->type)), (uint32_t) (idx->nb[0] / ggml_type_size(idx->type)),
+        (uint32_t) (idx->nb[1] / ggml_type_size(idx->type)), (uint32_t) (idx->nb[2] / ggml_type_size(idx->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        // Shape of dst
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3],
+        // Shape of idx
+        (uint32_t) (idx->ne[1]), (uint32_t) (idx->ne[2])
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(idx),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, idx),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, idx) },
+        { .binding = 2,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
+    };
+
+    uint32_t wg_x = CEIL_DIV(dst->ne[1] * dst->ne[2] * dst->ne[3], WEBGPU_MAX_WG_SIZE);
+
+    uint32_t        vectorized = src->type == GGML_TYPE_F32 && dst->ne[0] % 4 == 0;
+    webgpu_pipeline pipeline   = ctx->get_rows_pipelines[src->type][vectorized];
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx,
+                                          ggml_tensor *    src0,
+                                          ggml_tensor *    src1,
+                                          ggml_tensor *    dst) {
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) dst->ne[0],                                  // number of rows in result (M, transposed)
+        (uint32_t) dst->ne[1],                                  // number of columns in result (N)
+        (uint32_t) src0->ne[0],                                 // number of columns in src0/src1 (K)
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),  // stride (elements/blocks) of src0 in dimension 1
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),  // stride (elements/blocks) of src1 in dimension 1
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),  // stride (elements/blocks) of src0 in dimension 2
+        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),  // stride (elements/blocks) of src1 in dimension 2
+        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),  // stride (elements/blocks) of src0 in dimension 3
+        (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),  // stride (elements/blocks) of src1 in dimension 3
+        (uint32_t) src0->ne[2],                                 // batch size in dimension 2
+        (uint32_t) src0->ne[3],                                 // batch size in dimension 3
+        (uint32_t) (src1->ne[2] / src0->ne[2]),                 // broadcast in dimension 2
+        (uint32_t) (src1->ne[3] / src0->ne[3])                  // broadcast in dimension 3
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src0),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(src1),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src1) },
+        { .binding = 2,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst)  },
+    };
+
+    webgpu_pipeline pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][0];
+
+    uint32_t wg_x = CEIL_DIV(dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3], WEBGPU_MUL_MAT_WG_SIZE);
+    uint32_t wg_y = 1;
+
+    bool use_fast = false;
+    switch (src1->type) {
+        case GGML_TYPE_F16:
+            use_fast = (src0->type == GGML_TYPE_F16);
+            break;
+        case GGML_TYPE_F32:
+            switch (src0->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q4_0:
+                    use_fast = true;
+                    break;
+                default:
+                    break;
+            }
+            break;
+        default:
+            break;
+    }
+
+    if (use_fast) {
+        int vectorized = src0->ne[0] % 4 == 0 && dst->ne[0] % 4 == 0 && dst->ne[1] % 4 == 0;
+        if (dst->ne[1] == 1) {
+            // We don't support vectorized mul_mat_vec for quantized types
+            vectorized             = vectorized && (src0->type < 2);
+            pipeline               = ctx->mul_mat_vec_pipelines[src0->type][src1->type][vectorized];
+            uint32_t batches       = dst->ne[2] * dst->ne[3];
+            uint32_t output_groups = CEIL_DIV(dst->ne[0], WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG);
+            uint32_t total_wg      = output_groups * batches;
+            wg_x                   = total_wg % ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
+            wg_y = CEIL_DIV(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension);
+        } else {
+            pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][vectorized];
+            uint32_t wg_m;
+            uint32_t wg_n;
+#ifndef __EMSCRIPTEN__
+            if (ctx->global_ctx->capabilities.supports_subgroup_matrix) {
+                // The total number of subgroups/workgroups needed per matrix.
+                uint32_t wg_m_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_M * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M *
+                                        ctx->global_ctx->capabilities.sg_mat_m;
+                wg_m                  = CEIL_DIV(dst->ne[0], wg_m_sg_tile);
+                uint32_t wg_n_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_N * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N *
+                                        ctx->global_ctx->capabilities.sg_mat_n;
+                wg_n = CEIL_DIV(dst->ne[1], wg_n_sg_tile);
+            } else {
+#endif
+                uint32_t tile_m_s = WEBGPU_MUL_MAT_TILE_M * WEBGPU_MUL_MAT_WG_SIZE_M;
+                uint32_t tile_n_s = WEBGPU_MUL_MAT_TILE_N * WEBGPU_MUL_MAT_WG_SIZE_N;
+                wg_m              = CEIL_DIV(dst->ne[0], tile_m_s);
+                wg_n              = CEIL_DIV(dst->ne[1], tile_n_s);
+#ifndef __EMSCRIPTEN__
+            }
+#endif
+
+            wg_x = wg_m * wg_n * dst->ne[2] * dst->ne[3];
+        }
+    }
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, wg_y);
+}
+
+#ifndef __EMSCRIPTEN__
+static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx,
+                                             ggml_tensor *    Q,
+                                             ggml_tensor *    K,
+                                             ggml_tensor *    V,
+                                             ggml_tensor *    mask,
+                                             ggml_tensor *    sinks,
+                                             ggml_tensor *    dst) {
+    float scale = *(float *) dst->op_params;
+    float max_bias;
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+    float logit_softcap;
+    memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float));
+    if (logit_softcap != 0.0f) {
+        scale /= logit_softcap;
+    }
+    float n_head_log2 = float(1u << (uint32_t) floor(log2(Q->ne[2])));
+    float m0          = powf(2.0f, -(max_bias) / n_head_log2);
+    float m1          = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    const int has_mask  = (mask != nullptr);
+    const int has_sinks = (sinks != nullptr);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, Q) / ggml_type_size(Q->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, K) / ggml_type_size(K->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, V) / ggml_type_size(V->type)),
+        has_mask ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, mask) / ggml_type_size(mask->type)) : 0,
+        has_sinks ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, sinks) / ggml_type_size(sinks->type)) : 0,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) Q->ne[2],                              // number of heads
+        (uint32_t) Q->ne[1],                              // sequence length (Q)
+        (uint32_t) K->ne[1],                              // sequence length (K/V)
+        (uint32_t) (Q->nb[1] / ggml_type_size(Q->type)),  // stride (elements/blocks) of Q in dimension 1
+        (uint32_t) (Q->nb[2] / ggml_type_size(Q->type)),  // stride (elements/blocks) of Q in dimension 2
+        (uint32_t) (Q->nb[3] / ggml_type_size(Q->type)),  // stride (elements/blocks) of Q in dimension 3
+        (uint32_t) (K->nb[1] / ggml_type_size(K->type)),  // stride (elements/blocks) of K in dimension 1
+        (uint32_t) (K->nb[2] / ggml_type_size(K->type)),  // stride (elements/blocks) of K in dimension 2
+        (uint32_t) (K->nb[3] / ggml_type_size(K->type)),  // stride (elements/blocks) of K in dimension 3
+        (uint32_t) (V->nb[1] / ggml_type_size(V->type)),  // stride (elements/blocks) of V in dimension 1
+        (uint32_t) (V->nb[2] / ggml_type_size(V->type)),  // stride (elements/blocks) of V in dimension 2
+        (uint32_t) (V->nb[3] / ggml_type_size(V->type)),  // stride (elements/blocks) of V in dimension 3
+        has_mask ? (uint32_t) (mask->nb[3] / ggml_type_size(mask->type)) : 0,  // stride of mask dim 3
+        (uint32_t) (Q->ne[2] / K->ne[2]),  // repeat factor for K/V in dim 2 (MHA/MQA/GQA)
+        *(uint32_t *) &scale,              // scale (possibly adjusted for logit softcap)
+        *(uint32_t *) &max_bias,
+        *(uint32_t *) &logit_softcap,
+        *(uint32_t *) &n_head_log2,
+        *(uint32_t *) &m0,
+        *(uint32_t *) &m1
+
+    };
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(Q),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, Q),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, Q) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(K),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, K),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, K) },
+        { .binding = 2,
+         .buffer  = ggml_webgpu_tensor_buf(V),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, V),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, V) }
+    };
+    uint32_t binding_index = 3;
+    if (has_mask) {
+        entries.push_back({ .binding = binding_index++,
+                            .buffer  = ggml_webgpu_tensor_buf(mask),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, mask),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, mask) });
+    }
+    if (has_sinks) {
+        entries.push_back({ .binding = binding_index++,
+                            .buffer  = ggml_webgpu_tensor_buf(sinks),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, sinks),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, sinks) });
+    }
+    entries.push_back({ .binding = binding_index++,
+                        .buffer  = ggml_webgpu_tensor_buf(dst),
+                        .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                        .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+
+    bool kv_direct = (K->type == GGML_TYPE_F16) && (Q->ne[0] % ctx->global_ctx->capabilities.sg_mat_k == 0) &&
+                     (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0);
+
+    ggml_webgpu_flash_attn_pipeline_key key = {
+        .kv_type            = K->type,
+        .head_dim_qk        = (uint32_t) Q->ne[0],
+        .head_dim_v         = (uint32_t) V->ne[0],
+        .kv_direct          = kv_direct,
+        .has_mask           = static_cast<bool>(has_mask),
+        .has_sinks          = static_cast<bool>(has_sinks),
+        .uses_logit_softcap = logit_softcap != 0.0f,
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->flash_attn_pipelines.find(key);
+    if (it != ctx->flash_attn_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_flash_attn_shader_lib_context shader_lib_ctx = {
+            .key                = key,
+            .sg_mat_m           = ctx->global_ctx->capabilities.sg_mat_m,
+            .sg_mat_n           = ctx->global_ctx->capabilities.sg_mat_n,
+            .sg_mat_k           = ctx->global_ctx->capabilities.sg_mat_k,
+            .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize,
+            .max_subgroup_size  = ctx->global_ctx->capabilities.max_subgroup_size
+        };
+
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_flash_attn_shader(ctx->p, wgsl_flash_attn, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->flash_attn_pipelines.emplace(key, pipeline);
+    }
+
+    auto * decisions = static_cast<ggml_webgpu_flash_attn_shader_decisions *>(pipeline.context.get());
+
+    uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions->q_tile);
+    uint32_t wg_x        = wg_per_head * Q->ne[2] * Q->ne[3];  // wg per head * number of heads * number of batches
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+#endif
+
+static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    bool is_unary = dst->op == GGML_OP_UNARY;
+    bool inplace  = ggml_webgpu_tensor_equal(src, dst) || (dst->op == GGML_OP_FILL);
+    int  op       = is_unary ? (int) ggml_get_unary_op(dst) : dst->op;
+
+    ggml_webgpu_unary_pipeline_key pipeline_key = {
+        .type = dst->type, .op = op, .is_unary = is_unary, .inplace = inplace
+    };
+    ggml_webgpu_unary_shader_lib_context shader_lib_ctx = {
+        .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->unary_pipelines.find(pipeline_key);
+    if (it != ctx->unary_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_unary_shader(ctx->p, wgsl_unary, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->unary_pipelines.emplace(pipeline_key, pipeline);
+    }
+
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    uint32_t ne = (uint32_t) ggml_nelements(dst);
+
+    std::vector<uint32_t> params = { ne,
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+                                     (uint32_t) (src->nb[0] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+                                     (uint32_t) src->ne[0],
+                                     (uint32_t) src->ne[1],
+                                     (uint32_t) src->ne[2] };
+
+    ggml_tensor * effective_src = src;
+    if (is_unary) {
+        ggml_unary_op unary_op = ggml_get_unary_op(dst);
+        switch (unary_op) {
+            case GGML_UNARY_OP_XIELU:
+                {
+                    // Get float parameters and reinterpret their bit patterns as uint32_t
+                    // for passing through the params buffer
+                    float alpha_n = ggml_get_op_params_f32(dst, 1);
+                    float alpha_p = ggml_get_op_params_f32(dst, 2);
+                    float beta    = ggml_get_op_params_f32(dst, 3);
+                    float eps     = ggml_get_op_params_f32(dst, 4);
+                    params.push_back(*reinterpret_cast<const uint32_t *>(&alpha_n));
+                    params.push_back(*reinterpret_cast<const uint32_t *>(&alpha_p));
+                    params.push_back(*reinterpret_cast<const uint32_t *>(&beta));
+                    params.push_back(*reinterpret_cast<const uint32_t *>(&eps));
+                    break;
+                }
+            default:
+                break;
+        }
+    } else if (dst->op == GGML_OP_CLAMP) {
+        float clamp_min = ggml_get_op_params_f32(dst, 0);
+        float clamp_max = ggml_get_op_params_f32(dst, 1);
+        params.push_back(*reinterpret_cast<const uint32_t *>(&clamp_min));
+        params.push_back(*reinterpret_cast<const uint32_t *>(&clamp_max));
+    } else if (dst->op == GGML_OP_FILL) {
+        float fill_val = ggml_get_op_params_f32(dst, 0);
+        params.push_back(*reinterpret_cast<const uint32_t *>(&fill_val));
+        effective_src = dst;  // fill simply fills dst
+    }
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(effective_src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, effective_src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, effective_src) },
+    };
+    if (!inplace) {
+        entries.push_back({ .binding = 1,
+                            .buffer  = ggml_webgpu_tensor_buf(dst),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+    }
+
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
+                                            ggml_tensor *    src0,
+                                            ggml_tensor *    src1,
+                                            ggml_tensor *    dst) {
+    binary_overlap_flags flags = ggml_webgpu_detect_binary_overlap(src0, src1, dst);
+
+    ggml_webgpu_binary_pipeline_key pipeline_key = {
+        .type    = dst->type,
+        .op      = dst->op,
+        .inplace = flags.inplace,
+        .overlap = flags.overlap,
+    };
+    ggml_webgpu_binary_shader_lib_context shader_lib_ctx = {
+        .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->binary_pipelines.find(pipeline_key);
+    if (it != ctx->binary_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_binary_shader(ctx->p, wgsl_binary, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->binary_pipelines.emplace(pipeline_key, pipeline);
+    }
+
+    auto * decisions = static_cast<ggml_webgpu_argsort_shader_decisions *>(pipeline.context.get());
+
+    uint32_t ne = (uint32_t) ggml_nelements(dst);
+
+    std::vector<uint32_t> params = {
+        ne,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),
+        (uint32_t) src0->ne[0],
+        (uint32_t) src0->ne[1],
+        (uint32_t) src0->ne[2],
+        (uint32_t) src1->ne[0],
+        (uint32_t) src1->ne[1],
+        (uint32_t) src1->ne[2],
+        (uint32_t) src1->ne[3],
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries;
+
+    entries.push_back({
+        .binding = 0,
+        .buffer  = ggml_webgpu_tensor_buf(src0),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src0),
+    });
+
+    entries.push_back({
+        .binding = 1,
+        .buffer  = ggml_webgpu_tensor_buf(src1),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src1),
+    });
+
+    if (!flags.inplace && !flags.overlap) {
+        entries.push_back({ .binding = 2,
+                            .buffer  = ggml_webgpu_tensor_buf(dst),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+    }
+
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_rms_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    int inplace = ggml_webgpu_tensor_equal(src, dst);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        (uint32_t) src->ne[0],
+        (uint32_t) src->ne[1],
+        (uint32_t) src->ne[2],
+        (uint32_t) src->ne[3],
+        *(uint32_t *) dst->op_params  // epsilon, treated as f32 in the shader
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) }
+    };
+    if (!inplace) {
+        entries.push_back({ .binding = 1,
+                            .buffer  = ggml_webgpu_tensor_buf(dst),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+    }
+
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, ctx->rms_norm_pipelines[inplace], params,
+                                     entries, ggml_nrows(src));
+}
+
+static webgpu_command ggml_webgpu_rope(webgpu_context & ctx,
+                                       ggml_tensor *    src0,
+                                       ggml_tensor *    src1,
+                                       ggml_tensor *    src2,
+                                       ggml_tensor *    dst) {
+    const int inplace         = ggml_webgpu_tensor_equal(src0, dst);
+    const int has_freq_factor = (src2 != nullptr);
+
+    const int n_dims     = ((int32_t *) dst->op_params)[1];
+    const int mode       = ((int32_t *) dst->op_params)[2];
+    const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
+
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
+    memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
+    memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
+    memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
+
+    int sections[4];
+    memcpy(sections, (int32_t *) dst->op_params + 11, 4 * sizeof(int));
+
+    float theta_scale = powf(freq_base, -2.0f / n_dims);
+
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        src2 != nullptr ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)) : 0,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        (uint32_t) ggml_nelements(src0) / 2,
+        (uint32_t) src0->ne[0],
+        (uint32_t) src0->ne[1],
+        (uint32_t) src0->ne[2],
+        (uint32_t) n_dims,
+        (uint32_t) mode,
+        *(uint32_t *) &theta_scale,
+        *(uint32_t *) &attn_factor,
+        *(uint32_t *) &freq_scale,
+        *(uint32_t *) &ext_factor,
+        *(uint32_t *) &corr_dims[0],
+        *(uint32_t *) &corr_dims[1],
+        (uint32_t) sections[0],
+        (uint32_t) sections[1],
+        (uint32_t) sections[2],
+        (uint32_t) sections[3]
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src0),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(src1),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src1) }
+    };
+    uint32_t dst_binding = 2;
+    if (has_freq_factor) {
+        dst_binding = 3;
+        entries.push_back({ .binding = 2,
+                            .buffer  = ggml_webgpu_tensor_buf(src2),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, src2),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, src2) });
+    }
+    if (!inplace) {
+        entries.push_back({ .binding = dst_binding,
+                            .buffer  = ggml_webgpu_tensor_buf(dst),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+    }
+
+    webgpu_pipeline pipeline = ctx->rope_pipelines[dst->type][has_freq_factor][inplace];
+    uint32_t        wg_x     = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_glu(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
+    const int split = (src1 != nullptr);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        src1 != nullptr ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)) : 0,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
+        src1 != nullptr ? (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)) :
+                          (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        src1 != nullptr ? (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)) :
+                          (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        src1 != nullptr ? (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)) :
+                          (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        (uint32_t) ggml_nelements(dst),
+        (uint32_t) dst->ne[0],
+        (uint32_t) dst->ne[1],
+        (uint32_t) dst->ne[2],
+        (uint32_t) ((int32_t *) dst->op_params)[1],  // swapped
+        *(uint32_t *) &dst->op_params[2],            // alpha, for swiglu_oai
+        *(uint32_t *) &dst->op_params[3],            // limit, for swiglu_oai
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src0),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) },
+    };
+    uint32_t dst_binding = 1;
+    if (split) {
+        dst_binding = 2;
+        entries.push_back({ .binding = 1,
+                            .buffer  = ggml_webgpu_tensor_buf(src1),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, src1) });
+    }
+    entries.push_back({ .binding = dst_binding,
+                        .buffer  = ggml_webgpu_tensor_buf(dst),
+                        .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                        .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+
+    webgpu_pipeline pipeline = ctx->glu_pipelines[ggml_get_glu_op(dst)][dst->type][split];
+    uint32_t        wg_x     = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    int inplace = ggml_webgpu_tensor_equal(src, dst);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+        (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        (uint32_t) ggml_nelements(dst),
+        (uint32_t) src->ne[0],
+        (uint32_t) src->ne[1],
+        (uint32_t) src->ne[2],
+        *(uint32_t *) dst->op_params,     // scale
+        *(uint32_t *) &dst->op_params[1]  // bias
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) }
+    };
+    if (!inplace) {
+        entries.push_back({ .binding = 1,
+                            .buffer  = ggml_webgpu_tensor_buf(dst),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+    }
+
+    uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, ctx->scale_pipelines[inplace], params,
+                                     entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_soft_max(webgpu_context & ctx,
+                                           ggml_tensor *    src0,
+                                           ggml_tensor *    src1,
+                                           ggml_tensor *    src2,
+                                           ggml_tensor *    dst) {
+    const int inplace   = ggml_webgpu_tensor_equal(src0, dst);
+    const int mask_type = (src1 != nullptr) ? src1->type : 2;  // use 2 for no mask here
+    const int has_sink  = (src2 != nullptr);
+    float     max_bias;
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+    float n_head_log2 = float(1u << (uint32_t) floor(log2(src0->ne[2])));
+    float m0          = powf(2.0f, -(max_bias) / n_head_log2);
+    float m1          = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        mask_type < 2 ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)) : 0,
+        has_sink ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)) : 0,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
+        mask_type < 2 ? (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)) : 0,
+        mask_type < 2 ? (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)) : 0,
+        mask_type < 2 ? (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)) : 0,
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        (uint32_t) ggml_nelements(dst),
+        (uint32_t) src0->ne[0],
+        (uint32_t) src0->ne[1],
+        (uint32_t) src0->ne[2],
+        mask_type < 2 ? (uint32_t) src1->ne[2] : 0,
+        mask_type < 2 ? (uint32_t) src1->ne[3] : 0,
+        *(uint32_t *) dst->op_params,  // scale
+        *(uint32_t *) &max_bias,
+        *(uint32_t *) &n_head_log2,
+        *(uint32_t *) &m0,
+        *(uint32_t *) &m1
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src0),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) }
+    };
+    uint32_t binding_num = 1;
+    if (mask_type < 2) {
+        entries.push_back({ .binding = binding_num,
+                            .buffer  = ggml_webgpu_tensor_buf(src1),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, src1) });
+        binding_num++;
+    }
+    if (has_sink) {
+        entries.push_back({ .binding = binding_num,
+                            .buffer  = ggml_webgpu_tensor_buf(src2),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, src2),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, src2) });
+        binding_num++;
+    }
+    if (!inplace) {
+        entries.push_back({ .binding = binding_num,
+                            .buffer  = ggml_webgpu_tensor_buf(dst),
+                            .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+                            .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
+    }
+
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool,
+                                     ctx->soft_max_pipelines[mask_type][has_sink][inplace], params, entries,
+                                     ggml_nrows(dst));
+}
+
+static webgpu_command ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    std::vector<uint32_t> params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+                                     (uint32_t) src->ne[0] };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
+    };
+
+    ggml_webgpu_generic_shader_lib_context shader_lib_ctx = {
+        .vec4        = src->ne[0] % 4 == 0,
+        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->argmax_pipelines.find(shader_lib_ctx.vec4);
+    if (it != ctx->argmax_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_argmax, shader_lib_ctx, "argmax");
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        ctx->argmax_pipelines.emplace(shader_lib_ctx.vec4, pipeline);
+    }
+    uint32_t wg_x = ggml_nelements(dst);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    bool          is_top_k = dst->op == GGML_OP_TOP_K;
+    // ascending order is 0, descending order is 1
+    const int32_t order    = is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(dst, 0);
+
+    ggml_webgpu_argsort_shader_lib_context shader_lib_ctx = {
+        .max_wg_size        = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
+        .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize,
+        .order              = order
+    };
+
+    webgpu_pipeline argsort_pipeline;
+    auto            it = ctx->argsort_pipelines.find(order);
+    if (it != ctx->argsort_pipelines.end()) {
+        argsort_pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_argsort_shader(ctx->p, wgsl_argsort, shader_lib_ctx);
+        argsort_pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        argsort_pipeline.context = processed.decisions;
+        ctx->argsort_pipelines.emplace(order, argsort_pipeline);
+    }
+    auto * argsort_decisions = static_cast<ggml_webgpu_argsort_shader_decisions *>(argsort_pipeline.context.get());
+
+    webgpu_pipeline argsort_merge_pipeline;
+    it = ctx->argsort_merge_pipelines.find(order);
+    if (it != ctx->argsort_merge_pipelines.end()) {
+        argsort_merge_pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_argsort_merge_shader(ctx->p, wgsl_argsort_merge, shader_lib_ctx);
+        argsort_merge_pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        argsort_merge_pipeline.context = processed.decisions;
+        ctx->argsort_merge_pipelines.emplace(order, argsort_merge_pipeline);
+    }
+
+    const uint32_t src_ne0 = (uint32_t) src->ne[0];
+    const uint32_t nrows   = (uint32_t) ggml_nrows(src);
+    const uint32_t npr     = CEIL_DIV(src_ne0, argsort_decisions->wg_size);
+    const uint32_t block_size =
+        is_top_k ? std::min(argsort_decisions->wg_size, (uint32_t) dst->ne[0]) : argsort_decisions->wg_size;
+    uint32_t out_ne0 = src_ne0;
+    if (is_top_k) {
+        if (npr > 1) {
+            const uint32_t last_tile = src_ne0 - (npr - 1) * argsort_decisions->wg_size;
+            out_ne0                  = (npr - 1) * block_size + std::min(last_tile, block_size);
+        } else {
+            out_ne0 = block_size;
+        }
+    }
+
+    uint32_t merge_len    = block_size;
+    uint32_t merge_passes = 0;
+    while (merge_len < out_ne0) {
+        merge_len <<= 1;
+        merge_passes++;
+    }
+
+    const bool start_in_tmp = (merge_passes % 2) == 1;
+
+    const size_t dst_offset = ggml_webgpu_tensor_offset(dst);
+    const size_t idx_nbytes = out_ne0 * ggml_nrows(dst) * sizeof(int32_t);
+    const size_t tmp_offset =
+        ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment);
+    const size_t tmp_binding_size = ROUNDUP_POW2(idx_nbytes, WEBGPU_STORAGE_BUF_BINDING_MULT);
+    const size_t dst_binding_size =
+        ROUNDUP_POW2(idx_nbytes + ggml_webgpu_tensor_misalignment(ctx, dst), WEBGPU_STORAGE_BUF_BINDING_MULT);
+
+    const uint32_t offset_src  = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type));
+    const uint32_t offset_dst  = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type));
+    const uint32_t offset_tmp  = 0;
+    const uint32_t stride_src1 = (uint32_t) (src->nb[1] / ggml_type_size(src->type));
+    const uint32_t stride_src2 = (uint32_t) (src->nb[2] / ggml_type_size(src->type));
+    const uint32_t stride_src3 = (uint32_t) (src->nb[3] / ggml_type_size(src->type));
+    const uint32_t stride_idx1 = out_ne0;
+    const uint32_t stride_idx2 = out_ne0 * (uint32_t) dst->ne[1];
+    const uint32_t stride_idx3 = stride_idx2 * (uint32_t) dst->ne[2];
+
+    std::vector<webgpu_pipeline>                   pipelines;
+    std::vector<std::vector<uint32_t>>             params_list;
+    std::vector<std::vector<wgpu::BindGroupEntry>> entries_list;
+    std::vector<std::pair<uint32_t, uint32_t>>     workgroups_list;
+
+    const uint32_t init_offset       = start_in_tmp ? offset_tmp : offset_dst;
+    const size_t   init_align_offset = start_in_tmp ? tmp_offset : ggml_webgpu_tensor_align_offset(ctx, dst);
+    const size_t   init_binding_size = start_in_tmp ? tmp_binding_size : dst_binding_size;
+
+    std::vector<uint32_t> init_params = {
+        offset_src,  init_offset, stride_src1, stride_src2,           stride_src3,           stride_idx1,
+        stride_idx2, stride_idx3, src_ne0,     (uint32_t) src->ne[1], (uint32_t) src->ne[2], out_ne0,
+        block_size,  npr,         nrows
+    };
+
+    const uint32_t                    total_wg_init = npr * nrows;
+    const uint32_t                    max_wg    = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
+    const uint32_t                    wg_x_init = std::min(total_wg_init, max_wg);
+    const uint32_t                    wg_y_init = CEIL_DIV(total_wg_init, wg_x_init);
+    std::vector<wgpu::BindGroupEntry> init_entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1, .buffer = ggml_webgpu_tensor_buf(dst), .offset = init_align_offset, .size = init_binding_size }
+    };
+
+    pipelines.push_back(argsort_pipeline);
+    params_list.push_back(std::move(init_params));
+    entries_list.push_back(std::move(init_entries));
+    workgroups_list.push_back({ wg_x_init, wg_y_init });
+
+    if (merge_passes == 0) {
+        return ggml_backend_webgpu_build_multi(ctx->global_ctx, ctx->param_buf_pool, pipelines, params_list,
+                                               entries_list, workgroups_list);
+    }
+
+    bool     in_is_tmp = start_in_tmp;
+    uint32_t len       = block_size;
+    while (len < out_ne0) {
+        const uint32_t nm = CEIL_DIV(out_ne0, 2 * len);
+
+        const bool     out_is_tmp  = !in_is_tmp;
+        const uint32_t offset_in   = in_is_tmp ? offset_tmp : offset_dst;
+        const uint32_t offset_out  = out_is_tmp ? offset_tmp : offset_dst;
+        const size_t   align_in    = in_is_tmp ? tmp_offset : ggml_webgpu_tensor_align_offset(ctx, dst);
+        const size_t   align_out   = out_is_tmp ? tmp_offset : ggml_webgpu_tensor_align_offset(ctx, dst);
+        const size_t   size_in     = in_is_tmp ? tmp_binding_size : dst_binding_size;
+        const size_t   size_out    = out_is_tmp ? tmp_binding_size : dst_binding_size;
+        const uint32_t top_k_out   = (is_top_k && nm == 1) ? (uint32_t) dst->ne[0] : out_ne0;
+        const uint32_t stride_out1 = top_k_out;
+        const uint32_t stride_out2 = top_k_out * (uint32_t) dst->ne[1];
+        const uint32_t stride_out3 = stride_out2 * (uint32_t) dst->ne[2];
+
+        std::vector<uint32_t> merge_params = { offset_src,
+                                               offset_in,
+                                               offset_out,
+                                               stride_src1,
+                                               stride_src2,
+                                               stride_src3,
+                                               stride_idx1,
+                                               stride_idx2,
+                                               stride_idx3,
+                                               stride_out1,
+                                               stride_out2,
+                                               stride_out3,
+                                               out_ne0,
+                                               (uint32_t) src->ne[1],
+                                               (uint32_t) src->ne[2],
+                                               top_k_out,
+                                               len,
+                                               nm,
+                                               nrows };
+
+        std::vector<wgpu::BindGroupEntry> merge_entries = {
+            { .binding = 0,
+             .buffer  = ggml_webgpu_tensor_buf(src),
+             .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+             .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+            { .binding = 1, .buffer = ggml_webgpu_tensor_buf(dst), .offset = align_in, .size = size_in },
+            { .binding = 2, .buffer = ggml_webgpu_tensor_buf(dst), .offset = align_out, .size = size_out }
+        };
+
+        const uint32_t total_wg_merge = nm * nrows;
+        const uint32_t wg_x_merge     = std::min(total_wg_merge, max_wg);
+        const uint32_t wg_y_merge     = CEIL_DIV(total_wg_merge, wg_x_merge);
+        workgroups_list.push_back({ wg_x_merge, wg_y_merge });
+        pipelines.push_back(argsort_merge_pipeline);
+        params_list.push_back(std::move(merge_params));
+        entries_list.push_back(std::move(merge_entries));
+
+        len <<= 1;
+        in_is_tmp = !in_is_tmp;
+    }
+
+    return ggml_backend_webgpu_build_multi(ctx->global_ctx, ctx->param_buf_pool, pipelines, params_list, entries_list,
+                                           workgroups_list);
+}
+
+static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    std::vector<uint32_t> params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+                                     (uint32_t) src->ne[0] };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
+    };
+
+    ggml_webgpu_generic_shader_lib_context shader_lib_ctx = {
+        .vec4        = false,
+        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
+    };
+    webgpu_pipeline pipeline;
+    auto            it = ctx->cumsum_pipelines.find(1);
+    if (it != ctx->cumsum_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_cumsum, shader_lib_ctx, "cumsum");
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        ctx->cumsum_pipelines.emplace(1, pipeline);
+    }
+    uint32_t wg_x = ggml_nrows(dst);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
+    bool                  total_sum = dst->op == GGML_OP_SUM;
+    std::vector<uint32_t> params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+                                     total_sum ? 0 : (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+                                     total_sum ? 0 : (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+                                     total_sum ? 0 : (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+                                     total_sum ? static_cast<uint32_t>(ggml_nelements(src)) : (uint32_t) src->ne[0],
+                                     total_sum ? 1 : (uint32_t) src->ne[1],
+                                     total_sum ? 1 : (uint32_t) src->ne[2] };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
+        { .binding = 1,
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
+    };
+
+    ggml_webgpu_generic_shader_lib_context shader_lib_ctx = {
+        .vec4        = false,
+        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->sum_rows_pipelines.find(1);
+    if (it != ctx->sum_rows_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_sum_rows, shader_lib_ctx, "sum_rows");
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        ctx->sum_rows_pipelines.emplace(1, pipeline);
+    }
+    uint32_t wg_x = total_sum ? 1 : ggml_nrows(dst);
+    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
+}
+
+// Returns the encoded command, or std::nullopt if the operation is a no-op
+static std::optional<webgpu_command> ggml_webgpu_encode_node(webgpu_context ctx, ggml_tensor * node) {
+    if (ggml_is_empty(node)) {
+        return std::nullopt;
+    }
+    if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
+        return std::nullopt;
+    }
+    WEBGPU_LOG_DEBUG("ggml_webgpu_encode_node(" << node << ", " << ggml_op_name(node->op) << ")");
+
+    ggml_tensor * src0 = node->src[0];
+    ggml_tensor * src1 = node->src[1];
+    ggml_tensor * src2 = node->src[2];
+
+    switch (node->op) {
+            // no-ops
+        case GGML_OP_NONE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_RESHAPE:
+            return std::nullopt;
+        case GGML_OP_CPY:
+        case GGML_OP_CONT:
+            return ggml_webgpu_cpy(ctx, src0, node);
+        case GGML_OP_SET_ROWS:
+            return ggml_webgpu_set_rows(ctx, src0, src1, node);
+        case GGML_OP_GET_ROWS:
+            return ggml_webgpu_get_rows(ctx, src0, src1, node);
+        case GGML_OP_MUL_MAT:
+            return ggml_webgpu_mul_mat(ctx, src0, src1, node);
+        case GGML_OP_FLASH_ATTN_EXT:
+#ifndef __EMSCRIPTEN__
+            return ggml_webgpu_flash_attn(ctx, src0, src1, src2, node->src[3], node->src[4], node);
+#else
+            return std::nullopt;
+#endif
+        case GGML_OP_ADD:
+        case GGML_OP_SUB:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+            return ggml_webgpu_binary_op(ctx, src0, src1, node);
+        case GGML_OP_RMS_NORM:
+            return ggml_webgpu_rms_norm(ctx, src0, node);
+        case GGML_OP_ROPE:
+            return ggml_webgpu_rope(ctx, src0, src1, src2, node);
+        case GGML_OP_GLU:
+            return ggml_webgpu_glu(ctx, src0, src1, node);
+        case GGML_OP_SCALE:
+            return ggml_webgpu_scale(ctx, src0, node);
+        case GGML_OP_SOFT_MAX:
+            return ggml_webgpu_soft_max(ctx, src0, src1, src2, node);
+        case GGML_OP_UNARY:
+            return ggml_webgpu_unary_op(ctx, src0, node);
+        case GGML_OP_CLAMP:
+            return ggml_webgpu_unary_op(ctx, src0, node);
+        case GGML_OP_FILL:
+            return ggml_webgpu_unary_op(ctx, src0, node);
+        case GGML_OP_LOG:
+            return ggml_webgpu_unary_op(ctx, src0, node);
+        case GGML_OP_PAD:
+            return ggml_webgpu_pad(ctx, src0, node);
+        case GGML_OP_ARGMAX:
+            return ggml_webgpu_argmax(ctx, src0, node);
+        case GGML_OP_ARGSORT:
+            return ggml_webgpu_argsort(ctx, src0, node);
+        case GGML_OP_TOP_K:
+            // we reuse the same argsort implementation for top_k
+            return ggml_webgpu_argsort(ctx, src0, node);
+        case GGML_OP_CUMSUM:
+            return ggml_webgpu_cumsum(ctx, src0, node);
+        case GGML_OP_SUM:
+        case GGML_OP_SUM_ROWS:
+            return ggml_webgpu_sum_rows(ctx, src0, node);
+        default:
+            return std::nullopt;
+    }
+}
+
+static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_graph_compute(" << cgraph->n_nodes << " nodes)");
+
+    ggml_backend_webgpu_context * backend_ctx = (ggml_backend_webgpu_context *) backend->context;
+    webgpu_context                ctx         = backend_ctx->webgpu_ctx;
+
+    WEBGPU_CPU_PROFILE_TOTAL_START(graph_compute);
+
+    ctx->global_ctx->inflight_threads++;
+
+    std::vector<webgpu_command>            commands;
+    std::vector<webgpu_submission_futures> futures;
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        if (auto cmd = ggml_webgpu_encode_node(ctx, cgraph->nodes[i])) {
+            commands.push_back(*cmd);
+        }
+        // compute the batch size based on the number of inflight threads
+        uint32_t inflight_threads = ctx->global_ctx->inflight_threads;
+        uint32_t batch_size       = std::min(std::max(1u, WEBGPU_NUM_PARAM_BUFS / std::max(inflight_threads, 1u)),
+                                             WEBGPU_COMMAND_SUBMIT_BATCH_SIZE);
+        if (commands.size() >= batch_size) {
+            futures.push_back(ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool,
+                                                         &ctx->set_rows_error_buf_pool));
+            // Process events and check for completed submissions
+            ctx->global_ctx->instance.ProcessEvents();
+            ggml_backend_webgpu_wait(ctx->global_ctx, futures, false);
+            commands.clear();
+        }
+    }
+    if (!commands.empty()) {
+        webgpu_submission_futures new_futures =
+            ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool, &ctx->set_rows_error_buf_pool);
+        futures.push_back(new_futures);
+    }
+
+    ggml_backend_webgpu_wait(ctx->global_ctx, futures);
+    ctx->global_ctx->inflight_threads--;
+    WEBGPU_CPU_PROFILE_TOTAL_END(graph_compute, ctx->global_ctx);
+    return GGML_STATUS_SUCCESS;
+}
+
+static ggml_backend_i ggml_backend_webgpu_i = {
+    /* .get_name                = */ ggml_backend_webgpu_name,
+    /* .free                    = */ ggml_backend_webgpu_free,
+    /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_async        = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
+    /* .synchronize             = */ NULL,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_webgpu_graph_compute,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .graph_optimize          = */ NULL,
+};
+
+/* End GGML Backend Interface */
+
+/* GGML Backend Buffer Interface */
+
+static void ggml_backend_webgpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_webgpu_buffer_context * ctx = static_cast<ggml_backend_webgpu_buffer_context *>(buffer->context);
+    if (ctx != nullptr && ctx->buffer != nullptr) {
+        ctx->buffer.Destroy();
+        delete ctx;
+    }
+}
+
+// Returns the "fake" base pointer.
+static void * ggml_backend_webgpu_buffer_get_base(ggml_backend_buffer_t buffer) {
+    GGML_UNUSED(buffer);
+    return webgpu_ptr_base;
+}
+
+static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffer,
+                                                     ggml_tensor *         tensor,
+                                                     uint8_t               value,
+                                                     size_t                offset,
+                                                     size_t                size) {
+    if (size == 0) {
+        WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor: size is zero, nothing to do.");
+        return;
+    }
+
+    WEBGPU_CPU_PROFILE_TOTAL_START(memset_tensor);
+
+    ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buf_ctx->label << ", " << tensor << ", " << value
+                                                                 << ", " << offset << ", " << size << ")");
+
+    size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
+
+    // This is a trick to set all bytes of a u32 to the same 1 byte value.
+    uint32_t val32 = (uint32_t) value * 0x01010101;
+    ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32, total_offset, size);
+    WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, buf_ctx->global_ctx);
+}
+
+static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer,
+                                                  ggml_tensor *         tensor,
+                                                  const void *          data,
+                                                  size_t                offset,
+                                                  size_t                size) {
+    WEBGPU_CPU_PROFILE_TOTAL_START(set_tensor);
+    ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data
+                                                              << ", " << offset << ", " << size << ")");
+
+    size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
+
+    buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4);
+
+    if (size % 4 != 0) {
+        // If size is not a multiple of 4, we need to memset the remaining bytes
+        size_t remaining_size = size % 4;
+
+        // pack the remaining bytes into a uint32_t
+        uint32_t val32 = 0;
+
+        for (size_t i = 0; i < remaining_size; i++) {
+            ((uint8_t *) &val32)[i] = ((const uint8_t *) data)[size - remaining_size + i];
+        }
+        // memset the remaining bytes
+        ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32,
+                                          total_offset + (size - remaining_size), remaining_size);
+    } else {
+        // wait for WriteBuffer to complete
+        buf_ctx->global_ctx->instance.WaitAny(buf_ctx->global_ctx->queue.OnSubmittedWorkDone(
+                                                  wgpu::CallbackMode::AllowSpontaneous,
+                                                  [](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
+                                                      if (status != wgpu::QueueWorkDoneStatus::Success) {
+                                                          GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n",
+                                                                         std::string(message).c_str());
+                                                      }
+                                                  }),
+                                              UINT64_MAX);
+    }
+    WEBGPU_CPU_PROFILE_TOTAL_END(set_tensor, buf_ctx->global_ctx);
+}
+
+static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer,
+                                                  const ggml_tensor *   tensor,
+                                                  void *                data,
+                                                  size_t                offset,
+                                                  size_t                size) {
+    WEBGPU_CPU_PROFILE_TOTAL_START(get_tensor);
+    ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buf_ctx->label << ", " << tensor << ", " << data
+                                                              << ", " << offset << ", " << size << ")");
+    wgpu::Device device = buf_ctx->global_ctx->device;
+
+    size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
+
+    size_t final_size = size;
+    if (size % 4 != 0) {
+        // If size is not a multiple of 4, we need to round it up to the next multiple of 4
+        final_size = size + (4 - (size % 4));
+    }
+
+    std::lock_guard<std::recursive_mutex> lock(buf_ctx->global_ctx->mutex);
+
+    if (buf_ctx->global_ctx->get_tensor_staging_buf == nullptr ||
+        buf_ctx->global_ctx->get_tensor_staging_buf.GetSize() < final_size) {
+        // Create a new staging buffer if it doesn't exist or is too small
+        if (buf_ctx->global_ctx->get_tensor_staging_buf) {
+            buf_ctx->global_ctx->get_tensor_staging_buf.Destroy();
+        }
+        ggml_webgpu_create_buffer(device, buf_ctx->global_ctx->get_tensor_staging_buf, final_size,
+                                  wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "get_tensor_staging_buf");
+    }
+
+    // Copy the data from the buffer to the staging buffer
+    wgpu::CommandEncoder encoder = device.CreateCommandEncoder();
+    encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, buf_ctx->global_ctx->get_tensor_staging_buf, 0,
+                               final_size);
+    wgpu::CommandBuffer commands = encoder.Finish();
+
+    // Submit the command buffer to the queue
+    buf_ctx->global_ctx->queue.Submit(1, &commands);
+
+    // Map the staging buffer to read the data
+    ggml_backend_webgpu_map_buffer(buf_ctx->global_ctx, buf_ctx->global_ctx->get_tensor_staging_buf,
+                                   wgpu::MapMode::Read, 0, final_size);
+    // Must specify size here since the staging buffer might be larger than the tensor size
+    const void * mapped_range = buf_ctx->global_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size);
+
+    // Copy the data from the mapped range to the output buffer
+    std::memcpy(data, mapped_range, size);
+    buf_ctx->global_ctx->get_tensor_staging_buf.Unmap();
+    WEBGPU_CPU_PROFILE_TOTAL_END(get_tensor, buf_ctx->global_ctx);
+}
+
+static void ggml_backend_webgpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_clear(" << buffer << ", " << (uint32_t) value << ")");
+    WEBGPU_CPU_PROFILE_TOTAL_START(clear);
+    ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+    ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, value, 0, buffer->size);
+    WEBGPU_CPU_PROFILE_TOTAL_END(clear, buf_ctx->global_ctx);
+}
+
+static ggml_backend_buffer_i ggml_backend_webgpu_buffer_interface = {
+    /* .free_buffer     = */ ggml_backend_webgpu_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_webgpu_buffer_get_base,
+    /* .init_tensor     = */ NULL,  // TODO: optional, needed?
+    /* .memset_tensor   = */ ggml_backend_webgpu_buffer_memset_tensor,
+    /* .set_tensor      = */ ggml_backend_webgpu_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_webgpu_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,  // TODO: optional, implement this
+    /* .clear           = */ ggml_backend_webgpu_buffer_clear,
+    /* .reset           = */ NULL,  // TODO: optional, think it coordinates with .init_tensor
+};
+
+/* End GGML Backend Buffer Interface */
+
+/* GGML Backend Buffer Type Interface */
+
+static const char * ggml_backend_webgpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+    return ctx->device_name.c_str();
+}
+
+static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
+                                                                          size_t                     size) {
+    static std::atomic<int> buffer_count;
+    int                     buffer_id = buffer_count++;
+    std::string             buf_name  = "tensor_buf" + std::to_string(buffer_id);
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer_" << buffer_id << ": " << size << " bytes");
+
+    ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+    wgpu::Buffer                         buf;
+    ggml_webgpu_create_buffer(ctx->webgpu_global_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT),
+                              wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst,
+                              buf_name.c_str());
+
+    ggml_backend_webgpu_buffer_context * buf_ctx =
+        new ggml_backend_webgpu_buffer_context(buf, buf_name, ctx->webgpu_global_ctx);
+
+    return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size);
+}
+
+static size_t ggml_backend_webgpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    ggml_backend_webgpu_device_context * dev_ctx =
+        static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+    return dev_ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment;
+}
+
+// maxBufferSize might be larger, but you can't bind more than maxStorageBufferBindingSize to a single binding.
+static size_t ggml_backend_webgpu_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    ggml_backend_webgpu_device_context * dev_ctx =
+        static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+    return dev_ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize;
+}
+
+static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft,
+                                                             const ggml_tensor *        tensor) {
+    ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+    size_t                               res = ggml_nbytes(tensor);
+    switch (tensor->op) {
+        case GGML_OP_ARGSORT:
+            res = ROUNDUP_POW2(res * 2 + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment,
+                               WEBGPU_STORAGE_BUF_BINDING_MULT);
+            break;
+        case GGML_OP_TOP_K:
+            {
+                const ggml_tensor * src0 = tensor->src[0];
+                if (src0) {
+                    const size_t full = sizeof(int32_t) * ggml_nelements(src0);
+                    res               = ROUNDUP_POW2(
+                        full * 2 + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment,
+                        WEBGPU_STORAGE_BUF_BINDING_MULT);
+                }
+            }
+            break;
+        default:
+            break;
+    }
+    return res;
+}
+
+/* End GGML Backend Buffer Type Interface */
+
+/* GGML Backend Device Interface */
+
+static const char * ggml_backend_webgpu_device_get_name(ggml_backend_dev_t dev) {
+    ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
+    return ctx->device_name.c_str();
+}
+
+static const char * ggml_backend_webgpu_device_get_description(ggml_backend_dev_t dev) {
+    ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
+    return ctx->device_desc.c_str();
+}
+
+static void ggml_backend_webgpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+    ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
+    // TODO: for now, return maxBufferSize as both free and total memory
+    // Track https://github.com/gpuweb/gpuweb/issues/5505 for updates.
+    uint64_t                             max_buffer_size = ctx->webgpu_global_ctx->capabilities.limits.maxBufferSize;
+    // If we're on a 32-bit system, clamp to UINTPTR_MAX
+#if UINTPTR_MAX < UINT64_MAX
+    uint64_t max_ptr_size = static_cast<uint64_t>(UINTPTR_MAX);
+    if (max_buffer_size > max_ptr_size) {
+        max_buffer_size = max_ptr_size;
+    }
+#endif
+    *free  = static_cast<size_t>(max_buffer_size);
+    *total = static_cast<size_t>(max_buffer_size);
+}
+
+static enum ggml_backend_dev_type ggml_backend_webgpu_device_get_type(ggml_backend_dev_t dev) {
+    GGML_UNUSED(dev);
+    return GGML_BACKEND_DEVICE_TYPE_GPU;
+}
+
+static void ggml_backend_webgpu_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
+    props->name        = ggml_backend_webgpu_device_get_name(dev);
+    props->description = ggml_backend_webgpu_device_get_description(dev);
+    props->type        = ggml_backend_webgpu_device_get_type(dev);
+    ggml_backend_webgpu_device_get_memory(dev, &props->memory_free, &props->memory_total);
+    props->caps = {
+        /* .async                 = */ false,
+        /* .host_buffer           = */ false,
+        /* .buffer_from_host_ptr  = */ false,
+        /* .events                = */ false,
+    };
+}
+
+static ggml_guid_t ggml_backend_webgpu_guid(void) {
+    static const char * guid_str = "__ggml_webgpu :)";
+    return reinterpret_cast<ggml_guid_t>((void *) guid_str);
+}
+
+// Workgroup size is a common constant
+static std::vector<wgpu::ConstantEntry> ggml_webgpu_wg_size_entry(uint32_t wg_size) {
+    std::vector<wgpu::ConstantEntry> constants(1);
+    constants[0].key   = "wg_size";
+    constants[0].value = wg_size;
+    return constants;
+}
+
+static void ggml_webgpu_init_memset_pipeline(webgpu_global_context & ctx) {
+    // we use the maximum workgroup size for the memset pipeline
+    size_t max_threads = WEBGPU_MAX_WG_SIZE * ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
+    // Size the bytes_per_thread so that the largest buffer size can be handled
+    ctx->capabilities.memset_bytes_per_thread =
+        CEIL_DIV(ctx->capabilities.limits.maxStorageBufferBindingSize, max_threads);
+    std::vector<wgpu::ConstantEntry> constants(2);
+    constants[0].key         = "wg_size";
+    constants[0].value       = WEBGPU_MAX_WG_SIZE;
+    constants[1].key         = "bytes_per_thread";
+    constants[1].value       = ctx->capabilities.memset_bytes_per_thread;
+    ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(ctx->device, wgsl_memset, "memset", constants);
+}
+
+static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
+    // Q4/Q5/Q8 classic quantizations
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_1][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_0][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_1][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q8_0][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32");
+
+    // K-quantizations
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q2_K][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q3_K][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_K][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_K][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q6_K][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32");
+
+    // IQ quantizations (2-, 3-, 4-bit variants)
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XS][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_S][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32");
+
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_S][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32");
+
+    // 1-bit and 4-bit IQ variants
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_S][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_M][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_NL][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32");
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_XS][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32");
+
+    std::string proc_mul_mat_f32_f32;
+    std::string proc_mul_mat_f32_f32_vec;
+    std::string proc_mul_mat_f16_f32;
+    std::string proc_mul_mat_f16_f32_vec;
+    std::string proc_mul_mat_f16_f16;
+    std::string proc_mul_mat_f16_f16_vec;
+    std::string proc_mul_mat_q4_0_f32;
+    std::string proc_mul_mat_q4_0_f32_vec;
+
+    std::vector<wgpu::ConstantEntry> mul_mat_constants;
+#ifndef __EMSCRIPTEN__
+    if (webgpu_ctx->global_ctx->capabilities.supports_subgroup_matrix) {
+        std::map<std::string, std::string> sg_matrix_repls;
+        sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] =
+            std::to_string(webgpu_ctx->global_ctx->capabilities.max_subgroup_size);
+        sg_matrix_repls["WEBGPU_TILE_K"]            = std::to_string(WEBGPU_MUL_MAT_TILE_K);
+        sg_matrix_repls["WEBGPU_SUBGROUP_M"]        = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M);
+        sg_matrix_repls["WEBGPU_SUBGROUP_N"]        = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N);
+        sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M);
+        sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N);
+        sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"]     = std::to_string(webgpu_ctx->global_ctx->capabilities.sg_mat_m);
+        sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"]     = std::to_string(webgpu_ctx->global_ctx->capabilities.sg_mat_n);
+        sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"]     = std::to_string(webgpu_ctx->global_ctx->capabilities.sg_mat_k);
+        proc_mul_mat_f32_f32 = ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32, sg_matrix_repls);
+        proc_mul_mat_f32_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32_vec, sg_matrix_repls);
+        proc_mul_mat_f16_f32 = ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f32, sg_matrix_repls);
+        proc_mul_mat_f16_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f32_vec, sg_matrix_repls);
+        proc_mul_mat_f16_f16 = ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f16, sg_matrix_repls);
+        proc_mul_mat_f16_f16_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f16_vec, sg_matrix_repls);
+        proc_mul_mat_q4_0_f32 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_q4_0_f32, sg_matrix_repls);
+        proc_mul_mat_q4_0_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_q4_0_f32_vec, sg_matrix_repls);
+    } else {
+#endif
+        mul_mat_constants.push_back({ .key = "TILE_K", .value = WEBGPU_MUL_MAT_TILE_K });
+        mul_mat_constants.push_back({ .key = "WORKGROUP_SIZE_M", .value = WEBGPU_MUL_MAT_WG_SIZE_M });
+        mul_mat_constants.push_back({ .key = "WORKGROUP_SIZE_N", .value = WEBGPU_MUL_MAT_WG_SIZE_N });
+
+        std::map<std::string, std::string> reg_repls;
+        reg_repls["WEBGPU_TILE_M"] = std::to_string(WEBGPU_MUL_MAT_TILE_M);
+        reg_repls["WEBGPU_TILE_N"] = std::to_string(WEBGPU_MUL_MAT_TILE_N);
+
+        proc_mul_mat_f32_f32      = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f32_f32, reg_repls);
+        proc_mul_mat_f32_f32_vec  = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f32_f32_vec, reg_repls);
+        proc_mul_mat_f16_f32      = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f32, reg_repls);
+        proc_mul_mat_f16_f32_vec  = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f32_vec, reg_repls);
+        proc_mul_mat_f16_f16      = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f16, reg_repls);
+        proc_mul_mat_f16_f16_vec  = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f16_vec, reg_repls);
+        proc_mul_mat_q4_0_f32     = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_q4_0_f32, reg_repls);
+        proc_mul_mat_q4_0_f32_vec = ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_q4_0_f32_vec, reg_repls);
+#ifndef __EMSCRIPTEN__
+    }
+#endif
+
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_f32_f32.c_str(), "mul_mat_f32_f32", mul_mat_constants);
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_f32_f32_vec.c_str(), "mul_mat_f32_f32_vec", mul_mat_constants);
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f32.c_str(), "mul_mat_f16_f32", mul_mat_constants);
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f32_vec.c_str(), "mul_mat_f16_f32_vec", mul_mat_constants);
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f16.c_str(), "mul_mat_f16_f16", mul_mat_constants);
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f16_vec.c_str(), "mul_mat_f16_f16_vec", mul_mat_constants);
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_q4_0_f32.c_str(), "mul_mat_q4_0_f32", mul_mat_constants);
+    webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, proc_mul_mat_q4_0_f32_vec.c_str(), "mul_mat_q4_0_f32_vec", mul_mat_constants);
+
+    std::vector<wgpu::ConstantEntry> mul_mat_vec_constants(3);
+    mul_mat_vec_constants[0].key   = "WORKGROUP_SIZE";
+    mul_mat_vec_constants[0].value = WEBGPU_MUL_MAT_VEC_WG_SIZE;
+    mul_mat_vec_constants[1].key   = "TILE_K";
+    mul_mat_vec_constants[1].value = WEBGPU_MUL_MAT_VEC_TILE_K;
+    mul_mat_vec_constants[2].key   = "OUTPUTS_PER_WG";
+    mul_mat_vec_constants[2].value = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG;
+
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants);
+}
+
+static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
+
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants);
+
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_F16][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_I32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_i32, "get_rows_i32", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_0][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_0, "get_rows_q4_0", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_1][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_1, "get_rows_q4_1", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_0][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_0, "get_rows_q5_0", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_1][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_1, "get_rows_q5_1", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q8_0][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q8_0, "get_rows_q8_0", constants);
+
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q2_K][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q2_k, "get_rows_q2_k", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q3_K][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q3_k, "get_rows_q3_k", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_K][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_k, "get_rows_q4_k", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_K][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_k, "get_rows_q5_k", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q6_K][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants);
+
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XS][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_S][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_S][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_S][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq1_s, "get_rows_iq1_s", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_M][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq1_m, "get_rows_iq1_m", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_NL][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants);
+    webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_XS][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants);
+}
+
+static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
+
+    webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F32] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_f32, "cpy_f32_f32", constants);
+    webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_I32] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_i32, "cpy_f32_i32", constants);
+    webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F16] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_f16, "cpy_f32_f16", constants);
+    webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F32] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f32, "cpy_f16_f32", constants);
+    webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F16] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants);
+}
+
+static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE);
+
+    webgpu_ctx->rms_norm_pipelines[0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rms_norm, "rms_norm", constants);
+    webgpu_ctx->rms_norm_pipelines[1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants);
+}
+
+static void ggml_webgpu_init_rope_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
+
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32, "rope_f32", constants);
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants);
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants);
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants);
+
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16, "rope_f16", constants);
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants);
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants);
+    webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants);
+}
+
+static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
+
+    // REGLU
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f32, "reglu_f32", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f16, "reglu_f16", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][1] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f32_split, "reglu_f32_split", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][1] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f16_split, "reglu_f16_split", constants);
+
+    // GEGLU
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f32, "geglu_f32", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f16, "geglu_f16", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][1] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f32_split, "geglu_f32_split", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][1] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f16_split, "geglu_f16_split", constants);
+
+    // SWIGLU
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants);
+
+    // SWIGLU_OAI
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants);
+
+    // GEGLU_ERF
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants);
+
+    // GEGLU_QUICK
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants);
+    webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants);
+}
+
+static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
+
+    webgpu_ctx->scale_pipelines[0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32, "scale_f32", constants);
+    webgpu_ctx->scale_pipelines[1] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32_inplace,
+                                                                 "scale_f32_inplace", constants);
+}
+
+static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE);
+
+    // f32 (no mask)
+    webgpu_ctx->soft_max_pipelines[2][0][0] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants);
+    webgpu_ctx->soft_max_pipelines[2][0][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants);
+    webgpu_ctx->soft_max_pipelines[2][1][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants);
+    webgpu_ctx->soft_max_pipelines[2][1][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants);
+
+    // f32 mask (mask_type = 0)
+    webgpu_ctx->soft_max_pipelines[0][0][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants);
+    webgpu_ctx->soft_max_pipelines[0][0][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants);
+    webgpu_ctx->soft_max_pipelines[0][1][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants);
+    webgpu_ctx->soft_max_pipelines[0][1][1] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace,
+                                    "soft_max_f32_mask_f32_sink_inplace", constants);
+
+    // f16 mask (mask_type = 1)
+    webgpu_ctx->soft_max_pipelines[1][0][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants);
+    webgpu_ctx->soft_max_pipelines[1][0][1] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants);
+    webgpu_ctx->soft_max_pipelines[1][1][0] = ggml_webgpu_create_pipeline(
+        webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants);
+    webgpu_ctx->soft_max_pipelines[1][1][1] =
+        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace,
+                                    "soft_max_f32_mask_f16_sink_inplace", constants);
+}
+
+static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
+    wgpu::RequestAdapterOptions options = {};
+
+#ifndef __EMSCRIPTEN__
+    // TODO: track need for these toggles: https://issues.chromium.org/issues/42251215
+    const char * const          adapterEnabledToggles[] = { "vulkan_enable_f16_on_nvidia", "use_vulkan_memory_model" };
+    wgpu::DawnTogglesDescriptor adapterTogglesDesc;
+    adapterTogglesDesc.enabledToggles     = adapterEnabledToggles;
+    adapterTogglesDesc.enabledToggleCount = 2;
+    options.nextInChain                   = &adapterTogglesDesc;
+#endif
+
+    ctx->webgpu_global_ctx->instance.WaitAny(
+        ctx->webgpu_global_ctx->instance.RequestAdapter(
+            &options, wgpu::CallbackMode::AllowSpontaneous,
+            [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) {
+                if (status != wgpu::RequestAdapterStatus::Success) {
+                    GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message);
+                    return;
+                }
+                ctx->webgpu_global_ctx->adapter = std::move(adapter);
+            }),
+        UINT64_MAX);
+    GGML_ASSERT(ctx->webgpu_global_ctx->adapter != nullptr);
+
+    ctx->webgpu_global_ctx->adapter.GetLimits(&ctx->webgpu_global_ctx->capabilities.limits);
+
+    wgpu::AdapterInfo info{};
+#ifndef __EMSCRIPTEN__
+    wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{};
+    if (ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) {
+        info.nextInChain = &subgroup_matrix_configs;
+    }
+#endif
+    ctx->webgpu_global_ctx->adapter.GetInfo(&info);
+    wgpu::SupportedFeatures features;
+    ctx->webgpu_global_ctx->adapter.GetFeatures(&features);
+    // we require f16 support
+    GGML_ASSERT(ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ShaderF16));
+
+#ifndef __EMSCRIPTEN__
+    // Only support square f16 matrices of size 8 or 16 for now
+    bool valid_subgroup_matrix_config = false;
+    if (ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) {
+        for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) {
+            const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i];
+            if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) &&
+                config.componentType == wgpu::SubgroupMatrixComponentType::F16 &&
+                config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) {
+                ctx->webgpu_global_ctx->capabilities.sg_mat_m = config.M;
+                ctx->webgpu_global_ctx->capabilities.sg_mat_n = config.N;
+                ctx->webgpu_global_ctx->capabilities.sg_mat_k = config.K;
+                valid_subgroup_matrix_config                  = true;
+                break;
+            }
+        }
+    }
+    ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix = valid_subgroup_matrix_config;
+#endif
+
+    // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate.
+    // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter.
+    ctx->webgpu_global_ctx->capabilities.max_subgroup_size = info.subgroupMaxSize;
+    // Initialize device
+    std::vector<wgpu::FeatureName> required_features       = { wgpu::FeatureName::ShaderF16 };
+
+#ifndef __EMSCRIPTEN__
+    required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization);
+    if (ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix) {
+        required_features.push_back(wgpu::FeatureName::Subgroups);
+        required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix);
+    }
+#endif
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    required_features.push_back(wgpu::FeatureName::TimestampQuery);
+#endif
+
+    wgpu::DeviceDescriptor dev_desc;
+    dev_desc.requiredLimits       = &ctx->webgpu_global_ctx->capabilities.limits;
+    dev_desc.requiredFeatures     = required_features.data();
+    dev_desc.requiredFeatureCount = required_features.size();
+    dev_desc.SetDeviceLostCallback(
+        wgpu::CallbackMode::AllowSpontaneous,
+        [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) {
+            if (reason == wgpu::DeviceLostReason::Destroyed) {
+                return;
+            }
+            GGML_UNUSED(device);
+            GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast<int>(reason),
+                           std::string(message).c_str());
+        });
+    dev_desc.SetUncapturedErrorCallback(
+        [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) {
+            GGML_UNUSED(device);
+            GGML_ABORT("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast<int>(reason),
+                       std::string(message).c_str());
+        });
+
+#ifndef __EMSCRIPTEN__
+    // Enable Dawn-specific toggles to increase native performance
+    // TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these,
+    //       only for native performance?
+    const char * const deviceEnabledToggles[]  = { "skip_validation", "disable_robustness", "disable_workgroup_init",
+                                                   "disable_polyfills_on_integer_div_and_mod" };
+    const char * const deviceDisabledToggles[] = { "timestamp_quantization" };
+    wgpu::DawnTogglesDescriptor deviceTogglesDesc;
+    deviceTogglesDesc.enabledToggles      = deviceEnabledToggles;
+    deviceTogglesDesc.enabledToggleCount  = 4;
+    deviceTogglesDesc.disabledToggles     = deviceDisabledToggles;
+    deviceTogglesDesc.disabledToggleCount = 1;
+
+    dev_desc.nextInChain = &deviceTogglesDesc;
+#endif
+
+    ctx->webgpu_global_ctx->instance.WaitAny(
+        ctx->webgpu_global_ctx->adapter.RequestDevice(
+            &dev_desc, wgpu::CallbackMode::AllowSpontaneous,
+            [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) {
+                if (status != wgpu::RequestDeviceStatus::Success) {
+                    GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", std::string(message).c_str());
+                    return;
+                }
+                ctx->webgpu_global_ctx->device = std::move(device);
+            }),
+        UINT64_MAX);
+    GGML_ASSERT(ctx->webgpu_global_ctx->device != nullptr);
+
+    ggml_webgpu_init_memset_pipeline(ctx->webgpu_global_ctx);
+    ctx->webgpu_global_ctx->memset_buf_pool.init(ctx->webgpu_global_ctx->device, 1, WEBGPU_PARAMS_BUF_SIZE_BYTES,
+                                                 wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
+                                                 wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite);
+    ctx->webgpu_global_ctx->queue = ctx->webgpu_global_ctx->device.GetQueue();
+
+#ifdef GGML_WEBGPU_GPU_PROFILE
+    // Initialize buffer pool for timestamp queries, used for profiling
+    ctx->webgpu_global_ctx->timestamp_query_buf_pool.init(
+        ctx->webgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES,
+        wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc,
+        wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst);
+#endif
+
+    GGML_LOG_INFO(
+        "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | "
+        "device_desc: %s\n",
+        info.vendorID, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID,
+        std::string(info.device).c_str(), std::string(info.description).c_str());
+    return true;
+}
+
+static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) {
+    ggml_backend_webgpu_device_context * dev_ctx    = (ggml_backend_webgpu_device_context *) dev->context;
+    webgpu_context                       webgpu_ctx = std::make_shared<webgpu_context_struct>();
+    webgpu_ctx->global_ctx                          = dev_ctx->webgpu_global_ctx;
+    webgpu_ctx->param_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES,
+                                    wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
+                                    wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite);
+    webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS,
+                                             WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
+                                             wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage,
+                                             wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead);
+
+    ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
+    ggml_webgpu_init_get_rows_pipeline(webgpu_ctx);
+    ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
+    ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx);
+    ggml_webgpu_init_rope_pipeline(webgpu_ctx);
+    ggml_webgpu_init_glu_pipeline(webgpu_ctx);
+    ggml_webgpu_init_scale_pipeline(webgpu_ctx);
+    ggml_webgpu_init_soft_max_pipeline(webgpu_ctx);
+#ifdef GGML_WEBGPU_DEBUG
+    // Initialize debug buffers
+    ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->global_ctx->debug_host_buf,
+                              WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
+                              wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf");
+    ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->global_ctx->debug_dev_buf,
+                              WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
+                              wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf");
+#endif
+    return webgpu_ctx;
+}
+
+static ggml_backend_t ggml_backend_webgpu_backend_init(ggml_backend_dev_t dev, const char * params) {
+    GGML_UNUSED(params);
+
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_backend_init()");
+
+    ggml_backend_webgpu_device_context * dev_ctx = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
+
+    auto * backend_ctx      = new ggml_backend_webgpu_context();
+    backend_ctx->name       = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name;
+    backend_ctx->webgpu_ctx = initialize_webgpu_context(dev);
+
+    // See GGML Backend Interface section
+    auto * backend = new ggml_backend();
+    *backend       = {
+        /* .guid      = */ ggml_backend_webgpu_guid(),
+        /* .interface = */ ggml_backend_webgpu_i,
+        /* .device    = */ dev,
+        /* .context   = */ backend_ctx,
+    };
+    return backend;
+}
+
+static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) {
+    // See GGML Backend Buffer Type Interface section
+
+    static struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = {
+        /* .iface = */ {
+                        /* .get_name         = */ ggml_backend_webgpu_buffer_type_get_name,
+                        /* .alloc_buffer     = */ ggml_backend_webgpu_buffer_type_alloc_buffer,
+                        /* .get_alignment    = */ ggml_backend_webgpu_buffer_type_get_alignment,
+                        /* .get_max_size     = */ ggml_backend_webgpu_buffer_type_get_max_size,
+                        /* .get_alloc_size   = */ ggml_backend_webgpu_buffer_type_get_alloc_size,
+                        /* .is_host          = */ NULL,  // defaults to false
+        },
+        /* .device  = */
+        dev,
+        /* .context = */
+        NULL
+    };
+
+    return &ggml_backend_webgpu_buffer_type;
+}
+
+static bool ggml_backend_webgpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+    GGML_UNUSED(dev);
+    return buft->iface.get_name == ggml_backend_webgpu_buffer_type_get_name;
+}
+
+static bool ggml_webgpu_supported_qtype(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+            return true;
+        default:
+            return false;
+    }
+}
+
+static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+    ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
+
+    ggml_tensor * src0 = op->src[0];
+    ggml_tensor * src1 = op->src[1];
+    ggml_tensor * src2 = op->src[2];
+
+    // on smaller devices (or CI), tensors may be larger than the max storage buffer size
+    if (ggml_nbytes(op) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize ||
+        (src0 != nullptr &&
+         ggml_nbytes(src0) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) ||
+        (src1 != nullptr &&
+         ggml_nbytes(src1) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize)) {
+        return false;
+    }
+
+    bool supports_op = false;
+    switch (op->op) {
+        case GGML_OP_NONE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_RESHAPE:
+            supports_op = true;
+            break;
+        case GGML_OP_ADD:
+        case GGML_OP_SUB:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+            // TODO: support non-contiguous tensors, e.g. for MOE_EXPERT_REDUCE
+            // see https://github.com/ggml-org/llama.cpp/pull/16857
+            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type) &&
+                          (src1->type == op->type) && ggml_is_contiguous(src0) && ggml_is_contiguous(src1);
+            break;
+        case GGML_OP_CPY:
+        case GGML_OP_CONT:
+            supports_op = ((op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
+                           (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) ||
+                          (op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32);
+            break;
+        case GGML_OP_SET_ROWS:
+            supports_op = ((op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32) && src0->type == GGML_TYPE_F32 &&
+                           (src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32));
+            break;
+        case GGML_OP_GET_ROWS:
+            if (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_webgpu_supported_qtype(src0->type)) {
+                supports_op = (op->type == GGML_TYPE_F32);
+            } else if (src0->type == GGML_TYPE_I32) {
+                supports_op = op->type == GGML_TYPE_I32;
+            }
+            break;
+        case GGML_OP_MUL_MAT:
+            {
+                switch (src1->type) {
+                    case GGML_TYPE_F16:
+                        supports_op |= (src0->type == GGML_TYPE_F16);
+                        break;
+                    case GGML_TYPE_F32:
+                        switch (src0->type) {
+                            case GGML_TYPE_F32:
+                            case GGML_TYPE_F16:
+                            case GGML_TYPE_Q4_0:
+                            case GGML_TYPE_Q4_1:
+                            case GGML_TYPE_Q5_0:
+                            case GGML_TYPE_Q5_1:
+                            case GGML_TYPE_Q8_0:
+                            case GGML_TYPE_Q2_K:
+                            case GGML_TYPE_Q3_K:
+                            case GGML_TYPE_Q4_K:
+                            case GGML_TYPE_Q5_K:
+                            case GGML_TYPE_Q6_K:
+                            case GGML_TYPE_IQ2_XXS:
+                            case GGML_TYPE_IQ2_XS:
+                            case GGML_TYPE_IQ2_S:
+                            case GGML_TYPE_IQ3_XXS:
+                            case GGML_TYPE_IQ3_S:
+                            case GGML_TYPE_IQ1_S:
+                            case GGML_TYPE_IQ1_M:
+                            case GGML_TYPE_IQ4_NL:
+                            case GGML_TYPE_IQ4_XS:
+                                supports_op = true;
+                                break;
+                            default:
+                                break;
+                        }
+                    default:
+                        break;
+                }
+                break;
+            }
+        case GGML_OP_FLASH_ATTN_EXT:
+            {
+#ifndef __EMSCRIPTEN__
+                if (!ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix) {
+                    break;
+                }
+                // Head dimensions must fit in workgroup memory with minimum tile sizes
+                size_t     limit_bytes = ctx->webgpu_global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize;
+                const bool has_mask    = op->src[3] != nullptr;
+                const bool kv_direct   = src1->type == GGML_TYPE_F16 &&
+                                       (src0->ne[0] % ctx->webgpu_global_ctx->capabilities.sg_mat_k) == 0 &&
+                                       (src1->ne[1] % GGML_WEBGPU_KV_SEQ_PAD) == 0;
+                const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(
+                    ctx->webgpu_global_ctx->capabilities.sg_mat_m, ctx->webgpu_global_ctx->capabilities.sg_mat_n,
+                    (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask, kv_direct);
+                if (min_bytes > limit_bytes) {
+                    break;
+                }
+
+                supports_op = src0->type == GGML_TYPE_F32 &&
+                              (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 ||
+                               src1->type == GGML_TYPE_Q4_0 || src1->type == GGML_TYPE_Q8_0) &&
+                              src2->type == src1->type && op->type == GGML_TYPE_F32;
+#endif
+                break;
+            }
+        case GGML_OP_RMS_NORM:
+            supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32;
+            break;
+        case GGML_OP_ROPE:
+            supports_op = op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16;
+            break;
+        case GGML_OP_GLU:
+            switch (ggml_get_glu_op(op)) {
+                case GGML_GLU_OP_REGLU:
+                case GGML_GLU_OP_GEGLU:
+                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_GEGLU_ERF:
+                case GGML_GLU_OP_GEGLU_QUICK:
+                    supports_op = op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16;
+                    break;
+                case GGML_GLU_OP_SWIGLU_OAI:
+                    supports_op = op->type == GGML_TYPE_F32;
+                    break;
+                default:
+                    break;
+            }
+            break;
+        case GGML_OP_SCALE:
+            supports_op = op->type == GGML_TYPE_F32;
+            break;
+        case GGML_OP_SOFT_MAX:
+            supports_op = op->type == GGML_TYPE_F32;
+            break;
+        case GGML_OP_UNARY:
+            {
+                const ggml_unary_op UNARY_OP = ggml_get_unary_op(op);
+
+                switch (UNARY_OP) {
+                    case GGML_UNARY_OP_ABS:
+                    case GGML_UNARY_OP_SGN:
+                    case GGML_UNARY_OP_NEG:
+                    case GGML_UNARY_OP_STEP:
+                    case GGML_UNARY_OP_TANH:
+                    case GGML_UNARY_OP_ELU:
+                    case GGML_UNARY_OP_RELU:
+                    case GGML_UNARY_OP_SIGMOID:
+                    case GGML_UNARY_OP_GELU:
+                    case GGML_UNARY_OP_GELU_QUICK:
+                    case GGML_UNARY_OP_SILU:
+                    case GGML_UNARY_OP_HARDSWISH:
+                    case GGML_UNARY_OP_HARDSIGMOID:
+                    case GGML_UNARY_OP_EXP:
+                    case GGML_UNARY_OP_GELU_ERF:
+                    case GGML_UNARY_OP_SOFTPLUS:
+                    case GGML_UNARY_OP_EXPM1:
+                    case GGML_UNARY_OP_FLOOR:
+                    case GGML_UNARY_OP_CEIL:
+                    case GGML_UNARY_OP_ROUND:
+                    case GGML_UNARY_OP_TRUNC:
+                    case GGML_UNARY_OP_XIELU:
+                        supports_op =
+                            (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type);
+                        break;
+                    default:
+                        break;
+                }
+            }
+            break;
+        case GGML_OP_CLAMP:
+            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type);
+            break;
+        case GGML_OP_FILL:
+            supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32;
+            break;
+        case GGML_OP_LOG:
+            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type);
+            break;
+        case GGML_OP_PAD:
+            supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32;
+            break;
+        case GGML_OP_ARGMAX:
+            supports_op = op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32;
+            break;
+        case GGML_OP_ARGSORT:
+            supports_op = op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(src0);
+            break;
+        case GGML_OP_TOP_K:
+            supports_op = op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(src0);
+            break;
+        case GGML_OP_CUMSUM:
+            supports_op = op->type == GGML_TYPE_F32 && src0->type == op->type;
+            break;
+        case GGML_OP_SUM:
+        case GGML_OP_SUM_ROWS:
+            supports_op = op->type == GGML_TYPE_F32 && src0->type == op->type && ggml_is_contiguous_rows(src0);
+            break;
+        default:
+            break;
+    }
+    if (ggml_nbytes(op) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize ||
+        (src0 != nullptr &&
+         ggml_nbytes(src0) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) ||
+        (src1 != nullptr &&
+         ggml_nbytes(src1) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) ||
+        (src2 != nullptr &&
+         ggml_nbytes(src2) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize)) {
+        supports_op = false;
+        WEBGPU_LOG_DEBUG("ggml_webgpu op not supported due to size: ");
+    }
+
+    if (!supports_op) {
+        WEBGPU_LOG_DEBUG("ggml_webgpu op not supported: "
+                         << ggml_op_name(op->op) << " with types dst: " << ggml_type_name(op->type)
+                         << ", src0: " << (op->src[0] ? ggml_type_name(op->src[0]->type) : "null")
+                         << ", src1: " << (op->src[1] ? ggml_type_name(op->src[1]->type) : "null"));
+    } else {
+        WEBGPU_LOG_DEBUG("ggml_webgpu op supported: "
+                         << ggml_op_name(op->op) << " with types dst: " << ggml_type_name(op->type)
+                         << ", src0: " << (op->src[0] ? ggml_type_name(op->src[0]->type) : "null")
+                         << ", src1: " << (op->src[1] ? ggml_type_name(op->src[1]->type) : "null"));
+    }
+    return supports_op;
+}
+
+static struct ggml_backend_device_i ggml_backend_webgpu_device_i = {
+    /* .get_name             = */ ggml_backend_webgpu_device_get_name,
+    /* .get_description      = */ ggml_backend_webgpu_device_get_description,
+    /* .get_memory           = */ ggml_backend_webgpu_device_get_memory,
+    /* .get_type             = */ ggml_backend_webgpu_device_get_type,
+    /* .get_props            = */ ggml_backend_webgpu_device_get_props,
+    /* .init_backend         = */ ggml_backend_webgpu_backend_init,
+    /* .get_buffer_type      = */ ggml_backend_webgpu_device_get_buffer_type,
+    /* .get_host_buffer_type = */ NULL,
+    /* .buffer_from_host_ptr = */ NULL,
+    /* .supports_op          = */ ggml_backend_webgpu_device_supports_op,
+    /* .supports_buft        = */ ggml_backend_webgpu_device_supports_buft,
+    /* .offload_op           = */ NULL,
+    /* .event_new            = */ NULL,
+    /* .event_free           = */ NULL,
+    /* .event_synchronize    = */ NULL,
+};
+
+/* End GGML Backend Device Interface */
+
+/* GGML Backend Registration Interface */
+
+static const char * ggml_backend_webgpu_reg_get_name(ggml_backend_reg_t reg) {
+    ggml_backend_webgpu_reg_context * ctx = static_cast<ggml_backend_webgpu_reg_context *>(reg->context);
+    return ctx->name;
+}
+
+static size_t ggml_backend_webgpu_reg_get_device_count(ggml_backend_reg_t reg) {
+    ggml_backend_webgpu_reg_context * ctx = static_cast<ggml_backend_webgpu_reg_context *>(reg->context);
+    return ctx->device_count;
+}
+
+// Only one device is supported for now
+static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(index == 0);
+    WEBGPU_LOG_DEBUG("ggml_backend_reg_get_device()");
+
+    WEBGPU_CPU_PROFILE_TOTAL_START(reg_get_device);
+
+    ggml_backend_webgpu_reg_context * reg_ctx = static_cast<ggml_backend_webgpu_reg_context *>(reg->context);
+
+    create_webgpu_device(reg_ctx);
+
+    static ggml_backend_webgpu_device_context device_ctx;
+    device_ctx.device_name            = GGML_WEBGPU_NAME;
+    device_ctx.device_desc            = GGML_WEBGPU_NAME;
+    device_ctx.webgpu_global_ctx      = reg_ctx->webgpu_global_ctx;
+    // See GGML Backend Device Interface section
+    static ggml_backend_device device = {
+        /* .iface   = */ ggml_backend_webgpu_device_i,
+        /* .reg     = */ reg,
+        /* .context = */ &device_ctx,
+    };
+
+    WEBGPU_CPU_PROFILE_TOTAL_END(reg_get_device, reg_ctx->webgpu_global_ctx);
+    return &device;
+}
+
+static const struct ggml_backend_reg_i ggml_backend_webgpu_reg_i = {
+    /* .get_name         = */ ggml_backend_webgpu_reg_get_name,
+    /* .get_device_count = */ ggml_backend_webgpu_reg_get_device_count,
+    /* .get_device       = */ ggml_backend_webgpu_reg_get_device,
+    /* .get_proc_address = */ NULL,
+};
+
+/* End GGML Backend Registration Interface */
+
+ggml_backend_reg_t ggml_backend_webgpu_reg() {
+    WEBGPU_LOG_DEBUG("ggml_backend_webgpu_reg()");
+
+    static ggml_backend_webgpu_reg_context ctx;
+    ctx.name         = GGML_WEBGPU_NAME;
+    ctx.device_count = 1;
+
+    wgpu::InstanceDescriptor               instance_descriptor{};
+    std::vector<wgpu::InstanceFeatureName> instance_features = { wgpu::InstanceFeatureName::TimedWaitAny };
+    instance_descriptor.requiredFeatures                     = instance_features.data();
+    instance_descriptor.requiredFeatureCount                 = instance_features.size();
+
+#ifndef __EMSCRIPTEN__
+    const char * const          instanceEnabledToggles[] = { "allow_unsafe_apis" };
+    wgpu::DawnTogglesDescriptor instanceTogglesDesc;
+    instanceTogglesDesc.enabledToggles     = instanceEnabledToggles;
+    instanceTogglesDesc.enabledToggleCount = 1;
+    instance_descriptor.nextInChain        = &instanceTogglesDesc;
+#endif
+
+    wgpu::Instance inst             = wgpu::CreateInstance(&instance_descriptor);
+    ctx.webgpu_global_ctx           = webgpu_global_context(new webgpu_global_context_struct());
+    ctx.webgpu_global_ctx->instance = std::move(inst);
+
+#ifdef __EMSCRIPTEN__
+    if (ctx.webgpu_global_ctx->instance == nullptr) {
+        GGML_LOG_ERROR("ggml_webgpu: Failed to create WebGPU instance. Make sure either -sASYNCIFY or -sJSPI is set\n");
+        return nullptr;
+    }
+#endif
+    GGML_ASSERT(ctx.webgpu_global_ctx->instance != nullptr);
+
+    static ggml_backend_reg reg = {
+        /* .api_version = */ GGML_BACKEND_API_VERSION,
+        /* .iface       = */ ggml_backend_webgpu_reg_i,
+        /* .context     = */ &ctx,
+    };
+    return &reg;
+}
+
+ggml_backend_t ggml_backend_webgpu_init(void) {
+    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_webgpu_reg(), 0);
+
+    return ggml_backend_webgpu_backend_init(dev, nullptr);
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_webgpu_reg)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/pre_wgsl.hpp b/llama.cpp/ggml/src/ggml-webgpu/pre_wgsl.hpp
new file mode 100644
index 0000000..4d43594
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/pre_wgsl.hpp
@@ -0,0 +1,778 @@
+#ifndef PRE_WGSL_HPP
+#define PRE_WGSL_HPP
+
+#include <cctype>
+#include <fstream>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <string_view>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+namespace pre_wgsl {
+
+//==============================================================
+// Options
+//==============================================================
+struct Options {
+    std::string              include_path = ".";
+    std::vector<std::string> macros;
+};
+
+//==============================================================
+// Utility: trim
+//==============================================================
+static std::string trim(const std::string & s) {
+    size_t a = 0;
+    while (a < s.size() && std::isspace((unsigned char) s[a])) {
+        a++;
+    }
+    size_t b = s.size();
+    while (b > a && std::isspace((unsigned char) s[b - 1])) {
+        b--;
+    }
+    return s.substr(a, b - a);
+}
+
+static std::string trim_value(std::istream & is) {
+    std::string str;
+    std::getline(is, str);
+    return trim(str);
+}
+
+static bool isIdentChar(char c) {
+    return std::isalnum(static_cast<unsigned char>(c)) || c == '_';
+}
+
+static std::string expandMacrosRecursiveInternal(const std::string &                                  line,
+                                                 const std::unordered_map<std::string, std::string> & macros,
+                                                 std::unordered_set<std::string> &                    visiting);
+
+static std::string expandMacroValue(const std::string &                                  name,
+                                    const std::unordered_map<std::string, std::string> & macros,
+                                    std::unordered_set<std::string> &                    visiting) {
+    if (visiting.count(name)) {
+        throw std::runtime_error("Recursive macro: " + name);
+    }
+    visiting.insert(name);
+
+    auto it = macros.find(name);
+    if (it == macros.end()) {
+        visiting.erase(name);
+        return name;
+    }
+
+    const std::string & value = it->second;
+    if (value.empty()) {
+        visiting.erase(name);
+        return "";
+    }
+
+    std::string expanded = expandMacrosRecursiveInternal(value, macros, visiting);
+    visiting.erase(name);
+    return expanded;
+}
+
+static std::string expandMacrosRecursiveInternal(const std::string &                                  line,
+                                                 const std::unordered_map<std::string, std::string> & macros,
+                                                 std::unordered_set<std::string> &                    visiting) {
+    std::string result;
+    result.reserve(line.size());
+
+    size_t i = 0;
+    while (i < line.size()) {
+        if (isIdentChar(line[i])) {
+            size_t start = i;
+            while (i < line.size() && isIdentChar(line[i])) {
+                i++;
+            }
+            std::string token = line.substr(start, i - start);
+
+            auto it = macros.find(token);
+            if (it != macros.end()) {
+                result += expandMacroValue(token, macros, visiting);
+            } else {
+                result += token;
+            }
+        } else {
+            result += line[i];
+            i++;
+        }
+    }
+
+    return result;
+}
+
+static std::string expandMacrosRecursive(const std::string &                                  line,
+                                         const std::unordered_map<std::string, std::string> & macros) {
+    std::unordered_set<std::string> visiting;
+    return expandMacrosRecursiveInternal(line, macros, visiting);
+}
+
+//==============================================================
+// Tokenizer for expressions in #if/#elif
+//==============================================================
+class ExprLexer {
+  public:
+    enum Kind { END, IDENT, NUMBER, OP, LPAREN, RPAREN };
+
+    struct Tok {
+        Kind        kind;
+        std::string text;
+    };
+
+    explicit ExprLexer(std::string_view sv) : src(sv), pos(0) {}
+
+    Tok next() {
+        skipWS();
+        if (pos >= src.size()) {
+            return { END, "" };
+        }
+
+        char c = src[pos];
+
+        // number
+        if (std::isdigit((unsigned char) c)) {
+            size_t start = pos;
+            while (pos < src.size() && std::isdigit((unsigned char) src[pos])) {
+                pos++;
+            }
+            return { NUMBER, std::string(src.substr(start, pos - start)) };
+        }
+
+        // identifier
+        if (std::isalpha((unsigned char) c) || c == '_') {
+            size_t start = pos;
+            while (pos < src.size() && (std::isalnum((unsigned char) src[pos]) || src[pos] == '_')) {
+                pos++;
+            }
+            return { IDENT, std::string(src.substr(start, pos - start)) };
+        }
+
+        if (c == '(') {
+            pos++;
+            return { LPAREN, "(" };
+        }
+        if (c == ')') {
+            pos++;
+            return { RPAREN, ")" };
+        }
+
+        // multi-char operators
+        static const char * two_ops[] = { "==", "!=", "<=", ">=", "&&", "||", "<<", ">>" };
+        for (auto op : two_ops) {
+            if (src.substr(pos, 2) == op) {
+                pos += 2;
+                return { OP, std::string(op) };
+            }
+        }
+
+        // single-char operators
+        if (std::string("+-*/%<>!").find(c) != std::string::npos) {
+            pos++;
+            return { OP, std::string(1, c) };
+        }
+
+        // unexpected
+        pos++;
+        return { END, "" };
+    }
+
+  private:
+    std::string_view src;
+    size_t           pos;
+
+    void skipWS() {
+        while (pos < src.size() && std::isspace((unsigned char) src[pos])) {
+            pos++;
+        }
+    }
+};
+
+//==============================================================
+// Expression Parser (recursive descent)
+//==============================================================
+class ExprParser {
+  public:
+    ExprParser(std::string_view                                     expr,
+               const std::unordered_map<std::string, std::string> & macros,
+               std::unordered_set<std::string> &                    visiting) :
+        lex(expr),
+        macros(macros),
+        visiting(visiting) {
+        advance();
+    }
+
+    int parse() { return parseLogicalOr(); }
+
+  private:
+    ExprLexer                                            lex;
+    ExprLexer::Tok                                       tok;
+    const std::unordered_map<std::string, std::string> & macros;
+    std::unordered_set<std::string> &                    visiting;
+
+    void advance() { tok = lex.next(); }
+
+    bool acceptOp(const std::string & s) {
+        if (tok.kind == ExprLexer::OP && tok.text == s) {
+            advance();
+            return true;
+        }
+        return false;
+    }
+
+    bool acceptKind(ExprLexer::Kind k) {
+        if (tok.kind == k) {
+            advance();
+            return true;
+        }
+        return false;
+    }
+
+    int parseLogicalOr() {
+        int v = parseLogicalAnd();
+        while (acceptOp("||")) {
+            int rhs = parseLogicalAnd();
+            v       = (v || rhs);
+        }
+        return v;
+    }
+
+    int parseLogicalAnd() {
+        int v = parseEquality();
+        while (acceptOp("&&")) {
+            int rhs = parseEquality();
+            v       = (v && rhs);
+        }
+        return v;
+    }
+
+    int parseEquality() {
+        int v = parseRelational();
+        for (;;) {
+            if (acceptOp("==")) {
+                int rhs = parseRelational();
+                v       = (v == rhs);
+            } else if (acceptOp("!=")) {
+                int rhs = parseRelational();
+                v       = (v != rhs);
+            } else {
+                break;
+            }
+        }
+        return v;
+    }
+
+    int parseRelational() {
+        int v = parseShift();
+        for (;;) {
+            if (acceptOp("<")) {
+                int rhs = parseShift();
+                v       = (v < rhs);
+            } else if (acceptOp(">")) {
+                int rhs = parseShift();
+                v       = (v > rhs);
+            } else if (acceptOp("<=")) {
+                int rhs = parseShift();
+                v       = (v <= rhs);
+            } else if (acceptOp(">=")) {
+                int rhs = parseShift();
+                v       = (v >= rhs);
+            } else {
+                break;
+            }
+        }
+        return v;
+    }
+
+    int parseShift() {
+        int v = parseAdd();
+        for (;;) {
+            if (acceptOp("<<")) {
+                int rhs = parseAdd();
+                v       = (v << rhs);
+            } else if (acceptOp(">>")) {
+                int rhs = parseAdd();
+                v       = (v >> rhs);
+            } else {
+                break;
+            }
+        }
+        return v;
+    }
+
+    int parseAdd() {
+        int v = parseMult();
+        for (;;) {
+            if (acceptOp("+")) {
+                int rhs = parseMult();
+                v       = (v + rhs);
+            } else if (acceptOp("-")) {
+                int rhs = parseMult();
+                v       = (v - rhs);
+            } else {
+                break;
+            }
+        }
+        return v;
+    }
+
+    int parseMult() {
+        int v = parseUnary();
+        for (;;) {
+            if (acceptOp("*")) {
+                int rhs = parseUnary();
+                v       = (v * rhs);
+            } else if (acceptOp("/")) {
+                int rhs = parseUnary();
+                v       = (rhs == 0 ? 0 : v / rhs);
+            } else if (acceptOp("%")) {
+                int rhs = parseUnary();
+                v       = (rhs == 0 ? 0 : v % rhs);
+            } else {
+                break;
+            }
+        }
+        return v;
+    }
+
+    int parseUnary() {
+        if (acceptOp("!")) {
+            return !parseUnary();
+        }
+        if (acceptOp("-")) {
+            return -parseUnary();
+        }
+        if (acceptOp("+")) {
+            return +parseUnary();
+        }
+        return parsePrimary();
+    }
+
+    int parsePrimary() {
+        // '(' expr ')'
+        if (acceptKind(ExprLexer::LPAREN)) {
+            int v = parse();
+            if (!acceptKind(ExprLexer::RPAREN)) {
+                throw std::runtime_error("missing ')'");
+            }
+            return v;
+        }
+
+        // number
+        if (tok.kind == ExprLexer::NUMBER) {
+            int v = std::stoi(tok.text);
+            advance();
+            return v;
+        }
+
+        // defined(identifier)
+        if (tok.kind == ExprLexer::IDENT && tok.text == "defined") {
+            advance();
+            if (acceptKind(ExprLexer::LPAREN)) {
+                if (tok.kind != ExprLexer::IDENT) {
+                    throw std::runtime_error("expected identifier in defined()");
+                }
+                std::string name = tok.text;
+                advance();
+                if (!acceptKind(ExprLexer::RPAREN)) {
+                    throw std::runtime_error("missing ) in defined()");
+                }
+                return macros.count(name) ? 1 : 0;
+            } else {
+                // defined NAME
+                if (tok.kind != ExprLexer::IDENT) {
+                    throw std::runtime_error("expected identifier in defined NAME");
+                }
+                std::string name = tok.text;
+                advance();
+                return macros.count(name) ? 1 : 0;
+            }
+        }
+
+        // identifier -> treat as integer, if defined use its value else 0
+        if (tok.kind == ExprLexer::IDENT) {
+            std::string name = tok.text;
+            advance();
+            auto it = macros.find(name);
+            if (it == macros.end()) {
+                return 0;
+            }
+            if (it->second.empty()) {
+                return 1;
+            }
+            return evalMacroExpression(name, it->second);
+        }
+
+        // unexpected
+        return 0;
+    }
+
+    int evalMacroExpression(const std::string & name, const std::string & value) {
+        if (visiting.count(name)) {
+            throw std::runtime_error("Recursive macro: " + name);
+        }
+
+        visiting.insert(name);
+        ExprParser ep(value, macros, visiting);
+        int        v = ep.parse();
+        visiting.erase(name);
+        return v;
+    }
+};
+
+//==============================================================
+// Preprocessor
+//==============================================================
+class Preprocessor {
+  public:
+    explicit Preprocessor(Options opts = {}) : opts_(std::move(opts)) {
+        // Treat empty include path as current directory
+        if (opts_.include_path.empty()) {
+            opts_.include_path = ".";
+        }
+        parseMacroDefinitions(opts_.macros);
+    }
+
+    std::string preprocess_file(const std::string & filename, const std::vector<std::string> & additional_macros = {}) {
+        std::unordered_map<std::string, std::string> macros;
+        std::unordered_set<std::string>              predefined;
+        std::unordered_set<std::string>              include_stack;
+        buildMacros(additional_macros, macros, predefined);
+
+        std::string result = processFile(filename, macros, predefined, include_stack, DirectiveMode::All);
+        return result;
+    }
+
+    std::string preprocess(const std::string & contents, const std::vector<std::string> & additional_macros = {}) {
+        std::unordered_map<std::string, std::string> macros;
+        std::unordered_set<std::string>              predefined;
+        std::unordered_set<std::string>              include_stack;
+        buildMacros(additional_macros, macros, predefined);
+
+        std::string result = processString(contents, macros, predefined, include_stack, DirectiveMode::All);
+        return result;
+    }
+
+    std::string preprocess_includes_file(const std::string & filename) {
+        std::unordered_map<std::string, std::string> macros;
+        std::unordered_set<std::string>              predefined;
+        std::unordered_set<std::string>              include_stack;
+        std::string result = processFile(filename, macros, predefined, include_stack, DirectiveMode::IncludesOnly);
+        return result;
+    }
+
+    std::string preprocess_includes(const std::string & contents) {
+        std::unordered_map<std::string, std::string> macros;
+        std::unordered_set<std::string>              predefined;
+        std::unordered_set<std::string>              include_stack;
+        std::string result = processString(contents, macros, predefined, include_stack, DirectiveMode::IncludesOnly);
+        return result;
+    }
+
+  private:
+    Options                                      opts_;
+    std::unordered_map<std::string, std::string> global_macros;
+
+    enum class DirectiveMode { All, IncludesOnly };
+
+    struct Cond {
+        bool parent_active;
+        bool active;
+        bool taken;
+    };
+
+    //----------------------------------------------------------
+    // Parse macro definitions into global_macros
+    //----------------------------------------------------------
+    void parseMacroDefinitions(const std::vector<std::string> & macro_defs) {
+        for (const auto & def : macro_defs) {
+            size_t eq_pos = def.find('=');
+            if (eq_pos != std::string::npos) {
+                // Format: NAME=VALUE
+                std::string name    = trim(def.substr(0, eq_pos));
+                std::string value   = trim(def.substr(eq_pos + 1));
+                global_macros[name] = value;
+            } else {
+                // Format: NAME
+                std::string name    = trim(def);
+                global_macros[name] = "";
+            }
+        }
+    }
+
+    //----------------------------------------------------------
+    // Build combined macro map and predefined set for a preprocessing operation
+    //----------------------------------------------------------
+    void buildMacros(const std::vector<std::string> &               additional_macros,
+                     std::unordered_map<std::string, std::string> & macros,
+                     std::unordered_set<std::string> &              predefined) {
+        macros = global_macros;
+        predefined.clear();
+
+        for (const auto & [name, value] : global_macros) {
+            predefined.insert(name);
+        }
+
+        for (const auto & def : additional_macros) {
+            size_t      eq_pos = def.find('=');
+            std::string name, value;
+            if (eq_pos != std::string::npos) {
+                name  = trim(def.substr(0, eq_pos));
+                value = trim(def.substr(eq_pos + 1));
+            } else {
+                name  = trim(def);
+                value = "";
+            }
+
+            // Add to macros map (will override global if same name)
+            macros[name] = value;
+            predefined.insert(name);
+        }
+    }
+
+    //----------------------------------------------------------
+    // Helpers
+    //----------------------------------------------------------
+    std::string loadFile(const std::string & fname) {
+        std::ifstream f(fname);
+        if (!f.is_open()) {
+            throw std::runtime_error("Could not open file: " + fname);
+        }
+        std::stringstream ss;
+        ss << f.rdbuf();
+        return ss.str();
+    }
+
+    bool condActive(const std::vector<Cond> & cond) const {
+        if (cond.empty()) {
+            return true;
+        }
+        return cond.back().active;
+    }
+
+    //----------------------------------------------------------
+    // Process a file
+    //----------------------------------------------------------
+    std::string processFile(const std::string &                            name,
+                            std::unordered_map<std::string, std::string> & macros,
+                            const std::unordered_set<std::string> &        predefined_macros,
+                            std::unordered_set<std::string> &              include_stack,
+                            DirectiveMode                                  mode) {
+        if (include_stack.count(name)) {
+            throw std::runtime_error("Recursive include: " + name);
+        }
+
+        include_stack.insert(name);
+        std::string shader_code = loadFile(name);
+        std::string out         = processString(shader_code, macros, predefined_macros, include_stack, mode);
+        include_stack.erase(name);
+        return out;
+    }
+
+    std::string processIncludeFile(const std::string &                            fname,
+                                   std::unordered_map<std::string, std::string> & macros,
+                                   const std::unordered_set<std::string> &        predefined_macros,
+                                   std::unordered_set<std::string> &              include_stack,
+                                   DirectiveMode                                  mode) {
+        std::string full_path = opts_.include_path + "/" + fname;
+        return processFile(full_path, macros, predefined_macros, include_stack, mode);
+    }
+
+    //----------------------------------------------------------
+    // Process text
+    //----------------------------------------------------------
+    std::string processString(const std::string &                            shader_code,
+                              std::unordered_map<std::string, std::string> & macros,
+                              const std::unordered_set<std::string> &        predefined_macros,
+                              std::unordered_set<std::string> &              include_stack,
+                              DirectiveMode                                  mode) {
+        std::vector<Cond>  cond;  // Conditional stack for this shader
+        std::stringstream  out;
+        std::istringstream in(shader_code);
+        std::string        line;
+
+        while (std::getline(in, line)) {
+            std::string t = trim(line);
+
+            if (!t.empty() && t[0] == '#') {
+                bool handled = handleDirective(t, out, macros, predefined_macros, cond, include_stack, mode);
+                if (mode == DirectiveMode::IncludesOnly && !handled) {
+                    out << line << "\n";
+                }
+            } else {
+                if (mode == DirectiveMode::IncludesOnly) {
+                    out << line << "\n";
+                } else if (condActive(cond)) {
+                    // Expand macros in the line before outputting
+                    std::string expanded = expandMacrosRecursive(line, macros);
+                    out << expanded << "\n";
+                }
+            }
+        }
+
+        if (mode == DirectiveMode::All && !cond.empty()) {
+            throw std::runtime_error("Unclosed #if directive");
+        }
+
+        return out.str();
+    }
+
+    //----------------------------------------------------------
+    // Directive handler
+    //----------------------------------------------------------
+    bool handleDirective(const std::string &                            t,
+                         std::stringstream &                            out,
+                         std::unordered_map<std::string, std::string> & macros,
+                         const std::unordered_set<std::string> &        predefined_macros,
+                         std::vector<Cond> &                            cond,
+                         std::unordered_set<std::string> &              include_stack,
+                         DirectiveMode                                  mode) {
+        // split into tokens
+        std::string        body = t.substr(1);
+        std::istringstream iss(body);
+        std::string        cmd;
+        iss >> cmd;
+
+        if (cmd == "include") {
+            if (mode == DirectiveMode::All && !condActive(cond)) {
+                return true;
+            }
+            std::string file;
+            iss >> file;
+            if (file.size() >= 2 && file.front() == '"' && file.back() == '"') {
+                file = file.substr(1, file.size() - 2);
+            }
+            out << processIncludeFile(file, macros, predefined_macros, include_stack, mode);
+            return true;
+        }
+
+        if (mode == DirectiveMode::IncludesOnly) {
+            return false;
+        }
+
+        if (cmd == "define") {
+            if (!condActive(cond)) {
+                return true;
+            }
+            std::string name;
+            iss >> name;
+            // Don't override predefined macros from options
+            if (predefined_macros.count(name)) {
+                return true;
+            }
+            std::string value = trim_value(iss);
+            macros[name]      = value;
+            return true;
+        }
+
+        if (cmd == "undef") {
+            if (!condActive(cond)) {
+                return true;
+            }
+            std::string name;
+            iss >> name;
+            // Don't undef predefined macros from options
+            if (predefined_macros.count(name)) {
+                return true;
+            }
+            macros.erase(name);
+            return true;
+        }
+
+        if (cmd == "ifdef") {
+            std::string name;
+            iss >> name;
+            bool p = condActive(cond);
+            bool v = macros.count(name);
+            cond.push_back({ p, p && v, p && v });
+            return true;
+        }
+
+        if (cmd == "ifndef") {
+            std::string name;
+            iss >> name;
+            bool p = condActive(cond);
+            bool v = !macros.count(name);
+            cond.push_back({ p, p && v, p && v });
+            return true;
+        }
+
+        if (cmd == "if") {
+            std::string expr = trim_value(iss);
+            bool        p    = condActive(cond);
+            bool        v    = false;
+            if (p) {
+                std::unordered_set<std::string> visiting;
+                ExprParser                      ep(expr, macros, visiting);
+                v = ep.parse() != 0;
+            }
+            cond.push_back({ p, p && v, p && v });
+            return true;
+        }
+
+        if (cmd == "elif") {
+            std::string expr = trim_value(iss);
+
+            if (cond.empty()) {
+                throw std::runtime_error("#elif without #if");
+            }
+
+            Cond & c = cond.back();
+            if (!c.parent_active) {
+                c.active = false;
+                return true;
+            }
+
+            if (c.taken) {
+                c.active = false;
+                return true;
+            }
+
+            std::unordered_set<std::string> visiting;
+            ExprParser                      ep(expr, macros, visiting);
+            bool                            v = ep.parse() != 0;
+            c.active                          = v;
+            if (v) {
+                c.taken = true;
+            }
+            return true;
+        }
+
+        if (cmd == "else") {
+            if (cond.empty()) {
+                throw std::runtime_error("#else without #if");
+            }
+
+            Cond & c = cond.back();
+            if (!c.parent_active) {
+                c.active = false;
+                return true;
+            }
+            if (c.taken) {
+                c.active = false;
+            } else {
+                c.active = true;
+                c.taken  = true;
+            }
+            return true;
+        }
+
+        if (cmd == "endif") {
+            if (cond.empty()) {
+                throw std::runtime_error("#endif without #if");
+            }
+            cond.pop_back();
+            return true;
+        }
+
+        // Unknown directive
+        throw std::runtime_error("Unknown directive: #" + cmd);
+    }
+};
+
+}  // namespace pre_wgsl
+
+#endif  // PRE_WGSL_HPP
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argmax.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argmax.wgsl
new file mode 100644
index 0000000..ca5bfcc
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argmax.wgsl
@@ -0,0 +1,72 @@
+@group(0) @binding(0)
+#ifdef VEC4
+var<storage, read_write> src: array<vec4<f32>>;
+#define VEC_SIZE 4
+#else
+var<storage, read_write> src: array<f32>;
+#define VEC_SIZE 1
+#endif
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<i32>;
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_dst: u32, // in elements
+    ne0: u32,
+};
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+const FLOAT_MIN: f32 = -1.0e9;
+
+struct Pair {
+    value: f32,
+    index: i32
+};
+
+var<workgroup> shared_max: array<Pair, WG_SIZE>;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+    let row_idx = params.offset_src + wid.x * params.ne0;
+    var local_pair = Pair(FLOAT_MIN, -1);
+#ifdef VEC4
+    for (var col = lid.x; col < params.ne0/VEC_SIZE; col += WG_SIZE) {
+        let vec_val = src[row_idx / VEC_SIZE + col];
+        for (var v = 0u; v < VEC_SIZE; v++) {
+            let val = vec_val[v];
+            if (val >= local_pair.value) {
+                local_pair = Pair(val, i32(col * VEC_SIZE + v));
+            }
+        }
+    }
+#else
+    for (var col = lid.x; col < params.ne0; col += WG_SIZE) {
+        if (src[row_idx + col] >= local_pair.value) {
+            local_pair = Pair(src[row_idx + col], i32(col));
+        }
+    }
+#endif
+    shared_max[lid.x] = local_pair;
+    workgroupBarrier();
+    var offset: u32 = WG_SIZE >> 1;
+    while (offset > 0) {
+        if (lid.x < offset) {
+            let a = shared_max[lid.x];
+            let b = shared_max[lid.x + offset];
+            if (b.value > a.value) {
+                shared_max[lid.x] = b;
+            } else if (b.value == a.value && b.index > a.index) {
+                shared_max[lid.x] = b;
+            }
+        }
+        workgroupBarrier();
+        offset >>= 1;
+    }
+    if (lid.x == 0u) {
+        dst[params.offset_dst + wid.x] = shared_max[0].index;
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort.wgsl
new file mode 100644
index 0000000..46ed19f
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort.wgsl
@@ -0,0 +1,106 @@
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<i32>;
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_dst: u32, // in elements
+
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // src/dst dimensions
+    src_ne0: u32,
+    ne1: u32,
+    ne2: u32,
+
+    ne0: u32,
+    top_k: u32,
+
+    npr: u32,   // tiles per row
+    nrows: u32
+};
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+var<workgroup> shmem_idx: array<u32, WG_SIZE>;
+
+#if ORDER == 0
+#define EXTREME_VALUE 1e30
+#define SWAP_COMPARE_UP >
+#define SWAP_COMPARE_DOWN <
+#else
+#define EXTREME_VALUE -1e30
+#define SWAP_COMPARE_UP <
+#define SWAP_COMPARE_DOWN >
+#endif
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(num_workgroups) num_wg: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+    let linear = wid.x + wid.y * num_wg.x;
+    // guard against overprovisioned workgroups
+    if (linear >= params.npr * params.nrows) {
+        return;
+    }
+    let tile = linear % params.npr;
+    var row = linear / params.npr;
+    let i3 = row / (params.ne2 * params.ne1);
+    row = row % (params.ne2 * params.ne1);
+    let i2 = row / params.ne1;
+    let i1 = row % params.ne1;
+
+    let row_base = params.offset_src +
+        i1 * params.stride_src1 +
+        i2 * params.stride_src2 +
+        i3 * params.stride_src3;
+
+    let tile_base = tile * WG_SIZE;
+    let idx = tile_base + lid.x;
+    shmem_idx[lid.x] = select(params.src_ne0, idx, idx < params.src_ne0);
+    workgroupBarrier();
+
+    var k = 2u;
+    while (k <= WG_SIZE) {
+        var j = k >> 1;
+        while (j > 0) {
+            let ixj = lid.x ^ j;
+            if (ixj > lid.x) {
+                let dir_up = (lid.x & k) == 0;
+                let a_idx = shmem_idx[lid.x];
+                let b_idx = shmem_idx[ixj];
+                let a_val = select(EXTREME_VALUE, src[row_base + a_idx], a_idx < params.src_ne0);
+                let b_val = select(EXTREME_VALUE, src[row_base + b_idx], b_idx < params.src_ne0);
+                let should_swap = select(
+                    (a_val SWAP_COMPARE_DOWN b_val),
+                    (a_val SWAP_COMPARE_UP b_val),
+                    dir_up);
+                if (should_swap) {
+                    shmem_idx[lid.x] = b_idx;
+                    shmem_idx[ixj] = a_idx;
+                }
+            }
+            workgroupBarrier();
+            j >>= 1;
+        }
+        k <<= 1;
+    }
+
+    let out_idx = tile * params.top_k + lid.x;
+    if (out_idx < params.ne0 && lid.x < params.top_k) {
+        let row_dst = params.offset_dst +
+            i1 * params.stride_dst1 +
+            i2 * params.stride_dst2 +
+            i3 * params.stride_dst3;
+        dst[row_dst + out_idx] = i32(shmem_idx[lid.x]);
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort_merge.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort_merge.wgsl
new file mode 100644
index 0000000..9a77f6e
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort_merge.wgsl
@@ -0,0 +1,134 @@
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> idx_in: array<i32>;
+
+@group(0) @binding(2)
+var<storage, read_write> idx_out: array<i32>;
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_in: u32,  // in elements
+    offset_out: u32, // in elements
+
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_idx1: u32,
+    stride_idx2: u32,
+    stride_idx3: u32,
+
+    stride_out1: u32,
+    stride_out2: u32,
+    stride_out3: u32,
+
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+
+    top_k: u32,
+
+    len: u32,
+    nm: u32,
+    nrows: u32
+};
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+fn take_left(a_idx: i32, b_idx: i32, row_base: u32) -> bool {
+    let a_val = src[row_base + u32(a_idx)];
+    let b_val = src[row_base + u32(b_idx)];
+#if ORDER == 0
+    return a_val <= b_val;
+#else
+    return a_val >= b_val;
+#endif
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(num_workgroups) num_wg: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+    let linear = wid.x + wid.y * num_wg.x;
+    // guard against overprovisioned workgroups
+    if (linear >= params.nm * params.nrows) {
+        return;
+    }
+
+    let start = (linear % params.nm) * params.len * 2;
+    let len0 = min(params.len, params.ne0 - start);
+    let rem1 = select(0, params.ne0 - (start + params.len), params.ne0 > (start + params.len));
+    let len1 = min(params.len, rem1);
+    let total = len0 + len1;
+    let chunk = (total + WG_SIZE - 1u) / WG_SIZE;
+    let k0 = lid.x * chunk;
+    let k1 = min(min(k0 + chunk, total), params.top_k);
+    // guard against overprovisioned threads
+    if (k0 >= params.top_k || k0 >= total) {
+        return;
+    }
+
+    var row = linear / params.nm;
+    let i3 = row / (params.ne2 * params.ne1);
+    row = row % (params.ne2 * params.ne1);
+    let i2 = row / params.ne1;
+    let i1 = row % params.ne1;
+
+    let row_src = params.offset_src +
+        i1 * params.stride_src1 +
+        i2 * params.stride_src2 +
+        i3 * params.stride_src3;
+
+    let row_in = params.offset_in +
+        i1 * params.stride_idx1 +
+        i2 * params.stride_idx2 +
+        i3 * params.stride_idx3;
+
+    let row_out = params.offset_out +
+        i1 * params.stride_out1 +
+        i2 * params.stride_out2 +
+        i3 * params.stride_out3;
+
+
+    var low: u32 = select(0, k0 - len1, k0 > len1);
+    var high: u32 = min(k0, len0);
+
+    while (low < high) {
+        let mid = (low + high) >> 1;
+        let idx0 = idx_in[row_in + start + mid];
+        let idx1 = idx_in[row_in + start + params.len + (k0 - mid - 1)];
+        if (take_left(idx0, idx1, row_src)) {
+            low = mid + 1;
+        } else {
+            high = mid;
+        }
+    }
+
+    var i = low;
+    var j = k0 - i;
+    var k = k0;
+    while (k < k1) {
+        var take_l = false;
+        if (i >= len0) {
+            take_l = false;
+        } else if (j >= len1) {
+            take_l = true;
+        } else {
+            let idx0 = idx_in[row_in + start + i];
+            let idx1 = idx_in[row_in + start + params.len + j];
+            take_l = take_left(idx0, idx1, row_src);
+        }
+
+        let out_idx = select(
+            idx_in[row_in + start + params.len + j],
+            idx_in[row_in + start + i],
+            take_l);
+        idx_out[row_out + start + k] = out_idx;
+        i = select(i, i + 1, take_l);
+        j = select(j + 1, j, take_l);
+        k += 1;
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
new file mode 100644
index 0000000..55dd664
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
@@ -0,0 +1,107 @@
+enable f16;
+
+struct Params {
+    ne: u32,
+
+    // offsets in elements
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+
+    stride_src1_0: u32,
+    stride_src1_1: u32,
+    stride_src1_2: u32,
+    stride_src1_3: u32,
+
+    a_ne0: u32,
+    a_ne1: u32,
+    a_ne2: u32,
+
+    b_ne0: u32,
+    b_ne1: u32,
+    b_ne2: u32,
+    b_ne3: u32,
+};
+
+fn src1_index(_i: u32) -> u32 {
+    var i = _i;
+    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    let a_i2 = i / (params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne1 * params.a_ne0);
+    let a_i1 = i / params.a_ne0;
+    let a_i0 = i % params.a_ne0;
+
+    // handle repetition of b
+    // index loops back to the beginning and repeats after elements are exhausted = modulo
+    let b_i0 = a_i0 % params.b_ne0;
+    let b_i1 = a_i1 % params.b_ne1;
+    let b_i2 = a_i2 % params.b_ne2;
+    let b_i3 = a_i3 % params.b_ne3;
+
+    // compute index for position in b's flat array
+    return b_i0 * params.stride_src1_0 +
+           b_i1 * params.stride_src1_1 +
+           b_i2 * params.stride_src1_2 +
+           b_i3 * params.stride_src1_3;
+}
+
+#ifdef TYPE_F32
+#define DataType f32
+#endif
+#ifdef TYPE_F16
+#define DataType f16
+#endif
+
+@group(0) @binding(0)
+var<storage, read_write> src0: array<DataType>;
+
+@group(0) @binding(1)
+var<storage, read_write> src1 : array<DataType>;
+
+#ifdef INPLACE
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#elif defined(OVERLAP)
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#else
+@group(0) @binding(2)
+var<storage, read_write> dst: array<DataType>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+#endif
+
+fn op(a: DataType, b: DataType) -> DataType {
+#ifdef OP_ADD
+    return a + b;
+#elif defined(OP_SUB)
+    return a - b;
+#elif defined(OP_MUL)
+    return a * b;
+#elif defined(OP_DIV)
+    return a / b;
+#endif
+}
+
+fn update(dst_i: u32, src0_i: u32, src1_i: u32){
+    let result = op(src0[src0_i], src1[src1_i]);
+
+#ifdef INPLACE
+    src0[dst_i] = result;
+#elif defined(OVERLAP)
+    src1[dst_i] = result;
+#else
+    dst[dst_i] = result;
+#endif
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x < params.ne) {
+        update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
new file mode 100644
index 0000000..389c97b
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
@@ -0,0 +1,930 @@
+#decl(BYTE_HELPERS)
+
+fn get_byte(value: u32, index: u32) -> u32 {
+    return (value >> (index * 8)) & 0xFF;
+}
+
+fn get_byte_i32(value: u32, index: u32) -> i32 {
+    return bitcast<i32>(((value >> (index * 8)) & 0xFF) << 24) >> 24;
+}
+
+#enddecl(BYTE_HELPERS)
+
+#decl(Q4_0_T)
+struct q4_0 {
+    d: f16,
+    qs: array<f16, 8>
+};
+#enddecl(Q4_0_T)
+
+#decl(Q4_1_T)
+struct q4_1 {
+    d: f16,
+    m: f16,
+    qs: array<u32, 4>
+};
+#enddecl(Q4_1_T)
+
+#decl(Q5_0_T)
+struct q5_0 {
+    d: f16,
+    qh: array<f16, 2>,
+    qs: array<f16, 8>
+};
+#enddecl(Q5_0_T)
+
+#decl(Q5_1_T)
+struct q5_1 {
+    d: f16,
+    m: f16,
+    qh: u32,
+    qs: array<u32, 4>
+};
+#enddecl(Q5_1_T)
+
+#decl(Q8_0_T)
+struct q8_0 {
+    d: f16,
+    qs: array<f16, 16>
+};
+#enddecl(Q8_0_T)
+
+#decl(Q8_1_T)
+struct q8_1 {
+    d: f16,
+    m: f16,
+    qs: array<u32, 8>
+};
+#enddecl(Q8_1_T)
+
+#decl(Q2_K_T)
+struct q2_k {
+    scales: array<u32, 4>,
+    qs: array<u32, 16>,
+    d: f16,
+    dmin: f16
+};
+#enddecl(Q2_K_T)
+
+#decl(Q3_K_T)
+struct q3_k {
+    hmask: array<f16, 16>,
+    qs: array<f16, 32>,
+    scales: array<f16, 6>,
+    d: f16
+};
+#enddecl(Q3_K_T)
+
+#decl(Q45_K_SCALE_MIN)
+
+fn get_scale_min(is: u32, scales: array<u32, 3>) -> vec2<f32> {
+    if (is < 4) {
+        let sc_byte = get_byte(scales[is / 4], is % 4);
+        let min_byte = get_byte(scales[(is + 4) / 4], is % 4);
+        return vec2(f32(sc_byte & 63), f32(min_byte & 63));
+    } else {
+        let sc_min_lo = get_byte(scales[(is + 4) / 4], (is + 4) % 4);
+        let sc_hi = get_byte(scales[(is - 4) / 4], (is - 4) % 4);
+        let min_hi = get_byte(scales[is / 4], is % 4);
+        let sc = (sc_min_lo & 0xF) | ((sc_hi >> 6) << 4);
+        let m = (sc_min_lo >> 4) | ((min_hi >> 6) << 4);
+        return vec2(f32(sc), f32(m));
+    }
+}
+
+#enddecl(Q45_K_SCALE_MIN)
+
+#decl(Q4_K_T)
+struct q4_k {
+    d: f16,
+    dmin: f16,
+    scales: array<u32, 3>,
+    qs: array<u32, 32>
+};
+#enddecl(Q4_K_T)
+
+#decl(Q5_K_T)
+struct q5_k {
+    d: f16,
+    dmin: f16,
+    scales: array<u32, 3>,
+    qh: array<u32, 8>,
+    qs: array<u32, 32>
+};
+#enddecl(Q5_K_T)
+
+#decl(Q6_K_T)
+struct q6_k {
+    ql: array<f16, 64>,
+    qh: array<f16, 32>,
+    scales: array<f16, 8>,
+    d: f16
+};
+#enddecl(Q6_K_T)
+
+#decl(IQ2_XXS_T)
+struct iq2_xxs {
+    d: f16,
+    qs: array<f16, 32>
+};
+#enddecl(IQ2_XXS_T)
+
+#decl(IQ2_XS_T)
+struct iq2_xs {
+    d: f16,
+    qs: array<f16, 32>,
+    scales: array<f16, 4>
+};
+#enddecl(IQ2_XS_T)
+
+#decl(IQ2_S_T)
+struct iq2_s {
+    d: f16,
+    qs: array<f16, 32>,
+    qh: array<f16, 4>,
+    scales: array<f16, 4>
+};
+#enddecl(IQ2_S_T)
+
+#decl(IQ3_XSS_T)
+struct iq3_xxs {
+    d: f16,
+    qs: array<f16, 48>
+};
+#enddecl(IQ3_XSS_T)
+
+#decl(IQ3_S_T)
+struct iq3_s {
+    d: f16,
+    qs: array<f16, 32>,
+    qh: array<f16, 4>,
+    signs: array<f16, 16>,
+    scales: array<f16, 2>
+};
+#enddecl(IQ3_S_T)
+
+#decl(IQ1_S_T)
+struct iq1_s {
+    d: f16,
+    qs: array<f16, 16>,
+    qh: array<f16, 8>
+};
+#enddecl(IQ1_S_T)
+
+#decl(IQ1_M_T)
+struct iq1_m {
+    qs: array<u32, 8>,
+    qh: array<u32, 4>,
+    scales: array<u32, 2>
+};
+#enddecl(IQ1_M_T)
+
+#decl(IQ4_NL_T)
+struct iq4_nl {
+    d: f16,
+    qs: array<f16, 8>,
+};
+#enddecl(IQ4_NL_T)
+
+#decl(IQ4_XS_T)
+struct iq4_xs {
+    d: f16,
+    scales_h: f16,
+    scales_l: u32,
+    qs: array<u32, 32>
+};
+#enddecl(IQ4_XS_T)
+
+#decl(IQ23_TABLES)
+const kmask_iq2xs : array<u32, 2> = array<u32, 2>(
+    0x08040201u, // 1, 2, 4, 8
+    0x80402010u  // 16, 32, 64, 128
+);
+
+const ksigns_iq2xs: array<u32, 32> = array<u32, 32>(
+    0x03828100,0x87060584,0x8b0a0988,0x0f8e8d0c,
+    0x93121190,0x17969514,0x1b9a9918,0x9f1e1d9c,
+    0xa32221a0,0x27a6a524,0x2baaa928,0xaf2e2dac,
+    0x33b2b130,0xb73635b4,0xbb3a39b8,0x3fbebd3c,
+    0xc34241c0,0x47c6c544,0x4bcac948,0xcf4e4dcc,
+    0x53d2d150,0xd75655d4,0xdb5a59d8,0x5fdedd5c,
+    0x63e2e160,0xe76665e4,0xeb6a69e8,0x6feeed6c,
+    0xf37271f0,0x77f6f574,0x7bfaf978,0xff7e7dfc
+);
+#enddecl(IQ23_TABLES)
+
+#decl(IQ2_XXS_GRID)
+const iq2xxs_grid = array<u32, 512>(
+    0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808,
+    0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x082b0808, 0x08080808,
+    0x082b082b, 0x08080808, 0x082b2b08, 0x08080808, 0x082b2b2b, 0x08080808, 0x19080819, 0x08080808,
+    0x19081908, 0x08080808, 0x19190808, 0x08080808, 0x19192b08, 0x08080808, 0x192b0819, 0x08080808,
+    0x192b1908, 0x08080808, 0x2b080808, 0x08080808, 0x2b08082b, 0x08080808, 0x2b082b2b, 0x08080808,
+    0x2b2b082b, 0x08080808, 0x08080819, 0x08080819, 0x08081908, 0x08080819, 0x08190808, 0x08080819,
+    0x08191919, 0x08080819, 0x19080808, 0x08080819, 0x2b081908, 0x08080819, 0x2b192b08, 0x08080819,
+    0x08080808, 0x0808082b, 0x0808082b, 0x0808082b, 0x082b082b, 0x0808082b, 0x2b08082b, 0x0808082b,
+    0x08080819, 0x08081908, 0x08081908, 0x08081908, 0x08190808, 0x08081908, 0x082b0819, 0x08081908,
+    0x082b1908, 0x08081908, 0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19082b08, 0x08081908,
+    0x192b0808, 0x08081908, 0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b190808, 0x08081908,
+    0x2b2b1908, 0x08081908, 0x08080808, 0x08081919, 0x0808082b, 0x08081919, 0x08082b08, 0x08081919,
+    0x082b0808, 0x08081919, 0x1908192b, 0x08081919, 0x192b2b19, 0x08081919, 0x2b080808, 0x08081919,
+    0x2b190819, 0x08081919, 0x08082b19, 0x0808192b, 0x08190808, 0x0808192b, 0x19080808, 0x0808192b,
+    0x2b081908, 0x0808192b, 0x2b2b1908, 0x0808192b, 0x08080808, 0x08082b08, 0x08081919, 0x08082b08,
+    0x08082b08, 0x08082b08, 0x08191908, 0x08082b08, 0x082b2b08, 0x08082b08, 0x19080819, 0x08082b08,
+    0x19081908, 0x08082b08, 0x19190808, 0x08082b08, 0x1919082b, 0x08082b08, 0x2b082b08, 0x08082b08,
+    0x08081908, 0x08082b19, 0x19080808, 0x08082b19, 0x0808082b, 0x08082b2b, 0x08191908, 0x08082b2b,
+    0x08080819, 0x08190808, 0x08081908, 0x08190808, 0x08190808, 0x08190808, 0x082b0819, 0x08190808,
+    0x19080808, 0x08190808, 0x192b0808, 0x08190808, 0x2b081908, 0x08190808, 0x2b190808, 0x08190808,
+    0x2b191919, 0x08190808, 0x08080808, 0x08190819, 0x08082b08, 0x08190819, 0x082b0808, 0x08190819,
+    0x19190808, 0x08190819, 0x19192b2b, 0x08190819, 0x2b080808, 0x08190819, 0x082b1908, 0x0819082b,
+    0x19081919, 0x0819082b, 0x08080808, 0x08191908, 0x08082b08, 0x08191908, 0x082b0808, 0x08191908,
+    0x082b1919, 0x08191908, 0x19082b19, 0x08191908, 0x2b080808, 0x08191908, 0x08192b08, 0x08191919,
+    0x192b082b, 0x08191919, 0x08080808, 0x0819192b, 0x0819192b, 0x0819192b, 0x08080819, 0x08192b08,
+    0x08081908, 0x08192b08, 0x08190808, 0x08192b08, 0x19080808, 0x08192b08, 0x2b080819, 0x08192b08,
+    0x08080808, 0x08192b19, 0x08081919, 0x08192b19, 0x2b2b0808, 0x08192b19, 0x19190819, 0x08192b2b,
+    0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08082b2b, 0x082b0808, 0x19081908, 0x082b0808,
+    0x192b0819, 0x082b0808, 0x2b080808, 0x082b0808, 0x2b08082b, 0x082b0808, 0x082b2b19, 0x082b0819,
+    0x19082b08, 0x082b0819, 0x08080808, 0x082b082b, 0x0808082b, 0x082b082b, 0x08080819, 0x082b1908,
+    0x08081908, 0x082b1908, 0x08190808, 0x082b1908, 0x19080808, 0x082b1908, 0x1919192b, 0x082b1908,
+    0x08080808, 0x082b1919, 0x19080819, 0x082b1919, 0x192b1908, 0x082b1919, 0x2b190808, 0x082b192b,
+    0x08082b08, 0x082b2b08, 0x082b0808, 0x082b2b08, 0x2b191908, 0x082b2b08, 0x19081908, 0x082b2b2b,
+    0x08080819, 0x19080808, 0x08081908, 0x19080808, 0x08190808, 0x19080808, 0x08192b08, 0x19080808,
+    0x082b0819, 0x19080808, 0x082b1908, 0x19080808, 0x19080808, 0x19080808, 0x19082b08, 0x19080808,
+    0x1919192b, 0x19080808, 0x192b0808, 0x19080808, 0x2b080819, 0x19080808, 0x2b081908, 0x19080808,
+    0x2b190808, 0x19080808, 0x08080808, 0x19080819, 0x082b0808, 0x19080819, 0x192b0819, 0x19080819,
+    0x2b080808, 0x19080819, 0x2b081919, 0x19080819, 0x08080819, 0x1908082b, 0x08190808, 0x1908082b,
+    0x19082b08, 0x1908082b, 0x1919192b, 0x1908082b, 0x192b2b08, 0x1908082b, 0x08080808, 0x19081908,
+    0x08082b08, 0x19081908, 0x082b0808, 0x19081908, 0x2b080808, 0x19081908, 0x2b192b19, 0x19081908,
+    0x0819082b, 0x19081919, 0x082b1908, 0x19081919, 0x08080808, 0x1908192b, 0x08080819, 0x19082b08,
+    0x08081908, 0x19082b08, 0x08190808, 0x19082b08, 0x19080808, 0x19082b08, 0x19081919, 0x19082b08,
+    0x08080808, 0x19082b19, 0x19192b08, 0x19082b19, 0x192b0819, 0x19082b19, 0x2b08082b, 0x19082b19,
+    0x19081919, 0x19082b2b, 0x2b190808, 0x19082b2b, 0x08080808, 0x19190808, 0x08082b08, 0x19190808,
+    0x08190819, 0x19190808, 0x08192b19, 0x19190808, 0x082b0808, 0x19190808, 0x2b080808, 0x19190808,
+    0x2b082b08, 0x19190808, 0x08081908, 0x19190819, 0x1908082b, 0x19190819, 0x2b2b1908, 0x19190819,
+    0x2b190819, 0x1919082b, 0x2b190808, 0x19191908, 0x2b19082b, 0x19191908, 0x08082b2b, 0x19191919,
+    0x08080819, 0x1919192b, 0x19191908, 0x1919192b, 0x08080808, 0x19192b08, 0x08190819, 0x19192b08,
+    0x08192b19, 0x19192b08, 0x192b1908, 0x19192b08, 0x19080808, 0x19192b19, 0x08082b08, 0x19192b2b,
+    0x08081908, 0x192b0808, 0x08190808, 0x192b0808, 0x19080808, 0x192b0808, 0x192b2b08, 0x192b0808,
+    0x08080808, 0x192b0819, 0x19191919, 0x192b0819, 0x08192b08, 0x192b082b, 0x192b0808, 0x192b082b,
+    0x08080808, 0x192b1908, 0x08081919, 0x192b1908, 0x08190808, 0x192b1919, 0x0819082b, 0x192b1919,
+    0x2b081908, 0x192b1919, 0x1908082b, 0x192b2b08, 0x08080808, 0x2b080808, 0x0808082b, 0x2b080808,
+    0x08082b2b, 0x2b080808, 0x19080819, 0x2b080808, 0x2b08082b, 0x2b080808, 0x08081908, 0x2b080819,
+    0x08192b08, 0x2b080819, 0x19080808, 0x2b080819, 0x08190819, 0x2b08082b, 0x08080819, 0x2b081908,
+    0x08081908, 0x2b081908, 0x08190808, 0x2b081908, 0x08191919, 0x2b081908, 0x19080808, 0x2b081908,
+    0x192b0808, 0x2b081908, 0x08080808, 0x2b081919, 0x1908192b, 0x2b081919, 0x2b191908, 0x2b081919,
+    0x08082b19, 0x2b08192b, 0x19080808, 0x2b08192b, 0x192b0808, 0x2b08192b, 0x0808082b, 0x2b082b08,
+    0x08081908, 0x2b082b19, 0x08190819, 0x2b082b2b, 0x08081908, 0x2b190808, 0x08190808, 0x2b190808,
+    0x082b1908, 0x2b190808, 0x19080808, 0x2b190808, 0x2b2b0819, 0x2b190808, 0x0819192b, 0x2b190819,
+    0x2b080808, 0x2b190819, 0x19081919, 0x2b19082b, 0x08080808, 0x2b191908, 0x082b082b, 0x2b191908,
+    0x19081908, 0x2b191908, 0x19190819, 0x2b191919, 0x2b080819, 0x2b192b08, 0x082b0808, 0x2b192b19,
+    0x0808082b, 0x2b2b0808, 0x19190808, 0x2b2b0808, 0x2b081919, 0x2b2b0808, 0x08082b19, 0x2b2b0819,
+    0x08080808, 0x2b2b082b, 0x08192b08, 0x2b2b1908, 0x19190808, 0x2b2b2b08, 0x08081908, 0x2b2b2b19
+);
+#enddecl(IQ2_XXS_GRID)
+
+#decl(IQ2_XS_GRID)
+const iq2xs_grid = array<u32, 1024>(
+    0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808,
+    0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x0819192b, 0x08080808,
+    0x08192b19, 0x08080808, 0x082b0808, 0x08080808, 0x082b082b, 0x08080808, 0x082b1919, 0x08080808,
+    0x082b2b08, 0x08080808, 0x19080819, 0x08080808, 0x19081908, 0x08080808, 0x1908192b, 0x08080808,
+    0x19082b19, 0x08080808, 0x19190808, 0x08080808, 0x1919082b, 0x08080808, 0x19191919, 0x08080808,
+    0x19192b08, 0x08080808, 0x192b0819, 0x08080808, 0x192b1908, 0x08080808, 0x2b080808, 0x08080808,
+    0x2b08082b, 0x08080808, 0x2b081919, 0x08080808, 0x2b082b08, 0x08080808, 0x2b190819, 0x08080808,
+    0x2b191908, 0x08080808, 0x2b192b19, 0x08080808, 0x2b2b0808, 0x08080808, 0x08080819, 0x08080819,
+    0x08081908, 0x08080819, 0x0808192b, 0x08080819, 0x08082b19, 0x08080819, 0x08190808, 0x08080819,
+    0x0819082b, 0x08080819, 0x08191919, 0x08080819, 0x08192b08, 0x08080819, 0x08192b2b, 0x08080819,
+    0x082b0819, 0x08080819, 0x082b1908, 0x08080819, 0x19080808, 0x08080819, 0x1908082b, 0x08080819,
+    0x19081919, 0x08080819, 0x19082b08, 0x08080819, 0x19190819, 0x08080819, 0x19191908, 0x08080819,
+    0x192b0808, 0x08080819, 0x192b2b08, 0x08080819, 0x2b080819, 0x08080819, 0x2b081908, 0x08080819,
+    0x2b190808, 0x08080819, 0x08080808, 0x0808082b, 0x0808082b, 0x0808082b, 0x08081919, 0x0808082b,
+    0x08082b08, 0x0808082b, 0x08190819, 0x0808082b, 0x08191908, 0x0808082b, 0x082b0808, 0x0808082b,
+    0x19080819, 0x0808082b, 0x19081908, 0x0808082b, 0x19190808, 0x0808082b, 0x19191919, 0x0808082b,
+    0x2b080808, 0x0808082b, 0x2b082b2b, 0x0808082b, 0x08080819, 0x08081908, 0x08081908, 0x08081908,
+    0x0808192b, 0x08081908, 0x08082b19, 0x08081908, 0x08190808, 0x08081908, 0x0819082b, 0x08081908,
+    0x08191919, 0x08081908, 0x08192b08, 0x08081908, 0x082b0819, 0x08081908, 0x082b1908, 0x08081908,
+    0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19081919, 0x08081908, 0x19082b08, 0x08081908,
+    0x19190819, 0x08081908, 0x19191908, 0x08081908, 0x1919192b, 0x08081908, 0x192b0808, 0x08081908,
+    0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b190808, 0x08081908, 0x08080808, 0x08081919,
+    0x0808082b, 0x08081919, 0x08081919, 0x08081919, 0x08082b08, 0x08081919, 0x08190819, 0x08081919,
+    0x08191908, 0x08081919, 0x082b0808, 0x08081919, 0x19080819, 0x08081919, 0x19081908, 0x08081919,
+    0x19190808, 0x08081919, 0x192b0819, 0x08081919, 0x2b080808, 0x08081919, 0x08080819, 0x0808192b,
+    0x08081908, 0x0808192b, 0x08190808, 0x0808192b, 0x082b192b, 0x0808192b, 0x19080808, 0x0808192b,
+    0x1908082b, 0x0808192b, 0x2b081908, 0x0808192b, 0x08080808, 0x08082b08, 0x0808082b, 0x08082b08,
+    0x08081919, 0x08082b08, 0x08082b08, 0x08082b08, 0x08082b2b, 0x08082b08, 0x08190819, 0x08082b08,
+    0x08191908, 0x08082b08, 0x082b0808, 0x08082b08, 0x082b1919, 0x08082b08, 0x19080819, 0x08082b08,
+    0x19081908, 0x08082b08, 0x19190808, 0x08082b08, 0x19192b08, 0x08082b08, 0x2b080808, 0x08082b08,
+    0x2b2b0808, 0x08082b08, 0x2b2b2b2b, 0x08082b08, 0x08080819, 0x08082b19, 0x08081908, 0x08082b19,
+    0x08190808, 0x08082b19, 0x19080808, 0x08082b19, 0x2b080819, 0x08082b19, 0x2b082b19, 0x08082b19,
+    0x08080808, 0x08082b2b, 0x082b0808, 0x08082b2b, 0x082b2b08, 0x08082b2b, 0x2b19192b, 0x08082b2b,
+    0x2b2b0808, 0x08082b2b, 0x08080819, 0x08190808, 0x08081908, 0x08190808, 0x0808192b, 0x08190808,
+    0x08082b19, 0x08190808, 0x08190808, 0x08190808, 0x0819082b, 0x08190808, 0x08191919, 0x08190808,
+    0x08192b08, 0x08190808, 0x082b0819, 0x08190808, 0x082b1908, 0x08190808, 0x19080808, 0x08190808,
+    0x1908082b, 0x08190808, 0x19081919, 0x08190808, 0x19082b08, 0x08190808, 0x19190819, 0x08190808,
+    0x19191908, 0x08190808, 0x192b0808, 0x08190808, 0x192b2b2b, 0x08190808, 0x2b080819, 0x08190808,
+    0x2b081908, 0x08190808, 0x2b190808, 0x08190808, 0x08080808, 0x08190819, 0x0808082b, 0x08190819,
+    0x08081919, 0x08190819, 0x08082b08, 0x08190819, 0x08190819, 0x08190819, 0x08191908, 0x08190819,
+    0x082b0808, 0x08190819, 0x19080819, 0x08190819, 0x19081908, 0x08190819, 0x19190808, 0x08190819,
+    0x2b080808, 0x08190819, 0x2b191908, 0x08190819, 0x2b19192b, 0x08190819, 0x08080819, 0x0819082b,
+    0x08081908, 0x0819082b, 0x0808192b, 0x0819082b, 0x08190808, 0x0819082b, 0x19080808, 0x0819082b,
+    0x192b0808, 0x0819082b, 0x08080808, 0x08191908, 0x0808082b, 0x08191908, 0x08081919, 0x08191908,
+    0x08082b08, 0x08191908, 0x08190819, 0x08191908, 0x08191908, 0x08191908, 0x082b0808, 0x08191908,
+    0x19080819, 0x08191908, 0x19081908, 0x08191908, 0x19082b19, 0x08191908, 0x19190808, 0x08191908,
+    0x192b1908, 0x08191908, 0x2b080808, 0x08191908, 0x08080819, 0x08191919, 0x08081908, 0x08191919,
+    0x08190808, 0x08191919, 0x19080808, 0x08191919, 0x08080808, 0x0819192b, 0x08191908, 0x0819192b,
+    0x19082b19, 0x0819192b, 0x08080819, 0x08192b08, 0x08081908, 0x08192b08, 0x08190808, 0x08192b08,
+    0x0819082b, 0x08192b08, 0x19080808, 0x08192b08, 0x19191908, 0x08192b08, 0x2b08192b, 0x08192b08,
+    0x08080808, 0x08192b19, 0x08081919, 0x08192b19, 0x192b192b, 0x08192b19, 0x19190819, 0x08192b2b,
+    0x2b2b2b19, 0x08192b2b, 0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08081919, 0x082b0808,
+    0x08082b08, 0x082b0808, 0x08082b2b, 0x082b0808, 0x08190819, 0x082b0808, 0x08191908, 0x082b0808,
+    0x082b0808, 0x082b0808, 0x19080819, 0x082b0808, 0x19081908, 0x082b0808, 0x19190808, 0x082b0808,
+    0x2b080808, 0x082b0808, 0x2b2b0808, 0x082b0808, 0x08080819, 0x082b0819, 0x08081908, 0x082b0819,
+    0x08190808, 0x082b0819, 0x19080808, 0x082b0819, 0x19082b08, 0x082b0819, 0x192b1919, 0x082b0819,
+    0x08080808, 0x082b082b, 0x082b082b, 0x082b082b, 0x2b080808, 0x082b082b, 0x2b2b2b08, 0x082b082b,
+    0x08080819, 0x082b1908, 0x08081908, 0x082b1908, 0x08190808, 0x082b1908, 0x082b2b19, 0x082b1908,
+    0x19080808, 0x082b1908, 0x08080808, 0x082b1919, 0x19080819, 0x082b1919, 0x1919082b, 0x082b1919,
+    0x2b192b19, 0x082b1919, 0x08080819, 0x082b192b, 0x08192b2b, 0x082b192b, 0x2b2b192b, 0x082b192b,
+    0x08080808, 0x082b2b08, 0x08082b08, 0x082b2b08, 0x08082b2b, 0x082b2b08, 0x082b0808, 0x082b2b08,
+    0x19191919, 0x082b2b08, 0x2b082b08, 0x082b2b08, 0x2b2b082b, 0x082b2b08, 0x192b2b08, 0x082b2b19,
+    0x2b190808, 0x082b2b19, 0x08082b08, 0x082b2b2b, 0x082b0808, 0x082b2b2b, 0x2b08082b, 0x082b2b2b,
+    0x2b082b08, 0x082b2b2b, 0x2b082b2b, 0x082b2b2b, 0x08080819, 0x19080808, 0x08081908, 0x19080808,
+    0x0808192b, 0x19080808, 0x08082b19, 0x19080808, 0x08190808, 0x19080808, 0x0819082b, 0x19080808,
+    0x08191919, 0x19080808, 0x08192b08, 0x19080808, 0x082b0819, 0x19080808, 0x082b1908, 0x19080808,
+    0x19080808, 0x19080808, 0x1908082b, 0x19080808, 0x19081919, 0x19080808, 0x19082b08, 0x19080808,
+    0x19082b2b, 0x19080808, 0x19190819, 0x19080808, 0x19191908, 0x19080808, 0x192b0808, 0x19080808,
+    0x192b1919, 0x19080808, 0x2b080819, 0x19080808, 0x2b081908, 0x19080808, 0x2b190808, 0x19080808,
+    0x08080808, 0x19080819, 0x0808082b, 0x19080819, 0x08081919, 0x19080819, 0x08082b08, 0x19080819,
+    0x08190819, 0x19080819, 0x08191908, 0x19080819, 0x082b0808, 0x19080819, 0x19080819, 0x19080819,
+    0x19081908, 0x19080819, 0x19190808, 0x19080819, 0x2b080808, 0x19080819, 0x2b081919, 0x19080819,
+    0x2b2b082b, 0x19080819, 0x08080819, 0x1908082b, 0x08081908, 0x1908082b, 0x08190808, 0x1908082b,
+    0x0819082b, 0x1908082b, 0x082b2b19, 0x1908082b, 0x19080808, 0x1908082b, 0x08080808, 0x19081908,
+    0x0808082b, 0x19081908, 0x08081919, 0x19081908, 0x08082b08, 0x19081908, 0x08190819, 0x19081908,
+    0x08191908, 0x19081908, 0x08192b19, 0x19081908, 0x082b0808, 0x19081908, 0x19080819, 0x19081908,
+    0x19081908, 0x19081908, 0x19190808, 0x19081908, 0x2b080808, 0x19081908, 0x2b191908, 0x19081908,
+    0x08080819, 0x19081919, 0x08081908, 0x19081919, 0x08190808, 0x19081919, 0x082b1908, 0x19081919,
+    0x19080808, 0x19081919, 0x2b192b2b, 0x19081919, 0x08080808, 0x1908192b, 0x08082b2b, 0x1908192b,
+    0x19081908, 0x1908192b, 0x19190808, 0x1908192b, 0x08080819, 0x19082b08, 0x08081908, 0x19082b08,
+    0x08190808, 0x19082b08, 0x19080808, 0x19082b08, 0x19081919, 0x19082b08, 0x19191908, 0x19082b08,
+    0x192b082b, 0x19082b08, 0x08080808, 0x19082b19, 0x08190819, 0x19082b19, 0x19081908, 0x19082b19,
+    0x19190808, 0x19082b19, 0x192b2b19, 0x19082b19, 0x08081908, 0x19082b2b, 0x08080808, 0x19190808,
+    0x0808082b, 0x19190808, 0x08081919, 0x19190808, 0x08082b08, 0x19190808, 0x08190819, 0x19190808,
+    0x08191908, 0x19190808, 0x082b0808, 0x19190808, 0x082b2b08, 0x19190808, 0x19080819, 0x19190808,
+    0x19081908, 0x19190808, 0x19190808, 0x19190808, 0x2b080808, 0x19190808, 0x08080819, 0x19190819,
+    0x08081908, 0x19190819, 0x08190808, 0x19190819, 0x08191919, 0x19190819, 0x19080808, 0x19190819,
+    0x1908082b, 0x19190819, 0x08080808, 0x1919082b, 0x19081908, 0x1919082b, 0x2b2b2b2b, 0x1919082b,
+    0x08080819, 0x19191908, 0x08081908, 0x19191908, 0x08190808, 0x19191908, 0x082b0819, 0x19191908,
+    0x19080808, 0x19191908, 0x192b0808, 0x19191908, 0x2b080819, 0x19191908, 0x2b2b0819, 0x19191908,
+    0x08080808, 0x19191919, 0x08082b08, 0x19191919, 0x2b080808, 0x19191919, 0x2b082b08, 0x19191919,
+    0x082b0819, 0x1919192b, 0x192b2b08, 0x1919192b, 0x2b2b0819, 0x1919192b, 0x08080808, 0x19192b08,
+    0x08191908, 0x19192b08, 0x19080819, 0x19192b08, 0x19190808, 0x19192b08, 0x2b192b19, 0x19192b08,
+    0x08192b2b, 0x19192b19, 0x19080808, 0x19192b19, 0x1908082b, 0x19192b19, 0x2b081919, 0x19192b2b,
+    0x08080819, 0x192b0808, 0x08081908, 0x192b0808, 0x08190808, 0x192b0808, 0x19080808, 0x192b0808,
+    0x19191908, 0x192b0808, 0x192b082b, 0x192b0808, 0x2b08192b, 0x192b0808, 0x2b2b2b19, 0x192b0808,
+    0x08080808, 0x192b0819, 0x082b1908, 0x192b082b, 0x19082b2b, 0x192b082b, 0x2b19082b, 0x192b082b,
+    0x08080808, 0x192b1908, 0x0819192b, 0x192b1908, 0x08190808, 0x192b1919, 0x19080808, 0x192b1919,
+    0x19081919, 0x192b1919, 0x2b2b1908, 0x192b1919, 0x08080819, 0x192b2b08, 0x192b2b2b, 0x192b2b08,
+    0x082b1919, 0x192b2b19, 0x0808192b, 0x192b2b2b, 0x19191908, 0x192b2b2b, 0x192b082b, 0x192b2b2b,
+    0x08080808, 0x2b080808, 0x0808082b, 0x2b080808, 0x08081919, 0x2b080808, 0x08082b08, 0x2b080808,
+    0x08190819, 0x2b080808, 0x08191908, 0x2b080808, 0x082b0808, 0x2b080808, 0x082b2b2b, 0x2b080808,
+    0x19080819, 0x2b080808, 0x19081908, 0x2b080808, 0x19190808, 0x2b080808, 0x2b080808, 0x2b080808,
+    0x2b08082b, 0x2b080808, 0x2b2b2b08, 0x2b080808, 0x2b2b2b2b, 0x2b080808, 0x08080819, 0x2b080819,
+    0x08081908, 0x2b080819, 0x0808192b, 0x2b080819, 0x08190808, 0x2b080819, 0x19080808, 0x2b080819,
+    0x19190819, 0x2b080819, 0x19192b19, 0x2b080819, 0x08080808, 0x2b08082b, 0x082b0808, 0x2b08082b,
+    0x2b080808, 0x2b08082b, 0x2b08082b, 0x2b08082b, 0x2b2b0808, 0x2b08082b, 0x2b2b2b08, 0x2b08082b,
+    0x08080819, 0x2b081908, 0x08081908, 0x2b081908, 0x08190808, 0x2b081908, 0x0819082b, 0x2b081908,
+    0x08191919, 0x2b081908, 0x19080808, 0x2b081908, 0x192b0808, 0x2b081908, 0x2b082b19, 0x2b081908,
+    0x08080808, 0x2b081919, 0x19081908, 0x2b081919, 0x2b2b1919, 0x2b081919, 0x08192b08, 0x2b08192b,
+    0x192b2b2b, 0x2b08192b, 0x08080808, 0x2b082b08, 0x08082b08, 0x2b082b08, 0x082b1919, 0x2b082b08,
+    0x19192b2b, 0x2b082b08, 0x2b080808, 0x2b082b08, 0x2b08082b, 0x2b082b08, 0x2b2b2b08, 0x2b082b08,
+    0x0808192b, 0x2b082b19, 0x082b082b, 0x2b082b2b, 0x2b080808, 0x2b082b2b, 0x2b082b08, 0x2b082b2b,
+    0x2b19192b, 0x2b082b2b, 0x2b2b2b08, 0x2b082b2b, 0x08080819, 0x2b190808, 0x08081908, 0x2b190808,
+    0x08190808, 0x2b190808, 0x19080808, 0x2b190808, 0x1919192b, 0x2b190808, 0x2b081908, 0x2b190808,
+    0x08080808, 0x2b190819, 0x082b082b, 0x2b190819, 0x192b1908, 0x2b190819, 0x1919192b, 0x2b19082b,
+    0x2b082b19, 0x2b19082b, 0x08080808, 0x2b191908, 0x08081919, 0x2b191908, 0x19081908, 0x2b191908,
+    0x19190808, 0x2b191908, 0x19192b08, 0x2b191908, 0x082b2b19, 0x2b191919, 0x2b190808, 0x2b191919,
+    0x2b19082b, 0x2b191919, 0x19080819, 0x2b19192b, 0x19190819, 0x2b192b08, 0x2b2b192b, 0x2b192b08,
+    0x19082b19, 0x2b192b19, 0x08191919, 0x2b192b2b, 0x192b0808, 0x2b192b2b, 0x08080808, 0x2b2b0808,
+    0x0808082b, 0x2b2b0808, 0x08082b08, 0x2b2b0808, 0x08082b2b, 0x2b2b0808, 0x082b0808, 0x2b2b0808,
+    0x082b2b2b, 0x2b2b0808, 0x2b2b0808, 0x2b2b0808, 0x19190819, 0x2b2b0819, 0x19192b19, 0x2b2b0819,
+    0x2b2b192b, 0x2b2b0819, 0x08080808, 0x2b2b082b, 0x0808082b, 0x2b2b082b, 0x08082b08, 0x2b2b082b,
+    0x082b2b2b, 0x2b2b082b, 0x2b080808, 0x2b2b082b, 0x2b2b0808, 0x2b2b082b, 0x19080808, 0x2b2b1908,
+    0x2b191919, 0x2b2b1908, 0x192b1919, 0x2b2b192b, 0x2b192b08, 0x2b2b192b, 0x08082b2b, 0x2b2b2b08,
+    0x082b0808, 0x2b2b2b08, 0x082b082b, 0x2b2b2b08, 0x082b2b08, 0x2b2b2b08, 0x2b2b0808, 0x2b2b2b08,
+    0x2b2b2b08, 0x2b2b2b08, 0x08081908, 0x2b2b2b19, 0x2b081908, 0x2b2b2b19, 0x2b08192b, 0x2b2b2b19,
+    0x082b2b08, 0x2b2b2b2b, 0x082b2b2b, 0x2b2b2b2b, 0x2b190819, 0x2b2b2b2b, 0x2b2b2b2b, 0x2b2b2b2b
+);
+#enddecl(IQ2_XS_GRID)
+
+#decl(IQ2_S_GRID)
+const iq2s_grid = array<u32, 2048>(
+    0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808,
+    0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x0819192b, 0x08080808,
+    0x08192b19, 0x08080808, 0x082b0808, 0x08080808, 0x082b082b, 0x08080808, 0x082b1919, 0x08080808,
+    0x082b2b08, 0x08080808, 0x19080819, 0x08080808, 0x19081908, 0x08080808, 0x1908192b, 0x08080808,
+    0x19082b19, 0x08080808, 0x19190808, 0x08080808, 0x1919082b, 0x08080808, 0x19191919, 0x08080808,
+    0x19192b08, 0x08080808, 0x192b0819, 0x08080808, 0x192b1908, 0x08080808, 0x192b192b, 0x08080808,
+    0x192b2b19, 0x08080808, 0x2b080808, 0x08080808, 0x2b08082b, 0x08080808, 0x2b081919, 0x08080808,
+    0x2b082b08, 0x08080808, 0x2b190819, 0x08080808, 0x2b191908, 0x08080808, 0x2b2b0808, 0x08080808,
+    0x2b2b1919, 0x08080808, 0x2b2b2b2b, 0x08080808, 0x08080819, 0x08080819, 0x08081908, 0x08080819,
+    0x0808192b, 0x08080819, 0x08082b19, 0x08080819, 0x08190808, 0x08080819, 0x0819082b, 0x08080819,
+    0x08191919, 0x08080819, 0x08192b08, 0x08080819, 0x082b0819, 0x08080819, 0x082b1908, 0x08080819,
+    0x19080808, 0x08080819, 0x1908082b, 0x08080819, 0x19081919, 0x08080819, 0x19082b08, 0x08080819,
+    0x19190819, 0x08080819, 0x19191908, 0x08080819, 0x1919192b, 0x08080819, 0x19192b19, 0x08080819,
+    0x192b0808, 0x08080819, 0x192b1919, 0x08080819, 0x192b2b08, 0x08080819, 0x2b080819, 0x08080819,
+    0x2b081908, 0x08080819, 0x2b190808, 0x08080819, 0x2b19082b, 0x08080819, 0x2b191919, 0x08080819,
+    0x2b2b0819, 0x08080819, 0x2b2b1908, 0x08080819, 0x08080808, 0x0808082b, 0x0808082b, 0x0808082b,
+    0x08081919, 0x0808082b, 0x08082b08, 0x0808082b, 0x08190819, 0x0808082b, 0x08191908, 0x0808082b,
+    0x082b0808, 0x0808082b, 0x082b2b2b, 0x0808082b, 0x19080819, 0x0808082b, 0x19081908, 0x0808082b,
+    0x1908192b, 0x0808082b, 0x19082b19, 0x0808082b, 0x19190808, 0x0808082b, 0x19191919, 0x0808082b,
+    0x2b080808, 0x0808082b, 0x2b081919, 0x0808082b, 0x2b082b2b, 0x0808082b, 0x2b191908, 0x0808082b,
+    0x2b2b082b, 0x0808082b, 0x08080819, 0x08081908, 0x08081908, 0x08081908, 0x0808192b, 0x08081908,
+    0x08082b19, 0x08081908, 0x08190808, 0x08081908, 0x0819082b, 0x08081908, 0x08191919, 0x08081908,
+    0x08192b08, 0x08081908, 0x082b0819, 0x08081908, 0x082b1908, 0x08081908, 0x082b192b, 0x08081908,
+    0x082b2b19, 0x08081908, 0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19081919, 0x08081908,
+    0x19082b08, 0x08081908, 0x19082b2b, 0x08081908, 0x19190819, 0x08081908, 0x19191908, 0x08081908,
+    0x1919192b, 0x08081908, 0x19192b19, 0x08081908, 0x192b0808, 0x08081908, 0x192b082b, 0x08081908,
+    0x192b1919, 0x08081908, 0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b08192b, 0x08081908,
+    0x2b082b19, 0x08081908, 0x2b190808, 0x08081908, 0x2b191919, 0x08081908, 0x2b192b08, 0x08081908,
+    0x2b2b0819, 0x08081908, 0x2b2b1908, 0x08081908, 0x08080808, 0x08081919, 0x0808082b, 0x08081919,
+    0x08081919, 0x08081919, 0x08082b08, 0x08081919, 0x08082b2b, 0x08081919, 0x08190819, 0x08081919,
+    0x08191908, 0x08081919, 0x0819192b, 0x08081919, 0x08192b19, 0x08081919, 0x082b0808, 0x08081919,
+    0x082b1919, 0x08081919, 0x082b2b08, 0x08081919, 0x19080819, 0x08081919, 0x19081908, 0x08081919,
+    0x1908192b, 0x08081919, 0x19082b19, 0x08081919, 0x19190808, 0x08081919, 0x1919082b, 0x08081919,
+    0x19191919, 0x08081919, 0x19192b08, 0x08081919, 0x192b0819, 0x08081919, 0x192b1908, 0x08081919,
+    0x2b080808, 0x08081919, 0x2b08082b, 0x08081919, 0x2b081919, 0x08081919, 0x2b082b08, 0x08081919,
+    0x2b190819, 0x08081919, 0x2b191908, 0x08081919, 0x2b2b0808, 0x08081919, 0x08080819, 0x0808192b,
+    0x08081908, 0x0808192b, 0x0808192b, 0x0808192b, 0x08082b19, 0x0808192b, 0x08190808, 0x0808192b,
+    0x08191919, 0x0808192b, 0x19080808, 0x0808192b, 0x19081919, 0x0808192b, 0x19082b08, 0x0808192b,
+    0x19190819, 0x0808192b, 0x19191908, 0x0808192b, 0x192b0808, 0x0808192b, 0x2b080819, 0x0808192b,
+    0x2b081908, 0x0808192b, 0x2b190808, 0x0808192b, 0x08080808, 0x08082b08, 0x0808082b, 0x08082b08,
+    0x08081919, 0x08082b08, 0x08082b08, 0x08082b08, 0x08190819, 0x08082b08, 0x08191908, 0x08082b08,
+    0x0819192b, 0x08082b08, 0x08192b19, 0x08082b08, 0x082b0808, 0x08082b08, 0x082b1919, 0x08082b08,
+    0x082b2b2b, 0x08082b08, 0x19080819, 0x08082b08, 0x19081908, 0x08082b08, 0x1908192b, 0x08082b08,
+    0x19082b19, 0x08082b08, 0x19190808, 0x08082b08, 0x1919082b, 0x08082b08, 0x19191919, 0x08082b08,
+    0x19192b08, 0x08082b08, 0x192b0819, 0x08082b08, 0x192b1908, 0x08082b08, 0x2b080808, 0x08082b08,
+    0x2b081919, 0x08082b08, 0x2b191908, 0x08082b08, 0x2b2b2b2b, 0x08082b08, 0x08080819, 0x08082b19,
+    0x08081908, 0x08082b19, 0x08190808, 0x08082b19, 0x0819082b, 0x08082b19, 0x08191919, 0x08082b19,
+    0x08192b08, 0x08082b19, 0x082b0819, 0x08082b19, 0x19080808, 0x08082b19, 0x19081919, 0x08082b19,
+    0x19082b08, 0x08082b19, 0x19190819, 0x08082b19, 0x19191908, 0x08082b19, 0x192b0808, 0x08082b19,
+    0x2b080819, 0x08082b19, 0x2b190808, 0x08082b19, 0x08080808, 0x08082b2b, 0x08190819, 0x08082b2b,
+    0x08191908, 0x08082b2b, 0x082b082b, 0x08082b2b, 0x082b2b08, 0x08082b2b, 0x082b2b2b, 0x08082b2b,
+    0x19190808, 0x08082b2b, 0x2b192b19, 0x08082b2b, 0x08080819, 0x08190808, 0x08081908, 0x08190808,
+    0x0808192b, 0x08190808, 0x08082b19, 0x08190808, 0x08190808, 0x08190808, 0x0819082b, 0x08190808,
+    0x08191919, 0x08190808, 0x08192b08, 0x08190808, 0x082b0819, 0x08190808, 0x082b1908, 0x08190808,
+    0x082b192b, 0x08190808, 0x19080808, 0x08190808, 0x1908082b, 0x08190808, 0x19081919, 0x08190808,
+    0x19082b08, 0x08190808, 0x19190819, 0x08190808, 0x19191908, 0x08190808, 0x1919192b, 0x08190808,
+    0x19192b19, 0x08190808, 0x192b0808, 0x08190808, 0x192b082b, 0x08190808, 0x192b1919, 0x08190808,
+    0x192b2b08, 0x08190808, 0x2b080819, 0x08190808, 0x2b081908, 0x08190808, 0x2b08192b, 0x08190808,
+    0x2b190808, 0x08190808, 0x2b191919, 0x08190808, 0x2b192b08, 0x08190808, 0x2b2b0819, 0x08190808,
+    0x2b2b1908, 0x08190808, 0x08080808, 0x08190819, 0x0808082b, 0x08190819, 0x08081919, 0x08190819,
+    0x08082b08, 0x08190819, 0x08082b2b, 0x08190819, 0x08190819, 0x08190819, 0x08191908, 0x08190819,
+    0x0819192b, 0x08190819, 0x08192b19, 0x08190819, 0x082b0808, 0x08190819, 0x082b082b, 0x08190819,
+    0x082b1919, 0x08190819, 0x082b2b08, 0x08190819, 0x19080819, 0x08190819, 0x19081908, 0x08190819,
+    0x1908192b, 0x08190819, 0x19082b19, 0x08190819, 0x19190808, 0x08190819, 0x1919082b, 0x08190819,
+    0x19191919, 0x08190819, 0x19192b08, 0x08190819, 0x192b0819, 0x08190819, 0x192b1908, 0x08190819,
+    0x2b080808, 0x08190819, 0x2b08082b, 0x08190819, 0x2b081919, 0x08190819, 0x2b082b08, 0x08190819,
+    0x2b190819, 0x08190819, 0x2b191908, 0x08190819, 0x08080819, 0x0819082b, 0x08081908, 0x0819082b,
+    0x08082b19, 0x0819082b, 0x08190808, 0x0819082b, 0x08191919, 0x0819082b, 0x082b0819, 0x0819082b,
+    0x082b1908, 0x0819082b, 0x19080808, 0x0819082b, 0x19081919, 0x0819082b, 0x19190819, 0x0819082b,
+    0x19191908, 0x0819082b, 0x2b080819, 0x0819082b, 0x2b081908, 0x0819082b, 0x2b190808, 0x0819082b,
+    0x08080808, 0x08191908, 0x0808082b, 0x08191908, 0x08081919, 0x08191908, 0x08082b08, 0x08191908,
+    0x08190819, 0x08191908, 0x08191908, 0x08191908, 0x0819192b, 0x08191908, 0x08192b19, 0x08191908,
+    0x082b0808, 0x08191908, 0x082b1919, 0x08191908, 0x082b2b08, 0x08191908, 0x19080819, 0x08191908,
+    0x19081908, 0x08191908, 0x1908192b, 0x08191908, 0x19082b19, 0x08191908, 0x19190808, 0x08191908,
+    0x1919082b, 0x08191908, 0x19191919, 0x08191908, 0x19192b08, 0x08191908, 0x192b0819, 0x08191908,
+    0x192b1908, 0x08191908, 0x2b080808, 0x08191908, 0x2b08082b, 0x08191908, 0x2b081919, 0x08191908,
+    0x2b082b08, 0x08191908, 0x2b190819, 0x08191908, 0x2b191908, 0x08191908, 0x2b2b0808, 0x08191908,
+    0x08080819, 0x08191919, 0x08081908, 0x08191919, 0x0808192b, 0x08191919, 0x08082b19, 0x08191919,
+    0x08190808, 0x08191919, 0x0819082b, 0x08191919, 0x08191919, 0x08191919, 0x08192b08, 0x08191919,
+    0x082b0819, 0x08191919, 0x082b1908, 0x08191919, 0x19080808, 0x08191919, 0x1908082b, 0x08191919,
+    0x19081919, 0x08191919, 0x19082b08, 0x08191919, 0x19190819, 0x08191919, 0x19191908, 0x08191919,
+    0x192b0808, 0x08191919, 0x2b080819, 0x08191919, 0x2b081908, 0x08191919, 0x2b190808, 0x08191919,
+    0x08080808, 0x0819192b, 0x08081919, 0x0819192b, 0x08082b08, 0x0819192b, 0x08190819, 0x0819192b,
+    0x08191908, 0x0819192b, 0x082b0808, 0x0819192b, 0x19080819, 0x0819192b, 0x19081908, 0x0819192b,
+    0x19190808, 0x0819192b, 0x2b080808, 0x0819192b, 0x2b2b2b2b, 0x0819192b, 0x08080819, 0x08192b08,
+    0x08081908, 0x08192b08, 0x0808192b, 0x08192b08, 0x08082b19, 0x08192b08, 0x08190808, 0x08192b08,
+    0x08191919, 0x08192b08, 0x08192b08, 0x08192b08, 0x082b0819, 0x08192b08, 0x19080808, 0x08192b08,
+    0x1908082b, 0x08192b08, 0x19081919, 0x08192b08, 0x19082b08, 0x08192b08, 0x19190819, 0x08192b08,
+    0x19191908, 0x08192b08, 0x192b0808, 0x08192b08, 0x2b080819, 0x08192b08, 0x2b081908, 0x08192b08,
+    0x08080808, 0x08192b19, 0x0808082b, 0x08192b19, 0x08081919, 0x08192b19, 0x08082b08, 0x08192b19,
+    0x08190819, 0x08192b19, 0x08191908, 0x08192b19, 0x082b0808, 0x08192b19, 0x19080819, 0x08192b19,
+    0x19081908, 0x08192b19, 0x19190808, 0x08192b19, 0x192b2b19, 0x08192b19, 0x2b2b082b, 0x08192b19,
+    0x08081908, 0x08192b2b, 0x08190808, 0x08192b2b, 0x19080808, 0x08192b2b, 0x1919192b, 0x08192b2b,
+    0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08081919, 0x082b0808, 0x08082b08, 0x082b0808,
+    0x08190819, 0x082b0808, 0x08191908, 0x082b0808, 0x0819192b, 0x082b0808, 0x08192b19, 0x082b0808,
+    0x082b0808, 0x082b0808, 0x082b1919, 0x082b0808, 0x082b2b2b, 0x082b0808, 0x19080819, 0x082b0808,
+    0x19081908, 0x082b0808, 0x19190808, 0x082b0808, 0x1919082b, 0x082b0808, 0x19191919, 0x082b0808,
+    0x192b1908, 0x082b0808, 0x2b080808, 0x082b0808, 0x2b082b2b, 0x082b0808, 0x2b191908, 0x082b0808,
+    0x2b2b2b2b, 0x082b0808, 0x08080819, 0x082b0819, 0x08081908, 0x082b0819, 0x08190808, 0x082b0819,
+    0x0819082b, 0x082b0819, 0x08191919, 0x082b0819, 0x082b0819, 0x082b0819, 0x19080808, 0x082b0819,
+    0x1908082b, 0x082b0819, 0x19081919, 0x082b0819, 0x19190819, 0x082b0819, 0x19191908, 0x082b0819,
+    0x192b0808, 0x082b0819, 0x2b080819, 0x082b0819, 0x2b081908, 0x082b0819, 0x2b190808, 0x082b0819,
+    0x08080808, 0x082b082b, 0x08082b2b, 0x082b082b, 0x082b082b, 0x082b082b, 0x082b2b08, 0x082b082b,
+    0x082b2b2b, 0x082b082b, 0x19081908, 0x082b082b, 0x19190808, 0x082b082b, 0x2b082b08, 0x082b082b,
+    0x2b082b2b, 0x082b082b, 0x2b2b2b08, 0x082b082b, 0x08080819, 0x082b1908, 0x08081908, 0x082b1908,
+    0x0808192b, 0x082b1908, 0x08082b19, 0x082b1908, 0x08190808, 0x082b1908, 0x08191919, 0x082b1908,
+    0x08192b08, 0x082b1908, 0x082b0819, 0x082b1908, 0x082b1908, 0x082b1908, 0x19080808, 0x082b1908,
+    0x1908082b, 0x082b1908, 0x19081919, 0x082b1908, 0x19082b08, 0x082b1908, 0x19190819, 0x082b1908,
+    0x19191908, 0x082b1908, 0x192b0808, 0x082b1908, 0x2b080819, 0x082b1908, 0x2b081908, 0x082b1908,
+    0x2b190808, 0x082b1908, 0x08080808, 0x082b1919, 0x08081919, 0x082b1919, 0x08082b08, 0x082b1919,
+    0x08190819, 0x082b1919, 0x08191908, 0x082b1919, 0x082b0808, 0x082b1919, 0x19080819, 0x082b1919,
+    0x19081908, 0x082b1919, 0x19190808, 0x082b1919, 0x192b192b, 0x082b1919, 0x2b080808, 0x082b1919,
+    0x08080819, 0x082b192b, 0x08081908, 0x082b192b, 0x08190808, 0x082b192b, 0x19080808, 0x082b192b,
+    0x19192b19, 0x082b192b, 0x08080808, 0x082b2b08, 0x08081919, 0x082b2b08, 0x08190819, 0x082b2b08,
+    0x08191908, 0x082b2b08, 0x19080819, 0x082b2b08, 0x19081908, 0x082b2b08, 0x19190808, 0x082b2b08,
+    0x2b082b2b, 0x082b2b08, 0x2b2b2b2b, 0x082b2b08, 0x08080819, 0x082b2b19, 0x08081908, 0x082b2b19,
+    0x08190808, 0x082b2b19, 0x2b191919, 0x082b2b19, 0x08082b2b, 0x082b2b2b, 0x082b082b, 0x082b2b2b,
+    0x192b1908, 0x082b2b2b, 0x2b082b08, 0x082b2b2b, 0x2b082b2b, 0x082b2b2b, 0x08080819, 0x19080808,
+    0x08081908, 0x19080808, 0x0808192b, 0x19080808, 0x08082b19, 0x19080808, 0x08190808, 0x19080808,
+    0x0819082b, 0x19080808, 0x08191919, 0x19080808, 0x08192b08, 0x19080808, 0x08192b2b, 0x19080808,
+    0x082b0819, 0x19080808, 0x082b1908, 0x19080808, 0x082b192b, 0x19080808, 0x19080808, 0x19080808,
+    0x1908082b, 0x19080808, 0x19081919, 0x19080808, 0x19082b08, 0x19080808, 0x19082b2b, 0x19080808,
+    0x19190819, 0x19080808, 0x19191908, 0x19080808, 0x1919192b, 0x19080808, 0x19192b19, 0x19080808,
+    0x192b0808, 0x19080808, 0x192b082b, 0x19080808, 0x192b1919, 0x19080808, 0x2b080819, 0x19080808,
+    0x2b081908, 0x19080808, 0x2b190808, 0x19080808, 0x2b191919, 0x19080808, 0x2b192b08, 0x19080808,
+    0x2b2b0819, 0x19080808, 0x2b2b1908, 0x19080808, 0x08080808, 0x19080819, 0x0808082b, 0x19080819,
+    0x08081919, 0x19080819, 0x08082b08, 0x19080819, 0x08190819, 0x19080819, 0x08191908, 0x19080819,
+    0x0819192b, 0x19080819, 0x08192b19, 0x19080819, 0x082b0808, 0x19080819, 0x082b082b, 0x19080819,
+    0x082b1919, 0x19080819, 0x19080819, 0x19080819, 0x19081908, 0x19080819, 0x1908192b, 0x19080819,
+    0x19082b19, 0x19080819, 0x19190808, 0x19080819, 0x1919082b, 0x19080819, 0x19191919, 0x19080819,
+    0x19192b08, 0x19080819, 0x192b0819, 0x19080819, 0x192b1908, 0x19080819, 0x2b080808, 0x19080819,
+    0x2b08082b, 0x19080819, 0x2b081919, 0x19080819, 0x2b082b08, 0x19080819, 0x2b190819, 0x19080819,
+    0x2b191908, 0x19080819, 0x2b2b0808, 0x19080819, 0x08080819, 0x1908082b, 0x08081908, 0x1908082b,
+    0x08190808, 0x1908082b, 0x0819082b, 0x1908082b, 0x08191919, 0x1908082b, 0x08192b08, 0x1908082b,
+    0x082b1908, 0x1908082b, 0x19080808, 0x1908082b, 0x19081919, 0x1908082b, 0x19082b08, 0x1908082b,
+    0x19190819, 0x1908082b, 0x19191908, 0x1908082b, 0x192b0808, 0x1908082b, 0x2b080819, 0x1908082b,
+    0x2b081908, 0x1908082b, 0x08080808, 0x19081908, 0x0808082b, 0x19081908, 0x08081919, 0x19081908,
+    0x08082b08, 0x19081908, 0x08082b2b, 0x19081908, 0x08190819, 0x19081908, 0x08191908, 0x19081908,
+    0x0819192b, 0x19081908, 0x08192b19, 0x19081908, 0x082b0808, 0x19081908, 0x082b082b, 0x19081908,
+    0x082b1919, 0x19081908, 0x082b2b08, 0x19081908, 0x19080819, 0x19081908, 0x19081908, 0x19081908,
+    0x1908192b, 0x19081908, 0x19082b19, 0x19081908, 0x19190808, 0x19081908, 0x1919082b, 0x19081908,
+    0x19191919, 0x19081908, 0x19192b08, 0x19081908, 0x192b0819, 0x19081908, 0x192b1908, 0x19081908,
+    0x2b080808, 0x19081908, 0x2b08082b, 0x19081908, 0x2b081919, 0x19081908, 0x2b082b08, 0x19081908,
+    0x2b190819, 0x19081908, 0x2b191908, 0x19081908, 0x2b2b0808, 0x19081908, 0x08080819, 0x19081919,
+    0x08081908, 0x19081919, 0x0808192b, 0x19081919, 0x08082b19, 0x19081919, 0x08190808, 0x19081919,
+    0x0819082b, 0x19081919, 0x08191919, 0x19081919, 0x08192b08, 0x19081919, 0x082b0819, 0x19081919,
+    0x082b1908, 0x19081919, 0x19080808, 0x19081919, 0x1908082b, 0x19081919, 0x19081919, 0x19081919,
+    0x19082b08, 0x19081919, 0x19190819, 0x19081919, 0x19191908, 0x19081919, 0x192b0808, 0x19081919,
+    0x192b2b2b, 0x19081919, 0x2b080819, 0x19081919, 0x2b081908, 0x19081919, 0x2b190808, 0x19081919,
+    0x08080808, 0x1908192b, 0x0808082b, 0x1908192b, 0x08081919, 0x1908192b, 0x08082b08, 0x1908192b,
+    0x08190819, 0x1908192b, 0x08191908, 0x1908192b, 0x082b0808, 0x1908192b, 0x19080819, 0x1908192b,
+    0x19081908, 0x1908192b, 0x19190808, 0x1908192b, 0x2b080808, 0x1908192b, 0x2b2b1919, 0x1908192b,
+    0x08080819, 0x19082b08, 0x08081908, 0x19082b08, 0x08082b19, 0x19082b08, 0x08190808, 0x19082b08,
+    0x0819082b, 0x19082b08, 0x08191919, 0x19082b08, 0x08192b08, 0x19082b08, 0x082b0819, 0x19082b08,
+    0x082b1908, 0x19082b08, 0x19080808, 0x19082b08, 0x1908082b, 0x19082b08, 0x19081919, 0x19082b08,
+    0x19082b08, 0x19082b08, 0x19190819, 0x19082b08, 0x19191908, 0x19082b08, 0x192b0808, 0x19082b08,
+    0x2b081908, 0x19082b08, 0x2b190808, 0x19082b08, 0x08080808, 0x19082b19, 0x0808082b, 0x19082b19,
+    0x08081919, 0x19082b19, 0x08082b08, 0x19082b19, 0x08190819, 0x19082b19, 0x08191908, 0x19082b19,
+    0x082b0808, 0x19082b19, 0x19080819, 0x19082b19, 0x19081908, 0x19082b19, 0x19190808, 0x19082b19,
+    0x2b080808, 0x19082b19, 0x2b19192b, 0x19082b19, 0x08080819, 0x19082b2b, 0x08081908, 0x19082b2b,
+    0x08190808, 0x19082b2b, 0x19080808, 0x19082b2b, 0x08080808, 0x19190808, 0x0808082b, 0x19190808,
+    0x08081919, 0x19190808, 0x08082b08, 0x19190808, 0x08190819, 0x19190808, 0x08191908, 0x19190808,
+    0x0819192b, 0x19190808, 0x08192b19, 0x19190808, 0x082b0808, 0x19190808, 0x082b082b, 0x19190808,
+    0x082b1919, 0x19190808, 0x082b2b08, 0x19190808, 0x19080819, 0x19190808, 0x19081908, 0x19190808,
+    0x1908192b, 0x19190808, 0x19082b19, 0x19190808, 0x19190808, 0x19190808, 0x1919082b, 0x19190808,
+    0x19191919, 0x19190808, 0x19192b08, 0x19190808, 0x192b0819, 0x19190808, 0x192b1908, 0x19190808,
+    0x2b080808, 0x19190808, 0x2b08082b, 0x19190808, 0x2b081919, 0x19190808, 0x2b082b08, 0x19190808,
+    0x2b190819, 0x19190808, 0x2b191908, 0x19190808, 0x08080819, 0x19190819, 0x08081908, 0x19190819,
+    0x0808192b, 0x19190819, 0x08082b19, 0x19190819, 0x08190808, 0x19190819, 0x0819082b, 0x19190819,
+    0x08191919, 0x19190819, 0x08192b08, 0x19190819, 0x082b0819, 0x19190819, 0x082b1908, 0x19190819,
+    0x19080808, 0x19190819, 0x1908082b, 0x19190819, 0x19081919, 0x19190819, 0x19082b08, 0x19190819,
+    0x19190819, 0x19190819, 0x19191908, 0x19190819, 0x192b0808, 0x19190819, 0x2b080819, 0x19190819,
+    0x2b081908, 0x19190819, 0x2b190808, 0x19190819, 0x08080808, 0x1919082b, 0x08081919, 0x1919082b,
+    0x08082b08, 0x1919082b, 0x08190819, 0x1919082b, 0x08191908, 0x1919082b, 0x082b0808, 0x1919082b,
+    0x19080819, 0x1919082b, 0x19081908, 0x1919082b, 0x19190808, 0x1919082b, 0x192b2b19, 0x1919082b,
+    0x2b080808, 0x1919082b, 0x08080819, 0x19191908, 0x08081908, 0x19191908, 0x0808192b, 0x19191908,
+    0x08082b19, 0x19191908, 0x08190808, 0x19191908, 0x0819082b, 0x19191908, 0x08191919, 0x19191908,
+    0x08192b08, 0x19191908, 0x082b0819, 0x19191908, 0x082b1908, 0x19191908, 0x19080808, 0x19191908,
+    0x1908082b, 0x19191908, 0x19081919, 0x19191908, 0x19082b08, 0x19191908, 0x19190819, 0x19191908,
+    0x19191908, 0x19191908, 0x192b0808, 0x19191908, 0x2b080819, 0x19191908, 0x2b081908, 0x19191908,
+    0x2b190808, 0x19191908, 0x08080808, 0x19191919, 0x0808082b, 0x19191919, 0x08081919, 0x19191919,
+    0x08082b08, 0x19191919, 0x08190819, 0x19191919, 0x08191908, 0x19191919, 0x082b0808, 0x19191919,
+    0x19080819, 0x19191919, 0x19081908, 0x19191919, 0x19190808, 0x19191919, 0x2b080808, 0x19191919,
+    0x08080819, 0x1919192b, 0x08081908, 0x1919192b, 0x08190808, 0x1919192b, 0x082b192b, 0x1919192b,
+    0x19080808, 0x1919192b, 0x08080808, 0x19192b08, 0x0808082b, 0x19192b08, 0x08081919, 0x19192b08,
+    0x08082b08, 0x19192b08, 0x08190819, 0x19192b08, 0x08191908, 0x19192b08, 0x082b0808, 0x19192b08,
+    0x19080819, 0x19192b08, 0x19081908, 0x19192b08, 0x19190808, 0x19192b08, 0x19192b2b, 0x19192b08,
+    0x2b080808, 0x19192b08, 0x08080819, 0x19192b19, 0x08081908, 0x19192b19, 0x08190808, 0x19192b19,
+    0x19080808, 0x19192b19, 0x08080808, 0x19192b2b, 0x08192b19, 0x19192b2b, 0x2b081919, 0x19192b2b,
+    0x2b2b2b08, 0x19192b2b, 0x08080819, 0x192b0808, 0x08081908, 0x192b0808, 0x0808192b, 0x192b0808,
+    0x08190808, 0x192b0808, 0x0819082b, 0x192b0808, 0x08191919, 0x192b0808, 0x08192b08, 0x192b0808,
+    0x082b0819, 0x192b0808, 0x082b1908, 0x192b0808, 0x19080808, 0x192b0808, 0x19081919, 0x192b0808,
+    0x19082b08, 0x192b0808, 0x19190819, 0x192b0808, 0x19191908, 0x192b0808, 0x192b0808, 0x192b0808,
+    0x2b081908, 0x192b0808, 0x2b190808, 0x192b0808, 0x08080808, 0x192b0819, 0x0808082b, 0x192b0819,
+    0x08081919, 0x192b0819, 0x08082b08, 0x192b0819, 0x08190819, 0x192b0819, 0x08191908, 0x192b0819,
+    0x082b0808, 0x192b0819, 0x19080819, 0x192b0819, 0x19081908, 0x192b0819, 0x19190808, 0x192b0819,
+    0x2b080808, 0x192b0819, 0x2b192b19, 0x192b0819, 0x08081908, 0x192b082b, 0x08190808, 0x192b082b,
+    0x19080808, 0x192b082b, 0x1919192b, 0x192b082b, 0x2b2b0819, 0x192b082b, 0x08080808, 0x192b1908,
+    0x08081919, 0x192b1908, 0x08082b08, 0x192b1908, 0x08190819, 0x192b1908, 0x08191908, 0x192b1908,
+    0x082b0808, 0x192b1908, 0x19080819, 0x192b1908, 0x19081908, 0x192b1908, 0x19190808, 0x192b1908,
+    0x2b080808, 0x192b1908, 0x08080819, 0x192b1919, 0x08081908, 0x192b1919, 0x08190808, 0x192b1919,
+    0x19080808, 0x192b1919, 0x19082b2b, 0x192b1919, 0x192b2b08, 0x192b1919, 0x2b19082b, 0x192b1919,
+    0x08080808, 0x192b192b, 0x2b191908, 0x192b192b, 0x08080819, 0x192b2b08, 0x08081908, 0x192b2b08,
+    0x08190808, 0x192b2b08, 0x192b1919, 0x192b2b08, 0x2b192b08, 0x192b2b08, 0x08080808, 0x192b2b19,
+    0x082b2b2b, 0x192b2b19, 0x1908082b, 0x192b2b2b, 0x2b2b0819, 0x192b2b2b, 0x08080808, 0x2b080808,
+    0x0808082b, 0x2b080808, 0x08081919, 0x2b080808, 0x08082b08, 0x2b080808, 0x08190819, 0x2b080808,
+    0x08191908, 0x2b080808, 0x08192b19, 0x2b080808, 0x082b0808, 0x2b080808, 0x082b1919, 0x2b080808,
+    0x19080819, 0x2b080808, 0x19081908, 0x2b080808, 0x19190808, 0x2b080808, 0x1919082b, 0x2b080808,
+    0x19191919, 0x2b080808, 0x19192b08, 0x2b080808, 0x192b0819, 0x2b080808, 0x2b080808, 0x2b080808,
+    0x2b081919, 0x2b080808, 0x2b190819, 0x2b080808, 0x2b191908, 0x2b080808, 0x08080819, 0x2b080819,
+    0x08081908, 0x2b080819, 0x08082b19, 0x2b080819, 0x08190808, 0x2b080819, 0x0819082b, 0x2b080819,
+    0x08191919, 0x2b080819, 0x08192b08, 0x2b080819, 0x082b0819, 0x2b080819, 0x082b1908, 0x2b080819,
+    0x19080808, 0x2b080819, 0x1908082b, 0x2b080819, 0x19081919, 0x2b080819, 0x19082b08, 0x2b080819,
+    0x19190819, 0x2b080819, 0x19191908, 0x2b080819, 0x2b080819, 0x2b080819, 0x2b081908, 0x2b080819,
+    0x2b190808, 0x2b080819, 0x2b2b2b19, 0x2b080819, 0x08080808, 0x2b08082b, 0x08081919, 0x2b08082b,
+    0x08082b2b, 0x2b08082b, 0x08190819, 0x2b08082b, 0x08191908, 0x2b08082b, 0x19080819, 0x2b08082b,
+    0x19081908, 0x2b08082b, 0x19190808, 0x2b08082b, 0x08080819, 0x2b081908, 0x08081908, 0x2b081908,
+    0x0808192b, 0x2b081908, 0x08082b19, 0x2b081908, 0x08190808, 0x2b081908, 0x0819082b, 0x2b081908,
+    0x08191919, 0x2b081908, 0x08192b08, 0x2b081908, 0x082b0819, 0x2b081908, 0x19080808, 0x2b081908,
+    0x1908082b, 0x2b081908, 0x19081919, 0x2b081908, 0x19082b08, 0x2b081908, 0x19190819, 0x2b081908,
+    0x19191908, 0x2b081908, 0x192b0808, 0x2b081908, 0x2b080819, 0x2b081908, 0x2b081908, 0x2b081908,
+    0x2b190808, 0x2b081908, 0x08080808, 0x2b081919, 0x0808082b, 0x2b081919, 0x08081919, 0x2b081919,
+    0x08082b08, 0x2b081919, 0x08190819, 0x2b081919, 0x08191908, 0x2b081919, 0x082b0808, 0x2b081919,
+    0x19080819, 0x2b081919, 0x19081908, 0x2b081919, 0x19190808, 0x2b081919, 0x2b080808, 0x2b081919,
+    0x2b082b2b, 0x2b081919, 0x08080819, 0x2b08192b, 0x08081908, 0x2b08192b, 0x08190808, 0x2b08192b,
+    0x082b2b19, 0x2b08192b, 0x19080808, 0x2b08192b, 0x08080808, 0x2b082b08, 0x08081919, 0x2b082b08,
+    0x08190819, 0x2b082b08, 0x08191908, 0x2b082b08, 0x19080819, 0x2b082b08, 0x19081908, 0x2b082b08,
+    0x19190808, 0x2b082b08, 0x2b2b082b, 0x2b082b08, 0x08080819, 0x2b082b19, 0x08081908, 0x2b082b19,
+    0x19080808, 0x2b082b19, 0x192b1919, 0x2b082b19, 0x082b082b, 0x2b082b2b, 0x19192b08, 0x2b082b2b,
+    0x19192b2b, 0x2b082b2b, 0x2b08082b, 0x2b082b2b, 0x2b2b082b, 0x2b082b2b, 0x08080819, 0x2b190808,
+    0x08081908, 0x2b190808, 0x08082b19, 0x2b190808, 0x08190808, 0x2b190808, 0x0819082b, 0x2b190808,
+    0x08191919, 0x2b190808, 0x08192b08, 0x2b190808, 0x082b1908, 0x2b190808, 0x19080808, 0x2b190808,
+    0x1908082b, 0x2b190808, 0x19081919, 0x2b190808, 0x19082b08, 0x2b190808, 0x19190819, 0x2b190808,
+    0x19191908, 0x2b190808, 0x192b0808, 0x2b190808, 0x2b080819, 0x2b190808, 0x2b081908, 0x2b190808,
+    0x2b190808, 0x2b190808, 0x08080808, 0x2b190819, 0x08081919, 0x2b190819, 0x08190819, 0x2b190819,
+    0x08191908, 0x2b190819, 0x19080819, 0x2b190819, 0x19081908, 0x2b190819, 0x19190808, 0x2b190819,
+    0x19192b2b, 0x2b190819, 0x08080819, 0x2b19082b, 0x08081908, 0x2b19082b, 0x08190808, 0x2b19082b,
+    0x19080808, 0x2b19082b, 0x2b2b192b, 0x2b19082b, 0x08080808, 0x2b191908, 0x0808082b, 0x2b191908,
+    0x08081919, 0x2b191908, 0x08082b08, 0x2b191908, 0x08190819, 0x2b191908, 0x08191908, 0x2b191908,
+    0x082b0808, 0x2b191908, 0x19080819, 0x2b191908, 0x19081908, 0x2b191908, 0x19190808, 0x2b191908,
+    0x2b080808, 0x2b191908, 0x2b19192b, 0x2b191908, 0x08080819, 0x2b191919, 0x08081908, 0x2b191919,
+    0x08190808, 0x2b191919, 0x19080808, 0x2b191919, 0x2b192b08, 0x2b191919, 0x2b2b0819, 0x2b191919,
+    0x08080808, 0x2b19192b, 0x1908192b, 0x2b19192b, 0x192b1908, 0x2b19192b, 0x08080819, 0x2b192b08,
+    0x08081908, 0x2b192b08, 0x08190808, 0x2b192b08, 0x082b192b, 0x2b192b08, 0x19080808, 0x2b192b08,
+    0x2b2b2b19, 0x2b192b08, 0x08080808, 0x2b192b19, 0x19082b19, 0x2b192b19, 0x1919082b, 0x2b192b19,
+    0x2b190808, 0x2b192b2b, 0x08080808, 0x2b2b0808, 0x08081919, 0x2b2b0808, 0x08082b2b, 0x2b2b0808,
+    0x08191908, 0x2b2b0808, 0x082b082b, 0x2b2b0808, 0x082b2b2b, 0x2b2b0808, 0x19080819, 0x2b2b0808,
+    0x19081908, 0x2b2b0808, 0x19190808, 0x2b2b0808, 0x2b2b082b, 0x2b2b0808, 0x2b2b2b2b, 0x2b2b0808,
+    0x19080808, 0x2b2b0819, 0x192b1919, 0x2b2b0819, 0x0808082b, 0x2b2b082b, 0x08082b2b, 0x2b2b082b,
+    0x082b082b, 0x2b2b082b, 0x082b2b08, 0x2b2b082b, 0x082b2b2b, 0x2b2b082b, 0x2b08082b, 0x2b2b082b,
+    0x2b082b08, 0x2b2b082b, 0x2b082b2b, 0x2b2b082b, 0x2b2b2b08, 0x2b2b082b, 0x08080819, 0x2b2b1908,
+    0x08081908, 0x2b2b1908, 0x08190808, 0x2b2b1908, 0x19080808, 0x2b2b1908, 0x2b082b19, 0x2b2b1908,
+    0x2b2b1908, 0x2b2b1908, 0x08080808, 0x2b2b1919, 0x08192b19, 0x2b2b1919, 0x19190819, 0x2b2b192b,
+    0x08082b2b, 0x2b2b2b08, 0x082b2b08, 0x2b2b2b08, 0x2b2b082b, 0x2b2b2b08, 0x19191908, 0x2b2b2b19,
+    0x2b08192b, 0x2b2b2b19, 0x08082b08, 0x2b2b2b2b, 0x08082b2b, 0x2b2b2b2b, 0x082b0808, 0x2b2b2b2b,
+    0x082b082b, 0x2b2b2b2b, 0x082b2b08, 0x2b2b2b2b, 0x2b082b08, 0x2b2b2b2b, 0x2b2b2b2b, 0x2b2b2b2b
+);
+#enddecl(IQ2_S_GRID)
+
+#decl(IQ3_XSS_GRID)
+
+const iq3xxs_grid = array<u32, 256>(
+    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
+    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
+    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
+    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
+    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
+    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
+    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
+    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
+    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
+    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
+    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
+    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
+    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
+    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
+    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
+    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
+    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
+    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
+    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
+    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
+    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
+    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
+    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
+    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
+    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
+    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
+    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
+    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
+    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
+    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
+    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
+    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04
+);
+#enddecl(IQ3_XSS_GRID)
+
+#decl(IQ3_S_GRID)
+
+const iq3s_grid = array<u32, 512>(
+    0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
+    0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
+    0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
+    0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
+    0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
+    0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
+    0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
+    0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
+    0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
+    0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
+    0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
+    0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
+    0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
+    0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
+    0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
+    0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
+    0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
+    0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
+    0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
+    0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
+    0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
+    0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
+    0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
+    0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
+    0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
+    0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
+    0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
+    0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
+    0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
+    0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
+    0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
+    0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
+    0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
+    0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
+    0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
+    0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
+    0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
+    0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
+    0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
+    0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
+    0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
+    0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
+    0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
+    0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
+    0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
+    0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
+    0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
+    0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
+    0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
+    0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
+    0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
+    0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
+    0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
+    0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
+    0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
+    0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
+    0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
+    0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
+    0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
+    0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
+    0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
+    0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
+    0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
+    0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101
+);
+#enddecl(IQ3_S_GRID)
+
+#decl(IQ1_GRID)
+
+const IQ1_DELTA: f32 = 0.125;
+
+const iq1_grid = array<u32, 1024>(
+    0xfffdffff, 0xfff7fff0, 0xffccfff5, 0xffdfffc0, 0xffd7ffdd, 0xff30ffd5, 0xff03ff0c, 0xff10ff01,
+    0xff7dff7f, 0xff75ff77, 0xff5fff40, 0xff57ff5d, 0xfcf3ff55, 0xfcccfcf0, 0xfcc1fcc3, 0xfcc5fcc4,
+    0xfc3cfcd0, 0xfc34fc31, 0xfc00fc0d, 0xfc1cfc05, 0xfc11fc13, 0xfc70fc17, 0xfc43fc4c, 0xfc50fc41,
+    0xfdfdfdff, 0xfdf5fdf7, 0xfddffdc0, 0xfdd7fddd, 0xfd30fdd5, 0xfd04fd0c, 0xfd14fd13, 0xfd7dfd7f,
+    0xfd75fd77, 0xfd40fd4c, 0xfd5ffd44, 0xfd57fd5d, 0xf3ccfd55, 0xf3c1f3c3, 0xf33cf3d0, 0xf300f334,
+    0xf313f305, 0xf34cf310, 0xf350f344, 0xf0f3f0fc, 0xf0f1f0f0, 0xf0c7f0c0, 0xf0d4f0c5, 0xf030f03f,
+    0xf00ff035, 0xf003f00c, 0xf001f000, 0xf01ff004, 0xf010f01d, 0xf015f017, 0xf04cf07c, 0xf047f040,
+    0xf05cf045, 0xf050f053, 0xf054f051, 0xf1c4f1c3, 0xf133f13c, 0xf10df10f, 0xf107f100, 0xf11cf11f,
+    0xf114f111, 0xf14cf170, 0xf144f143, 0xf7fdf7ff, 0xf7f5f7f7, 0xf7dff7c0, 0xf7d7f7dd, 0xf730f7d5,
+    0xf701f70c, 0xf77ff710, 0xf777f77d, 0xf740f775, 0xf75df75f, 0xf755f757, 0xf4ccf4f0, 0xf4c4f4c3,
+    0xf4d0f4d3, 0xf40ff43c, 0xf400f40c, 0xf413f41c, 0xf44cf414, 0xf441f443, 0xf450f444, 0xf5fdf5ff,
+    0xf5f5f5f7, 0xf5dff5c0, 0xf5d7f5dd, 0xf530f5d5, 0xf504f50c, 0xf510f51c, 0xf57df57f, 0xf577f570,
+    0xf540f575, 0xf55df55f, 0xf555f557, 0xcfcccfcf, 0xcfc4cfc3, 0xcfd0cfd3, 0xcf33cf3c, 0xcf00cf0f,
+    0xcf1ccf07, 0xcf10cf13, 0xcf4ccf14, 0xcf41cf43, 0xcf50cf5c, 0xccf3ccfc, 0xccf4ccf1, 0xcccdcccf,
+    0xccc7ccc0, 0xccd3ccdc, 0xcc30ccd4, 0xcc0fcc35, 0xcc0dcc0c, 0xcc00cc03, 0xcc04cc01, 0xcc10cc1f,
+    0xcc4dcc73, 0xcc5ccc40, 0xcdcccc53, 0xcdc1cdc3, 0xcd3fcdd0, 0xcd34cd31, 0xcd00cd0d, 0xcd05cd07,
+    0xcd11cd13, 0xcd4ccd70, 0xcd41cd43, 0xc3fccd50, 0xc3f4c3f1, 0xc3c0c3c3, 0xc3c4c3c7, 0xc3d1c3dc,
+    0xc330c33c, 0xc337c331, 0xc30cc335, 0xc300c303, 0xc304c301, 0xc310c31d, 0xc373c317, 0xc34fc374,
+    0xc340c343, 0xc344c347, 0xc35cc345, 0xc350c353, 0xc0fdc354, 0xc0f5c0f0, 0xc0c3c0cc, 0xc0c1c0c0,
+    0xc0dfc0c4, 0xc0d0c0dd, 0xc0d5c0d7, 0xc033c03c, 0xc031c030, 0xc00dc00c, 0xc000c003, 0xc004c001,
+    0xc01cc005, 0xc010c013, 0xc014c011, 0xc07dc07f, 0xc070c073, 0xc075c077, 0xc04cc04f, 0xc040c043,
+    0xc044c041, 0xc05fc045, 0xc050c05d, 0xc1f3c1fc, 0xc1f1c1f0, 0xc1c1c1c0, 0xc1c5c1c7, 0xc1d1c1dc,
+    0xc13dc13f, 0xc130c133, 0xc135c137, 0xc100c10c, 0xc107c101, 0xc11cc104, 0xc110c113, 0xc114c117,
+    0xc171c115, 0xc14dc175, 0xc153c140, 0xc7ccc154, 0xc7d0c7c1, 0xc733c73c, 0xc734c731, 0xc700c70f,
+    0xc705c707, 0xc71cc71f, 0xc711c713, 0xc770c714, 0xc743c74c, 0xc4cfc750, 0xc4c0c4cd, 0xc4dcc4c5,
+    0xc43dc4d0, 0xc430c433, 0xc40cc437, 0xc400c403, 0xc404c401, 0xc41fc405, 0xc415c410, 0xc44cc474,
+    0xc440c44d, 0xc45cc447, 0xc454c451, 0xc5c1c5f4, 0xc5d1c5d3, 0xc531c533, 0xc50fc534, 0xc500c50d,
+    0xc51cc507, 0xc514c511, 0xc54cc570, 0xc545c541, 0xdffddfff, 0xdff5dff7, 0xdfdfdfc0, 0xdfd0dfdd,
+    0xdfd5dfd7, 0xdf0cdf30, 0xdf1cdf04, 0xdf7fdf10, 0xdf77df7d, 0xdf40df75, 0xdf5ddf5f, 0xdf57df50,
+    0xdcf0df55, 0xdcc3dccc, 0xdcd0dcc4, 0xdc33dc3d, 0xdc00dc34, 0xdc05dc07, 0xdc13dc1c, 0xdc11dc10,
+    0xdc4fdc70, 0xdc44dc41, 0xddfcdc50, 0xddf5ddf7, 0xddc0ddcc, 0xdddddddf, 0xddd5ddd7, 0xdd0cdd30,
+    0xdd04dd01, 0xdd7cdd10, 0xdd75dd77, 0xdd40dd4c, 0xdd5ddd5f, 0xdd55dd57, 0xd3c3d3f0, 0xd3c4d3c1,
+    0xd333d3d0, 0xd331d330, 0xd30dd334, 0xd307d300, 0xd311d305, 0xd34cd370, 0xd344d343, 0xd350d35c,
+    0xd0c0d0f4, 0xd0d4d0dc, 0xd030d03f, 0xd00cd037, 0xd000d003, 0xd01dd004, 0xd017d010, 0xd04fd074,
+    0xd040d043, 0xd045d047, 0xd053d05c, 0xd054d051, 0xd1cfd1f0, 0xd1c4d1cd, 0xd13cd1d0, 0xd100d134,
+    0xd11cd11f, 0xd173d114, 0xd14fd171, 0xd7ffd145, 0xd7f7d7fd, 0xd7c0d7f5, 0xd7ddd7df, 0xd7d5d7d7,
+    0xd70cd730, 0xd710d703, 0xd77dd77f, 0xd775d777, 0xd75dd75f, 0xd755d757, 0xd4ccd4f4, 0xd4c4d4c3,
+    0xd431d4d0, 0xd40dd434, 0xd41cd400, 0xd411d413, 0xd470d414, 0xd441d44f, 0xd453d444, 0xd5ffd450,
+    0xd5f7d5fd, 0xd5dfd5f5, 0xd5d7d5dd, 0xd530d5d5, 0xd501d50c, 0xd510d504, 0xd57dd57f, 0xd575d577,
+    0xd55fd540, 0xd557d55d, 0x3ff0d555, 0x3fc13fcc, 0x3f343fd0, 0x3f003f0d, 0x3f053f07, 0x3f133f1c,
+    0x3f433f11, 0x3f5c3f44, 0x3cff3f51, 0x3cf33cfc, 0x3cf43cf1, 0x3cc03ccd, 0x3cc73cc1, 0x3cdc3cc5,
+    0x3cd43cd1, 0x3c373c30, 0x3c0c3c35, 0x3c003c03, 0x3c043c01, 0x3c103c05, 0x3c153c17, 0x3c733c7c,
+    0x3c4f3c71, 0x3c403c4d, 0x3c5c3c5f, 0x3df03c5d, 0x3dc33dcc, 0x3dd03dc1, 0x3d0d3d3c, 0x3d053d00,
+    0x3d143d13, 0x3d433d74, 0x33fc3d50, 0x33c433c0, 0x333033d4, 0x33353337, 0x3303330c, 0x33013300,
+    0x331d331c, 0x33173310, 0x337c3315, 0x33743371, 0x334d334f, 0x335f3340, 0x3354335c, 0x30fd30fc,
+    0x30f530f0, 0x30c330cc, 0x30c130c0, 0x30df30c4, 0x30d530d0, 0x3033303c, 0x30313030, 0x300f3034,
+    0x3003300c, 0x30013000, 0x30043007, 0x3013301c, 0x30113010, 0x307d3014, 0x30703073, 0x304c3077,
+    0x30403043, 0x30443041, 0x30503045, 0x30553057, 0x31f031fc, 0x31c331f4, 0x31c731c0, 0x31dc31c5,
+    0x31d431d3, 0x313d313f, 0x31373130, 0x310c310f, 0x3100310d, 0x31043101, 0x3110311d, 0x317c3117,
+    0x31753170, 0x31403143, 0x3153315c, 0x37f03151, 0x37c037cc, 0x37d037c5, 0x3734373d, 0x3700370f,
+    0x371c3707, 0x37113713, 0x37703714, 0x3743374c, 0x37443741, 0x34fc3750, 0x34f134f0, 0x34cf34f5,
+    0x34c034c3, 0x34dc34c7, 0x34d134d3, 0x3430343f, 0x340c3435, 0x3403340d, 0x34013400, 0x341f3404,
+    0x3410341d, 0x34153411, 0x34743471, 0x3440344d, 0x34473441, 0x3453345c, 0x34543451, 0x353335c1,
+    0x35343531, 0x35073500, 0x35133505, 0x35433514, 0x0ffc3550, 0x0ff00ff3, 0x0ff40ff1, 0x0fc00fcd,
+    0x0fdc0fc5, 0x0fd40fd3, 0x0f300f3f, 0x0f0c0f37, 0x0f000f03, 0x0f040f01, 0x0f170f10, 0x0f740f71,
+    0x0f470f40, 0x0f5c0f5f, 0x0f540f51, 0x0cf70cf0, 0x0cf50cf4, 0x0cc30ccc, 0x0cc10cc0, 0x0cc40cc7,
+    0x0cd00cdf, 0x0cd70cd1, 0x0c3c0cd5, 0x0c300c33, 0x0c340c31, 0x0c0c0c0f, 0x0c030c0d, 0x0c010c00,
+    0x0c040c07, 0x0c1c0c05, 0x0c100c13, 0x0c140c11, 0x0c700c7d, 0x0c430c4c, 0x0c410c40, 0x0c5f0c44,
+    0x0c550c50, 0x0df10dfc, 0x0dc00dcd, 0x0ddc0dc5, 0x0d3d0dd3, 0x0d350d30, 0x0d030d0c, 0x0d010d00,
+    0x0d1d0d04, 0x0d700d10, 0x0d4d0d4f, 0x0d440d40, 0x0d530d45, 0x03f003f3, 0x03c303cc, 0x03c103c0,
+    0x03c403c7, 0x03d003dc, 0x03d503d7, 0x0333033c, 0x03310330, 0x03350334, 0x030c030f, 0x03000303,
+    0x03070301, 0x03050304, 0x031d031c, 0x03100313, 0x03140311, 0x0377037f, 0x034c0375, 0x03400343,
+    0x03440341, 0x0353035c, 0x03550350, 0x00fd00fc, 0x00f000f3, 0x00f400f1, 0x00cc00cf, 0x00c300cd,
+    0x00c100c0, 0x00c500c4, 0x00d300dc, 0x00d100d0, 0x003f00d4, 0x003d003c, 0x00300033, 0x00370031,
+    0x000f0034, 0x000d000c, 0x00000003, 0x00070001, 0x00050004, 0x001c001f, 0x00100013, 0x00170011,
+    0x00150014, 0x0073007c, 0x00740070, 0x004f0075, 0x0043004c, 0x00410040, 0x00440047, 0x0053005c,
+    0x00510050, 0x01ff0054, 0x01fd01fc, 0x01f101f3, 0x01f401f7, 0x01c301cc, 0x01c701c0, 0x01df01c4,
+    0x01dd01dc, 0x01d001d3, 0x01d701d1, 0x013c01d4, 0x01310130, 0x01340137, 0x010f0135, 0x010d010c,
+    0x01000103, 0x01070101, 0x01050104, 0x0113011c, 0x01140110, 0x0170017d, 0x01770171, 0x01750174,
+    0x0140014c, 0x015d0145, 0x01510150, 0x01540157, 0x07f007f3, 0x07f407f1, 0x07c007cf, 0x07dc07c7,
+    0x073007d5, 0x07350737, 0x0703070c, 0x07010700, 0x07040707, 0x071d071f, 0x07100713, 0x0774077d,
+    0x074d074f, 0x07470740, 0x0754075c, 0x04fd04fc, 0x04f504f0, 0x04c304cc, 0x04c104c0, 0x04d004c4,
+    0x0433043c, 0x04310430, 0x040f0434, 0x040d040c, 0x04000403, 0x04070401, 0x04050404, 0x0413041c,
+    0x04110410, 0x047c0414, 0x04740470, 0x0443044c, 0x04410440, 0x04440447, 0x05f30450, 0x05c005f7,
+    0x05df05c5, 0x05d105d0, 0x053005d4, 0x05340537, 0x0500050c, 0x05070501, 0x051d0504, 0x05170510,
+    0x057c0515, 0x054d0575, 0x05410540, 0x05450547, 0x1ff0055c, 0x1fc11fc3, 0x1fd01fc4, 0x1f0f1f33,
+    0x1f011f00, 0x1f051f07, 0x1f131f1c, 0x1f141f11, 0x1f411f7c, 0x1cfc1f50, 0x1cf11cf3, 0x1ccd1cf4,
+    0x1cdc1cc0, 0x1cd11cdd, 0x1c301cd4, 0x1c0c1c34, 0x1c011c00, 0x1c101c04, 0x1c151c11, 0x1c751c73,
+    0x1c401c4d, 0x1c511c5c, 0x1dcc1c54, 0x1dc41dc1, 0x1d3c1d3f, 0x1d001d31, 0x1d071d01, 0x1d701d1f,
+    0x1d411d4c, 0x13cc1d50, 0x13c013cd, 0x13c513c1, 0x13d113dc, 0x133f13d4, 0x1330133d, 0x13351337,
+    0x1303130c, 0x13011300, 0x13051304, 0x131d131f, 0x13731310, 0x13741370, 0x134d134f, 0x13401343,
+    0x13471341, 0x135c1345, 0x13541353, 0x10f710f0, 0x10cc10f5, 0x10c110c0, 0x103310c4, 0x10311030,
+    0x100f1034, 0x1003100c, 0x10011000, 0x101c1004, 0x10101013, 0x10141011, 0x10741071, 0x104c1075,
+    0x10411040, 0x10451044, 0x1050105d, 0x10571051, 0x11f411fd, 0x11df11c0, 0x11d711d1, 0x113f11d4,
+    0x11371130, 0x110c1135, 0x11001103, 0x11071101, 0x111f1105, 0x11171110, 0x117d117f, 0x11751170,
+    0x11411143, 0x11441147, 0x1153115f, 0x11551151, 0x17c417c1, 0x173c17d0, 0x1700170d, 0x171c1705,
+    0x17701714, 0x1747174c, 0x14fc1751, 0x14cf14f3, 0x14dc14c0, 0x14d114d3, 0x143f14d4, 0x1430143c,
+    0x14371431, 0x1403140c, 0x14011400, 0x141f1404, 0x14151410, 0x1473147d, 0x14401475, 0x1453145c,
+    0x14541450, 0x15c115cc, 0x153c15c7, 0x15341533, 0x1500150f, 0x15051507, 0x15101513, 0x15711514,
+    0x15471543, 0x15511545, 0x7ffd7fff, 0x7ff57ff7, 0x7fdd7fdf, 0x7fd57fd7, 0x7f0f7f30, 0x7f037f0c,
+    0x7f047f01, 0x7f7f7f10, 0x7f777f7d, 0x7f407f75, 0x7f5d7f5f, 0x7f557f57, 0x7ccc7cf0, 0x7cc17cc3,
+    0x7cd07cc4, 0x7c337c3c, 0x7c0f7c34, 0x7c007c0d, 0x7c077c01, 0x7c137c04, 0x7c147c11, 0x7c747c70,
+    0x7c417c43, 0x7c507c44, 0x7dfd7dff, 0x7df57df7, 0x7ddf7dc0, 0x7dd77ddd, 0x7d0c7dd5, 0x7d047d03,
+    0x7d7f7d10, 0x7d777d7d, 0x7d407d75, 0x7d5d7d5f, 0x7d557d57, 0x73c473c3, 0x7333733c, 0x7300730c,
+    0x731c7305, 0x73147313, 0x73447343, 0x70f470fc, 0x70c070cd, 0x70d170c5, 0x703f70d4, 0x7030703c,
+    0x700c7037, 0x70007003, 0x70047001, 0x70107005, 0x70177011, 0x707c7015, 0x70717073, 0x704f7074,
+    0x7040704d, 0x70517047, 0x71c171cc, 0x71d071c4, 0x7133713c, 0x71357134, 0x7100710f, 0x71057104,
+    0x7111711c, 0x71707115, 0x7145714c, 0x77ff7153, 0x77f777fd, 0x77c077f5, 0x77dd77df, 0x77d577d7,
+    0x7730773c, 0x7703770c, 0x77107704, 0x777f7714, 0x7777777d, 0x77407775, 0x775d775f, 0x77557757,
+    0x74f174f0, 0x74c374cc, 0x74d074c1, 0x7433743c, 0x74347431, 0x740d740f, 0x74057400, 0x7413741c,
+    0x74417470, 0x74507444, 0x75fd75ff, 0x75f575f7, 0x75df75c0, 0x75d775dd, 0x753075d5, 0x7503750c,
+    0x757f7501, 0x7577757d, 0x75407575, 0x755d755f, 0x75557557, 0x4fcc4ff0, 0x4fc74fc1, 0x4fd04fc4,
+    0x4f314f3c, 0x4f004f34, 0x4f054f07, 0x4f154f14, 0x4f4c4f70, 0x4f414f43, 0x4f504f44, 0x4cf34cfc,
+    0x4cf44cf1, 0x4cc04ccf, 0x4cc54cc7, 0x4cd34cdc, 0x4cd44cd1, 0x4c304c3f, 0x4c0c4c0f, 0x4c004c03,
+    0x4c044c01, 0x4c104c1d, 0x4c714c73, 0x4c404c4d, 0x4c5c4c47, 0x4c514c53, 0x4df04c54, 0x4dc34dcc,
+    0x4dd04dc4, 0x4d314d33, 0x4d0f4d34, 0x4d004d0d, 0x4d114d07, 0x4d704d14, 0x4d414d43, 0x43fc4d54,
+    0x43f143f3, 0x43c043cf, 0x43d143c7, 0x4335433f, 0x4303430c, 0x43014300, 0x43044307, 0x431c431f,
+    0x4310431d, 0x43714373, 0x4343434d, 0x43474340, 0x4354435c, 0x40f040ff, 0x40f540f7, 0x40cc40cf,
+    0x40c040c3, 0x40c440c1, 0x40d040dc, 0x40d540d4, 0x4033403c, 0x40314030, 0x400f4034, 0x400d400c,
+    0x40004003, 0x40074001, 0x40054004, 0x4013401c, 0x40114010, 0x407c4014, 0x40774070, 0x404d404c,
+    0x40404043, 0x40444041, 0x405f4045, 0x4050405d, 0x40554057, 0x41f341fc, 0x41c041cf, 0x41df41c4,
+    0x41d441d1, 0x41374130, 0x410c4134, 0x4100410d, 0x41044101, 0x41174110, 0x4173417d, 0x41754174,
+    0x4143414d, 0x41534140, 0x41544151, 0x47c147f0, 0x47d047c4, 0x4731473c, 0x470d470f, 0x47014700,
+    0x47134705, 0x47704710, 0x4741474c, 0x47504744, 0x44f144f3, 0x44cf44f4, 0x44c044cd, 0x44c544c7,
+    0x44dc44df, 0x44d144d3, 0x443d443f, 0x44374430, 0x440c4435, 0x44004403, 0x44044401, 0x4410441d,
+    0x44154411, 0x4473447c, 0x444d444f, 0x44454440, 0x4451445c, 0x45c045f0, 0x453345d0, 0x45344531,
+    0x4500450f, 0x451c4507, 0x454c4570, 0x45404543, 0x5fff4541, 0x5ff75ffd, 0x5fc05ff5, 0x5fdd5fdf,
+    0x5fd55fd7, 0x5f0c5f30, 0x5f015f03, 0x5f7f5f04, 0x5f775f7d, 0x5f405f75, 0x5f5d5f5f, 0x5f555f57,
+    0x5cf45cf0, 0x5cc35ccc, 0x5cc45cc1, 0x5c315cc5, 0x5c0c5c34, 0x5c075c00, 0x5c1c5c05, 0x5c705c13,
+    0x5c4d5c4f, 0x5c445c41, 0x5df75dfd, 0x5dcf5df5, 0x5ddd5dc4, 0x5dd55dd7, 0x5d0c5d30, 0x5d045d01,
+    0x5d7f5d10, 0x5d775d7d, 0x5d405d75, 0x5d5d5d5f, 0x5d555d57, 0x53d053c4, 0x5333533c, 0x5303530f,
+    0x53075300, 0x531c5305, 0x53115310, 0x53145317, 0x50f15370, 0x50cf50f4, 0x50c050cd, 0x50d150c7,
+    0x503d50d4, 0x500c5030, 0x50005003, 0x50045001, 0x50155010, 0x5073507c, 0x50715070, 0x504d5074,
+    0x50475040, 0x51cc51f0, 0x51c551c1, 0x51d051dc, 0x51315133, 0x510d5135, 0x51015100, 0x511f5107,
+    0x5171511d, 0x5140514f, 0x51445141, 0x5153515c, 0x57ff5151, 0x57f757fd, 0x57df57f5, 0x57d757dd,
+    0x570c57d5, 0x57015703, 0x577f5704, 0x5777577d, 0x57405775, 0x575d575f, 0x57555757, 0x54c354f0,
+    0x54dc54c4, 0x543c54d0, 0x5400540f, 0x541c5405, 0x54145411, 0x5441544f, 0x55fd55ff, 0x55f555f7,
+    0x55dd55df, 0x55d555d7, 0x5503550c, 0x557f5501, 0x5577557d, 0x55405575, 0x555d555f, 0x55555557
+);
+
+#enddecl(IQ1_GRID)
+
+#decl(IQ4_GRID)
+
+const kvalues_iq4nl = array<i32, 16>(
+    -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113
+);
+
+#enddecl(IQ4_GRID)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cpy.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cpy.tmpl.wgsl
new file mode 100644
index 0000000..b5e93b8
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cpy.tmpl.wgsl
@@ -0,0 +1,107 @@
+#define(VARIANTS)
+
+[
+  {
+    "REPLS": {
+      "SRC_TYPE": "f32",
+      "DST_TYPE": "f32"
+    }
+  },
+  {
+    "REPLS": {
+      "SRC_TYPE": "f32",
+      "DST_TYPE": "i32"
+    }
+  },
+  {
+    "REPLS": {
+      "SRC_TYPE": "f32",
+      "DST_TYPE": "f16"
+    }
+  },
+  {
+    "REPLS": {
+      "SRC_TYPE": "f16",
+      "DST_TYPE": "f16"
+    }
+  },
+  {
+    "REPLS": {
+      "SRC_TYPE": "f16",
+      "DST_TYPE": "f32"
+    }
+  }
+]
+
+#end(VARIANTS)
+
+#define(SHADER)
+enable f16;
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<{{SRC_TYPE}}>;
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<{{DST_TYPE}}>;
+
+struct Params {
+    ne: u32,            // total number of elements
+    offset_src: u32,    // in elements
+    offset_dst: u32,    // in elements
+
+    // Strides (in elements) — may be permuted
+    stride_src0: u32,
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_dst0: u32,
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // Logical shapes
+    src_ne0: u32,
+    src_ne1: u32,
+    src_ne2: u32,
+
+    dst_ne0: u32,
+    dst_ne1: u32,
+    dst_ne2: u32
+};
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+override wg_size: u32;
+@compute @workgroup_size(wg_size)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= params.ne) {
+        return;
+    }
+
+    var i = gid.x;
+    let i3 = i / (params.src_ne2 * params.src_ne1 * params.src_ne0);
+    i = i % (params.src_ne2 * params.src_ne1 * params.src_ne0);
+    let i2 = i / (params.src_ne1 * params.src_ne0);
+    i = i % (params.src_ne1 * params.src_ne0);
+    let i1 = i / params.src_ne0;
+    let i0 = i % params.src_ne0;
+
+    var j = gid.x;
+    let j3 = j / (params.dst_ne2 * params.dst_ne1 * params.dst_ne0);
+    j = j % (params.dst_ne2 * params.dst_ne1 * params.dst_ne0);
+    let j2 = j / (params.dst_ne1 * params.dst_ne0);
+    j = j % (params.dst_ne1 * params.dst_ne0);
+    let j1 = j / params.dst_ne0;
+    let j0 = j % params.dst_ne0;
+
+    let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 +
+                  i2 * params.stride_src2 + i3 * params.stride_src3;
+
+    let dst_idx = j0 * params.stride_dst0 + j1 * params.stride_dst1 +
+                  j2 * params.stride_dst2 + j3 * params.stride_dst3;
+
+    dst[params.offset_dst + dst_idx] = {{DST_TYPE}}((src[params.offset_src + src_idx]));
+}
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cumsum.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cumsum.wgsl
new file mode 100644
index 0000000..e622552
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cumsum.wgsl
@@ -0,0 +1,66 @@
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<f32>;
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_dst: u32, // in elements
+    ne0: u32,
+};
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+var<workgroup> shared_sum: array<f32, WG_SIZE>;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+    let row_idx = params.offset_src + wid.x * params.ne0;
+    let elems = (params.ne0 + WG_SIZE - 1) / WG_SIZE;
+    var local_sum: f32 = 0.0;
+    for (var col = lid.x * elems; col < (lid.x + 1) * elems && col < params.ne0; col ++) {
+        local_sum += src[row_idx + col];
+    }
+    shared_sum[lid.x] = local_sum;
+    workgroupBarrier();
+
+    // upsweep
+    var offset = 1u;
+    while (offset < WG_SIZE) {
+        let idx = (lid.x + 1) * offset * 2 - 1;
+        if (idx < WG_SIZE) {
+            shared_sum[idx] = shared_sum[idx] + shared_sum[idx - offset];
+        }
+        workgroupBarrier();
+        offset <<= 1;
+    }
+
+    // set last to 0 for exclusive sum
+    if (lid.x == 0) {
+        shared_sum[WG_SIZE - 1] = 0.0;
+    }
+    workgroupBarrier();
+
+    // downsweep
+    offset = WG_SIZE >> 1;
+    while (offset > 0) {
+        let idx = (lid.x + 1) * offset * 2 - 1;
+        if (idx < WG_SIZE) {
+            let t = shared_sum[idx - offset];
+            shared_sum[idx - offset] = shared_sum[idx];
+            shared_sum[idx] = shared_sum[idx] + t;
+        }
+        workgroupBarrier();
+        offset = offset >> 1;
+    }
+
+    // shared_sum[lid] is exclusive prefix sum up to this thread.
+    var running_sum = shared_sum[lid.x];
+    for (var col = lid.x * elems; col < (lid.x + 1) * elems && col < params.ne0; col ++) {
+        running_sum += src[row_idx + col];
+        dst[params.offset_dst + wid.x * params.ne0 + col] = running_sum;
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py
new file mode 100755
index 0000000..d61df5b
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py
@@ -0,0 +1,147 @@
+import os
+import re
+import ast
+import argparse
+
+
+def extract_block(text, name):
+    pattern = rf'#define\({name}\)\s*(.*?)#end\({name}\)'
+    match = re.search(pattern, text, re.DOTALL)
+    if not match:
+        raise ValueError(f"Missing block: {name}")
+    return match.group(1).strip()
+
+
+def parse_decls(decls_text):
+    decls = {}
+    for name, code in re.findall(r'#decl\((.*?)\)\s*(.*?)#enddecl\(\1\)', decls_text, re.DOTALL):
+        decls[name.strip()] = code.strip()
+    return decls
+
+
+def replace_repl_placeholders(variant, template_map):
+    for repl, code in variant["REPLS"].items():
+        for key, val in template_map.items():
+            # Match "key" and avoid matching subsequences using by using \b
+            code = re.sub(rf'\b{re.escape(str(key))}\b', str(val), code)
+        variant["REPLS"][repl] = code
+    return variant
+
+
+def replace_placeholders(shader_text, replacements):
+    for key, val in replacements.items():
+        # Match {{KEY}} literally, where KEY is escaped
+        pattern = r'{{\s*' + re.escape(key) + r'\s*}}'
+        shader_text = re.sub(pattern, str(val), shader_text)
+    return shader_text
+
+
+def expand_includes(shader, input_dir):
+    """
+    Replace #include "file" lines in the text with the contents of that file.
+    Searches for files relative to input_dir.
+    """
+    include_pattern = re.compile(r'^\s*#include\s+"([^"]+)"\s*$', re.MULTILINE)
+
+    def replacer(match):
+        fname = match.group(1)
+        file_path = os.path.join(input_dir, fname)
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"Included file not found: {file_path}")
+        with open(file_path, "r", encoding="utf-8") as f:
+            included_code = f.read()
+        # Recursively expand includes inside the included file
+        return expand_includes(included_code, input_dir)
+
+    return include_pattern.sub(replacer, shader)
+
+
+def write_shader(shader_name, shader_code, output_dir, outfile):
+    if output_dir:
+        wgsl_filename = os.path.join(output_dir, f"{shader_name}.wgsl")
+        with open(wgsl_filename, "w", encoding="utf-8") as f_out:
+            f_out.write(shader_code)
+    outfile.write(f'const char* wgsl_{shader_name} = R"({shader_code})";\n\n')
+
+
+def generate_variants(fname, input_dir, output_dir, outfile):
+    shader_path = os.path.join(input_dir, fname)
+    shader_base_name = fname.split(".")[0]
+
+    with open(shader_path, "r", encoding="utf-8") as f:
+        text = f.read()
+
+    try:
+        variants = ast.literal_eval(extract_block(text, "VARIANTS"))
+    except ValueError:
+        write_shader(shader_base_name, text, output_dir, outfile)
+    else:
+        try:
+            decls_map = parse_decls(extract_block(text, "DECLS"))
+        except ValueError:
+            decls_map = {}
+        try:
+            templates_map = ast.literal_eval(extract_block(text, "REPL_TEMPLATES"))
+        except ValueError:
+            templates_map = {}
+
+        for fname in sorted(os.listdir(input_dir)):
+            if fname.endswith(".tmpl"):
+                tmpl_path = os.path.join(input_dir, fname)
+                with open(tmpl_path, "r", encoding="utf-8") as f_tmpl:
+                    decls = f_tmpl.read()
+                    decls_map.update(parse_decls(decls))
+
+        shader_template = extract_block(text, "SHADER")
+        for variant in variants:
+            if "DECLS" in variant:
+                decls = variant["DECLS"]
+            else:
+                decls = []
+            decls_code = ""
+            for key in decls:
+                if key not in decls_map:
+                    raise ValueError(f"DECLS key '{key}' not found.")
+                decls_code += decls_map[key] + "\n\n"
+            final_shader = re.sub(r'\bDECLS\b', decls_code, shader_template)
+            if "REPLS" in variant:
+                variant = replace_repl_placeholders(variant, templates_map)
+                final_shader = replace_placeholders(final_shader, variant["REPLS"])
+                # second run to expand placeholders in repl_template
+                final_shader = replace_placeholders(final_shader, variant["REPLS"])
+            final_shader = expand_includes(final_shader, input_dir)
+
+            if "SHADER_NAME" in variant:
+                output_name = variant["SHADER_NAME"]
+            elif "SHADER_SUFFIX" in variant:
+                output_name = f"{shader_base_name}_" + variant["SHADER_SUFFIX"]
+            elif "REPLS" in variant and "SRC0_TYPE" in variant["REPLS"] and "SRC1_TYPE" in variant["REPLS"]:
+                output_name = f"{shader_base_name}_" + "_".join([variant["REPLS"]["SRC0_TYPE"], variant["REPLS"]["SRC1_TYPE"]])
+            elif "REPLS" in variant and "SRC_TYPE" in variant["REPLS"] and "DST_TYPE" in variant["REPLS"]:
+                output_name = f"{shader_base_name}_" + "_".join([variant["REPLS"]["SRC_TYPE"], variant["REPLS"]["DST_TYPE"]])
+            elif "REPLS" in variant and "TYPE" in variant["REPLS"]:
+                output_name = f"{shader_base_name}_" + variant["REPLS"]["TYPE"]
+            else:
+                output_name = shader_base_name
+            write_shader(output_name, final_shader, output_dir, outfile)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_dir", required=True)
+    parser.add_argument("--output_file", required=True)
+    parser.add_argument("--output_dir")
+    args = parser.parse_args()
+
+    if args.output_dir:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+    with open(args.output_file, "w", encoding="utf-8") as out:
+        out.write("// Auto-generated shader embedding\n\n")
+        for fname in sorted(os.listdir(args.input_dir)):
+            if fname.endswith(".wgsl"):
+                generate_variants(fname, args.input_dir, args.output_dir, out)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl
new file mode 100644
index 0000000..b682216
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl
@@ -0,0 +1,636 @@
+diagnostic(off, chromium.subgroup_matrix_uniformity);
+diagnostic(off, subgroup_uniformity);
+enable f16;
+enable subgroups;
+enable chromium_experimental_subgroup_matrix;
+
+#ifdef KV_F32
+#define KV_TYPE f32
+#else
+#define KV_TYPE f16
+#endif
+
+// Default values
+#define HEAD_DIM_QK 64
+#define HEAD_DIM_V 64
+
+// The number of rows/columns/k in a subgroup matrix. MxK * KxN = MxN
+// Note that the "K" here does not correspond to the K in attention's Q/K/V, it's just the common dimension.
+#define SG_MAT_M 8
+#define SG_MAT_N 8
+#define SG_MAT_K 8
+
+// Each workgroup processes one subgroup matrix of Q rows
+#define Q_TILE SG_MAT_M
+#define KV_TILE 16
+#define WG_SIZE 64
+
+// Number of subgroup-matrix-width blocks that span the KV tile. SG_MAT_N must divide KV_TILE.
+#define KV_BLOCKS (KV_TILE / SG_MAT_N)
+
+// Quantization constants/helpers
+#define BLOCK_SIZE 32
+#define BLOCKS_K ((HEAD_DIM_QK + BLOCK_SIZE - 1) / BLOCK_SIZE)
+#define BLOCKS_V ((HEAD_DIM_V + BLOCK_SIZE - 1) / BLOCK_SIZE)
+// number of quantized elements processed per thread
+#if defined(KV_Q4_0)
+#define NQ 16
+// Q4_0 has 32 elements, 1 f16 for scale, 8 f16 for 4-bit weights
+#define F16_PER_BLOCK 9
+#define WEIGHTS_PER_F16 4
+#elif defined(KV_Q8_0)
+#define NQ 8
+// Q8_0 has 32 elements, 1 f16 for scale, 16 f16 for 8-bit weights
+#define F16_PER_BLOCK 17
+#define WEIGHTS_PER_F16 2
+#endif
+#define F16_PER_THREAD (NQ / WEIGHTS_PER_F16)
+
+// Ok not to put these in a define block, compiler will remove if unused
+fn get_byte(value: u32, index: u32) -> u32 {
+    return (value >> (index * 8)) & 0xFF;
+}
+
+fn get_byte_i32(value: u32, index: u32) -> i32 {
+    return bitcast<i32>(((value >> (index * 8)) & 0xFF) << 24) >> 24;
+}
+
+struct Params {
+    offset_q: u32,
+    offset_k: u32,
+    offset_v: u32,
+    offset_mask: u32,
+    offset_sinks: u32,
+    offset_dst: u32,
+
+    // shapes of Q/K/V
+    n_heads: u32,
+    seq_len_q: u32,
+    seq_len_kv: u32,
+
+    // strides (in elements)
+    stride_q1: u32,
+    stride_q2: u32,
+    stride_q3: u32,
+    stride_k1: u32,
+    stride_k2: u32,
+    stride_k3: u32,
+    stride_v1: u32,
+    stride_v2: u32,
+    stride_v3: u32,
+    stride_mask3: u32,
+
+    // repeat factors for K/V, e.g., MHA vs. MQA vs. GQA
+    q_per_kv: u32,
+
+    // softmax params
+    scale: f32,
+    max_bias: f32,
+    logit_softcap: f32,
+    n_head_log2: f32,
+    m0: f32,
+    m1: f32,
+};
+
+@group(0) @binding(0) var<storage, read_write> Q: array<f32>;
+@group(0) @binding(1) var<storage, read_write> K: array<KV_TYPE>;
+@group(0) @binding(2) var<storage, read_write> V: array<KV_TYPE>;
+
+#if defined(MASK) && defined(SINKS)
+@group(0) @binding(3) var<storage, read_write> mask: array<f16>;
+@group(0) @binding(4) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 5
+#define PARAMS_BINDING 6
+#elif defined(MASK)
+@group(0) @binding(3) var<storage, read_write> mask: array<f16>;
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#elif defined(SINKS)
+@group(0) @binding(3) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#else
+#define DST_BINDING 3
+#define PARAMS_BINDING 4
+#endif
+
+@group(0) @binding(DST_BINDING) var<storage, read_write> dst: array<vec4<f32>>;
+@group(0) @binding(PARAMS_BINDING) var<uniform> params: Params;
+
+// Just a very small float value.
+const FLOAT_MIN: f32 = -1.0e9;
+
+// The number of Q rows processed per workgroup
+var<workgroup> q_shmem: array<f16, Q_TILE * HEAD_DIM_QK>;
+
+#ifndef KV_DIRECT
+const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);
+// we can reuse the same shmem for K and V since we only need one at a time
+var<workgroup> kv_shmem: array<f16, kv_shmem_size>;
+#endif
+
+var<workgroup> o_shmem: array<f16, Q_TILE * HEAD_DIM_V>; // output shmem
+
+#ifdef MASK
+// storage for mask values
+var<workgroup> mask_shmem: array<f16, Q_TILE * KV_TILE>;
+#endif
+
+// storage for output of Q*K^T scores for online softmax (S matrix from paper)
+// also storage for diagonal matrix during online softmax (P matrix from paper)
+// note that we reuse the same storage for both since we only need one at a time
+var<workgroup> inter_shmem: array<f16, Q_TILE * KV_TILE>;
+
+// Storage for row max and exp sum during online softmax
+var<workgroup> row_max_shmem: array<f32, Q_TILE>;
+var<workgroup> exp_sum_shmem: array<f32, Q_TILE>;
+
+fn calc_softmax_term(kv_idx: u32, q_tile_row: u32, slope: f32) -> f32 {
+    var v = select(FLOAT_MIN,
+                   f32(inter_shmem[kv_idx + q_tile_row * KV_TILE]) * params.scale,
+                   kv_idx < KV_TILE);
+#ifdef LOGIT_SOFTCAP
+    v = params.logit_softcap * tanh(v);
+#endif
+#ifdef MASK
+    let mask_val = select(0.0, f32(mask_shmem[q_tile_row * KV_TILE + kv_idx]), kv_idx < KV_TILE);
+    let mask_term = slope * mask_val;
+    v += mask_term;
+#endif
+    return v;
+}
+
+fn load_f32x4(buf: ptr<storage, array<vec4<f32>>, read_write>, scalar_index: u32) -> vec4<f32> {
+    return (*buf)[scalar_index >> 2u];
+}
+
+fn load_kvx4(buf: ptr<storage, array<vec4<KV_TYPE>>, read_write>, scalar_index: u32) -> vec4<KV_TYPE> {
+    return (*buf)[scalar_index >> 2u];
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
+    @builtin(local_invocation_id) local_id: vec3<u32>,
+    @builtin(subgroup_id) subgroup_id: u32,
+    @builtin(subgroup_size) subgroup_size: u32,
+    @builtin(num_subgroups) num_subgroups: u32,
+    @builtin(subgroup_invocation_id) sg_inv_id: u32) {
+
+    // initialize row max for online softmax
+    for (var i = local_id.x; i < Q_TILE; i += WG_SIZE) {
+        row_max_shmem[i] = FLOAT_MIN;
+        exp_sum_shmem[i] = 0.0;
+    }
+
+    for (var i = local_id.x; i < Q_TILE * HEAD_DIM_V; i += WG_SIZE) {
+        o_shmem[i] = 0.0;
+    }
+
+    // workgroups per head/batch
+    let wg_per_head = (params.seq_len_q + Q_TILE - 1u) / Q_TILE;
+    let wg_per_batch = wg_per_head * params.n_heads;
+
+    let dst2_stride = HEAD_DIM_V * params.n_heads;
+    let dst3_stride = dst2_stride * params.seq_len_q;
+
+    // batch index
+    let batch_idx = wg_id.x / wg_per_batch;
+    let q_batch_offset = params.offset_q + batch_idx * params.stride_q3;
+    let k_batch_offset = params.offset_k + batch_idx * params.stride_k3;
+    let v_batch_offset = params.offset_v + batch_idx * params.stride_v3;
+    let dst_batch_offset = params.offset_dst + batch_idx * dst3_stride;
+    let wg_in_batch = wg_id.x % wg_per_batch;
+
+    // head index
+    let head_idx = wg_in_batch / wg_per_head;
+    let q_head_offset = q_batch_offset + head_idx * params.stride_q2;
+    let k_head_idx = head_idx / params.q_per_kv;
+    let v_head_idx = k_head_idx;
+    let k_head_offset = k_batch_offset + k_head_idx * params.stride_k2;
+    let v_head_offset = v_batch_offset + v_head_idx * params.stride_v2;
+
+    // starting Q row for this workgroup
+    let wg_in_head = wg_in_batch % wg_per_head;
+    let q_row_start = wg_in_head * Q_TILE;
+
+#ifdef MASK
+    // mask offset
+    let mask_global_offset = params.offset_mask + batch_idx * params.stride_mask3 + q_row_start * params.seq_len_kv;
+#endif
+
+    // note that the output is permuted, the layout is [head_dim_v, n_heads, seq_len_q, batch_size]
+    let dst_global_offset = dst_batch_offset + q_row_start * dst2_stride + head_idx * HEAD_DIM_V;
+
+    let head = f32(head_idx);
+    let slope = select(1.0, select(pow(params.m1, 2.0 * (head - params.n_head_log2) + 1.0), pow(params.m0, head + 1.0), head < params.n_head_log2), params.max_bias > 0);
+
+    // load q tile into shared memory
+    for (var elem_idx = local_id.x; elem_idx < Q_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) {
+        let q_row = elem_idx / HEAD_DIM_QK;
+        let q_col = elem_idx % HEAD_DIM_QK;
+        let head_q_row = q_row_start + q_row;
+        let global_q_row_offset = q_head_offset + head_q_row * params.stride_q1;
+        q_shmem[elem_idx] = f16(select(
+            0.0,
+            Q[global_q_row_offset + q_col],
+            head_q_row < params.seq_len_q && q_col < HEAD_DIM_QK));
+    }
+
+    for (var kv_tile = 0u; kv_tile < params.seq_len_kv; kv_tile += KV_TILE) {
+      // clear inter_shmem to ensure zero-initialized accumulators
+        for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) {
+            inter_shmem[elem_idx] = 0.0;
+        }
+
+      // load k tile into shared memory
+#if defined(KV_Q4_0)
+      for (var elem_idx = local_id.x * NQ; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE * NQ) {
+          let blck_idx = elem_idx / BLOCK_SIZE;
+          let block_offset = (elem_idx % BLOCK_SIZE) / WEIGHTS_PER_F16;
+          let k_row = blck_idx / BLOCKS_K;
+          let global_k_row = kv_tile + k_row;
+          let block_k = blck_idx % BLOCKS_K;
+          let row_offset = k_row * HEAD_DIM_QK;
+
+          if (global_k_row < params.seq_len_kv) {
+              let global_block_idx = k_head_offset + global_k_row * params.stride_k1 + block_k;
+              let base_idx = global_block_idx * F16_PER_BLOCK;
+              let d = K[base_idx]; // scale
+              for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+                  let q_0 = K[base_idx + 1u + block_offset + j];
+                  let q_1 = K[base_idx + 1u + block_offset + j + 1];
+                  let q_packed = bitcast<u32>(vec2(q_0, q_1));
+                  for (var k = 0u; k < 4u; k++) {
+                      let q_byte = get_byte(q_packed, k);
+                      let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d;
+                      let q_lo = (f16(q_byte & 0xF) - 8.0) * d;
+                      let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
+                      kv_shmem[row_offset + idx] = q_lo;
+                      kv_shmem[row_offset + idx + 16u] = q_hi;
+                  }
+              }
+          }
+      }
+#elif defined(KV_Q8_0)
+      for (var elem_idx = local_id.x * NQ; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE * NQ) {
+          let blck_idx = elem_idx / BLOCK_SIZE;
+          let block_offset = (elem_idx % BLOCK_SIZE) / WEIGHTS_PER_F16;
+          let k_row = blck_idx / BLOCKS_K;
+          let global_k_row = kv_tile + k_row;
+          let block_k = blck_idx % BLOCKS_K;
+          let row_offset = k_row * HEAD_DIM_QK;
+
+          if (global_k_row < params.seq_len_kv) {
+              let global_block_idx = k_head_offset + global_k_row * params.stride_k1 + block_k;
+              let base_idx = global_block_idx * F16_PER_BLOCK;
+              let d = K[base_idx]; // scale
+              for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+                  let q_0 = K[base_idx + 1u + block_offset + j];
+                  let q_1 = K[base_idx + 1u + block_offset + j + 1];
+                  let q_packed = bitcast<u32>(vec2(q_0, q_1));
+                  for (var k = 0u; k < 4u; k++) {
+                      let q_byte = get_byte_i32(q_packed, k);
+                      let q_val = f16(q_byte) * d;
+                      let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
+                      kv_shmem[row_offset + idx] = q_val;
+                  }
+              }
+          }
+      }
+#elif defined(KV_DIRECT)
+      // Direct global loads for KV
+#else
+      for (var elem_idx = local_id.x; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) {
+          let k_row = elem_idx / HEAD_DIM_QK;
+          let k_col = elem_idx % HEAD_DIM_QK;
+          let global_k_row = kv_tile + k_row;
+          let global_k_row_offset = k_head_offset + global_k_row * params.stride_k1;
+          kv_shmem[elem_idx] = f16(select(
+              0.0,
+              K[global_k_row_offset + k_col],
+              global_k_row < params.seq_len_kv && k_col < HEAD_DIM_QK));
+      }
+#endif
+
+      workgroupBarrier();
+
+      // accumulate q block * k block into registers across the entire KV tile
+      // TODO: this loop seems to be the current largest bottleneck
+      // this bracket exists to scope the lifetime of variables, reducing register pressure
+      {
+#ifdef KV_DIRECT
+          let k_block_row = kv_tile + subgroup_id * SG_MAT_N;
+          var k_global_offset = k_head_offset + k_block_row * params.stride_k1;
+#else
+          var k_block_offset = subgroup_id * SG_MAT_N * HEAD_DIM_QK;
+#endif
+          for (var kv_block = subgroup_id; kv_block < KV_BLOCKS; kv_block += num_subgroups) {
+              let inter_offset = kv_block * SG_MAT_N;
+              var acc: subgroup_matrix_result<f16, SG_MAT_M, SG_MAT_N> = subgroupMatrixLoad<subgroup_matrix_result<f16, SG_MAT_M, SG_MAT_N>>(&inter_shmem, inter_offset, false, KV_TILE);
+
+              var q_cur = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_M, SG_MAT_K>>(&q_shmem, 0u, false, HEAD_DIM_QK);
+
+#ifdef KV_DIRECT
+              var k_cur = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&K, k_global_offset + 0u, true, params.stride_k1);
+#else
+              var k_cur = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&kv_shmem, k_block_offset + 0u, true, HEAD_DIM_QK);
+#endif
+
+              var t: u32 = 1u;
+              for (; t + 1u < HEAD_DIM_QK / SG_MAT_K; t += 2u) {
+                  let h0 = t * SG_MAT_K;
+                  var q0 = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_M, SG_MAT_K>>(&q_shmem, h0, false, HEAD_DIM_QK);
+#ifdef KV_DIRECT
+                  var k0 = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&K, k_global_offset + h0, true, params.stride_k1);
+#else
+                  var k0 = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&kv_shmem, k_block_offset + h0, true, HEAD_DIM_QK);
+#endif
+                  acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc);
+                  q_cur = q0;
+                  k_cur = k0;
+
+                  let h1 = (t + 1u) * SG_MAT_K;
+                  var q1g = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_M, SG_MAT_K>>(&q_shmem, h1, false, HEAD_DIM_QK);
+#ifdef KV_DIRECT
+                  var k1g = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&K, k_global_offset + h1, true, params.stride_k1);
+#else
+                  var k1g = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&kv_shmem, k_block_offset + h1, true, HEAD_DIM_QK);
+#endif
+                  acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc);
+                  q_cur = q1g;
+                  k_cur = k1g;
+              }
+
+              // handle odd tail
+              if (t < HEAD_DIM_QK / SG_MAT_K) {
+                  let h = t * SG_MAT_K;
+                  var qn = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_M, SG_MAT_K>>(&q_shmem, h, false, HEAD_DIM_QK);
+#ifdef KV_DIRECT
+                  var kn = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&K, k_global_offset + h, true, params.stride_k1);
+#else
+                  var kn = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(&kv_shmem, k_block_offset + h, true, HEAD_DIM_QK);
+#endif
+                  acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc);
+                  q_cur = qn;
+                  k_cur = kn;
+              }
+
+              acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc);
+
+#ifdef KV_DIRECT
+              k_global_offset += num_subgroups * SG_MAT_N * params.stride_k1;
+#else
+              k_block_offset += num_subgroups * SG_MAT_N * HEAD_DIM_QK;
+#endif
+              subgroupMatrixStore(&inter_shmem, inter_offset, acc, false, KV_TILE);
+          }
+      }
+
+
+#ifdef MASK
+      // load mask tile into shared memory for this KV block
+      // TODO: optimize and skip if mask is -INF for the entire tile
+      for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) {
+          let mask_row = elem_idx / KV_TILE;
+          let mask_col = elem_idx % KV_TILE;
+          let global_q_row = q_row_start + mask_row;
+          let global_k_col = kv_tile + mask_col;
+          let mask_in_bounds = global_q_row < params.seq_len_q && global_k_col < params.seq_len_kv;
+          let mask_idx = mask_global_offset + mask_row * params.seq_len_kv + global_k_col;
+          mask_shmem[elem_idx] = select(0.0, mask[mask_idx], mask_in_bounds);
+      }
+#endif
+
+      workgroupBarrier();
+
+      // online softmax
+      for (var q_tile_row = subgroup_id; q_tile_row < Q_TILE; q_tile_row += num_subgroups) {
+          let global_q_row = q_row_start + q_tile_row;
+          if (global_q_row >= params.seq_len_q) {
+              break;
+          }
+
+          // initialize running max for this row
+          var prev_max = row_max_shmem[q_tile_row];
+          var final_max = prev_max;
+          // pass 1: compute final max across the full KV tile in chunks
+          for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) {
+              let kv_idx = kv_offset + sg_inv_id;
+              let softmax_term = calc_softmax_term(kv_idx, q_tile_row, slope);
+              final_max = subgroupMax(max(final_max, softmax_term));
+          }
+
+          var total_exp_term: f32 = 0.0;
+          // pass 2: compute exp sum and write P using final_max
+          for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) {
+              let kv_idx = kv_offset + sg_inv_id;
+              let softmax_term = calc_softmax_term(kv_idx, q_tile_row, slope);
+              let cur_p = select(0.0,
+                                 exp(softmax_term - final_max),
+                                 kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE);
+              total_exp_term += subgroupAdd(cur_p);
+              if (kv_idx < KV_TILE) {
+                  inter_shmem[kv_idx + q_tile_row * KV_TILE] = f16(cur_p);
+              }
+          }
+
+          let cur_exp = exp(prev_max - final_max);
+
+          if (sg_inv_id == 0) {
+              row_max_shmem[q_tile_row] = final_max;
+              exp_sum_shmem[q_tile_row] = exp_sum_shmem[q_tile_row] * cur_exp + total_exp_term;
+          }
+
+          for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) {
+              let idx = q_tile_row * HEAD_DIM_V + elem_idx;
+              o_shmem[idx] = f16(f32(o_shmem[idx]) * cur_exp);
+          }
+      }
+
+      // load v tile into shared memory
+#if defined(KV_Q4_0)
+      for (var elem_idx = local_id.x * NQ; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE * NQ) {
+          let blck_idx = elem_idx / BLOCK_SIZE;
+          let block_offset = (elem_idx % BLOCK_SIZE) / WEIGHTS_PER_F16;
+          let v_row = blck_idx / BLOCKS_V;
+          let global_v_row = kv_tile + v_row;
+          let block_k = blck_idx % BLOCKS_V;
+          let row_offset = v_row * HEAD_DIM_V;
+
+          if (global_v_row < params.seq_len_kv) {
+              let global_block_idx = v_head_offset + global_v_row * params.stride_v1 + block_k;
+              let base_idx = global_block_idx * F16_PER_BLOCK;
+              let d = V[base_idx]; // scale
+              for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+                  let q_0 = V[base_idx + 1u + block_offset + j];
+                  let q_1 = V[base_idx + 1u + block_offset + j + 1];
+                  let q_packed = bitcast<u32>(vec2(q_0, q_1));
+                  for (var k = 0u; k < 4u; k++) {
+                      let q_byte = get_byte(q_packed, k);
+                      let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d;
+                      let q_lo = (f16(q_byte & 0xF) - 8.0) * d;
+                      let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
+                      kv_shmem[row_offset + idx] = q_lo;
+                      kv_shmem[row_offset + idx + 16u] = q_hi;
+                  }
+              }
+          }
+      }
+#elif defined(KV_Q8_0)
+      for (var elem_idx = local_id.x * NQ; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE * NQ) {
+          let blck_idx = elem_idx / BLOCK_SIZE;
+          let block_offset = (elem_idx % BLOCK_SIZE) / WEIGHTS_PER_F16;
+          let v_row = blck_idx / BLOCKS_V;
+          let global_v_row = kv_tile + v_row;
+          let block_k = blck_idx % BLOCKS_V;
+          let row_offset = v_row * HEAD_DIM_V;
+
+          if (global_v_row < params.seq_len_kv) {
+              let global_block_idx = v_head_offset + global_v_row * params.stride_v1 + block_k;
+              let base_idx = global_block_idx * F16_PER_BLOCK;
+              let d = V[base_idx]; // scale
+              for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+                  let q_0 = V[base_idx + 1u + block_offset + j];
+                  let q_1 = V[base_idx + 1u + block_offset + j + 1];
+                  let q_packed = bitcast<u32>(vec2(q_0, q_1));
+                  for (var k = 0u; k < 4u; k++) {
+                      let q_byte = get_byte_i32(q_packed, k);
+                      let q_val = f16(q_byte) * d;
+                      let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
+                      kv_shmem[row_offset + idx] = q_val;
+                  }
+              }
+          }
+      }
+#elif defined(KV_DIRECT)
+      // Direct global loads for KV
+#else
+      for (var elem_idx = local_id.x; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE) {
+          let v_row = elem_idx / HEAD_DIM_V;
+          let v_col = elem_idx % HEAD_DIM_V;
+          let global_v_row = kv_tile + v_row;
+          let global_v_row_offset = v_head_offset + global_v_row * params.stride_v1;
+          kv_shmem[elem_idx] = f16(select(
+              0.0,
+              V[global_v_row_offset + v_col],
+              global_v_row < params.seq_len_kv && v_col < HEAD_DIM_V));
+      }
+#endif
+
+      workgroupBarrier();
+
+      // we have P (Q_TILE x KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem
+      // we want to compute O += P * V across the full KV tile
+      for (var head_dim_block = subgroup_id * SG_MAT_N;
+           head_dim_block < HEAD_DIM_V;
+           head_dim_block += num_subgroups * SG_MAT_N) {
+              // load O submatrix from shared memory
+              var o_sg_mat: subgroup_matrix_result<f16, SG_MAT_M, SG_MAT_N> = subgroupMatrixLoad<subgroup_matrix_result<f16, SG_MAT_M, SG_MAT_N>>(
+                  &o_shmem,
+                  head_dim_block,
+                  false,
+                  HEAD_DIM_V
+              );
+              for (var kv_block = 0u; kv_block < KV_BLOCKS; kv_block++) {
+                  let p_offset = kv_block * SG_MAT_N;
+                  var p_sg_mat: subgroup_matrix_left<f16, SG_MAT_M, SG_MAT_K> = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_M, SG_MAT_K>>(
+                      &inter_shmem,
+                      p_offset,
+                      false,
+                      KV_TILE
+                  );
+
+                  // load V submatrix from global or shared memory
+#ifdef KV_DIRECT
+                  let v_block_row = kv_tile + kv_block * SG_MAT_N;
+                  let v_global_offset = v_head_offset + v_block_row * params.stride_v1 + head_dim_block;
+                  var v_sg_mat: subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N> = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(
+                      &V,
+                      v_global_offset,
+                      false,
+                      params.stride_v1
+                  );
+#else
+                  let v_block_offset = kv_block * SG_MAT_N * HEAD_DIM_V;
+                  var v_sg_mat: subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N> = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_K, SG_MAT_N>>(
+                      &kv_shmem,
+                      v_block_offset + head_dim_block,
+                      false,
+                      HEAD_DIM_V
+                  );
+#endif
+                  // O += P * V
+                  o_sg_mat = subgroupMatrixMultiplyAccumulate(p_sg_mat, v_sg_mat, o_sg_mat);
+              }
+              // store O back to shared memory
+              subgroupMatrixStore(&o_shmem, head_dim_block, o_sg_mat, false, HEAD_DIM_V);
+      }
+      workgroupBarrier();
+    }
+
+#ifdef SINKS
+    // add sinks (applied once after processing all KV tiles)
+    for (var q_tile_row = subgroup_id;
+         q_tile_row < Q_TILE;
+         q_tile_row += num_subgroups) {
+            // no need to process rows beyond seq_len_q
+            let global_q_row = q_row_start + q_tile_row;
+            if (global_q_row >= params.seq_len_q) {
+                break;
+            }
+
+            var prev_max = row_max_shmem[q_tile_row];
+
+            // for non-sink threads, exp(FLOAT_MIN) effectively zeroes out their contribution to the sum
+            let sink_val = select(FLOAT_MIN, sinks[params.offset_sinks + head_idx], sg_inv_id == 0);
+            let new_max = subgroupMax(max(prev_max, sink_val));
+            let max_exp = exp(prev_max - new_max);
+            let sink_exp = exp(sink_val - new_max);
+
+            let sink_exp_sum = subgroupAdd(sink_exp);
+
+            if (sg_inv_id == 0) {
+                exp_sum_shmem[q_tile_row] = exp_sum_shmem[q_tile_row] * max_exp + sink_exp_sum;
+            }
+
+            for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) {
+                let idx = q_tile_row * HEAD_DIM_V + elem_idx;
+                let val = f32(o_shmem[idx]) * max_exp;
+                o_shmem[idx] = f16(val);
+            }
+    }
+    workgroupBarrier();
+#endif
+    for (var q_tile_row = subgroup_id;
+        q_tile_row < Q_TILE;
+        q_tile_row += num_subgroups) {
+
+        let global_q_row = q_row_start + q_tile_row;
+        if (global_q_row >= params.seq_len_q) { break; }
+
+        let exp_sum = exp_sum_shmem[q_tile_row];
+        let scale = select(0.0, 1.0 / exp_sum, exp_sum != 0.0);
+
+        let row_base: u32 = dst_global_offset + q_tile_row * dst2_stride;
+
+        for (var elem_base = sg_inv_id * 4u;
+            elem_base < HEAD_DIM_V;
+            elem_base += subgroup_size * 4u) {
+
+            let i0 = q_tile_row * HEAD_DIM_V + (elem_base + 0u);
+            let i1 = q_tile_row * HEAD_DIM_V + (elem_base + 1u);
+            let i2 = q_tile_row * HEAD_DIM_V + (elem_base + 2u);
+            let i3 = q_tile_row * HEAD_DIM_V + (elem_base + 3u);
+
+            let v = vec4<f32>(
+                f32(o_shmem[i0]) * scale,
+                f32(o_shmem[i1]) * scale,
+                f32(o_shmem[i2]) * scale,
+                f32(o_shmem[i3]) * scale
+            );
+
+            let dst_vec_index: u32 = (row_base + elem_base) >> 2u;
+            dst[dst_vec_index] = v;
+        }
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.tmpl.wgsl
new file mode 100644
index 0000000..f80ce1f
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.tmpl.wgsl
@@ -0,0 +1,874 @@
+#define(VARIANTS)
+
+[
+  {
+    "SHADER_SUFFIX": "f32_vec",
+    "REPLS": {
+      "TYPE" : "vec4<f32>",
+      "DST_TYPE": "vec4<f32>",
+      "BLOCK_SIZE": 4
+    },
+    "DECLS": ["F32_VEC"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "f32",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 1
+    },
+    "DECLS": ["F32"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "f16",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 1
+    },
+    "DECLS": ["F16"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "i32",
+      "DST_TYPE": "i32",
+      "BLOCK_SIZE": 1
+    },
+    "DECLS": ["I32"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q4_0",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q4_0_T", "Q4_0"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q4_1",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q4_1_T", "Q4_1"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q5_0",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q5_0_T", "Q5_0"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q5_1",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q5_1_T", "Q5_1"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q8_0",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q8_0_T", "Q8_0"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q2_k",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q2_K_T", "Q2_K"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q3_k",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q3_K_T", "Q3_K"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q4_k",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["Q45_K_SCALE_MIN", "BYTE_HELPERS", "Q4_K_T", "Q4_K"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q5_k",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["Q45_K_SCALE_MIN", "BYTE_HELPERS", "Q5_K_T", "Q5_K"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "q6_k",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q6_K_T", "Q6_K"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "iq2_xxs",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_XXS_GRID", "IQ2_XXS_T", "IQ2_XXS"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "iq2_xs",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_XS_GRID", "IQ2_XS_T", "IQ2_XS"]
+  },
+  {
+    "REPLS": {
+      "TYPE": "iq2_s",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_S_GRID", "IQ2_S_T", "IQ2_S"]
+  },
+  {
+    "REPLS": {
+      "TYPE": "iq3_xxs",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ3_XSS_GRID", "IQ3_XSS_T", "IQ3_XSS"]
+  },
+  {
+    "REPLS": {
+      "TYPE": "iq3_s",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ3_S_GRID", "IQ3_S_T", "IQ3_S"]
+  },
+  {
+    "REPLS": {
+      "TYPE": "iq1_s",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ1_GRID", "IQ1_S_T", "IQ1_S"]
+  },
+  {
+    "REPLS": {
+      "TYPE": "iq1_m",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ1_GRID", "IQ1_M_T", "IQ1_M"]
+  },
+  {
+    "REPLS": {
+      "TYPE": "iq4_nl",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 32,
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ4_GRID", "IQ4_NL_T", "IQ4_NL"]
+  },
+  {
+    "REPLS": {
+      "TYPE": "iq4_xs",
+      "DST_TYPE": "f32",
+      "BLOCK_SIZE": 256,
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ4_GRID", "IQ4_XS_T", "IQ4_XS"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(F32_VEC)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    dst[(dst_base / 4) + offset] = src[(src_base / 4) + offset];
+}
+#enddecl(F32_VEC)
+
+#decl(F32)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    dst[dst_base + offset] = src[src_base + offset];
+}
+#enddecl(F32)
+
+#decl(F16)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    dst[dst_base + offset] = f32(src[src_base + offset]);
+}
+#enddecl(F16)
+
+#decl(I32)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    dst[dst_base + offset] = src[src_base + offset];
+}
+#enddecl(I32)
+
+#decl(Q4_0)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_q4_0 = src[src_base + offset];
+    let d = f32(block_q4_0.d);
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q4_0.qs[2 * j], block_q4_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0f) * d;
+            let q_lo = (f32(q_byte & 0xF) - 8.0f) * d;
+            let dst_offset = dst_base + offset * 32 + j * 4 + k;
+            dst[dst_offset] = q_lo;
+            dst[dst_offset + 16] = q_hi;
+        }
+    }
+}
+#enddecl(Q4_0)
+
+#decl(Q4_1)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_q4_1 = src[src_base + offset];
+    let d = f32(block_q4_1.d);
+    let m = f32(block_q4_1.m);
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = block_q4_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let q_hi = f32((q_byte >> 4) & 0xF) * d + m;
+            let q_lo = f32(q_byte & 0xF) * d + m;
+            let dst_offset = dst_base + offset * 32 + j * 4 + k;
+            dst[dst_offset] = q_lo;
+            dst[dst_offset + 16] = q_hi;
+        }
+    }
+}
+#enddecl(Q4_1)
+
+#decl(Q5_0)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_q5_0 = src[src_base + offset];
+    let d = f32(block_q5_0.d);
+    let qh_packed = bitcast<u32>(vec2(block_q5_0.qh[0], block_q5_0.qh[1]));
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q5_0.qs[2 * j], block_q5_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let qh_hi = (qh_packed >> (j * 4 + k + 12)) & 0x10;
+            let q_hi = (f32(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d;
+            let qh_lo = ((qh_packed >> (j * 4 + k)) << 4) & 0x10;
+            let q_lo = (f32((q_byte & 0xF) | qh_lo) - 16.0) * d;
+            let dst_offset = dst_base + offset * 32 + j * 4 + k;
+            dst[dst_offset] = q_lo;
+            dst[dst_offset + 16] = q_hi;
+        }
+    }
+}
+
+#enddecl(Q5_0)
+
+#decl(Q5_1)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_q5_1 = src[src_base + offset];
+    let d = f32(block_q5_1.d);
+    let m = f32(block_q5_1.m);
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = block_q5_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let qh_hi = (block_q5_1.qh >> (j * 4 + k + 12)) & 0x10;
+            let q_hi = f32(((q_byte >> 4) & 0xF) | qh_hi) * d + m;
+            let qh_lo = ((block_q5_1.qh >> (j * 4 + k)) << 4) & 0x10;
+            let q_lo = f32((q_byte & 0xF) | qh_lo) * d + m;
+            let dst_offset = dst_base + offset * 32 + j * 4 + k;
+            dst[dst_offset] = q_lo;
+            dst[dst_offset + 16] = q_hi;
+        }
+    }
+}
+#enddecl(Q5_1)
+
+#decl(Q8_0)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_q8_0 = src[src_base + offset];
+    let d = f32(block_q8_0.d);
+    for (var j: u32 = 0; j < 8; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q8_0.qs[2 * j], block_q8_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte_i32(q_packed, k);
+            let q_val = f32(q_byte) * d;
+            let dst_offset = dst_base + offset * 32 + j * 4 + k;
+            dst[dst_offset] = q_val;
+        }
+    }
+}
+#enddecl(Q8_0)
+
+#decl(Q2_K)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var dst_i = dst_base + offset * 256;
+    var is: u32 = 0;
+    // 2 halves of the block (128 elements each)
+    for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) {
+        // 4 groups (each group has 2 blocks of 16 elements)
+        for (var shift: u32 = 0; shift < 8; shift += 2) {
+            // 2 blocks
+            for (var k: u32 = 0; k < 32; k += 16) {
+                let sc = get_byte(block.scales[is / 4], is % 4);
+                is++;
+                let dl = d * f32(sc & 0xF);
+                let ml = m * f32(sc >> 4);
+                for (var l: u32 = 0u; l < 16; l++) {
+                    let q_idx = q_b_idx + k + l;
+                    let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                    let qs_val = (q_byte >> shift) & 3;
+                    dst[dst_i] = (f32(qs_val) * dl - ml);
+                    dst_i++;
+                }
+            }
+        }
+    }
+}
+#enddecl(Q2_K)
+
+#decl(Q3_K)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+
+    // extract 6-bit scales, which consist of 4-bits from first 8 bytes of scale,
+    // and 2-bits from the last 4 bytes
+    let kmask1: u32 = 0x03030303;
+    let kmask2: u32 = 0x0f0f0f0f;
+    var scale_vals: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        scale_vals[i] = bitcast<u32>(vec2(block.scales[2 * i], block.scales[2 * i + 1]));
+    }
+    var tmp: u32 = scale_vals[2];
+    scale_vals[2] = ((scale_vals[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+    scale_vals[3] = ((scale_vals[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+    scale_vals[0] = (scale_vals[0] & kmask2) | ((tmp & kmask1) << 4);
+    scale_vals[1] = (scale_vals[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+
+    // convert arrays of f16 -> u32
+    var hmask_vals: array<u32, 8>;
+    for (var i: u32 = 0; i < 8; i++) {
+        hmask_vals[i] = bitcast<u32>(vec2(block.hmask[2 * i], block.hmask[2 * i + 1]));
+    }
+    var qs_vals: array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qs_vals[i] = bitcast<u32>(vec2(block.qs[2 * i], block.qs[2 * i + 1]));
+    }
+
+    var dst_i = dst_base + offset * 256;
+    var is: u32 = 0;
+    var m: u32 = 1;
+    // 2 halves of the block (128 elements each)
+    for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) {
+        // 4 groups (each group has 2 blocks of 16 elements)
+        for (var shift: u32 = 0; shift < 8; shift += 2) {
+            // 2 blocks
+            for (var k: u32 = 0; k < 32; k += 16) {
+                let sc = get_byte(scale_vals[is / 4], is % 4);
+                is++;
+                let dl = d * (f32(sc) - 32.0);
+                for (var l: u32 = 0u; l < 16u; l++) {
+                    let q_idx = q_b_idx + k + l;
+                    let hm_idx = k + l;
+                    let q_byte = get_byte(qs_vals[q_idx / 4], q_idx % 4);
+                    let hmask_byte = get_byte(hmask_vals[hm_idx / 4], hm_idx % 4);
+                    let hm = select(4.0, 0.0, (hmask_byte & m) != 0);
+                    let qs_val = (q_byte >> shift) & 3;
+                    dst[dst_i] = (f32(qs_val) - hm) * dl;
+                    dst_i++;
+                }
+            }
+            m <<= 1;
+        }
+    }
+}
+#enddecl(Q3_K)
+
+#decl(Q4_K)
+// 8 blocks of 32 elements each
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var dst_i = dst_base + offset * 256;
+    var is: u32 = 0;
+    // 2 blocks each iteration
+    for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) {
+        for (var shift: u32 = 0; shift < 8; shift += 4) {
+            let scale_min = get_scale_min(is, block.scales);
+            is++;
+            let dl = d * scale_min.x;
+            let ml = m * scale_min.y;
+            for (var l: u32 = 0; l < 32; l++) {
+                let q_idx = q_b_idx + l;
+                let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                let qs_val = (q_byte >> shift) & 0xF;
+                dst[dst_i] = (f32(qs_val) * dl - ml);
+                dst_i++;
+            }
+        }
+    }
+}
+#enddecl(Q4_K)
+
+#decl(Q5_K)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var dst_i = dst_base + offset * 256;
+    var is: u32 = 0;
+    var u: u32 = 1;
+    // 2 blocks each iteration
+    for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) {
+        for (var shift: u32 = 0; shift < 8; shift += 4) {
+            let scale_min = get_scale_min(is, block.scales);
+            is++;
+            let dl = d * scale_min.x;
+            let ml = m * scale_min.y;
+            for (var l: u32 = 0; l < 32; l++) {
+                let q_idx = q_b_idx + l;
+                let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                let qh_byte = get_byte(block.qh[l / 4], l % 4);
+                let qs_val = (q_byte >> shift) & 0xF;
+                let qh_val = select(0.0, 16.0, (qh_byte & u) != 0);
+                dst[dst_i] = (f32(qs_val) + qh_val) * dl - ml;
+                dst_i++;
+            }
+            u <<= 1;
+        }
+    }
+}
+#enddecl(Q5_K)
+
+#decl(Q6_K)
+// 16 blocks of 16 elements each
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+
+    // convert arrays of f16 -> u32
+    var ql_vals: array<u32, 32>;
+    for (var i: u32 = 0; i < 32; i++) {
+        ql_vals[i] = bitcast<u32>(vec2(block.ql[2 * i], block.ql[2 * i + 1]));
+    }
+    var qh_vals: array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qh_vals[i] = bitcast<u32>(vec2(block.qh[2 * i], block.qh[2 * i + 1]));
+    }
+    var scale_vals: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        scale_vals[i] = bitcast<u32>(vec2(block.scales[2 * i], block.scales[2 * i + 1]));
+    }
+
+    var dst_i = dst_base + offset * 256;
+    var qh_b_idx: u32 = 0;
+    var sc_b_idx: u32 = 0;
+    for (var ql_b_idx: u32 = 0; ql_b_idx < 128; ql_b_idx += 64) {
+        for (var l: u32 = 0; l < 32; l++) {
+            let ql13_b = get_byte(ql_vals[(ql_b_idx + l) / 4], (ql_b_idx + l) % 4);
+            let ql24_b = get_byte(ql_vals[(ql_b_idx + l + 32) / 4], (ql_b_idx + l + 32) % 4);
+            let qh_b = get_byte(qh_vals[(qh_b_idx + l) / 4], (qh_b_idx + l) % 4);
+
+            let q1 = f32((ql13_b & 0xF) | ((qh_b & 3) << 4)) - 32.0;
+            let q2 = f32((ql24_b & 0xF) | (((qh_b >> 2) & 3) << 4)) - 32.0;
+            let q3 = f32((ql13_b >> 4) | (((qh_b >> 4) & 3) << 4)) - 32.0;
+            let q4 = f32((ql24_b >> 4) | (((qh_b >> 6) & 3) << 4)) - 32.0;
+
+            let is = l/16;
+            let is1 = sc_b_idx + is;
+            let sc1 = get_byte_i32(scale_vals[is1 / 4], is1 % 4);
+            let is2 = sc_b_idx + is + 2;
+            let sc2 = get_byte_i32(scale_vals[is2 / 4], is2 % 4);
+            let is3 = sc_b_idx + is + 4;
+            let sc3 = get_byte_i32(scale_vals[is3 / 4], is3 % 4);
+            let is4 = sc_b_idx + is + 6;
+            let sc4 = get_byte_i32(scale_vals[is4 / 4], is4 % 4);
+
+            dst[dst_i + l] = (q1 * f32(sc1)) * d;
+            dst[dst_i + l + 32] = (q2 * f32(sc2)) * d;
+            dst[dst_i + l + 64] = (q3 * f32(sc3)) * d;
+            dst[dst_i + l + 96] = (q4 * f32(sc4)) * d;
+        }
+        dst_i += 128;
+        qh_b_idx += 32;
+        sc_b_idx += 8;
+    }
+}
+
+#enddecl(Q6_K)
+
+#decl(IQ2_XXS)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    var dst_i = dst_base + offset * 256;
+    for (var ib: u32 = 0; ib < 32; ib += 4) {
+        let aux0 = bitcast<u32>(vec2(block.qs[ib], block.qs[ib + 1]));
+        let aux1 = bitcast<u32>(vec2(block.qs[ib + 2], block.qs[ib + 3]));
+        let db = d * (0.5 + f32(aux1 >> 28)) * 0.25;
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = get_byte(aux0, l) * 8;
+            let is = (aux1 >> (7 * l)) & 127;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2xxs_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                dst[dst_i] = db * f32(g) * m;
+                dst_i++;
+            }
+        }
+    }
+}
+#enddecl(IQ2_XXS)
+
+#decl(IQ2_XS)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    var dst_i = dst_base + offset * 256;
+    var scale_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.scales[0], block.scales[1])),
+        bitcast<u32>(vec2(block.scales[2], block.scales[3]))
+    );
+    for (var ib: u32 = 0; ib < 32; ib += 4) {
+        let s = get_byte(scale_vals[ib / 16], (ib % 16) / 4);
+        let db = array<f32, 2>(
+            d * (0.5 + f32(s & 0xF)) * 0.25,
+            d * (0.5 + f32(s >> 4)) * 0.25
+        );
+        for (var l: u32 = 0; l < 4; l++) {
+            let qs_val = bitcast<u32>(vec2(block.qs[ib + l], 0.0));
+            let ig = (qs_val & 511) * 8;
+            let is = qs_val >> 9;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            let dl = db[l/2];
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2xs_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                dst[dst_i] = dl * f32(g) * m;
+                dst_i++;
+            }
+        }
+    }
+}
+#enddecl(IQ2_XS)
+
+#decl(IQ2_S)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    var dst_i = dst_base + offset * 256;
+    var qs_vals : array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qs_vals[i] = bitcast<u32>(vec2(block.qs[i * 2], block.qs[i * 2 + 1]));
+    }
+    var qh_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.qh[0], block.qh[1])),
+        bitcast<u32>(vec2(block.qh[2], block.qh[3]))
+    );
+    var scale_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.scales[0], block.scales[1])),
+        bitcast<u32>(vec2(block.scales[2], block.scales[3]))
+    );
+    for (var ib: u32 = 0; ib < 8; ib ++) {
+        let s = get_byte(scale_vals[ib / 4], ib % 4);
+        let db = array<f32, 2>(
+            d * (0.5 + f32(s & 0xF)) * 0.25,
+            d * (0.5 + f32(s >> 4)) * 0.25
+        );
+        let qs_w = qs_vals[ib];
+        for (var l: u32 = 0; l < 4; l++) {
+            let qh_b = (get_byte(qh_vals[ib / 4], ib % 4) << (8 - 2 * l)) & 0x300;
+            let ig = (get_byte(qs_w, l) | qh_b) * 8;
+            let signs = get_byte(qs_vals[ib + 8], l);
+            let dl = db[l/2];
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2s_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                dst[dst_i] = dl * f32(g) * m;
+                dst_i++;
+            }
+        }
+    }
+}
+
+#enddecl(IQ2_S)
+
+#decl(IQ3_XSS)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    var dst_i = dst_base + offset * 256;
+    for (var ib: u32 = 0; ib < 16; ib += 2) {
+        let sc_sign = bitcast<u32>(vec2(block.qs[ib + 32], block.qs[ib + 33]));
+        let db = d * (0.5 + f32(sc_sign >> 28)) * 0.5;
+        for (var l: u32 = 0; l < 4; l++) {
+            let is = (sc_sign >> (7 * l)) & 127;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            let ig_val = bitcast<u32>(vec2(block.qs[ib * 2 + l], 0.0));
+            let ig1 = get_byte(ig_val, 0);
+            let ig2 = get_byte(ig_val, 1);
+            for (var j: u32 = 0; j < 4; j++) {
+                let g1 = get_byte(iq3xxs_grid[ig1], j);
+                let g2 = get_byte(iq3xxs_grid[ig2], j);
+                let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0);
+                let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0);
+                dst[dst_i] = db * f32(g1) * m1;
+                dst[dst_i + 4] = db * f32(g2) * m2;
+                dst_i++;
+            }
+            dst_i += 4;
+        }
+    }
+}
+#enddecl(IQ3_XSS)
+
+#decl(IQ3_S)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    var dst_i = dst_base + offset * 256;
+    var qh_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.qh[0], block.qh[1])),
+        bitcast<u32>(vec2(block.qh[2], block.qh[3]))
+    );
+    var sign_vals: array<u32, 8>;
+    for (var i: u32 = 0; i < 8; i++) {
+        sign_vals[i] = bitcast<u32>(vec2(block.signs[i * 2], block.signs[i * 2 + 1]));
+    }
+    var scale_vals = bitcast<u32>(vec2(block.scales[0], block.scales[1]));
+    for (var ib: u32 = 0; ib < 4; ib++) {
+        let s = get_byte(scale_vals, ib);
+        let db = array<f32, 2>(
+            d * (1.0 + 2.0 * f32(s & 0xF)),
+            d * (1.0 + 2.0 * f32(s >> 4))
+        );
+        for (var k: u32 = 0; k < 2; k++) {
+            let dl = db[k];
+            let qh_byte = get_byte(qh_vals[ib / 2], (ib % 2) * 2 + k);
+            let sign_w = sign_vals[ib * 2 + k];
+            for (var l: u32 = 0; l < 4; l++) {
+                let signs = get_byte(sign_w, l);
+                let ig_val = bitcast<u32>(vec2(block.qs[ib * 8 + k * 4 + l], 0.0));
+                let ig1 = get_byte(ig_val, 0) | ((qh_byte << ((8 - (2 * l)))) & 256);
+                let ig2 = get_byte(ig_val, 1) | ((qh_byte << ((7 - (2 * l)))) & 256);
+                for (var j: u32 = 0; j < 4; j++) {
+                    let g1 = get_byte(iq3s_grid[ig1], j);
+                    let g2 = get_byte(iq3s_grid[ig2], j);
+                    let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0);
+                    let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0);
+                    dst[dst_i] = dl * f32(g1) * m1;
+                    dst[dst_i + 4] = dl * f32(g2) * m2;
+                    dst_i++;
+                }
+                dst_i += 4;
+            }
+        }
+    }
+}
+#enddecl(IQ3_S)
+
+#decl(IQ1_S)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    var dst_i = dst_base + offset * 256;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let qh = bitcast<u32>(vec2(block.qh[ib], 0.0));
+        let dl = d * (2 * f32((qh >> 12) & 7) + 1);
+        let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000) != 0);
+        let qs_w = bitcast<u32>(vec2(block.qs[ib * 2], block.qs[ib * 2 + 1]));
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = (get_byte(qs_w, l) | (((qh >> (3 * l)) & 7) << 8)) * 8;
+            for (var j: u32 = 0; j < 8; j++) {
+                let gw = iq1_grid[(ig + j) / 16];
+                let g = (gw >> (((ig + j) % 16) * 2)) & 3;
+                let gs = bitcast<i32>(g << 30) >> 30;
+                dst[dst_i] = dl * (f32(gs) + delta);
+                dst_i++;
+            }
+        }
+    }
+}
+
+#enddecl(IQ1_S)
+
+#decl(IQ1_M)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+
+    let scale = ((block.scales[0] >> 12) & 0xF) | ((block.scales[0] >> 24) & 0x00F0) | ((block.scales[1] >> 4) & 0x0F00) | ((block.scales[1] >> 16) & 0xF000);
+    let d = f32(bitcast<vec2<f16>>(scale).x);
+    var dst_i = dst_base + offset * 256;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let sw = (block.scales[ib / 4] >> (16 * ((ib / 2) % 2))) & 0xFFFF;
+        let s1 : u32 = (sw >> (6 * (ib % 2))) & 0x7;
+        let s2 : u32 = (sw >> (6 * (ib % 2) + 3)) & 0x7;
+        var dl = array<f32, 2>(
+            d * f32(2 * s1 + 1),
+            d * f32(2 * s2 + 1)
+        );
+
+        let qh = block.qh[ib / 2] >> (16 * (ib % 2));
+        var idx = array<u32, 4>(
+            get_byte(block.qs[ib], 0) | ((qh << 8) & 0x700),
+            get_byte(block.qs[ib], 1) | ((qh << 4) & 0x700),
+            get_byte(block.qs[ib], 2) | ((qh) & 0x700),
+            get_byte(block.qs[ib], 3) | ((qh >> 4) & 0x700)
+        );
+        var delta = array<f32, 4>(
+            select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x08) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x80) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x08) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x80) != 0)
+        );
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = idx[l] * 8;
+            for (var j: u32 = 0; j < 8; j++) {
+                let gw = iq1_grid[(ig + j) / 16];
+                let g = (gw >> (((ig + j) % 16) * 2)) & 3;
+                let gs = bitcast<i32>(g << 30) >> 30;
+                dst[dst_i] = dl[l/2] * (f32(gs) + delta[l]);
+                dst_i++;
+            }
+        }
+    }
+}
+
+#enddecl(IQ1_M)
+
+#decl(IQ4_NL)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    var dst_i = dst_base + offset * 32;
+    var qs: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        qs[i] = bitcast<u32>(vec2(block.qs[i * 2], block.qs[i * 2 + 1]));
+    }
+    for (var j: u32 = 0; j < 16; j++) {
+        let qsb = get_byte(qs[j / 4], j % 4);
+        dst[dst_i] = d * f32(kvalues_iq4nl[qsb & 0xF]);
+        dst[dst_i + 16] = d * f32(kvalues_iq4nl[qsb >> 4]);
+        dst_i++;
+    }
+}
+#enddecl(IQ4_NL)
+
+#decl(IQ4_XS)
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block = src[src_base + offset];
+    let d = f32(block.d);
+    let scales_h = bitcast<u32>(vec2(block.scales_h, 0.0));
+    var dst_i = dst_base + offset * 256;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let ls = ((get_byte(block.scales_l, ib / 2) >> (4 * (ib % 2))) & 0xF) | (((scales_h >> (2 * ib)) & 3) << 4);
+        let dl = d * (f32(ls) - 32.0);
+        for (var j: u32 = 0; j < 16; j++) {
+            let iqs = ib * 16 + j;
+            let qsb = get_byte(block.qs[iqs / 4], iqs % 4);
+            dst[dst_i] = dl * f32(kvalues_iq4nl[qsb & 0xF]);
+            dst[dst_i + 16] = dl * f32(kvalues_iq4nl[qsb >> 4]);
+            dst_i++;
+        }
+        dst_i += 16;
+    }
+}
+#enddecl(IQ4_XS)
+
+#end(DECLS)
+
+#define(SHADER)
+
+enable f16;
+
+DECLS
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<{{TYPE}}>;
+
+@group(0) @binding(1)
+var<storage, read_write> idx: array<i32>;
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<{{DST_TYPE}}>;
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_idx: u32, // in elements
+    offset_dst: u32, // in elements
+
+    // Strides (in elements)
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_idx0: u32,
+    stride_idx1: u32,
+    stride_idx2: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // Shape of dst
+    ne0: u32,
+    n_rows: u32,
+    ne2: u32,
+    ne3: u32,
+
+    // Shape of idx
+    idx1: u32,
+    idx2: u32,
+};
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+override wg_size: u32;
+@compute @workgroup_size(wg_size)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= params.n_rows * params.ne2 * params.ne3) {
+        return;
+    }
+    var i = gid.x;
+    let i_dst3 = i / (params.ne2 * params.n_rows);
+
+    i = i % (params.ne2 * params.n_rows);
+    let i_dst2 = i / params.n_rows;
+    let i_dst1 = i % params.n_rows;
+
+    let i_idx2 = i_dst3 % params.idx2;
+    let i_idx1 = i_dst2 % params.idx1;
+    let i_idx0 = i_dst1;
+
+    let i_idx = params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2;
+
+    let idx_val = u32(idx[i_idx]);
+
+    let i_src_row = params.offset_src + idx_val * params.stride_src1 + i_dst2 * params.stride_src2 + i_dst3 * params.stride_src3;
+    let i_dst_row = params.offset_dst + i_dst1 * params.stride_dst1 + i_dst2 * params.stride_dst2 + i_dst3 * params.stride_dst3;
+
+    for (var i: u32 = 0; i < params.ne0/{{BLOCK_SIZE}}; i++) {
+      copy_elements(i_src_row, i_dst_row, i);
+    }
+}
+
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/glu.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/glu.tmpl.wgsl
new file mode 100644
index 0000000..03fcd54
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/glu.tmpl.wgsl
@@ -0,0 +1,323 @@
+#define(VARIANTS)
+
+[
+  {
+    "SHADER_NAME": "reglu_f32",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_SPLIT", "REGLU"]
+  },
+  {
+    "SHADER_NAME": "reglu_f32_split",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["SPLIT", "REGLU"]
+  },
+  {
+    "SHADER_NAME": "reglu_f16",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["NO_SPLIT", "REGLU"]
+  },
+  {
+    "SHADER_NAME": "reglu_f16_split",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["SPLIT", "REGLU"]
+  },
+  {
+    "SHADER_NAME": "geglu_f32",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_SPLIT", "GEGLU"]
+  },
+  {
+    "SHADER_NAME": "geglu_f32_split",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["SPLIT", "GEGLU"]
+  },
+  {
+    "SHADER_NAME": "geglu_f16",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["NO_SPLIT", "GEGLU"]
+  },
+  {
+    "SHADER_NAME": "geglu_f16_split",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["SPLIT", "GEGLU"]
+  },
+  {
+    "SHADER_NAME": "swiglu_f32",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_SPLIT", "SWIGLU"]
+  },
+  {
+    "SHADER_NAME": "swiglu_f32_split",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["SPLIT", "SWIGLU"]
+  },
+  {
+    "SHADER_NAME": "swiglu_f16",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["NO_SPLIT", "SWIGLU"]
+  },
+  {
+    "SHADER_NAME": "swiglu_f16_split",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["SPLIT", "SWIGLU"]
+  },
+  {
+    "SHADER_NAME": "swiglu_oai_f32",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_SPLIT", "SWIGLU_OAI"]
+  },
+  {
+    "SHADER_NAME": "swiglu_oai_f32_split",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["SPLIT", "SWIGLU_OAI"]
+  },
+  {
+    "SHADER_NAME": "geglu_erf_f32",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_SPLIT", "GEGLU_ERF"]
+  },
+  {
+    "SHADER_NAME": "geglu_erf_f32_split",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["SPLIT", "GEGLU_ERF"]
+  },
+  {
+    "SHADER_NAME": "geglu_erf_f16",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["NO_SPLIT", "GEGLU_ERF"]
+  },
+  {
+    "SHADER_NAME": "geglu_erf_f16_split",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["SPLIT", "GEGLU_ERF"]
+  },
+  {
+    "SHADER_NAME": "geglu_quick_f32",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_SPLIT", "GEGLU_QUICK"]
+  },
+  {
+    "SHADER_NAME": "geglu_quick_f32_split",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["SPLIT", "GEGLU_QUICK"]
+  },
+  {
+    "SHADER_NAME": "geglu_quick_f16",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["NO_SPLIT", "GEGLU_QUICK"]
+  },
+  {
+    "SHADER_NAME": "geglu_quick_f16_split",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["SPLIT", "GEGLU_QUICK"]
+  },
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(REGLU)
+fn op(a: {{TYPE}}, b: {{TYPE}}) -> {{TYPE}} {
+    return max(a, 0) * b;
+}
+#enddecl(REGLU)
+
+#decl(GEGLU)
+const SQRT_2_OVER_PI: {{TYPE}} = 0.79788456080286535587989211986876;
+const GELU_COEF_A: {{TYPE}} = 0.044715;
+
+fn op(a: {{TYPE}}, b: {{TYPE}}) -> {{TYPE}} {
+    let val = SQRT_2_OVER_PI * a * (1.0 + GELU_COEF_A * a * a);
+    return 0.5 * a * (2.0 - 2.0 / (exp(2 * val) + 1)) * b;
+}
+#enddecl(GEGLU)
+
+#decl(SWIGLU)
+fn op(a: {{TYPE}}, b: {{TYPE}}) -> {{TYPE}} {
+    return a / (1.0 + exp(-a)) * b;
+}
+#enddecl(SWIGLU)
+
+#decl(SWIGLU_OAI)
+fn op(a: f32, b: f32) -> f32 {
+  let xi = min(a, params.limit);
+  let gi = max(min(b, params.limit), -params.limit);
+  var out_glu = xi / (1.0 + exp(-xi * params.alpha));
+  out_glu = out_glu * (1.0 + gi);
+  return out_glu;
+}
+#enddecl(SWIGLU_OAI)
+
+#decl(GEGLU_ERF)
+const p_erf: {{TYPE}} = 0.3275911;
+const a1_erf: {{TYPE}} = 0.254829592;
+const a2_erf: {{TYPE}} = -0.284496736;
+const a3_erf: {{TYPE}} = 1.421413741;
+const a4_erf: {{TYPE}} = -1.453152027;
+const a5_erf: {{TYPE}} = 1.061405429;
+const SQRT_2_INV: {{TYPE}} = 0.7071067811865476;
+
+fn op(a: {{TYPE}}, b: {{TYPE}}) -> {{TYPE}} {
+  let a_div_sqr2 = a * SQRT_2_INV;
+  let sign_x = sign(a_div_sqr2);
+  let x = abs(a_div_sqr2);
+  let t = 1.0 / (1.0 + p_erf * x);
+  let y = 1.0 - (((((a5_erf * t + a4_erf) * t + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x));
+  let erf_approx = sign_x * y;
+  return 0.5 * a * (1.0 + erf_approx) * b;
+}
+#enddecl(GEGLU_ERF)
+
+#decl(GEGLU_QUICK)
+const GELU_QUICK_COEF: {{TYPE}} = -1.702;
+
+fn op(a: {{TYPE}}, b: {{TYPE}}) -> {{TYPE}} {
+    return a * (1.0 / (1.0 + exp(GELU_QUICK_COEF * a))) * b;
+}
+#enddecl(GEGLU_QUICK)
+
+#decl(NO_SPLIT)
+@group(0) @binding(1)
+var<storage, read_write> dst: array<{{TYPE}}>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+fn a_value(base: u32) -> {{TYPE}} {
+    let offset: u32 = select(0, params.ne0, params.swapped != 0);
+    return src0[base + offset];
+}
+
+fn b_value(base: u32) -> {{TYPE}} {
+    let offset: u32 = select(params.ne0, 0, params.swapped != 0);
+    return src0[base + offset];
+}
+#enddecl(NO_SPLIT)
+
+#decl(SPLIT)
+@group(0) @binding(1)
+var<storage, read_write> src1: array<{{TYPE}}>;
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<{{TYPE}}>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+fn a_value(base: u32) -> {{TYPE}} {
+    return src0[base];
+}
+
+fn b_value(base: u32) -> {{TYPE}} {
+    return src1[base];
+}
+#enddecl(SPLIT)
+
+#end(DECLS)
+
+#define(SHADER)
+
+enable f16;
+
+struct Params {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+
+    // Strides (in elements)
+    stride_src01: u32,
+    stride_src02: u32,
+    stride_src03: u32,
+
+    stride_src11: u32,
+    stride_src12: u32,
+    stride_src13: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // shape of dst
+    ne: u32,
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+
+    swapped: u32,
+    alpha: f32,
+    limit: f32,
+}
+
+@group(0) @binding(0)
+var<storage, read_write> src0: array<{{TYPE}}>;
+
+DECLS
+
+override wg_size: u32;
+@compute @workgroup_size(wg_size)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= params.ne) {
+        return;
+    }
+
+    var i = gid.x;
+    let i3 = i / (params.ne2 * params.ne1 * params.ne0);
+    i = i % (params.ne2 * params.ne1 * params.ne0);
+    let i2 = i / (params.ne1 * params.ne0);
+    i = i % (params.ne1 * params.ne0);
+    let i1 = i / params.ne0;
+    let i0 = i % params.ne0;
+
+    let i_a = params.offset_src0 + i3 * params.stride_src03 + i2 * params.stride_src02 + i1 * params.stride_src01 + i0;
+    let i_b = params.offset_src1 + i3 * params.stride_src13 + i2 * params.stride_src12 + i1 * params.stride_src11 + i0;
+    let i_dst = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1 + i0;
+
+    dst[i_dst] = op(a_value(i_a), b_value(i_b));
+}
+
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl
new file mode 100644
index 0000000..194d2d6
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl
@@ -0,0 +1,40 @@
+@group(0) @binding(0)
+var<storage, read_write> output_buffer: array<u32>;
+
+struct Params {
+    offset: u32, // in bytes
+    size: u32,   // in bytes
+    value: u32,  // 4 8-bit values, which are either repeating (memset_tensor) or may be separate (cleaning up unaligned set_tensor operations)
+};
+
+@group(0) @binding(1)
+var<uniform> params: Params;
+
+override wg_size: u32;
+override bytes_per_thread: u32;
+
+@compute @workgroup_size(wg_size)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    let i = gid.x * bytes_per_thread;
+    let start = params.offset;
+    let end = params.offset + params.size;
+
+    for (var j: u32 = 0u; j < bytes_per_thread; j += 4) {
+        let byte_index = start + i + j;
+        if (byte_index + 4 <= end) {
+            output_buffer[byte_index >> 2] = params.value;
+        } else {
+            // Handle tail (unaligned)
+            for (var k: u32 = 0; k < 4; k++) {
+                let idx = byte_index + k;
+                if (idx < end) {
+                    let word_idx = idx >> 2;
+                    let bit_offset = (idx & 3) * 8u;
+                    let mask = ~(0xffu << bit_offset);
+                    let existing = output_buffer[word_idx];
+                    output_buffer[word_idx] = (existing & mask) | (params.value & (0xffu << bit_offset));
+                }
+            }
+        }
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl
new file mode 100644
index 0000000..0f8e6e5
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl
@@ -0,0 +1,907 @@
+#define(VARIANTS)
+
+[
+  {
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "BLOCK_SIZE" : 1
+    },
+    "DECLS" : ["FLOAT"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "BLOCK_SIZE" : 1
+    },
+    "DECLS" : ["FLOAT"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "BLOCK_SIZE" : 1
+    },
+    "DECLS" : ["FLOAT"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q4_0",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q4_0_T", "Q4_0"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q4_1",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q4_1_T", "Q4_1"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q5_0",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q5_0_T", "Q5_0"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q5_1",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q5_1_T", "Q5_1"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q8_0",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q8_0_T", "Q8_0"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q2_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q2_K_T", "Q2_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q3_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q3_K_T", "Q3_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q4_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["Q45_K_SCALE_MIN", "BYTE_HELPERS", "Q4_K_T", "Q4_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q5_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["Q45_K_SCALE_MIN", "BYTE_HELPERS", "Q5_K_T", "Q5_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q6_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q6_K_T", "Q6_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq2_xxs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_XXS_GRID", "IQ2_XXS_T", "IQ2_XXS"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq2_xs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_XS_GRID", "IQ2_XS_T", "IQ2_XS"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq2_s",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_S_GRID", "IQ2_S_T", "IQ2_S"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq3_xxs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ3_XSS_GRID", "IQ3_XSS_T", "IQ3_XSS"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq3_s",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ3_S_GRID", "IQ3_S_T", "IQ3_S"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq1_s",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ1_GRID", "IQ1_S_T", "IQ1_S"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq1_m",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ1_GRID", "IQ1_M_T", "IQ1_M"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq4_nl",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32,
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ4_GRID", "IQ4_NL_T", "IQ4_NL"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq4_xs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256,
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ4_GRID", "IQ4_XS_T", "IQ4_XS"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(FLOAT)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    return f32(src0[src0_idx_base + offset]) * f32(src1[src1_idx_base + offset]);
+}
+#enddecl(FLOAT)
+
+#decl(Q4_0)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q4_0 = src0[src0_idx_base + offset];
+    let d = f32(block_q4_0.d);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q4_0.qs[2 * j], block_q4_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0f) * d;
+            let q_lo = (f32(q_byte & 0xF) - 8.0f) * d;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q4_0)
+
+#decl(Q4_1)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q4_1 = src0[src0_idx_base + offset];
+    let d = f32(block_q4_1.d);
+    let m = f32(block_q4_1.m);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = block_q4_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let q_hi = f32((q_byte >> 4) & 0xF) * d + m;
+            let q_lo = f32(q_byte & 0xF) * d + m;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q4_1)
+
+#decl(Q5_0)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q5_0 = src0[src0_idx_base + offset];
+    let d = f32(block_q5_0.d);
+    var sum: f32 = 0.0;
+    let qh_packed = bitcast<u32>(vec2(block_q5_0.qh[0], block_q5_0.qh[1]));
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q5_0.qs[2 * j], block_q5_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let qh_hi = (qh_packed >> (j * 4 + k + 12)) & 0x10;
+            let q_hi = (f32(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d;
+            let qh_lo = ((qh_packed >> (j * 4 + k)) << 4) & 0x10;
+            let q_lo = (f32((q_byte & 0xF) | qh_lo) - 16.0) * d;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q5_0)
+
+#decl(Q5_1)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q5_1 = src0[src0_idx_base + offset];
+    let d = f32(block_q5_1.d);
+    let m = f32(block_q5_1.m);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = block_q5_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let qh_hi = (block_q5_1.qh >> (j * 4 + k + 12)) & 0x10;
+            let q_hi = f32(((q_byte >> 4) & 0xF) | qh_hi) * d + m;
+            let qh_lo = ((block_q5_1.qh >> (j * 4 + k)) << 4) & 0x10;
+            let q_lo = f32((q_byte & 0xF) | qh_lo) * d + m;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q5_1)
+
+#decl(Q8_0)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q8_0 = src0[src0_idx_base + offset];
+    let d = f32(block_q8_0.d);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 8; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q8_0.qs[2 * j], block_q8_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte_i32(q_packed, k);
+            let q_val = f32(q_byte) * d;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_val * f32(src1[src1_offset]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q8_0)
+
+#decl(Q8_1)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q8_1 = src0[src0_idx_base + offset];
+    let d = f32(block_q8_1.d);
+    let m = f32(block_q8_1.m);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 8; j++) {
+        let q_packed = block_q8_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte_i32(q_packed, k);
+            let q_val = f32(q_byte) * d + m;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_val * f32(src1[src1_offset]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q8_1)
+
+#decl(Q2_K)
+// 16 blocks of 16 elements each
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    // 2 halves of the block (128 elements each)
+    for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) {
+        // 4 groups (each group has 2 blocks of 16 elements)
+        for (var shift: u32 = 0; shift < 8; shift += 2) {
+            // 2 blocks
+            for (var k: u32 = 0; k < 32; k += 16) {
+                let sc = get_byte(block.scales[is / 4], is % 4);
+                is++;
+                let dl = d * f32(sc & 0xF);
+                let ml = m * f32(sc >> 4);
+                for (var l: u32 = 0u; l < 16; l++) {
+                    let q_idx = q_b_idx + k + l;
+                    let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                    let qs_val = (q_byte >> shift) & 3;
+                    sum += (f32(qs_val) * dl - ml) * src1[src1_i];
+                    src1_i++;
+                }
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q2_K)
+
+#decl(Q3_K)
+// 16 blocks of 16 elements each
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+
+    // extract 6-bit scales, which consist of 4-bits from first 8 bytes of scale,
+    // and 2-bits from the last 4 bytes
+    let kmask1: u32 = 0x03030303;
+    let kmask2: u32 = 0x0f0f0f0f;
+    var scale_vals: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        scale_vals[i] = bitcast<u32>(vec2(block.scales[2 * i], block.scales[2 * i + 1]));
+    }
+    var tmp: u32 = scale_vals[2];
+    scale_vals[2] = ((scale_vals[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+    scale_vals[3] = ((scale_vals[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+    scale_vals[0] = (scale_vals[0] & kmask2) | ((tmp & kmask1) << 4);
+    scale_vals[1] = (scale_vals[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+
+    // convert arrays of f16 -> u32
+    var hmask_vals: array<u32, 8>;
+    for (var i: u32 = 0; i < 8; i++) {
+        hmask_vals[i] = bitcast<u32>(vec2(block.hmask[2 * i], block.hmask[2 * i + 1]));
+    }
+    var qs_vals: array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qs_vals[i] = bitcast<u32>(vec2(block.qs[2 * i], block.qs[2 * i + 1]));
+    }
+
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    var m: u32 = 1;
+    // 2 halves of the block (128 elements each)
+    for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) {
+        // 4 groups (each group has 2 blocks of 16 elements)
+        for (var shift: u32 = 0; shift < 8; shift += 2) {
+            // 2 blocks
+            for (var k: u32 = 0; k < 32; k += 16) {
+                let sc = get_byte(scale_vals[is / 4], is % 4);
+                is++;
+                let dl = d * (f32(sc) - 32.0);
+                for (var l: u32 = 0u; l < 16u; l++) {
+                    let q_idx = q_b_idx + k + l;
+                    let hm_idx = k + l;
+                    let q_byte = get_byte(qs_vals[q_idx / 4], q_idx % 4);
+                    let hmask_byte = get_byte(hmask_vals[hm_idx / 4], hm_idx % 4);
+                    let hm = select(4.0, 0.0, (hmask_byte & m) != 0);
+                    let qs_val = (q_byte >> shift) & 3;
+                    sum += ((f32(qs_val) - hm) * dl) * src1[src1_i];
+                    src1_i++;
+                }
+            }
+            m <<= 1;
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q3_K)
+
+#decl(Q4_K)
+// 8 blocks of 32 elements each
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    // 2 blocks each iteration
+    for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) {
+        for (var shift: u32 = 0; shift < 8; shift += 4) {
+            let scale_min = get_scale_min(is, block.scales);
+            is++;
+            let dl = d * scale_min.x;
+            let ml = m * scale_min.y;
+            for (var l: u32 = 0; l < 32; l++) {
+                let q_idx = q_b_idx + l;
+                let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                let qs_val = (q_byte >> shift) & 0xF;
+                sum += (f32(qs_val) * dl - ml) * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q4_K)
+
+#decl(Q5_K)
+// 8 blocks of 32 elements each
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    var u: u32 = 1;
+    // 2 blocks each iteration
+    for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) {
+        for (var shift: u32 = 0; shift < 8; shift += 4) {
+            let scale_min = get_scale_min(is, block.scales);
+            is++;
+            let dl = d * scale_min.x;
+            let ml = m * scale_min.y;
+            for (var l: u32 = 0; l < 32; l++) {
+                let q_idx = q_b_idx + l;
+                let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                let qh_byte = get_byte(block.qh[l / 4], l % 4);
+                let qs_val = (q_byte >> shift) & 0xF;
+                let qh_val = select(0.0, 16.0, (qh_byte & u) != 0);
+                sum += ((f32(qs_val) + qh_val) * dl - ml) * src1[src1_i];
+               src1_i++;
+            }
+            u <<= 1;
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q5_K)
+
+#decl(Q6_K)
+// 16 blocks of 16 elements each
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+
+    // convert arrays of f16 -> u32
+    var ql_vals: array<u32, 32>;
+    for (var i: u32 = 0; i < 32; i++) {
+        ql_vals[i] = bitcast<u32>(vec2(block.ql[2 * i], block.ql[2 * i + 1]));
+    }
+    var qh_vals: array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qh_vals[i] = bitcast<u32>(vec2(block.qh[2 * i], block.qh[2 * i + 1]));
+    }
+    var scale_vals: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        scale_vals[i] = bitcast<u32>(vec2(block.scales[2 * i], block.scales[2 * i + 1]));
+    }
+
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var qh_b_idx: u32 = 0;
+    var sc_b_idx: u32 = 0;
+    for (var ql_b_idx: u32 = 0; ql_b_idx < 128; ql_b_idx += 64) {
+        for (var l: u32 = 0; l < 32; l++) {
+            let ql13_b = get_byte(ql_vals[(ql_b_idx + l) / 4], (ql_b_idx + l) % 4);
+            let ql24_b = get_byte(ql_vals[(ql_b_idx + l + 32) / 4], (ql_b_idx + l + 32) % 4);
+            let qh_b = get_byte(qh_vals[(qh_b_idx + l) / 4], (qh_b_idx + l) % 4);
+
+            let q1 = f32((ql13_b & 0xF) | ((qh_b & 3) << 4)) - 32.0;
+            let q2 = f32((ql24_b & 0xF) | (((qh_b >> 2) & 3) << 4)) - 32.0;
+            let q3 = f32((ql13_b >> 4) | (((qh_b >> 4) & 3) << 4)) - 32.0;
+            let q4 = f32((ql24_b >> 4) | (((qh_b >> 6) & 3) << 4)) - 32.0;
+
+            let is = l/16;
+            let is1 = sc_b_idx + is;
+            let sc1 = get_byte_i32(scale_vals[is1 / 4], is1 % 4);
+            let is2 = sc_b_idx + is + 2;
+            let sc2 = get_byte_i32(scale_vals[is2 / 4], is2 % 4);
+            let is3 = sc_b_idx + is + 4;
+            let sc3 = get_byte_i32(scale_vals[is3 / 4], is3 % 4);
+            let is4 = sc_b_idx + is + 6;
+            let sc4 = get_byte_i32(scale_vals[is4 / 4], is4 % 4);
+
+            sum += d * f32(sc1) * q1 * src1[src1_i + l];
+            sum += d * f32(sc2) * q2 * src1[src1_i + l + 32];
+            sum += d * f32(sc3) * q3 * src1[src1_i + l + 64];
+            sum += d * f32(sc4) * q4 * src1[src1_i + l + 96];
+        }
+        src1_i += 128;
+        qh_b_idx += 32;
+        sc_b_idx += 8;
+    }
+    return sum;
+}
+
+#enddecl(Q6_K)
+
+#decl(IQ2_XXS)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 32; ib += 4) {
+        let aux0 = bitcast<u32>(vec2(block.qs[ib], block.qs[ib + 1]));
+        let aux1 = bitcast<u32>(vec2(block.qs[ib + 2], block.qs[ib + 3]));
+        let db = d * (0.5 + f32(aux1 >> 28)) * 0.25;
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = get_byte(aux0, l) * 8;
+            let is = (aux1 >> (7 * l)) & 127;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2xxs_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                sum += db * f32(g) * m * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ2_XXS)
+
+#decl(IQ2_XS)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var scale_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.scales[0], block.scales[1])),
+        bitcast<u32>(vec2(block.scales[2], block.scales[3]))
+    );
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 32; ib += 4) {
+        let s = get_byte(scale_vals[ib / 16], (ib % 16) / 4);
+        let db = array<f32, 2>(
+            d * (0.5 + f32(s & 0xF)) * 0.25,
+            d * (0.5 + f32(s >> 4)) * 0.25
+        );
+        for (var l: u32 = 0; l < 4; l++) {
+            let qs_val = bitcast<u32>(vec2(block.qs[ib + l], 0.0));
+            let ig = (qs_val & 511) * 8;
+            let is = qs_val >> 9;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            let dl = db[l/2];
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2xs_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                sum += dl * f32(g) * m * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ2_XS)
+
+#decl(IQ2_S)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var qs_vals : array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qs_vals[i] = bitcast<u32>(vec2(block.qs[i * 2], block.qs[i * 2 + 1]));
+    }
+    var qh_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.qh[0], block.qh[1])),
+        bitcast<u32>(vec2(block.qh[2], block.qh[3]))
+    );
+    var scale_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.scales[0], block.scales[1])),
+        bitcast<u32>(vec2(block.scales[2], block.scales[3]))
+    );
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib ++) {
+        let s = get_byte(scale_vals[ib / 4], ib % 4);
+        let db = array<f32, 2>(
+            d * (0.5 + f32(s & 0xF)) * 0.25,
+            d * (0.5 + f32(s >> 4)) * 0.25
+        );
+        let qs_w = qs_vals[ib];
+        for (var l: u32 = 0; l < 4; l++) {
+            let qh_b = (get_byte(qh_vals[ib / 4], ib % 4) << (8 - 2 * l)) & 0x300;
+            let ig = (get_byte(qs_w, l) | qh_b) * 8;
+            let signs = get_byte(qs_vals[ib + 8], l);
+            let dl = db[l/2];
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2s_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                sum += dl * f32(g) * m * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+
+#enddecl(IQ2_S)
+
+#decl(IQ3_XSS)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 16; ib += 2) {
+        let sc_sign = bitcast<u32>(vec2(block.qs[ib + 32], block.qs[ib + 33]));
+        let db = d * (0.5 + f32(sc_sign >> 28)) * 0.5;
+        for (var l: u32 = 0; l < 4; l++) {
+            let is = (sc_sign >> (7 * l)) & 127;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            let ig_val = bitcast<u32>(vec2(block.qs[ib * 2 + l], 0.0));
+            let ig1 = get_byte(ig_val, 0);
+            let ig2 = get_byte(ig_val, 1);
+            for (var j: u32 = 0; j < 4; j++) {
+                let g1 = get_byte(iq3xxs_grid[ig1], j);
+                let g2 = get_byte(iq3xxs_grid[ig2], j);
+                let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0);
+                let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0);
+                sum += db * f32(g1) * m1 * src1[src1_i];
+                sum += db * f32(g2) * m2 * src1[src1_i + 4];
+                src1_i++;
+            }
+            src1_i += 4;
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ3_XSS)
+
+#decl(IQ3_S)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var qh_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.qh[0], block.qh[1])),
+        bitcast<u32>(vec2(block.qh[2], block.qh[3]))
+    );
+    var sign_vals: array<u32, 8>;
+    for (var i: u32 = 0; i < 8; i++) {
+        sign_vals[i] = bitcast<u32>(vec2(block.signs[i * 2], block.signs[i * 2 + 1]));
+    }
+    var scale_vals = bitcast<u32>(vec2(block.scales[0], block.scales[1]));
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 4; ib++) {
+        let s = get_byte(scale_vals, ib);
+        let db = array<f32, 2>(
+            d * (1.0 + 2.0 * f32(s & 0xF)),
+            d * (1.0 + 2.0 * f32(s >> 4))
+        );
+        for (var k: u32 = 0; k < 2; k++) {
+            let dl = db[k];
+            let qh_byte = get_byte(qh_vals[ib / 2], (ib % 2) * 2 + k);
+            let sign_w = sign_vals[ib * 2 + k];
+            for (var l: u32 = 0; l < 4; l++) {
+                let signs = get_byte(sign_w, l);
+                let ig_val = bitcast<u32>(vec2(block.qs[ib * 8 + k * 4 + l], 0.0));
+                let ig1 = get_byte(ig_val, 0) | ((qh_byte << ((8 - (2 * l)))) & 256);
+                let ig2 = get_byte(ig_val, 1) | ((qh_byte << ((7 - (2 * l)))) & 256);
+                for (var j: u32 = 0; j < 4; j++) {
+                    let g1 = get_byte(iq3s_grid[ig1], j);
+                    let g2 = get_byte(iq3s_grid[ig2], j);
+                    let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0);
+                    let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0);
+                    sum += dl * f32(g1) * m1 * src1[src1_i];
+                    sum += dl * f32(g2) * m2 * src1[src1_i + 4];
+                    src1_i++;
+                }
+                src1_i += 4;
+            }
+        }
+    }
+    return sum;
+}
+#enddecl(IQ3_S)
+
+#decl(IQ1_S)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let qh = bitcast<u32>(vec2(block.qh[ib], 0.0));
+        let dl = d * (2 * f32((qh >> 12) & 7) + 1);
+        let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000) != 0);
+        let qs_w = bitcast<u32>(vec2(block.qs[ib * 2], block.qs[ib * 2 + 1]));
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = (get_byte(qs_w, l) | (((qh >> (3 * l)) & 7) << 8)) * 8;
+            for (var j: u32 = 0; j < 8; j++) {
+                let gw = iq1_grid[(ig + j) / 16];
+                let g = (gw >> (((ig + j) % 16) * 2)) & 3;
+                let gs = bitcast<i32>(g << 30) >> 30;
+                sum += dl * (f32(gs) + delta) * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ1_S)
+
+#decl(IQ1_M)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+
+    let scale = ((block.scales[0] >> 12) & 0xF) | ((block.scales[0] >> 24) & 0x00F0) | ((block.scales[1] >> 4) & 0x0F00) | ((block.scales[1] >> 16) & 0xF000);
+    let d = f32(bitcast<vec2<f16>>(scale).x);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let sw = (block.scales[ib / 4] >> (16 * ((ib / 2) % 2))) & 0xFFFF;
+        let s1 : u32 = (sw >> (6 * (ib % 2))) & 0x7;
+        let s2 : u32 = (sw >> (6 * (ib % 2) + 3)) & 0x7;
+        var dl = array<f32, 2>(
+            d * f32(2 * s1 + 1),
+            d * f32(2 * s2 + 1)
+        );
+
+        let qh = block.qh[ib / 2] >> (16 * (ib % 2));
+        var idx = array<u32, 4>(
+            get_byte(block.qs[ib], 0) | ((qh << 8) & 0x700),
+            get_byte(block.qs[ib], 1) | ((qh << 4) & 0x700),
+            get_byte(block.qs[ib], 2) | ((qh) & 0x700),
+            get_byte(block.qs[ib], 3) | ((qh >> 4) & 0x700)
+        );
+        var delta = array<f32, 4>(
+            select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x08) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x80) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x08) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x80) != 0)
+        );
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = idx[l] * 8;
+            for (var j: u32 = 0; j < 8; j++) {
+                let gw = iq1_grid[(ig + j) / 16];
+                let g = (gw >> (((ig + j) % 16) * 2)) & 3;
+                let gs = bitcast<i32>(g << 30) >> 30;
+                sum += dl[l/2] * (f32(gs) + delta[l]) * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ1_M)
+
+#decl(IQ4_NL)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 32;
+    var sum = 0.0;
+    var qs: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        qs[i] = bitcast<u32>(vec2(block.qs[i * 2], block.qs[i * 2 + 1]));
+    }
+    for (var j: u32 = 0; j < 16; j++) {
+        let qsb = get_byte(qs[j / 4], j % 4);
+        sum += d * f32(kvalues_iq4nl[qsb & 0xF]) * src1[src1_i];
+        sum += d * f32(kvalues_iq4nl[qsb >> 4]) * src1[src1_i + 16];
+        src1_i++;
+    }
+    return sum;
+}
+
+#enddecl(IQ4_NL)
+
+#decl(IQ4_XS)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let scales_h = bitcast<u32>(vec2(block.scales_h, 0.0));
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let ls = ((get_byte(block.scales_l, ib / 2) >> (4 * (ib % 2))) & 0xF) | (((scales_h >> (2 * ib)) & 3) << 4);
+        let dl = d * (f32(ls) - 32.0);
+        for (var j: u32 = 0; j < 16; j++) {
+            let iqs = ib * 16 + j;
+            let qsb = get_byte(block.qs[iqs / 4], iqs % 4);
+            sum += dl * f32(kvalues_iq4nl[qsb & 0xF]) * src1[src1_i];
+            sum += dl * f32(kvalues_iq4nl[qsb >> 4]) * src1[src1_i + 16];
+            src1_i++;
+        }
+        src1_i += 16;
+    }
+    return sum;
+}
+
+#enddecl(IQ4_XS)
+
+#end(DECLS)
+
+#define(SHADER)
+
+enable f16;
+
+DECLS
+
+struct MulMatParams {
+    offset_src0: u32, // in elements/blocks
+    offset_src1: u32, // in elements/blocks
+    offset_dst: u32, // in elements/blocks
+    m: u32,
+    n: u32,
+    k: u32,
+    // all strides are in elements/blocks
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // M rows, K columns
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // K rows, N columns (transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<f32>; // M rows, N columns
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+@compute @workgroup_size(256)
+fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
+    let total = params.m * params.n * params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3;
+    if (global_id.x >= total) {
+        return;
+    }
+
+    let dst2_stride = params.m * params.n;
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+
+    let dst3_idx = global_id.x / dst3_stride;
+    let src03_idx = dst3_idx / params.broadcast3; // src0 may be broadcast along the third dimension
+    let src13_idx = dst3_idx; // src1 is not broadcast
+    let dst3_rem = global_id.x % dst3_stride;
+
+    let dst2_idx = dst3_rem / dst2_stride;
+    let src02_idx = dst2_idx / params.broadcast2; // src0 may also be broadcast along the second dimension
+    let src12_idx = dst2_idx; // src1 is not broadcast
+
+    let dst2_rem = dst3_rem % dst2_stride;
+
+    let row = dst2_rem / params.m; // output row
+    let col = dst2_rem % params.m; // output column
+
+    let src0_idx_base = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02 + col * params.stride_01;
+    let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12 + row * params.stride_11;
+
+    var sum = 0.0;
+    for (var i: u32 = 0u; i < params.k/{{BLOCK_SIZE}}; i = i + 1u) {
+        sum += multiply_add(src0_idx_base, src1_idx_base, i);
+    }
+    dst[params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + row * params.m + col] = sum;
+}
+
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
new file mode 100644
index 0000000..109ff8d
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
@@ -0,0 +1,97 @@
+#decl(SHMEM_VEC)
+fn store_shmem(val: vec4<f16>, idx: u32) {
+    shmem[idx] = val.x;
+    shmem[idx + 1] = val.y;
+    shmem[idx + 2] = val.z;
+    shmem[idx + 3] = val.w;
+}
+#enddecl(SHMEM_VEC)
+
+#decl(SHMEM_SCALAR)
+fn store_shmem(val: f16, idx: u32) {
+    shmem[idx] = val;
+}
+#enddecl(SHMEM_SCALAR)
+
+#decl(INIT_SRC0_SHMEM_FLOAT)
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id * {{VEC_SIZE}}; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * {{VEC_SIZE}}) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+        let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+        let src0_val = select( // taking a slight performance hit to avoid oob
+            {{SRC0_TYPE}}(0.0),
+            src0[src0_idx/{{VEC_SIZE}}],
+            global_m < params.m && global_k < params.k);
+        store_shmem({{SHMEM_TYPE}}(src0_val), elem_idx);
+    }
+}
+
+#enddecl(INIT_SRC0_SHMEM_FLOAT)
+
+#decl(INIT_SRC1_SHMEM)
+
+fn init_shmem_src1(thread_id: u32, batch_offset: u32, offset_n: u32, k_outer: u32) {
+    for (var elem_idx = thread_id * {{VEC_SIZE}}; elem_idx < TILE_SRC1_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * {{VEC_SIZE}}) {
+        let tile_n = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_n = offset_n + tile_n;
+        let global_k = k_outer + tile_k;
+        let src1_idx = batch_offset + global_n * params.stride_11 + global_k;
+        let src1_val = select(
+            {{SRC1_TYPE}}(0.0),
+            src1[src1_idx/{{VEC_SIZE}}],
+            global_n < params.n && global_k < params.k);
+        store_shmem({{SHMEM_TYPE}}(src1_val), TILE_SRC0_SHMEM + elem_idx);
+    }
+}
+
+#enddecl(INIT_SRC1_SHMEM)
+
+#decl(INIT_SRC0_SHMEM_Q4_0)
+
+const BLOCK_SIZE = 32u;
+// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
+override BLOCKS_K = TILE_K/BLOCK_SIZE;
+const NQ = 16u;
+const F16_PER_BLOCK = 9u; // 1 scale + 8x4 packed weights
+const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
+const F16_PER_THREAD = NQ / WEIGHTS_PER_F16;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
+        let blck_idx = i / BLOCK_SIZE;
+        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+
+        let tile_m = blck_idx / BLOCKS_K;
+        let global_m = offset_m + tile_m;
+        let block_k = blck_idx % BLOCKS_K;
+        let global_k = k_outer / BLOCK_SIZE + block_k;
+
+        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
+            let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+            let scale_idx = src0_idx * F16_PER_BLOCK;
+            let d = src0[scale_idx];
+
+            for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+                let q_0 = src0[scale_idx + 1u + block_offset + j];
+                let q_1 = src0[scale_idx + 1u + block_offset + j + 1];
+
+                let q_packed = bitcast<u32>(vec2(q_0, q_1));
+                for (var k = 0u; k < 4u; k++) {
+                    let q_byte = get_byte(q_packed, k);
+                    let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d;
+                    let q_lo = (f16(q_byte & 0xF) - 8.0) * d;
+                    shmem[shmem_idx + j * 2 + k] = q_lo;
+                    shmem[shmem_idx + j * 2 + k + 16u] = q_hi;
+                }
+            }
+        }
+    }
+}
+
+#enddecl(INIT_SRC0_SHMEM_Q4_0)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.tmpl.wgsl
new file mode 100644
index 0000000..6b1dd26
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.tmpl.wgsl
@@ -0,0 +1,247 @@
+#define(VARIANTS)
+[
+  {
+    "SHADER_SUFFIX": "f32_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f32>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f32_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f16>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["BYTE_HELPERS", "VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["BYTE_HELPERS", "SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(VEC)
+fn store_val(acc: array<array<f16, TILE_N>, TILE_M>, tn: u32, tm: u32) -> vec4<f32> {
+    return vec4<f32>(f32(acc[tm][tn]), f32(acc[tm + 1][tn]), f32(acc[tm + 2][tn]), f32(acc[tm + 3][tn]));
+}
+#enddecl(VEC)
+
+#decl(SCALAR)
+fn store_val(acc: array<array<f16, TILE_N>, TILE_M>, tn: u32, tm: u32) -> f32 {
+    return f32(acc[tm][tn]);
+}
+#enddecl(SCALAR)
+
+#end(DECLS)
+
+#define(SHADER)
+enable f16;
+
+struct MulMatParams {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+    m: u32,
+    n: u32,
+    k: u32,
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // M rows, K columns
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // K rows, N columns (transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<{{DST_TYPE}}>; // M rows, N columns (transposed)
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+DECLS
+
+fn get_local_n(thread_id: u32) -> u32 {
+    return thread_id / WORKGROUP_SIZE_M;
+}
+fn get_local_m(thread_id: u32) -> u32 {
+    return thread_id % WORKGROUP_SIZE_M;
+}
+
+// TILE_M must be multiple of 4 for vec4 loads
+const TILE_M = {{WEBGPU_TILE_M}}u;
+const TILE_N = {{WEBGPU_TILE_N}}u;
+
+override WORKGROUP_SIZE_M: u32;
+override WORKGROUP_SIZE_N: u32;
+override TILE_K: u32;
+
+override TOTAL_WORKGROUP_SIZE = WORKGROUP_SIZE_M * WORKGROUP_SIZE_N;
+override TILE_SRC0_SHMEM = TILE_K * WORKGROUP_SIZE_M * TILE_M;
+override TILE_SRC1_SHMEM = TILE_K * WORKGROUP_SIZE_N * TILE_N;
+
+var<workgroup> shmem: array<f16, TILE_SRC0_SHMEM + TILE_SRC1_SHMEM>;
+
+@compute @workgroup_size(TOTAL_WORKGROUP_SIZE)
+fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
+        @builtin(local_invocation_id) local_id: vec3<u32>) {
+
+    let thread_id = local_id.x;
+    let local_m = get_local_m(thread_id);
+    let local_n = get_local_n(thread_id);
+
+    let wg_n_count = (params.n + WORKGROUP_SIZE_N * TILE_N - 1u) / (WORKGROUP_SIZE_N * TILE_N);
+    let wg_m_count = (params.m + WORKGROUP_SIZE_M * TILE_M - 1u) / (WORKGROUP_SIZE_M * TILE_M);
+    let wg_per_matrix = wg_m_count * wg_n_count;
+
+    let batch_idx = wg_id.x / wg_per_matrix;
+
+    let wg_in_batch = wg_id.x % wg_per_matrix;
+    let wg_m = wg_in_batch % wg_m_count;
+    let wg_n = wg_in_batch / wg_m_count;
+
+    let output_row_base = wg_m * WORKGROUP_SIZE_M * TILE_M + local_m * TILE_M;
+    let output_col_base = wg_n * WORKGROUP_SIZE_N * TILE_N + local_n * TILE_N;
+
+    let dst2_stride = params.m * params.n;
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+
+    let dst3_idx = batch_idx / (params.bs02 * params.broadcast2);
+    let src03_idx = dst3_idx / params.broadcast3;
+    let src13_idx = dst3_idx;
+    let dst2_idx = batch_idx % (params.bs02 * params.broadcast2);
+    let src02_idx = dst2_idx / params.broadcast2;
+    let src12_idx = dst2_idx;
+
+    let src0_batch_offset = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02;
+    let src1_batch_offset = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
+
+    let offset_m = wg_m * WORKGROUP_SIZE_M * TILE_M;
+    let offset_n = wg_n * WORKGROUP_SIZE_N * TILE_N;
+
+    var acc: array<array<f16, TILE_N>, TILE_M>;
+
+    for (var k_outer = 0u; k_outer < params.k; k_outer += TILE_K) {
+
+        // see mul_mat_decls.tmpl
+        init_shmem_src0(thread_id, src0_batch_offset, offset_m, k_outer);
+        init_shmem_src1(thread_id, src1_batch_offset, offset_n, k_outer);
+
+        workgroupBarrier();
+
+        let k_end = min(TILE_K, params.k - k_outer);
+
+        for (var k_inner = 0u; k_inner < k_end; k_inner++) {
+            var src0_tile: array<f16, TILE_M>;
+            for (var tm = 0u; tm < TILE_M; tm++) {
+                let src0_m = local_m * TILE_M + tm;
+                let src0_idx = k_inner + src0_m * TILE_K;
+                src0_tile[tm] = shmem[src0_idx];
+            }
+            for (var tn = 0u; tn < TILE_N; tn++) {
+                let src1_n = local_n * TILE_N + tn;
+                let src1_idx = src1_n * TILE_K + k_inner;
+                let src1_val = shmem[TILE_SRC0_SHMEM + src1_idx];
+                for (var tm = 0u; tm < TILE_M; tm++) {
+                      acc[tm][tn] += src0_tile[tm] * src1_val;
+                }
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+    let dst_batch_offset = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride;
+
+    for (var tn = 0u; tn < TILE_N; tn++) {
+        let global_col = output_col_base + tn;
+        if (global_col < params.n) {
+            for (var tm = 0u; tm < TILE_M; tm += {{VEC_SIZE}}) {
+                let global_row = output_row_base + tm;
+                if (global_row < params.m) {
+                    let dst_idx = dst_batch_offset + global_col * params.m + global_row;
+                    dst[dst_idx/{{VEC_SIZE}}] = store_val(acc, tn, tm);
+                }
+            }
+        }
+    }
+}
+
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.tmpl.wgsl
new file mode 100644
index 0000000..47c8ce3
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.tmpl.wgsl
@@ -0,0 +1,302 @@
+#define(VARIANTS)
+[
+  {
+    "SHADER_SUFFIX": "f32_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f32>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f32_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f16>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["BYTE_HELPERS", "VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["BYTE_HELPERS", "SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(VEC)
+fn store_dst(shmem_idx: u32, dst_idx: u32) {
+    dst[dst_idx] = vec4<f32>(
+        f32(shmem[shmem_idx]),
+        f32(shmem[shmem_idx + 1]),
+        f32(shmem[shmem_idx + 2]),
+        f32(shmem[shmem_idx + 3])
+    );
+}
+#enddecl(VEC)
+
+#decl(SCALAR)
+fn store_dst(shmem_idx: u32, dst_idx: u32) {
+    dst[dst_idx] = f32(shmem[shmem_idx]);
+}
+#enddecl(SCALAR)
+
+#end(DECLS)
+
+#define(SHADER)
+diagnostic(off, chromium.subgroup_matrix_uniformity);
+enable f16;
+enable subgroups;
+enable chromium_experimental_subgroup_matrix;
+
+struct MulMatParams {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+    m: u32,
+    n: u32,
+    k: u32,
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // M rows, K columns
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // K rows, N columns (transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<{{DST_TYPE}}>; // M rows, N columns (transposed)
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+DECLS
+
+// Note: These are string interpolated at build time, cannot use override constants due to limitations in
+// current Dawn version type definitions/matrix load requirements for constant memory sizes.
+const SUBGROUP_M = {{WEBGPU_SUBGROUP_M}}u;
+const SUBGROUP_N = {{WEBGPU_SUBGROUP_N}}u;
+// For portability we assume the max subgroup size, meaning some subgroups will be masked out if the
+// runtime subgroup size is smaller.
+const MAX_SUBGROUP_SIZE = {{WEBGPU_MAX_SUBGROUP_SIZE}}u;
+
+const EXPECTED_SUBGROUPS = SUBGROUP_M * SUBGROUP_N;
+
+const SUBGROUP_MATRIX_M_SIZE = {{WEBGPU_SG_MAT_M_SIZE}}u;
+const SUBGROUP_MATRIX_N_SIZE = {{WEBGPU_SG_MAT_N_SIZE}}u;
+const SUBGROUP_MATRIX_K_SIZE = {{WEBGPU_SG_MAT_K_SIZE}}u;
+
+const SUBGROUP_MATRIX_M = {{WEBGPU_SUBGROUP_MATRIX_M}}u;
+const SUBGROUP_MATRIX_N = {{WEBGPU_SUBGROUP_MATRIX_N}}u;
+
+const TILE_K = {{WEBGPU_TILE_K}}u;
+
+const WG_M_SG_TILE_SIZE = SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+const WG_N_SG_TILE_SIZE = SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+const TOTAL_WORKGROUP_SIZE = SUBGROUP_M * SUBGROUP_N * MAX_SUBGROUP_SIZE;
+const TILE_SRC0_SHMEM = TILE_K * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+const TILE_SRC1_SHMEM = TILE_K * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+const SG_MAT_ACCUM_SHMEM = SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_M_SIZE * SUBGROUP_MATRIX_N_SIZE;
+
+// We reuse shmem for accumulation matrices
+const SHMEM_SIZE = max(TILE_SRC0_SHMEM + TILE_SRC1_SHMEM, SG_MAT_ACCUM_SHMEM);
+
+var<workgroup> shmem: array<f16, SHMEM_SIZE>;
+
+@compute @workgroup_size(TOTAL_WORKGROUP_SIZE)
+fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
+        @builtin(local_invocation_id) local_id: vec3<u32>,
+        @builtin(subgroup_id) subgroup_id: u32) {
+
+    let thread_id = local_id.x;
+    let subgroup_m = subgroup_id % SUBGROUP_M;
+    let subgroup_n = subgroup_id / SUBGROUP_M;
+
+    let wg_m_count = (params.m + WG_M_SG_TILE_SIZE - 1) / WG_M_SG_TILE_SIZE;
+    let wg_n_count = (params.n + WG_N_SG_TILE_SIZE - 1) / WG_N_SG_TILE_SIZE;
+    let wg_per_matrix = wg_m_count * wg_n_count;
+
+    let batch_idx = wg_id.x / wg_per_matrix;
+
+    let wg_in_batch = wg_id.x % wg_per_matrix;
+    let wg_m = wg_in_batch % wg_m_count;
+    let wg_n = wg_in_batch / wg_m_count;
+
+    let dst2_stride = params.m * params.n;
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+
+    let dst3_idx = batch_idx / (params.bs02 * params.broadcast2);
+    let src03_idx = dst3_idx / params.broadcast3;
+    let src13_idx = dst3_idx;
+    let dst2_idx = batch_idx % (params.bs02 * params.broadcast2);
+    let src02_idx = dst2_idx / params.broadcast2;
+    let src12_idx = dst2_idx;
+
+    let src0_batch_offset = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02;
+    let src1_batch_offset = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
+
+    let offset_m = wg_m * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+    let offset_n = wg_n * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+    var acc_sg_mat : array<array<subgroup_matrix_result<f16, SUBGROUP_MATRIX_N_SIZE, SUBGROUP_MATRIX_M_SIZE>, SUBGROUP_MATRIX_N>, SUBGROUP_MATRIX_M>;
+
+    for (var k_outer = 0u; k_outer < params.k; k_outer += TILE_K) {
+
+        // see mul_mat_decls.tmpl
+        init_shmem_src0(thread_id, src0_batch_offset, offset_m, k_outer);
+        init_shmem_src1(thread_id, src1_batch_offset, offset_n, k_outer);
+
+        workgroupBarrier();
+
+        if (subgroup_id < EXPECTED_SUBGROUPS) {
+
+            for (var k_inner = 0u; k_inner < TILE_K; k_inner += SUBGROUP_MATRIX_K_SIZE) {
+
+                let src0_shmem_idx_base = subgroup_m * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE * TILE_K + k_inner;
+                var src0_sg_mats: array<subgroup_matrix_left<f16, SUBGROUP_MATRIX_K_SIZE, SUBGROUP_MATRIX_M_SIZE>, SUBGROUP_MATRIX_M>;
+                for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) {
+                    src0_sg_mats[m] = subgroupMatrixLoad<subgroup_matrix_left<f16, SUBGROUP_MATRIX_K_SIZE, SUBGROUP_MATRIX_M_SIZE>>(
+                        &shmem,
+                        src0_shmem_idx_base + m * SUBGROUP_MATRIX_M_SIZE * TILE_K,
+                        false,
+                        TILE_K
+                    );
+                }
+
+                let src1_shmem_idx_base = TILE_SRC0_SHMEM + subgroup_n * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE * TILE_K + k_inner;
+                for (var n = 0u; n < SUBGROUP_MATRIX_N; n++) {
+                    let src1_sg_mat = subgroupMatrixLoad<subgroup_matrix_right<f16, SUBGROUP_MATRIX_N_SIZE, SUBGROUP_MATRIX_K_SIZE>>(
+                        &shmem,
+                        src1_shmem_idx_base + n * SUBGROUP_MATRIX_N_SIZE * TILE_K,
+                        true,
+                        TILE_K
+                    );
+                    for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) {
+                        acc_sg_mat[m][n] = subgroupMatrixMultiplyAccumulate(src0_sg_mats[m], src1_sg_mat, acc_sg_mat[m][n]);
+                    }
+                }
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+    let dst_batch_offset = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride;
+
+    // Stage the subgroup matrix tiles into shared memory
+    // This uses WG_M_SG_TILE_SIZE as the stride (number of columns in the workgroup tile).
+    let WG_TILE_STRIDE = WG_M_SG_TILE_SIZE;
+    let tile_row_base_local = subgroup_n * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+    let tile_col_base_local = subgroup_m * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+
+    if (subgroup_id < EXPECTED_SUBGROUPS) { // 2-5% performance hit :(
+        for (var n = 0u; n < SUBGROUP_MATRIX_N; n++) {
+            for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) {
+                let local_row = tile_row_base_local + n * SUBGROUP_MATRIX_N_SIZE;
+                let local_col = tile_col_base_local + m * SUBGROUP_MATRIX_M_SIZE;
+                let out_base = local_row * WG_TILE_STRIDE + local_col;
+                subgroupMatrixStore(&shmem, out_base, acc_sg_mat[m][n], true, WG_TILE_STRIDE);
+            }
+        }
+    }
+
+    workgroupBarrier();
+
+    // Cooperative write: iterate over the entire workgroup tile
+    let tile_rows = WG_N_SG_TILE_SIZE;
+    let tile_cols = WG_M_SG_TILE_SIZE;
+    let total_tile_elems = tile_rows * tile_cols;
+    let tile_dst_row_base = wg_m * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+    let tile_dst_col_base = wg_n * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+    for (var idx = thread_id * {{VEC_SIZE}}; idx < total_tile_elems; idx += TOTAL_WORKGROUP_SIZE * {{VEC_SIZE}}) {
+        let local_row = idx % WG_TILE_STRIDE;
+        let local_col = idx / WG_TILE_STRIDE;
+
+        let global_row = tile_dst_row_base + local_row;
+        let global_col = tile_dst_col_base + local_col;
+
+        if (global_col < params.n && global_row < params.m) {
+            let dst_idx = dst_batch_offset + global_col * params.m + global_row;
+            store_dst(idx, dst_idx/{{VEC_SIZE}});
+        }
+    }
+}
+
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.tmpl.wgsl
new file mode 100644
index 0000000..ffbb640
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.tmpl.wgsl
@@ -0,0 +1,267 @@
+#define(VARIANTS)
+[
+  {
+    "SHADER_SUFFIX": "f32_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f32>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE": "vec4<f32>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f32_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE": "vec4<f32>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f16>",
+      "DST_TYPE": "vec4<f32>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["BYTE_HELPERS", "SCALAR", "MUL_ACC_Q4_0"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(VEC)
+fn inner_dot(src0_val: {{SRC0_TYPE}}, src1_val: {{SRC1_TYPE}}) -> f32 {
+    return f32(dot({{SRC1_TYPE}}(src0_val), src1_val));
+}
+
+fn store_val(group_base: u32) -> vec4<f32> {
+    return vec4<f32>(partial_sums[group_base],
+                     partial_sums[group_base + THREADS_PER_OUTPUT],
+                     partial_sums[group_base + THREADS_PER_OUTPUT * 2],
+                     partial_sums[group_base + THREADS_PER_OUTPUT * 3]);
+}
+#enddecl(VEC)
+
+#decl(SCALAR)
+fn inner_dot(src0_val: {{SRC0_TYPE}}, src1_val: {{SRC1_TYPE}}) -> f32 {
+    return f32(src0_val) * f32(src1_val);
+}
+
+fn store_val(group_base: u32) -> f32 {
+    return partial_sums[group_base];
+}
+#enddecl(SCALAR)
+
+#decl(MUL_ACC_FLOAT)
+
+fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 {
+    var local_sum = 0.0;
+    for (var i = tig * {{VEC_SIZE}}; i < tile_size; i += THREADS_PER_OUTPUT * {{VEC_SIZE}}) {
+        let a = src0[(idx_base + k_outer + i) / {{VEC_SIZE}}];
+        let b = shared_vector[i / {{VEC_SIZE}}];
+        local_sum += inner_dot(a, b);
+    }
+    return local_sum;
+}
+
+#enddecl(MUL_ACC_FLOAT)
+
+#decl(MUL_ACC_Q4_0)
+
+const BLOCK_SIZE = 32;
+const NQ = 16u; // number of weights per thread
+const F16_PER_BLOCK = 9u; // 1 scale + 8x4 packed weights
+const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
+const F16_PER_THREAD = NQ / WEIGHTS_PER_F16;
+
+fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 {
+    var local_sum = 0.0;
+    for (var i = tig * NQ; i < tile_size; i += THREADS_PER_OUTPUT * NQ) {
+        let blck_idx = i / BLOCK_SIZE;
+        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
+        let scale_idx = (idx_base + k_outer / BLOCK_SIZE + blck_idx) * F16_PER_BLOCK;
+        // each f16 contains offsets [block_offset, block_offset + 1] and [block_offset + 16, block_offset + 17]
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let d = f32(src0[scale_idx]);
+        for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+            let q_0 = src0[scale_idx + 1 + block_offset + j];
+            let q_1 = src0[scale_idx + 1 + block_offset + j + 1];
+            let q_packed = bitcast<u32>(vec2(q_0, q_1));
+            for (var k: u32 = 0; k < 4; k++) {
+                let q_byte = get_byte(q_packed, k);
+                let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0) * d;
+                let q_lo = (f32(q_byte & 0xF) - 8.0) * d;
+                local_sum += q_lo * shared_vector[shmem_idx + j * 2 + k];
+                local_sum += q_hi * shared_vector[shmem_idx + j * 2 + k + 16];
+            }
+        }
+    }
+    return local_sum;
+}
+
+#enddecl(MUL_ACC_Q4_0)
+
+#end(DECLS)
+
+#define(SHADER)
+enable f16;
+
+DECLS
+
+struct MulMatParams {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+    m: u32,
+    n: u32,
+    k: u32,
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // Matrix (M x K)
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // Vector (K x 1, transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<{{DST_TYPE}}>;  // Result vector (transposed)
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+override WORKGROUP_SIZE: u32;
+override TILE_K: u32;
+override OUTPUTS_PER_WG: u32;
+override THREADS_PER_OUTPUT = WORKGROUP_SIZE / OUTPUTS_PER_WG;
+
+// Shared memory for collaborative loading and reduction
+var<workgroup> shared_vector: array<{{SRC1_TYPE}}, TILE_K/{{VEC_SIZE}}>;  // Cache vector tile
+var<workgroup> partial_sums: array<f32, WORKGROUP_SIZE>;   // For reduction
+
+@compute @workgroup_size(WORKGROUP_SIZE)
+fn main(
+    @builtin(local_invocation_id) local_id: vec3<u32>,
+    @builtin(workgroup_id) wg_id: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>) {
+    let thread_id = local_id.x;
+
+    // Handle batch dimensions
+    let total_batches = params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3;
+    let wg_linear = wg_id.y * num_wg.x + wg_id.x;
+    let output_groups = (params.m + OUTPUTS_PER_WG - 1u) / OUTPUTS_PER_WG;
+    let batch_idx = wg_linear / output_groups;
+    if (batch_idx >= total_batches) {
+        return;
+    }
+
+    // Which of the outputs does this thread belong to?
+    let thread_group = thread_id / THREADS_PER_OUTPUT;
+    let thread_in_group = thread_id % THREADS_PER_OUTPUT;
+
+    // Each workgroup computes OUTPUTS_PER_WG consecutive outputs
+    let output_row = (wg_linear % output_groups) * OUTPUTS_PER_WG + thread_group;
+
+    let dst2_stride = params.m * params.n;
+    let dst2_idx = batch_idx % (params.bs02 * params.broadcast2);
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+    let dst3_idx = batch_idx / (params.bs02 * params.broadcast2);
+    let src03_idx = dst3_idx / params.broadcast3;
+    let src13_idx = dst3_idx;
+    let src02_idx = dst2_idx / params.broadcast2;
+    let src12_idx = dst2_idx;
+
+    let src0_idx_base = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02 + output_row * params.stride_01;
+    let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
+    let dst_idx = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + output_row;
+
+    var local_sum = 0.0;
+
+    // Each thread processes multiple K elements and accumulates
+    for (var k_tile = 0u; k_tile < params.k; k_tile += TILE_K) {
+        let tile_size = min(TILE_K, params.k - k_tile);
+
+        // Cooperatively load vector tile into shared memory (all threads)
+        for (var i = thread_id * {{VEC_SIZE}}; i < tile_size; i += WORKGROUP_SIZE * {{VEC_SIZE}}) {
+            shared_vector[i / {{VEC_SIZE}}] = src1[(src1_idx_base + k_tile + i) / {{VEC_SIZE}}];
+        }
+
+        workgroupBarrier();
+
+        if (output_row < params.m) {
+            local_sum += mul_acc(thread_in_group, tile_size, src0_idx_base, k_tile);
+        }
+
+        workgroupBarrier();
+    }
+
+    // Store partial sums and reduce within each partition
+    partial_sums[thread_id] = local_sum;
+    workgroupBarrier();
+    let group_base = thread_group * THREADS_PER_OUTPUT;
+    let thread_base = group_base + thread_in_group;
+    var offset = THREADS_PER_OUTPUT / 2;
+    while (offset > 0) {
+        if (thread_in_group < offset) {
+            partial_sums[thread_base] += partial_sums[thread_base + offset];
+        }
+        offset = offset / 2;
+        workgroupBarrier();
+    }
+
+    // Store back to global memory
+    if (output_row < params.m && thread_group % {{VEC_SIZE}} == 0 && thread_in_group == 0) {
+        dst[dst_idx / {{VEC_SIZE}}] = store_val(group_base);
+    }
+}
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/pad.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/pad.wgsl
new file mode 100644
index 0000000..ea63b9a
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/pad.wgsl
@@ -0,0 +1,86 @@
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<f32>;
+
+struct Params {
+    ne: u32,            // total number of elements
+    offset_src: u32,    // in elements
+    offset_dst: u32,    // in elements
+
+    // Strides (in elements)
+    stride_src0: u32,
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    // Logical shapes
+    src_ne0: u32,
+    src_ne1: u32,
+    src_ne2: u32,
+    src_ne3: u32,
+
+    dst_ne0: u32,
+    dst_ne1: u32,
+    dst_ne2: u32,
+    dst_ne3: u32,
+
+    // Pad sizes (in elements)
+    lp0: u32,
+    rp0: u32,
+    lp1: u32,
+    rp1: u32,
+    lp2: u32,
+    rp2: u32,
+    lp3: u32,
+    rp3: u32,
+};
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+fn wrap_around(idx: i32, n: u32) -> u32 {
+    return u32(idx + i32(n)) % n;
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= params.ne) {
+        return;
+    }
+
+    var i = gid.x;
+    let dst_plane = params.dst_ne2 * params.dst_ne1 * params.dst_ne0;
+    let i3 = i / dst_plane;
+    i = i % dst_plane;
+    let i2 = i / (params.dst_ne1 * params.dst_ne0);
+    i = i % (params.dst_ne1 * params.dst_ne0);
+    let i1 = i / params.dst_ne0;
+    let i0 = i % params.dst_ne0;
+
+    var value: f32 = 0.0;
+
+#ifdef CIRCULAR
+    let ci0 = wrap_around(i32(i0) - i32(params.lp0), params.src_ne0);
+    let ci1 = wrap_around(i32(i1) - i32(params.lp1), params.src_ne1);
+    let ci2 = wrap_around(i32(i2) - i32(params.lp2), params.src_ne2);
+    let ci3 = wrap_around(i32(i3) - i32(params.lp3), params.src_ne3);
+    let circular_src_idx = ci0 * params.stride_src0 + ci1 * params.stride_src1 +
+                           ci2 * params.stride_src2 + ci3 * params.stride_src3;
+    value = src[params.offset_src + circular_src_idx];
+#else
+    let is_src =
+        (i0 >= params.lp0 && i0 < params.dst_ne0 - params.rp0) &&
+        (i1 >= params.lp1 && i1 < params.dst_ne1 - params.rp1) &&
+        (i2 >= params.lp2 && i2 < params.dst_ne2 - params.rp2) &&
+        (i3 >= params.lp3 && i3 < params.dst_ne3 - params.rp3);
+    if (is_src) {
+        let src_idx = (i0 - params.lp0) * params.stride_src0 + (i1 - params.lp1) * params.stride_src1 +
+                      (i2 - params.lp2) * params.stride_src2 + (i3 - params.lp3) * params.stride_src3;
+        value = src[params.offset_src + src_idx];
+    }
+#endif
+
+    dst[params.offset_dst + gid.x] = value;
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm.wgsl
new file mode 100644
index 0000000..712b921
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm.wgsl
@@ -0,0 +1,123 @@
+#define(VARIANTS)
+
+[
+  {
+    "DECLS": ["NOT_INPLACE"]
+  },
+  {
+    "SHADER_SUFFIX": "inplace",
+    "DECLS": ["INPLACE"]
+  },
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(NOT_INPLACE)
+
+fn update(src_offset: u32, dst_offset: u32, scale: f32) {
+    dst[dst_offset] = scale * src[src_offset];
+}
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#enddecl(NOT_INPLACE)
+
+#decl(INPLACE)
+
+fn update(src_offset: u32, dst_offset: u32, scale: f32) {
+    src[dst_offset] = scale * src[src_offset];
+}
+
+@group(0) @binding(1)
+var<uniform> params: Params;
+
+#enddecl(INPLACE)
+
+#end(DECLS)
+
+#define(SHADER)
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_dst: u32, // in elements
+
+    // Strides (in elements)
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // Shape of src/dst
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+    ne3: u32,
+
+    eps: f32
+};
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+DECLS
+
+override wg_size: u32;
+var<workgroup> scratch: array<f32, wg_size>;
+
+@compute @workgroup_size(wg_size)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+
+    // one thread per row
+    var i = wid.x;
+    let i3 = i / (params.ne2 * params.ne1);
+    i = i % (params.ne2 * params.ne1);
+    let i2 = i / params.ne1;
+    let i1 = i % params.ne1;
+    let i_src_row = params.offset_src + i3 * params.stride_src3 + i2 * params.stride_src2 + i1 * params.stride_src1;
+    let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1;
+
+    let elems = (params.ne0 + wg_size - 1) / wg_size;
+
+    var sum = 0.0f;
+    var col = lid.x;
+    for (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+        sum += pow(src[i_src_row + col], 2.0);
+        col += wg_size;
+    }
+
+    scratch[lid.x] = sum;
+    workgroupBarrier();
+    var offset = wg_size / 2;
+    while (offset > 0) {
+        if (lid.x < offset) {
+            scratch[lid.x] += scratch[lid.x + offset];
+        }
+        offset = offset / 2;
+        workgroupBarrier();
+    }
+    sum = scratch[0];
+
+    let scale = 1.0/sqrt(sum/f32(params.ne0) + params.eps);
+    col = lid.x;
+    for (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+        update(i_src_row + col, i_dst_row + col, scale);
+        col += wg_size;
+    }
+}
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rope.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rope.tmpl.wgsl
new file mode 100644
index 0000000..84dc8db
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rope.tmpl.wgsl
@@ -0,0 +1,295 @@
+#define(VARIANTS)
+
+[
+  {
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_FF_BINDINGS", "NO_FF_FUNC", "ROTATE"]
+  },
+  {
+    "SHADER_SUFFIX": "f32_inplace",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["NO_FF_BINDINGS_INPLACE", "NO_FF_FUNC", "ROTATE_INPLACE"]
+  },
+  {
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["NO_FF_BINDINGS", "NO_FF_FUNC", "ROTATE"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_inplace",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["NO_FF_BINDINGS_INPLACE", "NO_FF_FUNC", "ROTATE_INPLACE"]
+  },
+  {
+   "SHADER_SUFFIX": "f32_ff",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["FF_BINDINGS", "FF_FUNC", "ROTATE"]
+  },
+  {
+   "SHADER_SUFFIX": "f32_ff_inplace",
+    "REPLS": {
+      "TYPE" : "f32",
+    },
+    "DECLS": ["FF_BINDINGS_INPLACE", "FF_FUNC", "ROTATE_INPLACE"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_ff",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["FF_BINDINGS", "FF_FUNC", "ROTATE"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_ff_inplace",
+    "REPLS": {
+      "TYPE" : "f16",
+    },
+    "DECLS": ["FF_BINDINGS_INPLACE", "FF_FUNC", "ROTATE_INPLACE"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(ROTATE)
+fn rotate(i_dst0: u32, i_dst1: u32, out0: f32, out1: f32) {
+    dst[i_dst0] = {{TYPE}}(out0);
+    dst[i_dst1] = {{TYPE}}(out1);
+}
+#enddecl(ROTATE)
+
+#decl(ROTATE_INPLACE)
+fn rotate(i_dst0: u32, i_dst1: u32, out0: f32, out1: f32) {
+    src0[i_dst0] = {{TYPE}}(out0);
+    src0[i_dst1] = {{TYPE}}(out1);
+}
+#enddecl(ROTATE_INPLACE)
+
+#decl(NO_FF_FUNC)
+fn freq_factor(i: u32) -> f32 {
+    return 1.0f;
+}
+#enddecl(NO_FF_FUNC)
+
+#decl(FF_FUNC)
+fn freq_factor(i: u32) -> f32 {
+    return src2[params.offset_src2 + i/2];
+}
+#enddecl(FF_FUNC)
+
+#decl(NO_FF_BINDINGS)
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<{{TYPE}}>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+#enddecl(NO_FF_BINDINGS)
+
+#decl(NO_FF_BINDINGS_INPLACE)
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#enddecl(NO_FF_BINDINGS_INPLACE)
+
+#decl(FF_BINDINGS)
+
+@group(0) @binding(2)
+var<storage, read_write> src2: array<f32>;
+
+@group(0) @binding(3)
+var<storage, read_write> dst: array<{{TYPE}}>;
+
+@group(0) @binding(4)
+var<uniform> params: Params;
+
+#enddecl(FF_BINDINGS)
+
+#decl(FF_BINDINGS_INPLACE)
+
+@group(0) @binding(2)
+var<storage, read_write> src2: array<f32>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+#enddecl(FF_BINDINGS_INPLACE)
+
+#end(DECLS)
+
+#define(SHADER)
+
+enable f16;
+
+struct Params {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_src2: u32,
+    offset_dst: u32,
+
+    // Strides (in elements)
+    stride_src01: u32,
+    stride_src02: u32,
+    stride_src03: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    n_threads: u32,
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+
+    n_dims: u32,
+    mode: u32,
+    theta_scale: f32,
+    attn_factor: f32,
+    freq_scale: f32,
+    ext_factor: f32,
+    corr_dim0: f32,
+    corr_dim1: f32,
+    sections0: u32,
+    sections1: u32,
+    sections2: u32,
+    sections3: u32
+};
+
+@group(0) @binding(0)
+var<storage, read_write> src0: array<{{TYPE}}>;
+
+@group(0) @binding(1)
+var<storage, read_write> src1: array<i32>;
+
+DECLS
+
+fn rope_yarn_ramp(low: f32, high: f32, i: u32) -> f32 {
+    let y = (f32(i / 2) - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// returns vector of (cos_theta, sin_theta)
+// TODO: check performance of instantiating once on the CPU and passed as buffer, since it's repeated per-row
+fn rope_yarn(theta_extrap: f32, i: u32) -> vec2<f32> {
+    var mscale = params.attn_factor;
+    var theta = params.freq_scale * theta_extrap;
+    if (params.ext_factor != 0.0f) {
+        let ramp_mix = rope_yarn_ramp(params.corr_dim0, params.corr_dim1, i) * params.ext_factor;
+        theta = theta * (1 - ramp_mix) + theta_extrap * ramp_mix;
+        mscale *= 1.0f + 0.1f * log(1.0f / params.freq_scale);
+    }
+    return vec2<f32>(cos(theta) * mscale, sin(theta) * mscale);
+}
+
+fn pair_base(i0: u32, div_2: bool) -> u32 {
+    if (div_2) {
+        return i0 / 2;
+    } else {
+        return i0;
+    }
+}
+
+fn pair_offset(is_neox: bool, is_mrope: bool, is_vision: bool) -> u32 {
+    if (is_vision) {
+        return params.n_dims;
+    } else if (is_neox || is_mrope) {
+        return params.n_dims / 2;
+    } else {
+        return 1;
+    }
+}
+
+override wg_size: u32;
+@compute @workgroup_size(wg_size)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    // two elements per thread
+    if (gid.x >= params.n_threads) {
+        return;
+    }
+
+    let is_neox = bool(params.mode & 2);
+    let is_mrope = bool(params.mode & 8);
+    let is_imrope = params.mode == 40;
+    let is_vision = params.mode == 24;
+
+    var i = gid.x * 2; // start index for this thread
+    let i3 = i / (params.ne2 * params.ne1 * params.ne0);
+    i = i % (params.ne2 * params.ne1 * params.ne0);
+    let i2 = i / (params.ne1 * params.ne0);
+    i = i % (params.ne1 * params.ne0);
+    let i1 = i / params.ne0;
+    let i0 = i % params.ne0;
+
+    let i_src_row = params.offset_src0 + i3 * params.stride_src03 + i2 * params.stride_src02 + i1 * params.stride_src01;
+    let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1;
+
+    if (i0 >= params.n_dims && !is_vision) {
+        let i_src = i_src_row + i0;
+        let i_dst = i_dst_row + i0;
+        rotate(i_dst, i_dst + 1, f32(src0[i_src]), f32(src0[i_src + 1]));
+        return;
+    }
+
+    var theta_base_mult: u32 = 0;
+    var theta_scale_pwr: u32 = i0 / 2;
+    if (is_mrope) {
+        let sect_dims = params.sections0 + params.sections1 + params.sections2 + params.sections3;
+        let sec_w = params.sections1 + params.sections0;
+        let sec_e = params.sections2 + sec_w;
+        let sector = (i0 / 2) % sect_dims;
+        if (is_imrope) {
+          if (sector % 3 == 1 && sector < 3 * params.sections1) {
+              theta_base_mult = 1;
+          } else if (sector % 3 == 2 && sector < 3 * params.sections2) {
+              theta_base_mult = 2;
+          } else if (sector % 3 == 0 && sector < 3 * params.sections0) {
+              theta_base_mult = 0;
+          } else {
+              theta_base_mult = 3;
+          }
+        } else {
+          if (sector >= params.sections0 && sector < sec_w) {
+              theta_base_mult = 1;
+              if (is_vision) {
+                  theta_scale_pwr = sector - params.sections0;
+              }
+          } else if (sector >= sec_w && sector < sec_e) {
+              theta_base_mult = 2;
+              if (is_vision) {
+                  theta_scale_pwr = sector - sec_w;
+              }
+          } else if (sector >= sec_e) {
+              if (is_vision) {
+                  theta_scale_pwr = sector - sec_e;
+                  theta_scale_pwr = (i0 / 2) % sec_e;
+              }
+              theta_base_mult = 3;
+          } else if (is_vision) {
+              theta_scale_pwr = sector;
+          }
+        }
+    }
+    let theta_base = f32(src1[params.offset_src1 + i2 + params.ne2 * theta_base_mult]) * pow(params.theta_scale, f32(theta_scale_pwr));
+    let thetas = rope_yarn(theta_base/freq_factor(i0), i0);
+
+    let i_src = i_src_row + pair_base(i0, is_neox || is_mrope || is_vision);
+    let i_dst = i_dst_row + pair_base(i0, is_neox || is_mrope || is_vision);
+
+    let x0 = f32(src0[i_src]);
+    let x1 = f32(src0[i_src + pair_offset(is_neox, is_mrope, is_vision)]);
+    rotate(i_dst, i_dst + pair_offset(is_neox, is_mrope, is_vision), x0 * thetas.x - x1 * thetas.y, x0 * thetas.y + x1 * thetas.x);
+}
+
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/scale.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/scale.tmpl.wgsl
new file mode 100644
index 0000000..040e80d
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/scale.tmpl.wgsl
@@ -0,0 +1,90 @@
+#define(VARIANTS)
+
+[
+  {
+    "SHADER_NAME": "scale_f32",
+    "DECLS": ["NOT_INPLACE"]
+  },
+  {
+    "SHADER_NAME": "scale_f32_inplace",
+    "DECLS": ["INPLACE"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(NOT_INPLACE)
+@group(0) @binding(1)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+fn store_scale(val: f32, offset: u32) {
+    dst[offset] = val;
+}
+#enddecl(NOT_INPLACE)
+
+#decl(INPLACE)
+@group(0) @binding(1)
+var<uniform> params: Params;
+
+fn store_scale(val: f32, offset: u32) {
+    src[offset] = val;
+}
+#enddecl(INPLACE)
+
+#end(DECLS)
+
+#define(SHADER)
+
+struct Params {
+    offset_src: u32,
+    offset_dst: u32,
+
+    // Strides (in elements)
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    ne: u32,
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+
+    scale: f32,
+    bias: f32
+};
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+DECLS
+
+override wg_size: u32;
+@compute @workgroup_size(wg_size)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= params.ne) {
+        return;
+    }
+
+    var i = gid.x;
+    let i3 = i / (params.ne2 * params.ne1 * params.ne0);
+    i = i % (params.ne2 * params.ne1 * params.ne0);
+    let i2 = i / (params.ne1 * params.ne0);
+    i = i % (params.ne1 * params.ne0);
+    let i1 = i / params.ne0;
+    let i0 = i % params.ne0;
+
+    let i_src = params.offset_src + i3 * params.stride_src3 + i2 * params.stride_src2 + i1 * params.stride_src1 + i0;
+    let i_dst = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1 + i0;
+
+    store_scale(src[i_src] * params.scale + params.bias, i_dst);
+}
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl
new file mode 100644
index 0000000..99e9192
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl
@@ -0,0 +1,109 @@
+enable f16;
+
+#ifdef DST_F32
+#define DST_INNER_TYPE f32
+#else
+#define DST_INNER_TYPE f16
+#endif
+
+#ifdef VEC4
+#define SRC_TYPE vec4<f32>
+#define DST_TYPE vec4<DST_INNER_TYPE>
+#define VEC_SIZE 4
+#else
+#define SRC_TYPE f32
+#define DST_TYPE DST_INNER_TYPE
+#define VEC_SIZE 1
+#endif
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<SRC_TYPE>;
+
+@group(0) @binding(1)
+var<storage, read_write> idx: array<u32>;
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<DST_TYPE>;
+
+#ifdef I64_IDX
+@group(0) @binding(3)
+var<storage, read_write> error: atomic<u32>;
+#define PARAMS_BINDING 4
+#else
+#define PARAMS_BINDING 3
+#endif
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_idx: u32, // in elements
+    offset_dst: u32, // in elements
+
+    // Strides (in elements)
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_idx0: u32,
+    stride_idx1: u32,
+    stride_idx2: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // Shape of src
+    ne0: u32,
+    n_rows: u32,
+    ne2: u32,
+    ne3: u32,
+
+    // Shape of idx
+    idx1: u32,
+    idx2: u32,
+};
+
+@group(0) @binding(PARAMS_BINDING)
+var<uniform> params: Params;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= (params.ne3 * params.ne2 * params.n_rows * params.ne0) / VEC_SIZE) {
+        return;
+    }
+
+    // getting the row from gid
+    let elems_per_row = params.ne0 / VEC_SIZE;
+    var i = gid.x / elems_per_row;
+
+    let i_src3 = i / (params.ne2 * params.n_rows);
+
+    i = i % (params.ne2 * params.n_rows);
+    let i_src2 = i / params.n_rows;
+    let i_src1 = i % params.n_rows;
+
+    let i_idx2 = i_src3 % params.idx2;
+    let i_idx1 = i_src2 % params.idx1;
+    let i_idx0 = i_src1;
+
+#ifdef I64_IDX
+    let idx_high = (params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2) * 2;
+
+    let idx_val = idx[idx_high];
+    let idx_low_val = idx[idx_high + 1];
+
+    if (idx_low_val != 0) {
+        // Upper bits of index are not zero, output will be incorrect
+        atomicStore(&error, 1);
+        return;
+    }
+#else
+    let idx_i = params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2;
+    let idx_val = idx[idx_i];
+#endif
+
+    let i_dst_row = params.offset_dst + idx_val * params.stride_dst1 + i_src2 * params.stride_dst2 + i_src3 * params.stride_dst3;
+    let i_src_row = params.offset_src + i_src1 * params.stride_src1 + i_src2 * params.stride_src2 + i_src3 * params.stride_src3;
+
+    let col_idx = (gid.x % elems_per_row);
+    dst[i_dst_row/VEC_SIZE + col_idx] = DST_TYPE(src[i_src_row/VEC_SIZE + col_idx]);
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/soft_max.tmpl.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/soft_max.tmpl.wgsl
new file mode 100644
index 0000000..c74dc4c
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/soft_max.tmpl.wgsl
@@ -0,0 +1,345 @@
+#define(VARIANTS)
+[
+  {
+    "SHADER_NAME": "soft_max_f32",
+    "DECLS": ["BASE_BINDINGS", "NOT_INPLACE", "NO_MASK", "NO_SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_inplace",
+    "DECLS": ["BASE_BINDINGS_INPLACE", "INPLACE", "NO_MASK", "NO_SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_sink",
+    "DECLS": ["SINK_BINDINGS", "NOT_INPLACE", "NO_MASK", "SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_sink_inplace",
+    "DECLS": ["SINK_BINDINGS_INPLACE", "INPLACE", "NO_MASK", "SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f32",
+    "REPLS": {
+      "MASK_TYPE" : "f32",
+    },
+    "DECLS": ["MASK_BINDINGS", "NOT_INPLACE", "MASK", "NO_SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f32_inplace",
+    "REPLS": {
+      "MASK_TYPE" : "f32",
+    },
+    "DECLS": ["MASK_BINDINGS_INPLACE", "INPLACE", "MASK", "NO_SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f16",
+    "REPLS": {
+      "MASK_TYPE" : "f16",
+    },
+    "DECLS": ["MASK_BINDINGS", "NOT_INPLACE", "MASK", "NO_SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f16_inplace",
+    "REPLS": {
+      "MASK_TYPE" : "f16",
+    },
+    "DECLS": ["MASK_BINDINGS_INPLACE", "INPLACE", "MASK", "NO_SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f32_sink",
+    "REPLS": {
+      "MASK_TYPE" : "f32",
+    },
+    "DECLS": ["MASK_SINK_BINDINGS", "NOT_INPLACE", "MASK", "SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f32_sink_inplace",
+    "REPLS": {
+      "MASK_TYPE" : "f32",
+    },
+    "DECLS": ["MASK_SINK_BINDINGS_INPLACE", "INPLACE", "MASK", "SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f16_sink",
+    "REPLS": {
+      "MASK_TYPE" : "f16",
+    },
+    "DECLS": ["MASK_SINK_BINDINGS", "NOT_INPLACE", "MASK", "SINK"]
+  },
+  {
+    "SHADER_NAME": "soft_max_f32_mask_f16_sink_inplace",
+    "REPLS": {
+      "MASK_TYPE" : "f16",
+    },
+    "DECLS": ["MASK_SINK_BINDINGS_INPLACE", "INPLACE", "MASK", "SINK"]
+  }
+]
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(BASE_BINDINGS)
+@group(0) @binding(1)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+#enddecl(BASE_BINDINGS)
+
+#decl(BASE_BINDINGS_INPLACE)
+@group(0) @binding(1)
+var<uniform> params: Params;
+#enddecl(BASE_BINDINGS_INPLACE)
+
+#decl(SINK_BINDINGS)
+@group(0) @binding(1)
+var<storage, read_write> sinks: array<f32>;
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+#enddecl(SINK_BINDINGS)
+
+#decl(SINK_BINDINGS_INPLACE)
+@group(0) @binding(1)
+var<storage, read_write> sinks: array<f32>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+#enddecl(SINK_BINDINGS_INPLACE)
+
+#decl(MASK_BINDINGS)
+@group(0) @binding(1)
+var<storage, read_write> mask: array<{{MASK_TYPE}}>;
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+#enddecl(MASK_BINDINGS)
+
+#decl(MASK_BINDINGS_INPLACE)
+@group(0) @binding(1)
+var<storage, read_write> mask: array<{{MASK_TYPE}}>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+#enddecl(MASK_BINDINGS_INPLACE)
+
+#decl(MASK_SINK_BINDINGS)
+@group(0) @binding(1)
+var<storage, read_write> mask: array<{{MASK_TYPE}}>;
+
+@group(0) @binding(2)
+var<storage, read_write> sinks: array<f32>;
+
+@group(0) @binding(3)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(4)
+var<uniform> params: Params;
+#enddecl(MASK_SINK_BINDINGS)
+
+#decl(MASK_SINK_BINDINGS_INPLACE)
+@group(0) @binding(1)
+var<storage, read_write> mask: array<{{MASK_TYPE}}>;
+
+@group(0) @binding(2)
+var<storage, read_write> sinks: array<f32>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+#enddecl(MASK_SINK_BINDINGS_INPLACE)
+
+#decl(NOT_INPLACE)
+fn inter_value(i: u32) -> f32 {
+    return dst[i];
+}
+
+fn update(i: u32, val: f32) {
+    dst[i] = val;
+}
+#enddecl(NOT_INPLACE)
+
+#decl(INPLACE)
+fn inter_value(i: u32) -> f32 {
+    return src[i];
+}
+
+fn update(i: u32, val: f32) {
+    src[i] = val;
+}
+#enddecl(INPLACE)
+
+#decl(NO_MASK)
+fn mask_val(i: u32) -> f32 {
+    return 0.0;
+}
+#enddecl(NO_MASK)
+
+#decl(MASK)
+fn mask_val(i: u32) -> f32 {
+    return f32(mask[i]);
+}
+#enddecl(MASK)
+
+#decl(NO_SINK)
+fn lower_max_bound(i2: u32) -> f32 {
+    return -1e30;
+}
+
+fn add_sinks(val: f32, i2: u32, max_val: f32) -> f32 {
+    return val;
+}
+#enddecl(NO_SINK)
+
+#decl(SINK)
+fn lower_max_bound(i2: u32) -> f32 {
+    return sinks[params.offset_sinks + i2];
+}
+
+fn add_sinks(val: f32, i2: u32, max_val: f32) -> f32 {
+    return val + exp(sinks[params.offset_sinks + i2] - max_val);
+}
+#enddecl(SINK)
+
+#end(DECLS)
+
+#define(SHADER)
+enable f16;
+
+struct Params {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_sinks: u32,
+    offset_dst: u32,
+
+    // Strides (in elements)
+    stride_src01: u32,
+    stride_src02: u32,
+    stride_src03: u32,
+
+    stride_src11: u32,
+    stride_src12: u32,
+    stride_src13: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // shape of src0/dst
+    ne: u32,
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+
+    // shape of src1
+    ne12: u32,
+    ne13: u32,
+
+    scale: f32,
+    max_bias: f32,
+    n_head_log2: f32,
+    m0: f32,
+    m1: f32,
+};
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+DECLS
+
+const CACHE_SIZE: u32 = 16;
+
+override wg_size: u32;
+var<workgroup> scratch: array<f32, wg_size>;
+
+@compute @workgroup_size(wg_size)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+
+    var i = wid.x;
+    let i3 = i / (params.ne2 * params.ne1);
+    i = i % (params.ne2 * params.ne1);
+    let i2 = i / params.ne1;
+    let i1 = i % params.ne1;
+    let i_src0_row = params.offset_src0 + i3 * params.stride_src03 + i2 * params.stride_src02 + i1 * params.stride_src01;
+    let i_src1_row = params.offset_src1 + (i3 % params.ne13) * params.stride_src13 + (i2 % params.ne12) * params.stride_src12 + i1 * params.stride_src11;
+    let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1;
+    let elems = (params.ne0 + wg_size - 1) / wg_size;
+
+    let head = f32(i2);
+    let slope = select(1, select(pow(params.m1, 2 * (head - params.n_head_log2) + 1), pow(params.m0, head + 1), head < params.n_head_log2), params.max_bias > 0);
+
+    var cache: array<f32, CACHE_SIZE>;
+
+    var max_val = lower_max_bound(i2);
+    var col = lid.x;
+    for (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+        let val = src[i_src0_row + col] * params.scale + slope * mask_val(i_src1_row + col);
+        max_val = max(max_val, val);
+        if (col < CACHE_SIZE) {
+            cache[col] = val;
+        }
+        col += wg_size;
+    }
+
+    scratch[lid.x] = max_val;
+    workgroupBarrier();
+    var offset = wg_size / 2;
+    while (offset > 0) {
+        if (lid.x < offset) {
+            scratch[lid.x] = max(scratch[lid.x], scratch[lid.x + offset]);
+        }
+        offset = offset / 2;
+        workgroupBarrier();
+    }
+    let row_max = scratch[0];
+    workgroupBarrier();
+
+    var sum = 0.0f;
+    col = lid.x;
+    for (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+        let val = select(src[i_src0_row + col] * params.scale + slope * mask_val(i_src1_row + col),
+                         cache[col], col < CACHE_SIZE);
+        let ex = exp(val - row_max);
+        sum += ex;
+        if (col < CACHE_SIZE) {
+            cache[col] = ex;
+        } else {
+            update(i_dst_row + col, ex);
+        }
+        col += wg_size;
+    }
+
+    scratch[lid.x] = sum;
+    workgroupBarrier();
+    offset = wg_size / 2;
+    while (offset > 0) {
+        if (lid.x < offset) {
+            scratch[lid.x] += scratch[lid.x + offset];
+        }
+        offset = offset / 2;
+        workgroupBarrier();
+    }
+    let row_sum = add_sinks(scratch[0], i2, row_max);
+
+    let sum_recip = 1.0 / row_sum;
+    col = lid.x;
+    for  (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+        update(i_dst_row + col, select(inter_value(i_dst_row + col), cache[col], col < CACHE_SIZE) * sum_recip);
+        col += wg_size;
+    }
+}
+#end(SHADER)
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/sum_rows.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/sum_rows.wgsl
new file mode 100644
index 0000000..6ea2de9
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/sum_rows.wgsl
@@ -0,0 +1,55 @@
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<f32>;
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_dst: u32, // in elements
+
+    // Strides (in elements)
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    ne0: u32,
+    ne1: u32,
+    ne2: u32
+};
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+var<workgroup> shared_sum: array<f32, WG_SIZE>;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+
+    var i = wid.x;
+    let i3 = i / (params.ne2 * params.ne1);
+    i = i % (params.ne2 * params.ne1);
+    let i2 = i / params.ne1;
+    let i1 = i % params.ne1;
+    let i_src_row = params.offset_src + i3 * params.stride_src3 + i2 * params.stride_src2 + i1 * params.stride_src1;
+    var local_sum: f32 = 0.0;
+    for (var col = lid.x; col < params.ne0; col += WG_SIZE) {
+        local_sum += src[i_src_row + col];
+    }
+    shared_sum[lid.x] = local_sum;
+    workgroupBarrier();
+    // reduce within workgroup
+    var offset: u32 = WG_SIZE >> 1;
+    while (offset > 0) {
+        if (lid.x < offset) {
+            shared_sum[lid.x] = shared_sum[lid.x] + shared_sum[lid.x + offset];
+        }
+        workgroupBarrier();
+        offset >>= 1;
+    }
+
+    if (lid.x == 0) {
+        dst[params.offset_dst + wid.x] = shared_sum[0];
+    }
+}
diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl
new file mode 100644
index 0000000..d639d98
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl
@@ -0,0 +1,179 @@
+#ifdef TYPE_F16
+enable f16;
+#define TYPE f16
+#else
+#define TYPE f32
+#endif
+
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<TYPE>;
+
+#ifndef INPLACE
+@group(0) @binding(1)
+var<storage, read_write> dst: array<TYPE>;
+#define PARAMS_BINDING 2
+#else
+#define PARAMS_BINDING 1
+#endif
+
+struct Params {
+    ne: u32,            // total number of elements
+    offset_src: u32,    // in elements
+    offset_dst: u32,    // in elements
+
+    // Strides (in elements)
+    stride_src0: u32,
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    // Logical shapes
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+#ifdef CLAMP
+    clamp_min: f32,
+    clamp_max: f32,
+#endif
+#ifdef FILL
+    fill_val: f32,
+#endif
+#ifdef XIELU
+    alpha_n: f32,
+    alpha_p: f32,
+    beta: f32,
+    eps: f32,
+#endif
+
+};
+
+@group(0) @binding(PARAMS_BINDING)
+var<uniform> params: Params;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= params.ne) {
+      return;
+    }
+    var i = gid.x;
+    let i3 = i / (params.ne2 * params.ne1 * params.ne0);
+    i = i % (params.ne2 * params.ne1 * params.ne0);
+    let i2 = i / (params.ne1 * params.ne0);
+    i = i % (params.ne1 * params.ne0);
+    let i1 = i / params.ne0;
+    let i0 = i % params.ne0;
+
+    let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 +
+                  i2 * params.stride_src2 + i3 * params.stride_src3;
+
+#ifdef ABS
+    let res = abs(src[params.offset_src + src_idx]);
+#endif
+#ifdef SGN
+    let res = select(TYPE(select(0.0, -1.0, src[params.offset_src + src_idx] < 0.0)), TYPE(1.0),
+                     src[params.offset_src + src_idx] > 0.0);
+#endif
+#ifdef NEG
+    let res = -src[params.offset_src + src_idx];
+#endif
+#ifdef STEP
+    let res = TYPE(select(0.0, 1.0, src[params.offset_src + src_idx] > 0.0));
+#endif
+#ifdef TANH
+    let res = tanh(clamp(src[params.offset_src + src_idx], -9.010913, 9.010913));
+#endif
+#ifdef RELU
+    let res = select(0.0, src[params.offset_src + src_idx], src[params.offset_src + src_idx] > 0.0);
+#endif
+#ifdef ELU
+    let res = select(exp(src[params.offset_src + src_idx]) - 1.0, src[params.offset_src + src_idx],
+                     src[params.offset_src + src_idx] > 0.0);
+#endif
+#ifdef HARDSIGMOID
+    let res = min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0));
+#endif
+#ifdef SIGMOID
+    let res = 1.0 / (1.0 + exp(-src[params.offset_src + src_idx]));
+#endif
+#ifdef SILU
+    let res = src[params.offset_src + src_idx] / (1.0 + exp(-src[params.offset_src + src_idx]));
+#endif
+#ifdef EXP
+    let res = exp(src[params.offset_src + src_idx]);
+#endif
+#ifdef LOG
+    let res = TYPE(log(f32(src[params.offset_src + src_idx])));
+#endif
+#ifdef CLAMP
+    let res = clamp(src[params.offset_src + src_idx], TYPE(params.clamp_min), TYPE(params.clamp_max));
+#endif
+#ifdef FILL
+    let res = TYPE(params.fill_val);
+#endif
+#ifdef HARDSWISH
+    let res = src[params.offset_src + src_idx] *
+              min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0));
+#endif
+#ifdef GELU
+    let res = 0.5 * src[params.offset_src + src_idx] *
+              (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) *
+                               (src[params.offset_src + src_idx] +
+                                0.044715 * pow(src[params.offset_src + src_idx], 3.0)),
+                               -9.010913, 9.010913)));
+#endif
+#ifdef GELU_QUICK
+    let res = src[params.offset_src + src_idx] * 0.5 *
+              (1.0 + tanh(clamp(0.79788456 *
+                               (src[params.offset_src + src_idx] +
+                                0.044715 * src[params.offset_src + src_idx] *
+                                    src[params.offset_src + src_idx] * src[params.offset_src + src_idx]),
+                               -9.010913, 9.010913)));
+#endif
+#ifdef GELU_ERF
+    let res = 0.5 * src[params.offset_src + src_idx] *
+              (1.0 + tanh(clamp(0.79788456 *
+                               (src[params.offset_src + src_idx] +
+                                0.044715 * src[params.offset_src + src_idx] *
+                                    src[params.offset_src + src_idx] * src[params.offset_src + src_idx]),
+                               -9.010913, 9.010913)));
+#endif
+#ifdef XIELU
+    let res =
+        select(((exp(min(src[params.offset_src + src_idx], TYPE(params.eps))) - 1.0) -
+                src[params.offset_src + src_idx]) *
+                   TYPE(params.alpha_n) +
+               TYPE(params.beta) * src[params.offset_src + src_idx],
+               TYPE(params.alpha_p) * src[params.offset_src + src_idx] *
+                   src[params.offset_src + src_idx] +
+                   TYPE(params.beta) * src[params.offset_src + src_idx],
+               src[params.offset_src + src_idx] > 0.0);
+#endif
+#ifdef SOFTPLUS
+    let src_f32 = f32(src[params.offset_src + src_idx]);
+    let res = TYPE(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0));
+#endif
+#ifdef EXPM1
+    let res = exp(src[params.offset_src + src_idx]) - 1.0;
+#endif
+#ifdef FLOOR
+    let res = floor(src[params.offset_src + src_idx]);
+#endif
+#ifdef CEIL
+    let res = ceil(src[params.offset_src + src_idx]);
+#endif
+#ifdef ROUND
+    let src_f32 = f32(src[params.offset_src + src_idx]);
+    let result = select(ceil(src_f32 - 0.5), floor(src_f32 + 0.5), src_f32 >= 0.0);
+    let res = TYPE(result);
+#endif
+#ifdef TRUNC
+    let res = trunc(src[params.offset_src + src_idx]);
+#endif
+
+#ifdef INPLACE
+    src[params.offset_src + src_idx] = res;
+#else
+    dst[params.offset_dst + gid.x] = res;
+#endif
+}