/*
    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"

#ifdef __EMSCRIPTEN__
#    include <emscripten/emscripten.h>
#endif

#include <webgpu/webgpu_cpp.h>

#include <atomic>
#include <condition_variable>
#include <cstdint>
#include <cstring>
#ifdef GGML_WEBGPU_GPU_PROFILE
#    include <iomanip>
#endif
#if defined(GGML_WEBGPU_DEBUG) || defined(GGML_WEBGPU_CPU_PROFILE) || defined(GGML_WEBGPU_GPU_PROFILE)
#    include <iostream>
#endif
#include <map>
#include <memory>
#include <mutex>
#include <optional>
#include <string>
#include <utility>
#include <vector>

#define ROUNDUP_POW2(x, pow2) (((x) + ((pow2) - 1)) & ~((pow2) - 1))
#define CEIL_DIV(M, N)        (((M) + (N) - 1) / (N))

// Return a rectangular grid of workgroups with minimal over-provisioned workgroups.
// Assumes that the total number of workgroups does not exceed max_per_dim^2.
static inline void compute_2d_workgroups(uint32_t total_wg, uint32_t max_per_dim, uint32_t & wg_x, uint32_t & wg_y) {
    wg_y = std::max(1u, CEIL_DIV(total_wg, max_per_dim));
    wg_x = CEIL_DIV(total_wg, wg_y);
}

#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       32
#    define WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES 16  // e.g. enough for two timestamps
#endif

/* Constants */

#define WEBGPU_NUM_PARAM_BUFS                96u
#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE     32u
#define WEBGPU_WAIT_ANY_TIMEOUT_MS           100
// 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_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

// Track https://github.com/gpuweb/gpuweb/issues/5315 for fixes to
// implementations so this can be removed, necessary only for get_rows right now
#define WEBGPU_MAX_WG_SIZE 288

/* 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);

// Holds a pool of parameter buffers for WebGPU operations
struct webgpu_buf_pool {
    std::vector<wgpu::Buffer> 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;
    size_t                  cur_pool_size;
    size_t                  max_pool_size;
    wgpu::Device            device;
    wgpu::BufferUsage       dev_buf_usage;
    size_t                  buf_size;
    bool                    should_grow;

    void init(wgpu::Device      device,
              int               num_bufs,
              size_t            buf_size,
              wgpu::BufferUsage dev_buf_usage,
              bool              should_grow   = false,
              size_t            max_pool_size = WEBGPU_NUM_PARAM_BUFS * 2) {
        this->max_pool_size = max_pool_size;
        this->cur_pool_size = num_bufs;
        this->device        = device;
        this->dev_buf_usage = dev_buf_usage;
        this->buf_size      = buf_size;
        this->should_grow   = should_grow;
        for (int i = 0; i < num_bufs; i++) {
            wgpu::Buffer dev_buf;
            ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf");
            free.push_back(dev_buf);
        }
    }

    wgpu::Buffer alloc_bufs() {
        std::unique_lock<std::mutex> lock(mutex);
        if (!free.empty()) {
            wgpu::Buffer buf = free.back();
            free.pop_back();
            return buf;
        }

        // Try growing the pool if no free buffers
        if (free.empty() && cur_pool_size < max_pool_size && should_grow) {
            cur_pool_size++;
            lock.unlock();  // avoid deadlock between this lock and Dawn's internal locks when buffers are freed in callbacks
            wgpu::Buffer dev_buf;
            ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf");

            if (!dev_buf) {
                GGML_ABORT("webgpu_buf_pool: failed to allocate buffers");
            }
            return dev_buf;
        }
        cv.wait(lock, [this] { return !free.empty(); });
        wgpu::Buffer buf = free.back();
        free.pop_back();
        return buf;
    }

    void free_bufs(std::vector<wgpu::Buffer> 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 & buf : free) {
            if (buf) {
                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_command {
    uint32_t                  num_kernels;
    wgpu::CommandBuffer       commands;
    std::vector<wgpu::Buffer> params_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

#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;

struct webgpu_submission {
    wgpu::FutureWaitInfo submit_done;
#ifdef GGML_WEBGPU_GPU_PROFILE
    std::vector<wgpu::FutureWaitInfo> profile_futures;
#endif
};

// 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;

    std::unique_ptr<ggml_webgpu_shader_lib> shader_lib;

    webgpu_buf_pool param_buf_pool;
    wgpu::Buffer    set_rows_dev_error_buf;
    wgpu::Buffer    set_rows_host_error_buf;

    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 */

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 */

static bool ggml_backend_webgpu_handle_wait_status(wgpu::WaitStatus status, bool allow_timeout = false) {
    switch (status) {
        case wgpu::WaitStatus::Success:
            return true;
        case wgpu::WaitStatus::TimedOut:
            if (allow_timeout) {
                return false;
            }
            GGML_LOG_ERROR("ggml_webgpu: WaitAny timed out unexpectedly\n");
            return false;
        case wgpu::WaitStatus::Error:
            GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an error\n");
            return false;
        default:
            GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an unknown status\n");
            return false;
    }
}

#ifdef GGML_WEBGPU_GPU_PROFILE
static void ggml_backend_webgpu_erase_completed_futures(std::vector<wgpu::FutureWaitInfo> & futures) {
    futures.erase(std::remove_if(futures.begin(), futures.end(),
                                 [](const wgpu::FutureWaitInfo & info) { return info.completed; }),
                  futures.end());
}

static void ggml_backend_webgpu_wait_profile_futures(webgpu_global_context &             ctx,
                                                     std::vector<wgpu::FutureWaitInfo> & futures,
                                                     bool                                block) {
    if (futures.empty()) {
        return;
    }

    uint64_t timeout_ms = block ? UINT64_MAX : 0;
    if (block) {
        while (!futures.empty()) {
            auto waitStatus = ctx->instance.WaitAny(futures.size(), futures.data(), timeout_ms);
            if (ggml_backend_webgpu_handle_wait_status(waitStatus)) {
                ggml_backend_webgpu_erase_completed_futures(futures);
            }
        }
    } else {
        auto waitStatus = ctx->instance.WaitAny(futures.size(), futures.data(), timeout_ms);
        if (ggml_backend_webgpu_handle_wait_status(waitStatus, true)) {
            ggml_backend_webgpu_erase_completed_futures(futures);
        }
    }
}
#endif

// Wait for the queue to finish processing all submitted work
static void ggml_backend_webgpu_wait(webgpu_global_context &          ctx,
                                     std::vector<webgpu_submission> & subs,
                                     bool                             block = true) {
    if (subs.empty()) {
        return;
    }

    bool blocking_wait = block || subs.size() >= WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD;
    while (blocking_wait) {
        auto waitStatus = ctx->instance.WaitAny(1, &subs[0].submit_done, WEBGPU_WAIT_ANY_TIMEOUT_MS * 1e6);
        if (ggml_backend_webgpu_handle_wait_status(waitStatus, true)) {
#ifdef GGML_WEBGPU_GPU_PROFILE
            ggml_backend_webgpu_wait_profile_futures(ctx, subs[0].profile_futures, true);
#endif
            subs.erase(subs.begin());
        }
        blocking_wait = (block && !subs.empty()) || subs.size() >= WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD;
    }

    if (subs.empty()) {
        return;
    }

    // Poll each submit future once and remove completed submissions.
    for (auto sub = subs.begin(); sub != subs.end();) {
        auto waitStatus = ctx->instance.WaitAny(1, &sub->submit_done, 0);
        bool success    = ggml_backend_webgpu_handle_wait_status(waitStatus, true);
#ifdef GGML_WEBGPU_GPU_PROFILE
        ggml_backend_webgpu_wait_profile_futures(ctx, sub->profile_futures, false);
        if (success && sub->profile_futures.empty()) {
#else
        if (success) {
#endif
            sub = subs.erase(sub);
        } else {
            ++sub;
        }
    }
}

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 ggml_backend_webgpu_submit(webgpu_global_context &       ctx,
                                                    std::vector<webgpu_command> & commands,
                                                    webgpu_buf_pool &             param_buf_pool) {
    std::vector<wgpu::CommandBuffer> command_buffers;
    std::vector<wgpu::Buffer>        params_bufs;
    webgpu_submission                submission;
#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());
    }
    ctx->queue.Submit(command_buffers.size(), command_buffers.data());

    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);
        });
    submission.submit_done = { p_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 });
            });
        submission.profile_futures.push_back({ f });
    }
#endif
    return submission;
}

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) {
    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<wgpu::Buffer>    params_bufs_list;
    std::vector<wgpu::BindGroup> bind_groups;

    for (size_t i = 0; i < pipelines.size(); i++) {
        wgpu::Buffer params_bufs = param_buf_pool.alloc_bufs();

        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, .offset = 0, .size = params_bufs.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 (size_t i = 0; i < params_bufs_list.size(); i++) {
        ctx->queue.WriteBuffer(params_bufs_list[i], 0, params_list[i].data(), params_list[i].size() * sizeof(uint32_t));
    }

#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.num_kernels           = pipelines.size();
#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) {
    return ggml_backend_webgpu_build_multi(ctx, param_buf_pool,
                                           {
                                               pipeline
    },
                                           { std::move(params) }, { std::move(bind_group_entries) },
                                           { { wg_x, wg_y } });
}

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_command>    commands = { command };
    std::vector<webgpu_submission> sub      = { ggml_backend_webgpu_submit(ctx, commands, ctx->memset_buf_pool) };
}

/** 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 (" << std::fixed << std::setprecision(2)
                  << 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
    bool src_overlap;
};

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);
    flags.src_overlap          = ggml_webgpu_tensor_overlap(src0, src1);

    return flags;
}

static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_cpy_pipeline(shader_lib_ctx);

    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)),
        // 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, 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_set(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
    const bool inplace = ggml_webgpu_tensor_equal(src0, dst);

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .inplace     = inplace,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_set_pipeline(shader_lib_ctx);

    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());

    const uint32_t ne            = inplace ? (uint32_t) ggml_nelements(src1) : (uint32_t) ggml_nelements(dst);
    const uint32_t dst_type_size = (uint32_t) ggml_type_size(dst->type);

    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) (((const int32_t *) dst->op_params)[3] / dst_type_size),

        (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)),

        1u,
        (uint32_t) (((const int32_t *) dst->op_params)[0] / dst_type_size),
        (uint32_t) (((const int32_t *) dst->op_params)[1] / dst_type_size),
        (uint32_t) (((const int32_t *) dst->op_params)[2] / dst_type_size),

        (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;
    uint32_t                          binding_index = 0;
    if (!inplace) {
        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) });
        binding_index++;
    }
    entries.push_back({ .binding = binding_index,
                        .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 = binding_index + 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_pad(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0 = src, .dst = dst, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_pad_pipeline(shader_lib_ctx);

    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 webgpu_command ggml_webgpu_solve_tri(webgpu_context & ctx,
                                            ggml_tensor *    src0,
                                            ggml_tensor *    src1,
                                            ggml_tensor *    dst) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0               = src0,
        .src1               = src1,
        .dst                = dst,
        .max_wg_size        = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_solve_tri_pipeline(shader_lib_ctx);

    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());

    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) (src0->nb[0] / ggml_type_size(src0->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) (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) (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)),

        (uint32_t) src1->ne[0],
        (uint32_t) dst->ne[2],
        (uint32_t) dst->ne[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)  }
    };

    const uint32_t wg_x = CEIL_DIV((uint32_t) src1->ne[0], decisions->wg_size);
    const uint32_t wg_y = (uint32_t) (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);
}

static webgpu_command ggml_webgpu_ssm_conv(webgpu_context & ctx,
                                           ggml_tensor *    src0,
                                           ggml_tensor *    src1,
                                           ggml_tensor *    dst) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
    };

    webgpu_pipeline pipeline  = ctx->shader_lib->get_ssm_conv_pipeline(shader_lib_ctx);
    auto *          decisions = static_cast<ggml_webgpu_ssm_conv_shader_decisions *>(pipeline.context.get());

    const uint32_t token_tiles = CEIL_DIV((uint32_t) dst->ne[1], decisions->tokens_per_wg);

    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) (src0->nb[1] / ggml_type_size(src0->type)),
        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
        (uint32_t) (src1->nb[1] / ggml_type_size(src1->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) src1->ne[0],
        (uint32_t) src0->ne[1],
        (uint32_t) dst->ne[1],
        (uint32_t) dst->ne[2],
        token_tiles,
    };

    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)  }
    };

    const uint32_t wg_x = CEIL_DIV((uint32_t) src0->ne[1], decisions->block_size);
    const uint32_t wg_y = token_tiles * (uint32_t) dst->ne[2];
    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, wg_y);
}

static webgpu_command ggml_webgpu_gated_delta_net(webgpu_context & ctx,
                                                  ggml_tensor *    src0,
                                                  ggml_tensor *    src1,
                                                  ggml_tensor *    src2,
                                                  ggml_tensor *    src3,
                                                  ggml_tensor *    src4,
                                                  ggml_tensor *    src5,
                                                  ggml_tensor *    dst) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .src2        = src2,
        .src3        = src3,
        .src4        = src4,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_gated_delta_net_pipeline(shader_lib_ctx);

    const uint32_t s_v      = (uint32_t) src2->ne[0];
    const uint32_t h        = (uint32_t) src2->ne[1];
    const uint32_t n_tokens = (uint32_t) src2->ne[2];
    const uint32_t n_seqs   = (uint32_t) src2->ne[3];
    const float    scale    = 1.0f / sqrtf((float) s_v);
    uint32_t       scale_u32;
    memcpy(&scale_u32, &scale, sizeof(scale_u32));

    std::vector<uint32_t> params = {
        h,
        n_tokens,
        n_seqs,
        s_v * h * n_tokens * n_seqs,

        (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) (src2->nb[1] / ggml_type_size(src2->type)),
        (uint32_t) (src2->nb[2] / ggml_type_size(src2->type)),
        (uint32_t) (src2->nb[3] / ggml_type_size(src2->type)),

        (uint32_t) (src4->nb[1] / ggml_type_size(src4->type)),
        (uint32_t) (src4->nb[2] / ggml_type_size(src4->type)),
        (uint32_t) (src4->nb[3] / ggml_type_size(src4->type)),

        (uint32_t) src0->ne[1],
        (uint32_t) (src2->ne[3] / src0->ne[3]),
        scale_u32,
    };

    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(src2),
         .offset  = ggml_webgpu_tensor_align_offset(ctx, src2),
         .size    = ggml_webgpu_tensor_binding_size(ctx, src2) },
        { .binding = 3,
         .buffer  = ggml_webgpu_tensor_buf(src3),
         .offset  = ggml_webgpu_tensor_align_offset(ctx, src3),
         .size    = ggml_webgpu_tensor_binding_size(ctx, src3) },
        { .binding = 4,
         .buffer  = ggml_webgpu_tensor_buf(src4),
         .offset  = ggml_webgpu_tensor_align_offset(ctx, src4),
         .size    = ggml_webgpu_tensor_binding_size(ctx, src4) },
        { .binding = 5,
         .buffer  = ggml_webgpu_tensor_buf(src5),
         .offset  = ggml_webgpu_tensor_align_offset(ctx, src5),
         .size    = ggml_webgpu_tensor_binding_size(ctx, src5) },
        { .binding = 6,
         .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, pipeline, params, entries, h, n_seqs);
}

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_shader_lib_context shader_lib_ctx = {
        .src0        = src,
        .src1        = idx,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_set_rows_pipeline(shader_lib_ctx);

    auto * decisions = static_cast<ggml_webgpu_set_rows_shader_decisions *>(pipeline.context.get());

    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 (decisions->i64_idx) {
        entries.push_back({ .binding = 3,
                            .buffer  = ctx->set_rows_dev_error_buf,
                            .offset  = 0,
                            .size    = ctx->set_rows_dev_error_buf.GetSize() });
    }

    uint32_t threads;
    if (decisions->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);
}

// 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 webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx,
                                           ggml_tensor *    src,
                                           ggml_tensor *    idx,
                                           ggml_tensor *    dst) {
    const bool float_parallel = src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16 || src->type == GGML_TYPE_I32;

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src,
        .src1        = nullptr,
        .dst         = dst,
        .max_wg_size = WEBGPU_MAX_WG_SIZE,
    };

    webgpu_pipeline pipeline  = ctx->shader_lib->get_get_rows_pipeline(shader_lib_ctx);
    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());

    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)),
                                     (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)),
                                     (uint32_t) dst->ne[0],
                                     (uint32_t) dst->ne[1],
                                     (uint32_t) dst->ne[2],
                                     (uint32_t) dst->ne[3],
                                     (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 blocks_per_row = (uint32_t) (dst->ne[0] / (src->type == GGML_TYPE_F32 && dst->ne[0] % 4 == 0 ? 4 : 1));
    uint32_t total_rows     = (uint32_t) (dst->ne[1] * dst->ne[2] * dst->ne[3]);
    uint32_t total_threads  = float_parallel ? blocks_per_row * total_rows : total_rows;
    uint32_t wg_x           = CEIL_DIV(total_threads, 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_mul_mat(webgpu_context & ctx,
                                          ggml_tensor *    src0,
                                          ggml_tensor *    src1,
                                          ggml_tensor *    dst) {
    // Determine if this is a mat-vec operation
    bool is_vec = (dst->ne[1] == 1);

    // Determine if we should use fast path
    bool use_fast = false;
    switch (src1->type) {
        case GGML_TYPE_F16:
            use_fast = (src0->type == GGML_TYPE_F16);
            break;
        case GGML_TYPE_F32:
            // TODO: implement better mat-mat for k-quants, mat-vec for all k-quants except q6_K
            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_Q8_1:
                case GGML_TYPE_Q6_K:
                    use_fast = true;
                    break;
                case GGML_TYPE_Q2_K:
                case GGML_TYPE_Q3_K:
                case GGML_TYPE_Q4_K:
                case GGML_TYPE_Q5_K:
                    // we don't have fast mat-vec for these types, but we do have (semi) fast mat-mat
                    use_fast = !is_vec;
                    break;
                default:
                    break;
            }
            break;
        default:
            break;
    }

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0                     = src0,
        .src1                     = src1,
        .dst                      = dst,
        .max_wg_size              = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .supports_subgroup_matrix = ctx->global_ctx->capabilities.supports_subgroup_matrix,
        .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,
        .max_subgroup_size        = ctx->global_ctx->capabilities.max_subgroup_size,
    };

    // Get or create pipeline
    webgpu_pipeline pipeline;

    if (use_fast && is_vec) {
        pipeline = ctx->shader_lib->get_mul_mat_vec_pipeline(shader_lib_ctx);
    } else if (use_fast) {
        pipeline = ctx->shader_lib->get_mul_mat_fast_pipeline(shader_lib_ctx);
    } else {
        pipeline = ctx->shader_lib->get_mul_mat_legacy_pipeline(shader_lib_ctx);
    }

    // Build params
    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],
        (uint32_t) dst->ne[1],
        (uint32_t) src0->ne[0],
        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),
        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
        (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),
        (uint32_t) src0->ne[2],
        (uint32_t) src0->ne[3],
        (uint32_t) (src1->ne[2] / src0->ne[2]),
        (uint32_t) (src1->ne[3] / src0->ne[3])
    };

    // Build bind group entries
    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)  },
    };

    // Calculate workgroup dimensions
    uint32_t       wg_x           = 1;
    uint32_t       wg_y           = 1;
    const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;

    if (use_fast && is_vec) {
        auto * decisions = static_cast<ggml_webgpu_mul_mat_vec_shader_decisions *>(pipeline.context.get());

        uint32_t batches       = dst->ne[2] * dst->ne[3];
        uint32_t output_groups = CEIL_DIV(dst->ne[0], decisions->outputs_per_wg);
        uint32_t total_wg      = output_groups * batches;
        compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y);
    } else if (use_fast) {
        auto * decisions = static_cast<ggml_webgpu_mul_mat_shader_decisions *>(pipeline.context.get());

        // Fast-path tiled/subgroup calculations
        uint32_t wg_m;
        uint32_t wg_n;
        if (decisions->use_subgroup_matrix) {
            uint32_t wg_m_sg_tile =
                decisions->subgroup_m * decisions->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 =
                decisions->subgroup_n * decisions->subgroup_matrix_n * ctx->global_ctx->capabilities.sg_mat_n;
            wg_n = CEIL_DIV(dst->ne[1], wg_n_sg_tile);
        } else {
            uint32_t tile_m_s = decisions->tile_m * decisions->wg_size_m;
            uint32_t tile_n_s = decisions->tile_n * decisions->wg_size_n;
            wg_m              = CEIL_DIV(dst->ne[0], tile_m_s);
            wg_n              = CEIL_DIV(dst->ne[1], tile_n_s);
        }
        uint32_t total_wg = wg_m * wg_n * dst->ne[2] * dst->ne[3];
        compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y);

    } else {  // legacy
        auto *   decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
        uint32_t wg_size   = decisions->wg_size;
        uint32_t total_wg  = CEIL_DIV(dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3], wg_size);
        compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y);
    }

    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) });

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0               = Q,
        .src1               = K,
        .src2               = V,
        .src3               = mask,
        .src4               = sinks,
        .dst                = dst,
        .max_wg_size        = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize,
        .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,
        .max_subgroup_size  = ctx->global_ctx->capabilities.max_subgroup_size,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_pipeline(shader_lib_ctx);

    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);

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src,
        .src1        = nullptr,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .inplace     = inplace,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_unary_pipeline(shader_lib_ctx);

    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_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .inplace     = flags.inplace,
        .overlap     = flags.overlap,
        .src_overlap = flags.src_overlap,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_binary_pipeline(shader_lib_ctx);

    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());

    uint32_t ne = (uint32_t) ggml_nelements(dst);

    size_t src0_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src0);
    size_t src1_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src1);

    uint32_t offset_merged_src0 = 0;
    uint32_t offset_merged_src1 = 0;
    if (flags.src_overlap) {
        size_t min_off     = std::min(src0_webgpu_tensor_align_offset, src1_webgpu_tensor_align_offset);
        offset_merged_src0 = (uint32_t) ((src0_webgpu_tensor_align_offset - min_off) / ggml_type_size(src0->type));
        offset_merged_src1 = (uint32_t) ((src1_webgpu_tensor_align_offset - min_off) / ggml_type_size(src0->type));
    }

    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)),
        offset_merged_src0,
        offset_merged_src1,
        (uint32_t) (src0->nb[0] / ggml_type_size(src0->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) (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;

    if (flags.src_overlap) {
        size_t merged_offset = std::min(src0_webgpu_tensor_align_offset, src1_webgpu_tensor_align_offset);
        size_t merged_end    = std::max(src0_webgpu_tensor_align_offset + ggml_webgpu_tensor_binding_size(ctx, src0),
                                        src1_webgpu_tensor_align_offset + ggml_webgpu_tensor_binding_size(ctx, src1));
        entries.push_back({
            .binding = 0,
            .buffer  = ggml_webgpu_tensor_buf(src0),
            .offset  = merged_offset,
            .size    = merged_end - merged_offset,
        });
        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),
        });
    } else {
        entries.push_back({
            .binding = 0,
            .buffer  = ggml_webgpu_tensor_buf(src0),
            .offset  = src0_webgpu_tensor_align_offset,
            .size    = ggml_webgpu_tensor_binding_size(ctx, src0),
        });
        entries.push_back({
            .binding = 1,
            .buffer  = ggml_webgpu_tensor_buf(src1),
            .offset  = src1_webgpu_tensor_align_offset,
            .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_concat(webgpu_context & ctx,
                                         ggml_tensor *    src0,
                                         ggml_tensor *    src1,
                                         ggml_tensor *    dst) {
    uint32_t ne  = (uint32_t) ggml_nelements(dst);
    uint32_t dim = (uint32_t) dst->op_params[0];

    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) (src0->nb[0] / ggml_type_size(src0->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) (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) dst->ne[0],
        (uint32_t) dst->ne[1],
        (uint32_t) dst->ne[2],
        (uint32_t) dst->ne[3],
        dim,
        (uint32_t) src0->ne[dim]
    };

    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)  }
    };

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
    };

    webgpu_pipeline pipeline  = ctx->shader_lib->get_concat_pipeline(shader_lib_ctx);
    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
    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_repeat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * dst) {
    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, dst) / ggml_type_size(dst->type)),
                                     (uint32_t) (src0->nb[0] / ggml_type_size(src0->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) (src0->ne[0]),
                                     (uint32_t) (src0->ne[1]),
                                     (uint32_t) (src0->ne[2]),
                                     (uint32_t) (src0->ne[3]),
                                     (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(src0),
         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) },
        { .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_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
    };

    webgpu_pipeline pipeline  = ctx->shader_lib->get_repeat_pipeline(shader_lib_ctx);
    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
    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_row_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
    bool 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) });
    }

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .inplace     = inplace,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_row_norm_pipeline(shader_lib_ctx);
    return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, 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) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .src2        = src2,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .inplace     = ggml_webgpu_tensor_equal(src0, dst),
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_rope_pipeline(shader_lib_ctx);

    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());

    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;
    float freq_scale;
    float ext_factor;
    float attn_factor;
    float beta_fast;
    float 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) });
    }

    uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), 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_glu(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_glu_pipeline(shader_lib_ctx);

    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());

    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) });

    uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), 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_scale(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
    bool inplace = ggml_webgpu_tensor_equal(src, dst);

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src,
        .src1        = nullptr,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .inplace     = inplace,
    };

    webgpu_pipeline pipeline  = ctx->shader_lib->get_scale_pipeline(shader_lib_ctx);
    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());

    // params unchanged
    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
    };

    // bindgroups unchanged
    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), 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_soft_max(webgpu_context & ctx,
                                           ggml_tensor *    src0,
                                           ggml_tensor *    src1,
                                           ggml_tensor *    src2,
                                           ggml_tensor *    dst) {
    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0        = src0,
        .src1        = src1,
        .src2        = src2,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .inplace     = ggml_webgpu_tensor_equal(src0, dst),
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_soft_max_pipeline(shader_lib_ctx);

    const int inplace  = ggml_webgpu_tensor_equal(src0, dst);
    const int has_mask = (src1 != nullptr);
    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)),
        has_mask ? (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)),
        has_mask ? (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)) : 0,
        has_mask ? (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)) : 0,
        has_mask ? (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],
        has_mask ? (uint32_t) src1->ne[2] : 0,
        has_mask ? (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 (has_mask) {
        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, pipeline, 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_shader_lib_context shader_lib_ctx = {
        .src0 = src, .dst = dst, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_argmax_pipeline(shader_lib_ctx);
    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;

    ggml_webgpu_shader_lib_context shader_lib_ctx = {
        .src0               = src,
        .src1               = nullptr,
        .dst                = dst,
        .max_wg_size        = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
        .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize,
    };

    webgpu_pipeline argsort_pipeline = ctx->shader_lib->get_argsort_pipeline(shader_lib_ctx);
    auto * argsort_decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(argsort_pipeline.context.get());

    webgpu_pipeline argsort_merge_pipeline = ctx->shader_lib->get_argsort_merge_pipeline(shader_lib_ctx);

    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_shader_lib_context shader_lib_ctx = {
        .src0        = src,
        .src1        = nullptr,
        .dst         = dst,
        .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_cumsum_pipeline(shader_lib_ctx);
    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_shader_lib_context shader_lib_ctx = {
        .src0 = src, .dst = dst, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
    };

    webgpu_pipeline pipeline = ctx->shader_lib->get_sum_rows_pipeline(shader_lib_ctx);

    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:
            return ggml_webgpu_set(ctx, src0, src1, 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_CONCAT:
            return ggml_webgpu_concat(ctx, src0, src1, node);
        case GGML_OP_REPEAT:
            return ggml_webgpu_repeat(ctx, src0, node);
        case GGML_OP_RMS_NORM:
        case GGML_OP_L2_NORM:
            return ggml_webgpu_row_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:
        case GGML_OP_CLAMP:
        case GGML_OP_FILL:
        case GGML_OP_LOG:
        case GGML_OP_SQR:
        case GGML_OP_SQRT:
        case GGML_OP_SIN:
        case GGML_OP_COS:
        case GGML_OP_DIAG:
        case GGML_OP_TRI:
            return ggml_webgpu_unary_op(ctx, src0, node);
        case GGML_OP_SOLVE_TRI:
            return ggml_webgpu_solve_tri(ctx, src0, src1, node);
        case GGML_OP_SSM_CONV:
            return ggml_webgpu_ssm_conv(ctx, src0, src1, node);
        case GGML_OP_GATED_DELTA_NET:
            return ggml_webgpu_gated_delta_net(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5], 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:
        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);

    std::vector<webgpu_command>    commands;
    std::vector<webgpu_submission> subs;
    uint32_t                       num_batched_kernels = 0;
    bool                           contains_set_rows   = false;

    for (int i = 0; i < cgraph->n_nodes; i++) {
        if (cgraph->nodes[i]->op == GGML_OP_SET_ROWS) {
            contains_set_rows = true;
        }
        if (auto cmd = ggml_webgpu_encode_node(ctx, cgraph->nodes[i])) {
            commands.push_back(*cmd);
            num_batched_kernels += cmd.value().num_kernels;
        }

        if (num_batched_kernels >= WEBGPU_COMMAND_SUBMIT_BATCH_SIZE) {
            num_batched_kernels = 0;
            subs.push_back(ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool));
            // Process events and check for completed submissions
            ctx->global_ctx->instance.ProcessEvents();
            ggml_backend_webgpu_wait(ctx->global_ctx, subs, false);
            commands.clear();
        }
    }
    if (!commands.empty()) {
        subs.push_back(ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool));
        commands.clear();
    }

    // If there are SET_ROWS operations in this graph, copy the error buffers to the host for checking.
    if (contains_set_rows) {
        wgpu::CommandEncoder encoder = ctx->global_ctx->device.CreateCommandEncoder();
        encoder.CopyBufferToBuffer(ctx->set_rows_dev_error_buf, 0, ctx->set_rows_host_error_buf, 0,
                                   ctx->set_rows_host_error_buf.GetSize());
        wgpu::CommandBuffer set_rows_commands = encoder.Finish();
        ctx->global_ctx->queue.Submit(1, &set_rows_commands);
        ggml_backend_webgpu_map_buffer(ctx->global_ctx, ctx->set_rows_host_error_buf, wgpu::MapMode::Read, 0,
                                       ctx->set_rows_host_error_buf.GetSize());
        const uint32_t * error_data = (const uint32_t *) ctx->set_rows_host_error_buf.GetConstMappedRange();
        if (*error_data) {
            GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported.");
        }
        ctx->set_rows_host_error_buf.Unmap();
    }

    ggml_backend_webgpu_wait(ctx->global_ctx, subs);
    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);
    }
    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);
}

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 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->shader_lib = std::make_unique<ggml_webgpu_shader_lib>(dev_ctx->webgpu_global_ctx->device);
    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, true);
    ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->set_rows_dev_error_buf,
                              WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
                              wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "set_rows_dev_error_buf");
    ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->set_rows_host_error_buf,
                              WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
                              wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "set_rows_host_error_buf");

#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:
            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type) &&
                          (src1->type == op->type);
            break;
        case GGML_OP_CONCAT:
            supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32);
            break;
        case GGML_OP_REPEAT:
            supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32 || src0->type == GGML_TYPE_I16);
            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:
            supports_op = src0->type == src1->type && src0->type == op->type &&
                          (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_I32);
            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:
        case GGML_OP_L2_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_TRI:
            supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32;
            break;
        case GGML_OP_DIAG:
            supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32;
            break;
        case GGML_OP_SOLVE_TRI:
            supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
            break;
        case GGML_OP_SSM_CONV:
            supports_op = op->type == GGML_TYPE_F32;
            break;
        case GGML_OP_GATED_DELTA_NET:
            {
                const uint32_t s_v = (uint32_t) src2->ne[0];
                supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 &&
                              src2->type == GGML_TYPE_F32 && op->src[3]->type == GGML_TYPE_F32 &&
                              op->src[4]->type == GGML_TYPE_F32 && op->src[5]->type == GGML_TYPE_F32 &&
                              s_v <= ctx->webgpu_global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
            }
            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_SQR:
            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type);
            break;
        case GGML_OP_SQRT:
            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type);
            break;
        case GGML_OP_SIN:
            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type);
            break;
        case GGML_OP_COS:
            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)
