// transcribe-load-common.cpp - shared model-load scaffolding.
//
// See transcribe-load-common.h for rationale. The functions here are the
// common backend-init and tensor-stream logic shared by per-family load().

#include "transcribe-load-common.h"

#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml.h"
#include "gguf.h"
// Under GGML_BACKEND_DL the Metal symbols live in a loadable module and are
// not link-visible here, so the capability query below is compiled out.
#if defined(TRANSCRIBE_HAS_METAL) && !defined(TRANSCRIBE_GGML_BACKEND_DL)
#    include "ggml-metal.h"
#endif
#include "transcribe-backend.h"
#include "transcribe-log.h"
#include "transcribe-path.h"

#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <ios>
#include <stdexcept>
#include <vector>

namespace transcribe::load_common {

// GPUs below MTLGPUFamilyApple7 (Intel iGPUs, AMD dGPUs on Intel Macs) have
// no simdgroup matrix multiply; ggml's fallback matmul kernels silently
// produce garbage transcripts there (Handy issue #1608). No generic ggml
// capability exposes this — MUL_MAT's supports_op only needs simdgroup
// *reduction*, which these GPUs do report — so ask Metal directly.
bool metal_backend_lacks_simdgroup_mm(ggml_backend_t be, ggml_backend_dev_t dev) {
    // Test hook: force the "missing" verdict by device-name substring
    // ("*" matches any) so the gate can be exercised on healthy hardware.
    if (const char * match = std::getenv("TRANSCRIBE_TEST_METAL_NO_SIMDGROUP_MM");
        match != nullptr && match[0] != '\0') {
        const char * name = ggml_backend_dev_name(dev);
        if (std::strcmp(match, "*") == 0 || (name != nullptr && std::strstr(name, match) != nullptr)) {
            return true;
        }
    }
#if defined(TRANSCRIBE_HAS_METAL) && !defined(TRANSCRIBE_GGML_BACKEND_DL)
    // ggml_backend_metal_supports_family asserts the backend is Metal; guard
    // so a classify/is_metal disagreement can never abort the process.
    if (ggml_backend_is_metal(be)) {
        return !ggml_backend_metal_supports_family(be, /*MTLGPUFamilyApple7=*/7);
    }
#endif
    (void) be;
    return false;
}

namespace {

// Device init can throw from GPU drivers. Treat that like nullptr so AUTO can
// keep probing and explicit backend requests fail cleanly.
//
// Test hook: TRANSCRIBE_TEST_DEV_INIT_THROW is inert when unset or empty,
// "*" matches every device, otherwise it matches by device-name substring.
ggml_backend_t dev_init_checked(ggml_backend_dev_t dev, const char * error_tag) {
    const char * name = "?";
    try {
        if (const char * n = ggml_backend_dev_name(dev); n != nullptr) {
            name = n;
        }
        if (const char * match = std::getenv("TRANSCRIBE_TEST_DEV_INIT_THROW"); match != nullptr && match[0] != '\0') {
            if (std::strcmp(match, "*") == 0 || std::strstr(name, match) != nullptr) {
                throw std::runtime_error("TRANSCRIBE_TEST_DEV_INIT_THROW fault injection");
            }
        }
        return ggml_backend_dev_init(dev, nullptr);
    } catch (const std::exception & e) {
        log_msg(TRANSCRIBE_LOG_LEVEL_WARN, "%s: device \"%s\" init threw: %s - skipping it", error_tag, name, e.what());
        return nullptr;
    } catch (...) {
        log_msg(TRANSCRIBE_LOG_LEVEL_WARN, "%s: device \"%s\" init threw an unknown exception - skipping it", error_tag,
                name);
        return nullptr;
    }
}

// Try to discover and initialize the first device whose classified
// BackendKind matches `wanted`, visiting candidates in gpu_probe_order
// (every discrete GPU before any integrated GPU). On success returns
// the initialized backend and writes the classified kind of the device
// that actually succeeded to out_kind (which may differ from `wanted`
// when `wanted == BackendKind::OtherGpu`). Returns nullptr if no
// matching device initializes.
//
// When `wanted == BackendKind::OtherGpu`, this acts as an "any GPU of
// any vendor" probe — used by the AUTO path. Critically, out_kind is
// derived from the device that actually initialized, not from a
// separate post-hoc registry walk, so a failing first-GPU followed by
// a succeeding second-GPU yields the correct kind.
ggml_backend_t try_init_kind(BackendKind wanted, const char * error_tag, BackendKind & out_kind) {
    const size_t n = ggml_backend_dev_count();

    std::vector<enum ggml_backend_dev_type> dev_types;
    dev_types.reserve(n);
    for (size_t i = 0; i < n; ++i) {
        dev_types.push_back(ggml_backend_dev_type(ggml_backend_dev_get(i)));
    }

    for (const size_t i : gpu_probe_order(dev_types)) {
        ggml_backend_dev_t dev = ggml_backend_dev_get(i);

        if (wanted != BackendKind::OtherGpu && classify_device(dev) != wanted) {
            continue;
        }

        ggml_backend_t be = dev_init_checked(dev, error_tag);
        if (be == nullptr) {
            continue;
        }

        const BackendKind kind = classify_device(dev);

        // A Metal device without simdgroup matmul yields garbage transcripts
        // (see metal_backend_lacks_simdgroup_mm): skip it under AUTO, honor
        // an explicit Metal request with a warning.
        if (kind == BackendKind::Metal && metal_backend_lacks_simdgroup_mm(be, dev)) {
            const char * dname = ggml_backend_dev_name(dev);
            if (wanted == BackendKind::OtherGpu) {
                log_msg(TRANSCRIBE_LOG_LEVEL_WARN,
                        "%s: skipping Metal device \"%s\": no simdgroup matrix multiply "
                        "(pre-Apple7 GPU)",
                        error_tag, dname != nullptr ? dname : "?");
                safe_backend_free(be);
                continue;
            }
            log_msg(TRANSCRIBE_LOG_LEVEL_WARN,
                    "%s: Metal device \"%s\" has no simdgroup matrix multiply "
                    "(pre-Apple7 GPU); transcription may be incorrect and very slow",
                    error_tag, dname != nullptr ? dname : "?");
        }

        log_msg(TRANSCRIBE_LOG_LEVEL_INFO, "%s: using %s backend: %s", error_tag, kind_name(kind),
                ggml_backend_dev_name(dev));
        out_kind = kind;
        return be;
    }
    return nullptr;
}

// Append every ACCEL device as a scheduler backend. ACCEL backends
// (BLAS, AMX, …) accelerate specific ops on host memory, so they
// layer cleanly on top of both CPU and GPU primaries. They are
// excluded only on strict-CPU requests, where the whole point is to
// avoid any backend dispatch ambiguity.
void append_accel_backends(std::vector<ggml_backend_t> & out, const char * error_tag) {
    const size_t n = ggml_backend_dev_count();
    for (size_t i = 0; i < n; ++i) {
        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
        if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_ACCEL) {
            continue;
        }
        ggml_backend_t be = dev_init_checked(dev, error_tag);
        if (be == nullptr) {
            continue;
        }

        log_msg(TRANSCRIBE_LOG_LEVEL_INFO, "%s: using accel backend: %s", error_tag, ggml_backend_dev_name(dev));
        out.push_back(be);
    }
}

// Initialize the CPU backend. Always runs — it is the universal
// fallback and the strict-CPU primary.
ggml_backend_t init_cpu_backend(const char * error_tag) {
    ggml_backend_t cpu_be = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
    if (cpu_be == nullptr) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: failed to initialize CPU backend", error_tag);
    }
    return cpu_be;
}

bool valid_backend_request(int raw) {
    switch (raw) {
        case TRANSCRIBE_BACKEND_AUTO:
        case TRANSCRIBE_BACKEND_CPU:
        case TRANSCRIBE_BACKEND_METAL:
        case TRANSCRIBE_BACKEND_VULKAN:
        case TRANSCRIBE_BACKEND_CPU_ACCEL:
        case TRANSCRIBE_BACKEND_CUDA:
            return true;
    }
    return false;
}

// Resolve a BackendPlan for an explicit device selection (gpu_device > 0).
// `dev_index` is a global ggml registry index. The selected device becomes
// the primary; `requested` constrains what kind it must be. Only GPU/IGPU
// devices are selectable this way — strict/accel CPU requests reject a
// non-zero gpu_device before reaching here. The assembled plan mirrors the
// specific-GPU path: primary GPU, then ACCEL, then CPU last as the fallback.
transcribe_status init_backends_explicit_index(transcribe_backend_request requested,
                                               int                        dev_index,
                                               const char *               error_tag,
                                               BackendPlan &              out) {
    const size_t n = ggml_backend_dev_count();
    if (dev_index < 0 || static_cast<size_t>(dev_index) >= n) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: gpu_device %d out of range [0, %zu)", error_tag, dev_index, n);
        return TRANSCRIBE_ERR_INVALID_ARG;
    }

    ggml_backend_dev_t dev      = ggml_backend_dev_get(static_cast<size_t>(dev_index));
    const auto         dev_type = ggml_backend_dev_type(dev);
    if (dev_type != GGML_BACKEND_DEVICE_TYPE_GPU && dev_type != GGML_BACKEND_DEVICE_TYPE_IGPU) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: gpu_device %d (%s) is not a GPU device", error_tag, dev_index,
                ggml_backend_dev_name(dev));
        return TRANSCRIBE_ERR_INVALID_ARG;
    }

    const BackendKind got = classify_device(dev);

    // A specific vendor request pins the kind; AUTO accepts any GPU.
    BackendKind wanted = BackendKind::Unknown;  // Unknown == "any GPU" (AUTO)
    switch (requested) {
        case TRANSCRIBE_BACKEND_METAL:
            wanted = BackendKind::Metal;
            break;
        case TRANSCRIBE_BACKEND_VULKAN:
            wanted = BackendKind::Vulkan;
            break;
        case TRANSCRIBE_BACKEND_CUDA:
            wanted = BackendKind::Cuda;
            break;
        case TRANSCRIBE_BACKEND_AUTO:
            break;
        default:
            // CPU / CPU_ACCEL never reach here (caller rejects nonzero
            // gpu_device for them); anything else is a programming error.
            return TRANSCRIBE_ERR_INVALID_ARG;
    }
    if (wanted != BackendKind::Unknown && got != wanted) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: gpu_device %d is a %s device but %s was requested", error_tag,
                dev_index, kind_name(got), kind_name(wanted));
        return TRANSCRIBE_ERR_INVALID_ARG;
    }

    ggml_backend_t gpu_be = dev_init_checked(dev, error_tag);
    if (gpu_be == nullptr) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: failed to initialize gpu_device %d (%s)", error_tag, dev_index,
                ggml_backend_dev_name(dev));
        return TRANSCRIBE_ERR_BACKEND;
    }
    log_msg(TRANSCRIBE_LOG_LEVEL_INFO, "%s: using %s backend (gpu_device %d): %s", error_tag, kind_name(got), dev_index,
            ggml_backend_dev_name(dev));

    // An explicit device index is always honored: warn only (same gate as
    // try_init_kind).
    if (got == BackendKind::Metal && metal_backend_lacks_simdgroup_mm(gpu_be, dev)) {
        const char * dname = ggml_backend_dev_name(dev);
        log_msg(TRANSCRIBE_LOG_LEVEL_WARN,
                "%s: Metal device \"%s\" has no simdgroup matrix multiply "
                "(pre-Apple7 GPU); transcription may be incorrect and very slow",
                error_tag, dname != nullptr ? dname : "?");
    }

    out.primary      = gpu_be;
    out.primary_kind = got;
    out.scheduler_list.push_back(gpu_be);

    append_accel_backends(out.scheduler_list, error_tag);

    ggml_backend_t cpu_be = init_cpu_backend(error_tag);
    if (cpu_be == nullptr) {
        return TRANSCRIBE_ERR_BACKEND;
    }
    out.scheduler_list.push_back(cpu_be);
    return TRANSCRIBE_OK;
}

}  // namespace

transcribe_status init_backends(transcribe_backend_request requested,
                                int                        gpu_device,
                                const char *               error_tag,
                                BackendPlan &              out) {
    // Read the request as raw bytes before any enum-typed load: a C caller
    // can pass any int here, and loading an out-of-range value through the
    // enum lvalue is UB in C++ (UBSan traps). The raw value is validated by
    // the switch; only valid values are stored back as the enum.
    int requested_raw = TRANSCRIBE_BACKEND_AUTO;
    std::memcpy(&requested_raw, &requested, sizeof(requested_raw));

    out           = BackendPlan{};
    out.requested = static_cast<transcribe_backend_request>(
        valid_backend_request(requested_raw) ? requested_raw : TRANSCRIBE_BACKEND_AUTO);

    // Explicit device selection. 0 is "auto / first of kind" and falls
    // through to the per-request logic below; a negative index is always
    // invalid; a positive index pins a specific GPU/IGPU device.
    if (gpu_device < 0) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: gpu_device must be >= 0 (got %d)", error_tag, gpu_device);
        return TRANSCRIBE_ERR_INVALID_ARG;
    }
    if (gpu_device > 0) {
        if (!valid_backend_request(requested_raw)) {
            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: invalid transcribe_backend_request value %d", error_tag,
                    requested_raw);
            return TRANSCRIBE_ERR_INVALID_ARG;
        }
        // gpu_device names a GPU; a CPU-only request has nothing to select.
        if (requested_raw == TRANSCRIBE_BACKEND_CPU || requested_raw == TRANSCRIBE_BACKEND_CPU_ACCEL) {
            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: gpu_device %d is invalid for a CPU backend request", error_tag,
                    gpu_device);
            return TRANSCRIBE_ERR_INVALID_ARG;
        }
        return init_backends_explicit_index(out.requested, gpu_device, error_tag, out);
    }

    // Explicit switch over the enum so an unknown / garbage value
    // from a C caller never silently collapses into AUTO. Unknown
    // values are a programming error on the caller's side, not a
    // fallback we want to tolerate.
    switch (requested_raw) {
        case TRANSCRIBE_BACKEND_CPU:
        case TRANSCRIBE_BACKEND_CPU_ACCEL:
            {
                // CPU primary. CPU_ACCEL additionally layers the host-memory
                // accelerators (BLAS/AMX/…, GGML_BACKEND_DEVICE_TYPE_ACCEL) onto the
                // scheduler. Both set primary_kind == Cpu so CPU-keyed policy (e.g.
                // F16→F32 conv pointwise promotion) triggers identically.
                //
                // ggml requires the CPU backend to sit last in the scheduler list,
                // so accel backends (when included) go in first.
                const bool     with_accel = (requested == TRANSCRIBE_BACKEND_CPU_ACCEL);
                ggml_backend_t cpu_be     = init_cpu_backend(error_tag);
                if (cpu_be == nullptr) {
                    return TRANSCRIBE_ERR_BACKEND;
                }
                log_msg(TRANSCRIBE_LOG_LEVEL_INFO, "%s: using cpu backend (%s)", error_tag,
                        with_accel ? "with accel" : "strict");
                out.primary      = cpu_be;
                out.primary_kind = BackendKind::Cpu;
                if (with_accel) {
                    append_accel_backends(out.scheduler_list, error_tag);
                }
                out.scheduler_list.push_back(cpu_be);
                return TRANSCRIBE_OK;
            }

        case TRANSCRIBE_BACKEND_METAL:
        case TRANSCRIBE_BACKEND_VULKAN:
        case TRANSCRIBE_BACKEND_CUDA:
            {
                // Specific GPU backend request: must find a matching device
                // or fail. ACCEL is still layered on because it's host-memory
                // and orthogonal to the GPU/CPU split.
                BackendKind wanted = BackendKind::Unknown;
                switch (requested) {
                    case TRANSCRIBE_BACKEND_METAL:
                        wanted = BackendKind::Metal;
                        break;
                    case TRANSCRIBE_BACKEND_VULKAN:
                        wanted = BackendKind::Vulkan;
                        break;
                    case TRANSCRIBE_BACKEND_CUDA:
                        wanted = BackendKind::Cuda;
                        break;
                    default:
                        break;
                }
                BackendKind    got_kind = BackendKind::Unknown;
                ggml_backend_t gpu_be   = try_init_kind(wanted, error_tag, got_kind);
                if (gpu_be == nullptr) {
                    log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: %s backend requested but not available", error_tag,
                            kind_name(wanted));
                    return TRANSCRIBE_ERR_BACKEND;
                }
                out.primary      = gpu_be;
                out.primary_kind = got_kind;
                out.scheduler_list.push_back(gpu_be);

                append_accel_backends(out.scheduler_list, error_tag);

                ggml_backend_t cpu_be = init_cpu_backend(error_tag);
                if (cpu_be == nullptr) {
                    return TRANSCRIBE_ERR_BACKEND;
                }
                out.scheduler_list.push_back(cpu_be);
                return TRANSCRIBE_OK;
            }

        case TRANSCRIBE_BACKEND_AUTO:
            {
                // AUTO: take the first GPU device that successfully
                // initializes, regardless of vendor, in gpu_probe_order
                // (discrete before integrated). If every GPU fails init
                // or none is compiled in, fall through to CPU + ACCEL.
                //
                // try_init_kind yields the classified kind of the device
                // that actually succeeded, so a failing-then-succeeding probe
                // can't misclassify primary_kind.
                BackendKind    got_kind = BackendKind::Unknown;
                ggml_backend_t gpu_be   = try_init_kind(BackendKind::OtherGpu, error_tag, got_kind);
                if (gpu_be != nullptr) {
                    out.primary      = gpu_be;
                    out.primary_kind = got_kind;
                    out.scheduler_list.push_back(gpu_be);
                }

                append_accel_backends(out.scheduler_list, error_tag);

                ggml_backend_t cpu_be = init_cpu_backend(error_tag);
                if (cpu_be == nullptr) {
                    // If we already have at least a GPU or ACCEL backend,
                    // losing CPU is catastrophic — the scheduler needs CPU
                    // as a fallback for every op it can't dispatch
                    // elsewhere. Fail hard.
                    return TRANSCRIBE_ERR_BACKEND;
                }

                // If the GPU probe failed and AUTO picked nothing so far, CPU
                // becomes the primary.
                if (out.primary == nullptr) {
                    out.primary      = cpu_be;
                    out.primary_kind = BackendKind::Cpu;
                }
                out.scheduler_list.push_back(cpu_be);
                return TRANSCRIBE_OK;
            }
    }

    // Unknown enumerator: reject loudly so callers catch ABI drift
    // during development. Do not let "everything else" silently map
    // to AUTO — that hides bugs.
    log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: invalid transcribe_backend_request value %d", error_tag, requested_raw);
    return TRANSCRIBE_ERR_INVALID_ARG;
}

transcribe_status stream_tensor_data(const std::string &  path,
                                     const gguf_context * gguf_data,
                                     ggml_context *       ctx_meta,
                                     const char *         error_tag) {
    // std::ifstream rather than going through gguf's loader a third
    // time: ifstream::seekg takes a streamoff (signed 64-bit on every
    // platform we target), so multi-GB tensor offsets work without
    // #ifdef'ing fseeko vs _fseeki64.
    std::ifstream fin(path_from_utf8(path), std::ios::binary);
    if (!fin) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: failed to reopen %s for tensor data", error_tag, path.c_str());
        return TRANSCRIBE_ERR_GGUF;
    }

    const size_t         data_offset = gguf_get_data_offset(gguf_data);
    std::vector<uint8_t> staging;

    for (ggml_tensor * t = ggml_get_first_tensor(ctx_meta); t != nullptr; t = ggml_get_next_tensor(ctx_meta, t)) {
        const int64_t idx = gguf_find_tensor(gguf_data, t->name);
        if (idx < 0) {
            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: tensor \"%s\" not in gguf data", error_tag, t->name);
            return TRANSCRIBE_ERR_GGUF;
        }
        const size_t toffset = gguf_get_tensor_offset(gguf_data, idx);
        const size_t nbytes  = ggml_nbytes(t);

        const std::streamoff abs_offset =
            static_cast<std::streamoff>(data_offset) + static_cast<std::streamoff>(toffset);
        fin.seekg(abs_offset);
        if (!fin) {
            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: seek failed for tensor \"%s\"", error_tag, t->name);
            return TRANSCRIBE_ERR_GGUF;
        }

        if (staging.size() < nbytes) {
            staging.resize(nbytes);
        }
        fin.read(reinterpret_cast<char *>(staging.data()), static_cast<std::streamsize>(nbytes));
        if (!fin) {
            log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: short read for tensor \"%s\" (%zu bytes)", error_tag, t->name,
                    nbytes);
            return TRANSCRIBE_ERR_GGUF;
        }

        // ggml_backend_tensor_set is the right call regardless of
        // backend: on host buffers (CPU + Metal unified memory on
        // Apple Silicon) it's a memcpy; on discrete GPUs it does
        // the upload.
        ggml_backend_tensor_set(t, staging.data(), 0, nbytes);
    }

    return TRANSCRIBE_OK;
}

transcribe_status promote_conv_pw_f16_to_f32_on_cpu(const BackendPlan &                plan,
                                                    const std::vector<ConvPwF32Slot> & slots,
                                                    const char *                       error_tag,
                                                    ggml_context **                    out_ctx,
                                                    ggml_backend_buffer_t *            out_buffer) {
    // Key off the classified primary kind, not off ACCEL/CPU ordering in
    // the backend list, so a strict-CPU request reliably triggers promotion
    // even when an ACCEL backend sorts ahead of CPU.
    if (plan.primary_kind != BackendKind::Cpu) {
        return TRANSCRIBE_OK;
    }
    if (plan.primary == nullptr) {
        return TRANSCRIBE_OK;
    }

    if (slots.empty()) {
        return TRANSCRIBE_OK;
    }

    // New ctx sized for exactly the replacement tensors plus a small
    // slack. no_alloc=true — ggml_backend_alloc_ctx_tensors will
    // allocate the storage buffer separately below.
    const size_t     ctx_size = slots.size() * ggml_tensor_overhead() + 256;
    ggml_init_params params   = { ctx_size, nullptr, true };
    ggml_context *   ctx      = ggml_init(params);
    if (ctx == nullptr) {
        return TRANSCRIBE_ERR_BACKEND;
    }

    // Allocate F32 replacements in the new ctx, matching each source's
    // full n-d shape. Names are copied so debug dumps still find them.
    std::vector<ggml_tensor *> replacements;
    replacements.reserve(slots.size());
    for (const auto & s : slots) {
        ggml_tensor * r = ggml_new_tensor(ctx, GGML_TYPE_F32, ggml_n_dims(s.src), s.src->ne);
        if (r == nullptr) {
            ggml_free(ctx);
            return TRANSCRIBE_ERR_BACKEND;
        }
        ggml_set_name(r, s.src->name);
        replacements.push_back(r);
    }

    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, plan.primary);
    if (buffer == nullptr) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: conv_pw f32 promotion buffer alloc failed", error_tag);
        ggml_free(ctx);
        return TRANSCRIBE_ERR_BACKEND;
    }
    ggml_backend_buffer_set_usage(buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);

    // Dequantize each F16 tensor into its F32 replacement.
    const auto * f16_traits = ggml_get_type_traits(GGML_TYPE_F16);
    if (f16_traits == nullptr || f16_traits->to_float == nullptr) {
        log_msg(TRANSCRIBE_LOG_LEVEL_WARN, "%s: no f16 to_float trait — skipping conv pw promotion", error_tag);
        // Partial success: the ctx + buffer are already allocated but
        // unused. Free them so the caller's outparams stay nullptr,
        // matching the "do nothing" contract.
        safe_buffer_free(buffer);
        ggml_free(ctx);
        return TRANSCRIBE_OK;
    }

    std::vector<uint8_t> f16_staging;
    std::vector<float>   f32_staging;
    for (size_t i = 0; i < slots.size(); ++i) {
        ggml_tensor * src       = slots[i].src;
        ggml_tensor * dst       = replacements[i];
        const int64_t n_elem    = ggml_nelements(src);
        const size_t  f16_bytes = ggml_nbytes(src);
        const size_t  f32_bytes = static_cast<size_t>(n_elem) * sizeof(float);

        if (f16_staging.size() < f16_bytes) {
            f16_staging.resize(f16_bytes);
        }
        if (f32_staging.size() < static_cast<size_t>(n_elem)) {
            f32_staging.resize(n_elem);
        }

        ggml_backend_tensor_get(src, f16_staging.data(), 0, f16_bytes);
        f16_traits->to_float(f16_staging.data(), f32_staging.data(), n_elem);
        ggml_backend_tensor_set(dst, f32_staging.data(), 0, f32_bytes);

        *slots[i].dst_slot = dst;
    }

    *out_ctx    = ctx;
    *out_buffer = buffer;

    log_msg(TRANSCRIBE_LOG_LEVEL_INFO,
            "%s: promoted %zu conv pointwise weights from F16 → F32 "
            "for CPU backend",
            error_tag, slots.size());
    return TRANSCRIBE_OK;
}

ReadF32Result read_f32_tensor_checked(gguf_context *       gguf_ctx,
                                      const std::string &  gguf_path,
                                      const char *         tensor_name,
                                      size_t               expected_elems,
                                      const char *         error_tag,
                                      std::vector<float> & out) {
    // Clear on entry so stale data cannot leak on any return path.
    out.clear();

    const int64_t idx = gguf_find_tensor(gguf_ctx, tensor_name);
    if (idx < 0) {
        return ReadF32Result::Absent;
    }

    // Validate type is F32.
    const enum ggml_type ttype = gguf_get_tensor_type(gguf_ctx, idx);
    if (ttype != GGML_TYPE_F32) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: tensor \"%s\" has type %d, expected F32 (%d)", error_tag, tensor_name,
                static_cast<int>(ttype), static_cast<int>(GGML_TYPE_F32));
        return ReadF32Result::BadType;
    }

    const size_t nbytes = static_cast<size_t>(gguf_get_tensor_size(gguf_ctx, idx));

    // Validate alignment: byte count must be a multiple of sizeof(float).
    if (nbytes == 0 || (nbytes % sizeof(float)) != 0) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
                "%s: tensor \"%s\" has %zu bytes "
                "(not a multiple of %zu)",
                error_tag, tensor_name, nbytes, sizeof(float));
        return ReadF32Result::BadSize;
    }

    const size_t n_elems = nbytes / sizeof(float);

    // Validate expected element count when the caller knows the shape.
    if (expected_elems > 0 && n_elems != expected_elems) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: tensor \"%s\" has %zu elements, expected %zu", error_tag, tensor_name,
                n_elems, expected_elems);
        return ReadF32Result::BadSize;
    }

    // Read from the GGUF data section. Use the same overflow-safe
    // offset pattern as stream_tensor_data: cast each addend to
    // std::streamoff individually rather than adding two size_t values
    // that could wrap on 32-bit builds (not a real platform today, but
    // consistent).
    const size_t data_off = gguf_get_data_offset(gguf_ctx);
    const size_t t_off    = gguf_get_tensor_offset(gguf_ctx, idx);

    std::ifstream fin(path_from_utf8(gguf_path), std::ios::binary);
    if (!fin) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: failed to open %s for tensor \"%s\"", error_tag, gguf_path.c_str(),
                tensor_name);
        return ReadF32Result::ReadErr;
    }

    const std::streamoff abs_offset = static_cast<std::streamoff>(data_off) + static_cast<std::streamoff>(t_off);
    fin.seekg(abs_offset);
    if (!fin) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR, "%s: seek failed for tensor \"%s\"", error_tag, tensor_name);
        return ReadF32Result::ReadErr;
    }

    out.resize(n_elems);
    fin.read(reinterpret_cast<char *>(out.data()), static_cast<std::streamsize>(nbytes));

    if (!fin || static_cast<size_t>(fin.gcount()) != nbytes) {
        log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
                "%s: short read for tensor \"%s\" "
                "(got %zu of %zu bytes)",
                error_tag, tensor_name, static_cast<size_t>(fin.gcount()), nbytes);
        out.clear();
        return ReadF32Result::ReadErr;
    }

    return ReadF32Result::Ok;
}

}  // namespace transcribe::load_common