mirror of
https://github.com/leejet/stable-diffusion.cpp.git
synced 2026-11-10 19:06:33 +00:00
182 lines
8.7 KiB
C++
182 lines
8.7 KiB
C++
#include "core/cpu/registration.h"
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#include "core/ggml_extensions.h"
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#ifndef SD_USE_UPSTREAM_GGML
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#include <algorithm>
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#include <cmath>
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#include <cstring>
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namespace sd_cpu {
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static void transform(float* values, int group) {
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const float scale = 1.f / std::sqrt(float(group));
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for (int i = 0; i < group; ++i)
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values[i] *= scale;
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for (int stride = 1; stride < group; stride *= 4)
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for (int base = 0; base < group; base += 4 * stride)
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for (int j = 0; j < stride; ++j) {
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const int i = base + j;
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const float a = values[i], b = values[i + stride], c = values[i + 2 * stride], d = values[i + 3 * stride];
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values[i] = a + b + c - d;
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values[i + stride] = a + b - c + d;
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values[i + 2 * stride] = a - b + c + d;
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values[i + 3 * stride] = -a + b + c + d;
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}
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}
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struct I8Execution {
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SDI8Operation op;
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int64_t rows;
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int8_t* quant;
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float* scales;
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float* output;
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};
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static void quantize(ggml_tensor*, int ith, int nth, void* data) {
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auto& e = *static_cast<I8Execution*>(data);
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const int64_t k = e.op.input->ne[0];
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const int group = e.op.group_size;
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const auto* input = static_cast<const float*>(e.op.input->data);
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for (int64_t row = ith; row < e.rows; row += nth) {
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const float* src = input + row * k;
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int8_t* dst = e.quant + row * k;
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float amax = 0.f, values[256];
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auto visit = [&](auto fn) { if (!group) { for (int64_t i = 0; i < k; ++i) fn(i, src[i]); }
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else for (int64_t base = 0; base < k; base += group) { std::memcpy(values, src + base, group * sizeof(float)); transform(values, group); for (int j = 0; j < group; ++j) fn(base + j, values[j]); } };
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visit([&](int64_t, float value) { amax = std::max(amax, std::fabs(value)); });
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const float scale = amax / 127.f, inverse = scale == 0.f ? 0.f : 1.f / scale;
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e.scales[row] = scale;
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if (scale == 0.f)
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std::memset(dst, 0, k);
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else
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visit([&](int64_t i, float value) { dst[i] = static_cast<int8_t>(std::max(-127L, std::min(127L, std::lrintf(value * inverse)))); });
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}
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}
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static void multiply(ggml_tensor*, int ith, int nth, void* data) {
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auto& e = *static_cast<I8Execution*>(data);
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const auto& op = e.op;
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const int64_t k = op.weight->ne[0], n = op.weight->ne[1];
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const auto* w = static_cast<const int8_t*>(op.weight->data);
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const auto* ws = op.weight_scale ? static_cast<const float*>(op.weight_scale->data) : nullptr;
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const auto* bias = op.bias ? static_cast<const float*>(op.bias->data) : nullptr;
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for (int64_t index = ith; index < e.rows * n; index += nth) {
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const int64_t row = index / n, column = index % n;
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uint32_t sum = 0;
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for (int64_t i = 0; i < k; ++i)
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sum += uint32_t(int32_t(w[column * k + i]) * int32_t(e.quant[row * k + i]));
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int32_t signed_sum;
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std::memcpy(&signed_sum, &sum, sizeof(sum));
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float value = float(signed_sum) * e.scales[row];
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if (ws)
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value *= ws[column];
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if (bias)
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value += bias[column];
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e.output[index] = value;
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}
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}
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static bool overlaps(const ggml_tensor* dst, const ggml_tensor* src) {
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if (!src)
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return false;
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const auto a = reinterpret_cast<uintptr_t>(dst->data), b = reinterpret_cast<uintptr_t>(src->data);
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return a <= b ? b - a < ggml_nbytes(dst) : a - b < ggml_nbytes(src);
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}
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static size_t quant_workspace(const ggml_tensor* node, const SDI8Operation& op) {
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return op.input->type == GGML_TYPE_F32 ? (size_t(ggml_nelements(op.input)) + 3) / 4 * 4 + size_t(ggml_nrows(node)) * 4 : 0;
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}
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static bool i8_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
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SDI8Operation op;
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return sd_ggml_i8_describe(node, &op);
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}
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static bool convrot_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
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int group;
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return sd_ggml_convrot_describe(node, &group);
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}
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static ggml_status i8_prepare(ggml_backend_t, ggml_tensor* const* nodes, int, void**, size_t* bytes, void*) {
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SDI8Operation op;
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if (!sd_ggml_i8_describe(nodes[0], &op))
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return GGML_STATUS_FAILED;
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*bytes = quant_workspace(nodes[0], op) + ggml_nbytes(nodes[0]);
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return GGML_STATUS_SUCCESS;
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}
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static ggml_status convrot_prepare(ggml_backend_t, ggml_tensor* const* nodes, int, void**, size_t* bytes, void*) {
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*bytes = ggml_nbytes(nodes[0]);
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return GGML_STATUS_SUCCESS;
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}
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static void destroy(void*, void*) {}
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static ggml_ext_cpu_parallel_fn parallel(ggml_backend_t backend) {
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return reinterpret_cast<ggml_ext_cpu_parallel_fn>(ggml_backend_reg_get_proc_address(ggml_backend_dev_backend_reg(ggml_backend_get_device(backend)), "ggml_ext_cpu_parallel_v1"));
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}
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static ggml_status i8_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int, void*, void*) {
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auto* dst = nodes[0];
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I8Execution e = {};
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if (!sd_ggml_i8_describe(dst, &e.op))
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return GGML_STATUS_FAILED;
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e.rows = ggml_nrows(dst);
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auto run = parallel(context->backend);
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if (!run)
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return GGML_STATUS_FAILED;
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char* scratch = static_cast<char*>(ggml_backend_buffer_get_base(context->workspace));
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const size_t quant_size = quant_workspace(dst, e.op);
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if (e.op.input->type == GGML_TYPE_F32) {
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e.quant = reinterpret_cast<int8_t*>(scratch);
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e.scales = reinterpret_cast<float*>(scratch + (size_t(ggml_nelements(e.op.input)) + 3) / 4 * 4);
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auto status = run(context->backend, dst, quantize, &e);
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if (status != GGML_STATUS_SUCCESS)
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return status;
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} else {
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e.quant = static_cast<int8_t*>(e.op.input->data);
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e.scales = e.op.packed ? reinterpret_cast<float*>(e.quant + e.op.input->ne[0] * ((e.rows + 3) / 4 * 4)) : static_cast<float*>(e.op.input_scale->data);
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}
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bool alias = false;
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for (auto* input : dst->src)
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alias |= overlaps(dst, input);
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e.output = alias ? reinterpret_cast<float*>(scratch + quant_size) : static_cast<float*>(dst->data);
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auto status = run(context->backend, dst, multiply, &e);
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if (status == GGML_STATUS_SUCCESS && alias)
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std::memcpy(dst->data, e.output, ggml_nbytes(dst));
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return status;
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}
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static ggml_status convrot_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int, void*, void*) {
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auto* dst = nodes[0];
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I8Execution e = {};
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if (!sd_ggml_convrot_describe(dst, &e.op.group_size))
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return GGML_STATUS_FAILED;
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auto run = parallel(context->backend);
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if (!run)
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return GGML_STATUS_FAILED;
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e.op.input = dst->src[0];
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e.rows = ggml_nrows(e.op.input);
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bool alias = overlaps(dst, e.op.input);
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e.quant = static_cast<int8_t*>(alias ? ggml_backend_buffer_get_base(context->workspace) : dst->data);
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const int64_t k = dst->ne[0], padded = (e.rows + 3) / 4 * 4;
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e.scales = reinterpret_cast<float*>(e.quant + k * padded);
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auto status = run(context->backend, dst, quantize, &e);
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if (status == GGML_STATUS_SUCCESS) {
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std::memset(e.quant + k * e.rows, 0, k * (padded - e.rows));
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if (alias)
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std::memcpy(dst->data, e.quant, ggml_nbytes(dst));
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}
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return status;
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}
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void register_i8(ggml_backend_dev_t device) {
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ggml_ext_kernel_desc d = {};
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d.abi_version = GGML_EXT_ABI_VERSION;
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d.priority = 100;
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d.destroy = destroy;
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d.name = "sd.linear_i8.cpu.v1";
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d.op = GGML_OP_CUSTOM;
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d.custom_op = sd_ggml_i8_op();
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d.supports = i8_supports;
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d.prepare = i8_prepare;
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d.execute = i8_execute;
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GGML_ASSERT(ggml_ext_register_kernel(device, &d));
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d.name = "sd.quantize_i8_convrot.cpu.v1";
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d.op = GGML_OP_CUSTOM;
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d.custom_op = sd_ggml_convrot_op();
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d.supports = convrot_supports;
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d.prepare = convrot_prepare;
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d.execute = convrot_execute;
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GGML_ASSERT(ggml_ext_register_kernel(device, &d));
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}
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}
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#endif
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