DarthAffe/stable-diffusion.cpp
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refactor: move model-specific operators and fusions into backend extensions (#2253)

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leejet authored and GitHub committed 2026-11-01 21:18:37 +08:00
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commit 45a63ac460
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src/core/cpu/int8.cpp
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#include "core/cpu/registration.h"
#include "core/ggml_extensions.h"
#ifndef SD_USE_UPSTREAM_GGML
#include <algorithm>
#include <cmath>
#include <cstring>
namespace sd_cpu {
static void transform(float* values, int group) {
const float scale = 1.f / std::sqrt(float(group));
for (int i = 0; i < group; ++i)
values[i] *= scale;
for (int stride = 1; stride < group; stride *= 4)
for (int base = 0; base < group; base += 4 * stride)
for (int j = 0; j < stride; ++j) {
const int i = base + j;
const float a = values[i], b = values[i + stride], c = values[i + 2 * stride], d = values[i + 3 * stride];
values[i] = a + b + c - d;
values[i + stride] = a + b - c + d;
values[i + 2 * stride] = a - b + c + d;
values[i + 3 * stride] = -a + b + c + d;
}
}
struct I8Execution {
SDI8Operation op;
int64_t rows;
int8_t* quant;
float* scales;
float* output;
};
static void quantize(ggml_tensor*, int ith, int nth, void* data) {
auto& e = *static_cast<I8Execution*>(data);
const int64_t k = e.op.input->ne[0];
const int group = e.op.group_size;
const auto* input = static_cast<const float*>(e.op.input->data);
for (int64_t row = ith; row < e.rows; row += nth) {
const float* src = input + row * k;
int8_t* dst = e.quant + row * k;
float amax = 0.f, values[256];
auto visit = [&](auto fn) { if (!group) { for (int64_t i = 0; i < k; ++i) fn(i, src[i]); }
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]); } };
visit([&](int64_t, float value) { amax = std::max(amax, std::fabs(value)); });
const float scale = amax / 127.f, inverse = scale == 0.f ? 0.f : 1.f / scale;
e.scales[row] = scale;
if (scale == 0.f)
std::memset(dst, 0, k);
else
visit([&](int64_t i, float value) { dst[i] = static_cast<int8_t>(std::max(-127L, std::min(127L, std::lrintf(value * inverse)))); });
}
}
static void multiply(ggml_tensor*, int ith, int nth, void* data) {
auto& e = *static_cast<I8Execution*>(data);
const auto& op = e.op;
const int64_t k = op.weight->ne[0], n = op.weight->ne[1];
const auto* w = static_cast<const int8_t*>(op.weight->data);
const auto* ws = op.weight_scale ? static_cast<const float*>(op.weight_scale->data) : nullptr;
const auto* bias = op.bias ? static_cast<const float*>(op.bias->data) : nullptr;
for (int64_t index = ith; index < e.rows * n; index += nth) {
const int64_t row = index / n, column = index % n;
uint32_t sum = 0;
for (int64_t i = 0; i < k; ++i)
sum += uint32_t(int32_t(w[column * k + i]) * int32_t(e.quant[row * k + i]));
int32_t signed_sum;
std::memcpy(&signed_sum, &sum, sizeof(sum));
float value = float(signed_sum) * e.scales[row];
if (ws)
value *= ws[column];
if (bias)
value += bias[column];
e.output[index] = value;
}
}
static bool overlaps(const ggml_tensor* dst, const ggml_tensor* src) {
if (!src)
return false;
const auto a = reinterpret_cast<uintptr_t>(dst->data), b = reinterpret_cast<uintptr_t>(src->data);
return a <= b ? b - a < ggml_nbytes(dst) : a - b < ggml_nbytes(src);
}
static size_t quant_workspace(const ggml_tensor* node, const SDI8Operation& op) {
return op.input->type == GGML_TYPE_F32 ? (size_t(ggml_nelements(op.input)) + 3) / 4 * 4 + size_t(ggml_nrows(node)) * 4 : 0;
}
static bool i8_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
SDI8Operation op;
return sd_ggml_i8_describe(node, &op);
}
static bool convrot_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
int group;
return sd_ggml_convrot_describe(node, &group);
}
static ggml_status i8_prepare(ggml_backend_t, ggml_tensor* const* nodes, int, void**, size_t* bytes, void*) {
SDI8Operation op;
if (!sd_ggml_i8_describe(nodes[0], &op))
return GGML_STATUS_FAILED;
*bytes = quant_workspace(nodes[0], op) + ggml_nbytes(nodes[0]);
return GGML_STATUS_SUCCESS;
}
static ggml_status convrot_prepare(ggml_backend_t, ggml_tensor* const* nodes, int, void**, size_t* bytes, void*) {
*bytes = ggml_nbytes(nodes[0]);
return GGML_STATUS_SUCCESS;
}
static void destroy(void*, void*) {}
static ggml_ext_cpu_parallel_fn parallel(ggml_backend_t backend) {
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"));
}
static ggml_status i8_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int, void*, void*) {
auto* dst = nodes[0];
I8Execution e = {};
if (!sd_ggml_i8_describe(dst, &e.op))
return GGML_STATUS_FAILED;
e.rows = ggml_nrows(dst);
auto run = parallel(context->backend);
if (!run)
return GGML_STATUS_FAILED;
char* scratch = static_cast<char*>(ggml_backend_buffer_get_base(context->workspace));
const size_t quant_size = quant_workspace(dst, e.op);
if (e.op.input->type == GGML_TYPE_F32) {
e.quant = reinterpret_cast<int8_t*>(scratch);
e.scales = reinterpret_cast<float*>(scratch + (size_t(ggml_nelements(e.op.input)) + 3) / 4 * 4);
auto status = run(context->backend, dst, quantize, &e);
if (status != GGML_STATUS_SUCCESS)
return status;
} else {
e.quant = static_cast<int8_t*>(e.op.input->data);
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);
}
bool alias = false;
for (auto* input : dst->src)
alias |= overlaps(dst, input);
e.output = alias ? reinterpret_cast<float*>(scratch + quant_size) : static_cast<float*>(dst->data);
auto status = run(context->backend, dst, multiply, &e);
if (status == GGML_STATUS_SUCCESS && alias)
std::memcpy(dst->data, e.output, ggml_nbytes(dst));
return status;
}
static ggml_status convrot_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int, void*, void*) {
auto* dst = nodes[0];
I8Execution e = {};
if (!sd_ggml_convrot_describe(dst, &e.op.group_size))
return GGML_STATUS_FAILED;
auto run = parallel(context->backend);
if (!run)
return GGML_STATUS_FAILED;
e.op.input = dst->src[0];
e.rows = ggml_nrows(e.op.input);
bool alias = overlaps(dst, e.op.input);
e.quant = static_cast<int8_t*>(alias ? ggml_backend_buffer_get_base(context->workspace) : dst->data);
const int64_t k = dst->ne[0], padded = (e.rows + 3) / 4 * 4;
e.scales = reinterpret_cast<float*>(e.quant + k * padded);
auto status = run(context->backend, dst, quantize, &e);
if (status == GGML_STATUS_SUCCESS) {
std::memset(e.quant + k * e.rows, 0, k * (padded - e.rows));
if (alias)
std::memcpy(dst->data, e.quant, ggml_nbytes(dst));
}
return status;
}
void register_i8(ggml_backend_dev_t device) {
ggml_ext_kernel_desc d = {};
d.abi_version = GGML_EXT_ABI_VERSION;
d.priority = 100;
d.destroy = destroy;
d.name = "sd.linear_i8.cpu.v1";
d.op = GGML_OP_CUSTOM;
d.custom_op = sd_ggml_i8_op();
d.supports = i8_supports;
d.prepare = i8_prepare;
d.execute = i8_execute;
GGML_ASSERT(ggml_ext_register_kernel(device, &d));
d.name = "sd.quantize_i8_convrot.cpu.v1";
d.op = GGML_OP_CUSTOM;
d.custom_op = sd_ggml_convrot_op();
d.supports = convrot_supports;
d.prepare = convrot_prepare;
d.execute = convrot_execute;
GGML_ASSERT(ggml_ext_register_kernel(device, &d));
}
}
#endif
src/core/cpu/registration.cpp
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#include "core/cpu/registration.h"
#ifndef SD_USE_UPSTREAM_GGML
namespace sd_cpu {
void register_extensions(ggml_backend_dev_t device) {
register_i8(device);
register_rope(device);
}
}
#endif
src/core/cpu/registration.h
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#ifndef __SD_CORE_CPU_REGISTRATION_H__
#define __SD_CORE_CPU_REGISTRATION_H__
#include "ggml-backend.h"
#ifndef SD_USE_UPSTREAM_GGML
namespace sd_cpu {
void register_extensions(ggml_backend_dev_t device);
void register_rope(ggml_backend_dev_t device);
void register_i8(ggml_backend_dev_t device);
}
#endif
#endif
src/core/cpu/rope.cpp
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#include "core/cpu/registration.h"
#include "core/ggml_extensions.h"
#ifndef SD_USE_UPSTREAM_GGML
#include <cmath>
#include <cstring>
namespace sd_cpu {
static bool rope_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
return sd_ggml_rope_describe(node);
}
struct RopeExecution {
const ggml_tensor* rope;
const ggml_tensor* norm;
const ggml_tensor* weight;
};
static void rope_compute(ggml_tensor* dst, int ith, int nth, void* user_data) {
const auto& execution = *static_cast<const RopeExecution*>(user_data);
const auto* rope = execution.rope;
const auto* x = execution.norm ? execution.norm->src[0] : rope->src[0];
const auto* pe = rope->src[1];
int32_t interleaved;
std::memcpy(&interleaved, ggml_ext_op_attributes(rope), sizeof(interleaved));
float eps = 0.0f;
if (execution.norm) {
std::memcpy(&eps, execution.norm->op_params, sizeof(eps));
}
const int64_t half_dim = pe->ne[2];
const int64_t rows = x->ne[1] * x->ne[2] * x->ne[3];
for (int64_t row = ith; row < rows; row += nth) {
const int64_t token = row % x->ne[2];
const int64_t head = (row / x->ne[2]) % x->ne[1];
const int64_t batch = row / (x->ne[2] * x->ne[1]);
const char* input = static_cast<const char*>(x->data) + head * x->nb[1] + token * x->nb[2] + batch * x->nb[3];
const char* pos = static_cast<const char*>(pe->data) + token * pe->nb[3];
float* output = static_cast<float*>(dst->data) + row * x->ne[0];
float scale = 1.0f;
if (execution.norm) {
double sum = 0.0;
for (int64_t col = 0; col < x->ne[0]; ++col) {
const float v = *reinterpret_cast<const float*>(input + col * x->nb[0]);
sum += static_cast<double>(v * v);
}
const float mean = static_cast<float>(sum / x->ne[0]);
scale = 1.0f / std::sqrt(mean + eps);
}
auto value = [&](int64_t col) {
float v = *reinterpret_cast<const float*>(input + col * x->nb[0]);
if (execution.norm) {
const auto* weight = static_cast<const char*>(execution.weight->data);
v = (v * scale) * *reinterpret_cast<const float*>(weight + col * execution.weight->nb[0]);
}
return v;
};
for (int64_t j = 0; j < half_dim; ++j) {
const int64_t i0 = interleaved ? 2 * j : j;
const int64_t i1 = interleaved ? 2 * j + 1 : j + half_dim;
const float a = value(i0);
const float b = value(i1);
const char* m = pos + j * pe->nb[2];
const float c = *reinterpret_cast<const float*>(m);
const float s = *reinterpret_cast<const float*>(m + pe->nb[1]);
if (interleaved) {
const float neg_s = *reinterpret_cast<const float*>(m + pe->nb[0]);
const float c1 = *reinterpret_cast<const float*>(m + pe->nb[0] + pe->nb[1]);
output[i0] = a * c + b * neg_s;
output[i1] = a * s + b * c1;
} else {
output[i0] = a * c - b * s;
output[i1] = a * s + b * c;
}
}
for (int64_t col = 2 * half_dim; col < x->ne[0]; ++col) {
output[col] = value(col);
}
}
}
static int rope_fusion_match(ggml_backend_dev_t, const ggml_cgraph* graph, int first, void*) {
auto* view = const_cast<ggml_cgraph*>(graph);
if (first + 2 >= ggml_graph_n_nodes(view)) {
return 0;
}
const auto* norm = ggml_graph_node(view, first);
const auto* mul = ggml_graph_node(view, first + 1);
const auto* rope = ggml_graph_node(view, first + 2);
if (norm->op != GGML_OP_RMS_NORM || norm->type != GGML_TYPE_F32 || !norm->src[0] ||
norm->src[0]->type != GGML_TYPE_F32 || norm->src[0]->nb[0] != sizeof(float) ||
mul->op != GGML_OP_MUL || mul->type != GGML_TYPE_F32 || rope->src[0] != mul ||
ggml_ext_op_get(rope) != sd_ggml_rope_op() ||
!sd_ggml_rope_describe(rope) || !ggml_are_same_shape(norm, mul)) {
return 0;
}
const auto* weight = mul->src[0] == norm ? mul->src[1] : (mul->src[1] == norm ? mul->src[0] : nullptr);
if (!weight || weight->type != GGML_TYPE_F32 || weight->ne[0] != norm->ne[0] ||
weight->ne[1] != 1 || weight->ne[2] != 1 || weight->ne[3] != 1) {
return 0;
}
return 3;
}
static ggml_status rope_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int count, void*, void*) {
auto device = ggml_backend_get_device(context->backend);
auto reg = ggml_backend_dev_backend_reg(device);
auto run = reinterpret_cast<ggml_ext_cpu_parallel_fn>(ggml_backend_reg_get_proc_address(reg, "ggml_ext_cpu_parallel_v1"));
GGML_ASSERT(run);
RopeExecution execution = {nodes[count - 1], nullptr, nullptr};
if (count == 3) {
execution.norm = nodes[0];
execution.weight = nodes[1]->src[0] == nodes[0] ? nodes[1]->src[1] : nodes[1]->src[0];
}
return run(context->backend, nodes[count - 1], rope_compute, &execution);
}
void register_rope(ggml_backend_dev_t device) {
ggml_ext_kernel_desc desc = {};
desc.abi_version = GGML_EXT_ABI_VERSION;
desc.name = "sd.rope_apply.cpu.v1";
desc.op = GGML_OP_CUSTOM;
desc.custom_op = sd_ggml_rope_op();
desc.priority = 100;
desc.supports = rope_supports;
desc.execute = rope_execute;
GGML_ASSERT(ggml_ext_register_kernel(device, &desc));
desc.name = "sd.rms_norm_mul_rope.cpu.v1";
desc.op = GGML_OP_RMS_NORM;
desc.custom_op = nullptr;
desc.priority = 200;
desc.supports = nullptr;
desc.match = rope_fusion_match;
GGML_ASSERT(ggml_ext_register_kernel(device, &desc));
}
}
#endif