feat: add custom modulate operator and ConvRot fusion (#2258)

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leejet authored and GitHub committed 2026-11-02 01:19:28 +08:00
1 parent 133087deb8
commit 290148d2e3
36 files changed
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@@ -0,0 +1,82 @@
#include "core/cpu/registration.h"
#include "core/ggml_extensions.h"
#ifndef SD_USE_UPSTREAM_GGML
#include <cstring>
namespace sd_cpu {
static bool modulate_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
return sd_ggml_modulate_describe(node);
}
static float modulate_value(const ggml_tensor* t, const int64_t* index) {
size_t offset = 0;
for (int d = 0; d < GGML_MAX_DIMS; ++d) {
offset += (index[d] % t->ne[d]) * t->nb[d];
}
return *reinterpret_cast<const float*>(static_cast<const char*>(t->data) + offset);
}
static void modulate_compute(ggml_tensor* dst, int ith, int nth, void*) {
auto* output = static_cast<float*>(dst->data);
const int64_t rows = ggml_nrows(dst);
for (int64_t row = ith; row < rows; row += nth) {
int64_t index[] = {0, row % dst->ne[1], (row / dst->ne[1]) % dst->ne[2], row / (dst->ne[1] * dst->ne[2])};
for (int64_t col = 0; col < dst->ne[0]; ++col) {
index[0] = col;
const float x = modulate_value(dst->src[0], index);
// Match separate ggml MUL/ADD rounding even with contraction enabled.
volatile float product = x * modulate_value(dst->src[1], index);
volatile float scaled = x + product;
output[row * dst->ne[0] + col] = dst->src[2] ? scaled + modulate_value(dst->src[2], index) : scaled;
}
}
}
static ggml_status modulate_prepare(ggml_backend_t, ggml_tensor* const* nodes, int, void**, size_t* bytes, void*) {
*bytes = sd_ggml_modulate_workspace_size(nodes[0]);
return GGML_STATUS_SUCCESS;
}
static void modulate_destroy(void*, void*) {}
static ggml_status modulate_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int, void*, void*) {
auto reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(context->backend));
auto run = reinterpret_cast<ggml_ext_cpu_parallel_fn>(ggml_backend_reg_get_proc_address(reg, "ggml_ext_cpu_parallel_v1"));
GGML_ASSERT(run);
auto* dst = nodes[0];
const auto address = reinterpret_cast<uintptr_t>(dst->data);
bool alias = false;
for (const auto* input : dst->src) {
if (input) {
const auto src = reinterpret_cast<uintptr_t>(input->data);
alias |= address < src + ggml_nbytes(input) && src < address + ggml_nbytes(dst);
}
}
if (!alias) {
return run(context->backend, dst, modulate_compute, nullptr);
}
ggml_tensor temporary = *dst;
temporary.data = ggml_backend_buffer_get_base(context->workspace);
const auto status = run(context->backend, &temporary, modulate_compute, nullptr);
if (status == GGML_STATUS_SUCCESS) {
std::memcpy(dst->data, temporary.data, ggml_nbytes(dst));
}
return status;
}
void register_modulate(ggml_backend_dev_t device) {
ggml_ext_kernel_desc desc = {};
desc.abi_version = GGML_EXT_ABI_VERSION;
desc.name = "sd.modulate.cpu.v1";
desc.op = GGML_OP_CUSTOM;
desc.custom_op = sd_ggml_modulate_op();
desc.priority = 100;
desc.supports = modulate_supports;
desc.prepare = modulate_prepare;
desc.destroy = modulate_destroy;
desc.execute = modulate_execute;
GGML_ASSERT(ggml_ext_register_kernel(device, &desc));
}
}
#endif
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@@ -6,6 +6,7 @@ namespace sd_cpu {
void register_extensions(ggml_backend_dev_t device) {
register_i8(device);
register_rope(device);
register_modulate(device);
}
}
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@@ -7,6 +7,7 @@
namespace sd_cpu {
void register_extensions(ggml_backend_dev_t device);
void register_rope(ggml_backend_dev_t device);
void register_modulate(ggml_backend_dev_t device);
void register_i8(ggml_backend_dev_t device);
}
#endif