DarthAffe/stable-diffusion.cpp
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revert: restore public backend extension integration

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leejet committed 2026-11-01 04:09:12 +08:00
1 parent a013932674
commit 75e8157b01
26 files changed
+590 -358
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src/core/cpu/int8.cpp
+12 -3
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@@ -101,18 +101,24 @@ namespace sd_cpu {
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 = parallel(context->backend, dst, quantize, &e);
auto status = run(context->backend, dst, quantize, &e);
if (status != GGML_STATUS_SUCCESS)
return status;
} else {
@@ -123,7 +129,7 @@ namespace sd_cpu {
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 = parallel(context->backend, dst, multiply, &e);
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;
@@ -133,13 +139,16 @@ namespace sd_cpu {
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 = parallel(context->backend, dst, quantize, &e);
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)