DarthAffe/stable-diffusion.cpp
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refactor: unify runner lifecycles and weight residency (#1940)

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leejet authored and GitHub committed 2026-09-06 22:30:45 +08:00
1 parent 6c57cc3b38
commit 462d675018
78 files changed
+2963 -2051
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src/core/ggml_graph_cut.h
+13 -41
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@@ -6,19 +6,13 @@
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "ggml-backend.h"
#include "ggml.h"
namespace sd::ggml_graph_cut {
// Streaming residency for a segment's params.
enum class SegmentResidency : uint8_t {
STREAMED = 0,
RESIDENT = 1,
};
struct Segment {
enum InputType {
INPUT_EXTERNAL = 0,
@@ -33,38 +27,28 @@ namespace sd::ggml_graph_cut {
int node_index = -1;
};
size_t compute_buffer_size = 0;
size_t output_bytes = 0;
size_t input_external_bytes = 0;
size_t input_previous_cut_bytes = 0;
size_t input_param_bytes = 0;
size_t compute_buffer_size = 0;
std::string group_name;
std::vector<int> internal_node_indices;
std::vector<int> output_node_indices;
std::vector<InputRef> input_refs;
SegmentResidency residency = SegmentResidency::STREAMED;
std::unordered_set<std::string> future_cut_names;
std::unordered_set<std::string> live_cut_names;
};
struct Plan {
struct InputShape {
int leaf_index = -1;
ggml_type type = GGML_TYPE_COUNT;
std::array<int64_t, GGML_MAX_DIMS> ne = {0, 0, 0, 0};
};
bool available = false;
bool has_cuts = false;
bool valid = true;
int n_nodes = 0;
int n_leafs = 0;
std::vector<InputShape> input_shapes;
bool available = false;
bool has_cuts = false;
bool valid = true;
size_t compute_buffer_size = 0;
std::vector<uint64_t> layout;
std::vector<std::string> leaf_names;
std::vector<std::pair<int, std::string>> cut_markers;
std::vector<Segment> segments;
};
struct PlanCache {
Plan graph_cut_plan;
Plan budgeted_graph_cut_plan;
size_t budgeted_graph_cut_plan_max_vram_bytes = 0;
};
static constexpr const char* GGML_RUNNER_CUT_PREFIX = "ggml_runner_cut:";
@@ -89,13 +73,12 @@ namespace sd::ggml_graph_cut {
ggml_backend_buffer_t tensor_buffer(const ggml_tensor* tensor);
ggml_tensor* cache_source_tensor(ggml_tensor* tensor);
size_t cache_tensor_bytes(const ggml_tensor* tensor);
// Plans ignore runtime bindings; allocator reservations must include them.
std::vector<uint64_t> graph_layout(ggml_cgraph* graph, bool include_bindings);
bool plan_matches_graph(ggml_cgraph* gf, const Plan& plan);
ggml_tensor* output_tensor(ggml_cgraph* gf, const Segment& segment, size_t output_index);
ggml_tensor* input_tensor(ggml_cgraph* gf, const Segment::InputRef& input_ref);
std::vector<ggml_tensor*> param_tensors(ggml_cgraph* gf, const Segment& segment);
std::unordered_set<std::string> collect_future_input_names(ggml_cgraph* gf,
const Plan& plan,
size_t current_segment_index);
ggml_cgraph* build_segment_graph(ggml_cgraph* gf,
const Segment& segment,
ggml_context** graph_ctx_out);
@@ -109,23 +92,12 @@ namespace sd::ggml_graph_cut {
ggml_cgraph* gf,
const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const char* log_desc);
Plan apply_max_vram_budget(ggml_cgraph* gf,
const Plan& base_plan,
size_t max_graph_vram_bytes,
ggml_backend_t backend,
const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const char* log_desc);
Plan resolve_plan(ggml_backend_t backend,
ggml_cgraph* gf,
PlanCache* cache,
size_t max_graph_vram_bytes,
const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const char* log_desc);
// Mark leading segments resident after reserving streamed execution headroom.
void annotate_residency(Plan& plan,
size_t max_graph_vram_bytes,
bool prefetch_enabled);
} // namespace sd::ggml_graph_cut
#endif // __SD_CORE_GGML_GRAPH_CUT_H__