stable-diffusion.cpp/src/core/runner_cache.h

67 lines
2.3 KiB
C++

#ifndef __SD_CORE_RUNNER_CACHE_H__
#define __SD_CORE_RUNNER_CACHE_H__
#include <map>
#include <memory>
#include <string>
#include <unordered_set>
#include "ggml-backend.h"
namespace sd::ggml_graph_cut {
struct Segment;
}
namespace sd {
struct CachedTensor {
ggml_context* context = nullptr;
ggml_backend_buffer_t buffer = nullptr;
ggml_tensor* tensor = nullptr;
~CachedTensor();
static std::unique_ptr<CachedTensor> copy(ggml_backend_t backend,
const std::string& name,
ggml_tensor* source);
};
using CachedTensors = std::map<std::string, std::unique_ptr<CachedTensor>>;
class RunnerCache {
ggml_backend_t backend_;
CachedTensors committed_;
CachedTensors pending_;
std::map<std::string, ggml_tensor*> outputs_;
public:
explicit RunnerCache(ggml_backend_t backend)
: backend_(backend) {}
RunnerCache(const RunnerCache&) = delete;
RunnerCache& operator=(const RunnerCache&) = delete;
ggml_tensor* get(const std::string& name) const;
void stage(const std::string& name, ggml_tensor* tensor);
const std::map<std::string, ggml_tensor*>& outputs() const { return outputs_; }
size_t pending_bytes(ggml_cgraph* graph) const;
size_t resident_bytes(ggml_backend_dev_t device) const;
bool capture(ggml_cgraph* graph);
void graph_end(bool success);
void clear();
};
class GraphCutTensorCache {
ggml_backend_t backend_;
CachedTensors tensors_;
public:
explicit GraphCutTensorCache(ggml_backend_t backend)
: backend_(backend) {}
ggml_tensor* get(const std::string& name) const;
size_t resident_bytes(ggml_backend_dev_t device) const;
size_t estimate_output_bytes(ggml_cgraph* graph,
const ggml_graph_cut::Segment& segment) const;
bool capture(ggml_cgraph* graph, const ggml_graph_cut::Segment& segment, const char* log_desc);
void prune(const std::unordered_set<std::string>& keep_names);
void clear() { tensors_.clear(); }
};
}
#endif // __SD_CORE_RUNNER_CACHE_H__