Datasets:

Modalities:
Text
Formats:
text
Size:
< 1K
ArXiv:
Libraries:
Datasets
dlxj
todo: 基于 CUDA 13.0 编译
2517be1
#pragma once
// clang-format off
#include "ggml.h"
#include "ggml-backend-impl.h"
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include <cstdint>
// clang-format on
// ggml_tensor is serialized into apir_rpc_tensor
struct apir_rpc_tensor {
uint64_t id;
uint32_t type;
uint64_t buffer;
uint32_t ne[GGML_MAX_DIMS];
uint32_t nb[GGML_MAX_DIMS];
uint32_t op;
int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
int32_t flags;
uint64_t src[GGML_MAX_SRC];
uint64_t view_src;
uint64_t view_offs;
uint64_t data;
char name[GGML_MAX_NAME];
char padding[4];
};
/* frontend */
apir_rpc_tensor apir_serialize_tensor(const ggml_tensor * tensor);
void apir_serialize_graph(const ggml_cgraph * cgraph, std::vector<uint8_t> & output);
/* backend */
void apir_track_backend_buffer(ggml_backend_buffer_t buffer);
bool apir_untrack_backend_buffer(ggml_backend_buffer_t buffer);
std::unordered_set<ggml_backend_buffer_t> apir_get_track_backend_buffers();
void apir_add_tensor(ggml_tensor * tensor,
std::vector<apir_rpc_tensor> & tensors,
std::unordered_set<ggml_tensor *> & visited);
ggml_tensor * apir_deserialize_tensor(ggml_context * ctx, const apir_rpc_tensor * tensor);
ggml_tensor * apir_create_node(uint64_t id,
ggml_context * ctx,
const std::unordered_map<uint64_t, const apir_rpc_tensor *> & tensor_ptrs,
std::unordered_map<uint64_t, ggml_tensor *> & tensor_map);
ggml_cgraph * apir_deserialize_graph(uint32_t n_nodes,
uint32_t n_tensors,
const apir_rpc_tensor * tensors,
const uint64_t * nodes);