File size: 4,978 Bytes
2517be1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | #include "ggml-backend-impl.h"
#include "ggml-decoder.h"
#include "ggml-impl.h"
#include <algorithm>
#include <cstddef>
#include <memory>
#include <openvino/runtime/core.hpp>
#include <openvino/runtime/infer_request.hpp>
#include <string>
#include <unordered_map>
#include <vector>
struct graph_key {
int n_nodes;
std::string first_node_name;
std::string last_node_name;
graph_key(const ggml_cgraph * cgraph) : n_nodes(cgraph->n_nodes) {
if (n_nodes > 0) {
first_node_name = cgraph->nodes[0]->name;
last_node_name = cgraph->nodes[n_nodes - 1]->name;
}
}
bool operator==(const graph_key & other) const {
return n_nodes == other.n_nodes && first_node_name == other.first_node_name &&
last_node_name == other.last_node_name;
}
};
struct graph_key_hash {
size_t operator()(const graph_key & key) const {
size_t h = std::hash<int>{}(key.n_nodes);
if (key.n_nodes > 0) {
h ^= std::hash<std::string>{}(key.first_node_name) + 0x9e3779b9 + (h << 6) + (h >> 2);
h ^= std::hash<std::string>{}(key.last_node_name) + 0x9e3779b9 + (h << 6) + (h >> 2);
}
return h;
}
};
struct ov_runtime_context {
std::mutex ov_compute_mutex;
std::string device;
bool stateful;
std::unordered_map<graph_key, std::shared_ptr<GgmlOvDecoder>, graph_key_hash> decoder_cache;
std::unordered_map<graph_key, std::shared_ptr<ov::InferRequest>, graph_key_hash> infer_request_cache;
std::unordered_map<graph_key, std::shared_ptr<ov::InferRequest>, graph_key_hash> infer_request_cache_prefill;
std::unordered_map<graph_key, std::vector<std::string>, graph_key_hash> ov_input_names_cache;
std::unordered_map<graph_key, std::vector<std::string>, graph_key_hash> ov_output_names_cache;
//TODO: Stateful is only supported for single request at a time.
// Simultanous stateful inference request support to be added.
size_t stateful_kv_size;
std::map<std::string, std::string> kv_state_input_name_map;
ov_runtime_context() :
device("CPU"),
stateful(false),
stateful_kv_size(0) {}
};
enum ggml_status ov_graph_compute(struct ggml_cgraph * cgraph, ggml_backend_t backend);
enum ggml_status ov_graph_compute_dynamic(struct ggml_cgraph * cgraph, std::shared_ptr<ov_runtime_context> r_ctx);
enum ggml_status ov_graph_compute_static(struct ggml_cgraph * cgraph, std::shared_ptr<ov_runtime_context> r_ctx);
size_t checksum(const void * data, size_t size);
void print_input_tensor_info(const std::string & name, const ov::Tensor & tensor);
void print_output_tensor_info(const std::string & name, const ov::Tensor & tensor, const void * output_dst);
template <typename T>
std::vector<T> pad_input(const T * data,
size_t rows,
size_t cols,
size_t padded_rows,
size_t padded_cols,
T pad_value) {
std::vector<T> padded(padded_rows * padded_cols, pad_value);
for (size_t i = 0; i < std::min(rows, padded_rows); ++i) {
for (size_t j = 0; j < std::min(cols, padded_cols); ++j) {
padded[i * padded_cols + j] = data[i * cols + j];
}
}
return padded;
}
template <typename T>
std::vector<T> pad_input(const ggml_tensor * tensor, size_t padded_rows, size_t padded_cols, T pad_value) {
return pad_input<T>(reinterpret_cast<const T *>(tensor->data),
static_cast<size_t>(tensor->ne[1]), // rows
static_cast<size_t>(tensor->ne[0]), // cols
padded_rows, padded_cols, pad_value);
}
void set_zero_diagonal(std::vector<float> & matrix, size_t rows, size_t cols);
const ggml_tensor * get_inp_pos_tensor(struct ggml_cgraph * cgraph);
bool get_is_prefill(const ggml_tensor * inp_pos);
ov::Tensor get_ov_input_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder, const std::string & param_name);
ov::Tensor get_ov_input_tensor_static_decode(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
const std::string & param_name);
ov::Tensor get_ov_input_tensor_static_prefill(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
const std::string & param_name,
int chunk_index);
ov::Tensor create_ov_output_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
std::shared_ptr<ov::InferRequest> infer_request,
int output_index,
const ggml_tensor * ggml_tensor);
bool is_naive(struct ggml_cgraph * cgraph);
enum ggml_status naive_compute(struct ggml_cgraph * cgraph,
ov::Core & core,
const std::string & device,
const ov::AnyMap & config);
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