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llama.cpp
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llama.cpp/ggml/src/ggml-openvino/utils.cpp

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#include "utils.h"
#include "ggml-impl.h"
#include "ggml-openvino/ggml-decoder.h"
#include "ggml.h"
#include "openvino/frontend.hpp"
#include "openvino/input_model.hpp"
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstdlib>
#include <cstring>
#include <iomanip>
#include <iostream>
#include <memory>
#include <mutex>
#include <openvino/core/any.hpp>
#include <openvino/core/graph_util.hpp>
#include <openvino/core/shape.hpp>
#include <openvino/core/type/float16.hpp>
#include <openvino/frontend/manager.hpp>
#include <openvino/openvino.hpp>
#include <openvino/runtime/compiled_model.hpp>
#include <openvino/runtime/infer_request.hpp>
#include <openvino/runtime/intel_npu/properties.hpp>
#include <openvino/runtime/properties.hpp>
#include <openvino/runtime/tensor.hpp>
#include <string>
#include <unordered_map>
#include <vector>
// Suppress deprecation warning for ov::Tensor::data()
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
static ov::Core core;
enum ggml_status ov_graph_compute(ggml_cgraph * cgraph) {
auto get_device = [&] {
std::string device = getenv("GGML_OPENVINO_DEVICE") ? getenv("GGML_OPENVINO_DEVICE") : "CPU";
auto available_devices = core.get_available_devices();
if (std::find(available_devices.begin(), available_devices.end(), device) == available_devices.end()) {
GGML_LOG_WARN("GGML OpenVINO Backend: device %s is not available, fallback to CPU\n", device.c_str());
device = "CPU";
}
return device;
};
if (getenv("GGML_OPENVINO_DUMP_CGRAPH")) {
std::string filename = "cgraph.txt";
GgmlOvDecoder::dump_cgraph(cgraph, filename);
}
static const auto device = get_device();
static const auto is_static = device == "NPU" ? true : false;
return is_static ? ov_graph_compute_static(cgraph) : ov_graph_compute_dynamic(cgraph, device);
}
enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, const std::string & device) {
static auto is_static = false;
static auto config = get_ov_compile_config(device);
// if (is_naive(cgraph)) {
// return naive_compute(cgraph, core, device, config);
// }
auto start_time = ggml_time_us();
static std::mutex cache_mutex;
static std::unordered_map<graph_key, std::shared_ptr<GgmlOvDecoder>, graph_key_hash> decoder_cache;
static std::unordered_map<graph_key, std::shared_ptr<ov::InferRequest>, graph_key_hash> infer_request_cache;
static std::unordered_map<graph_key, std::vector<std::string>, graph_key_hash> ov_input_names_cache;
static std::unordered_map<graph_key, std::vector<std::string>, graph_key_hash> ov_output_names_cache;
std::shared_ptr<GgmlOvDecoder> ggml_decoder;
std::shared_ptr<ov::InferRequest> infer_request;
ModelParams m_params;
ComputeParams c_params;
std::tie(m_params, c_params) = GgmlOvDecoder::compute_llm_params(cgraph, is_static);
const auto key = compute_graph_key(cgraph);
bool cache_hit;
int64_t decoder_end_time;
int64_t conversion_end_time;
int64_t compile_end_time;
int64_t infer_end_time;
{
std::lock_guard<std::mutex> lock(cache_mutex);
auto it = decoder_cache.find(key);
cache_hit = it != decoder_cache.end();
if (cache_hit) {
ggml_decoder = it->second;
cache_hit = ggml_decoder->get_model_params().can_reuse_statically(m_params);
}
if (cache_hit) {
std::map<std::string, std::shared_ptr<ov::Node>> model_weights;
ggml_decoder = decoder_cache[key];
ggml_decoder->set_compute_params(c_params);
ggml_decoder->set_model_params(m_params);
ggml_decoder->add_extra_inputs();
infer_request = infer_request_cache[key];
decoder_end_time = ggml_time_us();
conversion_end_time = decoder_end_time;
compile_end_time = decoder_end_time;
} else {
infer_request_cache.erase(key);
std::shared_ptr<ov::Model> model;
auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph, get_types_to_requant(device));
ggml_decoder = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights, is_static);
decoder_end_time = ggml_time_us();
auto input_model = std::make_shared<ov::frontend::ggml::InputModel>(ggml_decoder);
model = ov::frontend::ggml::FrontEnd::convert(input_model);
ggml_decoder->clear_model_weights();
conversion_end_time = ggml_time_us();
if (getenv("GGML_OPENVINO_DUMP_IR")) {
char timestamped_filename[64];
auto timestamp = (long long) ggml_time_us();
snprintf(timestamped_filename, sizeof(timestamped_filename), "model_%lld.xml", timestamp);
ov::serialize(model, timestamped_filename);
}
auto compiled_model = core.compile_model(model, device, config);
compile_end_time = ggml_time_us();
infer_request = std::make_shared<ov::InferRequest>(compiled_model.create_infer_request());
infer_request_cache[key] = infer_request;
decoder_cache[key] = ggml_decoder;
std::vector<std::string> ov_input_names;
std::vector<std::string> ov_output_names;
for (const auto & ov_param : model->get_parameters()) {
ov_input_names.push_back(ov_param->get_friendly_name());
}
for (const auto & ov_output : model->get_results()) {
ov_output_names.push_back(ov_output->get_friendly_name());
}
ov_input_names_cache[key] = std::move(ov_input_names);
ov_output_names_cache[key] = std::move(ov_output_names);
}
}
auto ov_input_names = ov_input_names_cache[key];
auto ov_output_names = ov_output_names_cache[key];
for (size_t i = 0; i < ov_input_names.size(); i++) {
auto param_name = ov_input_names[i];
auto input_tensor = get_ov_input_tensor(ggml_decoder, param_name);
infer_request->set_input_tensor(i, input_tensor);
if (getenv("GGML_OPENVINO_DEBUG_INPUT")) {
print_input_tensor_info(param_name, input_tensor);
}
}
for (size_t i = 0; i < ov_output_names.size(); i++) {
auto output_tensor = get_ov_output_tensor(ggml_decoder, ov_output_names[i]);
infer_request->set_output_tensor(i, output_tensor);
}
infer_request->infer();
infer_end_time = ggml_time_us();
if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
for (size_t i = 0; i < ov_output_names.size(); i++) {
const auto output_tensor = infer_request->get_output_tensor(i);
print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data());
}
}
if (getenv("GGML_OPENVINO_PROFILING")) {
GGML_LOG_INFO("\nGGML OpenVINO Backend: \n");
GGML_LOG_INFO(" - Graph decoder Time: %ld ms \n", (decoder_end_time - start_time) / 1000);
if (!cache_hit) {
GGML_LOG_INFO(" - Graph conversion Time: %ld ms \n", (conversion_end_time - decoder_end_time) / 1000);
GGML_LOG_INFO(" - Graph compile Time: %ld ms \n", (compile_end_time - conversion_end_time) / 1000);
}
GGML_LOG_INFO(" - Graph Inference Time: %ld ms \n", (infer_end_time - compile_end_time) / 1000);
}
return GGML_STATUS_SUCCESS;
}
enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph) {
auto get_prefill_chunk_size = [] {
const char * chunk_size_str = getenv("GGML_OPENVINO_PREFILL_CHUNK_SIZE");
if (chunk_size_str && atoi(chunk_size_str) > 0) {
return atoi(chunk_size_str);
}
return 256;
};
static std::string device = "NPU";
static auto is_static = true;
static auto prefill_chunk_size = get_prefill_chunk_size();
static auto config = get_ov_compile_config(device);
if (is_naive(cgraph)) {
return naive_compute(cgraph, core, device, config);
}
auto start_time = ggml_time_us();
static std::mutex cache_mutex;
static std::unordered_map<graph_key, std::shared_ptr<GgmlOvDecoder>, graph_key_hash> decoder_cache;
static std::unordered_map<graph_key, std::shared_ptr<ov::InferRequest>, graph_key_hash> infer_request_cache;
static std::unordered_map<graph_key, std::shared_ptr<ov::InferRequest>, graph_key_hash> infer_request_cache_prefill;
static std::unordered_map<graph_key, std::vector<std::string>, graph_key_hash> ov_input_names_cache;
static std::unordered_map<graph_key, std::vector<std::string>, graph_key_hash> ov_output_names_cache;
std::shared_ptr<GgmlOvDecoder> ggml_decoder;
std::shared_ptr<ov::InferRequest> infer_request;
ModelParams m_params;
ComputeParams c_params;
std::tie(m_params, c_params) = GgmlOvDecoder::compute_llm_params(cgraph, is_static);
const auto * inp_pos = get_inp_pos_tensor(cgraph);
const auto is_prefill = get_is_prefill(inp_pos);
const auto key = compute_graph_key(cgraph);
bool cache_hit;
int64_t decoder_end_time;
int64_t conversion_end_time;
int64_t compile_end_time;
int64_t infer_end_time;
{
std::lock_guard<std::mutex> lock(cache_mutex);
auto it = decoder_cache.find(key);
cache_hit = it != decoder_cache.end();
if (cache_hit) {
ggml_decoder = it->second;
cache_hit = ggml_decoder->get_model_params().can_reuse_statically(m_params);
}
if (cache_hit) {
std::map<std::string, std::shared_ptr<ov::Node>> model_weights;
ggml_decoder = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights, is_static,
is_prefill, prefill_chunk_size);
decoder_cache[key] = ggml_decoder;
infer_request = is_prefill ? infer_request_cache_prefill[key] : infer_request_cache[key];
decoder_end_time = ggml_time_us();
conversion_end_time = decoder_end_time;
compile_end_time = decoder_end_time;
} else {
infer_request_cache.erase(key);
infer_request_cache_prefill.erase(key);
std::shared_ptr<ov::Model> model;
auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph, get_types_to_requant(device));
auto ggml_decoder_prefill = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights,
is_static, true, prefill_chunk_size);
auto ggml_decoder_decode = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights,
is_static, false, prefill_chunk_size);
decoder_end_time = ggml_time_us();
auto input_model_prefill = std::make_shared<ov::frontend::ggml::InputModel>(ggml_decoder_prefill);
auto input_model_decode = std::make_shared<ov::frontend::ggml::InputModel>(ggml_decoder_decode);
auto model_prefill = ov::frontend::ggml::FrontEnd::convert(input_model_prefill);
ggml_decoder_prefill->clear_model_weights();
auto model_decode = ov::frontend::ggml::FrontEnd::convert(input_model_decode);
ggml_decoder_decode->clear_model_weights();
conversion_end_time = ggml_time_us();
if (getenv("GGML_OPENVINO_DUMP_IR")) {
char timestamped_filename[64];
auto timestamp = (long long) ggml_time_us();
snprintf(timestamped_filename, sizeof(timestamped_filename), "model_prefill_%lld.xml", timestamp);
ov::serialize(model_prefill, timestamped_filename);
snprintf(timestamped_filename, sizeof(timestamped_filename), "model_decode_%lld.xml", timestamp);
ov::serialize(model_decode, timestamped_filename);
}
auto compiled_model_prefill = core.compile_model(model_prefill, device, get_ov_compile_config(device));
auto compiled_model_decode = core.compile_model(model_decode, device, get_ov_compile_config(device));
infer_request_cache_prefill[key] =
std::make_shared<ov::InferRequest>(compiled_model_prefill.create_infer_request());
infer_request_cache[key] = std::make_shared<ov::InferRequest>(compiled_model_decode.create_infer_request());
compile_end_time = ggml_time_us();
model = is_prefill ? model_prefill : model_decode;
ggml_decoder = is_prefill ? ggml_decoder_prefill : ggml_decoder_decode;
infer_request = is_prefill ? infer_request_cache_prefill[key] : infer_request_cache[key];
decoder_cache[key] = ggml_decoder;
std::vector<std::string> ov_input_names;
std::vector<std::string> ov_output_names;
for (const auto & ov_param : model->get_parameters()) {
ov_input_names.push_back(ov_param->get_friendly_name());
}
for (const auto & ov_output : model->get_results()) {
ov_output_names.push_back(ov_output->get_friendly_name());
}
ov_input_names_cache[key] = std::move(ov_input_names);
ov_output_names_cache[key] = std::move(ov_output_names);
}
}
auto ov_input_names = ov_input_names_cache[key];
auto ov_output_names = ov_output_names_cache[key];
if (is_prefill) {
auto inp_len = inp_pos->ne[0];
for (int chunk_index = 0; chunk_index * prefill_chunk_size < inp_len; chunk_index++) {
for (size_t i = 0; i < ov_input_names.size(); i++) {
auto param_name = ov_input_names[i];
auto input_tensor = get_ov_input_tensor_static_prefill(ggml_decoder, param_name, chunk_index);
infer_request->set_input_tensor(i, input_tensor);
if (getenv("GGML_OPENVINO_DEBUG_INPUT")) {
const auto input_tensor = infer_request->get_input_tensor(i);
print_input_tensor_info(param_name, input_tensor);
}
}
for (size_t i = 0; i < ov_output_names.size(); i++) {
auto output_tensor = get_ov_output_tensor(ggml_decoder, ov_output_names[i]);
infer_request->set_output_tensor(i, output_tensor);
}
infer_request->infer();
if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
for (size_t i = 0; i < ov_output_names.size(); i++) {
const auto output_tensor = infer_request->get_output_tensor(i);
print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data());
}
}
}
infer_end_time = ggml_time_us();
} else {
for (size_t i = 0; i < ov_input_names.size(); i++) {
auto param_name = ov_input_names[i];
auto input_tensor = get_ov_input_tensor_static_decode(ggml_decoder, param_name);
infer_request->set_input_tensor(i, input_tensor);
if (getenv("GGML_OPENVINO_DEBUG_INPUT")) {
const auto input_tensor = infer_request->get_input_tensor(i);
print_input_tensor_info(param_name, input_tensor);
}
}
for (size_t i = 0; i < ov_output_names.size(); i++) {
auto output_tensor = get_ov_output_tensor(ggml_decoder, ov_output_names[i]);
infer_request->set_output_tensor(i, output_tensor);
}
infer_request->infer();
infer_end_time = ggml_time_us();
if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
for (size_t i = 0; i < ov_output_names.size(); i++) {
const auto output_tensor = infer_request->get_output_tensor(i);
print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data());
}
}
}
if (getenv("GGML_OPENVINO_PROFILING")) {
GGML_LOG_INFO("\nGGML OpenVINO Backend: \n");
GGML_LOG_INFO(" - Graph decoder Time: %ld ms \n", (decoder_end_time - start_time) / 1000);
if (!cache_hit) {
GGML_LOG_INFO(" - Graph conversion Time: %ld ms \n", (conversion_end_time - decoder_end_time) / 1000);
GGML_LOG_INFO(" - Graph compile Time: %ld ms \n", (compile_end_time - conversion_end_time) / 1000);
}
GGML_LOG_INFO(" - Graph Inference Time: %ld ms \n", (infer_end_time - compile_end_time) / 1000);
}
return GGML_STATUS_SUCCESS;
}
ov::AnyMap get_ov_compile_config(const std::string & device) {
ov::AnyMap config;
auto * cache_dir = getenv("GGML_OPENVINO_CACHE_DIR");
if (device == "NPU") {
config = {
{"NPU_COMPILER_DYNAMIC_QUANTIZATION", "YES" },
{"NPU_USE_NPUW", "YES" },
{"NPUW_DEVICES", "NPU" },
{"NPUW_FOLD", "YES" },
{"NPUW_WEIGHTS_BANK", "shared"},
{"NPUW_FUNCALL_FOR_ALL", "YES" },
{"NPUW_FUNCALL_ASYNC", "YES" },
{"NPUW_DQ", "YES" },
{"NPUW_DQ_FULL", "NO" },
};
if (cache_dir) {
config["NPUW_CACHE_DIR"] = cache_dir;
}
} else if (cache_dir) {
core.set_property(ov::cache_dir(cache_dir));
}
return config;
}
std::map<ggml_type, ExtraQuantType> get_types_to_requant(const std::string & device) {
if (device == "NPU") {
return {
{GGML_TYPE_Q4_0, ExtraQuantType::Q4_0_128},
{GGML_TYPE_Q4_1, ExtraQuantType::Q4_0_128},
{GGML_TYPE_Q4_K, ExtraQuantType::Q4_0_128},
{GGML_TYPE_Q6_K, ExtraQuantType::F16 },
{GGML_TYPE_Q5_K, ExtraQuantType::F16 },
};
}
if (device == "GPU") {
return {
// gs16 will be supported on openvino-2025.4
{GGML_TYPE_Q6_K, ExtraQuantType::Q8_0_32},
};
}
return {};
}
bool is_naive(ggml_cgraph * cgraph) {
constexpr int naive_graph_size_threshold = 20;
return cgraph->n_nodes < naive_graph_size_threshold;
}
enum ggml_status naive_compute(ggml_cgraph * cgraph,
ov::Core & core,
const std::string & device,
const ov::AnyMap & config) {
if (cgraph->n_nodes == 1 && (cgraph->nodes[0]->op == GGML_OP_NONE || cgraph->nodes[0]->op == GGML_OP_VIEW)) {
return GGML_STATUS_SUCCESS;
}
if (cgraph->nodes[0]->op == GGML_OP_FLASH_ATTN_EXT) {
return GGML_STATUS_FAILED;
}
auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph);
auto decoder = std::make_shared<GgmlOvDecoder>(cgraph, model_weights);
auto input_model = std::make_shared<ov::frontend::ggml::InputModel>(decoder);
auto naive = true;
auto model = ov::frontend::ggml::FrontEnd::convert(input_model, naive);
if (getenv("GGML_OPENVINO_DUMP_IR")) {
ov::serialize(model, "IR_naive.xml");
}
auto infer_request = core.compile_model(model, device, config).create_infer_request();
auto ov_params = model->get_parameters();
for (size_t i = 0; i < ov_params.size(); i++) {
auto param_name = ov_params[i]->get_friendly_name();
auto input_tensor = get_ov_input_tensor(decoder, param_name);
infer_request.set_input_tensor(i, input_tensor);
}
auto ov_results = model->get_results();
for (size_t i = 0; i < ov_results.size(); i++) {
auto result_name = ov_results[i]->get_friendly_name();
auto output_tensor = get_ov_output_tensor(decoder, result_name);
infer_request.set_output_tensor(i, output_tensor);
}
infer_request.infer();
return GGML_STATUS_SUCCESS;
}
namespace {
ov::Tensor convert_ggml_input_to_ov(std::shared_ptr<GgmlOvDecoder> ggml_decoder, const std::string & name) {
const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(name);
auto * input_data = ggml_tensor->data;
ov::Shape input_shape;
if (ggml_tensor->op == GGML_OP_VIEW) {
// This case is added to make test-backend-ops work
input_shape = ggml_decoder->get_shape(ggml_tensor->view_src);
} else {
input_shape = ggml_decoder->get_shape(ggml_tensor);
}
auto input_tensor = ov::Tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape, input_data);
return input_tensor;
}
} // namespace
ov::Tensor get_ov_input_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder, const std::string & param_name) {
ov::Tensor input_tensor;
if (ggml_decoder->get_model_extra_inputs().find(param_name) != ggml_decoder->get_model_extra_inputs().end()) {
input_tensor = *ggml_decoder->get_model_extra_input_values().at(param_name);
} else {
input_tensor = convert_ggml_input_to_ov(ggml_decoder, param_name);
}
return input_tensor;
}
ov::Tensor get_ov_input_tensor_static_decode(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
const std::string & param_name) {
const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(param_name);
const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor);
if (param_name == "inp_pos" || param_name == "inp_tokens" ||
(op->op == GGML_OP_SET_ROWS && op->src[1] == ggml_tensor)) {
assert(ggml_tensor->ne[0] == 1);
ov::Shape input_shape = {1, 1, 1, 1};
ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape);
if (ggml_tensor->type == GGML_TYPE_I32) {
*input_tensor.data<int32_t>() = *((int32_t *) ggml_tensor->data);
} else if (ggml_tensor->type == GGML_TYPE_I64) {
*input_tensor.data<int64_t>() = *((int64_t *) ggml_tensor->data);
} else {
throw std::runtime_error("Unexpected tensor type for " + param_name);
}
return input_tensor;
}
if (param_name == "inp_out_ids") {
ov::Shape input_shape = {1, 1, 1, 1};
ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape);
int32_t inp_out_id = *((int32_t *) ggml_tensor->data);
assert(ggml_tensor->ne[0] == 1);
assert(inp_out_id == 0);
*input_tensor.data<int32_t>() = inp_out_id;
return input_tensor;
}
if (param_name.find("KQ_mask") == 0) {
size_t context_size = ggml_decoder->get_ctx_size();
std::vector<float> padded_data = pad_input<float>(ggml_tensor, 1, context_size, -INFINITY);
ov::Tensor input_tensor(ov::element::f32, ov::Shape{1, 1, 1, context_size});
auto * data_ptr = input_tensor.data<float>();
std::copy(padded_data.begin(), padded_data.begin() + context_size, data_ptr);
return input_tensor;
}
return get_ov_input_tensor(ggml_decoder, param_name);
}
ov::Tensor get_ov_input_tensor_static_prefill(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
const std::string & param_name,
int chunk_index) {
const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(param_name);
const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor);
const size_t input_len = ggml_decoder->get_input_len();
const size_t chunk_size = ggml_decoder->m_prefill_chunk_size;
const size_t chunk_valid_size = std::min(chunk_size, input_len - chunk_index * chunk_size);
const size_t chunk_pad_size = chunk_size - chunk_valid_size;
if (param_name == "inp_pos" || param_name == "inp_tokens" ||
(op->op == GGML_OP_SET_ROWS && op->src[1] == ggml_tensor)) {
ov::Shape input_shape = {1, 1, 1, chunk_size};
ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape);
// copy the chunk_index-th chunk from ggml_tensor
size_t element_size = ggml_type_size(ggml_tensor->type);
void * input_data = (char *) ggml_tensor->data + chunk_index * chunk_size * element_size;
std::memcpy(input_tensor.data(), input_data, chunk_valid_size * element_size);
// pad the rest with last_value + 1, so that kv's of padded positions are inserted
// to the next row after the valids row in the kvcache
if (chunk_pad_size > 0) {
if (ggml_tensor->type == GGML_TYPE_I32) {
int32_t last_value =
*((int32_t *) ggml_tensor->data + (chunk_index * chunk_size + chunk_valid_size - 1));
int32_t * output_data = input_tensor.data<int32_t>();
std::fill(output_data + chunk_valid_size, output_data + chunk_size, last_value + 1);
} else if (ggml_tensor->type == GGML_TYPE_I64) {
int64_t last_value =
*((int64_t *) ggml_tensor->data + (chunk_index * chunk_size + chunk_valid_size - 1));
int64_t * output_data = input_tensor.data<int64_t>();
std::fill(output_data + chunk_valid_size, output_data + chunk_size, last_value + 1);
} else {
throw std::runtime_error("Unexpected tensor type for " + param_name);
}
}
return input_tensor;
}
if (param_name == "inp_out_ids") {
size_t output_len = ggml_decoder->get_compute_params().output_len;
ov::Shape input_shape = {1, 1, 1, output_len};
ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape);
if (ggml_tensor->ne[0] == 0) {
*input_tensor.data<int32_t>() = 0;
} else {
auto * data_addr = input_tensor.data<int32_t>();
for (size_t i = 0; i < output_len; i++) {
data_addr[i] = ((int32_t *) ggml_tensor->data)[i] % chunk_size;
}
}
return input_tensor;
}
if (param_name.find("KQ_mask") == 0) {
size_t cols = ggml_tensor->ne[0];
size_t rows = ggml_tensor->ne[1];
float * ggml_data = (float *) ggml_tensor->data + chunk_index * chunk_size * cols;
size_t chunk_valid_rows = std::min(chunk_size, rows - chunk_index * chunk_size);
size_t context_size = ggml_decoder->get_ctx_size();
std::vector<float> padded_data =
pad_input<float>(ggml_data, chunk_valid_rows, cols, chunk_size, context_size, -INFINITY);
set_zero_diagonal(padded_data, chunk_size, context_size);
ov::Tensor input_tensor(ov::element::f32, ov::Shape{1, 1, chunk_size, context_size});
auto * data_ptr = input_tensor.data<float>();
std::copy(padded_data.begin(), padded_data.begin() + chunk_size * context_size, data_ptr);
return input_tensor;
}
return get_ov_input_tensor(ggml_decoder, param_name);
}
ov::Tensor get_ov_output_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder, const std::string & result_name) {
auto * ggml_tensor = ggml_decoder->get_model_outputs().at(result_name);
auto output_type = ggml_decoder->get_ov_type(ggml_tensor);
auto output_shape = ggml_decoder->get_shape(ggml_tensor);
if (ggml_decoder->is_static() && result_name == "result_output") {
output_shape[1] = 1;
}
ov::Tensor output_tensor(output_type, output_shape, ggml_tensor->data);
return output_tensor;
}
size_t checksum(const void * data, size_t size) {
const uint8_t * bytes = static_cast<const uint8_t *>(data);
size_t sum = 0;
for (size_t i = 0; i < size; ++i) {
sum += (uint8_t) i;
sum += bytes[i];
}
return sum;
}
void print_input_tensor_info(const std::string & name, const ov::Tensor & tensor) {
std::cout << "Input name: " << name << ", Input shape: " << tensor.get_shape() << ", Address: " << tensor.data()
<< std::endl;
switch (tensor.get_element_type()) {
case ov::element::f32: {
if (name.find("KQ_mask") == std::string::npos) {
std::cout << *(tensor.data<float>()) << std::endl;
} else {
size_t rows = tensor.get_shape()[2];
size_t cols = tensor.get_shape()[3];
auto * data = tensor.data<float>();
for (size_t i = 0; i < rows; ++i) {
for (size_t j = 0; j < cols; ++j) {
float val = data[i * cols + j];
if (std::isinf(val) && val < 0) {
std::cout << std::setw(5) << "-inf";
} else {
std::cout << std::setw(5) << val;
}
}
std::cout << std::endl;
}
}
break;
}
case ov::element::f16:
std::cout << *(tensor.data<ov::float16>()) << std::endl;
break;
case ov::element::i32:
for (size_t i = 0; i < tensor.get_size(); ++i) {
std::cout << tensor.data<int32_t>()[i] << " ";
}
std::cout << std::endl;
break;
case ov::element::i64:
for (size_t i = 0; i < tensor.get_size(); ++i) {
std::cout << tensor.data<int64_t>()[i] << " ";
}
std::cout << std::endl;
break;
default:
break;
}
}
void print_output_tensor_info(const std::string & name, const ov::Tensor & tensor, const void * output_dst) {
std::cout << "Output name: " << name << ", Output shape: " << tensor.get_shape() << ", Address: " << output_dst
<< std::endl;
auto print_float_stats = [](const std::string & type_name, size_t size, auto get_value) {
if (size == 0) {
return;
}
float first = get_value(0);
float min = first;
float max = first;
double sum = first;
for (size_t i = 1; i < size; ++i) {
float v = get_value(i);
if (v < min) {
min = v;
}
if (v > max) {
max = v;
}
sum += v;
}
double mean = sum / size;
std::cout << std::right << std::setw(6) << type_name << std::right << std::setw(12) << "First" << std::setw(12)
<< "Min" << std::setw(12) << "Max" << std::setw(12) << "Mean" << std::endl;
std::cout << std::right << std::setw(6) << "" << std::right << std::setw(12) << first << std::setw(12) << min
<< std::setw(12) << max << std::setw(12) << mean << std::endl;
};
switch (tensor.get_element_type()) {
case ov::element::f32: {
const float * data = tensor.data<float>();
size_t size = tensor.get_size();
print_float_stats("[f32]", size, [data](size_t i) { return data[i]; });
break;
}
case ov::element::f16: {
const ov::float16 * data = tensor.data<ov::float16>();
size_t size = tensor.get_size();
print_float_stats("[f16]", size, [data](size_t i) { return static_cast<float>(data[i]); });
break;
}
default:
break;
}
}
void set_zero_diagonal(std::vector<float> & matrix, size_t rows, size_t cols) {
for (size_t i = 0; i < rows; ++i) {
size_t diag_col = std::min(i, cols - 1);
matrix[i * cols + diag_col] = 0.0f;
}
}
const ggml_tensor * get_inp_pos_tensor(ggml_cgraph * cgraph) {
for (int i = 0; i < cgraph->n_nodes; ++i) {
auto * op = cgraph->nodes[i];
for (int j = 0; j < GGML_MAX_SRC; ++j) {
auto * src = op->src[j];
if (src == nullptr) {
break;
}
if (std::string(src->name) == "inp_pos") {
return src;
}
}
}
GGML_LOG_ERROR("get_inp_pos_tensor: inp_pos not found in cgraph");
throw std::runtime_error("get_inp_pos_tensor: inp_pos not found in cgraph");
}
bool get_is_prefill(const ggml_tensor * inp_pos) {
return inp_pos->ne[0] > 1;
}
graph_key compute_graph_key(ggml_cgraph * cgraph) {
graph_key key;
key.n_nodes = cgraph->n_nodes;
if (cgraph->n_nodes > 0) {
key.first_node_name = std::string(cgraph->nodes[0]->name);
key.last_node_name = std::string(cgraph->nodes[cgraph->n_nodes - 1]->name);
} else {
key.first_node_name = "";
key.last_node_name = "";
}
return key;
}
#pragma GCC diagnostic pop
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