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#pragma once
#include <unordered_map>
#include "onnx/onnx_pb.h"
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/onnx/onnxifi_graph_info.h"
#include "caffe2/onnx/onnxifi_init.h"
#include "caffe2/utils/string_utils.h"
namespace caffe2 {
template <typename T, typename Context>
class OnnxifiOp final : public Operator<Context> {
struct TensorInfo {
TensorInfo() {}
TensorInfo(TensorInfo&&) = default;
TensorInfo& operator=(TensorInfo&&) = default;
std::vector<uint64_t> dims;
uint64_t onnxifi_type;
};
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
OnnxifiOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws) {
lib_ = onnx::initOnnxifiLibrary();
backend_graph_map_ptr_ = onnx::getOnnxBackendGraphMap();
CAFFE_ENFORCE(lib_, "Cannot initialize ONNXIFI library");
auto onnx_model_str =
this->template GetSingleArgument<std::string>("onnx_model", "");
CAFFE_ENFORCE(!onnx_model_str.empty(), "onnx_model cannot be empty");
// Setup input/output descriptor templates
input_names_ =
this->template GetRepeatedArgument<std::string>("input_names");
output_names_ =
this->template GetRepeatedArgument<std::string>("output_names");
CAFFE_ENFORCE_EQ(input_names_.size(), operator_def.input_size());
CAFFE_ENFORCE_EQ(output_names_.size(), operator_def.output_size());
for (const auto& input : input_names_) {
input_desc_.push_back(onnxTensorDescriptorV1());
input_desc_.back().name = input.c_str();
}
int output_idx = 0;
for (const auto& output : output_names_) {
output_desc_.push_back(onnxTensorDescriptorV1());
output_desc_.back().name = output.c_str();
// For output, we try to get its output size hint
const std::string key = c10::str("output_shape_hint_", output_idx);
auto output_shape_hint = this->template GetRepeatedArgument<int>(key);
if (!output_shape_hint.empty()) {
TensorInfo info;
info.onnxifi_type = output_shape_hint.front();
for (int i = 1; i < output_shape_hint.size(); ++i) {
info.dims.push_back(output_shape_hint[i]);
}
output_shape_hints_.emplace(output_idx, std::move(info));
}
++output_idx;
}
// Encode arguments starting with "custom_" to backend
std::vector<uint64_t> property_pointers;
std::vector<int64_t> int_args;
std::vector<float> float_args;
BuildPropertyList(operator_def, &property_pointers, &int_args, &float_args);
// Pull the weights from workspace and feed it to the backend through
// setGraphIO. Notice that since we may have rewritten the net, we need to
// map the weight names
auto initializers =
this->template GetRepeatedArgument<std::string>("initializers");
CAFFE_ENFORCE_EQ(
initializers.size() % 2, 0, "initializers should come in pairs");
std::unordered_set<std::string> initializer_set;
std::unordered_map<std::string, std::string> input_mapping;
for (auto it = initializers.begin(); it != initializers.end(); ++it) {
auto key = *it++;
input_mapping.emplace(key, *it);
initializer_set.emplace(key);
}
Workspace mapped_ws(ws, input_mapping);
std::vector<std::string> weight_names;
std::vector<std::vector<uint64_t>> weight_shapes;
auto weight_descs = BuildInitializationList(
&mapped_ws, &initializer_set, &weight_names, &weight_shapes);
BuildBackendAndGraph(property_pointers, onnx_model_str, weight_descs);
}
~OnnxifiOp() {
backend_graph_shared_ptr_.reset();
backend_graph_map_ptr_->remove(op_id_string_);
}
bool RunOnDevice() override;
private:
uint64_t SetOutputShapeAndType(int output_idx, std::vector<size_t>* dims) {
uint64_t type = ONNXIFI_DATATYPE_FLOAT32;
const auto it = output_shape_hints_.find(output_idx);
if (it != output_shape_hints_.end()) {
std::copy(
it->second.dims.begin(),
it->second.dims.end(),
std::back_inserter(*dims));
type = it->second.onnxifi_type;
}
return type;
}
void BuildPropertyList(
const OperatorDef& /* unused */,
std::vector<uint64_t>* property_list,
std::vector<int64_t>* /* unused */,
std::vector<float>* /* unused */) {
property_list->push_back(ONNXIFI_BACKEND_PROPERTY_NONE);
}
void BuildBackendAndGraph(
const std::vector<uint64_t>& property_pointers,
const std::string& onnx_model_str,
const std::vector<onnxTensorDescriptorV1>& weight_descs) {
op_id_string_ =
this->template GetSingleArgument<std::string>("net_pos", "");
auto net_id_string =
this->template GetSingleArgument<std::string>("model_id", "");
op_id_string_ += net_id_string;
backend_graph_shared_ptr_ = backend_graph_map_ptr_->lookup(op_id_string_);
if (backend_graph_shared_ptr_ == nullptr) {
// Build the Onnxifi engine
std::vector<onnxBackendID> backend_ids;
auto backend_index =
this->template GetSingleArgument<int>("backend_id", 0);
CAFFE_ENFORCE_EQ(
lib_->onnxGetBackendIDs(nullptr, &num_backends_),
ONNXIFI_STATUS_FALLBACK);
CAFFE_ENFORCE_GT(
num_backends_, 0, "At least 1 onnxifi backend should be available");
CAFFE_ENFORCE_LT(
backend_index,
num_backends_,
"Backend idx out of bound: ",
backend_index,
", #backends: ",
num_backends_);
backend_ids.resize(num_backends_);
CAFFE_ENFORCE_EQ(
lib_->onnxGetBackendIDs(backend_ids.data(), &num_backends_),
ONNXIFI_STATUS_SUCCESS);
backend_id_ = backend_ids[backend_index];
CAFFE_ENFORCE_EQ(
lib_->onnxInitBackend(
backend_id_, property_pointers.data(), &backend_),
ONNXIFI_STATUS_SUCCESS);
// Release unused backend ids.
for (auto i = 0; i < num_backends_; ++i) {
if (i == backend_index) {
continue;
}
lib_->onnxReleaseBackendID(backend_ids[i]);
}
CAFFE_ENFORCE_EQ(
lib_->onnxInitGraph(
backend_,
nullptr,
onnx_model_str.size(),
(const void*)(onnx_model_str.c_str()),
weight_descs.size(),
weight_descs.data(),
&graph_),
ONNXIFI_STATUS_SUCCESS);
backend_graph_shared_ptr_ = backend_graph_map_ptr_->insert(
op_id_string_,
onnx::BackendGraphInfo(backend_id_, backend_, graph_, lib_));
// This checks if our insertion was successful or some other thread did
// the insert in the meantime.
if (backend_graph_shared_ptr_->backend_id != backend_id_ ||
backend_graph_shared_ptr_->backend != backend_ ||
backend_graph_shared_ptr_->graph != graph_) {
lib_->onnxReleaseBackendID(backend_id_);
lib_->onnxReleaseBackend(backend_);
lib_->onnxReleaseGraph(graph_);
}
}
backend_id_ = backend_graph_shared_ptr_->backend_id;
backend_ = backend_graph_shared_ptr_->backend;
graph_ = backend_graph_shared_ptr_->graph;
}
std::vector<onnxTensorDescriptorV1> BuildInitializationList(
Workspace* ws,
std::unordered_set<std::string>* initialization_list,
std::vector<std::string>* weight_names,
std::vector<std::vector<uint64_t>>* weight_shapes);
// pointer to loaded onnxifi library
onnxifi_library* lib_{nullptr};
onnx::OnnxBackendGraphMap* backend_graph_map_ptr_;
std::string op_id_string_;
onnxBackendID backend_id_{nullptr};
onnxBackend backend_{nullptr};
onnxGraph graph_{nullptr};
onnx::SharedPtrBackendGraphInfo backend_graph_shared_ptr_;
size_t num_backends_{0};
// input/output descriptors
std::vector<onnxTensorDescriptorV1> input_desc_;
std::vector<onnxTensorDescriptorV1> output_desc_;
// We bind the op input/output by position while ONNXIFI binds input/output by
// names. In addition, op input/output names can be writtten by, for example,
// memonger. We cache the original input/output name of ONNX object here and
// bind them by position.
std::vector<std::string> input_names_;
std::vector<std::string> output_names_;
std::vector<std::vector<uint64_t>> input_shapes_;
std::vector<std::vector<uint64_t>> output_shapes_;
// output shape hints
std::unordered_map<int, TensorInfo> output_shape_hints_;
};
} // namespace caffe2
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