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pybind_state.cc
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#include "pybind_state.h"
#include <chrono>
#include <future>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <c10/macros/Macros.h>
#include "caffe2/core/blob_serialization.h"
#include "caffe2/core/blob_stats.h"
#include "caffe2/core/db.h"
#include "caffe2/core/numa.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/stats.h"
#include "caffe2/core/transform.h"
#include "caffe2/observers/profile_observer.h"
#include "caffe2/observers/runcnt_observer.h"
#include "caffe2/observers/time_observer.h"
#include "caffe2/onnx/backend.h"
#include "caffe2/onnx/helper.h"
#include "caffe2/onnx/offline_tensor.h"
#include "caffe2/onnx/onnx_exporter.h"
#include "caffe2/opt/converter.h"
#include "caffe2/opt/fusion.h"
#include "caffe2/opt/mobile.h"
#include "caffe2/opt/onnxifi_transformer.h"
#include "caffe2/opt/optimize_ideep.h"
#include "caffe2/opt/passes.h"
#include "caffe2/opt/shape_info.h"
#include "caffe2/opt/fakefp16_transform.h"
#include "caffe2/predictor/emulator/data_filler.h"
#include "caffe2/predictor/predictor.h"
#include "caffe2/python/pybind_state_registry.h"
#include "caffe2/utils/cpuid.h"
#include "caffe2/utils/proto_convert.h"
#include "caffe2/utils/string_utils.h"
#include "torch/csrc/autograd/variable.h"
#include "torch/csrc/jit/python/module_python.h"
// Because of CMake setup, we can't depend on script module here just yet -
// it pulls in generated files from a different directory and it
// probabilistically breaks the build.
// TODO: enable if once shared libraries are unified in CMake
#ifdef FBCODE_CAFFE2
#include "torch/script.h"
#endif
namespace caffe2 {
namespace python {
// A dummy variable to overcome the pybind11 py::arg::operator= ambiguity
// for some earlier versions of pybind11.
constexpr bool kPyBindFalse = false;
namespace py = pybind11;
// gWorkspaces allows us to define and switch between multiple workspaces in
// Python.
static std::map<std::string, std::unique_ptr<Workspace>> gWorkspaces;
// gWorkspace is the pointer to the current workspace. The ownership is kept
// by the gWorkspaces map.
static Workspace* gWorkspace = nullptr;
static std::string gCurrentWorkspaceName;
BlobFetcherBase::~BlobFetcherBase() {}
BlobFeederBase::~BlobFeederBase() {}
C10_DEFINE_TYPED_REGISTRY(
BlobFetcherRegistry,
TypeIdentifier,
BlobFetcherBase,
std::unique_ptr);
C10_DEFINE_TYPED_REGISTRY(
BlobFeederRegistry,
caffe2::DeviceType,
BlobFeederBase,
std::unique_ptr);
REGISTER_BLOB_FETCHER((TypeMeta::Id<Tensor>()), TensorFetcher);
REGISTER_BLOB_FEEDER(CPU, TensorFeeder<CPUContext>);
Workspace* GetCurrentWorkspace() {
return gWorkspace;
}
Workspace* GetWorkspaceByName(const std::string &name) {
if (gWorkspaces.count(name)) {
return gWorkspaces[name].get();
}
return nullptr;
}
class StringFetcher : public BlobFetcherBase {
public:
py::object Fetch(const Blob& blob) override {
return py::bytes(blob.Get<string>());
}
};
REGISTER_BLOB_FETCHER((TypeMeta::Id<string>()), StringFetcher);
#ifdef FBCODE_CAFFE2
class ScriptModuleFetcher : public BlobFetcherBase {
public:
pybind11::object Fetch(const Blob& blob) override {
return py::cast(*blob.Get<std::unique_ptr<torch::jit::Module>>());
}
};
REGISTER_BLOB_FETCHER(
(TypeMeta::Id<std::unique_ptr<torch::jit::Module>>()),
caffe2::python::ScriptModuleFetcher);
#endif
static_assert(
sizeof(int) == sizeof(int32_t),
"We make an assumption that int is always int32 for numpy "
"type mapping.");
int CaffeToNumpyType(const TypeMeta meta) {
#ifdef USE_NUMPY
static std::map<TypeIdentifier, int> numpy_type_map{
{TypeMeta::Id<bool>(), NPY_BOOL},
{TypeMeta::Id<double>(), NPY_DOUBLE},
{TypeMeta::Id<float>(), NPY_FLOAT},
{TypeMeta::Id<std::complex<double>>(), NPY_COMPLEX128},
{TypeMeta::Id<std::complex<float>>(), NPY_COMPLEX64},
{TypeMeta::Id<at::Half>(), NPY_FLOAT16},
{TypeMeta::Id<int>(), NPY_INT},
{TypeMeta::Id<int8_t>(), NPY_INT8},
{TypeMeta::Id<int16_t>(), NPY_INT16},
{TypeMeta::Id<int64_t>(), NPY_LONGLONG},
{TypeMeta::Id<uint8_t>(), NPY_UINT8},
{TypeMeta::Id<uint16_t>(), NPY_UINT16},
{TypeMeta::Id<std::string>(), NPY_OBJECT},
// Note: Add more types here.
};
const auto it = numpy_type_map.find(meta.id());
return it == numpy_type_map.end() ? -1 : it->second;
#else
CAFFE_THROW("Caffe2 compiled without NumPy support.");
#endif // USE_NUMPY
}
const TypeMeta NumpyTypeToCaffe(int numpy_type) {
#ifdef USE_NUMPY
static std::map<int, TypeMeta> caffe_type_map{
{NPY_BOOL, TypeMeta::Make<bool>()},
{NPY_DOUBLE, TypeMeta::Make<double>()},
{NPY_FLOAT, TypeMeta::Make<float>()},
{NPY_FLOAT16, TypeMeta::Make<at::Half>()},
{NPY_INT, TypeMeta::Make<int>()},
{NPY_INT8, TypeMeta::Make<int8_t>()},
{NPY_INT16, TypeMeta::Make<int16_t>()},
{NPY_INT64, TypeMeta::Make<int64_t>()},
{NPY_LONG,
sizeof(long) == sizeof(int) ? TypeMeta::Make<int>()
: TypeMeta::Make<int64_t>()},
{NPY_LONGLONG, TypeMeta::Make<int64_t>()},
{NPY_UINT8, TypeMeta::Make<uint8_t>()},
{NPY_UINT16, TypeMeta::Make<uint16_t>()},
{NPY_OBJECT, TypeMeta::Make<std::string>()},
{NPY_UNICODE, TypeMeta::Make<std::string>()},
{NPY_STRING, TypeMeta::Make<std::string>()},
// Note: Add more types here.
};
static TypeMeta unknown_type;
const auto it = caffe_type_map.find(numpy_type);
return it == caffe_type_map.end() ? unknown_type : it->second;
#else
CAFFE_THROW("Caffe2 compiled without NumPy support.");
#endif // USE_NUMPY
}
template <typename Registry>
std::function<const char*(const string&)> DefinitionGetter(
const Registry* registry) {
return [registry](const string& name) { return registry->HelpMessage(name); };
}
void switchWorkspaceInternal(const std::string& name, bool create_if_missing) {
if (gWorkspaces.count(name)) {
gCurrentWorkspaceName = name;
gWorkspace = gWorkspaces[name].get();
return;
}
CAFFE_ENFORCE(create_if_missing);
std::unique_ptr<Workspace> new_workspace(new Workspace());
gWorkspace = new_workspace.get();
gWorkspaces.insert(std::make_pair(name, std::move(new_workspace)));
gCurrentWorkspaceName = name;
}
namespace python_detail {
// Python Op implementations.
using FuncRegistry = std::unordered_map<std::string, Func>;
FuncRegistry& gRegistry() {
// Always leak the objects registered here.
static FuncRegistry* r = new FuncRegistry();
return *r;
}
const Func& getOpFunc(const std::string& token) {
CAFFE_ENFORCE(
gRegistry().count(token),
"Python operator for ",
token,
" is not available. If you use distributed training it probably means "
"that python implementation has to be registered in each of the workers");
return gRegistry()[token];
}
const Func& getGradientFunc(const std::string& token) {
return getOpFunc(token + "_gradient");
}
py::object fetchBlob(Workspace* ws, const std::string& name) {
CAFFE_ENFORCE(ws->HasBlob(name), "Can't find blob: ", name);
const caffe2::Blob& blob = *(ws->GetBlob(name));
auto fetcher = CreateFetcher(blob.meta().id());
if (fetcher) {
return fetcher->Fetch(blob);
} else {
// If there is no fetcher registered, return a metainfo string.
// If all branches failed, we will return a metainfo string.
std::stringstream ss;
ss << caffe2::string(name) << ", a C++ native class of type "
<< blob.TypeName() << ".";
return py::bytes(ss.str());
}
}
// This function can only return true, but keeping it for backward compatibility
bool feedBlob(
Blob* blob,
const py::object& arg,
const py::object device_option) {
DeviceOption option;
if (!device_option.is(py::none())) {
// If we have a device option passed in, read it.
CAFFE_ENFORCE(ParseProtoFromLargeString(
py::bytes(device_option).cast<std::string>(), &option));
}
#ifdef USE_NUMPY
if (PyArray_Check(arg.ptr())) { // numpy array
PyArrayObject* array = reinterpret_cast<PyArrayObject*>(arg.ptr());
auto feeder = CreateFeeder(option.device_type());
CAFFE_ENFORCE(feeder, "Unknown device type encountered in FeedBlob.");
feeder->Feed(option, array, blob, true); /* default to inplace feed */
return true;
}
#else
CAFFE_THROW("Caffe2 compiled without NumPy support.");
#endif // USE_NUMPY
if (PyBytes_Check(arg.ptr()) || PyUnicode_Check(arg.ptr())) {
*blob->GetMutable<std::string>() = arg.cast<std::string>();
return true;
}
#ifdef FBCODE_CAFFE2
if (auto module = torch::jit::as_module(arg)) {
blob->GetMutable<std::unique_ptr<torch::jit::Module>>()->reset(
new torch::jit::Module(*module));
return true;
}
#endif
CAFFE_THROW(
"Unexpected type of argument - only numpy array or string are "
"supported for feeding");
return false;
}
Blob deserializeBlob(const string& content) {
Blob blob;
DeserializeBlob(content, &blob);
return blob;
}
} // namespace python_detail
class GetPythonGradient : public GradientMakerBase {
public:
using GradientMakerBase::GradientMakerBase;
std::vector<OperatorDef> GetGradientDefs() override {
CAFFE_ENFORCE(Def().type() == "Python" || Def().type() == "PythonDLPack");
ArgumentHelper helper(Def());
auto gradOutputIndices =
helper.GetRepeatedArgument<int>("grad_output_indices");
auto gradInputIndices =
helper.GetRepeatedArgument<int>("grad_input_indices");
std::vector<std::string> gradientInputs;
for (int i = 0; i < def_.input_size(); ++i) {
gradientInputs.push_back(I(i));
}
for (int i = 0; i < def_.output_size(); ++i) {
gradientInputs.push_back(O(i));
}
if (gradOutputIndices.size() > 0) {
for (int i = 0; i < gradOutputIndices.size(); ++i) {
int GO_i = gradOutputIndices[i];
gradientInputs.push_back(GO(GO_i));
}
} else {
for (int i = 0; i < def_.output_size(); ++i) {
gradientInputs.push_back(GO(i));
}
}
std::vector<std::string> gradientOutputs;
if (gradInputIndices.size() > 0) {
for (int i = 0; i < gradInputIndices.size(); ++i) {
int GI_i = gradInputIndices[i];
gradientOutputs.push_back(GI(GI_i));
}
} else {
for (int i = 0; i < def_.input_size(); ++i) {
gradientOutputs.push_back(GI(i));
}
}
std::string grad_op_name = "PythonGradient";
if (Def().type() == "PythonDLPack") {
grad_op_name = "PythonDLPackGradient";
}
return SingleGradientDef(grad_op_name, "", gradientInputs, gradientOutputs);
}
};
REGISTER_CPU_OPERATOR(Python, PythonOp<CPUContext, false>);
REGISTER_CPU_OPERATOR(PythonGradient, PythonGradientOp<CPUContext, false>);
// Always allow running in-place
OPERATOR_SCHEMA(Python).AllowInplace([](int, int) { return true; });
OPERATOR_SCHEMA(PythonGradient).AllowInplace([](int, int) { return true; });
REGISTER_GRADIENT(Python, GetPythonGradient);
REGISTER_CPU_OPERATOR(PythonDLPack, PythonOp<CPUContext, true>);
REGISTER_CPU_OPERATOR(PythonDLPackGradient, PythonGradientOp<CPUContext, true>);
OPERATOR_SCHEMA(PythonDLPack).AllowInplace([](int, int) { return true; });
OPERATOR_SCHEMA(PythonDLPackGradient).AllowInplace([](int, int) {
return true;
});
REGISTER_GRADIENT(PythonDLPack, GetPythonGradient);
class BackgroundPlan {
public:
BackgroundPlan(Workspace* ws, PlanDef def) : ws_(ws), def_(def) {}
void run() {
fut_ =
std::async(std::launch::async, [this]() { return ws_->RunPlan(def_); });
}
bool isDone() {
CAFFE_ENFORCE(fut_.valid());
auto status = fut_.wait_for(std::chrono::milliseconds(0));
return status == std::future_status::ready;
}
bool isSucceeded() {
CAFFE_ENFORCE(isDone());
return fut_.get();
}
private:
Workspace* ws_;
PlanDef def_;
std::future<bool> fut_;
};
void addObjectMethods(py::module& m) {
py::class_<NetBase>(m, "Net")
.def(
"run",
[](NetBase* net) {
py::gil_scoped_release g;
CAFFE_ENFORCE(net->Run());
})
.def("cancel",
[](NetBase* net) {
py::gil_scoped_release g;
net->Cancel();
});
py::class_<ObserverBase<NetBase>>(m, "Observer")
.def(
"average_time",
[](ObserverBase<NetBase>* ob) {
auto* cast_ob = dynamic_cast_if_rtti<TimeObserver*>(ob);
CAFFE_ENFORCE(
cast_ob, "Observer does not implement this function.");
return cast_ob->average_time();
})
.def(
"average_time_children",
[](ObserverBase<NetBase>* ob) {
auto* cast_ob = dynamic_cast_if_rtti<TimeObserver*>(ob);
CAFFE_ENFORCE(
cast_ob, "Observer does not implement this function.");
return cast_ob->average_time_children();
})
.def("debug_info", [](ObserverBase<NetBase>* ob) {
return ob->debugInfo();
});
py::class_<Blob>(m, "Blob")
.def(
"serialize",
[](const Blob& blob, const std::string& name) -> py::bytes {
return SerializeBlob(blob, name);
})
.def(
"deserialize",
[](Blob* blob, py::bytes serialized) {
DeserializeBlob(serialized, blob);
})
.def(
"fetch",
[](const Blob& blob) {
auto fetcher = CreateFetcher(blob.meta().id());
CAFFE_ENFORCE(
fetcher,
"Could not fetch for blob of type: ",
blob.meta().name());
return fetcher->Fetch(blob);
})
.def("is_tensor", [](Blob* blob) { return blob->IsType<Tensor>(); })
// return any device Tensor
.def(
"as_tensor",
[](Blob* blob) {
CAFFE_ENFORCE(
blob->IsType<Tensor>(),
"Passed in blob doesn't contain Tensor and instead has ",
blob->meta());
return py::cast(&blob->Get<Tensor>());
},
py::return_value_policy::reference_internal)
// legacy API that resets tensor to CPUTensor if it's not already
.def(
"tensor",
[](Blob* blob) { return py::cast(BlobGetMutableTensor(blob, CPU)); },
py::return_value_policy::reference_internal)
.def(
"_feed",
&python_detail::feedBlob,
"Feed an input array or string, with the (optional) DeviceOption",
py::arg("arg"),
py::arg("device_option") = py::none())
.def("_wrap_tensor_impl", [](Blob* blob, void* ptr) {
auto p = c10::intrusive_ptr<c10::TensorImpl, at::UndefinedTensorImpl>::
unsafe_reclaim_from_nonowning(static_cast<c10::TensorImpl*>(ptr));
TORCH_CHECK(p.defined(), "Can't wrap undefined tensor");
TORCH_CHECK(
!p->requires_grad(), "Can wrap only non-requires-grad tensor");
auto at_tensor = at::Tensor::wrap_tensor_impl(std::move(p));
BlobSetTensor(blob, Tensor(std::move(at_tensor)));
});
py::class_<DLPackWrapper<CPUContext>>(m, "DLPackTensorCPU")
.def_property_readonly(
"data",
[](DLPackWrapper<CPUContext>* t) -> py::object {
CAFFE_ENFORCE_EQ(
t->device_option.device_type(),
PROTO_CPU,
"Expected CPU device option for CPU tensor");
return t->data();
},
"Return DLPack tensor with tensor's data.")
.def(
"feed",
[](DLPackWrapper<CPUContext>* t, py::object obj) {
CAFFE_ENFORCE_EQ(
t->device_option.device_type(),
PROTO_CPU,
"Expected CPU device option for CPU tensor");
t->feed(obj);
},
"Copy data from given DLPack tensor into this tensor.")
.def_property_readonly(
"_shape",
[](const DLPackWrapper<CPUContext>& t) {
auto* tensor = t.tensor;
// TODO: This is marginally less efficient than it could
// be, since we're doing an extra allocation we didn't
// need to do. But I don't remember how to clue in
// pybind11 how to convert ArrayRef to vector.
return tensor->sizes().vec();
})
.def(
"_reshape",
[](DLPackWrapper<CPUContext>* t, std::vector<int64_t> dims) {
auto* tensor = t->tensor;
tensor->Resize(dims);
});
py::class_<TensorCPU>(m, "TensorCPU")
.def_property_readonly(
"data",
[](TensorCPU* t) -> py::object {
if (t->dtype() == TypeMeta{}) {
// keep this behavior for backward compatibility
t->mutable_data<float>();
}
auto res = TensorFetcher().FetchTensor(*t, false);
return res.obj;
},
"Return numpy array pointing to this tensor's data if possible. "
"Otherwise (e.g. for strings) copies the data (same as fetch).")
.def(
"feed",
[](TensorCPU* t, py::object obj) {
#ifdef USE_NUMPY
if (!PyArray_Check(obj.ptr())) {
CAFFE_THROW(
"Unexpected type of argument -- expected numpy array");
}
*t = TensorFeeder<CPUContext>().FeedTensor(
DeviceOption{}, reinterpret_cast<PyArrayObject*>(obj.ptr()));
#else
CAFFE_THROW("Caffe2 compiled without NumPy support.");
#endif // USE_NUMPY
},
"Copy data from given numpy array into this tensor.")
.def(
"fetch",
[](TensorCPU* t) {
auto res = TensorFetcher().FetchTensor(*t, true);
return res.obj;
},
"Copy data from this tensor into a new numpy array.")
.def(
"init",
[](Tensor* t, std::vector<int64_t> dims, int caffe_type) {
const auto& meta =
DataTypeToTypeMeta((TensorProto::DataType)caffe_type);
CAFFE_ENFORCE(
!TensorFetcher().NeedsCopy(t, meta),
"Cannot init tensor of this type. Use `feed` instead.");
t->Resize(dims);
t->raw_mutable_data(meta);
},
"Initialize this tensor to given shape and data type. "
"Fail if the given data type cannot be accessed from python.")
.def(
"_tensor_impl_raw_handle",
[](TensorCPU* t) -> void* {
auto p = t->getIntrusivePtr();
// We return a raw non-owning pointer here, we rely on surrounding
// code to keep the original tensor alive
return p.get();
})
.def_property_readonly(
"_shape", [](const TensorCPU& t) { return t.sizes().vec(); })
.def("_reshape", [](TensorCPU* t, std::vector<int64_t> dims) {
t->Resize(dims);
});
py::class_<Workspace>(m, "Workspace")
.def(py::init<>())
.def(py::init<Workspace*>())
.def_property_readonly(
"nets",
[](Workspace* self) {
CHECK_NOTNULL(self);
std::map<std::string, py::object> nets;
for (const auto& name : self->Nets()) {
LOG(INFO) << "name: " << name;
nets[name] = py::cast(self->GetNet(name));
}
return nets;
},
py::return_value_policy::reference_internal)
.def_property_readonly(
"blobs",
[](Workspace* self) {
CHECK_NOTNULL(self);
std::map<std::string, py::object> blobs;
for (const auto& name : self->Blobs()) {
blobs[name] = py::cast(self->GetBlob(name));
}
return blobs;
},
py::return_value_policy::reference_internal)
.def(
"_create_net",
[](Workspace* self, py::bytes def, bool overwrite) -> py::object {
caffe2::NetDef proto;
CAFFE_ENFORCE(
ParseProtoFromLargeString(def.cast<std::string>(), &proto));
NetBase* net = self->CreateNet(proto, overwrite);
CAFFE_ENFORCE(net);
return py::cast(net);
},
py::return_value_policy::reference_internal,
py::arg("def"),
py::arg("overwrite") = kPyBindFalse)
.def(
"create_blob",
[](Workspace* self, const std::string& name) -> py::object {
return py::cast(self->CreateBlob(name));
},
py::return_value_policy::reference_internal)
.def(
"_remove_blob",
[](Workspace* self, const std::string& name) -> py::bool_ {
return self->RemoveBlob(name);
})
.def("fetch_blob", &python_detail::fetchBlob)
.def(
"has_blob",
[](Workspace* self, const std::string& name) {
return self->HasBlob(name);
})
.def(
"_run_net",
[](Workspace* self, py::bytes def) {
caffe2::NetDef proto;
CAFFE_ENFORCE(
ParseProtoFromLargeString(def.cast<std::string>(), &proto));
py::gil_scoped_release g;
CAFFE_ENFORCE(self->RunNetOnce(proto));
})
.def(
"_run_operator",
[](Workspace* self, py::bytes def) {
caffe2::OperatorDef proto;
CAFFE_ENFORCE(
ParseProtoFromLargeString(def.cast<std::string>(), &proto));
py::gil_scoped_release g;
CAFFE_ENFORCE(self->RunOperatorOnce(proto));
})
.def(
"_run_plan",
[](Workspace* self, py::bytes def) {
caffe2::PlanDef proto;
CAFFE_ENFORCE(
ParseProtoFromLargeString(def.cast<std::string>(), &proto));
py::gil_scoped_release g;
CAFFE_ENFORCE(self->RunPlan(proto));
})
.def(
"_last_failed_op_net_position",
[](Workspace* self) {
CAFFE_ENFORCE(self);
return (int)self->last_failed_op_net_position;
})
.def_property_readonly_static("current", [](py::object /* type */) {
auto ws = gWorkspaces.find(gCurrentWorkspaceName);
CAFFE_ENFORCE(ws != gWorkspaces.end());
CAFFE_ENFORCE(ws->second.get());
return py::cast(ws->second.get(), py::return_value_policy::reference);
});
py::class_<BackgroundPlan, std::shared_ptr<BackgroundPlan>>(
m, "BackgroundPlan")
.def("is_done", &BackgroundPlan::isDone)
.def("is_succeeded", &BackgroundPlan::isSucceeded);
// Gradients
py::class_<GradientWrapper>(m, "GradientWrapper")
.def(py::init<>())
.def_readwrite("dense", &GradientWrapper::dense_)
.def_readwrite("indices", &GradientWrapper::indices_)
.def_readwrite("values", &GradientWrapper::values_)
.def("is_sparse", &GradientWrapper::IsSparse)
.def("is_dense", &GradientWrapper::IsDense)
.def("is_empty", &GradientWrapper::IsEmpty);
m.def(
"get_gradient_defs",
[](py::bytes op_def, std::vector<GradientWrapper> output_gradients) {
OperatorDef def;
CAFFE_ENFORCE(
ParseProtoFromLargeString(op_def.cast<std::string>(), &def));
CAFFE_ENFORCE(caffe2::GradientRegistry()->Has(def.type()));
const auto& meta = GetGradientForOp(def, output_gradients);
std::vector<py::bytes> grad_ops;
for (const auto& op : meta.ops_) {
grad_ops.push_back(
SerializeAsString_EnforceCheck(op, "addObjectMethods"));
}
return std::pair<std::vector<py::bytes>, std::vector<GradientWrapper>>{
grad_ops, meta.g_input_};
},
pybind11::return_value_policy::copy);
// DB
py::class_<db::Transaction>(m, "Transaction")
.def("put", &db::Transaction::Put)
.def("commit", &db::Transaction::Commit);
py::class_<db::Cursor>(m, "Cursor")
.def("supports_seek", &db::Cursor::SupportsSeek)
.def("seek_to_first", &db::Cursor::SeekToFirst)
.def("next", &db::Cursor::Next)
.def("key", [](db::Cursor* self) -> py::bytes { return self->key(); })
.def("value", [](db::Cursor* self) -> py::bytes { return self->value(); })
.def("valid", &db::Cursor::Valid);
py::enum_<db::Mode>(m, "Mode")
.value("read", db::Mode::READ)
.value("write", db::Mode::WRITE)
.value("new", db::Mode::NEW)
.export_values();
py::class_<db::DB /*, std::unique_ptr<DB>*/>(m, "DB")
.def("new_transaction", &db::DB::NewTransaction)
.def("new_cursor", &db::DB::NewCursor)
.def("close", &db::DB::Close);
m.def("create_db", &db::CreateDB);
m.def("registered_dbs", []() {
return caffe2::db::Caffe2DBRegistry()->Keys();
});
// OpSchema
py::class_<OpSchema> op_schema(m, "OpSchema");
op_schema.def_property_readonly("file", &OpSchema::file)
.def_property_readonly("line", &OpSchema::line)
.def_property_readonly("private", &OpSchema::private_op)
.def_property_readonly(
"doc", &OpSchema::doc, py::return_value_policy::reference)
.def_property_readonly("args", &OpSchema::args)
.def_property_readonly("input_desc", &OpSchema::input_desc)
.def_property_readonly("output_desc", &OpSchema::output_desc)
.def_property_readonly("max_input", &OpSchema::max_input)
.def_property_readonly("max_output", &OpSchema::max_output)
.def_property_readonly("min_input", &OpSchema::min_input)
.def_property_readonly("min_output", &OpSchema::min_output)
.def_property_readonly("inf", &OpSchema::inf)
// Note: this does not work yet, we will need to figure out how to pass
// protobuf objects.
.def("infer_tensor", &OpSchema::InferTensor)
.def("CalculateOutput", &OpSchema::CalculateOutput)
.def("inplace_enforced", &OpSchema::inplace_enforced)
.def("num_inputs_allowed", &OpSchema::num_inputs_allowed)
.def("num_outputs_allowed", &OpSchema::num_outputs_allowed)
.def("num_inputs_outputs_allowed", &OpSchema::num_inputs_outputs_allowed)
.def_static(
"get", &OpSchemaRegistry::Schema, py::return_value_policy::reference)
.def_static(
"get_cpu_impl",
DefinitionGetter(CPUOperatorRegistry()),
py::return_value_policy::reference)
.def_static(
"get_cuda_impl",
DefinitionGetter(CUDAOperatorRegistry()),
py::return_value_policy::reference)
.def_static(
"get_gradient_impl",
DefinitionGetter(GradientRegistry()),
py::return_value_policy::reference);
py::class_<OpSchema::Argument>(op_schema, "Argument")
.def_property_readonly("name", &OpSchema::Argument::name)
.def_property_readonly("description", &OpSchema::Argument::description)
.def_property_readonly("required", &OpSchema::Argument::is_required);
py::class_<caffe2::onnx::Caffe2Ops>(m, "Caffe2Ops")
.def(py::init([](const std::vector<py::bytes>& init_ops,
const std::vector<py::bytes>& ops,
const std::vector<std::string>& interface_blobs) {
auto* c2ops = new caffe2::onnx::Caffe2Ops();
for (const auto& s : init_ops) {
ParseProtoFromLargeString(
s.cast<std::string>(), c2ops->init_ops.Add());
}
for (const auto& s : ops) {
ParseProtoFromLargeString(s.cast<std::string>(), c2ops->ops.Add());
}
for (const auto& s : interface_blobs) {
auto* tmp = c2ops->interface_blobs.Add();
*tmp = s;
}
return c2ops;
}));
py::class_<caffe2::onnx::DummyName>(m, "DummyName")
.def(py::init<>())
.def(
"reset",
[](caffe2::onnx::DummyName& instance, const py::object& args) {
if (args.is(py::none())) {
instance.Reset(std::unordered_set<std::string>());
} else {
instance.Reset(args.cast<std::unordered_set<std::string>>());
}
},
"Reset the dummy name generator",
py::arg("args") = py::none())
.def(
"new_dummy_name",
[](caffe2::onnx::DummyName& instance) -> std::string {
return instance.NewDummyName();
});
py::class_<caffe2::onnx::Caffe2BackendRep>(m, "Caffe2BackenRep")
.def(py::init<>())
.def(
"init_net",
[](caffe2::onnx::Caffe2BackendRep& instance) {
const auto& init_net = instance.init_net();
std::string out;
init_net.SerializeToString(&out);
return py::bytes(out);
})
.def(
"pred_net",
[](caffe2::onnx::Caffe2BackendRep& instance) {
const auto& pred_net = instance.pred_net();
std::string out;
pred_net.SerializeToString(&out);
return py::bytes(out);
})
.def(
"external_outputs",
[](caffe2::onnx::Caffe2BackendRep& instance) {
std::vector<std::string> outputs;
for (const auto& o : instance.pred_net().external_output()) {
outputs.emplace_back(o);
}
return outputs;
})
.def(
"external_inputs",
[](caffe2::onnx::Caffe2BackendRep& instance) {
std::vector<std::string> inputs;
for (const auto& o : instance.pred_net().external_input()) {
inputs.emplace_back(o);
}
return inputs;
})
.def(
"uninitialized_inputs",
[](caffe2::onnx::Caffe2BackendRep& instance) {
return instance.uninitialized_inputs();
})
.def(
"run",
[](caffe2::onnx::Caffe2BackendRep& instance,
std::map<std::string, py::object> inputs)
-> std::vector<py::object> {
caffe2::Predictor::TensorMap tensors_data{};
for (const auto pair : inputs) {
const auto& name = pair.first;
const auto& input = pair.second;
#ifdef USE_NUMPY
CAFFE_ENFORCE(
PyArray_Check(input.ptr()),
"Input must be of type numpy array.");
PyArrayObject* array =
reinterpret_cast<PyArrayObject*>(input.ptr());
tensors_data.emplace(
name,
TensorFeeder<CPUContext>().FeedTensor(DeviceOption(), array));
#else
CAFFE_THROW("Caffe2 was compiled without NumPy support.");
#endif // USE_NUMPY
}
caffe2::Predictor::TensorList out;
instance.RunMap(tensors_data, &out);
std::vector<py::object> pyout;
for (auto& t : out) {
pyout.push_back(TensorFetcher().FetchTensor(t, true).obj);
}
return pyout;
})
.def(
"run",
[](caffe2::onnx::Caffe2BackendRep& instance,
std::vector<py::object> inputs) -> std::vector<py::object> {
std::vector<TensorCPU> tensors_data;
#ifdef USE_NUMPY
for (auto i = 0; i < inputs.size(); ++i) {
auto input = inputs[i];
CAFFE_ENFORCE(
PyArray_Check(input.ptr()),
"Input must be of type numpy array.");
PyArrayObject* array =
reinterpret_cast<PyArrayObject*>(input.ptr());
tensors_data.push_back(
TensorFeeder<CPUContext>().FeedTensor(DeviceOption(), array));
}
#else
CAFFE_THROW("Caffe2 was compiled without NumPy support.");
#endif // USE_NUMPY
std::vector<TensorCPU> out;
instance.Run(tensors_data, &out);
std::vector<py::object> pyout;
for (auto& t : out) {
pyout.push_back(TensorFetcher().FetchTensor(t, true).obj);
}
return pyout;
});
py::class_<caffe2::onnx::Caffe2Backend>(m, "Caffe2Backend")
.def(py::init<>())
.def(py::init<caffe2::onnx::DummyName*>())
.def(
"support_onnx_import",
[](caffe2::onnx::Caffe2Backend& instance,
const std::string& op) -> bool { return instance.SupportOp(op); })
.def(
"prepare",
[](caffe2::onnx::Caffe2Backend& instance,
const py::bytes& onnx_model_str,
const std::string& device,
const std::vector<caffe2::onnx::Caffe2Ops>& extras) {
auto* rep = instance.Prepare(
onnx_model_str.cast<std::string>(), device, extras);
return rep;
})
.def(
"convert_node",
[](caffe2::onnx::Caffe2Backend& instance,
const py::bytes& node_str,
const std::vector<py::bytes>& value_infos_bytes,
int opset_version) -> std::vector<std::vector<py::bytes>> {
// Note that we return two lists of serialized ops. The first set is
// init_ops and the second set is ops for pred net. When converting
// RNN related op, it is possible that we will create ops in the
// init_net. Hence the return structure here
caffe2::onnx::ValueInfoMap value_infos{};
for (const auto& vi_bytes : value_infos_bytes) {
::ONNX_NAMESPACE::ValueInfoProto vi{};
vi.ParseFromString(vi_bytes);
auto name = vi.name();
value_infos.emplace(std::move(name), std::move(vi));
}
auto c2ops = instance.ConvertNode(
node_str.cast<std::string>(), {value_infos, opset_version});
std::vector<std::vector<py::bytes>> vals;
vals.emplace_back();
auto& init_vals = vals.back();
for (const auto& init_op : c2ops.init_ops) {
std::string out;
init_op.SerializeToString(&out);
init_vals.emplace_back(py::bytes(out));
}
vals.emplace_back();
auto& normal_vals = vals.back();
for (const auto& op : c2ops.ops) {
std::string out;
op.SerializeToString(&out);
normal_vals.emplace_back(py::bytes(out));
}
return vals;
},
py::arg("node_str"),
py::arg("value_infos_bytes") = std::vector<py::bytes>{},
py::arg("opset_version") = kKnownOpsetVersion)
.def(
"_build_tensor_filling_op",
[](caffe2::onnx::Caffe2Backend& instance,
const py::bytes& tensor_proto_str,
const std::string& name = "") -> py::bytes {
caffe2::OperatorDef op;
::ONNX_NAMESPACE::TensorProto tp;
ParseProtoFromLargeString(tensor_proto_str, &tp);
instance.BuildTensorFillingOp(&op, tp, name);
std::string out;
op.SerializeToString(&out);
return py::bytes(out);
});
py::class_<Predictor>(m, "Predictor")
.def(py::init([](py::bytes init_net, py::bytes predict_net) {
CAFFE_ENFORCE(gWorkspace);
NetDef init_net_, predict_net_;
CAFFE_ENFORCE(ParseProtoFromLargeString(
init_net.cast<std::string>(), &init_net_));
CAFFE_ENFORCE(ParseProtoFromLargeString(
predict_net.cast<std::string>(), &predict_net_));
return new Predictor(
makePredictorConfig(init_net_, predict_net_, gWorkspace));
}))
.def(
"run",
[](Predictor& instance,
std::vector<py::object> inputs) -> std::vector<py::object> {
std::vector<Tensor> tensors_data;
#ifdef USE_NUMPY
for (auto i = 0; i < inputs.size(); ++i) {
auto input = inputs[i];
CAFFE_ENFORCE(
PyArray_Check(input.ptr()),
"Input must be of type numpy array.");
PyArrayObject* array =
reinterpret_cast<PyArrayObject*>(input.ptr());
tensors_data.push_back(
TensorFeeder<CPUContext>().FeedTensor(DeviceOption(), array));
}
#else
CAFFE_THROW("Caffe2 was compiled without NumPy support.");
#endif // USE_NUMPY
std::vector<TensorCPU> out;
instance(tensors_data, &out);
std::vector<py::object> pyout;
for (auto& t : out) {
pyout.push_back(TensorFetcher().FetchTensor(t, true).obj);
}