m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'c4eb5d0378'
${ noResults }
Paddle/paddle/fluid/pybind/pybind.cc

2843 lines
113 KiB
Raw Blame History Escape

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <Python.h>
#include <algorithm>
#include <cstdlib>
#include <map>
#include <memory>
#include <mutex> // NOLINT // for call_once
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/io/fs.h"
#include "paddle/fluid/framework/ir/coalesce_grad_tensor_pass.h"
#include "paddle/fluid/framework/ir/pass_builder.h"
#include "paddle/fluid/framework/load_op_lib.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/framework/parallel_executor.h"
#include "paddle/fluid/framework/prune.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/save_load_util.h"
#include "paddle/fluid/framework/scope_pool.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/trainer.h"
#include "paddle/fluid/framework/type_defs.h"
#include "paddle/fluid/framework/version.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/memory/allocation/allocator_strategy.h"
#include "paddle/fluid/memory/allocation/mmap_allocator.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/common_infer_shape_functions.h"
#include "paddle/fluid/operators/py_func_op.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/monitor.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/pybind/box_helper_py.h"
#include "paddle/fluid/pybind/compatible.h"
#include "paddle/fluid/pybind/const_value.h"
#include "paddle/fluid/pybind/data_set_py.h"
#include "paddle/fluid/pybind/exception.h"
#include "paddle/fluid/pybind/fleet_wrapper_py.h"
#include "paddle/fluid/pybind/generator_py.h"
#include "paddle/fluid/pybind/global_value_getter_setter.h"
#include "paddle/fluid/pybind/gloo_context_py.h"
#include "paddle/fluid/pybind/gloo_wrapper_py.h"
#include "paddle/fluid/pybind/heter_wrapper_py.h"
#include "paddle/fluid/pybind/imperative.h"
#include "paddle/fluid/pybind/inference_api.h"
#include "paddle/fluid/pybind/ir.h"
#include "paddle/fluid/pybind/pybind_boost_headers.h"
#ifdef PADDLE_WITH_NCCL
#include "paddle/fluid/pybind/nccl_wrapper_py.h"
#endif
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/pybind/protobuf.h"
#include "paddle/fluid/pybind/pybind.h" // NOLINT
#include "paddle/fluid/pybind/reader_py.h"
#include "paddle/fluid/pybind/tensor_py.h"
#include "paddle/fluid/string/to_string.h"
#ifdef PADDLE_WITH_CUDA
#ifdef PADDLE_WITH_NCCL
#include "paddle/fluid/operators/nccl/nccl_gpu_common.h"
#endif
#include "paddle/fluid/platform/cuda_profiler.h"
#include "paddle/fluid/platform/gpu_info.h"
#endif
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/platform/xpu_info.h"
#endif
#ifdef PADDLE_WITH_DISTRIBUTE
#include "paddle/fluid/pybind/communicator_py.h"
#endif
#ifdef PADDLE_WITH_CRYPTO
#include "paddle/fluid/pybind/crypto.h"
#endif
#include "pybind11/stl.h"
DECLARE_bool(use_mkldnn);
// disable auto conversion to list in Python
PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);
PYBIND11_MAKE_OPAQUE(paddle::framework::FetchUnmergedList);
PYBIND11_MAKE_OPAQUE(paddle::framework::FetchList);
PYBIND11_MAKE_OPAQUE(paddle::framework::FetchType);
namespace paddle {
namespace pybind {
bool IsCompiledWithCUDA() {
#ifndef PADDLE_WITH_CUDA
return false;
#else
return true;
#endif
}
bool IsCompiledWithXPU() {
#ifndef PADDLE_WITH_XPU
return false;
#else
return true;
#endif
}
bool IsCompiledWithMKLDNN() {
#ifndef PADDLE_WITH_MKLDNN
return false;
#else
return true;
#endif
}
bool SupportsBfloat16() {
#ifndef PADDLE_WITH_MKLDNN
return false;
#else
if (platform::MayIUse(platform::cpu_isa_t::avx512_core))
return true;
else
return false;
#endif
}
bool IsCompiledWithBrpc() {
#ifndef PADDLE_WITH_DISTRIBUTE
return false;
#endif
#ifdef PADDLE_WITH_GRPC
return false;
#endif
return true;
}
bool IsCompiledWithDIST() {
#ifdef PADDLE_WITH_DISTRIBUTE
return true;
#else
return false;
#endif
}
template <typename PlaceType1, typename PlaceType2>
static inline bool IsSamePlace(const PlaceType1 &p1, const PlaceType2 &p2) {
return paddle::platform::Place(p1) == paddle::platform::Place(p2);
}
template <typename PlaceType>
static inline int PlaceIndex(const PlaceType &p) {
return static_cast<int>(paddle::platform::Place(p).which());
}
static PyObject *GetPythonAttribute(PyObject *obj, const char *attr_name) {
// NOTE(zjl): PyObject_GetAttrString would return nullptr when attr_name
// is not inside obj, but it would also set the error flag of Python.
// If the error flag is set in C++, C++ code would not raise Exception,
// but Python would raise Exception once C++ call ends.
// To avoid unexpected Exception raised in Python, we check whether
// attribute exists before calling PyObject_GetAttrString.
//
// Caution: PyObject_GetAttrString would increase reference count of PyObject.
// Developer should call Py_DECREF manually after the attribute is not used.
if (PyObject_HasAttrString(obj, attr_name)) {
return PyObject_GetAttrString(obj, attr_name);
} else {
return nullptr;
}
}
template <typename T>
static T PyObjectCast(PyObject *obj) {
try {
return py::cast<T>(py::handle(obj));
} catch (py::cast_error &) {
PADDLE_THROW(platform::errors::InvalidArgument(
"Python object is not type of %s, the real type is %s",
typeid(T).name(), obj->ob_type->tp_name));
}
}
using PyNameVarBaseMap = std::unordered_map<std::string, py::handle>;
static std::vector<std::shared_ptr<imperative::VarBase>> GetVarBaseList(
const PyNameVarBaseMap &state_dict) {
std::vector<std::shared_ptr<imperative::VarBase>> vec_res;
vec_res.reserve(state_dict.size());
for (auto &para : state_dict) {
PyObject *py_obj = para.second.ptr();
if (!py_obj || py_obj == Py_None) {
PADDLE_THROW(platform::errors::InvalidArgument(
"The parameter [%s] to save is None", para.first));
}
vec_res.emplace_back(
PyObjectCast<std::shared_ptr<imperative::VarBase>>(py_obj));
}
return vec_res;
}
static std::vector<std::string> inline GetNameList(
const py::handle &py_handle) {
std::vector<std::string> vec_res;
PyObject *py_obj = py_handle.ptr(); // get underlying PyObject
// Python None is not nullptr in C++!
if (!py_obj || py_obj == Py_None) {
PADDLE_THROW(platform::errors::InvalidArgument(
"The parameter list to save is None"));
}
if (PyList_Check(py_obj)) {
size_t len = PyList_GET_SIZE(py_obj);
vec_res.reserve(len);
const char *kNameField = "name";
for (size_t i = 0; i < len; ++i) {
PyObject *py_name =
PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kNameField);
PADDLE_ENFORCE_NOT_NULL(py_name,
platform::errors::InvalidArgument(
"The name of parameter to save is None"));
vec_res.emplace_back(PyObjectCast<std::string>(py_name));
Py_DECREF(py_name);
}
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The parameters to save is not a list"));
}
return vec_res;
}
static void inline CreateVariableIfNotExit(
const py::handle &py_handle, const framework::Scope &scope,
const framework::Executor *exe = nullptr) {
std::vector<std::string> vec_res;
PyObject *py_obj = py_handle.ptr(); // get underlying PyObject
// Python None is not nullptr in C++!
if (!py_obj || py_obj == Py_None) {
PADDLE_THROW(
platform::errors::InvalidArgument("The parameter list to set is None"));
}
if (PyList_Check(py_obj)) {
size_t len = PyList_GET_SIZE(py_obj);
vec_res.reserve(len);
const char *kNameField = "name";
const char *kVarDescField = "desc";
for (size_t i = 0; i < len; ++i) {
PyObject *py_name =
PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kNameField);
PADDLE_ENFORCE_NOT_NULL(py_name,
platform::errors::InvalidArgument(
"The name of parameter to set is None"));
auto para_name = PyObjectCast<std::string>(py_name);
Py_DECREF(py_name);
auto var = scope.FindVar(para_name);
if (var == nullptr) {
PADDLE_ENFORCE_NOT_NULL(exe,
platform::errors::InvalidArgument(
"Parameter not Initialized, "
"Please set argument [executor] not None "
"or run startup program first"));
PyObject *py_var_desc =
PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kVarDescField);
PADDLE_ENFORCE_NOT_NULL(
py_var_desc, platform::errors::InvalidArgument(
"The var_desc of parameter to set is None"));
auto var_desc = PyObjectCast<framework::VarDesc>(py_var_desc);
Py_DECREF(py_var_desc);
var = const_cast<framework::Scope *>(&scope)->Var(para_name);
auto *tensor_temp = var->GetMutable<framework::LoDTensor>();
tensor_temp->Resize(framework::make_ddim(var_desc.GetShape()));
tensor_temp->mutable_data(exe->GetPlace(), var_desc.GetDataType());
}
}
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The parameters to set is not a list"));
}
return;
}
static void AssertStaticGraphAndDygraphGradMakerNoDiff() {
std::set<std::string> ops;
for (auto &pair : framework::OpInfoMap::Instance().map()) {
bool has_static_grad_maker = (pair.second.grad_op_maker_ != nullptr);
bool has_dygraph_grad_maker =
(pair.second.dygraph_grad_op_maker_ != nullptr);
if (has_static_grad_maker ^ has_dygraph_grad_maker) {
bool has_kernel =
(framework::OperatorWithKernel::AllOpKernels().count(pair.first) > 0);
if (has_kernel) {
ops.insert(pair.first);
} else {
VLOG(5) << pair.first << " has no kernels, skip";
}
}
}
PADDLE_ENFORCE_EQ(ops.empty(), true,
platform::errors::Unimplemented(
"OperatorWithKernel [%s] have only static graph grad "
"maker or have only dygraph grad maker, which is not "
"allowed",
string::join_strings(ops, ',')));
}
#ifdef PADDLE_WITH_AVX
PYBIND11_MODULE(core_avx, m) {
#else
PYBIND11_MODULE(core_noavx, m) {
#endif
// Not used, just make sure cpu_info.cc is linked.
paddle::platform::CpuTotalPhysicalMemory();
paddle::memory::allocation::UseAllocatorStrategyGFlag();
AssertStaticGraphAndDygraphGradMakerNoDiff();
m.doc() = "C++ core of PaddlePaddle";
// using framework in this function. Since it is inside a function, it will
// not cause namespace pollution.
using namespace paddle::framework; // NOLINT
BindException(&m);
m.def("set_num_threads", &platform::SetNumThreads);
#ifdef PADDLE_WITH_CUDA
m.def("cudnn_version", &platform::CudnnVersion);
#endif
m.def("from_dlpack", [](py::capsule *dltensor) {
DLManagedTensor *dmt = reinterpret_cast<DLManagedTensor *>(
PyCapsule_GetPointer(dltensor->ptr(), "dltensor"));
PyCapsule_SetName(dltensor->ptr(), "used_dltensor");
DLTensor dl = dmt->dl_tensor;
Tensor tensor;
if (dl.ctx.device_type == kDLCPU) {
paddle::framework::TensorFromDLPack(dl, &tensor);
}
#ifdef PADDLE_WITH_CUDA
if (dl.ctx.device_type == kDLGPU) {
paddle::framework::TensorFromDLPack(dl, &tensor);
}
#endif
return tensor;
});
m.def("_save_static_dict",
[](const std::string &str_file_name, const py::handle &vec_var_list,
const Scope &scope) {
std::vector<std::string> vec_name_list = GetNameList(vec_var_list);
SaveStaticNameListToDisk(str_file_name, vec_name_list, scope);
});
m.def("_load_static_dict",
[](const std::string &str_file_name, const py::handle &vec_var_list,
const Scope &scope, const Executor *executor) {
std::vector<std::string> vec_name_list = GetNameList(vec_var_list);
CreateVariableIfNotExit(vec_var_list, scope, executor);
LoadStaticNameListFromDisk(str_file_name, vec_name_list, scope);
});
m.def("_create_loaded_parameter",
[](const py::handle &vec_var_list, const Scope &scope,
const Executor *executor) {
CreateVariableIfNotExit(vec_var_list, scope, executor);
});
m.def("_save_dygraph_dict", [](const std::string &str_file_name,
const PyNameVarBaseMap &state_dict) {
auto vec_var_base_list = GetVarBaseList(state_dict);
SaveDygraphVarBaseListToDisk(str_file_name, vec_var_base_list);
});
m.def("_load_dygraph_dict", [](const std::string &str_file_name) {
auto load_tensor = LoadDygraphVarBaseListFromDisk(str_file_name);
std::unordered_map<std::string, std::shared_ptr<imperative::VarBase>>
map_output;
for (size_t i = 0; i < load_tensor.size(); ++i) {
map_output.emplace(load_tensor[i]->Name(), load_tensor[i]);
}
return map_output;
});
m.def("save_op_version_info", [](framework::ProgramDesc &desc) {
framework::compatible::pb::OpVersionMap pb_vmap{desc.OpVersionMap()};
framework::compatible::SaveOpVersions(
framework::compatible::OpVersionRegistrar::GetInstance()
.GetVersionMap(),
&pb_vmap);
});
m.def("set_printoptions", [](const py::kwargs &kwargs) {
auto &print_opt = framework::PrintOptions::Instance();
if (kwargs.contains("precision")) {
print_opt.precision = kwargs["precision"].cast<int>();
}
if (kwargs.contains("threshold")) {
print_opt.threshold = kwargs["threshold"].cast<int>();
}
if (kwargs.contains("edgeitems")) {
print_opt.edgeitems = kwargs["edgeitems"].cast<int>();
}
if (kwargs.contains("linewidth")) {
print_opt.linewidth = kwargs["linewidth"].cast<int>();
}
if (kwargs.contains("sci_mode")) {
print_opt.sci_mode = kwargs["sci_mode"].cast<bool>();
}
VLOG(4) << "Set printoptions: precision=" << print_opt.precision
<< ", threshold=" << print_opt.threshold
<< ", edgeitems=" << print_opt.edgeitems
<< ", linewidth=" << print_opt.linewidth
<< ", sci_mode=" << print_opt.sci_mode;
});
m.def("broadcast_shape", [](const std::vector<int64_t> &x_dim,
const std::vector<int64_t> &y_dim) {
return vectorize(operators::details::BroadcastTwoDims(
make_ddim(x_dim), make_ddim(y_dim), -1));
});
m.def(
"_append_python_callable_object_and_return_id",
[](py::object py_obj) -> size_t {
return paddle::operators::AppendPythonCallableObjectAndReturnId(py_obj);
});
m.def("_get_use_default_grad_op_desc_maker_ops",
[] { return OpInfoMap::Instance().GetUseDefaultGradOpDescMakerOps(); });
m.def("_get_all_register_op_kernels", [] {
auto &all_kernels = paddle::framework::OperatorWithKernel::AllOpKernels();
std::unordered_map<std::string, std::vector<std::string>> all_kernels_info;
for (auto &kernel_pair : all_kernels) {
auto op_type = kernel_pair.first;
std::vector<std::string> kernel_types;
for (auto &info_pair : kernel_pair.second) {
paddle::framework::OpKernelType kernel_type = info_pair.first;
kernel_types.push_back(
paddle::framework::KernelTypeToString(kernel_type));
}
all_kernels_info.emplace(op_type, kernel_types);
}
return all_kernels_info;
});
// NOTE(zjl): ctest would load environment variables at the beginning even
// though we have not `import paddle.fluid as fluid`. So we add this API
// to enable eager deletion mode in unittest.
m.def("_set_eager_deletion_mode", &paddle::framework::SetEagerDeletionMode);
m.def("_set_fuse_parameter_group_size",
&paddle::framework::ir::SetFuseParameterGroupsSize);
m.def("_set_fuse_parameter_memory_size",
&paddle::framework::ir::SetFuseParameterMemorySize);
m.add_object("_cleanup",
py::capsule([]() { ScopePool::Instance().Clear(); }));
m.def("_set_paddle_lib_path", &paddle::platform::dynload::SetPaddleLibPath);
m.def("_promote_types_if_complex_exists",
&paddle::framework::PromoteTypesIfComplexExists);
BindImperative(&m);
py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
.def("__array__", [](Tensor &self) { return TensorToPyArray(self); })
.def("_is_initialized",
[](const Tensor &self) { return self.IsInitialized(); })
.def("_get_dims",
[](const Tensor &self) { return vectorize(self.dims()); })
.def("_set_dims",
[](Tensor &self, const std::vector<int64_t> &dim) {
self.Resize(make_ddim(dim));
})
.def("_set_layout",
[](Tensor &self, const std::string &layout) {
self.set_layout(StringToDataLayout(layout));
})
.def("_alloc_float",
[](Tensor &self, paddle::platform::CUDAPlace &place) {
self.mutable_data<float>(place);
})
.def("_alloc_float",
[](Tensor &self, paddle::platform::XPUPlace &place) {
self.mutable_data<float>(place);
})
.def("_alloc_float",
[](Tensor &self, paddle::platform::CPUPlace &place) {
self.mutable_data<float>(place);
})
.def("_alloc_double",
[](Tensor &self, paddle::platform::CPUPlace &place) {
self.mutable_data<double>(place);
})
.def("_alloc_int",
[](Tensor &self, paddle::platform::CPUPlace &place) {
self.mutable_data<int>(place);
})
.def("_alloc_int",
[](Tensor &self, paddle::platform::XPUPlace &place) {
self.mutable_data<int>(place);
})
.def("_alloc_int",
[](Tensor &self, paddle::platform::CUDAPlace &place) {
self.mutable_data<int>(place);
})
.def("_alloc_int",
[](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
self.mutable_data<int>(place);
})
.def("_alloc_float",
[](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
self.mutable_data<float>(place);
})
.def("_mutable_data",
[](Tensor &self, paddle::platform::CPUPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(self.mutable_data(place, type));
})
.def("_mutable_data",
[](Tensor &self, paddle::platform::XPUPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(self.mutable_data(place, type));
})
.def("_mutable_data",
[](Tensor &self, paddle::platform::CUDAPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(self.mutable_data(place, type));
})
.def("_mutable_data",
[](Tensor &self, paddle::platform::CUDAPinnedPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(self.mutable_data(place, type));
})
.def("_clear", &Tensor::clear)
.def("set", SetTensorFromPyArray<paddle::platform::CPUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::XPUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::CUDAPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::CUDAPinnedPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false,
R"DOC(
Set the data of LoDTensor on place with given numpy array.
Args:
lod (numpy.ndarray): The data to set.
place (CPUPlace|CUDAPlace|XPUPlace|CUDAPinnedPlace): The place where the
LoDTensor is to be set.
zero_copy (bool, optional): Whether to share memory with the input numpy array.
This parameter only works with CPUPlace. Default: False.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
)DOC")
.def("shape", [](Tensor &self) { return vectorize(self.dims()); }, R"DOC(
Return the shape of LoDTensor.
Returns:
list[int]: The shape of LoDTensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
print(t.shape()) # [5, 30]
)DOC")
.def("_to_dlpack",
[](Tensor &self) {
DLPackTensor dlpack_tensor(self, 1);
DLManagedTensor *dmt =
dlpack_tensor.ToCudfCompatibleDLManagedTensor();
auto capsule = py::capsule(
static_cast<void *>(dmt), "dltensor", [](PyObject *ptr) {
if (ptr) {
auto dltensor = new DLManagedTensor;
try {
dltensor = reinterpret_cast<DLManagedTensor *>(
PyCapsule_GetPointer(ptr, "used_dltensor"));
return;
} catch (...) {
dltensor = reinterpret_cast<DLManagedTensor *>(
PyCapsule_GetPointer(ptr, "dltensor"));
}
dltensor->deleter(dltensor);
}
});
return capsule;
})
.def("_set_float_element", TensorSetElement<float>)
.def("_get_float_element", TensorGetElement<float>)
.def("_set_double_element", TensorSetElement<double>)
.def("_get_double_element", TensorGetElement<double>)
.def("_place", [](Tensor &self) { return self.place(); })
.def("_dtype", [](Tensor &self) { return self.type(); })
.def("_layout",
[](Tensor &self) { return DataLayoutToString(self.layout()); })
.def("_share_data_with", &Tensor::ShareDataWith)
.def("__getitem__", PySliceTensor, py::return_value_policy::reference)
.def("__str__", [](const Tensor &self) {
std::stringstream ostr;
ostr << self;
return ostr.str();
});
// TODO(cql): add reference: en_user_guide_lod_tensor
py::class_<LoDTensor, Tensor>(m, "LoDTensor", R"DOC(
LoDTensor is a Tensor with optional LoD (Level of Details) information,
it can be used for variable-length sequences,
see :ref:`user_guide_lod_tensor` for details.
LoDTensor can be converted to numpy array using :code:`numpy.array(lod_tensor)`.
You can skip the following explanation if you don't need to know details
of LoDTensor.
The following two examples show how to use LODtensor to represent
variable-length sequences.
Example 1:
Suppose x is a LoDTensor representing a variable-length sequence.
It contains two logical subsequences, the length of first logical sequence
is 2 (e.g., number of samples is 2), the length of second logical sequence
is 3, and the total length is 5. The data of the first logical sequence is
[1, 2], [3, 4], and the data of the second logical sequence is [5, 6],
[7, 8], [9, 10]. The data dimension of each sample is 2. So, the final
shape of the LoDTensor is [5, 2], of which 5 is the total length and 2 is
the dimension of each sample.
Logically, we can represent the variable-length sequence in two ways: one
is in the form of recursive sequence lengths, that is,
x.recursive_sequence_lengths=[[2, 3]]; the other is in the form of offsets,
that is, x.lod=[[0, 2, 2+3]]. These two representations are equivalent, and
you can set and retrieve recursive_sequence_lengths or LoD through the
corresponding interfaces of LoDTensor introduced later.
Actually, in order to access sequence faster, Paddle uses offset to store
different lengths of sequences.
Therefore, the operations on recursive_sequence_lengths will be converted
to the operations on LoD eventually.
.. code-block:: python
y.data = [[1, 2], [3, 4],
[5, 6], [7, 8],
[9, 10], [11, 12], [13, 14]]
y.shape = [2+2+3, 2]
y.recursive_sequence_lengths = [[2, 1], [2, 2, 3]]
y.lod = [[0, 2, 3], [0, 2, 4, 7]]
Example 2:
LoD may have more than one level (for example, a paragraph may have more
than one sentence and a sentence may have more than one word). Suppose y
is a LoDTensor and its lod_level is 2.
From level = 0, there are two logical sequences, the length of which is
2 and 1, respectively, indicating that the first logical sequence contains
two sub-sequences and the second logical sequence contains one sub-sequence.
From level = 1, the lengths of two sub-sequences contained by the first
logical sequence is 2 and 2, and the length of sub-sequence contained by
the second logical sequence is 3.
Therefore, the LoDTensor is represented in the form of recursive sequence
lengths as y.recursive_sequence_lengths=[[2,1], [2,2,3]]; and equally, in
the form of offset, it is represented as y.lod=[[0,2,3], [0,2,4,7]].
.. code-block:: python
y.data = [[1, 2], [3, 4],
[5, 6], [7, 8],
[9, 10], [11, 12], [13, 14]]
y.shape = [2+2+3, 2]
y.recursive_sequence_lengths = [[2, 1], [2, 2, 3]]
y.lod = [[0, 2, 3], [0, 2, 4, 7]]
Examples:
.. code-block:: python
import paddle.fluid as fluid
t = fluid.LoDTensor()
)DOC")
.def("__array__", [](Tensor &self) { return TensorToPyArray(self); })
.def("__init__",
[](LoDTensor &instance, const std::vector<std::vector<size_t>>
&recursive_sequence_lengths) {
LoD new_lod;
new_lod.reserve(recursive_sequence_lengths.size());
std::copy(recursive_sequence_lengths.begin(),
recursive_sequence_lengths.end(),
std::back_inserter(new_lod));
LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
PADDLE_ENFORCE_EQ(
CheckLoD(new_offset_lod, -1), true,
platform::errors::InvalidArgument(
"The provided recursive_sequence_lengths info is invalid, "
"the LoD converted by recursive_sequence_lengths is %s",
new_lod));
new (&instance) LoDTensor(new_offset_lod);
})
.def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
// We implement offset based LOD in C++ while we use length based with
// Python API. So we changed set_lod to set_recursive_sequence_lengths
// to
// avoid misuse.
// The discussion is here:
// https://github.com/PaddlePaddle/Paddle/issues/10855
.def("set_lod",
[](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
// the input lod is offset-based level-of-detail info
LoD new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
PADDLE_ENFORCE_EQ(
CheckLoD(new_lod, vectorize(self.dims()).front()), true,
platform::errors::InvalidArgument(
"The provided LoD is invalid, the LoD is %s", new_lod));
self.set_lod(new_lod);
},
py::arg("lod"), R"DOC(
Set LoD of the LoDTensor.
Args:
lod (list[list[int]]): The lod to set.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
t.set_lod([[0, 2, 5]])
print(t.lod()) # [[0, 2, 5]]
)DOC")
.def("set_recursive_sequence_lengths",
[](LoDTensor &self, const std::vector<std::vector<size_t>>
&recursive_sequence_lengths) {
// the input recursive_sequence_lengths is length-based
// level-of-detail info
LoD new_lod;
new_lod.reserve(recursive_sequence_lengths.size());
std::copy(recursive_sequence_lengths.begin(),
recursive_sequence_lengths.end(),
std::back_inserter(new_lod));
LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
PADDLE_ENFORCE_EQ(
CheckLoD(new_offset_lod, vectorize(self.dims()).front()), true,
platform::errors::InvalidArgument(
"The provided recursive_sequence_lengths info is invalid, "
"the LoD converted by recursive_sequence_lengths is "
"%s",
new_lod));
self.set_lod(new_offset_lod);
},
py::arg("recursive_sequence_lengths"), R"DOC(
Set LoD of the LoDTensor according to recursive sequence lengths.
For example, if recursive_sequence_lengths=[[2, 3]], which means
there are two sequences with length 2 and 3 respectively, the
corresponding lod would be [[0, 2, 2+3]], i.e., [[0, 2, 5]].
Args:
recursive_sequence_lengths (list[list[int]]): The recursive sequence lengths.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
t.set_recursive_sequence_lengths([[2, 3]])
print(t.recursive_sequence_length()) # [[2, 3]]
print(t.lod()) # [[0, 2, 5]]
)DOC")
.def("lod",
[](LoDTensor &self) -> std::vector<std::vector<size_t>> {
// output the offset-based lod info
LoD lod = self.lod();
std::vector<std::vector<size_t>> new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
return new_lod;
},
R"DOC(
Return the LoD of the LoDTensor.
Returns:
list[list[int]]: The lod of the LoDTensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
t.set_lod([[0, 2, 5]])
print(t.lod()) # [[0, 2, 5]]
)DOC")
// Set above comments of set_lod.
.def("recursive_sequence_lengths",
[](LoDTensor &self) -> std::vector<std::vector<size_t>> {
// output the length-based lod info
LoD lod = ConvertToLengthBasedLoD(self.lod());
std::vector<std::vector<size_t>> new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
return new_lod;
},
R"DOC(
Return the recursive sequence lengths corresponding to of the LodD
of the LoDTensor.
Returns:
list[list[int]]: The recursive sequence lengths.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
t.set_recursive_sequence_lengths([[2, 3]])
print(t.recursive_sequence_lengths()) # [[2, 3]]
)DOC")
.def("has_valid_recursive_sequence_lengths",
[](LoDTensor &self) -> bool {
// Check that the lod info is valid and match the outermost
// dimension of the LoDTensor data
return CheckLoD(self.lod(), vectorize(self.dims()).front());
},
R"DOC(
Check whether the LoD of the LoDTensor is valid.
Returns:
bool: Whether the LoD is valid.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
t.set_recursive_sequence_lengths([[2, 3]])
print(t.has_valid_recursive_sequence_lengths()) # True
)DOC")
.def("__getitem__", PySliceTensor, py::return_value_policy::reference,
R"DOC(
Slice the original Tensor, and remove the LoD information.
Returns:
out (Tensor): new Tensor(NOT LoDTensor).
)DOC")
.def("__str__",
[](const LoDTensor &self) {
std::stringstream ostr;
ostr << self;
return ostr.str();
})
.def("_copy", [](const LoDTensor &self, const platform::Place &place) {
// follow fetch_op's inplementation
LoDTensor dst;
if (self.IsInitialized() && self.numel() > 0) {
TensorCopySync(self, place, &dst);
} else {
// Not copy, if the src tensor is empty.
dst.clear();
dst.Resize({0});
}
dst.set_lod(self.lod());
return dst;
#ifdef _WIN32
});
#else
})
.def(py::pickle(
[](const LoDTensor &t) { // __getstate__
auto holder = t.Holder();
PADDLE_ENFORCE_EQ(
platform::is_cpu_place(holder->place()), true,
platform::errors::PreconditionNotMet(
"LoDTensor is not on CPU."
"Now only LoDTensor on CPU can be serialized."));
auto* mmap_writer_allocation =
dynamic_cast<memory::allocation::MemoryMapWriterAllocation *>(
holder.get());
PADDLE_ENFORCE_NOT_NULL(mmap_writer_allocation,
platform::errors::PreconditionNotMet(
"LoDTensor is not in shared memory."
"Now only LoDTensor on shared memory can be serialized."));
int type_idx = static_cast<int>(t.type());
return py::make_tuple(mmap_writer_allocation->ipc_name(),
mmap_writer_allocation->size(),
type_idx, vectorize(t.dims()), t.lod());
},
[](py::tuple t) { // __setstate__
if (t.size() != 5)
throw std::runtime_error("Invalid LoDTensor state!");
// 1. Create a new C++ instance
LoDTensor tensor;
// 2. Rebuild Allocation
const std::string &ipc_name = t[0].cast<std::string>();
size_t size = t[1].cast<size_t>();
auto shared_reader_holder =
memory::allocation::RebuildMemoryMapReaderAllocation(
ipc_name, size);
// 3. Maintain global fd set
VLOG(3) << "LoDTensor ipc name: " << ipc_name;
memory::allocation::MemoryMapFdSet::Instance().Insert(ipc_name);
// 4. Rebuild LoDTensor
tensor.ResetHolderWithType(shared_reader_holder,
static_cast<proto::VarType::Type>(t[2].cast<int>()));
tensor.Resize(make_ddim(t[3].cast<std::vector<int>>()));
tensor.set_lod(t[4].cast<framework::LoD>());
return tensor;
}));
#endif
py::class_<SelectedRows>(m, "SelectedRows")
.def("__init__",
[](SelectedRows &instance) { new (&instance) SelectedRows(); })
.def("__init__",
[](SelectedRows &instance, const std::vector<int64_t> rows,
const int64_t &height) {
new (&instance) SelectedRows(rows, height);
})
.def("get_tensor",
[](SelectedRows &self) { return self.mutable_value(); },
py::return_value_policy::reference)
.def("numel",
[](SelectedRows &self) -> int64_t { return self.value().numel(); })
.def("set_height", &SelectedRows::set_height)
.def("height", &SelectedRows::height)
.def("set_rows",
[](SelectedRows &self, std::vector<int64_t> rows) {
#ifndef PADDLE_WITH_CUDA
self.set_rows(rows);
#else
Vector<int64_t> new_rows(rows);
self.set_rows(new_rows);
#endif
})
.def("sync_index", [](SelectedRows &instance) { instance.SyncIndex(); })
.def("rows", [](SelectedRows &self) {
auto rows = self.rows();
std::vector<int64_t> new_rows;
new_rows.reserve(rows.size());
std::copy(rows.begin(), rows.end(), std::back_inserter(new_rows));
return new_rows;
});
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
)DOC")
.def(py::init<>())
.def("is_int", [](const Variable &var) { return var.IsType<int>(); })
.def("set_int",
[](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
.def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
.def("is_float", [](const Variable &var) { return var.IsType<float>(); })
.def("set_float",
[](Variable &var, float val) -> void {
*var.GetMutable<float>() = val;
})
.def("get_float",
[](const Variable &var) -> float { return var.Get<float>(); })
.def("get_tensor",
[](Variable &self) -> LoDTensor * {
return self.GetMutable<LoDTensor>();
},
py::return_value_policy::reference)
.def("get_bytes",
[](Variable &self) {
return py::bytes(*self.GetMutable<std::string>());
})
.def("get_lod_rank_table",
[](Variable &self) { return self.GetMutable<LoDRankTable>(); },
py::return_value_policy::reference)
.def("get_selected_rows",
[](Variable &self) -> SelectedRows * {
return self.GetMutable<SelectedRows>();
},
py::return_value_policy::reference)
.def("get_lod_tensor_array",
[](Variable &self) { return self.GetMutable<LoDTensorArray>(); },
py::return_value_policy::reference)
.def("get_fetch_list",
[](Variable &self) { return self.GetMutable<FetchList>(); },
py::return_value_policy::reference)
#if (defined(PADDLE_WITH_NCCL))
.def("get_communicator",
[](Variable &self) -> platform::Communicator * {
return self.GetMutable<platform::Communicator>();
},
py::return_value_policy::reference)
#endif
.def("get_reader",
[](Variable &self) -> framework::ReaderHolder * {
PADDLE_ENFORCE_EQ(
self.IsType<framework::ReaderHolder>(), true,
platform::errors::InvalidArgument(
"The variable is not type of ReaderHolder."));
return self.GetMutable<framework::ReaderHolder>();
},
py::return_value_policy::reference)
.def("set_scope", [](Variable &self, Scope &scope) {
auto scope_vec = self.GetMutable<std::vector<framework::Scope *>>();
scope_vec->emplace_back(&scope);
});
BindReader(&m);
py::class_<Scope>(m, "_Scope", R"DOC(
Scope is an association of a name to Variable. All variables belong to Scope.
Variables in a parent scope can be retrieved from local scope.
You need to specify a scope to run a Net, i.e., `exe.Run(&scope)`.
One net can run in different scopes and update different variable in the
scope.
You can create var in a scope and get it from the scope.
Examples:
.. code-block:: python
import paddle.fluid as fluid
# create tensor from a scope and set value to it.
param = scope.var('Param').get_tensor()
param_array = np.full((height, row_numel), 5.0).astype("float32")
param.set(param_array, place)
)DOC")
.def("_remove_from_pool",
[](Scope &self) { ScopePool::Instance().Remove(&self); })
.def("var",
[](Scope &self, const std::string &name) -> Variable * {
return self.Var(name);
},
py::arg("name"),
R"DOC(
Find or create variable named :code:`name` in the current scope.
If the variable named :code:`name` does not exist in the
current scope, the variable would be created. Otherwise,
return the existing variable.
Args:
name (str): the variable name.
Returns:
out (core.Variable): the found or created variable.
)DOC",
py::return_value_policy::reference)
.def("find_var", &Scope::FindVar, py::arg("name"),
R"DOC(
Find variable named :code:`name` in the current scope or
its parent scope. Return None if not found.
Args:
name (str): the variable name.
Returns:
out (core.Variable|None): the found variable or None.
)DOC",
py::return_value_policy::reference)
.def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
R"DOC(
Create a new sub-scope of the current scope.
Returns:
out (core._Scope): the created sub-scope.
)DOC",
py::return_value_policy::reference)
.def("drop_kids", &Scope::DropKids,
R"DOC(
Delete all sub-scopes of the current scope.
)DOC")
.def("_kids", &Scope::kids);
m.def("Scope",
[]() -> Scope * {
auto *s = new Scope();
ScopePool::Instance().Insert(std::unique_ptr<Scope>(s));
return s;
},
R"DOC(
Create a new scope.
Returns:
out (core._Scope): the created scope.
)DOC",
py::return_value_policy::reference);
//! @note: Be careful! PyBind will return std::string as an unicode, not
//! Python str. If you want a str object, you should cast them in Python.
m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
std::vector<py::bytes> ret_values;
for (auto &iter : OpInfoMap::Instance().map()) {
auto &info = iter.second;
if (info.HasOpProtoAndChecker()) {
std::string str;
PADDLE_ENFORCE_EQ(
info.Proto().SerializeToString(&str), true,
platform::errors::Fatal(
"Serialize OpProto Error. This could be a bug of Paddle."));
ret_values.emplace_back(str);
}
}
return ret_values;
});
m.def("get_op_attrs_default_value",
[](py::bytes byte_name) -> paddle::framework::AttributeMap {
std::string op_type = byte_name;
paddle::framework::AttributeMap res;
auto info = OpInfoMap::Instance().GetNullable(op_type);
if (info != nullptr) {
if (info->HasOpProtoAndChecker()) {
auto op_checker = info->Checker();
res = op_checker->GetAttrsDefaultValuesMap();
}
}
return res;
});
m.def(
"get_grad_op_desc", [](const OpDesc &op_desc,
const std::unordered_set<std::string> &no_grad_set,
const std::vector<BlockDesc *> &grad_sub_block) {
std::unordered_map<std::string, std::string> grad_to_var;
std::vector<std::unique_ptr<OpDesc>> grad_op_descs =
framework::OpInfoMap::Instance()
.Get(op_desc.Type())
.GradOpMaker()(op_desc, no_grad_set, &grad_to_var,
grad_sub_block);
std::vector<OpDesc *> grad_op_desc_ptrs(grad_op_descs.size());
std::transform(grad_op_descs.begin(), grad_op_descs.end(),
grad_op_desc_ptrs.begin(),
[](std::unique_ptr<OpDesc> &p) { return p.release(); });
return std::make_pair(grad_op_desc_ptrs, grad_to_var);
});
m.def("has_grad_op_maker", [](const std::string op_type) {
return framework::OpInfoMap::Instance().Get(op_type).HasGradOpMaker();
});
m.def("has_non_empty_grad_op_maker", [](const std::string op_type) {
return framework::OpInfoMap::Instance()
.Get(op_type)
.HasNonEmptyGradOpMaker();
});
m.def("has_infer_inplace", [](const std::string op_type) {
return framework::OpInfoMap::Instance().Get(op_type).HasInferInplace();
});
m.def("infer_no_need_buffer_slots",
[](const std::string op_type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs) {
auto infer_func = framework::OpInfoMap::Instance()
.Get(op_type)
.NoNeedBufferVarsInferer();
if (infer_func) {
return infer_func(inputs, outputs, attrs);
} else {
std::unordered_set<std::string> empty = {};
return empty;
}
});
m.def("prune", [](const ProgramDesc &origin,
const std::set<std::string> &feeded_var_names,
const std::vector<std::array<size_t, 2>> &targets) {
ProgramDesc prog_with_targets(origin);
for (const auto &t : targets) {
prog_with_targets.MutableBlock(t[0])->Op(t[1])->SetIsTarget(true);
}
proto::ProgramDesc pruned_desc;
auto pruned_origin_block_id_map =
Prune(*prog_with_targets.Proto(), feeded_var_names, &pruned_desc);
return std::make_tuple(ProgramDesc(pruned_desc),
pruned_origin_block_id_map);
});
m.def("prune_backward",
[](const framework::ProgramDesc &program) {
return PruneBackward(program);
},
R"DOC(
Prune the backward part of a program, mostly called in
program.clone(for_test=True).
Args:
program (ProgramDesc): The original program.
Returns:
tuple(ProgramDesc, map<int, int>): The first part is
the pruned program desc, and the second part is a map
which contains the id pair of pruned block and corresponding
origin block.
)DOC");
m.def("empty_var_name",
[]() { return std::string(framework::kEmptyVarName); });
m.def("grad_var_suffix",
[]() { return std::string(framework::kGradVarSuffix); });
m.def_submodule(
"var_names",
"The module will return special predefined variable name in Paddle")
.def("empty", []() { return kEmptyVarName; })
.def("temp", []() { return kTempVarName; });
// clang-format off
py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
.def_static("create",
[](paddle::platform::CPUPlace& place)
-> paddle::platform::DeviceContext* {
return new paddle::platform::CPUDeviceContext();
})
.def_static("create",
[](paddle::platform::XPUPlace& place)
-> paddle::platform::DeviceContext* {
#ifndef PADDLE_WITH_XPU
PADDLE_THROW(
platform::errors::PermissionDenied(
"Cannot use XPUPlace in CPU/GPU version, "
"Please recompile or reinstall Paddle with XPU support."));
#else
return new paddle::platform::XPUDeviceContext(place);
#endif
})
.def_static("create",
[](paddle::platform::CUDAPlace& place)
-> paddle::platform::DeviceContext* {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW(
platform::errors::PermissionDenied(
"Cannot use CUDAPlace in CPU only version, "
"Please recompile or reinstall Paddle with CUDA support."));
#else
return new paddle::platform::CUDADeviceContext(place);
#endif
})
.def_static("create",
[](paddle::platform::CUDAPinnedPlace& place)
-> paddle::platform::DeviceContext* {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW(
platform::errors::PermissionDenied(
"Cannot use CUDAPinnedPlace in CPU only version, "
"Please recompile or reinstall Paddle with CUDA support."));
#else
return new paddle::platform::CUDAPinnedDeviceContext(place);
#endif
});;
// clang-format on
#if defined(PADDLE_WITH_NCCL)
py::class_<platform::Communicator>(m, "Communicator").def(py::init<>());
#endif
py::class_<platform::CUDAPlace>(m, "CUDAPlace", R"DOC(
CUDAPlace is a descriptor of a device.
It represents a GPU device allocated or to be allocated with Tensor or LoDTensor.
Each CUDAPlace has a dev_id to indicate the graphics card ID represented by the current CUDAPlace,
staring from 0.
The memory of CUDAPlace with different dev_id is not accessible.
Numbering here refers to the logical ID of the visible graphics card, not the actual ID of the graphics card.
You can set visible GPU devices by setting the `CUDA_VISIBLE_DEVICES` environment variable.
When the program starts, visible GPU devices will be numbered from 0.
If `CUDA_VISIBLE_DEVICES` is not set, all devices are visible by default,
and the logical ID is the same as the actual ID.
Parameters:
id (int): GPU device ID.
Examples:
.. code-block:: python
import paddle
place = paddle.CUDAPlace(0)
)DOC")
.def("__init__",
[](platform::CUDAPlace &self, int dev_id) {
#ifdef PADDLE_WITH_CUDA
if (UNLIKELY(dev_id < 0)) {
LOG(ERROR) << string::Sprintf(
"Invalid CUDAPlace(%d), device id must be 0 or "
"positive integer",
dev_id);
std::exit(-1);
}
if (UNLIKELY(dev_id >= platform::GetCUDADeviceCount())) {
if (platform::GetCUDADeviceCount() == 0) {
LOG(ERROR) << "Cannot use GPU because there is no GPU "
"detected on your "
"machine.";
std::exit(-1);
} else {
LOG(ERROR) << string::Sprintf(
"Invalid CUDAPlace(%d), must inside [0, %d), because GPU "
"number on your machine is %d",
dev_id, platform::GetCUDADeviceCount(),
platform::GetCUDADeviceCount());
std::exit(-1);
}
}
new (&self) platform::CUDAPlace(dev_id);
#else
LOG(ERROR) << string::Sprintf(
"Cannot use GPU because you have installed CPU version "
"PaddlePaddle.\n"
"If you want to use GPU, please try to install GPU version "
"PaddlePaddle by: pip install paddlepaddle-gpu\n"
"If you only have CPU, please change CUDAPlace(%d) to be "
"CPUPlace().\n",
dev_id);
std::exit(-1);
#endif
})
#ifdef PADDLE_WITH_CUDA
.def("get_device_id",
[](const platform::CUDAPlace &self) { return self.GetDeviceId(); })
.def("_type", &PlaceIndex<platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::Place>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CPUPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::XPUPlace>)
.def("_equals",
&IsSamePlace<platform::CUDAPlace, platform::CUDAPinnedPlace>)
.def("_get_device_id",
[](platform::CUDAPlace &self) -> int { return self.GetDeviceId(); })
#endif
.def("__repr__", string::to_string<const platform::CUDAPlace &>)
.def("__str__", string::to_string<const platform::CUDAPlace &>);
py::class_<platform::XPUPlace>(m, "XPUPlace", R"DOC(
**Note**:
Examples:
.. code-block:: python
import paddle.fluid as fluid
xpu_place = fluid.XPUPlace(0)
)DOC")
.def("__init__",
[](platform::XPUPlace &self, int dev_id) {
#ifdef PADDLE_WITH_XPU
if (UNLIKELY(dev_id < 0)) {
LOG(ERROR) << string::Sprintf(
"Invalid XPUPlace(%d), device id must be 0 or "
"positive integer",
dev_id);
std::exit(-1);
}
if (UNLIKELY(dev_id >= platform::GetXPUDeviceCount())) {
if (platform::GetXPUDeviceCount() == 0) {
LOG(ERROR) << "Cannot use XPU because there is no XPU "
"detected on your "
"machine.";
std::exit(-1);
} else {
LOG(ERROR) << string::Sprintf(
"Invalid XPUPlace(%d), must inside [0, %d), because XPU "
"number on your machine is %d",
dev_id, platform::GetXPUDeviceCount(),
platform::GetXPUDeviceCount());
std::exit(-1);
}
}
new (&self) platform::XPUPlace(dev_id);
#else
LOG(ERROR) << string::Sprintf(
"Cannot use XPU because you have installed CPU/GPU version "
"PaddlePaddle.\n"
"If you want to use XPU, please try to install XPU version "
"PaddlePaddle by: pip install paddlepaddle-xpu\n"
"If you only have CPU, please change XPUPlace(%d) to be "
"CPUPlace().\n",
dev_id);
std::exit(-1);
#endif
})
#ifdef PADDLE_WITH_XPU
.def("_type", &PlaceIndex<platform::XPUPlace>)
.def("_equals", &IsSamePlace<platform::XPUPlace, platform::Place>)
.def("_equals", &IsSamePlace<platform::XPUPlace, platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::XPUPlace, platform::CPUPlace>)
.def("_equals", &IsSamePlace<platform::XPUPlace, platform::XPUPlace>)
.def("_equals",
&IsSamePlace<platform::XPUPlace, platform::CUDAPinnedPlace>)
.def("get_device_id",
[](const platform::XPUPlace &self) { return self.GetDeviceId(); })
#endif
.def("__repr__", string::to_string<const platform::XPUPlace &>)
.def("__str__", string::to_string<const platform::XPUPlace &>);
#ifdef PADDLE_WITH_XPU
m.def("get_xpu_device_count", platform::GetXPUDeviceCount);
#endif
py::class_<paddle::platform::CPUPlace>(m, "CPUPlace", R"DOC(
CPUPlace is a descriptor of a device.
It represents a CPU device on which a tensor will be allocated and a model will run.
Examples:
.. code-block:: python
import paddle
cpu_place = paddle.CPUPlace()
)DOC")
.def(py::init<>())
.def("_type", &PlaceIndex<platform::CPUPlace>)
.def("_equals", &IsSamePlace<platform::CPUPlace, platform::Place>)
.def("_equals", &IsSamePlace<platform::CPUPlace, platform::XPUPlace>)
.def("_equals", &IsSamePlace<platform::CPUPlace, platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::CPUPlace, platform::CPUPlace>)
.def("_equals",
&IsSamePlace<platform::CPUPlace, platform::CUDAPinnedPlace>)
.def("__repr__", string::to_string<const platform::CPUPlace &>)
.def("__str__", string::to_string<const platform::CPUPlace &>);
py::class_<paddle::platform::CUDAPinnedPlace>(m, "CUDAPinnedPlace", R"DOC(
CUDAPinnedPlace is a descriptor of a device.
It refers to the page locked memory allocated by the CUDA function `cudaHostAlloc()` in the host memory.
The host operating system will not paging and exchanging the memory.
It can be accessed through direct memory access technology to speed up the copy of data between the host and GPU.
For more information on CUDA data transfer and `pinned memory`,
please refer to `official document <https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#pinned-memory>`_ .
Examples:
.. code-block:: python
import paddle
place = paddle.CUDAPinnedPlace()
)DOC")
.def("__init__",
[](platform::CUDAPinnedPlace &self) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW(platform::errors::PermissionDenied(
"Cannot use CUDAPinnedPlace in CPU only version, "
"Please recompile or reinstall Paddle with CUDA support."));
#endif
new (&self) platform::CUDAPinnedPlace();
})
.def("_type", &PlaceIndex<platform::CUDAPinnedPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPinnedPlace, platform::Place>)
.def("_equals",
&IsSamePlace<platform::CUDAPinnedPlace, platform::CUDAPlace>)
.def("_equals",
&IsSamePlace<platform::CUDAPinnedPlace, platform::XPUPlace>)
.def("_equals",
&IsSamePlace<platform::CUDAPinnedPlace, platform::CPUPlace>)
.def("_equals",
&IsSamePlace<platform::CUDAPinnedPlace, platform::CUDAPinnedPlace>)
.def("__repr__", string::to_string<const platform::CUDAPinnedPlace &>)
.def("__str__", string::to_string<const platform::CUDAPinnedPlace &>);
py::class_<platform::Place>(m, "Place")
.def(py::init<>())
.def("_type", &PlaceIndex<platform::Place>)
.def("_equals", &IsSamePlace<platform::Place, platform::Place>)
.def("_equals", &IsSamePlace<platform::Place, platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::Place, platform::CPUPlace>)
.def("_equals", &IsSamePlace<platform::Place, platform::XPUPlace>)
.def("_equals", &IsSamePlace<platform::Place, platform::CUDAPinnedPlace>)
.def("is_gpu_place",
[](platform::Place &self) { return platform::is_gpu_place(self); })
.def("is_cpu_place",
[](platform::Place &self) { return platform::is_cpu_place(self); })
.def("is_xpu_place",
[](platform::Place &self) { return platform::is_xpu_place(self); })
.def("is_cuda_pinned_place",
[](platform::Place &self) {
return platform::is_cuda_pinned_place(self);
})
.def("gpu_device_id",
[](platform::Place &self) {
return BOOST_GET_CONST(platform::CUDAPlace, self).device;
})
.def("xpu_device_id",
[](platform::Place &self) {
return BOOST_GET_CONST(platform::XPUPlace, self).device;
})
.def("set_place", [](platform::Place &self,
const platform::Place &other) { self = other; })
.def("set_place",
[](platform::Place &self, const platform::CPUPlace &cpu_place) {
self = cpu_place;
})
.def("set_place",
[](platform::Place &self, const platform::XPUPlace &xpu_place) {
self = xpu_place;
})
.def("set_place",
[](platform::Place &self, const platform::CUDAPlace &gpu_place) {
self = gpu_place;
})
.def("set_place",
[](platform::Place &self,
const platform::CUDAPinnedPlace &cuda_pinned_place) {
self = cuda_pinned_place;
})
.def("__repr__", string::to_string<const platform::Place &>)
.def("__str__", string::to_string<const platform::Place &>);
py::class_<OperatorBase>(m, "Operator")
.def_static(
"create",
[](py::bytes protobin) {
proto::OpDesc desc;
PADDLE_ENFORCE_EQ(desc.ParsePartialFromString(protobin), true,
platform::errors::InvalidArgument(
"Cannot parse user input to OpDesc"));
PADDLE_ENFORCE_EQ(
desc.IsInitialized(), true,
platform::errors::InvalidArgument(
"The provided OpDesc is not initialized, the reason is: %s",
desc.InitializationErrorString()));
return OpRegistry::CreateOp(desc);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::CPUPlace &place) { self.Run(scope, place); })
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::XPUPlace &place) { self.Run(scope, place); })
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::CUDAPlace &place) { self.Run(scope, place); })
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::CUDAPinnedPlace &place) {
self.Run(scope, place);
})
.def("type",
[](const OperatorBase &op) -> std::string { return op.Type(); })
.def("outputs",
[](const OperatorBase &op)
-> std::map<std::string, std::vector<std::string>> {
return op.Outputs();
})
.def("output_vars",
[](const OperatorBase &op) { return op.OutputVars(true); })
.def("inputs", [](const OperatorBase &op) { return op.Inputs(); })
.def("input_vars", [](const OperatorBase &op) { return op.InputVars(); })
.def("__str__", &OperatorBase::DebugString)
.def("no_intermediate_outputs",
[](const OperatorBase &op) { return op.OutputVars(false); })
.def("support_gpu", &OperatorBase::SupportGPU);
py::class_<framework::ExecutorPrepareContext>(m, "ExecutorPrepareContext")
.def(py::init<const ProgramDesc &, size_t>());
py::class_<framework::TrainerBase, std::shared_ptr<framework::TrainerBase>>(
m, "TrainerBase")
.def("get_worker_scope",
[](TrainerBase &self, int thread_id) -> Scope * {
return self.GetWorkerScope(thread_id);
},
py::return_value_policy::reference)
.def("finalize", &TrainerBase::Finalize);
py::class_<framework::Executor>(m, "Executor")
.def(py::init<const platform::Place &>())
.def("close", &Executor::Close)
.def("run_from_dataset", &Executor::RunFromDataset,
py::call_guard<py::gil_scoped_release>())
.def("release_trainer", &Executor::ReleaseTrainer,
py::call_guard<py::gil_scoped_release>())
.def("init_for_dataset",
[](Executor &self, const ProgramDesc &prog,
const std::string &trainer_desc, Scope *scope,
Dataset *dataset) -> std::shared_ptr<TrainerBase> {
pybind11::gil_scoped_release release;
return self.InitForDataset(prog, trainer_desc, scope, dataset);
})
.def("run_from_dataset",
[](Executor &self, std::shared_ptr<TrainerBase> trainer) {
pybind11::gil_scoped_release release;
self.RunFromDataset(trainer);
})
.def("run_prepared_ctx",
[](Executor &self, ExecutorPrepareContext *ctx, Scope *scope,
std::map<std::string, const LoDTensor *> *feed_targets,
std::map<std::string, FetchType *> *fetch_targets,
bool create_local_scope = true, bool create_vars = true,
const std::string &feed_holder_name = "feed",
const std::string &fetch_holder_name = "fetch") {
pybind11::gil_scoped_release release;
self.RunPreparedContext(ctx, scope, feed_targets, fetch_targets,
create_local_scope, create_vars,
feed_holder_name, fetch_holder_name);
})
.def("run_prepared_ctx",
[](Executor &self, ExecutorPrepareContext *ctx, Scope *scope,
bool create_local_scope = true, bool create_vars = true,
bool keep_kids = false) {
pybind11::gil_scoped_release release;
self.RunPreparedContext(ctx, scope, create_local_scope,
create_vars, keep_kids);
})
.def("prepare",
[](Executor &self, const ProgramDesc &program, int block_id,
const std::vector<std::string> &skip_ref_cnt_vars =
std::vector<std::string>(),
bool force_disable_gc = false) {
pybind11::gil_scoped_release release;
return self.Prepare(program, block_id, skip_ref_cnt_vars,
force_disable_gc);
})
.def("create_variables", &Executor::CreateVariables)
.def("run", [](Executor &self, const ProgramDesc &prog, Scope *scope,
int block_id, bool create_local_scope, bool create_vars,
const std::vector<std::string> &fetch_vars) {
pybind11::gil_scoped_release release;
self.Run(prog, scope, block_id, create_local_scope, create_vars,
fetch_vars);
});
m.def("init_gflags", framework::InitGflags);
m.def("init_glog", framework::InitGLOG);
m.def("load_op_library", framework::LoadOpLib);
m.def("init_devices", []() { framework::InitDevices(); });
m.def("is_compiled_with_cuda", IsCompiledWithCUDA);
m.def("is_compiled_with_xpu", IsCompiledWithXPU);
m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN);
m.def("supports_bfloat16", SupportsBfloat16);
m.def("is_compiled_with_brpc", IsCompiledWithBrpc);
m.def("is_compiled_with_dist", IsCompiledWithDIST);
m.def("_cuda_synchronize", [](const platform::CUDAPlace &place) {
platform::DeviceContextPool::Instance().Get(place)->Wait();
});
m.def("get_float_stats", []() {
std::vector<paddle::platform::ExportedStatValue<float>> float_stats;
paddle::platform::StatRegistry<float>::Instance().publish(float_stats);
std::unordered_map<std::string, float> stats_map;
for (const auto &stat : float_stats) {
stats_map[stat.key] = stat.value;
}
return stats_map;
});
m.def("get_int_stats", []() {
std::vector<paddle::platform::ExportedStatValue<int64_t>> int_stats;
paddle::platform::StatRegistry<int64_t>::Instance().publish(int_stats);
std::unordered_map<std::string, int64_t> stats_map;
for (const auto &stat : int_stats) {
stats_map[stat.key] = stat.value;
}
return stats_map;
});
m.def("run_cmd",
[](const std::string &cmd, int time_out = -1,
int sleep_inter = -1) -> const std::string {
return paddle::framework::shell_get_command_output(cmd, time_out,
sleep_inter);
},
py::arg("cmd"), py::arg("time_out") = -1, py::arg("sleep_inter") = -1);
m.def("shell_execute_cmd",
[](const std::string &cmd, int time_out = 0, int sleep_inter = 0,
bool redirect_stderr = false) -> std::vector<std::string> {
return paddle::framework::shell_execute_cmd(
cmd, time_out, sleep_inter, redirect_stderr);
},
py::arg("cmd"), py::arg("time_out") = 0, py::arg("sleep_inter") = 0,
py::arg("redirect_stderr") = false);
#ifdef PADDLE_WITH_CUDA
m.def("is_float16_supported", [](const platform::CUDAPlace &place) -> bool {
// Only GPUs with Compute Capability >= 53 support float16
return platform::GetCUDAComputeCapability(place.device) >= 53;
});
#endif
m.def("set_feed_variable", framework::SetFeedVariable);
m.def("get_fetch_variable",
[](const Scope &scope, const std::string &var_name,
size_t index) -> py::object {
auto &var = framework::GetFetchVariable(scope, var_name, index);
if (data_is_lod_tensor(var)) {
return py::cast(BOOST_GET(LoDTensor, var));
} else {
return py::cast(BOOST_GET(LoDTensorArray, var));
}
});
m.def("get_variable_tensor", framework::GetVariableTensor);
m.def("_is_program_version_supported", IsProgramVersionSupported);
BindProgramDesc(&m);
BindBlockDesc(&m);
BindVarDsec(&m);
BindOpDesc(&m);
BindConstValue(&m);
BindGlobalValueGetterSetter(&m);
py::class_<framework::LoDRankTable>(m, "LodRankTable")
.def("items", [](framework::LoDRankTable &table) {
std::vector<std::pair<size_t, size_t>> res;
for (auto &item : table.items()) {
res.push_back({item.index, item.length});
}
return res;
});
py::class_<LoDTensorArray>(m, "LoDTensorArray", R"DOC(
LoDTensorArray is array of LoDTensor, it supports operator[], len() and for-loop iteration.
Examples:
.. code-block:: python
import paddle.fluid as fluid
arr = fluid.LoDTensorArray()
)DOC")
.def("__init__",
[](LoDTensorArray &instance) { new (&instance) LoDTensorArray(); })
.def("__getitem__",
[](LoDTensorArray &self, size_t i) { return &self.at(i); },
py::return_value_policy::reference)
.def("__len__", [](LoDTensorArray &self) { return self.size(); })
.def("__setitem__",
[](LoDTensorArray &self, size_t i, const LoDTensor &t) {
PADDLE_ENFORCE_LT(i, self.size(),
platform::errors::InvalidArgument(
"The index to set is larger than the size "
"of LoDTensorArray."));
self[i].ShareDataWith(t);
self[i].set_lod(t.lod());
})
.def("append",
[](LoDTensorArray &self, const LoDTensor &t) {
self.emplace_back();
self.back().ShareDataWith(t);
self.back().set_lod(t.lod());
},
py::arg("tensor"), R"DOC(
Append a LoDensor to LoDTensorArray.
Args:
tensor (LoDTensor): The LoDTensor to be appended.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
arr = fluid.LoDTensorArray()
t = fluid.LoDTensor()
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
arr.append(t)
)DOC")
.def("_move_to_list",
[](LoDTensorArray &self) -> py::list {
py::list res(self.size());
for (size_t i = 0; i < self.size(); ++i) {
res[i] = py::cast(std::move(self[i]));
}
self.clear();
return res;
},
py::return_value_policy::take_ownership);
py::class_<FetchList>(m, "FetchList", R"DOC( FetchList is a
vector of boost::variant<LoDTensor, LoDTensorArray>.
)DOC")
.def("_move_to_list",
[](FetchList &self) -> py::list {
py::list res(self.size());
for (size_t i = 0; i < self.size(); ++i) {
if (data_is_lod_tensor(self[i])) {
auto &data = BOOST_GET(LoDTensor, self[i]);
res[i] = py::cast(std::move(data));
} else {
auto &data = BOOST_GET(LoDTensorArray, self[i]);
py::list tmp(data.size());
for (size_t j = 0; j < data.size(); ++j) {
tmp[j] = py::cast(std::move(data[j]));
}
res[i] = std::move(tmp);
}
}
self.clear();
return res;
},
py::return_value_policy::take_ownership)
.def("append",
[](FetchList &self, const LoDTensor &t) {
self.emplace_back();
auto &lod_tensor = BOOST_GET(LoDTensor, self.back());
lod_tensor.ShareDataWith(t);
lod_tensor.set_lod(t.lod());
},
py::arg("var"))
.def("append",
[](FetchList &self, const LoDTensorArray &t) {
self.emplace_back();
auto &lod_tensor_array = BOOST_GET(LoDTensorArray, self.back());
for (size_t i = 0; i < t.size(); ++i) {
lod_tensor_array[i].ShareDataWith(t[i]);
lod_tensor_array[i].set_lod(t[i].lod());
}
},
py::arg("var"));
py::class_<FetchUnmergedList>(m, "FetchUnmergedList", R"DOC(
FetchUnmergedList is 2-D array of FetchType(boost::variant(LoDTensor, LoDTensorArray)).
)DOC")
.def("_move_to_list",
[](FetchUnmergedList &self) -> py::list {
py::list res(self.size());
for (size_t i = 0; i < self.size(); ++i) {
py::list tmp(self[i].size());
for (size_t j = 0; j < self[i].size(); ++j) {
if (data_is_lod_tensor(self[i][j])) {
auto &var = BOOST_GET(LoDTensor, self[i][j]);
tmp[j] = py::cast(std::move(var));
} else {
auto &var = BOOST_GET(LoDTensorArray, self[i][j]);
py::list tmp_array(var.size());
for (size_t k = 0; k < var.size(); ++k) {
tmp_array[k] = std::move(var[k]);
}
tmp[j] = std::move(tmp_array);
}
}
res[i] = std::move(tmp);
self[i].clear();
}
self.clear();
return res;
},
py::return_value_policy::take_ownership);
m.def("op_support_gpu", OpSupportGPU);
#ifdef PADDLE_WITH_CUDA
m.def("get_cuda_device_count", platform::GetCUDADeviceCount);
#ifndef _WIN32
m.def("nvprof_init", platform::CudaProfilerInit);
m.def("nvprof_start", platform::CudaProfilerStart);
m.def("nvprof_stop", platform::CudaProfilerStop);
#endif
#endif
py::enum_<platform::TracerOption>(m, "TracerOption", py::arithmetic())
.value("kDefault", platform::TracerOption::kDefault)
.value("kOpDetail", platform::TracerOption::kOpDetail)
.value("kAllOpDetail", platform::TracerOption::kAllOpDetail)
.export_values();
py::enum_<platform::ProfilerState>(m, "ProfilerState", py::arithmetic())
.value("kDisabled", platform::ProfilerState::kDisabled)
.value("kCPU", platform::ProfilerState::kCPU)
.value("kCUDA", platform::ProfilerState::kCUDA)
.value("kAll", platform::ProfilerState::kAll)
.export_values();
py::enum_<platform::EventSortingKey>(m, "EventSortingKey", py::arithmetic())
.value("kDefault", platform::EventSortingKey::kDefault)
.value("kCalls", platform::EventSortingKey::kCalls)
.value("kTotal", platform::EventSortingKey::kTotal)
.value("kMin", platform::EventSortingKey::kMin)
.value("kMax", platform::EventSortingKey::kMax)
.value("kAve", platform::EventSortingKey::kAve)
.export_values();
m.def("set_tracer_option", platform::SetTracerOption);
m.def("enable_profiler", platform::EnableProfiler);
m.def("disable_profiler", platform::DisableProfiler);
m.def("is_profiler_enabled", platform::IsProfileEnabled);
m.def("reset_profiler", platform::ResetProfiler);
m.def("get_pass", [](const std::string &pass_type) {
auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
return std::shared_ptr<framework::ir::Pass>(std::move(pass));
});
m.def("size_of_dtype", framework::SizeOfType);
#ifdef PADDLE_WITH_CUDA
m.def("set_cublas_switch", platform::SetAllowTF32Cublas);
m.def("get_cublas_switch", platform::AllowTF32Cublas);
#endif // PADDLE_WITH_CUDA
using VarQuantScale =
std::unordered_map<std::string, std::pair<bool, LoDTensor>>;
py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
pass.def(py::init())
.def("has", &ir::Pass::Has)
.def("set_not_owned",
[](ir::Pass &self, const std::string &attr_name, ProgramDesc &attr) {
self.SetNotOwned<ProgramDesc>(attr_name, &attr);
})
.def(
"set",
[](ir::Pass &self, const std::string &name, const std::string &attr) {
self.Set<std::string>(name, new std::string(attr));
})
.def("set", [](ir::Pass &self, const std::string &name,
bool val) { self.Set<bool>(name, new bool(val)); })
.def("set", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("set",
[](ir::Pass &self, const std::string &name,
std::unordered_set<std::string> set) {
self.Set(name, new std::unordered_set<std::string>(set));
})
.def("set",
[](ir::Pass &self, const std::string &name,
std::unordered_set<int> set) {
self.Set(name, new std::unordered_set<int>(set));
})
.def("set",
[](ir::Pass &self, const std::string &name, VarQuantScale scales) {
self.Set(name, new VarQuantScale(scales));
})
.def("type", &ir::Pass::Type)
.def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
self.Apply(graph.get());
});
py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(
m, "PassBuilder");
pb.def(py::init())
.def("append_pass",
[](ir::PassBuilder &self,
const std::string &pass_type) -> std::shared_ptr<ir::Pass> {
return self.AppendPass(pass_type);
})
.def("all_passes", [](ir::PassBuilder &self) { return self.AllPasses(); })
.def("insert_pass",
[](ir::PassBuilder &self, size_t idx, const std::string &pass_type) {
return self.InsertPass(idx, pass_type);
})
.def("remove_pass",
[](ir::PassBuilder &self, size_t idx) { self.RemovePass(idx); });
// -- python binds for parallel executor.
py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy", R"DOC(
ExecutionStrategy allows the user to more preciously control how to run
the program in ParallelExecutor by setting the property.
Returns:
ExecutionStrategy: An ExecutionStrategy object.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
import paddle.nn.functional as F
paddle.enable_static()
x = static.data(name='x', shape=[None, 13], dtype='float32')
y = static.data(name='y', shape=[None, 1], dtype='float32')
y_predict = static.nn.fc(input=x, size=1, act=None)
cost = F.square_error_cost(input=y_predict, label=y)
avg_loss = paddle.mean(cost)
sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_loss)
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_threads = 4
train_exe = static.ParallelExecutor(use_cuda=False,
loss_name=avg_loss.name,
exec_strategy=exec_strategy)
)DOC");
py::enum_<ExecutionStrategy::UseDevice>(exec_strategy, "UseDevice")
.value("CPU", ExecutionStrategy::UseDevice::kCPU)
.value("CUDA", ExecutionStrategy::UseDevice::kCUDA)
.value("XPU", ExecutionStrategy::UseDevice::kXPU);
exec_strategy.def(py::init())
.def_property(
"num_threads",
[](const ExecutionStrategy &self) { return self.num_threads_; },
[](ExecutionStrategy &self, size_t num_threads) {
self.num_threads_ = num_threads;
},
R"DOC(
The type is INT, num_threads represents the size of thread pool that
used to run the operators of the current program in ParallelExecutor.
If :math:`num\_threads=1`, all the operators will execute one by one,
but the order maybe difference between iterations.
If it is not set, it will be set in ParallelExecutor according to the
device type and device count, for GPU, :math:`num\_threads=device\_count*4`, for CPU,
:math:`num\_threads=CPU\_NUM*4`, the explanation of:math:`CPU\_NUM` is in ParallelExecutor.
if it is not set, ParallelExecutor will get the cpu count by calling
`multiprocessing.cpu_count()`. Default 0.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_threads = 4
)DOC")
.def_property(
"_use_device",
[](const ExecutionStrategy &self) { return self.use_device_; },
[](ExecutionStrategy &self, ExecutionStrategy::UseDevice use_device) {
self.use_device_ = use_device;
}) // NOTE(liuyuhui): Doesn't add doc for 'use_device', because
// use_device isn‘t exposed to users.
.def_property(
"allow_op_delay",
[](const ExecutionStrategy &self) { return self.allow_op_delay_; },
[](ExecutionStrategy &self, bool allow_op_delay) {
self.allow_op_delay_ = allow_op_delay;
},
R"DOC(The type is BOOL, allow_op_delay represents whether to delay the
communication operators to run, it may make the execution faster.
Note that this option is invalid now, and it will be removed in
next version. Default False.)DOC")
.def_property(
"num_iteration_per_drop_scope",
[](const ExecutionStrategy &self) {
return self.num_iteration_per_drop_scope_;
},
[](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
},
R"DOC(The type is INT, num_iteration_per_drop_scope indicates how
many iterations to clean up the temp variables which
is generated during execution. It may make the execution faster,
because the temp variable's shape maybe the same between two iterations.
Default 100.
.. note::
1. If you fetch data when calling the 'run', the ParallelExecutor
will clean up the temp variables at the end of the current iteration.
2. In some NLP model, it may cause the GPU memory is insufficient,
in this case, you should reduce `num_iteration_per_drop_scope`.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_iteration_per_drop_scope = 10
)DOC")
.def_property(
"num_iteration_per_run",
[](const ExecutionStrategy &self) {
return self.num_iteration_per_run_;
},
[](ExecutionStrategy &self, size_t num_iteration_per_run) {
self.num_iteration_per_run_ = num_iteration_per_run;
},
R"DOC(This config that how many iteration the executor will run when
user call exe.run() in python。Default: 1.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_iteration_per_run = 10
)DOC")
.def_property(
"use_thread_barrier",
[](const ExecutionStrategy &self) { return self.thread_barrier_; },
[](ExecutionStrategy &self, bool use_thread_barrier) {
self.thread_barrier_ = use_thread_barrier;
},
R"DOC(This config that the this is distributed training with parameter server
)DOC")
.def_property("_dry_run",
[](const ExecutionStrategy &self) { return self.dry_run_; },
[](ExecutionStrategy &self, bool dry_run) {
self.dry_run_ = dry_run;
});
exec_strategy.def_property(
"use_experimental_executor",
[](const ExecutionStrategy &self) {
return self.type_ == ExecutionStrategy::kExperimental;
},
[](ExecutionStrategy &self, bool experimental) {
self.type_ = experimental ? ExecutionStrategy::kExperimental
: ExecutionStrategy::kDefault;
});
py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy", R"DOC(
BuildStrategy allows the user to more preciously control how to
build the SSA Graph in ParallelExecutor by setting the property.
Returns:
BuildStrategy: An BuildStrategy object.
Examples:
.. code-block:: python
import os
import paddle
import paddle.static as static
paddle.enable_static()
os.environ['CPU_NUM'] = str(2)
places = static.cpu_places()
data = static.data(name="x", shape=[None, 1], dtype="float32")
hidden = static.nn.fc(input=data, size=10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
build_strategy = static.BuildStrategy()
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce
program = static.CompiledProgram(static.default_main_program())
program = program.with_data_parallel(loss_name=loss.name,
build_strategy=build_strategy,
places=places)
)DOC");
py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
.value("Reduce", BuildStrategy::ReduceStrategy::kReduce)
.value("AllReduce", BuildStrategy::ReduceStrategy::kAllReduce);
py::enum_<BuildStrategy::GradientScaleStrategy>(build_strategy,
"GradientScaleStrategy")
.value("CoeffNumDevice",
BuildStrategy::GradientScaleStrategy::kCoeffNumDevice)
.value("One", BuildStrategy::GradientScaleStrategy::kOne)
.value("Customized", BuildStrategy::GradientScaleStrategy::kCustomized);
build_strategy.def(py::init())
.def_property(
"reduce_strategy",
[](const BuildStrategy &self) { return self.reduce_; },
[](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.reduce_ = strategy;
},
R"DOC((fluid.BuildStrategy.ReduceStrategy, optional): there are two reduce
strategies in ParallelExecutor, AllReduce and Reduce. If you want
that all the parameters' optimization are done on all devices independently,
you should choose AllReduce; otherwise, if you choose Reduce, all the parameters'
optimization will be evenly distributed to different devices, and then
broadcast the optimized parameter to other devices.
Default is 'AllReduce'.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce
)DOC")
.def_property(
"gradient_scale_strategy",
[](const BuildStrategy &self) { return self.gradient_scale_; },
[](BuildStrategy &self,
BuildStrategy::GradientScaleStrategy strategy) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.gradient_scale_ = strategy;
},
R"DOC((paddle.static.BuildStrategy.GradientScaleStrategy, optional): there are three
ways of defining :math:`loss@grad` in ParallelExecutor, that is, CoeffNumDevice,
One and Customized. By default, ParallelExecutor sets the :math:`loss@grad`
according to the number of devices. If you want to customize :math:`loss@grad`,
you can choose Customized. Default is 'CoeffNumDevice'.
Examples:
.. code-block:: python
import numpy
import os
import paddle
import paddle.static as static
paddle.enable_static()
use_cuda = True
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
exe = static.Executor(place)
# NOTE: If you use CPU to run the program, you need
# to specify the CPU_NUM, otherwise, paddle will use
# all the number of the logic core as the CPU_NUM,
# in that case, the batch size of the input should be
# greater than CPU_NUM, if not, the process will be
# failed by an exception.
if not use_cuda:
os.environ['CPU_NUM'] = str(2)
places = static.cpu_places()
else:
places = static.cuda_places()
data = static.data(name='X', shape=[None, 1], dtype='float32')
hidden = static.nn.fc(input=data, size=10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
exe.run(static.default_startup_program())
build_strategy = static.BuildStrategy()
build_strategy.gradient_scale_strategy = \
static.BuildStrategy.GradientScaleStrategy.Customized
compiled_prog = static.CompiledProgram(
static.default_main_program()).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy,
places=places)
dev_count = len(places)
x = numpy.random.random(size=(10, 1)).astype('float32')
loss_grad = numpy.ones((dev_count)).astype("float32") * 0.01
loss_grad_name = loss.name+"@GRAD"
loss_data = exe.run(compiled_prog,
feed={"X": x, loss_grad_name : loss_grad},
fetch_list=[loss.name, loss_grad_name])
)DOC")
.def_property(
"debug_graphviz_path",
[](const BuildStrategy &self) { return self.debug_graphviz_path_; },
[](BuildStrategy &self, const std::string &path) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.debug_graphviz_path_ = path;
},
R"DOC((str, optional): debug_graphviz_path indicates the path that
writing the SSA Graph to file in the form of graphviz.
It is useful for debugging. Default is empty string, that is, ""
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.debug_graphviz_path = "./graph"
)DOC")
.def_property(
"enable_sequential_execution",
[](const BuildStrategy &self) {
return self.enable_sequential_execution_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.enable_sequential_execution_ = b;
},
R"DOC((bool, optional): If set True, the execution order of ops would
be the same as what is in the program. Default is False.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.enable_sequential_execution = True
)DOC")
.def_property(
"remove_unnecessary_lock",
[](const BuildStrategy &self) {
return self.remove_unnecessary_lock_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.remove_unnecessary_lock_ = b;
},
R"DOC((bool, optional): If set True, some locks in GPU ops would be
released and ParallelExecutor would run faster. Default is True.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.remove_unnecessary_lock = True
)DOC")
.def_property(
"num_trainers",
[](const BuildStrategy &self) { return self.num_trainers_; },
[](BuildStrategy &self, int num_trainers) {
#ifdef WIN32
PADDLE_THROW(platform::errors::Unavailable(
"Distribution mode is not supported on Windows platform."));
#endif
self.num_trainers_ = num_trainers;
})
.def_property(
"trainers_endpoints",
[](const BuildStrategy &self) { return self.trainers_endpoints_; },
[](BuildStrategy &self,
const std::vector<std::string> &trainers_endpoints) {
self.trainers_endpoints_ = trainers_endpoints;
})
.def_property("trainer_id",
[](const BuildStrategy &self) { return self.trainer_id_; },
[](BuildStrategy &self, int trainer_id) {
self.trainer_id_ = trainer_id;
})
.def_property(
"nccl_comm_num",
[](const BuildStrategy &self) { return self.nccl_comm_num_; },
[](BuildStrategy &self, int nccl_comm_num) {
self.nccl_comm_num_ = nccl_comm_num;
})
.def_property("use_hierarchical_allreduce",
[](const BuildStrategy &self) {
return self.use_hierarchical_allreduce_;
},
[](BuildStrategy &self, bool use) {
self.use_hierarchical_allreduce_ = use;
})
.def_property("hierarchical_allreduce_inter_nranks",
[](const BuildStrategy &self) {
return self.hierarchical_allreduce_inter_nranks_;
},
[](BuildStrategy &self, int nranks) {
self.hierarchical_allreduce_inter_nranks_ = nranks;
})
.def_property(
"fuse_elewise_add_act_ops",
[](const BuildStrategy &self) {
return self.fuse_elewise_add_act_ops_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.fuse_elewise_add_act_ops_ = b;
},
R"DOC((bool, optional): fuse_elewise_add_act_ops indicate whether
to fuse elementwise_add_op and activation_op,
it may make the execution faster. Default is False.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_elewise_add_act_ops = True
)DOC")
.def_property(
"fuse_bn_act_ops",
[](const BuildStrategy &self) { return self.fuse_bn_act_ops_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.fuse_bn_act_ops_ = b;
},
R"DOC((bool, optional): fuse_bn_act_ops indicate whether
to fuse batch_norm and activation_op,
it may make the execution faster. Default is False.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_bn_act_ops = True
)DOC")
.def_property(
"fuse_bn_add_act_ops",
[](const BuildStrategy &self) { return self.fuse_bn_add_act_ops_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.fuse_bn_add_act_ops_ = b;
},
R"DOC((bool, optional): fuse_bn_add_act_ops indicate whether
to fuse batch_norm, elementwise_add and activation_op,
it may make the execution faster. Default is True
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_bn_add_act_ops = True
)DOC")
.def_property(
"enable_auto_fusion",
[](const BuildStrategy &self) { return self.enable_auto_fusion_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.enable_auto_fusion_ = b;
},
R"DOC((bool, optional): Whether to enable fusing subgraph to a
fusion_group. Now we only support fusing subgraph that composed
of elementwise-like operators, such as elementwise_add/mul
without broadcast and activations.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.enable_auto_fusion = True
)DOC")
.def_property(
"fuse_relu_depthwise_conv",
[](const BuildStrategy &self) {
return self.fuse_relu_depthwise_conv_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.fuse_relu_depthwise_conv_ = b;
},
R"DOC((bool, optional): fuse_relu_depthwise_conv indicate whether
to fuse relu and depthwise_conv2d,
it will save GPU memory and may make the execution faster.
This options is only available in GPU devices.
Default is False.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_relu_depthwise_conv = True
)DOC")
.def_property("fuse_broadcast_ops",
[](const BuildStrategy &self) {
return self.fuse_broadcast_ops_ == true ||
self.fuse_broadcast_ops_ == boost::none;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, "
"cannot be configured again."));
self.fuse_broadcast_ops_ = b;
},
R"DOC((bool, optional): fuse_broadcast_op indicates whether
to fuse the broadcast ops. Note that, in Reduce mode,
fusing broadcast ops may make the program faster. Because
fusing broadcast OP equals delaying the execution of all
broadcast Ops, in this case, all nccl streams are used only
for NCCLReduce operations for a period of time. Default False.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_broadcast_ops = True
)DOC")
.def_property("fuse_all_optimizer_ops",
[](const BuildStrategy &self) {
return self.fuse_all_optimizer_ops_ == true ||
self.fuse_all_optimizer_ops_ == boost::none;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, "
"cannot be configured again."));
self.fuse_all_optimizer_ops_ = b;
})
.def_property(
"sync_batch_norm",
[](const BuildStrategy &self) { return self.sync_batch_norm_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
self.sync_batch_norm_ = b;
},
R"DOC((bool, optional): sync_batch_norm indicates whether to use
synchronous batch normalization which synchronizes the mean
and variance through multi-devices in training phase.
Current implementation doesn't support FP16 training and CPU.
And only synchronous on one machine, not all machines.
Default is False.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.sync_batch_norm = True
)DOC")
.def_property(
"memory_optimize",
[](const BuildStrategy &self) -> py::object {
if (self.memory_optimize_) {
return py::cast(self.memory_optimize_.get());
} else {
return py::cast(nullptr);
}
},
[](BuildStrategy &self, const py::handle &value) {
auto *py_obj = value.ptr();
if (py_obj == nullptr || py_obj == Py_None) {
self.memory_optimize_ = boost::none;
} else if (PyBool_Check(py_obj)) {
self.memory_optimize_ = (py_obj == Py_True);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"BuildStrategy.memory_optimize must be set to None, False or "
"True"));
}
},
R"DOC((bool, optional): memory opitimize aims to save total memory
consumption, set to True to enable it.
Default None. None means framework would choose to use or not use
this strategy automatically. Currently, None means that it is
enabled when GC is disabled, and disabled when GC is enabled.
True means enabling and False means disabling. Default is None.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.memory_optimize = True
)DOC")
.def_property(
"is_distribution",
[](const BuildStrategy &self) { return self.is_distribution_; },
[](BuildStrategy &self, bool b) {
#ifdef WIN32
if (b) {
PADDLE_THROW(platform::errors::Unavailable(
"Distribution mode is not supported on Windows platform."));
}
#else
self.is_distribution_ = b;
#endif
})
.def_property("async_mode",
[](const BuildStrategy &self) { return self.async_mode_; },
[](BuildStrategy &self, bool b) { self.async_mode_ = b; })
.def_property(
"enable_inplace",
[](const BuildStrategy &self) { return self.enable_inplace_; },
[](BuildStrategy &self, bool b) { self.enable_inplace_ = b; })
.def_property(
"enable_addto",
[](const BuildStrategy &self) { return self.enable_addto_; },
[](BuildStrategy &self, bool b) { self.enable_addto_ = b; })
.def_property(
"fuse_all_reduce_ops",
[](const BuildStrategy &self) {
return self.fuse_all_reduce_ops_ == true ||
self.fuse_all_reduce_ops_ == boost::none;
},
[](BuildStrategy &self, bool b) { self.fuse_all_reduce_ops_ = b; })
.def_property("enable_backward_optimizer_op_deps",
[](const BuildStrategy &self) {
return self.enable_backward_optimizer_op_deps_;
},
[](BuildStrategy &self, bool b) {
self.enable_backward_optimizer_op_deps_ = b;
})
.def_property(
"cache_runtime_context",
[](const BuildStrategy &self) { return self.cache_runtime_context_; },
[](BuildStrategy &self, bool b) { self.cache_runtime_context_ = b; })
.def_property(
"mkldnn_enabled_op_types",
[](const BuildStrategy &self) {
return self.mkldnn_enabled_op_types_;
},
[](BuildStrategy &self,
const std::unordered_set<std::string> &mkldnn_enabled_op_types) {
self.mkldnn_enabled_op_types_ = mkldnn_enabled_op_types;
})
.def("_finalize_strategy_and_create_passes",
[](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
return self.CreatePassesFromStrategy(true);
},
R"DOC(Allow user to customized passes. Normally model-specific
optimization passes should be defined in this way. BuildStrategy
cannot be updated after being finalized.)DOC");
pe.def(py::init<const std::vector<platform::Place> &,
const std::vector<std::string> &, const std::string &,
Scope *, std::vector<Scope *> &, const ExecutionStrategy &,
const BuildStrategy &, ir::Graph *>())
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
// We still cannot get local_scope from this vector, since the element
// of vec<Scope*> will be freed by Python GC. We can only return Scope*
// one by one and mark them as reference.
.def("local_scopes",
[](ParallelExecutor &self) -> std::vector<Scope *> * {
return &self.GetLocalScopes();
},
py::return_value_policy::reference)
.def("drop_local_exe_scopes", &ParallelExecutor::DropLocalExeScopes)
.def("_need_create_local_exe_scopes",
&ParallelExecutor::NeedCreateLocalExeScope)
.def("feed_tensors_into_local_scopes",
&ParallelExecutor::FeedTensorsIntoLocalScopes)
.def("feed_and_split_tensor_into_local_scopes",
&ParallelExecutor::FeedAndSplitTensorIntoLocalScopes)
.def("run",
[](ParallelExecutor &self,
const std::vector<std::string> &fetch_tensors,
bool return_merged) -> py::object {
paddle::framework::FetchResultType ret;
{
pybind11::gil_scoped_release release;
ret = self.Run(fetch_tensors, return_merged);
}
if (return_merged) {
return py::cast(
std::move(BOOST_GET(paddle::framework::FetchList, ret)));
} else {
return py::cast(std::move(
BOOST_GET(paddle::framework::FetchUnmergedList, ret)));
}
})
.def("device_count", &ParallelExecutor::DeviceCount);
BindFleetWrapper(&m);
#ifdef PADDLE_WITH_PSLIB
BindHeterWrapper(&m);
#endif
BindGlooWrapper(&m);
BindBoxHelper(&m);
#ifdef PADDLE_WITH_BOX_PS
BindBoxWrapper(&m);
#endif
#ifdef PADDLE_WITH_NCCL
BindNCCLWrapper(&m);
#endif
#ifdef PADDLE_WITH_GLOO
BindGlooContext(&m);
#endif
BindGraph(&m);
BindNode(&m);
BindInferenceApi(&m);
BindCompatible(&m);
BindDataset(&m);
BindGenerator(&m);
#ifdef PADDLE_WITH_CRYPTO
BindCrypto(&m);
#endif
#ifdef PADDLE_WITH_DISTRIBUTE
BindCommunicator(&m);
BindCommunicatorContext(&m);
BindLargeScaleKV(&m);
#endif
}
} // namespace pybind
} // namespace paddle
Reference in new issue View Git Blame Copy Permalink