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pybind.cc
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pybind.cc
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/* 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/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/garbage_collector.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_compatible_info.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_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/trainer.h"
#include "paddle/fluid/framework/version.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/memory/allocation/allocator_strategy.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/py_func_op.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/cpu_info.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/place.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/pybind/box_helper_py.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/global_value_getter_setter.h"
#include "paddle/fluid/pybind/imperative.h"
#include "paddle/fluid/pybind/inference_api.h"
#include "paddle/fluid/pybind/ir.h"
#ifndef _WIN32
#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
#ifndef _WIN32
#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_DISTRIBUTE
#include "paddle/fluid/pybind/communicator_py.h"
#endif
#include "pybind11/stl.h"
DEFINE_bool(reader_queue_speed_test_mode, false,
"If set true, the queue.pop will only get data from queue but not "
"remove the data from queue for speed testing");
DECLARE_bool(use_mkldnn);
#ifdef PADDLE_WITH_NGRAPH
DECLARE_bool(use_ngraph);
#endif
// disable auto conversion to list in Python
PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);
namespace paddle {
namespace pybind {
bool IsCompiledWithCUDA() {
#ifndef PADDLE_WITH_CUDA
return false;
#else
return true;
#endif
}
bool IsCompiledWithMKLDNN() {
#ifndef PADDLE_WITH_MKLDNN
return false;
#else
return true;
#endif
}
bool IsCompiledWithNGRAPH() {
#ifndef PADDLE_WITH_NGRAPH
return false;
#else
return true;
#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("Python object is not type of %s", typeid(T).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 ¶ : state_dict) {
PyObject *py_obj = para.second.ptr();
if (!py_obj || py_obj == Py_None) {
PADDLE_THROW("Save parameter [%s] is None", para.first);
}
const char *kIVarField = "_ivar";
PyObject *py_ivar = GetPythonAttribute(py_obj, kIVarField);
PADDLE_ENFORCE_NOT_NULL(py_ivar, "Can not find ivar in Variable");
vec_res.emplace_back(
PyObjectCast<std::shared_ptr<imperative::VarBase>>(py_ivar));
Py_DECREF(py_ivar);
}
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("Save parameter list 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);
vec_res.emplace_back(PyObjectCast<std::string>(py_name));
Py_DECREF(py_name);
}
} else {
PADDLE_THROW("Set parameter should be 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("Save parameter list 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);
auto para_name = PyObjectCast<std::string>(py_name);
Py_DECREF(py_name);
auto var = scope.FindVar(para_name);
if (var == nullptr) {
PADDLE_ENFORCE_NE(exe, nullptr,
"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);
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("Set parameter should be a list");
}
return;
}
#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();
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);
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_compatible_info", [](framework::ProgramDesc &desc) {
framework::OpCompatibleMap op_compatible_map;
op_compatible_map.InitOpCompatibleMap();
return op_compatible_map.ConvertToProto(desc.OpCompatibleMap());
});
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(); });
// 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);
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::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::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::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::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|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("_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,
"the provided recursive_sequence_lengths info is invalid");
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,
"the provided lod info is invalid");
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,
"the provided recursive_sequence_lengths info is invalid");
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;
});
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_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)
#if (defined(PADDLE_WITH_CUDA) && !defined(_WIN32))
.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);
return self.GetMutable<framework::ReaderHolder>();
},
py::return_value_policy::reference);
BindReader(&m);
using LoDTensorBlockingQueue =
::paddle::operators::reader::LoDTensorBlockingQueue;
using LoDTensorBlockingQueueHolder =
::paddle::operators::reader::LoDTensorBlockingQueueHolder;
py::class_<LoDTensorBlockingQueue, std::shared_ptr<LoDTensorBlockingQueue>>(
m, "LoDTensorBlockingQueue", "")
.def("push",
[](LoDTensorBlockingQueue &self,
const std::vector<framework::LoDTensor> &lod_tensor_vec) {
pybind11::gil_scoped_release release;
return self.Push(lod_tensor_vec);
})
.def("size", &LoDTensorBlockingQueue::Size)
.def("capacity", &LoDTensorBlockingQueue::Cap)
.def("close", &LoDTensorBlockingQueue::Close)
.def("kill", &LoDTensorBlockingQueue::Kill)
.def("is_closed", &LoDTensorBlockingQueue::IsClosed);
m.def("init_lod_tensor_blocking_queue",
[](Variable &var,
size_t capacity) -> std::shared_ptr<LoDTensorBlockingQueue> {
VLOG(1) << "init_lod_tensor_blocking_queue";
auto *holder = var.GetMutable<LoDTensorBlockingQueueHolder>();
holder->InitOnce(capacity, FLAGS_reader_queue_speed_test_mode);
return holder->GetQueue();
},
py::return_value_policy::copy);
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.