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[Eager] publish python c api for eager (#37550)
* refine a test case, test=develop * publish python c api for eager, test=develop * revert modify about test_allclose_layer.py, test=develop * refine, test=develop * refine, test=develop * refine, test=develop * refine, test=develop * refine, test=develop * refine, test=develop * delete numpy includes, use pybind11 numpy.h, test=develop * refine, test=develop * refine, test=develop * refine, test=develop * suport eager error msg, and add grad test case, test=develop * refine, test=develop * refine, test=develop
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/* Copyright (c) 2021 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. */ | ||
// disable numpy compile error | ||
#include <Python.h> | ||
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#include <string> | ||
#include <vector> | ||
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#include "paddle/fluid/eager/api/all.h" | ||
#include "paddle/fluid/eager/autograd_meta.h" | ||
#include "paddle/fluid/eager/utils.h" | ||
#include "paddle/fluid/memory/allocation/allocator.h" | ||
#include "paddle/fluid/memory/memcpy.h" | ||
#include "paddle/fluid/platform/enforce.h" | ||
#include "paddle/fluid/pybind/eager.h" | ||
#include "paddle/fluid/pybind/eager_utils.h" | ||
#include "paddle/pten/common/data_type.h" | ||
#include "paddle/pten/core/convert_utils.h" | ||
#include "paddle/pten/core/dense_tensor.h" | ||
#include "paddle/pten/include/core.h" | ||
#pragma GCC diagnostic ignored "-Wmissing-field-initializers" | ||
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namespace paddle { | ||
namespace pybind { | ||
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namespace py = ::pybind11; | ||
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PyTypeObject* p_eager_tensor_type; | ||
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PyObject* eagertensor_new(PyTypeObject* type, PyObject* args, | ||
PyObject* kwargs) { | ||
PyObject* obj = type->tp_alloc(type, 0); | ||
if (obj) { | ||
auto v = reinterpret_cast<EagerTensorObject*>(obj); | ||
new (&(v->eagertensor)) egr::EagerTensor(); | ||
} | ||
return obj; | ||
} | ||
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static void eagertensor_dealloc(EagerTensorObject* self) { | ||
self->eagertensor.~EagerTensor(); | ||
Py_TYPE(self)->tp_free(reinterpret_cast<PyObject*>(self)); | ||
} | ||
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extern struct PyGetSetDef variable_properties[]; | ||
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extern PyMethodDef variable_methods[]; | ||
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PyTypeObject eager_tensor_type = { | ||
PyVarObject_HEAD_INIT(NULL, 0) "core_avx.eager.EagerTensor", /* tp_name */ | ||
sizeof(EagerTensorObject), /* tp_basicsize */ | ||
0, /* tp_itemsize */ | ||
(destructor)eagertensor_dealloc, /* tp_dealloc */ | ||
0, /* tp_vectorcall_offset */ | ||
0, /* tp_getattr */ | ||
0, /* tp_setattr */ | ||
0, /* tp_reserved */ | ||
0, /* tp_repr */ | ||
0, /* tp_as_number */ | ||
0, /* tp_as_sequence */ | ||
0, /* tp_as_mapping */ | ||
0, /* tp_hash */ | ||
0, /* tp_call */ | ||
0, /* tp_str */ | ||
0, /* tp_getattro */ | ||
0, /* tp_setattro */ | ||
0, /* tp_as_buffer */ | ||
Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | | ||
Py_TPFLAGS_HEAPTYPE, /* tp_flags */ | ||
0, /* tp_doc */ | ||
0, /* tp_traverse */ | ||
0, /* tp_clear */ | ||
0, /* tp_richcompare */ | ||
0, /* tp_weaklistoffset */ | ||
0, /* tp_iter */ | ||
0, /* tp_iternext */ | ||
variable_methods, /* tp_methods */ | ||
0, /* tp_members */ | ||
variable_properties, /* tp_getset */ | ||
0, /* tp_base */ | ||
0, /* tp_dict */ | ||
0, /* tp_descr_get */ | ||
0, /* tp_descr_set */ | ||
0, /* tp_dictoffset */ | ||
0, /* tp_init */ | ||
0, /* tp_alloc */ | ||
eagertensor_new, /* tp_new */ | ||
0, /* tp_free */ | ||
0, /* tp_is_gc */ | ||
0, /* tp_bases */ | ||
0, /* tp_mro */ | ||
0, /* tp_cache */ | ||
0, /* tp_subclasses */ | ||
0, /* tp_weaklist */ | ||
0, /* tp_del */ | ||
0, /* tp_version_tag */ | ||
0 /* tp_finalize */ | ||
}; | ||
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void BindEager(pybind11::module* module) { | ||
auto m = module->def_submodule("eager"); | ||
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p_eager_tensor_type = &eager_tensor_type; | ||
if (PyType_Ready(&eager_tensor_type) < 0) { | ||
PADDLE_THROW(platform::errors::Fatal( | ||
"Init Paddle erroe in BindEager(PyType_Ready).")); | ||
return; | ||
} | ||
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Py_INCREF(&eager_tensor_type); | ||
if (PyModule_AddObject(m.ptr(), "EagerTensor", | ||
reinterpret_cast<PyObject*>(&eager_tensor_type)) < 0) { | ||
Py_DECREF(&eager_tensor_type); | ||
Py_DECREF(m.ptr()); | ||
PADDLE_THROW(platform::errors::Fatal( | ||
"Init Paddle erroe in BindEager(PyModule_AddObject).")); | ||
return; | ||
} | ||
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BindFunctions(m.ptr()); | ||
} | ||
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} // namespace pybind | ||
} // namespace paddle |
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/* Copyright (c) 2021 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. */ | ||
#pragma once | ||
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#include <Python.h> | ||
#include "pybind11/pybind11.h" | ||
#include "pybind11/stl.h" | ||
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namespace paddle { | ||
namespace pybind { | ||
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void BindEager(pybind11::module* m); | ||
void BindFunctions(PyObject* module); | ||
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} // namespace pybind | ||
} // namespace paddle |
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/* Copyright (c) 2021 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. */ | ||
// disable numpy compile error | ||
#include <Python.h> | ||
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#include <string> | ||
#include <vector> | ||
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#include "pybind11/numpy.h" | ||
#include "pybind11/pybind11.h" | ||
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#include "paddle/fluid/eager/accumulation/accumulation_node.h" | ||
#include "paddle/fluid/eager/api/all.h" | ||
#include "paddle/fluid/eager/autograd_meta.h" | ||
#include "paddle/fluid/eager/backward.h" | ||
#include "paddle/fluid/eager/utils.h" | ||
#include "paddle/fluid/memory/allocation/allocator.h" | ||
#include "paddle/fluid/memory/memcpy.h" | ||
#include "paddle/fluid/platform/enforce.h" | ||
#include "paddle/fluid/pybind/eager.h" | ||
#include "paddle/fluid/pybind/eager_utils.h" | ||
#include "paddle/fluid/pybind/exception.h" | ||
#include "paddle/pten/api/lib/utils/allocator.h" | ||
#include "paddle/pten/api/lib/utils/storage.h" | ||
#include "paddle/pten/api/lib/utils/tensor_utils.h" | ||
#include "paddle/pten/common/data_type.h" | ||
#include "paddle/pten/core/convert_utils.h" | ||
#include "paddle/pten/core/dense_tensor.h" | ||
#include "paddle/pten/include/core.h" | ||
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namespace paddle { | ||
namespace pybind { | ||
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namespace py = ::pybind11; | ||
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extern PyTypeObject* p_eager_tensor_type; | ||
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size_t PyArray_Size_(PyObject* numpy_data) { | ||
size_t res = 1; | ||
auto dims = pybind11::detail::array_proxy(numpy_data)->dimensions; | ||
auto nd = pybind11::detail::array_proxy(numpy_data)->nd; | ||
while (nd--) { | ||
res *= (*dims++); | ||
} | ||
return res; | ||
} | ||
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class EagerNumpyAllocation : public paddle::memory::allocation::Allocation { | ||
public: | ||
explicit EagerNumpyAllocation(PyObject* numpy_data, pten::DataType dtype) | ||
: Allocation( | ||
static_cast<void*>(pybind11::detail::array_proxy(numpy_data)->data), | ||
pten::DataTypeSize(dtype) * PyArray_Size_(numpy_data), | ||
paddle::platform::CPUPlace()), | ||
arr_(numpy_data) { | ||
PADDLE_ENFORCE_NOT_NULL(arr_, platform::errors::InvalidArgument( | ||
"The underlying PyObject pointer of " | ||
"numpy array cannot be nullptr")); | ||
PADDLE_ENFORCE_NE( | ||
arr_, Py_None, | ||
platform::errors::PreconditionNotMet( | ||
"The underlying PyObject pointer of numpy array cannot be None")); | ||
Py_INCREF(arr_); | ||
} | ||
~EagerNumpyAllocation() override { | ||
py::gil_scoped_acquire gil; | ||
Py_DECREF(arr_); | ||
} | ||
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private: | ||
PyObject* arr_; | ||
}; | ||
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static PyObject* eager_api_set_expected_place(PyObject* self, PyObject* args, | ||
PyObject* kwargs) { | ||
EAGER_TRY | ||
auto place = CastPyArg2Place(PyTuple_GET_ITEM(args, 0), 0); | ||
egr::Controller::Instance().SetExpectedPlace(place); | ||
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Py_INCREF(Py_None); | ||
return Py_None; | ||
EAGER_CATCH_AND_THROW_RETURN_NULL | ||
} | ||
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static PyObject* eager_api_scale(PyObject* self, PyObject* args, | ||
PyObject* kwargs) { | ||
EAGER_TRY | ||
// TODO(jiabin): Sync Tensor and Variable here when we support | ||
egr::EagerTensor ret = | ||
egr::scale(reinterpret_cast<EagerTensorObject*>(PyTuple_GET_ITEM(args, 0)) | ||
->eagertensor, | ||
CastPyArg2AttrFloat(PyTuple_GET_ITEM(args, 1), 1), | ||
CastPyArg2AttrFloat(PyTuple_GET_ITEM(args, 2), 2), | ||
CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3), | ||
CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 4), 4)); | ||
return ToPyObject(ret); | ||
EAGER_CATCH_AND_THROW_RETURN_NULL | ||
} | ||
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static PyObject* eager_api_numpy_to_tensor(PyObject* numpy_data, | ||
pten::DataType dtype, | ||
const paddle::platform::Place& place, | ||
bool stop_gradient) { | ||
std::vector<int64_t> vec_dims; | ||
auto numpy_shape = pybind11::detail::array_proxy(numpy_data)->dimensions; | ||
int rank = pybind11::detail::array_proxy(numpy_data)->nd; | ||
for (int i = 0; i < rank; i++) { | ||
vec_dims.push_back(static_cast<int64_t>(numpy_shape[i])); | ||
} | ||
paddle::framework::DDim dims = paddle::framework::make_ddim(vec_dims); | ||
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// TODO(jiabin): Support GPU later | ||
auto meta = pten::DenseTensorMeta(dtype, dims); | ||
auto holder = std::make_shared<EagerNumpyAllocation>(numpy_data, dtype); | ||
auto shared_storage = | ||
pten::make_intrusive<paddle::experimental::SharedStorage>(holder, 0); | ||
std::shared_ptr<pten::DenseTensor> densetensor( | ||
new pten::DenseTensor(std::move(shared_storage), std::move(meta))); | ||
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PyObject* obj = p_eager_tensor_type->tp_alloc(p_eager_tensor_type, 0); | ||
if (obj) { | ||
auto v = reinterpret_cast<EagerTensorObject*>(obj); | ||
new (&(v->eagertensor)) egr::EagerTensor(); | ||
v->eagertensor.set_impl(densetensor); | ||
v->eagertensor.set_name(egr::Controller::Instance().GenerateUniqueName()); | ||
auto meta = egr::EagerUtils::autograd_meta(&(v->eagertensor)); | ||
meta->SetStopGradient(stop_gradient); | ||
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// Created tensor will be leaf tensor | ||
// So we append AccumulationNode to it. | ||
auto accumulation_node = std::make_shared<egr::GradNodeAccumulation>(); | ||
meta->SetGradNode(accumulation_node); | ||
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// TODO(jiabin): Shall we increase ref cnt here to make python ref cnt num | ||
// correctly? | ||
} else { | ||
PADDLE_THROW(platform::errors::Fatal( | ||
"tp_alloc return null, can not new a PyObject.")); | ||
} | ||
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return obj; | ||
} | ||
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static PyObject* eager_api_to_tensor(PyObject* self, PyObject* args, | ||
PyObject* kwargs) { | ||
EAGER_TRY | ||
// TODO(jiabin): Support Kwargs here | ||
PyObject* data = PyTuple_GET_ITEM(args, 0); | ||
auto str_dtype = CastPyArg2AttrString(PyTuple_GET_ITEM(args, 1), 1); | ||
pten::DataType dtype = pten::String2DataType(str_dtype); | ||
auto place = CastPyArg2Place(PyTuple_GET_ITEM(args, 2), 2); | ||
bool stop_gradient = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3); | ||
// TODO(jiabin): Support this when python given name | ||
// auto str_name = CastPyArg2AttrString(PyTuple_GET_ITEM(args, 4), 4); | ||
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if (pybind11::detail::npy_api::get().PyArray_Check_(data)) { | ||
return eager_api_numpy_to_tensor(data, dtype, place, stop_gradient); | ||
} else { | ||
PADDLE_THROW(platform::errors::InvalidArgument( | ||
"Eater to_tensor only support numpy to tensor.")); | ||
Py_INCREF(Py_None); | ||
return Py_None; | ||
} | ||
EAGER_CATCH_AND_THROW_RETURN_NULL | ||
} | ||
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static PyObject* eager_api_retain_grad_for_tensor(PyObject* self, | ||
PyObject* args, | ||
PyObject* kwargs) { | ||
EAGER_TRY | ||
egr::egr_utils_api::RetainGradForTensor( | ||
CastPyArg2EagerTensor(PyTuple_GET_ITEM(args, 0), 0)); | ||
Py_INCREF(Py_None); | ||
return Py_None; | ||
EAGER_CATCH_AND_THROW_RETURN_NULL | ||
} | ||
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static PyObject* eager_api_run_backward(PyObject* self, PyObject* args, | ||
PyObject* kwargs) { | ||
EAGER_TRY | ||
auto tensors = CastPyArg2VectorOfEagerTensor(PyTuple_GET_ITEM(args, 0), 0); | ||
auto grad_tensors = | ||
CastPyArg2VectorOfEagerTensor(PyTuple_GET_ITEM(args, 1), 1); | ||
RunBackward(tensors, grad_tensors, | ||
CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 2), 2)); | ||
Py_INCREF(Py_None); | ||
return Py_None; | ||
EAGER_CATCH_AND_THROW_RETURN_NULL | ||
} | ||
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PyMethodDef variable_functions[] = { | ||
{"to_tensor", (PyCFunction)(void (*)(void))eager_api_to_tensor, | ||
METH_VARARGS | METH_KEYWORDS, NULL}, | ||
{"scale", (PyCFunction)(void (*)(void))eager_api_scale, | ||
METH_VARARGS | METH_KEYWORDS, NULL}, | ||
{"_set_expected_place", | ||
(PyCFunction)(void (*)(void))eager_api_set_expected_place, | ||
METH_VARARGS | METH_KEYWORDS, NULL}, | ||
{"retain_grad_for_tensor", | ||
(PyCFunction)(void (*)(void))eager_api_retain_grad_for_tensor, | ||
METH_VARARGS | METH_KEYWORDS, NULL}, | ||
{"run_backward", (PyCFunction)(void (*)(void))eager_api_run_backward, | ||
METH_VARARGS | METH_KEYWORDS, NULL}, | ||
{NULL, NULL, 0, NULL}}; | ||
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void BindFunctions(PyObject* module) { | ||
if (PyModule_AddFunctions(module, variable_functions) < 0) { | ||
PADDLE_THROW(platform::errors::Fatal( | ||
"Init Paddle erroe in BindFunctions(PyModule_AddFunctions).")); | ||
return; | ||
} | ||
} | ||
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} // namespace pybind | ||
} // namespace paddle |
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