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eager_method.cc
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/
eager_method.cc
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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
#if defined(_MSC_VER)
#include <BaseTsd.h>
typedef SSIZE_T ssize_t;
#endif
#include <Python.h>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/eager/accumulation/accumulation_node.h"
#include "paddle/fluid/eager/api/all.h"
#include "paddle/fluid/eager/api/generated/fluid_generated/dygraph_forward_api.h"
#include "paddle/fluid/eager/autograd_meta.h"
#include "paddle/fluid/eager/grad_node_info.h"
#include "paddle/fluid/eager/hooks.h"
#include "paddle/fluid/eager/utils.h"
#include "paddle/fluid/framework/convert_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/fluid/pybind/slice_utils.h"
#include "paddle/fluid/pybind/uva_utils.h"
#include "paddle/phi/api/include/api.h"
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/core/compat/convert_utils.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "pybind11/detail/internals.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#pragma GCC diagnostic ignored "-Wmissing-field-initializers"
#include "paddle/fluid/eager/api/generated/eager_generated/forwards/dygraph_functions.h"
#include "paddle/fluid/framework/python_headers.h"
#include "paddle/fluid/memory/allocation/mmap_allocator.h"
#include "paddle/fluid/pybind/tensor_py.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace paddle {
namespace pybind {
extern void InitTensorWithNumpyValue(TensorObject* self,
const pybind11::object& array,
const paddle::platform::Place& place,
bool zero_copy);
extern PyTypeObject* p_tensor_type;
Py_ssize_t GetSliceIndexFromPyObject(PyObject* obj) {
if (PyObject_IsInstance(obj, reinterpret_cast<PyObject*>(p_tensor_type))) {
VLOG(6) << "Call GetSliceIndexFromTensor in Eager";
paddle::experimental::Tensor tensor = CastPyArg2Tensor(obj, 0);
PADDLE_ENFORCE_EQ(
tensor.initialized(),
true,
paddle::platform::errors::InvalidArgument(
"We can only support initialized tensor in slice, however we got "
"uninitialized tensor %s, please check your code.",
tensor.name()));
return GetSliceIndexFromTensor((*static_cast<phi::DenseTensor*>(
CastPyArg2Tensor(obj, 0).impl().get())));
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"We should only get paddle::experimental::Tensor or VarBase in this "
"method, when you reach this means we got another type index."));
}
}
bool PyCheckTensor(PyObject* obj) {
return PyObject_IsInstance(obj, reinterpret_cast<PyObject*>(p_tensor_type));
}
static PyObject* tensor_method_numpy(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
auto& api = pybind11::detail::npy_api::get();
if (!self->tensor.impl()) {
Py_intptr_t py_dims[paddle::framework::DDim::kMaxRank];
Py_intptr_t py_strides[paddle::framework::DDim::kMaxRank];
py_dims[0] = 0;
py_strides[0] = 0;
PyObject* array = api.PyArray_NewFromDescr_(
api.PyArray_Type_,
api.PyArray_DescrFromType_(pybind11::detail::npy_api::NPY_FLOAT_),
1,
py_dims,
py_strides,
nullptr,
pybind11::detail::npy_api::NPY_ARRAY_ALIGNED_ |
pybind11::detail::npy_api::NPY_ARRAY_WRITEABLE_,
nullptr);
return array;
}
auto tensor_dims = self->tensor.shape();
auto numpy_dtype = TensorDtype2NumpyDtype(self->tensor.type());
auto sizeof_dtype = paddle::framework::DataTypeSize(self->tensor.type());
Py_intptr_t py_dims[paddle::framework::DDim::kMaxRank];
Py_intptr_t py_strides[paddle::framework::DDim::kMaxRank];
size_t numel = 1;
for (int i = tensor_dims.size() - 1; i >= 0; --i) {
py_dims[i] = static_cast<size_t>(tensor_dims[i]);
py_strides[i] = sizeof_dtype * numel;
numel *= py_dims[i];
}
PyObject* array = api.PyArray_NewFromDescr_(
api.PyArray_Type_,
api.PyArray_DescrFromType_(numpy_dtype),
tensor_dims.size(),
py_dims,
py_strides,
nullptr,
pybind11::detail::npy_api::NPY_ARRAY_ALIGNED_ |
pybind11::detail::npy_api::NPY_ARRAY_WRITEABLE_,
nullptr);
if (!self->tensor.impl()->initialized()) {
if (tensor_dims.size() == 0) {
py_dims[0] = 0;
py_strides[0] = 0;
PyObject* array = api.PyArray_NewFromDescr_(
api.PyArray_Type_,
api.PyArray_DescrFromType_(numpy_dtype),
1,
py_dims,
py_strides,
nullptr,
pybind11::detail::npy_api::NPY_ARRAY_ALIGNED_ |
pybind11::detail::npy_api::NPY_ARRAY_WRITEABLE_,
nullptr);
return array;
}
return array;
}
if (self->tensor.is_cpu() || self->tensor.is_gpu_pinned()) {
platform::CPUPlace place;
if (self->tensor.is_selected_rows()) {
VLOG(6) << "Getting SelectedRows's numpy value";
auto* selected_rows =
static_cast<phi::SelectedRows*>(self->tensor.impl().get());
auto* dense_tensor = static_cast<paddle::framework::LoDTensor*>(
selected_rows->mutable_value());
// deep copy
paddle::memory::Copy(
place,
reinterpret_cast<void*>(pybind11::detail::array_proxy(array)->data),
place,
dense_tensor->data(),
sizeof_dtype * numel);
} else {
VLOG(6) << "Getting DenseTensor's numpy value";
auto dense_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(self->tensor.impl());
// deep copy
paddle::memory::Copy(
place,
reinterpret_cast<void*>(pybind11::detail::array_proxy(array)->data),
place,
dense_tensor->data(),
sizeof_dtype * numel);
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
} else if (self->tensor.is_gpu()) {
#if defined(PADDLE_WITH_CUDA)
gpuMemcpyKind kind = cudaMemcpyDeviceToHost;
#elif defined(PADDLE_WITH_HIP)
gpuMemcpyKind kind = hipMemcpyDeviceToHost;
#endif
if (self->tensor.is_selected_rows()) {
VLOG(6) << "Getting SelectedRows's numpy value";
auto* selected_rows =
static_cast<phi::SelectedRows*>(self->tensor.impl().get());
auto* dense_tensor = static_cast<paddle::framework::LoDTensor*>(
selected_rows->mutable_value());
paddle::platform::GpuMemcpySync(
pybind11::detail::array_proxy(array)->data,
dense_tensor->data(),
paddle::framework::DataTypeSize(dense_tensor->dtype()) *
dense_tensor->numel(),
kind);
} else {
VLOG(6) << "Getting DenseTensor's numpy value";
auto dense_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(self->tensor.impl());
paddle::platform::GpuMemcpySync(
pybind11::detail::array_proxy(array)->data,
dense_tensor->data(),
paddle::framework::DataTypeSize(dense_tensor->dtype()) *
dense_tensor->numel(),
kind);
}
#endif
#ifdef PADDLE_WITH_CUSTOM_DEVICE
} else if (self->tensor.is_custom_device()) {
if (self->tensor.is_selected_rows()) {
VLOG(6) << "Getting SelectedRows's numpy value";
auto* selected_rows =
static_cast<phi::SelectedRows*>(self->tensor.impl().get());
auto* dense_tensor = static_cast<paddle::framework::LoDTensor*>(
selected_rows->mutable_value());
phi::DeviceManager::GetDeviceWithPlace(self->tensor.place())
->MemoryCopyD2H(
pybind11::detail::array_proxy(array)->data,
dense_tensor->data(),
paddle::framework::DataTypeSize(dense_tensor->dtype()) *
dense_tensor->numel());
} else {
VLOG(6) << "Getting DenseTensor's numpy value";
auto dense_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(self->tensor.impl());
phi::DeviceManager::GetDeviceWithPlace(self->tensor.place())
->MemoryCopyD2H(
pybind11::detail::array_proxy(array)->data,
dense_tensor->data(),
paddle::framework::DataTypeSize(dense_tensor->dtype()) *
dense_tensor->numel());
}
#endif
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Tensor.numpy() only support cpu tensor."));
RETURN_PY_NONE
}
return array;
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_numpy_for_string_tensor(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
auto& api = pybind11::detail::npy_api::get();
if (!self->tensor.impl() || !self->tensor.impl()->initialized()) {
VLOG(6) << "The StringTensor is uninitialized. Return the empty string "
"numpy array.";
Py_intptr_t py_dims[paddle::framework::DDim::kMaxRank];
Py_intptr_t py_strides[paddle::framework::DDim::kMaxRank];
py_dims[0] = 0;
py_strides[0] = 0;
PyObject* array = api.PyArray_NewFromDescr_(
api.PyArray_Type_,
api.PyArray_DescrFromType_(pybind11::detail::npy_api::NPY_UNICODE_),
1,
py_dims,
py_strides,
nullptr,
pybind11::detail::npy_api::NPY_ARRAY_ALIGNED_ |
pybind11::detail::npy_api::NPY_ARRAY_WRITEABLE_,
nullptr);
return array;
}
if (self->tensor.is_cpu()) {
VLOG(6) << "Getting StringTensor's numpy value";
auto string_tensor =
std::dynamic_pointer_cast<phi::StringTensor>(self->tensor.impl());
const auto* st_ptr = string_tensor->data();
auto numel = self->tensor.numel();
auto tensor_dims = self->tensor.shape();
// Get the max unicode length of StringTensor to create numpy unicode string
// array.
auto* longest_pstring = std::max_element(
st_ptr, st_ptr + numel, [](const auto& a, const auto& b) {
auto a_unicode_len =
phi::strings::GetUnicodeStrLen(a.data(), a.size());
auto b_unicode_len =
phi::strings::GetUnicodeStrLen(b.data(), b.size());
return a_unicode_len < b_unicode_len;
});
size_t max_unicode_length = phi::strings::GetUnicodeStrLen(
longest_pstring->data(), longest_pstring->size());
max_unicode_length = (max_unicode_length == 0) ? 1 : max_unicode_length;
VLOG(6) << "The max unicode length is " << max_unicode_length;
auto sp = std::make_unique<uint32_t[]>(max_unicode_length * numel);
auto py_array_data = sp.get();
memset(py_array_data, 0, max_unicode_length * numel * sizeof(uint32_t));
for (int64_t i = 0; i < numel; ++i) {
auto curr_unicode_len =
phi::strings::GetUnicodeStrLen(st_ptr[i].data(), st_ptr[i].size());
phi::strings::GetUnicodeStr(st_ptr[i].data(),
py_array_data + i * max_unicode_length,
curr_unicode_len);
}
py::array array(py::dtype("U" + std::to_string(max_unicode_length)),
tensor_dims,
{},
py_array_data);
return array.release().ptr();
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"StringTensor.numpy() only support cpu tensor."));
RETURN_PY_NONE
}
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method__is_initialized(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
return ToPyObject(self->tensor.initialized());
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method__is_dense_tensor_hold_allocation(
TensorObject* self, PyObject* args, PyObject* kwargs) {
EAGER_TRY
auto dense_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(self->tensor.impl());
if (dense_tensor) {
return ToPyObject(dense_tensor->IsInitialized());
} else {
return ToPyObject(false);
}
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static void IncreaseTensorReferenceCountUntilCopyComplete(
const paddle::experimental::Tensor& tensor, const platform::Place& place) {
auto place_ = platform::is_gpu_place(place) ? place : tensor.place();
auto tracer = egr::Controller::Instance().GetCurrentTracer();
auto gc = tracer->MutableGarbageCollectorIfNotExists(place_);
// Note(dev): This is an empty callback, the only way is to "reference"
// inner memory Holder, so it will not be destructed until the kernels
// launched at current stream of given place is finished, such as
// CUDAPinned Mem -> CUDA by cudamemcpyAsync.
auto callback = [tensor, place_]() {
VLOG(3) << "Run callback of Tensor:" << tensor.name() << " at place "
<< place_;
};
gc->DirectClearCallback(callback);
}
static PyObject* tensor_method__copy_to(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
auto place = CastPyArg2Place(PyTuple_GET_ITEM(args, 0), 0);
bool blocking = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 1), 1);
auto cp_tensor = self->tensor.copy_to(place, blocking);
if (!blocking) {
IncreaseTensorReferenceCountUntilCopyComplete(self->tensor, place);
}
egr::EagerUtils::autograd_meta(&cp_tensor)->SetStopGradient(true);
egr::EagerUtils::autograd_meta(&cp_tensor)
->SetPersistable(
egr::EagerUtils::autograd_meta(&(self->tensor))->Persistable());
return ToPyObject(cp_tensor);
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_cpu(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
auto cp_tensor = self->tensor.copy_to(phi::CPUPlace(), true);
egr::EagerUtils::autograd_meta(&cp_tensor)->SetStopGradient(true);
egr::EagerUtils::autograd_meta(&cp_tensor)
->SetPersistable(
egr::EagerUtils::autograd_meta(&(self->tensor))->Persistable());
return ToPyObject(cp_tensor);
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_reconstruct_from_(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
paddle::experimental::Tensor src_tensor =
CastPyArg2Tensor(PyTuple_GET_ITEM(args, 0), 0);
std::string orig_name = self->tensor.name();
VLOG(6) << "Start Reconstructing Tensor from" << src_tensor.name() << " to "
<< orig_name;
self->tensor = src_tensor;
// Recover source name
self->tensor.set_name(orig_name);
VLOG(6) << "Finished Reconstructing Tensor from" << src_tensor.name()
<< " to " << self->tensor.name();
RETURN_PY_NONE
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_copy_(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
paddle::experimental::Tensor src_tensor =
CastPyArg2Tensor(PyTuple_GET_ITEM(args, 0), 0);
bool blocking = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 1), 1);
VLOG(6) << "Start Copy Tensor " << src_tensor.name() << " to "
<< self->tensor.name();
if (!self->tensor.initialized()) {
egr::EagerUtils::autograd_meta(&(self->tensor))
->SetStopGradient(
egr::EagerUtils::autograd_meta(&(src_tensor))->StopGradient());
egr::EagerUtils::autograd_meta(&(self->tensor))
->SetPersistable(
egr::EagerUtils::autograd_meta(&(src_tensor))->Persistable());
if (src_tensor.initialized()) {
self->tensor.copy_(src_tensor, src_tensor.place(), blocking);
}
} else {
if (src_tensor.initialized()) {
self->tensor.copy_(src_tensor, self->tensor.place(), blocking);
}
}
VLOG(6) << "Finish Copy Tensor " << src_tensor.name() << " to "
<< self->tensor.name();
RETURN_PY_NONE
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_retain_grads(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
if (egr::Controller::Instance().HasGrad()) {
auto meta = egr::EagerUtils::autograd_meta(&(self->tensor));
if (!meta->GetMutableGradNode()) {
VLOG(6) << "Make grad node of tensor: " << self->tensor.name()
<< "become accumulation node";
meta->SetGradNode(std::make_shared<egr::GradNodeAccumulation>(meta));
}
egr::egr_utils_api::RetainGradForTensor(self->tensor);
}
RETURN_PY_NONE
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_clear_gradient(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
VLOG(4) << "ClearGradient " << self->tensor.name();
Py_ssize_t args_num = PyTuple_Size(args);
bool set_to_zero = true;
if (args_num == (Py_ssize_t)1) {
set_to_zero = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 0), 0);
}
paddle::experimental::Tensor* grad;
bool is_leaf = egr::egr_utils_api::IsLeafTensor(self->tensor);
if (is_leaf) {
grad = egr::EagerUtils::mutable_grad(self->tensor);
PADDLE_ENFORCE(grad != nullptr,
paddle::platform::errors::Fatal(
"Detected NULL grad"
"Please check if you have manually cleared"
"the grad inside autograd_meta"));
} else {
auto meta = egr::EagerUtils::unsafe_autograd_meta(self->tensor);
grad = meta->MutableGrad();
}
if (grad->impl()) {
if (grad->is_selected_rows()) {
auto selected_rows =
std::dynamic_pointer_cast<phi::SelectedRows>(grad->impl());
if (selected_rows->mutable_value()->IsInitialized()) {
selected_rows->mutable_rows()->clear();
selected_rows->mutable_value()->clear();
}
} else if (grad->is_dense_tensor()) {
if (grad->initialized()) {
if (set_to_zero) {
auto* grad_t = static_cast<phi::DenseTensor*>(grad->impl().get());
auto* dev_ctx =
platform::DeviceContextPool::Instance().Get(grad_t->place());
phi::funcs::set_constant(*dev_ctx, grad_t, 0.0);
if (is_leaf) {
std::static_pointer_cast<egr::GradNodeAccumulation>(
egr::EagerUtils::grad_node(self->tensor))
->SetFakeEmpty(true);
}
} else {
VLOG(4) << "Gradient of " << self->tensor.name()
<< " is initialized, will be released.";
auto dense_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(grad->impl());
dense_tensor->MoveMemoryHolder();
}
}
}
}
RETURN_PY_NONE
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor__zero_grads(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
VLOG(4) << "ZeroGrads " << self->tensor.name();
if (egr::egr_utils_api::IsLeafTensor(self->tensor)) {
// Add RetainGrad as PostHook to AccumulationNode
paddle::experimental::Tensor* grad =
egr::EagerUtils::mutable_grad(self->tensor);
PADDLE_ENFORCE(grad != nullptr,
paddle::platform::errors::Fatal(
"Detected NULL grad"
"Please check if you have manually cleared"
"the grad inside autograd_meta"));
if (grad->initialized()) {
if (grad->is_dense_tensor()) {
auto* t = static_cast<phi::DenseTensor*>(grad->impl().get());
auto* dev_ctx = platform::DeviceContextPool::Instance().Get(t->place());
phi::funcs::set_constant(*dev_ctx, t, 0.0);
} else {
grad->set_impl(paddle::experimental::zeros_like(*(grad)).impl());
}
}
} else {
auto meta = egr::EagerUtils::unsafe_autograd_meta(self->tensor);
if (meta->MutableGrad()->initialized()) {
if (meta->MutableGrad()->is_dense_tensor()) {
auto* t =
static_cast<phi::DenseTensor*>(meta->MutableGrad()->impl().get());
auto* dev_ctx = platform::DeviceContextPool::Instance().Get(t->place());
phi::funcs::set_constant(*dev_ctx, t, 0.0);
} else {
meta->MutableGrad()->set_impl(
paddle::experimental::zeros_like(*(meta->MutableGrad())).impl());
}
}
}
RETURN_PY_NONE
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor__share_buffer_to(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
paddle::experimental::Tensor* dst_ptr =
&(reinterpret_cast<TensorObject*>(PyTuple_GET_ITEM(args, 0))->tensor);
PADDLE_ENFORCE_EQ(self->tensor.initialized(),
true,
platform::errors::InvalidArgument(
"Tensor %s has not been initialized! please initialize "
"src tensor before share_buffer_with to other.",
self->tensor.name()));
auto* src_tensor =
static_cast<paddle::framework::Tensor*>(self->tensor.impl().get());
if (!dst_ptr->defined()) {
dst_ptr->set_impl(std::make_shared<phi::DenseTensor>());
}
auto dst_tensor =
static_cast<paddle::framework::Tensor*>(dst_ptr->impl().get());
dst_tensor->ShareBufferWith(*src_tensor);
dst_tensor->ShareDataTypeWith(*src_tensor);
RETURN_PY_NONE
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor__is_shared_buffer_with(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
paddle::experimental::Tensor* dst_ptr =
&(reinterpret_cast<TensorObject*>(PyTuple_GET_ITEM(args, 0))->tensor);
PADDLE_ENFORCE_EQ(self->tensor.initialized(),
true,
platform::errors::InvalidArgument(
"Tensor %s has not been initialized! please initialize "
"src tensor before share_buffer_with to other.",
self->tensor.name()));
bool res = false;
if (!self->tensor.defined() || !dst_ptr->defined()) {
return ToPyObject(res);
}
auto* self_ptr =
static_cast<paddle::framework::Tensor*>(self->tensor.impl().get());
auto dst_tensor =
static_cast<paddle::framework::Tensor*>(dst_ptr->impl().get());
res = dst_tensor->IsSharedBufferWith(*self_ptr);
return ToPyObject(res);
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor__share_underline_tensor_to(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
paddle::experimental::Tensor* src_ptr =
&(reinterpret_cast<TensorObject*>(PyTuple_GET_ITEM(args, 0))->tensor);
PADDLE_ENFORCE_EQ(self->tensor.initialized(),
true,
platform::errors::InvalidArgument(
"Tensor %s has not been initialized! please initialize "
"src tensor before share_buffer_with to other.",
self->tensor.name()));
src_ptr->set_impl(self->tensor.impl());
RETURN_PY_NONE
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor__is_shared_underline_tensor_with(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
paddle::experimental::Tensor src_tensor =
CastPyArg2Tensor(PyTuple_GET_ITEM(args, 0), 0);
PADDLE_ENFORCE_EQ(src_tensor.initialized(),
true,
platform::errors::InvalidArgument(
"Tensor %s has not been initialized! please initialize "
"src tensor before share_buffer_with to other.",
src_tensor.name()));
bool res = false;
if (!self->tensor.defined() || !src_tensor.defined()) {
return ToPyObject(res);
}
res = (self->tensor.impl().get() == src_tensor.impl().get());
return ToPyObject(res);
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_detach(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
PADDLE_ENFORCE_EQ(
self->tensor.initialized(),
true,
platform::errors::InvalidArgument("Tensor %s has not been initialized!",
self->tensor.name()));
PyObject* obj = p_tensor_type->tp_alloc(p_tensor_type, 0);
if (obj) {
auto v = reinterpret_cast<TensorObject*>(obj);
new (&(v->tensor)) paddle::experimental::Tensor();
v->tensor.set_impl(self->tensor.impl());
v->tensor.set_name(egr::Controller::Instance().GenerateUniqueName());
auto autograd_meta_src = egr::EagerUtils::autograd_meta(&(self->tensor));
auto autograd_meta = egr::EagerUtils::autograd_meta(&(v->tensor));
autograd_meta->SetPersistable(autograd_meta_src->Persistable());
} else {
PADDLE_THROW(platform::errors::Fatal(
"tp_alloc return null, can not new a PyObject."));
}
return obj;
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_get_underline_tensor(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
if (!self->tensor.defined()) {
// The original `get_tensor` method of Variable will create a empty tensor
phi::DenseTensor empty_tensor;
return ToPyObject(&empty_tensor);
}
if (self->tensor.is_dense_tensor()) {
auto* tensor =
static_cast<paddle::framework::LoDTensor*>(self->tensor.impl().get());
VLOG(6) << "tensor: " << tensor->IsInitialized();
return ToPyObject(tensor);
} else {
RETURN_PY_NONE
}
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_get_underline_selected_rows(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
if (!self->tensor.defined()) {
RETURN_PY_NONE
}
if (self->tensor.is_selected_rows()) {
auto* selected_rows =
static_cast<phi::SelectedRows*>(self->tensor.impl().get());
return ToPyObject(selected_rows);
} else {
RETURN_PY_NONE
}
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor__getitem_index_not_tensor(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
PyObject* _index = PyTuple_GET_ITEM(args, 0);
VLOG(4) << "Call _getitem_index_not_tensor";
std::vector<int> slice_axes, slice_starts, slice_ends, slice_strides,
decrease_axis, none_axes, infer_flags, list_select_idxs;
// if index is a list, list_select_flag will be true
bool list_select_flag = false;
PADDLE_ENFORCE_EQ(
self->tensor.initialized(),
true,
platform::errors::InvalidArgument(
"tensor %s has not been initialized, we can only slice initialized "
"tensor please init it first with numpy or other tensor.",
self->tensor.name()));
auto tensor = static_cast<phi::DenseTensor*>(self->tensor.impl().get());
ParseIndexingSlice(tensor,
_index,
&slice_axes,
&slice_starts,
&slice_ends,
&slice_strides,
&decrease_axis,
&none_axes,
&infer_flags,
&list_select_idxs,
&list_select_flag);
auto out = slice_axes.empty() && !list_select_flag
? self->tensor
: paddle::experimental::Tensor(
egr::Controller::Instance().GenerateUniqueName());
if (!slice_axes.empty()) {
framework::AttributeMap attrs = {{"axes", slice_axes},
{"starts", slice_starts},
{"ends", slice_ends},
{"infer_flags", infer_flags},
{"decrease_axis", decrease_axis}};
std::string op_type = "slice";
for (auto stride : slice_strides) {
if (stride != 1) {
op_type = "strided_slice";
attrs.insert({"strides", slice_strides});
attrs.erase("decrease_axis");
break;
}
}
std::vector<int64_t> slice_axes_tmp(slice_axes.begin(), slice_axes.end());
std::vector<int64_t> infer_flags_tmp(infer_flags.begin(),
infer_flags.end());
std::vector<int64_t> decrease_axis_tmp(decrease_axis.begin(),
decrease_axis.end());
if (op_type == "slice") {
out = slice_final_state_dygraph_function(self->tensor,
slice_axes_tmp,
slice_starts,
slice_ends,
infer_flags_tmp,
decrease_axis_tmp);
} else if (op_type == "strided_slice") {
out = strided_slice_final_state_dygraph_function(
self->tensor, slice_axes, slice_starts, slice_ends, slice_strides);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Slice is only support slice and strided_slice, but we got %s which "
"is impossible, please check your code first or contact us by "
"issue. ",
op_type));
}
}
if (!none_axes.empty()) {
// Deal with cases when all axes are decreased.
// After slice, the shape of out is [1], which should have been
// [], but Paddle doesn't support scalar.
// In order to ensure the correctness of the final shape of out,
// one dimension of out needs to be decreased.
// For example:
// # x.shape: (2,3,4)
// out = x[0, 1, 1, None] # out.shape : (1)
if (static_cast<int>(decrease_axis.size()) == tensor->dims().size()) {
none_axes.pop_back();
}
if (!none_axes.empty()) {
// Deal with cases that decrease_axes is not empty
// For example:
// # x.shape: (2,3,4)
// out = x[0, 0:2, None] # out.shape : (2, 1, 4)
for (auto& axis : none_axes) {
int len = 0;
for (int da : decrease_axis) {
if (da < axis) {
len++;
}
}
axis -= len;
}
paddle::experimental::Tensor new_out;
new_out = unsqueeze_final_state_dygraph_function(out, none_axes);
return ToPyObject(new_out);
}
}
// the index is a list
if (list_select_flag) {
auto select_index = paddle::experimental::Tensor(
egr::Controller::Instance().GenerateUniqueName());
auto idx_tensor = std::make_shared<phi::DenseTensor>();
select_index.set_impl(idx_tensor);
auto* dev_ctx = platform::DeviceContextPool::Instance().Get(
egr::Controller::Instance().GetExpectedPlace());
paddle::framework::TensorFromVector(
list_select_idxs, *dev_ctx, idx_tensor.get());
framework::AttributeMap attrs = {{"dim", 0}};
out = index_select_final_state_dygraph_function(
self->tensor, select_index, 0);
}
return ToPyObject(out);
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor__getitem_from_offset(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
auto ptr = static_cast<phi::DenseTensor*>(self->tensor.impl().get());
PADDLE_ENFORCE_NOT_NULL(ptr,
platform::errors::InvalidArgument(
"%s is not a DenseTensor.", self->tensor.name()));
const auto& tensor = *ptr;
PADDLE_ENFORCE_EQ(
tensor.IsInitialized(),
true,
platform::errors::InvalidArgument(
"Tensor of %s is Empty, please check if it has no data.",
self->tensor.name()));
const auto& tensor_dims = tensor.dims();
std::vector<size_t> dims(tensor_dims.size());
std::vector<size_t> strides(tensor_dims.size());
size_t numel = 1;
for (int i = tensor_dims.size() - 1; i >= 0; --i) {
strides[i] = numel;
dims[i] = static_cast<size_t>(tensor_dims[i]);
numel *= dims[i];
}
size_t offset = 0;
if (PyTuple_Size(args) == 0) {
PADDLE_ENFORCE_EQ(numel,
1,
platform::errors::InvalidArgument(
"only one element tensors can be converted to Python "
"scalars when no input coordinates"));
} else if (PyTuple_Size(args) == 1) {
offset = CastPyArg2AttrLong(PyTuple_GET_ITEM(args, 0), 0);
PADDLE_ENFORCE_LT(
offset,
numel,
platform::errors::InvalidArgument(
"index %d is out of bounds for size %d", offset, numel));
} else {
PADDLE_ENFORCE_EQ(PyTuple_Size(args),
dims.size(),
platform::errors::InvalidArgument(
"incorrect number of indices for Tensor"));
for (Py_ssize_t i = 0; i < PyTuple_Size(args); ++i) {
size_t index = CastPyArg2AttrLong(PyTuple_GET_ITEM(args, i), i);
PADDLE_ENFORCE_LT(
index,
dims[i],
platform::errors::InvalidArgument(
"index %d is out fo bounds for axis %d with size %d",
index,
i,
dims[i]));
offset += index * strides[i];
}
}
#define PD_FOR_EACH_DENSE_TENSOR_DATA_TYPE(_) \
_(bool, DataType::BOOL) \
_(int8_t, DataType::INT8) \
_(uint8_t, DataType::UINT8) \
_(int16_t, DataType::INT16) \
_(uint16_t, DataType::UINT16) \
_(int32_t, DataType::INT32) \
_(uint32_t, DataType::UINT32) \
_(int64_t, DataType::INT64) \
_(uint64_t, DataType::UINT64) \
_(bfloat16, DataType::BFLOAT16) \
_(float16, DataType::FLOAT16) \
_(float, DataType::FLOAT32) \
_(double, DataType::FLOAT64) \
_(complex64, DataType::COMPLEX64) \
_(complex128, DataType::COMPLEX128)
#define TENSOR_TO_PY_SCALAR(T, proto_type) \
if (tensor.dtype() == proto_type) { \
auto numpy_dtype = TensorDtype2NumpyDtype(proto_type); \
T b = paddle::pybind::TensorGetElement<T>(tensor, offset); \
Py_intptr_t py_dims[paddle::framework::DDim::kMaxRank]; \
Py_intptr_t py_strides[paddle::framework::DDim::kMaxRank]; \
py_dims[0] = 1; \
py_strides[0] = 1; \
auto& api = pybind11::detail::npy_api::get(); \
PyObject* array = api.PyArray_NewFromDescr_( \
api.PyArray_Type_, \
api.PyArray_DescrFromType_(numpy_dtype), \
1, \
py_dims, \
py_strides, \
nullptr, \
pybind11::detail::npy_api::NPY_ARRAY_ALIGNED_ | \
pybind11::detail::npy_api::NPY_ARRAY_WRITEABLE_, \
nullptr); \
std::memcpy( \
reinterpret_cast<void*>(pybind11::detail::array_proxy(array)->data), \
static_cast<void*>(&b), \
sizeof(b)); \
return array; \
}
PD_FOR_EACH_DENSE_TENSOR_DATA_TYPE(TENSOR_TO_PY_SCALAR);
#undef TENSOR_TO_PY_SCALAR
PADDLE_THROW(platform::errors::Unimplemented(
"Unsupported tensor data type: %s", tensor.dtype()));
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method__setitem_eager_tensor(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
VLOG(4) << "Call __setitem_eager_tensor";
auto self_tensor = static_cast<phi::DenseTensor*>(self->tensor.impl().get());
PyObject* _index = PyTuple_GET_ITEM(args, 0);
PyObject* value_obj = PyTuple_GET_ITEM(args, 1);
// NOTE(zhiqiu): PyTuple_Pack increases refcount while PyTuple_New
// https://github.com/python/cpython/blob/24b63c695ae0a95b06379eaadace66735abac1e2/Objects/tupleobject.c#L251
PyObject* index_ptr =
!PyTuple_Check(_index) ? PyTuple_Pack(1, _index) : _index;
DEFINE_PADDLE_SCOPE_GUARD([index_ptr, &_index]() {
if (!PyTuple_Check(_index)) {
Py_DECREF(index_ptr);
VLOG(4) << "Call Py_DECREF";
}
});
// 1. Check argumnets
bool parse_index = true;
// Check whether _index can be parsed.
const int size = PyTuple_GET_SIZE(index_ptr);
for (int dim = 0; dim < size; ++dim) {
PyObject* slice_item = PyTuple_GetItem(index_ptr, dim);
if (!(PyCheckInteger(slice_item) || PySlice_Check(slice_item) ||
slice_item == Py_Ellipsis || slice_item == Py_None)) {
parse_index = false;
break;
}
}
// 2. Call op set_value to speed up if the condition is met,
// otherwise call TensorToPyArray.
// TODO(liym27): Try not to call TensorToPyArray because it always
// copys data to cpu place, which reduces performance.