Files
pytorch/torch/csrc/autograd/python_legacy_variable.cpp
Will Feng 8cde4c4d22 Remove Variable::Impl and DifferentiableViewImpl (#17072)
Summary:
As part of the Variable/Tensor merge work: https://github.com/pytorch/pytorch/issues/13638, we make the following changes in this PR:
1. Remove the `Variable::Impl` class and the `DifferentiableViewImpl` class
2. Change all `Variable.data()` call sites to either use `Variable` directly, or use `Variable.tensor_data()`
3. Remove `Variable.data()` API
3. Add `Variable.variable_data()` that matches `tensor.data` in Python API, which creates a new `Variable` that shares the same storage and tensor metadata with the original `Variable`, but with a completely new autograd history.

After this PR, Variable doesn't wrap a Tensor internally anymore, and both Variable and Tensor use the same TensorImpl class as its `impl_`. The only difference is that Variable always has AutogradMeta in its TensorImpl, but Tensor doesn't.

**Note that this PR is BC-breaking in the following use cases:**

**Use Case 1:**
Previously, `x.data = y` works even if `x` and `y` are of different TensorImpl type (e.g. `x` is a CPU dense tensor whose impl is of type TensorImpl, while `y` is a CPU sparse tensor whose impl is of type SparseTensorImpl). However, after this PR, `x.data = y` doesn't work anymore if `x` and `y` are of different TensorImpl type, because the underlying implementation `variable.set_data(tensor)` no longer works if `variable` and `tensor` have different TensorImpl type.

**Use Case 2:**
If a tensor `x`'s `grad` is sparse, accumulating dense gradients to `x` will change the tensor that `x.grad` is pointing to. This is better illustrated with the following example:
```python
params = torch.tensor([1.5, 1.5]).requires_grad_()
with torch.no_grad():
    # Change gradient to a sparse tensor
    params.grad = torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.]))

grad_saved = params.grad
params.backward(torch.tensor([1.5, 1.5]))
assert id(grad_saved) == id(params.grad)  # This will fail after this PR
```
The assertion in the last line will fail after this PR, because adding dense gradients to sparse gradients will change the `params.grad` tensor reference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17072

Differential Revision: D14075257

Pulled By: yf225

fbshipit-source-id: 0e681df641270dea586042dd26db59f2e76b5957
2019-05-23 21:09:04 -07:00

135 lines
5.2 KiB
C++

#include <torch/csrc/autograd/python_legacy_variable.h>
#include <ATen/ATen.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/autograd/python_function.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/tensor/python_tensor.h>
#include <torch/csrc/jit/tracer.h>
using namespace at;
namespace torch { namespace autograd {
static PyObject *THPVariable_pynew(PyTypeObject* type, PyObject *args, PyObject *kwds) {
HANDLE_TH_ERRORS
THPObjectPtr _data;
PyObject *data = nullptr;
PyObject *grad_fn = nullptr;
char is_volatile = 0;
char requires_grad = 0;
const char* name = nullptr;
const char *accepted_args[] = {"data", "requires_grad", "volatile", "_grad_fn", "name", nullptr};
if (!PyArg_ParseTupleAndKeywords(args, kwds, "|ObbOz", (char**)accepted_args,
&data, &requires_grad, &is_volatile, &grad_fn, &name))
return nullptr;
if (grad_fn == Py_None)
grad_fn = nullptr;
if (is_volatile) {
PyErr_WarnEx(PyExc_UserWarning,
"volatile was removed and now has no effect. Use `with torch.no_grad():` "
"instead.", 1);
}
if (is_volatile && requires_grad) {
throw ValueError("Variable can't be volatile and require_grad at the same time!");
}
if (grad_fn && !THPFunction_Check(grad_fn)) {
throw TypeError("_grad_fn has to be a Function object or None, but got %s",
Py_TYPE(grad_fn)->tp_name);
}
Tensor tensor;
if (!data || data == Py_None) {
// For legacy serialization code, create an empty tensor. This is also used
// by nn.Parameter() with no arguments.
auto scalar_type = torch::tensors::get_default_scalar_type();
auto var = at::empty({0}, torch::tensors::get_default_tensor_type().options(scalar_type));
tensor = static_cast<Variable&>(var).tensor_data();
} else if (THPVariable_Check(data)) {
tensor = ((THPVariable*)data)->cdata.tensor_data();
} else {
throw torch::TypeError("Variable data has to be a tensor, but got %s",
Py_TYPE(data)->tp_name);
}
Variable var;
if (grad_fn) {
auto grad_fn_ = THPFunction_asFunction((THPFunction*)grad_fn);
Edge edge(grad_fn_, grad_fn_->add_input_metadata(tensor));
var = make_variable(std::move(tensor), std::move(edge));
} else {
var = make_variable(std::move(tensor), requires_grad);
}
if (name) {
var.set_name(name);
}
if (jit::tracer::isTracing() && data && data != Py_None && THPVariable_Check(data)) {
if (auto *v = jit::tracer::getValueTrace(((THPVariable*)data)->cdata)) {
jit::tracer::setValueTrace(var, v);
}
}
return THPVariable_Wrap(std::move(var));
END_HANDLE_TH_ERRORS
}
PyTypeObject THPLegacyVariableType = {
PyVarObject_HEAD_INIT(nullptr, 0)
"torch._C._LegacyVariableBase", /* tp_name */
0, /* tp_basicsize */
0, /* tp_itemsize */
nullptr, /* tp_dealloc */
nullptr, /* tp_print */
nullptr, /* tp_getattr */
nullptr, /* tp_setattr */
nullptr, /* tp_reserved */
nullptr, /* tp_repr */
nullptr, /* tp_as_number */
nullptr, /* tp_as_sequence */
nullptr, /* tp_as_mapping */
nullptr, /* tp_hash */
nullptr, /* tp_call */
nullptr, /* tp_str */
nullptr, /* tp_getattro */
nullptr, /* tp_setattro */
nullptr, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */
nullptr, /* tp_doc */
nullptr, /* tp_traverse */
nullptr, /* tp_clear */
nullptr, /* tp_richcompare */
0, /* tp_weaklistoffset */
nullptr, /* tp_iter */
nullptr, /* tp_iternext */
nullptr, /* tp_methods */
nullptr, /* tp_members */
nullptr, /* tp_getset */
nullptr, /* tp_base */
nullptr, /* tp_dict */
nullptr, /* tp_descr_get */
nullptr, /* tp_descr_set */
0, /* tp_dictoffset */
nullptr, /* tp_init */
nullptr, /* tp_alloc */
THPVariable_pynew /* tp_new */
};
void init_legacy_variable(PyObject *module) {
if (PyType_Ready(&THPLegacyVariableType) < 0) {
throw python_error();
}
auto obj = (PyObject*)&THPLegacyVariableType;
Py_INCREF(obj);
if (PyModule_AddObject(module, "_LegacyVariableBase", obj) < 0) {
throw python_error();
}
}
}} // namespace torch::autograd