Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30915
Since we now have C++14, we don't need these c10::guts helpers anymore
ghstack-source-id: 95777609
Test Plan: waitforsandcastle
Differential Revision: D18869639
fbshipit-source-id: 97716f932297c64c6e814410ac47b444c33d4e2e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28287
This PR eliminates the static distinction between
Tensor and Variable. Every Variable is a Tensor, no need to static_cast
or call the Variable constructor.
To do this, I need Tensor to have API parity with Variable. I have already
moved most of the methods I don't want in Tensor off Variable.
These implementations are all placed in Tensor.cpp.
One API difference is that all Variable methods now have const, so we no longer
have faux const-correctness (see https://github.com/zdevito/ATen/issues/27 for
back story)
This diff is BC breaking in a few ways:
- Because torch::autograd::Variable is now just an alias of at::Tensor, ADL for
`torch::autograd` functions no longer works, you have to explicitly qualify
them with `torch::autograd` (examples: `torch/nn/parallel/data_parallel.h`)
- Because Variable and Tensor are now the same type, code which assumes that
they are different types (e.g., for the purposes of templating, or enable_if checks)
will not work until you delete the (now) redundant overload/specialization.
(examples: `torch/nn/modules/container/any.h`, `torch/csrc/utils/pybind.h`)
Some other notes:
- I'm not sure what was going with the old template implementation of `extract_vars`,
but I couldn't get the sfinae version to work. Replacing it with an overloading based version
made it work.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D18571426
Pulled By: ezyang
fbshipit-source-id: 2ea8151e5f1d8512cdebf1345399642e68b707b8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28605
This was added because std::to_string isn't available in libstc++
on Android. Use it in more places to get the PyTorch Android
build working with libstdc++.
Test Plan: Internal android build.
Reviewed By: jerryzh168
Differential Revision: D18099520
fbshipit-source-id: 17a2b617c2d21deadd0fdac1db849823637981fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25467
Use Layout/Device more directly in these cases.
ghstack-source-id: 89289651
Test Plan: sandcastle and ossci
Differential Revision: D17131883
fbshipit-source-id: ab3c6d1c879b7f26f20a2378364c852dc37508fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24393
Ability to register hook on a variable, similar to python autograd API. register_hook will take a function as argument and create a CppFunctionPreHook similar to PyFunctionPreHook.
It will return the index of the hook which can be passed to remove_hook to disable the hook.
Test Plan: Added tests.
Differential Revision: D16861722
fbshipit-source-id: d08047f932e38c7bde04283a18b2d0311c8ad604
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23803
Custom `forward()` can return a `Variable` in case of single outputs instead of returning a `variable_list` of size 1.
Test Plan: Modified tests involving single output forward functions.
Reviewed By: ezyang
Differential Revision: D16673857
Pulled By: ezyang
fbshipit-source-id: c96d9473b48ad99e6736a68d334b333a917498b7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23572
### **(The stack from #23020 was moved into this PR)**
Adding API for custom autograd operations, with user defined forward and backward, [like in python](https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd).
The custom operation should be a subclass of Function, with static forward and backward functions. `forward()` can accept any arguments similar to the Python API and `backward()` should accept a variable list as an argument.
Both `forward()` and `backward() `accept a AutogradContext* which can be used to share data between them.
Variables can be saved in the context using `save_for_backward()` and other data can be saved in the map `save` in the form of `<std::string, at::IValue>` pairs. Variables saved in forward can be accessed with `get_saved_variables()`.
Example usage:
```
class MyFunction : public Function<MyFunction> {
public:
static variable_list forward(AutogradContext *ctx, int n, Variable var) {
// Save data for backward in context
ctx->saved_data["n"] = n;
return {var};
}
static variable_list backward(AutogradContext *ctx, variable_list grad_output) {
// Use data saved in forward
auto n = ctx->saved_data["n"].toInt();
return {grad_output[0]*n};
}
};
```
Then, it can be used with:
```
Variable x;
MyFunction::apply(6, x);
```
Also AutogradContext has methods to mark outputs as non differentiable and mark inputs as dirty similar to the [Python API](ff23a02ac4/torch/autograd/function.py (L26)).
Test Plan: Added tests for the custom autograd function API based on test_autograd.py. Currently only the tests for the basic functionality have been added. More tests will be added later.
Differential Revision: D16583428
fbshipit-source-id: 0bd42f19ce37bcd99d3080d16195ad74d40d0413
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/22631
Test Plan:
test suite
Imported from OSS
Differential Revision: D16185040
fbshipit-source-id: 9b83749f6c9cd05d13f54a3bb4801e263293252b
