Summary:
Based on https://github.com/pytorch/pytorch/pull/12413, with the following additional changes:
- Inside `native_functions.yml` move those outplace operators right next to everyone's corresponding inplace operators for convenience of checking if they match when reviewing
- `matches_jit_signature: True` for them
- Add missing `scatter` with Scalar source
- Add missing `masked_fill` and `index_fill` with Tensor source.
- Add missing test for `scatter` with Scalar source
- Add missing test for `masked_fill` and `index_fill` with Tensor source by checking the gradient w.r.t source
- Add missing docs to `tensor.rst`
Differential Revision: D14069925
Pulled By: ezyang
fbshipit-source-id: bb3f0cb51cf6b756788dc4955667fead6e8796e5
Summary:
- Moved a few functions from `autograd` namespace to `aten` namespace to be visible from JIT nativeResolver.
- Added a hack to loop up keyword only argument. Will add proper support for kw only later
- Simulate function overload in aten using `_<number>` as function name suffix.
- Even `forward` returns multiple outputs like in `kthvalue`, there's at most one requires grad that we currently support.
- Removed the `TensorList` related ops here since partial `TensorList` support is prone to bugs. Our symbolic diff for `cat` was never tested with autodiff, and it seems broken. Need to find another proper way to support these ops(either by properly supporting `TensorList` or sth like `prim::ConstantChunk` and leave them for next PR.
Ops supported in this PR:
```
erf
expand_as
index
kthvalue
mean
permute
pow
rsub
select
sqrt
squeeze
t
to
topk
transpose
view
var
embedding
logsumexp
// grad is None
_dim_arange
contiguous
nonzero
ones_like
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16689
Differential Revision: D14020806
Pulled By: ailzhang
fbshipit-source-id: a5e2c144a7be5a0d39d7ac5f93cb402ec12503a5
Summary:
So that things like below can be JITable, and available in C++ API:
```python
import torch
torch.jit.script
def f(x, y, z):
x.index_add(0, y, z)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12413
Differential Revision: D13899948
Pulled By: suo
fbshipit-source-id: b0006b4bee2d1085c813733e1037e2dcde4ce626
Summary:
Partially fixes: https://github.com/pytorch/pytorch/issues/394
Implementation detail:
Codegen is modified to generate codes that looks like below:
```C++
static PyObject * THPVariable_svd(PyObject* self_, PyObject* args, PyObject* kwargs)
{
HANDLE_TH_ERRORS
static PythonArgParser parser({
"svd(Tensor input, bool some=True, bool compute_uv=True, *, TensorList[3] out=None)",
}, /*traceable=*/true);
ParsedArgs<6> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
static PyStructSequence_Field fields0[] = {
{"U", ""}, {"S", ""}, {"V", ""}, {nullptr}
};
static PyStructSequence_Desc desc0 = {
"torch.return_types.svd_out", nullptr,
fields0, 3
};
static PyTypeObject type0;
static bool namedtuple_type_initialized0 = false;
if (!namedtuple_type_initialized0) {
PyStructSequence_InitType(&type0, &desc0);
namedtuple_type_initialized0 = true;
}
static PyStructSequence_Field fields1[] = {
{"U", ""}, {"S", ""}, {"V", ""}, {nullptr}
};
static PyStructSequence_Desc desc1 = {
"torch.return_types.svd", nullptr,
fields1, 3
};
static PyTypeObject type1;
static bool namedtuple_type_initialized1 = false;
if (!namedtuple_type_initialized1) {
PyStructSequence_InitType(&type1, &desc1);
namedtuple_type_initialized1 = true;
}
if (r.idx == 0) {
if (r.isNone(3)) {
return wrap(&type1, dispatch_svd(r.tensor(0), r.toBool(1), r.toBool(2)));
} else {
auto results = r.tensorlist_n<3>(3);
return wrap(&type0, dispatch_svd(r.tensor(0), r.toBool(1), r.toBool(2), results[0], results[1], results[2]));
}
}
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
```
Types are defined as static member of `THPVariable_${op_name}` functions, and initialized at the first time the function is called.
When parsing function prototypes in `native_functions.yaml`, the parser will set the specified name as `field_name` when see things like `-> (Tensor t1, ...)`. These field names will be the field names of namedtuple. The class of namedtuples will be named `torch.return_types.${op_name}`.
In some python 2, `PyStructSequence` is not a subtype of tuple, so we have to create some functions to check if an object is a tuple or namedtuple for compatibility issue.
Operators in `native_functions.yaml` are changed such that only `max` and `svd` are generated as namedtuple. Tests are added for these two operators to see if the return value works as expected. Docs for these two ops are also updated to explicitly mention the return value is a namedtuple. More ops will be added in later PRs.
There is some issue with Windows build of linker unable to resolve `PyStructSequence_UnnamedField`, and some workaround is added to deal with this case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15429
Differential Revision: D13709678
Pulled By: ezyang
fbshipit-source-id: 23a511c9436977098afc49374e9a748b6e30bccf
Summary:
4GB is still too large and leads to CUDA OOM failures.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15959
Differential Revision: D13635146
Pulled By: mrshenli
fbshipit-source-id: 3dc34a03d6ed65c458839d8fa37cd05bf3bc8106
Summary:
Changes originally in this PR:
1. Move Variable::Impl data members into TensorImpl as `AutogradMeta` struct
2. Change Variable::Impl functions to use data members in `AutogradMeta` struct
3. Add `shallow_copy_and_detach()` function to each subclass of TensorImpl
4. Do shallow copy when the user calls `make_variable(tensor)` / `make_variable_view(tensor)` / `variable.set_data(tensor)` / `variable.detach()`
Changes moved from https://github.com/pytorch/pytorch/pull/13645:
1. Add a flag to Variable to disallow size/stride/storage_ptr changes from in-place operations such as `resize_` / `resize_as_` / `set_` / `transpose_`, and set this flag to true when people call `tensor.data` in Python.
2. Write text in the docs to actively discourage changing the shape or storage of `tensor_detached` and expecting `tensor` to also be updated.
This is the 1st+2nd PR mentioned in https://github.com/pytorch/pytorch/issues/13638.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13827
Differential Revision: D13507173
Pulled By: yf225
fbshipit-source-id: b177b08438d534a8197e34e1ad4a837e2db0ed6a
Summary:
Followup PR of #14904, and the stretch goal of #12653.
Directly calculate coordinates in the original tensor using column index in the result tensor. Every GPU thread takes care of a column (two numbers) in the output tensor.
The implementation detects and handles precision loss during calculating the square root of a `int64_t` variable, and supports tensors with up to `row * column = 2 ^ 59` numbers.
Algorithm details are describe in [comments of TensorFactories.cu](23ddb6f58a/aten/src/ATen/native/cuda/TensorFactories.cu (L109-L255)).
zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15203
Reviewed By: zou3519
Differential Revision: D13517695
Pulled By: mrshenli
fbshipit-source-id: 86b305d22cac08c8962a3b0cf8e9e620b7ec33ea
Summary:
update roll to behave as in numpy.roll when dimension to roll not specified.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13588
Differential Revision: D12964295
Pulled By: nairbv
fbshipit-source-id: de9cdea1a937773033f081f8c1505a40e4e08bc1
Summary:
- fixes weights-contiguous requirement for THCUNN Convolutions
- Add tests that conv backward pass works for non-contiguous weights
- fix RNN tests / error messages to be consistent and pass
- relax weight grad precision for fp16 for a particular test
- fix regression of CMAKE_PREFIX_PATH not passing through
- add missing skipIfNoLapack annotations where needed
Differential Revision: D12918456
Pulled By: soumith
fbshipit-source-id: 8642d36bffcc6f2957800d6afa1e10bef2a91d05
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12777
Enables JIT tests in FBCode. Changes pybind11 code to avoid mixing py::args with positinally matched arguments because old versions of PyBind11 leak memory in this case.
Reviewed By: jamesr66a
Differential Revision: D10419708
fbshipit-source-id: 74bc466001b5d363132d1af32e96841b38601827
