* change irfft signal_sizes arg to be the last
* add docs for fft, ifft, rfft, irfft; update doc for stft
* fix typo in window function docs
* improve gradcheck error message
* implement backward of fft, ifft, rfft, irfft
* add grad tests for fft, ifft, rfft, irfft
* fix nits and typos from #6118
* address comments
* Implemented log2 and log10
* Re-add incorrectly removed files
* Fix minor bugs
* Fix log1p docs
* Add a try-except for python2 math module in log2 test
* Revert changes made to aten/doc/*
* Fix docstring errors
* Fix windows build
This changes type(tensor) to return `torch.Tensor` instead of
`torch.autograd.Variable`.
This requires a few implementation changes:
- torch.Tensor is now a regular Python class instead of a
pseudo-factory like torch.FloatTensor/torch.DoubleTensor
- torch.autograd.Variable is just a shell with a __new__ function.
Since no instanes are constructed it doesn't have any methods.
- Adds torch.get_default_dtype() since torch.Tensor.dtype returns
<attribute 'dtype' of 'torch._C._TensorBase' objects>
* 1. Add logdet and slogdet in ATen side
2. Previously, det can return result with incorrect sign upon seeing symmetric
matrices. This is caused by the wrong assumption I had on SVD (when input is
symmetric U=V^T). This fixes it.
3. Moreover, after fixing 2 now QR is always needed for det forward. So I moved
SVD to backward call. Since this is a specific variant of SVD, it is named as
_svd_with_positive_UV_det, with derivative.yaml entry being svd_backward.
4. Updated/added backward functions for det, logdet and slogdet, which uses
_svd_with_positive_UV_det and svd_backward inside.
5. Optimized svd_backward:
a. Avoid unnecessary kernels when only sigma has gradient (this is the usual
case, and also true with *det backward functions).
b. Fix SVD double backward by avoiding a nan.
* 1. Add/update grad checks for det, logdet, and slogdet.
2. Fix an incorrect check for dim_args_idx in test_autograd.py
3. Add option to only test a subset of output values, specified by
test_output_indices, for cases like slogdet where only the
second output is differentiable.
4. Add better doc for the test generating list.
* Add/improve output tests for det, logdet and slogdet
Add a scaling to random matrices so closeness checks are more robust
* Remove unnecessaery Variable wrappers in some test files
* Add logdet slogdet docs
* Improve an err msg in THTensorLapack.c
* add inverse-based backward for invertible matrices
use svd only for non-invertible case, so don't need the special variant anymore
* use LU rather than QR
* Implement torch.reshape and Tensor.reshape
This implements reshape which has similar semantics to numpy.reshape. It
will return a view of the source tensor if possible. Otherwise, it
returns a copy.
* Remove in-place reshape_ that was an alias for resize_
* Update documentation
Add script::Module C++ class to represent script modules
switch AST -> IR conversion to work on Modules/Methods rather than raw graphs
function-only AST -> IR conversion is just a simplified case where there is
only one module with a single method and no parameters.
introduce SugaredValue in compiler.h to represent values in scope in a script
function that are not first-class and that get desugared. This is used to
represent the module's self parameter, as well as python function calls,
and method calls on tensor
provide a Python ScriptModule that provides a nice API on top of script::Module
allowing for the definition of script modules with methods, parameters,
and submodules
Not in this PR but intended for the future:
ScriptModule actually subclasses nn.Module, with most methods implemented
Unification of tracedmodule and script module functionality into one container class.
Detailed changelog:
* Switch compiler over to using Module, but don't
use them yet.
* Remove intermediate attribute encoding in compiler
* Create SugaredValue object to handle resolution
of compiled module.
* switch to_ir to modules, implement Select
* hacky python wrappers
* Private ScriptModule
* Add `define` to script module
* Attributes use TK_LIST_LITERAL
this anticipates adding a real list literal expression to the language.
* Add a metaclass to make sure script stubs are registered
* Add a test
* Doc createResolutionCallback
* Docs and minor editing
* Address PR comments
* Document
* Fix unicode issue
* Improvize documentation
1. Add formula for erf, erfinv
2. Make exp, expm1 similar to log, log1p
3. Symbol change in ge, le, ne, isnan
* Fix minor nit in the docstring
* More doc improvements
1. Added some formulae
2. Complete scanning till "Other Operations" in Tensor docs
* Add more changes
1. Modify all torch.Tensor wherever required
* Fix Conv docs
1. Fix minor nits in the references for LAPACK routines
* Improve Pooling docs
1. Fix lint error
* Improve docs for RNN, Normalization and Padding
1. Fix flake8 error for pooling
* Final fixes for torch.nn.* docs.
1. Improve Loss Function documentation
2. Improve Vision Layers documentation
* Fix lint error
* Improve docstrings in torch.nn.init
* Fix lint error
* Fix minor error in torch.nn.init.sparse
* Fix Activation and Utils Docs
1. Fix Math Errors
2. Add explicit clean to Makefile in docs to prevent running graph generation script
while cleaning
3. Fix utils docs
* Make PYCMD a Makefile argument, clear up prints in the build_activation_images.py
* Fix batch norm doc error
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
Variable.item() converts one-element tensors to standard Python numbers.
This operates like float(var) or int(var) depending on
the data type of the Variable.
* Replace async with non_blocking for Python 3.7 upgrade
* Remove trailing whitespace
* Give _cuda and _type kwargs and accept async for compatibility
* Rename async to non_blocking in all C++ code
* Add entries for async in python_variable_methods
* Friendlier backward compatibility for cuda and type
* Add torch.take and Tensor.put_
These are similar to numpy.take and numpy.put. The take function allows
you to linearly index into a tensor without viewing it as a 1D tensor
first. The output has the same shape as the indices. The put function
copies value into a tensor also using linear indices.
