* Add batched linear solver to torch.gesv()
Fixes#3164
Picks up from #4502
I moved `gesv` to ATen.
Adds bindings for MAGMA's `gesv_batched` function for CUDA.
For CPU, runs `THLapack(gesv)` in a for loop.
The new function supports arbitrary batch dimensions (and broadcasting
of those dimensions). For example, the 4-d tensor `A x B x M x M` should
be treated as having batch-size `(A x B)`.
The overhead of creating the magma_queue_t is: ~350000 microseconds
the first time it's called and ~6 microseconds every time after that.
* Tests and docs
* Address comments
* Address comments
* Rebase
* Address comments
* Fix rebase
* Addressed comments
* Address comments
* Address comments
* Addressed comments
* Implement torch.as_tensor, similar to numpy.asarray.
torch.as_tensor behaves like torch.tensor except it avoids copies if possible; so also somewhat like tensor.new but without the size overloads.
I didn't add a requires_grad field, because we haven't decided on the semantics such as as_param.
* Remove requires_grad for doc.
* Add big warning about averagin to KLDivLoss documentation #6622
Also: An (independent) change in diagonal docstring tensor
formatting.
* Improve note with example
Thank you Richard Zou!
* use log_softmax
* Enhance diagonal
This patch
- adds Tensor.diagonal to complement torch.diagonal
- implements diagonal natively in ATen
- makes diagonal a view
- implements taking arbitrary diagonals
- implements diagonal backward instead of referring
to the (more limited) diag
* add tests, copy diagonal code to backward for double differentiability
* improve tests and doc comment. Thank you, Adam!
* Mark diagonal as view function in gen_autograd.py, use simple backward.
* More factory functions
Changes:
- Added the remaining factory and factory-like functions
- Better argument reuse via string templates
- Link under torch.rst's Creation Ops to the randomized creation ops
* Add double tick around False
* fix flake8
* Fix False
* Clarify comment: hopefully it is clearer now
* start at generic trilinear
* Implement einsum (fixes#1889)
This provides a simple implementation of einsum. It is built on
top of the work for computing bilinear (#6110).
It uses a naive left-to-right resolution at the moment.
Autograd is able to differentiate by itself.
The obvious unsupported feature is taking diagonals (einsum('ii->i',(a,)).
* add tests and docs
* fix flake8
* clean diff
* rebase on current master to resolve conflicting String wrapping
* clean up after rebase
* better commentary in einsum and sumproduct_pair
* don't say fixme if it's fixed and rename num_outputs to num_output_dims
* adapt python wrapper to use std::string instead of String to avoid typedef at::String
* typos and some vector to array conversion
* fix accidental python<->python3 change
* really fix bad rebase
Changes:
- Deleted docs for old constructor. Add link to new `torch.tensor` ctor
- Add docs for `torch.tensor`
- Add some info on dtypes to the top of `tensors.rst`.
* Update docs for torch.zeros factory method
If this looks good, I'll submit another PR rewriting the other factory
methods in this fashion.
* Address comments
* Better explanation for device default
* Add variable argument back
* s/set/sequence/g
* Remove class from torch.strided
* 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
* 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
* Fix some minor errors in existing docs.
* Fix Convolution and Pooling docs in torch.nn.functional
* Cleaned up torch.nn.functional docs
* Address @SsnL 's comments
* Add multiplication sign missing in docs
* Fix more typos, and clear some warnings
* Change infinity symbol in LPPool2d
* Revert some changes in torch.nn.functional
* Few more minor changes
* 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
* 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
* Fix catArray in THTensor
Asserts that the inputs have the same size except in the
cat dimension or are empty (or a mix of both).
* Fix catArray for THCTensor
* Document torch.cat shape checks
* Fix types
