Tensor methods - Githubissues

Here's a complete list of tensor method we need to implement:

[x] $abs
[x] $abs_
[x] $acos
[x] $acos_
[x] $add
[x] $add_
[x] $addbmm
[x] $addbmm_
[x] $addcdiv
[x] $addcdiv_
[x] $addcmul
[x] $addcmul_
[x] $addmm
[x] $addmm_
[x] $addmv
[x] $addmv_
[x] $addr
[x] $addr_
[x] $all
[x] $allclose
[x] $any
[ ] $apply_
[x] $argmax
[x] $argmin
[x] $asin
[x] $asin_
[x] $atan
[x] $atan_
[x] $atan2
[x] $atan2_
[x] $baddbmm
[x] $baddbmm_
[x] $bernoulli
[x] $bernoulli_
[x] $bmm
[x] $btrifact
[x] $btrifact_with_info
[x] $btrisolve
[x] $byte
[ ] $ByteTensor
[x] $cauchy_
[x] $ceil
[x] $ceil_
[x] $char
[x] $cholesky
[x] $chunk
[x] $clamp
[x] $clamp_
[x] $clone
[x] $contiguous
[x] $copy_
[x] $cos
[x] $cos_
[x] $cosh
[x] $cosh_
[x] $cpu
[x] $cross
[x] $cuda
[x] $cumprod
[x] $cumsum
[x] $data_ptr
[x] $det
[x] $device
[x] $diag
[x] $diag_embed
[x] $dim
[x] $dist
[x] $div
[x] $div_
[x] $dot
[x] $double
[x] $eig
[x] $element_size
[x] $eq
[ ] $eq_ (waiting because there's a bug on pytorch )
[x] $equal
[x] $erf
[x] $erf_
[x] $erfc
[x] $erfc_
[x] $erfinv
[x] $erfinv_
[x] $exp
[x] $exp_
[x] $expand
[x] $expand_as
[x] $expm1
[x] $expm1_
[x] $exponential_ - need to think about generators.
[x] $fill_
[x] $flatten
[x] $flip
[x] $float
[x] $floor
[x] $floor_
[x] $fmod
[x] $fmod_
[x] $frac
[x] $frac_
[x] $gather
[x] $ge
[x] $ge_
[x] $gels
[x] $geometric_
[x] $geqrf
[x] $ger
[x] $gesv
[x] $get_device
[x] $gt
[x] $gt_
[x] $half
[x] $histc
[x] $index_add_
[x] $index_copy_
[x] $index_fill_
[x] $index_put_
[x] $index_select
[x] $int
[x] $inverse
[x] $is_contiguous
[x] $is_cuda
[ ] $is_pinned
[x] $is_set_to
[x] $is_signed
[x] $item
[x] $kthvalue
[x] $le
[x] $le_
[x] $lerp
[ ] $lerp_
[x] $log
[x] $log_
[ ] $log_normal_
[x] $log10
[x] $log10_
[x] $log1p
[x] $log1p_
[x] $log2
[x] $log2_
[ ] $logdet
[x] $logsumexp
[ ] $long
[ ] $lt
[ ] $lt_
[ ] $map_
[ ] $masked_fill_
[ ] $masked_scatter_
[ ] $masked_select
[ ] $matmul
[ ] $matrix_power
[x] $max
[x] $mean
[x] $median
[x] $min
[x] $mm
[x] $mode
[x] $mul
[ ] $mul_
[ ] $multinomial
[ ] $mv
[ ] $mvlgamma
[ ] $mvlgamma_
[ ] $narrow
[ ] $ndimension
[ ] $ne
[ ] $ne_
[ ] $neg
[ ] $neg_
[ ] $nelement
[ ] $new_empty
[ ] $new_full
[ ] $new_ones
[ ] $new_tensor
[ ] $new_zeros
[ ] $nonzero
[ ] $norm
[ ] $normal_
[ ] $numel
[ ] $numpy
[ ] $orgqr
[ ] $ormqr
[ ] $permute
[ ] $pin_memory
[ ] $pinverse
[ ] $potrf
[ ] $potri
[ ] $potrs
[x] $pow
[x] $pow_
[ ] $prod
[ ] $pstrf
[ ] $put_
[ ] $qr
[ ] $random_
[ ] $reciprocal
[ ] $reciprocal_
[ ] $remainder
[ ] $remainder_
[ ] $renorm
[ ] $renorm_
[ ] $repeat
[ ] $requires_grad_
[ ] $reshape
[ ] $reshape_as
[ ] $resize_
[ ] $resize_as_
[ ] $round
[ ] $round_
[x] $rsqrt
[ ] $rsqrt_
[ ] $scatter_
[ ] $scatter_add_
[ ] $select
[ ] $set_
[ ] $share_memory_
[ ] $short
[x] $sigmoid
[x] $sigmoid_
[ ] $sign
[ ] $sign_
[x] $sin
[x] $sin_
[x] $sinh
[ ] $sinh_
[ ] $size
[ ] $slogdet
[ ] $sort
[ ] $sparse_mask
[ ] $split
[ ] $sqrt
[ ] $sqrt_
[ ] $squeeze
[ ] $squeeze_
[x] $std
[ ] $storage
[ ] $storage_offset
[ ] $storage_type
[ ] $stride
[x] $sub
[x] $sub_
[x] $sum
[ ] $svd
[ ] $symeig
[ ] $t
[ ] $t_
[ ] $take
[x] $tan
[x] $tan_
[x] $tanh
[x] $tanh_
[ ] $Tensor
[ ] $to
[ ] $to_sparse
[ ] $tolist
[ ] $topk
[ ] $trace
[ ] $transpose
[ ] $transpose_
[x] $tril
[ ] $tril_
[x] $triu
[ ] $triu_
[ ] $trtrs
[ ] $trunc
[ ] $trunc_
[ ] $type
[ ] $type_as
[ ] $unfold
[ ] $uniform_
[x] $unique
[x] $unsqueeze
[x] $unsqueeze_
[x] $var
[ ] $view
[ ] $view_as
[ ] $zero_

The code used to create the list:

library(httr)
library(xml2)
library(magrittr)

docs <- httr::GET("https://pytorch.org/docs/stable/tensors.html")

docs %>% 
  content() %>% 
  xml2::xml_find_all("//code[@class='descname']") %>% 
  xml2::xml_text() %>% 
  unique() %>% 
  sort() %>% 
  paste0("- [ ] `$", ., "`") %>% 
  cat(sep = "\n")

dfalbel / torch

Tensor methods #25