Updating to libtorch v2.0.1

.github/workflows/lantern.yaml

@@ -19,7 +19,6 @@ jobs:
           - {os: macOS, version: cpu-m1, runner: [self-hosted, m1]}
 
           - {os: ubuntu, version: cpu, runner: ubuntu-latest}
-          - {os: ubuntu, version: cu11.6, runner: [self-hosted, gce, disk]}
           - {os: ubuntu, version: cu11.7, runner: [self-hosted, gce, disk]}
 
           - {os: windows, version: cpu, runner: windows-2019}
@@ -33,12 +32,9 @@ jobs:
 
           # specify the CUDA patch for each major/minor version.
           # required for cuda installation
-          - config: {version: cu11.6}
-            cuda: 11.6
-            cuda_patch: 1
           - config: {version: cu11.7}
             cuda: 11.7
-            cuda_patch: 0
+            cuda_patch: 1
 
         exclude:
           - config: {os: macOS}

.github/workflows/main.yaml

@@ -33,8 +33,7 @@ jobs:
           - {os: ubuntu, r_version: release, version: cpu, runner: ubuntu-20.04}
           # the precxx11abi R version is whichever is specified in the selected container.
           - {os: centos, r_version: '', version: cpu, runner: ubuntu-20.04, precxx11abi: 1}
-          - {os: ubuntu, r_version: release, version: cu11.6, runner: [self-hosted, gce, gpu]}
-          - {os: ubuntu, r_version: release, version: cu11.7, runner: [self-hosted, gce, gpu]}
+          - {os: ubuntu, r_version: release, version: cu11.7, runner: [self-hosted, gpu-local]}
 
           - {os: windows, r_version: release, version: cpu, runner: windows-latest}
           - {os: windows, r_version: 3.6, version: cpu, runner: windows-latest}
@@ -43,12 +42,9 @@ jobs:
 
           - config: {os: centos, precxx11abi: 1}
             container: rstudio/r-base:4.2-centos7
-
-          - config: {os: ubuntu, version: cu11.6}
-            container: {image: 'nvidia/cuda:11.6.0-cudnn8-devel-ubuntu18.04', options: '--gpus all --runtime=nvidia'}
 
           - config: {os: ubuntu, version: cu11.7}
-            container: {image: 'nvidia/cuda:11.7.0-cudnn8-devel-ubuntu18.04', options: '--gpus all --runtime=nvidia'}
+            container: {image: 'nvidia/cuda:11.7.1-cudnn8-devel-ubuntu18.04', options: '--gpus all --runtime=nvidia'}
 
     runs-on: ${{ matrix.config.runner }}
     container: ${{ matrix.container }}

NAMESPACE

@@ -801,7 +801,6 @@ export(torch_sub)
 export(torch_subtract)
 export(torch_sum)
 export(torch_svd)
-export(torch_symeig)
 export(torch_t)
 export(torch_take)
 export(torch_tan)

R/distributions-multivariate_normal.R

@@ -57,12 +57,8 @@
   # Ref: https://nbviewer.jupyter.org/gist/fehiepsi/5ef8e09e61604f10607380467eb82006#Precision-to-scale_tril
   Lf <- linalg_cholesky(torch_flip(P, c(-2, -1)))
   L_inv <- torch_transpose(torch_flip(Lf, c(-2, -1)), -2, -1)
-  torch_linalg_solve_triangular(
-    L_inv,
-    torch_eye(head2(P$shape, -1), 
-    upper = FALSE,
-    dtype = P$dtype, device = P$device),
-  )
+  Id <- torch_eye(head2(P$shape, -1), dtype=P$dtype, device=P$device)
+  torch_linalg_solve_triangular(L_inv, Id, upper = FALSE)
 }
 
 MultivariateNormal <- R6::R6Class(

R/gen-method.R

@@ -277,39 +277,52 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "_is_zerotensor", function() {  args <- list()
+Tensor$set("public", "_is_all_true", function() {  args <- list()
 args <- c(list(self = self), args)
 expected_types <- list(self = "Tensor")
 nd_args <- "self"
-return_types <- list(list('bool'))
+return_types <- list(list('Tensor'))
 call_c_function(
-  fun_name = '_is_zerotensor',
+  fun_name = '_is_all_true',
   args = args,
   expected_types = expected_types,
   nd_args = nd_args,
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "_neg_view", function() {  args <- list()
+Tensor$set("public", "_is_any_true", function() {  args <- list()
 args <- c(list(self = self), args)
 expected_types <- list(self = "Tensor")
 nd_args <- "self"
 return_types <- list(list('Tensor'))
 call_c_function(
-  fun_name = '_neg_view',
+  fun_name = '_is_any_true',
   args = args,
   expected_types = expected_types,
   nd_args = nd_args,
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "_nested_tensor_layer_norm", function(weight, bias, eps) {  args <- mget(x = c("weight", "bias", "eps"))
+Tensor$set("public", "_is_zerotensor", function() {  args <- list()
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", eps = "double")
-nd_args <- c("self", "weight", "bias", "eps")
+expected_types <- list(self = "Tensor")
+nd_args <- "self"
+return_types <- list(list('bool'))
+call_c_function(
+  fun_name = '_is_zerotensor',
+  args = args,
+  expected_types = expected_types,
+  nd_args = nd_args,
+  return_types = return_types,
+  fun_type = 'method'
+)})
+Tensor$set("public", "_neg_view", function() {  args <- list()
+args <- c(list(self = self), args)
+expected_types <- list(self = "Tensor")
+nd_args <- "self"
 return_types <- list(list('Tensor'))
 call_c_function(
-  fun_name = '_nested_tensor_layer_norm',
+  fun_name = '_neg_view',
   args = args,
   expected_types = expected_types,
   nd_args = nd_args,
@@ -5269,19 +5282,6 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "prelu_backward", function(grad_output, weight) {  args <- mget(x = c("grad_output", "weight"))
-args <- c(list(self = self), args)
-expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor")
-nd_args <- c("grad_output", "self", "weight")
-return_types <- list(list("Tensor", "Tensor"))
-call_c_function(
-  fun_name = 'prelu_backward',
-  args = args,
-  expected_types = expected_types,
-  nd_args = nd_args,
-  return_types = return_types,
-  fun_type = 'method'
-)})
 Tensor$set("public", "prod", function(dim, keepdim = FALSE, dtype = NULL) {  args <- mget(x = c("dim", "keepdim", "dtype"))
 args <- c(list(self = self), args)
 expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool", 
@@ -6417,7 +6417,8 @@ call_c_function(
 )})
 Tensor$set("public", "squeeze", function(dim) {  args <- mget(x = c("dim"))
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
+expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname", "IntArrayRef"
+))
 nd_args <- c("self", "dim")
 return_types <- list(list('Tensor'))
 call_c_function(
@@ -6430,7 +6431,8 @@ call_c_function(
 )})
 Tensor$set("public", "squeeze_", function(dim) {  args <- mget(x = c("dim"))
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"))
+expected_types <- list(self = "Tensor", dim = c("int64_t", "IntArrayRef", "Dimname"
+))
 nd_args <- c("self", "dim")
 return_types <- list(list('Tensor'))
 call_c_function(
@@ -6455,11 +6457,11 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "std", function(dim, correction, unbiased = TRUE, keepdim = FALSE) {  args <- mget(x = c("dim", "correction", "unbiased", "keepdim"))
+Tensor$set("public", "std", function(dim, unbiased = TRUE, keepdim = FALSE) {  args <- mget(x = c("dim", "unbiased", "keepdim"))
 args <- c(list(self = self), args)
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
-    correction = "int64_t", unbiased = "bool", keepdim = "bool")
-nd_args <- c("self", "dim", "correction")
+    unbiased = "bool", keepdim = "bool")
+nd_args <- c("self", "dim")
 return_types <- list(list('Tensor'))
 call_c_function(
   fun_name = 'std',
@@ -6642,19 +6644,6 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "symeig", function(eigenvectors = FALSE, upper = TRUE) {  args <- mget(x = c("eigenvectors", "upper"))
-args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor", eigenvectors = "bool", upper = "bool")
-nd_args <- "self"
-return_types <- list(list("Tensor", "Tensor"))
-call_c_function(
-  fun_name = 'symeig',
-  args = args,
-  expected_types = expected_types,
-  nd_args = nd_args,
-  return_types = return_types,
-  fun_type = 'method'
-)})
 Tensor$set("public", "t", function() {  args <- list()
 args <- c(list(self = self), args)
 expected_types <- list(self = "Tensor")
@@ -6841,9 +6830,10 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "to_sparse", function(sparse_dim) {  args <- mget(x = c("sparse_dim"))
+Tensor$set("public", "to_sparse", function(layout = NULL, sparse_dim, blocksize = NULL, dense_dim = NULL) {  args <- mget(x = c("layout", "sparse_dim", "blocksize", "dense_dim"))
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor", sparse_dim = "int64_t")
+expected_types <- list(self = "Tensor", layout = "Layout", sparse_dim = "int64_t", 
+    blocksize = "IntArrayRef", dense_dim = "int64_t")
 nd_args <- c("self", "sparse_dim")
 return_types <- list(list('Tensor'))
 call_c_function(
@@ -6854,9 +6844,9 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "to_sparse_bsc", function(blocksize) {  args <- mget(x = c("blocksize"))
+Tensor$set("public", "to_sparse_bsc", function(blocksize, dense_dim = NULL) {  args <- mget(x = c("blocksize", "dense_dim"))
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor", blocksize = "IntArrayRef")
+expected_types <- list(self = "Tensor", blocksize = "IntArrayRef", dense_dim = "int64_t")
 nd_args <- c("self", "blocksize")
 return_types <- list(list('Tensor'))
 call_c_function(
@@ -6867,9 +6857,9 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "to_sparse_bsr", function(blocksize) {  args <- mget(x = c("blocksize"))
+Tensor$set("public", "to_sparse_bsr", function(blocksize, dense_dim = NULL) {  args <- mget(x = c("blocksize", "dense_dim"))
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor", blocksize = "IntArrayRef")
+expected_types <- list(self = "Tensor", blocksize = "IntArrayRef", dense_dim = "int64_t")
 nd_args <- c("self", "blocksize")
 return_types <- list(list('Tensor'))
 call_c_function(
@@ -6880,9 +6870,9 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "to_sparse_csc", function() {  args <- list()
+Tensor$set("public", "to_sparse_csc", function(dense_dim = NULL) {  args <- mget(x = c("dense_dim"))
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor")
+expected_types <- list(self = "Tensor", dense_dim = "int64_t")
 nd_args <- "self"
 return_types <- list(list('Tensor'))
 call_c_function(
@@ -6893,9 +6883,9 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "to_sparse_csr", function() {  args <- list()
+Tensor$set("public", "to_sparse_csr", function(dense_dim = NULL) {  args <- mget(x = c("dense_dim"))
 args <- c(list(self = self), args)
-expected_types <- list(self = "Tensor")
+expected_types <- list(self = "Tensor", dense_dim = "int64_t")
 nd_args <- "self"
 return_types <- list(list('Tensor'))
 call_c_function(
@@ -7223,11 +7213,11 @@ call_c_function(
   return_types = return_types,
   fun_type = 'method'
 )})
-Tensor$set("public", "var", function(dim, correction, unbiased = TRUE, keepdim = FALSE) {  args <- mget(x = c("dim", "correction", "unbiased", "keepdim"))
+Tensor$set("public", "var", function(dim, unbiased = TRUE, keepdim = FALSE) {  args <- mget(x = c("dim", "unbiased", "keepdim"))
 args <- c(list(self = self), args)
 expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), 
-    correction = "int64_t", unbiased = "bool", keepdim = "bool")
-nd_args <- c("self", "dim", "correction")
+    unbiased = "bool", keepdim = "bool")
+nd_args <- c("self", "dim")
 return_types <- list(list('Tensor'))
 call_c_function(
   fun_name = 'var',
@@ -7291,7 +7281,8 @@ call_c_function(
 )})
 Tensor$set("public", "where", function(condition, other) {  args <- mget(x = c("condition", "other"))
 args <- c(list(self = self), args)
-expected_types <- list(condition = "Tensor", self = "Tensor", other = "Tensor")
+expected_types <- list(condition = "Tensor", self = "Tensor", other = c("Tensor", 
+"Scalar"))
 nd_args <- c("condition", "self", "other")
 return_types <- list(list('Tensor'))
 call_c_function(

R/gen-namespace-docs.R

@@ -5161,59 +5161,11 @@ NULL
 #' @export
 NULL
 
-
-#' Symeig
-#'
-#' @section symeig(input, eigenvectors=False, upper=TRUE) -> (Tensor, Tensor) :
-#'
-#' This function returns eigenvalues and eigenvectors
-#' of a real symmetric matrix `input` or a batch of real symmetric matrices,
-#' represented by a namedtuple (eigenvalues, eigenvectors).
-#' 
-#' This function calculates all eigenvalues (and vectors) of `input`
-#' such that \eqn{\mbox{input} = V \mbox{diag}(e) V^T}.
-#' 
-#' The boolean argument `eigenvectors` defines computation of
-#' both eigenvectors and eigenvalues or eigenvalues only.
-#' 
-#' If it is `FALSE`, only eigenvalues are computed. If it is `TRUE`,
-#' both eigenvalues and eigenvectors are computed.
-#' 
-#' Since the input matrix `input` is supposed to be symmetric,
-#' only the upper triangular portion is used by default.
-#' 
-#' If `upper` is `FALSE`, then lower triangular portion is used.
-#' 
-#' @note The eigenvalues are returned in ascending order. If `input` is a batch of matrices,
-#'           then the eigenvalues of each matrix in the batch is returned in ascending order.
-#' 
-#' @note Irrespective of the original strides, the returned matrix `V` will
-#'           be transposed, i.e. with strides `V.contiguous().transpose(-1, -2).stride()`.
-#' 
-#' @note Extra care needs to be taken when backward through outputs. Such
-#'           operation is really only stable when all eigenvalues are distinct.
-#'           Otherwise, `NaN` can appear as the gradients are not properly defined.
-#'
-#'
-#' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more                    batch dimensions consisting of symmetric matrices.
-#' @param eigenvectors (boolean, optional) controls whether eigenvectors have to be computed
-#' @param upper (boolean, optional) controls whether to consider upper-triangular or lower-triangular region
-#'
-#' @name torch_symeig
-#'
-#' @export
-NULL
-
-
 #' Eig
 #'
 #' @section eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor) :
 #'
 #' Computes the eigenvalues and eigenvectors of a real square matrix.
-#' 
-#' @note
-#'     Since eigenvalues and eigenvectors might be complex, backward pass is supported only
-#'     for [`torch_symeig`]
 #'
 #'
 #' @param self (Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed

R/gen-namespace-examples.R

@@ -2393,29 +2393,6 @@ NULL
 NULL
 # -> triangular_solve <-
 
-# -> symeig: 27bc25d51797de06954ef84fde11f765 <-
-#'
-#' @name torch_symeig
-#'
-#' @examples
-#'
-#' a = torch_randn(c(5, 5))
-#' a = a + a$t()  # To make a symmetric
-#' a
-#' o = torch_symeig(a, eigenvectors=TRUE)
-#' e = o[[1]]
-#' v = o[[2]]
-#' e
-#' v
-#' a_big = torch_randn(c(5, 2, 2))
-#' a_big = a_big + a_big$transpose(-2, -1)  # To make a_big symmetric
-#' o = a_big$symeig(eigenvectors=TRUE)
-#' e = o[[1]]
-#' v = o[[2]]
-#' torch_allclose(torch_matmul(v, torch_matmul(e$diag_embed(), v$transpose(-2, -1))), a_big)
-NULL
-# -> symeig <-
-
 # -> eig: 94b4710518b0b3d3bf08c75dda217258 <-
 #'
 #' @name torch_eig