Renamed user-facing API functions to match base R names: nv_sine() -> nv_sin(), nv_cosine() -> nv_cos(), nv_ceil() -> nv_ceiling(), nv_cholesky() -> nv_chol(). The corresponding primitives were renamed in step: prim_sine() -> prim_sin(), prim_cosine() -> prim_cos(), prim_cholesky() -> prim_chol().
nv_solve() no longer requires a to be symmetric positive-definite as it uses LU instead of Cholesky decomposition. Because of this, it is no longer differentiable, as the reverse rule for LU is not implemented yet.
nv_chol() / prim_chol() now default to lower = FALSE (upper-triangular factor), matching base R’s chol(). Previously defaulted to lower = TRUE.
New Features
Linear algebra
New matrix-decomposition primitives and corresponding nv_*() functions: qr, lu, svd, eigh. None of them implement a reverse rule yet.
The overloaded %% operator now calls the new nv_mod() to be consistent with base R.
The reverse rule for prim_reduce_prod() no longer produces NaN / Inf gradients when the input contains zeros.
The CI now actually runs the torch-comparison tests.
nv_runif() not properly respects the lower argument.
anvl 0.2.0
Breaking Changes
The package was renamed from anvil to anvl to avoid a conflict with the Bioconductor package AnVIL.
AnvilTensor/nv_tensor were renamed to AnvlArray and nv_array to be more in line with R’s array(). Also, nv_aten() was renamed to nv_aval().
Subsetting with list() (e.g. x[list(1, 3)]) is no longer supported. Use array() to wrap the indices instead, e.g. x[array(c(1L, 3L))]. This mirrors the input convention used everywhere else in the package.
Removed debug mode.
Remove NSE support for nvl_if. It now requires passing 0-argument closures as true and false arguments.
Primitives renamed from nvl_* to prim_*. The underlying primitive object containing the rules and metadata is now part of the JitPrimitive function via the primitive attribute.
New Features
Better composability: jit()ted functions can now be used in other jit()-calls. This is the mechanism underlying the new eager mode.
Eager mode was added: This means, you can now do nv_add(1, nv_array(1:2)) and it will actually perform the computation and not only do type inference.
An experimental {quickr} backend was added It only runs on CPU for now and supports a subset of available operations. You can enable it via the backend argument in jit() and nv_array() or via the anvl.default_backend option.
New primitives:
nvl_cholesky() to compute the Cholesky decomposition of a matrix.
nvl_triangular_solve() to solve a system of linear equations with a triangular matrix.
New API functions (+ corresponding R generic implementations):
nv_diag() to create a diagonal matrix from a 1-D tensor.
nv_cholesky() to compute the Cholesky decomposition of a matrix.
nv_device() constructs a backend-specific device object (e.g. nv_device("cpu")) that can be passed as device to array constructors like nv_fill() or nv_iota().
Many operations are now done asynchronously, which improves performance, especially on GPUs.
Bug Fixes
+-Inf/NaN are correctly created for f64 when inlined into the XLA exectuable (#182). This caused wrong results with e.g. nv_reduce_max() when working with f64.
