NEWS.md

anvl (development version)

Features

• New nv_lower_tri() and nv_upper_tri() (with nv_lower_tri_like() / nv_upper_tri_like()) return a boolean triangular mask for a given shape, mirroring base R's lower.tri() / upper.tri(). As in base R, the main diagonal is excluded by default; pass diagonal = 0L to include it. Use nv_tril() / nv_triu() to zero out a triangle of an existing array.

• trace_fn() gained an optimize argument controlling which graph optimization passes run on the traced graph. TRUE runs all passes, FALSE (default) runs none, and a character vector (e.g. c("inline_scalars", "remove_unused_constants")) selects a subset. jit() and xla() always trace with all passes enabled.

• New nv_dnorm() computes the normal distribution's probability density function (or, with log = TRUE, its log-density).

• nv_array(), nv_scalar(), as_array(), and the as.integer() / as.double() / as.logical() / as.vector() methods for AnvlArray gained a check argument that opts into scanning for NA values during host -> device and device -> host transfers. See the "Gotchas" vignette.

• nv_var() and nv_sd() now default to dims = NULL, which reduces over all dimensions and returns a scalar, consistent with the other reductions.

Performance

• Most nv_*() API functions are now JIT-compiled internally (via a new @jit roxygen roclet), speeding up eager-mode execution.

• Tracing (trace_fn()) performance has been improved.

• Tracing now accumulates primitive calls in a fastmap::fastqueue (amortised-O(1) append) instead of an R list grown with c() (copy-on-modify, O(n^2)). Tracing large unrolled graphs is substantially faster, e.g. ~1.36x for an 8000-op chain, with the gain growing with graph size.

• StableHLO lowering forwards the trace-time output types to the hlo_* builders (via an output_types argument passed to the lowering rules), so stablehlo skips redundant type inference when lowering the graph.

Bug fixes

• NULL is now treated as an empty node when flattening and unflattening trees. It contributes no leaves but is preserved structurally, so functions with optional arguments (e.g. function(x, y = NULL)) round-trip correctly.

• nv_argmax() / nv_argmin() and nv_cummax() / nv_cummin() now break ties order-independently, so they return the same result on GPU as on CPU (#368). nv_argmax() / nv_argmin() prefer the smallest index; nv_cummax() / nv_cummin() prefer the last occurrence.

• nv_diag() now errors on non-1-D input instead of silently producing an incorrect result.

anvl 0.3.0

Breaking Changes

• nv_empty() / nv_empty_like() return arrays with unspecified contents (no longer zero-initialized).

New Features

• On CPU, jitted XLA functions now back every non-aliased output with an R-owned RAWSXP. anvl appends a phantom donated input per unaliased output during lowering, allocates pjrt::pjrt_empty() buffers at execute time, and pjrt migrates the keepalive onto the output XPtr. The output's host bytes are then managed by R's GC.
• 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_reduce_mean() was renamed to nv_mean().
• 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.
• New API functions:
  • nv_triangular_solve() (wraps the already-existing prim_triangular_solve()).
  • nv_det() and nv_determinant(). The latter can also be called via the determinant() generic.
  • nv_inv(), which can also be called via solve(operand) (missing second argument).
• qr, chol, and solve from base R now dispatch to nv_qr(), nv_chol(), and nv_solve() on AnvlArray / AnvlBox inputs.

Element-wise math

• New unary primitives and corresponding nv_*() functions: acos, acosh, asin, asinh, atan, atanh, cosh, sinh, digamma, lgamma, polygamma, erf, erf_inv, erfc.
• New API functions nv_mod() (flooring remainder) and nv_trunc() (truncation toward zero).

Cumulative reductions

• New primitives and corresponding nv_*() functions: cumsum, cumprod, cummax, cummin. prim_cumprod() does not yet have a reverse rule.

Sorting and searching

• New primitives prim_sort(), prim_top_k(), prim_reduce(), prim_argmax(), prim_argmin().
• New API functions:
  • nv_sort() / nv_argsort() -- sort along a dimension, or return the permutation that does.
  • nv_top_k() -- the k largest values along a dimension.
  • nv_median() / nv_quantile() -- median / quantiles along a dimension. median() dispatches to nv_median().
  • nv_argmax() / nv_argmin() -- index of the maximum / minimum along a dimension (ties broken by smallest index).
  • nv_select() -- select a slice along a dimension by index.

Array construction / shape

• nv_array() gained a byrow argument that fills the array from an R object in row-major order, mirroring matrix(byrow = TRUE) (#165).
• New nv_matrix(data, nrow, ncol, ...) which works like R's matrix().
• New API functions nv_rbind() and nv_cbind() and corresponding rbind() / cbind() generics.
• New API function nv_flatten() for flattening to 1-D.

Misc

• New AnvlArray -> R vector converters: as.numeric(), as.double(), as.integer(), as.logical(), as.vector().
• New function await() that blocks until the underlying computation has finished.
• New tree utilities map_tree() and pmap_tree() for applying functions leaf-wise over (possibly nested) lists.
• Added support for range generic.
• Improved NaN handling across various primitives and API functions.

Other

• nv_reduce_sum(), nv_reduce_prod(), nv_reduce_max(), nv_reduce_min(), nv_reduce_any(), nv_reduce_all() and nv_mean() now default dims = NULL, which reduces over all dimensions and returns a scalar. Previously, dims was required.

Bug Fixes

• 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_eye() to create an identity matrix.
  • nv_solve() to solve a system of linear equations.
  • 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().
  • nv_crossprod() and nv_tcrossprod() for matrix cross-products.
  • nv_outer() for the outer product.
  • nv_extract_diag() to extract the diagonal of a matrix.
  • nv_trace() to compute the trace of a matrix.
  • nv_tril() and nv_triu() to extract lower/upper triangular parts.
  • nv_squeeze() and nv_unsqueeze() to drop or add length-1 dimensions.
  • nv_log2() and nv_log10().
  • nv_is_infinite() and nv_is_nan().
  • nv_sd() and nv_var() for standard deviation and variance.
• jit() now accepts integer positions for the static argument.
• New S3 methods dim(), nrow(), ncol(), and length() for anvl arrays.
• Printing tensors via nv_print() now also works on GPUs.
• R vectors of length 1 and arrays are now auto-converted when being passed to jitted functions.
• Improved device handling in jit()

Performance

• 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.
• Corrected argument checks in nv_iota().
• Fix check that wrt arguments in gradient() must be floats.
• nv_subset() and nv_subset_assign() now error on trailing-comma subscripts (#273).

Documentation

• New vignette on implementing Gaussian Processes.
• New vignette on implementing Metropolis-Hastings sampling.

Platform support and installation

• An installation guide was added.
• Linux on ARM is now supported (CPU only).
• To use the CUDA backend, it is now possible to install the cuda12.8 package (see installation guide), which only requires a compatible CUDA driver.

anvl 0.1.0

Initial release