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.
