Taichi v0.3.9 released

[yuanming]

Changes since v0.3.0:

• (Dec 24, 2019) v0.3.9
  • ti.classfunc decorator for functions within a data_oriented class
  • [Expr/Vector/Matrix].to_torch now has a extra argument device, which specifies the device placement for returned torch tensor, and should have type torch.device. Default=None.
  • Cross-device (CPU/GPU) taichi/PyTorch interaction support, when using to_torch/from_torch.
  • #kernels compiled during external array IO significantly reduced (from matrix size to 1)
• (Dec 23, 2019) v0.3.8 released.
  • Breaking change: ti.data_oriented decorator introduced. Please decorate all your Taichi data-oriented objects using this decorator. To invoke the gradient versions of classmethod, for example, A.forward, simply use A.forward.grad() instead of A.forward(__gradient=True) (obsolete).
• (Dec 22, 2019) v0.3.5 released.
  • Maximum tensor dimensionality is 8 now (used to be 4). I.e., you can now allocate up to 8-D tensors.
• (Dec 22, 2019) v0.3.4 released.
  • 2D and 3D polar decomposition (R, S = ti.polar_decompose(A, ti.f32)) and svd (U, sigma, V = ti.svd(A, ti.f32)) support. Note that sigma is a 3x3 diagonal matrix.
  • Fixed documentation versioning
  • Allow expr_init with ti.core.DataType as inputs, so that ti.core.DataType can be used as ti.func parameter
• (Dec 20, 2019) v0.3.3 released.
  • Loud failure message when calling nested kernels. Closed #310
  • DiffTaichi examples moved to a standalone repo
  • Fixed documentation versioning
  • Correctly differentiating kernels with multiple offloaded statements
• (Dec 18, 2019) v0.3.2 released
  • Vector.norm now comes with a parameter eps (=0 by default), and returns sqrt(\sum_i(x_i ^ 2) + eps). A non-zero eps safe guards the operator’s gradient on zero vectors during differentiable programming.
• (Dec 17, 2019) v0.3.1 released.
  • Removed dependency on glibc 2.27