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Changelog

  • (Jan 20, 2020) v0.3.25 released.
  • (Jan 19, 2020) v0.3.24 released.
    • % and // now follow Python semantics. Use ti.raw_mod for C-style % semantics (by Chujie Zeng [Psycho7])
    • Parallel range-fors now supports non-compile-time constant bounds. For example, for i in range(bound[0]) is supported
  • (Jan 18, 2020) v0.3.23 released.
    • Taichi kernel calls now releases Python GIL
  • (Jan 17, 2020) v0.3.22 released.
    • ti.atomic_add() now returns the old value (by Ye Kuang [k-ye])
    • Experimental patch to Windows systems with malformed BIOS info (by Chujie Zeng [Psycho7])
    • ti.__version__ now returns the version triple, e.g. (0, 3, 22)
    • Fixed a CPU multithreading bug
    • Avoid accessor IR printing when setting ti.cfg.print_ir = True
    • Added ti.cfg.print_accessor_ir
    • Removed dependency on x86_64 SIMD intrinsics
    • Improved doc
  • (Jan 11, 2020) v0.3.21 released.
    • GUI fix for OS X 10.14 and 10.15 (by Ye Kuang [k-ye]).
    • Minor improvements on documentation and profiler
  • (Jan 2, 2020) v0.3.20 released.
    • Support ti.static(ti.grouped(ti.ndrange(...)))
  • (Jan 2, 2020) v0.3.19 released.
    • Added ti.atan2(y, x)
    • Improved error msg when using float point numbers as tensor indices
  • (Jan 1, 2020) v0.3.18 released.
    • Added ti.GUI class
    • Improved the performance of performance ti.Matrix.fill
  • (Dec 31, 2019) v0.3.17 released.
    • Fixed cuda context conflict with PyTorch (thanks to @Xingzhe He for reporting)
    • Support ti.Matrix.T() for transposing a matrix
    • Iteratable ti.static(ti.ndrange)
    • Fixed ti.Matrix.identity()
    • Added ti.Matrix.one() (create a matrix with 1 as all the entries)
    • Improved ir_printer on SNodes
    • Better support for dynamic SNodes.
      • Struct-for's on dynamic nodes supported
      • ti.length and ti.append to query and manipulate dynamic nodes
  • (Dec 29, 2019) v0.3.16 released.
    • Fixed ndrange-fors with local variables (thanks to Xingzhe He for reporting this issue)
  • (Dec 28, 2019) v0.3.15 released.
    • Multi-dimensional parallel range-for using ti.ndrange:
  @ti.kernel
  def fill_3d():
    # Parallelized for all 3 <= i < 8, 1 <= j < 6, 0 <= k < 9
    for i, j, k in ti.ndrange((3, 8), (1, 6), 9):
      x[i, j, k] = i + j + k
  • (Dec 28, 2019) v0.3.14 released.
    • GPU random number generator support for more than 1024x1024 threads
    • Parallelized element list generation on GPUs. Struct-fors significantly sped up.
    • ti and tid (debug mode) CLI commands
  • (Dec 26, 2019) v0.3.13 released.
    • ti.append now returns the list length before appending
    • Fixed for loops with 0 iterations
    • Set ti.get_runtime().set_verbose_kernel_launch(True) to log kernel launches
    • Distinguish / and // following the Python convention
    • Allow using local variables as kernel argument type annotations
  • (Dec 25, 2019) v0.3.11 released.
    • Support multiple kernels with the same name, especially in the OOP cases where multiple member kernels share the same name
    • Basic dynamic node support (ti.append, ti.length) in the new LLVM backend
    • Fixed struct-for loops on 0-D tensors
  • (Dec 24, 2019) v0.3.10 released.
    • assert <condition> statement supported in Taichi kernels.
    • Comparison operator chaining (e.g. 1 < x <3) supported in Taichi kernels.
  • (Dec 24, 2019) v0.3.9 released.
    • 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
  • (Dec 17, 2019) v0.3.0 released.
    • Documentation significantly improved
    • break statements supported in while loops
    • CPU multithreading enabled by default
  • (Dec 16, 2019) v0.2.6 released.
    • ti.GUI.set_image(np.ndarray/Taichi tensor)
    • Inplace adds are atomic by default. E.g., x[i] += j is equivalent to ti.atomic_add(x[i], j)
    • ti.func arguments are forced to pass by value
    • min/max can now take more than two arguments, e.g. max(a, b, c, d)
    • Matrix operators transposed, trace, polar_decompose, determinant promoted to ti scope. I.e., users can now use ti.transposed(M) instead of ti.Matrix.transposed(M)
    • ti.get_runtime().set_verbose(False) to eliminate verbose outputs
    • LLVM backend now supports multithreading on CPUs
    • LLVM backend now supports random number generators (ti.random(ti.i32/i64/f32/f64)
  • (Dec 5, 2019) v0.2.3 released.
    • Simplified interaction between Taichi, numpy and PyTorch
      • taichi_scalar_tensor.to_numpy()/from_numpy(numpy_array)
      • taichi_scalar_tensor.to_torch()/from_torch(torch_array)
  • (Dec 4, 2019) v0.2.2 released.
    • Argument type ti.ext_arr() now takes PyTorch tensors
  • (Dec 3, 2019) v0.2.1 released.
    • Improved type mismatch error message
    • native min/max supprt
    • Tensor access index dimensionality checking
    • Matrix.to_numpy, Matrix.zero, Matrix.identity, Matrix.fill
    • Warning instead of error on lossy stores
    • Added some initial support for cross-referencing local variables in different offloaded blocks.
  • (Nov 28, 2019) v0.2.0 released.
    • More friendly syntax error when passing non-compile-time-constant values to ti.static
    • Systematically resolved the variable name resolution issue
    • Better interaction with numpy:
      • numpy arrays passed as a ti.ext_arr() [examples]
        • i32/f32/i64/f64 data type support for numpy
        • Multidimensional numpy arrays now supported in Taichi kernels
      • Tensor.to_numpy() and Tensor.from_numpy(numpy.ndarray) supported [examples]
      • Corresponding PyTorch tensor interaction will be supported very soon. Now only 1D f32 PyTorch tensors supproted when using ti.ext_arr(). Please use numpy arrays as intermediate buffers for now
    • Indexing arrays with an incorrect number of indices now results in a syntax error
    • Tensor shape reflection: [examples]
      • Tensor.dim() to retrieve the dimensionality of a global tensor
      • Tensor.shape() to retrieve the shape of a global tensor
      • Note the above queries will cause data structures to be materialized
    • struct-for (e.g. for i, j in x) now supports iterating over tensors with non power-of-two dimensions
    • Handy tensor filling: [examples]
      • Tensor.fill(x) to set all entries to x
      • Matrix.fill(x) to set all entries to x, where x can be a scalar or ti.Matrix of the same size
    • Reduced python package size
    • struct-for with grouped indices for better metaprogramming, especially in writing dimensionality-independent code, in e.g. physical simulation: [examples]
for I in ti.grouped(x): # I is a vector of size x.dim() and data type i32
  x[I] = 0

# If tensor x is 2D
for I in ti.grouped(x): # I is a vector of size x.dim() and data type i32
  y[I + ti.Vector([0, 1])] = I[0] + I[1]
# is equivalent to
for i, j in x:
  y[i, j + 1] = i + j

  • (Nov 27, 2019) v0.1.5 released.

    • Better modular programming support
    • Disalow the use of ti.static outside Taichi kernels
    • Documentation improvements (WIP)
    • Codegen bug fixes
    • Special thanks to Andrew Spielberg and KLozes for bug report and feedback.

  • (Nov 22, 2019) v0.1.3 released.

    • Object-oriented programming. [Example]
    • native Python function translation in Taichi kernels:
      • Use print instead of ti.print
      • Use int() instead of ti.cast(x, ti.i32) (or ti.cast(x, ti.i64) if your default integer precision is 64 bit)
      • Use float() instead of ti.cast(x, ti.f32) (or ti.cast(x, ti.f64) if your default float-point precision is 64 bit)
      • Use abs instead of ti.abs
      • Use ti.static_print for compile-time printing

  • (Nov 16, 2019) v0.1.0 released. Fixed PyTorch interface.

  • (Nov 12, 2019) v0.0.87 released.

    • Added experimental Windows support with a [known issue] regarding virtual memory allocation, which will potentially limit the scalability of Taichi programs (If you are a Windows expert, please let me know how to solve this. Thanks!). Most examples work on Windows now.
    • CUDA march autodetection;
    • Complex kernel to override autodiff.

  • (Nov 4, 2019) v0.0.85 released.

    • ti.stop_grad for stopping gradients during backpropagation. [Example];
    • Compatibility improvements on Linux and OS X;
    • Minor bug fixes.

The Legacy Taichi Library [Legacy branch]

The legacy Taichi library is an open-source computer graphics library written in C++14 and wrapped friendly with Python. It is no longer maintained since we have switched to the Taichi programming language and compiler.

News

Getting Started (Legacy)