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Documentations	Chat	taichi-nightly	taichi-nightly-cuda-10-0	taichi-nightly-cuda-10-1

# Python 3.6/3.7 needed

# CPU only. No GPU/CUDA needed. (Linux, OS X and Windows)
python3 -m pip install taichi-nightly

# With GPU (CUDA 10.0) support (Linux only)
python3 -m pip install taichi-nightly-cuda-10-0

# With GPU (CUDA 10.1) support (Linux only)
python3 -m pip install taichi-nightly-cuda-10-1

	Linux (CUDA)	OS X (10.14)	Windows
Build
PyPI

Related papers

• (SIGGRAPH Asia 2019) High-Performance Computation on Sparse Data Structures [Video] [BibTex]
  • by Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand
• (ICLR 2020) Differentiable Programming for Physical Simulation [Video] [BibTex] [Code]
  • by Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand

Short-term goals

• (Done) Fully implement the LLVM backend to replace the legacy source-to-source C++/CUDA backends (By Dec 2019)
  • The only missing features compared to the old source-to-source backends:
    • Vectorization on CPUs. Given most users who want performance are using GPUs (CUDA), this is given low priprity.
    • Automatic shared memory utilization. Postponed until Feb/March 2020.
• (WIP) Tune the performance of the LLVM backend to match that of the legacy source-to-source backends (By the end of Jan 2020)
• (WIP) Redesign memory allocator

Updates

• (Jan 3, 2020) v0.3.20 released.
  • Support for loops with 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.

• (Nov 1, 2019) v0.0.77 released.

  • Python wheels now support OS X 10.14+;
  • LLVM is now the default backend. No need to install gcc-7 or clang-7 anymore. To use legacy backends, export TI_LLVM=0;
  • LLVM compilation speed is improved by 2x;
  • More friendly syntax error messages.

• (Oct 30, 2019) v0.0.72 released.

  • LLVM GPU backend now as fast as the legacy (yet optimized) CUDA backend. To enable, export TI_LLVM=1;
  • Bug fixes: LLVM struct for list generation.

• (Oct 29, 2019) v0.0.71 released. LLVM GPU backend performance greatly improved. Frontend compiler now emits readable syntax error messages.

• (Oct 28, 2019) v0.0.70 released. This version comes with experimental LLVM backends for x86_64 and CUDA (via NVVM/PTX). GPU kernel compilation speed is improved by 10x. To enable, update the taichi package and export TI_LLVM=1.

• (Oct 24, 2019) Python wheels (v0.0.61) released for Python 3.6/3.7 and CUDA 10.0/10.1 on Ubuntu 16.04+. Contributors of this release include Yuanming Hu, robbertvc, Zhoutong Zhang, Tao Du, Srinivas Kaza, and Kenneth Lozes.

• (Oct 22, 2019) Added support for kernel templates. Kernel templates allow users to pass in taichi tensors and compile-time constants as kernel parameters.

• (Oct 9, 2019) Compatibility improvements. Added a basic PyTorch interface. [Example].

Notes:

• You still need to clone this repo for demo scripts under examples. You do not need to execute install.py or dev_setup.py. After installation using pip you can simply go to examples and execute, e.g., python3 mpm_fluid.py.
• Make sure you clear your legacy Taichi installation (if applicable) by cleaning the environment variables (delete TAICHI_REPO_DIR, and remove legacy taichi from PYTHONPATH) in your .bashrc or .zshrc. Or you can simply do this in your shell to temporarily clear them:

export PYTHONPATH=
export TAICHI_REPO_DIR=

The Taichi Library [Legacy branch]

Taichi is an open-source computer graphics library that aims to provide easy-to-use infrastructures for computer graphics R&D. It's written in C++14 and wrapped friendly with Python.

News

• May 17, 2019: Giga-Voxel SPGrid Topology Optimization Solver is released!
• March 4, 2019: MLS-MPM/CPIC solver is now MIT-licensed!
• August 14, 2018: MLS-MPM/CPIC solver reloaded! It delivers 4-14x performance boost over the previous state of the art on CPUs.

Getting Started (Legacy)