Merge branch 'master' of https://github.com/taichi-dev/taichi

.github/workflows/presubmit.yml

@@ -206,8 +206,6 @@ jobs:
       - name: Build
         shell: powershell
         run: |
-          $env:TAICHI_REPO_DIR = "D:\a\taichi\taichi"
-          $env:PYTHONPATH = "$env:TAICHI_REPO_DIR\python"
           cd C:\
           Remove-item alias:curl
           curl --retry 10 --retry-delay 5 https://github.com/taichi-dev/taichi_assets/releases/download/llvm10/taichi-llvm-10.0.0-msvc2019.zip -LO
@@ -216,28 +214,19 @@ jobs:
           7z x clang-10.0.0-win.zip -otaichi_clang
           $env:PATH += ";C:\taichi_llvm\bin"
           $env:PATH += ";C:\taichi_clang\bin"
-          $env:PATH += ";$env:TAICHI_REPO_DIR\bin"
           clang --version
           cd D:\a\taichi\taichi
-          python -m pip install numpy
-          python -m pip install pybind11
+          python -m pip install --user numpy pybind11 gitpython
           python misc/ci_setup.py ci
-          mkdir build
-          cd build
-          cmake .. -G"Visual Studio 16 2019" -A x64 -DPYTHON_EXECUTABLE="$env:PYTHON" -DLLVM_DIR="C:\taichi_llvm\lib\cmake\llvm"
-          msbuild /p:Configuration=RelWithDebInfo /p:Platform=x64 /m taichi.sln
-          cd ..
         env:
           PYTHON: C:\hostedtoolcache\windows\Python\3.7.9\x64\python.exe
+          CI_SETUP_CMAKE_ARGS: -G "Visual Studio 16 2019" -A x64 -DLLVM_DIR=C:\taichi_llvm\lib\cmake\llvm
 
       - name: Test
         shell: powershell
         run: |
-          $env:TAICHI_REPO_DIR = "D:\a\taichi\taichi"
-          $env:PYTHONPATH = "$env:TAICHI_REPO_DIR\python"
           $env:PATH += ";C:\taichi_llvm\bin"
           $env:PATH += ";C:\taichi_clang\bin"
-          $env:PATH += ";$env:TAICHI_REPO_DIR\bin"
           python -c "import taichi"
           python examples/algorithm/laplace.py
           python bin/taichi diagnose

MANIFEST.in

@@ -4,7 +4,7 @@ include python/*.py
 include *.cfg
 include python/taichi/*.md
 include python/taichi/assets/*
-include python/taichi/examples/*
+recursive-include python/taichi/examples *.py
 include python/taichi/tests/*
 include python/taichi/lib/*.so
 include python/taichi/lib/*.pyd

appveyor.yml

@@ -50,16 +50,13 @@ build_script:
   - 7z x clang-10.0.0-win.zip -otaichi_clang
   - set PATH=C:\taichi_llvm\bin;%PATH%;
   - set PATH=C:\taichi_clang\bin;%PATH%
-  - set PYTHONPATH=%TAICHI_REPO_DIR%/python
-  - set PATH=%TAICHI_REPO_DIR%\bin;%PATH%
   - clang --version
   - cd C:\taichi
+  - set CI_SETUP_CMAKE_ARGS=-G "Visual Studio 16 2019" -A x64 -DLLVM_DIR=C:\taichi_llvm\lib\cmake\llvm
+  # This is to fix the bug caused by execnet 1.2, the library xdist uses to schedule tests.
+  # Reverting to v1.1 solves this bug(Python Fatal Error: IOError).
+  - "%PYTHON% -m pip install -U execnet==1.1"
   - "%PYTHON% misc/ci_setup.py ci"
-  - mkdir build
-  - cd build
-  - cmake .. -G"Visual Studio 16 2019" -A x64 -DPYTHON_EXECUTABLE="%PYTHON%" -DLLVM_DIR="C:\taichi_llvm\lib\cmake\llvm"
-  - msbuild /p:Configuration=RelWithDebInfo /p:Platform=x64 /m taichi.sln
-  - cd ..
   - '%PYTHON% -c "import taichi"'
   - "%PYTHON% examples/algorithm/laplace.py"
   - "%PYTHON% bin/taichi diagnose"

misc/ci_setup.py

@@ -255,23 +255,21 @@ def run(self):
             print('PYTHONPATH={}'.format(os.environ['PYTHONPATH']))
 
             execute_command('echo $PYTHONPATH')
-        elif get_os_name() != 'win':
+        else:
             # compile ..
             arg = environ.get('CI_SETUP_CMAKE_ARGS', '')
+            os.environ['TAICHI_CMAKE_ARGS'] = arg
             os.makedirs('build', exist_ok=True)
-            execute_command(
-                f'TAICHI_CMAKE_ARGS="{arg}" {sys.executable} setup.py install --user'
-            )
+            execute_command(f'{sys.executable} setup.py install --user')
         return
         if test_installation():
             print('  Successfully Installed Taichi at {}.'.format(
                 self.repo_dir))
-            if get_os_name() != 'win':
-                if execute_command('ti') != 0:
-                    print('  Warning: shortcut "ti" does not work.')
-                print('  Please execute')
-                print('    source ', get_shell_rc_name())
-                print('  or restart your terminal.')
+            if execute_command('ti') != 0:
+                print('  Warning: shortcut "ti" does not work.')
+            print('  Please execute')
+            print('    source ', get_shell_rc_name())
+            print('  or restart your terminal.')
         else:
             print('  Error: installation failed.')
             exit(-1)

python/taichi/lang/__init__.py

@@ -76,7 +76,19 @@ def memory_profiler_print():
 
 
 extension = _ti_core.Extension
-is_extension_supported = _ti_core.is_extension_supported
+
+
+def is_extension_supported(arch, ext):
+    """Checks whether an extension is supported on an arch.
+
+    Args:
+        arch (taichi_core.Arch): Specified arch.
+        ext (taichi_core.Extension): Specified extension.
+
+    Returns:
+        bool: Whether `ext` is supported on `arch`.
+    """
+    return _ti_core.is_extension_supported(arch, ext)
 
 
 def reset():
@@ -289,13 +301,37 @@ def loop_unique(val, covers=None):
 
 
 def polar_decompose(A, dt=None):
+    """Perform polar decomposition (A=UP) for arbitrary size matrix.
+
+    Mathematical concept refers to https://en.wikipedia.org/wiki/Polar_decomposition.
+    This is only a wrapper for :func:`taichi.lang.linalg.polar_decompose`.
+
+    Args:
+        A (ti.Matrix(n, n)): input nxn matrix `A`.
+        dt (DataType): date type of elements in matrix `A`, typically accepts ti.f32 or ti.f64.
+
+    Returns:
+        Decomposed nxn matrices `U` and `P`.
+    """
     if dt is None:
         dt = impl.get_runtime().default_fp
     from .linalg import polar_decompose
     return polar_decompose(A, dt)
 
 
 def svd(A, dt=None):
+    """Perform singular value decomposition (A=USV^T) for arbitrary size matrix.
+
+    Mathematical concept refers to https://en.wikipedia.org/wiki/Singular_value_decomposition.
+    This is only a wrappers for :func:`taichi.lang.linalg.svd`.
+
+    Args:
+        A (ti.Matrix(n, n)): input nxn matrix `A`.
+        dt (DataType): date type of elements in matrix `A`, typically accepts ti.f32 or ti.f64.
+
+    Returns:
+        Decomposed nxn matrices `U`, 'S' and `V`.
+    """
     if dt is None:
         dt = impl.get_runtime().default_fp
     from .linalg import svd
@@ -305,23 +341,16 @@ def svd(A, dt=None):
 def eig(A, dt=None):
     """Compute the eigenvalues and right eigenvectors of a real matrix.
 
-    Parameters
-    ----------
-    A: ti.Matrix(n, n)
-        2D Matrix for which the eigenvalues and right eigenvectors will be computed.
-    dt: Optional[DataType]
-        The datatype for the eigenvalues and right eigenvectors
-
-    Returns
-    -------
-    eigenvalues: ti.Matrix(n, 2)
-        The eigenvalues in complex form. Each row stores one eigenvalue. The first number
-        of the eigenvalue represents the real part and the second number represents the
-        imaginary part.
-    eigenvectors: ti.Matrix(n*2, n)
-        The eigenvectors in complex form. Each column stores one eigenvector. Each eigenvector
-        consists of n entries, each of which is represented by two numbers for its real part
-        and imaginary part.
+    Mathematical concept refers to https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix.
+    2D implementation refers to :func:`taichi.lang.linalg.eig2x2`.
+
+    Args:
+        A (ti.Matrix(n, n)): 2D Matrix for which the eigenvalues and right eigenvectors will be computed.
+        dt (DataType): The datatype for the eigenvalues and right eigenvectors.
+
+    Returns:
+        eigenvalues (ti.Matrix(n, 2)): The eigenvalues in complex form. Each row stores one eigenvalue. The first number of the eigenvalue represents the real part and the second number represents the imaginary part.
+        eigenvectors (ti.Matrix(n*2, n)): The eigenvectors in complex form. Each column stores one eigenvector. Each eigenvector consists of n entries, each of which is represented by two numbers for its real part and imaginary part.
     """
     if dt is None:
         dt = impl.get_runtime().default_fp
@@ -334,19 +363,16 @@ def eig(A, dt=None):
 def sym_eig(A, dt=None):
     """Compute the eigenvalues and right eigenvectors of a real symmetric matrix.
 
-    Parameters
-    ----------
-    A: ti.Matrix(n, n)
-        Symmetric Matrix for which the eigenvalues and right eigenvectors will be computed.
-    dt: Optional[DataType]
-        The datatype for the eigenvalues and right eigenvectors
-
-    Returns
-    -------
-    eigenvalues: ti.Vector(n)
-        The eigenvalues. Each entry store one eigen value.
-    eigenvectors: ti.Matrix(n, n)
-        The eigenvectors. Each column stores one eigenvector.
+    Mathematical concept refers to https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix.
+    2D implementation refers to :func:`taichi.lang.linalg.sym_eig2x2`.
+
+    Args:
+        A (ti.Matrix(n, n)): Symmetric Matrix for which the eigenvalues and right eigenvectors will be computed.
+        dt (DataType): The datatype for the eigenvalues and right eigenvectors.
+
+    Returns:
+        eigenvalues (ti.Vector(n)): The eigenvalues. Each entry store one eigen value.
+        eigenvectors (ti.Matrix(n, n)): The eigenvectors. Each column stores one eigenvector.
     """
     assert all(A == A.transpose()), "A needs to be symmetric"
     if dt is None:
@@ -358,6 +384,16 @@ def sym_eig(A, dt=None):
 
 
 def randn(dt=None):
+    """Generates a random number from standard normal distribution.
+
+    Implementation refers to :func:`taichi.lang.random.randn`.
+
+    Args:
+        dt (DataType): The datatype for the generated random number.
+
+    Returns:
+        The generated random number.
+    """
     if dt is None:
         dt = impl.get_runtime().default_fp
     from .random import randn
@@ -609,6 +645,14 @@ def stat_write(key, value):
 
 
 def is_arch_supported(arch):
+    """Checks whether an arch is supported on the machine.
+
+    Args:
+        arch (taichi_core.Arch): Specified arch.
+
+    Returns:
+        bool: Whether `arch` is supported on the machine.
+    """
     arch_table = {
         cuda: _ti_core.with_cuda,
         metal: _ti_core.with_metal,
@@ -631,6 +675,11 @@ def is_arch_supported(arch):
 
 
 def supported_archs():
+    """Gets all supported archs on the machine.
+
+    Returns:
+        List[taichi_core.Arch]: All supported archs on the machine.
+    """
     archs = [cpu, cuda, metal, opengl, cc]
 
     wanted_archs = os.environ.get('TI_WANTED_ARCHS', '')