[Refactor] [lang] Deprecate x.data_type() and use x.dtype instead

docs/meta.rst

@@ -17,55 +17,124 @@ Taichi kernels are *lazily instantiated* and a lot of computation can happen at
 Template metaprogramming
 ------------------------
 
+Taichi tensors oftenly are used as globals. But you may use ``ti.template()``
+as type hint to pass a tensor as argument. For example:
+
 .. code-block:: python
 
     @ti.kernel
     def copy(x: ti.template(), y: ti.template()):
         for i in x:
             y[i] = x[i]
 
+    a = ti.var(ti.f32, 4)
+    b = ti.var(ti.f32, 4)
+    c = ti.var(ti.f32, 12)
+    d = ti.var(ti.f32, 12)
+    copy(a, b)
+    copy(c, d)
+
+
+As shown by the above example, template programming may enable us to reuse our
+code and improve better flexibility.
+
 
 Dimensionality-independent programming using grouped indices
 ------------------------------------------------------------
 
+However, the ``copy`` template shown above is not perfect, i.e., it can only be
+used to copy 1D tensors. What if we want to copy 2D tensors? Do we have to write
+another kernel?
+
+.. code-block:: python
+
+    @ti.kernel
+    def copy2d(x: ti.template(), y: ti.template()):
+        for i, j in x:
+            y[i, j] = x[i, j]
+
+Not necessary! Taichi provides ``ti.grouped`` syntax which enable you to get
+loop indices into a grouped vector, therefore unify different dimensionalities.
+For example:
+
 .. code-block:: python
 
     @ti.kernel
     def copy(x: ti.template(), y: ti.template()):
         for I in ti.grouped(y):
+            # I is a vector with same dimensionality with x and data type i32
+            # If y is 0D, then I = None
+            # If y is 1D, then I = ti.Vector([i])
+            # If y is 2D, then I = ti.Vector([i, j])
+            # If y is 3D, then I = ti.Vector([i, j, k])
+            # ...
             x[I] = y[I]
 
     @ti.kernel
     def array_op(x: ti.template(), y: ti.template()):
-        # If tensor x is 2D
-        for I in ti.grouped(x): # I is a vector of size x.dim() and data type i32
+        # if tensor x is 2D:
+        for I in ti.grouped(x): # I is simply a 2D vector with x data type i32
             y[I + ti.Vector([0, 1])] = I[0] + I[1]
-        # is equivalent to
+
+        # then it is equivalent to:
         for i, j in x:
             y[i, j + 1] = i + j
 
-Tensor size reflection
-----------------------
 
-Sometimes it will be useful to get the dimensionality (``tensor.dim()``) and shape (``tensor.shape()``) of tensors.
-These functions can be used in both Taichi kernels and python scripts.
+Tensor meta data
+----------------
+
+Sometimes it will be useful to get the data type (``tensor.dtype``) and shape (``tensor.shape``) of tensors.
+These attributes can be accessed in both Taichi kernels and python scripts.
 
 .. code-block:: python
 
   @ti.func
-  def print_tensor_size(x: ti.template()):
-    print(x.dim())
-    for i in ti.static(range(x.dim())):
-      print(x.shape()[i])
+  def print_tensor_info(x: ti.template()):
+    print('Tensor dimensionality is', len(x.shape))
+    for i in ti.static(range(len(x.shape))):
+      print('Size alone dimension', i, 'is', x.shape[i])
+    ti.static_print('Tensor data type is', x.dtype)
+
+See :ref:`scalar_tensor` for more details.
+
+.. note::
+
+    For sparse tensors, the full domain shape will be returned.
+
+
+Matrix & vector meta data
+-------------------------
+
+Sometimes it will also be useful to get the matrix column and row numbers when
+you want to write dimensionality-independent code, such as reusing code between
+2D/3D physical simulations.
+
+``matrix.m`` equals to the column number of matrix, while ``matrix.n`` equals to
+the row number of matrix.
+Since vectors are considered as matrices with one column, ``vector.n`` is simply
+the dimensionality of vector.
+
+.. code-block:: python
+
+  @ti.kernel
+  def foo():
+    matrix = ti.Matrix([[1, 2], [3, 4], [5, 6]])
+    print(matrix.n)  # 2
+    print(matrix.m)  # 3
+    vector = ti.Vector([7, 8, 9])
+    print(vector.n)  # 3
+    print(vector.m)  # 1
+
 
-For sparse tensors, the full domain shape will be returned.
 
 Compile-time evaluations
 ------------------------
+
 Using compile-time evaluation will allow certain computations to happen when kernels are being instantiated.
 This saves the overhead of those computations at runtime.
 
-* Use ``ti.static`` for compile-time branching (for those who come from C++17, this is `if constexpr <https://en.cppreference.com/w/cpp/language/if>`_.)
+* Use ``ti.static`` for compile-time branching (for those who come from C++17, this is `if constexpr <https://en.cppreference.com/w/cpp/language/if>`_.):
 
 .. code-block:: python
 
@@ -77,32 +146,20 @@ This saves the overhead of those computations at runtime.
        x[0] = 1
 
 
-* Use ``ti.static`` for forced loop unrolling
+* Use ``ti.static`` for forced loop unrolling:
 
 .. code-block:: python
 
- @ti.kernel
- def g2p(f: ti.i32):
- for p in range(0, n_particles):
-  base = ti.cast(x[f, p] * inv_dx - 0.5, ti.i32)
-  fx = x[f, p] * inv_dx - ti.cast(base, real)
-  w = [0.5 * ti.sqr(1.5 - fx), 0.75 - ti.sqr(fx - 1.0),
-       0.5 * ti.sqr(fx - 0.5)]
-  new_v = ti.Vector([0.0, 0.0])
-  new_C = ti.Matrix([[0.0, 0.0], [0.0, 0.0]])
-
-  # Unrolled 9 iterations for higher performance
-  for i in ti.static(range(3)):
-    for j in ti.static(range(3)):
-      dpos = ti.cast(ti.Vector([i, j]), real) - fx
-      g_v = grid_v_out[base(0) + i, base(1) + j]
-      weight = w[i](0) * w[j](1)
-      new_v += weight * g_v
-      new_C += 4 * weight * ti.outer_product(g_v, dpos) * inv_dx
-
-  v[f + 1, p] = new_v
-  x[f + 1, p] = x[f, p] + dt * v[f + 1, p]
-  C[f + 1, p] = new_C
+  @ti.kernel
+  def func():
+    for i in ti.static(range(4)):
+        print(i)
+
+    # is equivalent to:
+    print(0)
+    print(1)
+    print(2)
+    print(3)
 
 
 When to use for loops with ``ti.static``
@@ -120,7 +177,7 @@ For example, code for resetting this tensor of vectors should be
    @ti.kernel
    def reset():
      for i in x:
-       for j in ti.static(range(3)):
+       for j in ti.static(range(x.n)):
          # The inner loop must be unrolled since j is a vector index instead
          # of a global tensor index.
          x[i][j] = 0

docs/scalar_tensor.rst

@@ -107,49 +107,33 @@ You can access an element of the Taichi tensor by an index or indices.
 Meta data
 ---------
 
-.. function:: a.dim()
 
-    :parameter a: (Tensor) the tensor
-    :return: (scalar) the length of ``a``
-
-    ::
-
-        x = ti.var(ti.i32, (6, 5))
-        x.dim()  # 2
-
-        y = ti.var(ti.i32, 6)
-        y.dim()  # 1
-
-        z = ti.var(ti.i32, ())
-        z.dim()  # 0
-
-
-.. function:: a.shape()
+.. attribute:: a.shape
 
     :parameter a: (Tensor) the tensor
     :return: (tuple) the shape of tensor ``a``
 
     ::
 
         x = ti.var(ti.i32, (6, 5))
-        x.shape()  # (6, 5)
+        x.shape  # (6, 5)
 
         y = ti.var(ti.i32, 6)
-        y.shape()  # (6,)
+        y.shape  # (6,)
 
         z = ti.var(ti.i32, ())
-        z.shape()  # ()
+        z.shape  # ()
 
 
-.. function:: a.data_type()
+.. function:: a.dtype
 
     :parameter a: (Tensor) the tensor
     :return: (DataType) the data type of ``a``
 
     ::
 
         x = ti.var(ti.i32, (2, 3))
-        x.data_type()  # ti.i32
+        x.dtype  # ti.i32
 
 
 .. function:: a.parent(n = 1)

docs/snode.rst

@@ -33,32 +33,19 @@ See :ref:`layout` for more details. ``ti.root`` is the root node of the data str
         assert x.snode() == y.snode()
 
 
-.. function:: tensor.shape()
+.. function:: tensor.shape
 
     :parameter tensor: (Tensor)
     :return: (tuple of integers) the shape of tensor
 
-    Equivalent to ``tensor.snode().shape()``.
+    Equivalent to ``tensor.snode().shape``.
 
     For example,
 
     ::
 
         ti.root.dense(ti.ijk, (3, 5, 4)).place(x)
-        x.shape() # returns (3, 5, 4)
-
-
-.. function:: tensor.dim()
-
-    :parameter tensor: (Tensor)
-    :return: (scalar) the dimensionality of the tensor
-
-    Equivalent to ``len(tensor.shape())``.
-
-    ::
-
-        ti.root.dense(ti.ijk, (8, 9, 10)).place(x)
-        x.dim()  # 3
+        x.shape # returns (3, 5, 4)
 
 
 .. function:: tensor.snode()
@@ -74,7 +61,7 @@ See :ref:`layout` for more details. ``ti.root`` is the root node of the data str
         x.snode()
 
 
-.. function:: snode.shape()
+.. function:: snode.shape
 
     :parameter snode: (SNode)
     :return: (tuple) the size of node along that axis
@@ -85,29 +72,16 @@ See :ref:`layout` for more details. ``ti.root`` is the root node of the data str
         blk2 = blk1.dense(ti.i,  3)
         blk3 = blk2.dense(ti.jk, (5, 2))
         blk4 = blk3.dense(ti.k,  2)
-        blk1.shape()  # ()
-        blk2.shape()  # (3, )
-        blk3.shape()  # (3, 5, 2)
-        blk4.shape()  # (3, 5, 4)
-
-
-.. function:: snode.dim()
-
-    :parameter snode: (SNode)
-    :return: (scalar) the dimensionality of ``snode``
-
-    Equivalent to ``len(snode.shape())``.
-
-    ::
-
-        blk1 = ti.root.dense(ti.ijk, (8, 9, 10))
-        ti.root.dim()  # 0
-        blk1.dim()     # 3
+        blk1.shape  # ()
+        blk2.shape  # (3, )
+        blk3.shape  # (3, 5, 2)
+        blk4.shape  # (3, 5, 4)
 
 
-.. function:: snode.parent()
+.. function:: snode.parent(n = 1)
 
     :parameter snode: (SNode)
+    :parameter n: (optional, scalar) the number of parent steps, i.e. ``n=1`` for parent, ``n=2`` grandparent, etc.
     :return: (SNode) the parent node of ``snode``
 
     ::
@@ -118,6 +92,10 @@ See :ref:`layout` for more details. ``ti.root`` is the root node of the data str
         blk1.parent()  # ti.root
         blk2.parent()  # blk1
         blk3.parent()  # blk2
+        blk3.parent(1) # blk2
+        blk3.parent(2) # blk1
+        blk3.parent(3) # ti.root
+        blk3.parent(4) # None
 
 
 Node types

docs/vector.rst

@@ -191,6 +191,9 @@ Methods
     Vectors are special matrices with only 1 column. In fact, ``ti.Vector`` is just an alias of ``ti.Matrix``.
 
 
+Meta data
+---------
+
 .. attribute:: a.n
 
    :parameter a: (Vector or tensor of Vector)

python/taichi/lang/expr.py

@@ -68,7 +68,7 @@ def initialize_accessor(self):
             return
         snode = self.ptr.snode()
 
-        if self.snode().data_type() == f32 or self.snode().data_type() == f64:
+        if self.dtype == f32 or self.dtype == f64:
 
             def getter(*key):
                 assert len(key) == taichi_lang_core.get_max_num_indices()
@@ -78,7 +78,7 @@ def setter(value, *key):
                 assert len(key) == taichi_lang_core.get_max_num_indices()
                 snode.write_float(key, value)
         else:
-            if taichi_lang_core.is_signed(self.snode().data_type()):
+            if taichi_lang_core.is_signed(self.dtype):
 
                 def getter(*key):
                     assert len(key) == taichi_lang_core.get_max_num_indices()
@@ -135,15 +135,19 @@ def shape(self):
     def dim(self):
         return len(self.shape)
 
+    @property
+    def dtype(self):
+        return self.snode().dtype
+
+    @deprecated('x.data_type()', 'x.dtype')
     def data_type(self):
-        return self.snode().data_type()
+        return self.snode().dtype
 
     @python_scope
     def to_numpy(self):
         from .meta import tensor_to_ext_arr
         import numpy as np
-        arr = np.zeros(shape=self.shape,
-                       dtype=to_numpy_type(self.snode().data_type()))
+        arr = np.zeros(shape=self.shape, dtype=to_numpy_type(self.dtype))
         tensor_to_ext_arr(self, arr)
         import taichi as ti
         ti.sync()
@@ -154,7 +158,7 @@ def to_torch(self, device=None):
         from .meta import tensor_to_ext_arr
         import torch
         arr = torch.zeros(size=self.shape,
-                          dtype=to_pytorch_type(self.snode().data_type()),
+                          dtype=to_pytorch_type(self.dtype),
                           device=device)
         tensor_to_ext_arr(self, arr)
         import taichi as ti