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[Doc] Improve type.rst readability #1618

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146 changes: 123 additions & 23 deletions docs/type.rst
Original file line number Diff line number Diff line change
@@ -1,8 +1,33 @@
Type system
===========

Taichi supports common numerical data types. Each type is denoted as
a character indicating its *category* and a number of *precision bits*, e.g., ``i32`` and ``f64``.

The *category* can be one of:

- ``i`` for signed integers, e.g. 233, -666
- ``u`` for unsigned integers, e.g. 233, 666
- ``f`` for floating point numbers, e.g. 2.33, 1e-4

The *digital number* can be one of:

- ``8``
- ``16``
- ``32``
- ``64``

It represents how many **bits** are used in storing the data.
The larger the bit number, the higher the precision is.

For example, the two most commonly used types:

- ``i32`` represents a 32-bit signed integer.
- ``f32`` represents a 32-bit floating pointer number.

Supported types
---------------

Currently, supported basic types in Taichi are

- int8 ``ti.i8``
Expand Down Expand Up @@ -46,54 +71,129 @@ Currently, supported basic types in Taichi are
(OK: supported, EXT: require extension, N/A: not available)


Boolean types should be represented using ``ti.i32``.
.. note::

Boolean types are represented using ``ti.i32``.


Binary operations on different types will give you a promoted type, following the C programming language, e.g.

- ``i32 + f32 = f32``
- ``f32 + f64 = f64``
- ``i32 + i64 = i64``
Type promotion
--------------

Binary operations on different types will give you a promoted type, following the C programming language convention, e.g.:

- ``i32 + f32 = f32`` (integer + float = float)
- ``i32 + i64 = i64`` (less-bits + more-bits = more-bits)

Basically it will try to choose the more precise type to contain the result value.


.. _default_precisions:

Default precisions
------------------

By default, numerical literals have 32-bit precisions.
By default, all numerical literals have 32-bit precisions.
For example, ``42`` has type ``ti.i32`` and ``3.14`` has type ``ti.f32``.

Default integer and float-point precisions (``default_ip`` and ``default_fp``) can be specified when initializing Taichi:

.. code-block:: python

ti.init(..., default_fp=ti.f32)
ti.init(..., default_fp=ti.f64)
ti.init(default_fp=ti.f32)
ti.init(default_fp=ti.f64)

ti.init(default_ip=ti.i32)
ti.init(default_ip=ti.i64)


Also note that you may use ``float`` or ``int`` in type definitions as aliases
for default precisions, e.g.:

.. code-block:: python

ti.init(default_ip=ti.i64, default_fp=ti.f32)

x = ti.var(float, 5)
y = ti.var(int, 5)
# is equivalent to:
x = ti.var(ti.f32, 5)
y = ti.var(ti.i64, 5)
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def func(a: float) -> int:
...

# is equivalent to:
def func(a: ti.f32) -> ti.i64:
...

ti.init(..., default_ip=ti.i32)
ti.init(..., default_ip=ti.i64)


Type casts
----------

Use ``ti.cast`` to cast scalar values.
Implicit casts
**************

WARNING: The type of a variable is **determinated on it's initialization**.
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When a *low-precision* variable is assigned to a *high-precision* variable, it will be
implicitly promoted to the *wide* type and no warning will be raised:

.. code-block:: python

a = 1.7
a = 1
print(a) # 1.0

When a *high-precision* variable is assigned to a *low-precision* type, it will be
implicitly down-cast into the *low-precision* type and Taichi will raise a warning:

.. code-block:: python

a = 1.4
b = ti.cast(a, ti.i32)
c = ti.cast(b, ti.f32)
a = 1
a = 1.7
print(a) # 1

Explicit casts
**************

You may use ``ti.cast`` to explicitly cast scalar values between different types:

.. code-block:: python

# Equivalently, use ``int()`` and ``float()``
# to convert values to default float-point/integer types
b = int(a)
c = float(b)
a = 1.7
b = ti.cast(a, ti.i32) # 1
c = ti.cast(b, ti.f32) # 1.0

# Element-wise casts in matrices
mat = ti.Matrix([[3.0, 0.0], [0.3, 0.1]])
mat_int = mat.cast(int)
mat_int2 = mat.cast(ti.i32)
Equivalently, use ``int()`` and ``float()`` to convert values to float-point or
integer types of default precisions:

.. code-block:: python

a = 1.7
b = int(a) # 1
c = float(a) # 1.0

Casting vectors and matrices
****************************

Type casts applied to vectors/matrices are element-wise:

.. code-block:: python

u = ti.Vector([2.3, 4.7])
v = int(u) # ti.Vector([2, 4])
# If you are using ti.i32 as default_ip, this is equivalent to:
v = ti.cast(u, ti.i32) # ti.Vector([2, 4])

Bit casting
***********

Use ``ti.bit_cast`` to bit-cast a value into another data type. The underlying bits will be preserved in this cast.
The new type must have the same width as the the old type.
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For example, bit-casting ``i32`` to ``f64`` is not allowed. Use this operation with caution.
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or example, bit-casting ``i32`` to ``f64`` is not allowed. Use this operation with caution.
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.. code-block::

For people from C++, ``ti.bit_cast`` is equivalent to ``reinterpret_cast``.