[lang] [refactor] deprecate @boardcast_if_scalar, all use @binary and @unary

python/taichi/lang/common_ops.py

@@ -0,0 +1,56 @@
+class TaichiOperations:
+    def __neg__(self):
+        import taichi as ti
+        return ti.neg(self)
+
+    def __abs__(self):
+        import taichi as ti
+        return ti.abs(self)
+
+    def __add__(self, other):
+        import taichi as ti
+        return ti.add(self, other)
+
+    __radd__ = __add__
+
+    def __sub__(self, other):
+        import taichi as ti
+        return ti.sub(self, other)
+
+    def __rsub__(self, other):
+        import taichi as ti
+        return ti.sub(other, self)
+
+    def __mul__(self, other):
+        import taichi as ti
+        return ti.mul(self, other)
+
+    __rmul__ = __mul__
+
+    def __truediv__(self, other):
+        import taichi as ti
+        return ti.truediv(self, other)
+
+    def __rtruediv__(self, other):
+        import taichi as ti
+        return ti.truediv(other, self)
+
+    def __floordiv__(self, other):
+        import taichi as ti
+        return ti.floordiv(self, other)
+
+    def __rfloordiv__(self, other):
+        import taichi as ti
+        return ti.floordiv(other, self)
+
+    def __mod__(self, other):
+        import taichi as ti
+        return ti.mod(self, other)
+
+    def __pow__(self, other, modulo=None):
+        import taichi as ti
+        return ti.pow(self, other)
+
+    def __rpow__(self, other, modulo=None):
+        import taichi as ti
+        return ti.pow(other, self)

python/taichi/lang/expr.py

@@ -1,10 +1,11 @@
 from .core import taichi_lang_core
 from .util import *
+from .common_ops import TaichiOperations
 import traceback
 
 
 # Scalar, basic data type
-class Expr:
+class Expr(TaichiOperations):
     materialize_layout_callback = None
     layout_materialized = False
 
@@ -46,70 +47,23 @@ def stack_info():
         # remove the confusing last line
         return '\n'.join(raw.split('\n')[:-3]) + '\n'
 
-    def __add__(self, other):
-        other = Expr(other)
-        return Expr(taichi_lang_core.expr_add(self.ptr, other.ptr),
-                    tb=self.stack_info())
-
-    __radd__ = __add__
-
-    def __neg__(self):
-        return Expr(taichi_lang_core.expr_neg(self.ptr), tb=self.stack_info())
-
-    def __sub__(self, other):
-        other = Expr(other)
-        return Expr(taichi_lang_core.expr_sub(self.ptr, other.ptr),
-                    tb=self.stack_info())
-
-    def __rsub__(self, other):
-        other = Expr(other)
-        return Expr(taichi_lang_core.expr_sub(other.ptr, self.ptr))
-
-    def __mul__(self, other):
-        if is_taichi_class(other) and hasattr(other, '__rmul__'):
-            return other.__rmul__(self)
-        else:
-            other = Expr(other)
-            return Expr(taichi_lang_core.expr_mul(self.ptr, other.ptr))
-
-    __rmul__ = __mul__
-
-    def __truediv__(self, other):
-        return Expr(taichi_lang_core.expr_truediv(self.ptr, Expr(other).ptr))
-
-    def __rtruediv__(self, other):
-        return Expr(taichi_lang_core.expr_truediv(Expr(other).ptr, self.ptr))
-
-    def __floordiv__(self, other):
-        return Expr(taichi_lang_core.expr_floordiv(self.ptr, Expr(other).ptr))
-
-    def __rfloordiv__(self, other):
-        return Expr(taichi_lang_core.expr_floordiv(Expr(other).ptr, self.ptr))
-
-    def __mod__(self, other):
-        other = Expr(other)
-        quotient = Expr(taichi_lang_core.expr_floordiv(self.ptr, other.ptr))
-        multiply = Expr(taichi_lang_core.expr_mul(other.ptr, quotient.ptr))
-        return Expr(taichi_lang_core.expr_sub(self.ptr, multiply.ptr))
-
     def __iadd__(self, other):
         self.atomic_add(other)
 
     def __isub__(self, other):
         self.atomic_sub(other)
 
     def __imul__(self, other):
-        self.assign(Expr(taichi_lang_core.expr_mul(self.ptr, other.ptr)))
+        import taichi as ti
+        self.assign(ti.mul(self, other))
 
     def __itruediv__(self, other):
-        self.assign(
-            Expr(taichi_lang_core.expr_truediv(self.ptr,
-                                               Expr(other).ptr)))
+        import taichi as ti
+        self.assign(ti.truediv(self, other))
 
     def __ifloordiv__(self, other):
-        self.assign(
-            Expr(taichi_lang_core.expr_floordiv(self.ptr,
-                                                Expr(other).ptr)))
+        import taichi as ti
+        self.assign(ti.floordiv(self, other))
 
     def __iand__(self, other):
         self.atomic_and(other)
@@ -120,6 +74,7 @@ def __ior__(self, other):
     def __ixor__(self, other):
         self.atomic_xor(other)
 
+    # TODO: move to ops.py: ti.cmp_le
     def __le__(self, other):
         other = Expr(other)
         return Expr(taichi_lang_core.expr_cmp_le(self.ptr, other.ptr))
@@ -314,39 +269,6 @@ def fill(self, val):
         from .meta import fill_tensor
         fill_tensor(self, val)
 
-    def __rpow__(self, power, modulo=None):
-        # Python will try Matrix.__pow__ first so we don't have to worry whether `power` is `Matrix`
-        return Expr(power).__pow__(self, modulo)
-
-    def __pow__(self, power, modulo=None):
-        import taichi as ti
-        if ti.is_taichi_class(power):
-            return power.element_wise_binary(lambda x, y: pow(y, x), self)
-        if not isinstance(power, int) or abs(power) > 100:
-            return Expr(taichi_lang_core.expr_pow(self.ptr, Expr(power).ptr))
-        if power == 0:
-            return Expr(1)
-        negative = power < 0
-        power = abs(power)
-        tmp = self
-        ret = None
-        while power:
-            if power & 1:
-                if ret is None:
-                    ret = tmp
-                else:
-                    ret = ti.expr_init(ret * tmp)
-            tmp = ti.expr_init(tmp * tmp)
-            power >>= 1
-        if negative:
-            return 1 / ret
-        else:
-            return ret
-
-    def __abs__(self):
-        import taichi as ti
-        return ti.abs(self)
-
     def __ti_int__(self):
         import taichi as ti
         return ti.cast(self, ti.get_runtime().default_ip)

python/taichi/lang/matrix.py

@@ -4,6 +4,7 @@
 import numbers
 import numpy as np
 from .util import to_numpy_type, to_pytorch_type
+from .common_ops import TaichiOperations
 
 
 def broadcast_if_scalar(func):
@@ -15,7 +16,7 @@ def broadcasted(self, other, *args, **kwargs):
     return broadcasted
 
 
-class Matrix:
+class Matrix(TaichiOperations):
     is_taichi_class = True
 
     def __init__(self,
@@ -42,12 +43,14 @@ def __init__(self,
                     assert row.n == rows[
                         0].n, "input vectors must be the same shape"
                 self.m = rows[0].n
+                # l-value copy:
                 self.entries = [row(i) for row in rows for i in range(row.n)]
             elif isinstance(rows[0], list):
                 for row in rows:
                     assert len(row) == len(
                         rows[0]), "input lists must be the same shape"
                 self.m = len(rows[0])
+                # l-value copy:
                 self.entries = [x for row in rows for x in row]
             else:
                 raise Exception(
@@ -168,113 +171,12 @@ def __matmul__(self, other):
                     ret(i, j).assign(ret(i, j) + self(i, k) * other(k, j))
         return ret
 
-    @broadcast_if_scalar
-    def __pow__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(self(i, j)**other(i, j))
-        return ret
-
-    @broadcast_if_scalar
-    def __rpow__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(other(i, j)**self(i, j))
-        return ret
-
-    @broadcast_if_scalar
-    def __div__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(self(i, j) / other(i, j))
-        return ret
-
-    @broadcast_if_scalar
-    def __rtruediv__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(other(i, j) / self(i, j))
-        return ret
-
     def broadcast(self, scalar):
         ret = Matrix(self.n, self.m, empty=True)
         for i in range(self.n * self.m):
             ret.entries[i] = scalar
         return ret
 
-    @broadcast_if_scalar
-    def __truediv__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(self(i, j) / other(i, j))
-        return ret
-
-    @broadcast_if_scalar
-    def __floordiv__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(self(i, j) // other(i, j))
-        return ret
-
-    @broadcast_if_scalar
-    def __mul__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(self(i, j) * other(i, j))
-        return ret
-
-    __rmul__ = __mul__
-
-    @broadcast_if_scalar
-    def __add__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(self(i, j) + other(i, j))
-        return ret
-
-    __radd__ = __add__
-
-    @broadcast_if_scalar
-    def __sub__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(self(i, j) - other(i, j))
-        return ret
-
-    def __neg__(self):
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(-self(i, j))
-        return ret
-
-    @broadcast_if_scalar
-    def __rsub__(self, other):
-        assert self.n == other.n and self.m == other.m
-        ret = Matrix(self.n, self.m)
-        for i in range(self.n):
-            for j in range(self.m):
-                ret(i, j).assign(other(i, j) - self(i, j))
-        return ret
-
     def linearize_entry_id(self, *args):
         assert 1 <= len(args) <= 2
         if len(args) == 1 and isinstance(args[0], (list, tuple)):
@@ -379,7 +281,7 @@ def abs(self):
 
     def trace(self):
         assert self.n == self.m
-        sum = self(0, 0)
+        sum = expr.Expr(self(0, 0))
         for i in range(1, self.n):
             sum = sum + self(i, i)
         return sum
@@ -390,8 +292,9 @@ def inverse(self):
             return Matrix([1 / self(0, 0)])
         elif self.n == 2:
             inv_det = impl.expr_init(1.0 / self.determinant(self))
+            # Dis: https://github.com/taichi-dev/taichi/pull/943#issuecomment-626344323
             return inv_det * Matrix([[self(1, 1), -self(0, 1)],
-                                     [-self(1, 0), self(0, 0)]])
+                                     [-self(1, 0), self(0, 0)]]).variable()
         elif self.n == 3:
             n = 3
             import taichi as ti
@@ -524,6 +427,7 @@ def diag(dim, val):
     def loop_range(self):
         return self.entries[0]
 
+    # TODO
     @broadcast_if_scalar
     def augassign(self, other, op):
         if not isinstance(other, Matrix):