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

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

python/taichi/lang/matrix.py

@@ -524,8 +524,13 @@ def shape(self):
     def dim(self):
         return len(self.shape)
 
+    @property
+    def dtype(self):
+        return self.loop_range().dtype
+
+    @deprecated('x.data_type()', 'x.dtype')
     def data_type(self):
-        return self.loop_range().data_type()
+        return self.dtype
 
     def make_grad(self):
         ret = self.empty_copy()
@@ -619,9 +624,7 @@ def to_numpy(self, keep_dims=False, as_vector=None):
         if not self.is_global():
             return np.array(self.entries).reshape(shape_ext)
 
-        ret = np.empty(self.loop_range().shape + shape_ext,
-                       dtype=to_numpy_type(
-                           self.loop_range().snode().data_type()))
+        ret = np.empty(self.shape + shape_ext, dtype=to_numpy_type(self.dtype))
         from .meta import matrix_to_ext_arr
         matrix_to_ext_arr(self, ret, as_vector)
         import taichi as ti
@@ -633,9 +636,8 @@ def to_torch(self, device=None, keep_dims=False):
         import torch
         as_vector = self.m == 1 and not keep_dims
         shape_ext = (self.n, ) if as_vector else (self.n, self.m)
-        ret = torch.empty(self.loop_range().shape + shape_ext,
-                          dtype=to_pytorch_type(
-                              self.loop_range().snode().data_type()),
+        ret = torch.empty(self.shape + shape_ext,
+                          dtype=to_pytorch_type(self.dtype),
                           device=device)
         from .meta import matrix_to_ext_arr
         matrix_to_ext_arr(self, ret, as_vector)

python/taichi/lang/snode.py

@@ -64,9 +64,14 @@ def parent(self, n=1):
             return impl.root
         return SNode(p)
 
-    def data_type(self):
+    @property
+    def dtype(self):
         return self.ptr.data_type()
 
+    @deprecated('x.data_type()', 'x.dtype')
+    def data_type(self):
+        return self.dtype
+
     @deprecated('x.dim()', 'len(x.shape)')
     def dim(self):
         return len(self.shape)

python/taichi/misc/gui.py

@@ -80,7 +80,7 @@ def set_image(self, img):
         import taichi as ti
 
         if isinstance(img, ti.Expr):
-            if ti.core.is_integral(img.data_type()) or len(img.shape) != 2:
+            if ti.core.is_integral(img.dtype) or len(img.shape) != 2:
                 # Images of uint is not optimized by xxx_to_image
                 self.img = self.cook_image(img.to_numpy())
             else:
@@ -92,7 +92,7 @@ def set_image(self, img):
                 ti.sync()
 
         elif isinstance(img, ti.Matrix):
-            if ti.core.is_integral(img.data_type()):
+            if ti.core.is_integral(img.dtype):
                 self.img = self.cook_image(img.to_numpy())
             else:
                 # Type matched! We can use an optimized copy kernel.

tests/python/test_tensor_reflection.py

@@ -13,7 +13,7 @@ def test_POT():
     ti.root.dense(ti.i, n).dense(ti.j, m).dense(ti.k, p).place(val)
 
     assert val.shape == (n, m, p)
-    assert val.data_type() == ti.i32
+    assert val.dtype == ti.i32
 
 
 @ti.all_archs
@@ -30,7 +30,7 @@ def test_non_POT():
     blk3.place(val)
 
     assert val.shape == (n, m, p)
-    assert val.data_type() == ti.i32
+    assert val.dtype == ti.i32
 
 
 @ti.all_archs
@@ -46,7 +46,7 @@ def test_unordered():
     blk3 = blk2.dense(ti.j, p)
     blk3.place(val)
 
-    assert val.data_type() == ti.i32
+    assert val.dtype == ti.i32
     assert val.shape == (n, m, p)
     assert val.snode().parent(0) == val.snode()
     assert val.snode().parent() == blk3
@@ -79,7 +79,7 @@ def test_unordered_matrix():
     blk3.place(val)
 
     assert val.shape == (n, m, p)
-    assert val.data_type() == ti.i32
+    assert val.dtype == ti.i32
     assert val.loop_range().snode().parent(0) == val.loop_range().snode()
     assert val.loop_range().snode().parent() == blk3
     assert val.loop_range().snode().parent(1) == blk3
@@ -104,8 +104,10 @@ def test_deprecated():
     blk3.place(val, mat)
 
     assert val.dim() == 3
+    assert val.data_type() == ti.f32
     assert val.shape() == (n, m, p)
     assert mat.dim() == 3
+    assert mat.data_type() == ti.i32
     assert mat.shape() == (n, m, p)
     assert blk3.dim() == 3
     assert blk3.shape() == (n, m, p)