Tensor.int and Tensor.bool (#5317)

docs/tensor/ops.md

@@ -106,3 +106,5 @@
 ::: tinygrad.Tensor.bitcast
 ::: tinygrad.Tensor.float
 ::: tinygrad.Tensor.half
+::: tinygrad.Tensor.int
+::: tinygrad.Tensor.bool

test/test_ops.py

@@ -1866,6 +1866,13 @@ class TestOps(unittest.TestCase):
     helper_test_op([(32,10)], lambda x: x.masked_fill((x>0.1).detach(), -math.inf))
     helper_test_op([(32,10)], lambda x: x.masked_fill((x<0.1).detach(), -math.inf))
 
+  def test_cast(self):
+    helper_test_op([(3, 3)], lambda x: x.float())
+    helper_test_op(None, lambda x: x.float(), vals=[[0, 1, 2, 3]], forward_only=True)
+    helper_test_op(None, lambda x: x.float(), vals=[[True, False]], forward_only=True)
+    helper_test_op([(3, 3)], lambda x: x.int(), forward_only=True)
+    helper_test_op([(3, 3)], lambda x: x.bool(), forward_only=True)
+
 if __name__ == '__main__':
   np.random.seed(1337)
   unittest.main(verbosity=2)

tinygrad/tensor.py

@@ -2837,12 +2837,23 @@ class Tensor:
     smoothing = label_smoothing * (log_probs.mean(-1) * loss_mask).sum()
     return -((1 - label_smoothing) * (log_probs * y).sum() + smoothing) / loss_mask.sum()
 
+  # ***** convenience stuff *****
+
+  @property
+  def ndim(self) -> int: return len(self.shape)
+  def numel(self) -> sint: return prod(self.shape)
+  def element_size(self) -> int: return self.dtype.itemsize
+  def nbytes(self) -> int: return self.numel() * self.element_size()
+  def is_floating_point(self) -> bool: return dtypes.is_float(self.dtype)
+  def size(self, dim=None) -> Union[sint, Tuple[sint, ...]]: return self.shape if dim is None else self.shape[dim]
+
   # ***** cast ops *****
 
   def llvm_bf16_cast(self, dtype:DType):
     # hack for devices that don't support bfloat16
     assert self.dtype == dtypes.bfloat16
     return self.to("LLVM").bitcast(dtypes.uint16).cast(dtypes.uint32).mul(1<<16).bitcast(dtypes.float32).cast(dtype)
+
   def cast(self, dtype:DType) -> Tensor:
     """
     Casts `self` to the given `dtype`.
@@ -2857,6 +2868,7 @@ class Tensor:
     ```
     """
     return self if self.dtype == dtype else F.Cast.apply(self, dtype=dtype)
+
   def bitcast(self, dtype:DType) -> Tensor:
     """
     Bitcasts `self` to the given `dtype` of the same itemsize.
@@ -2874,6 +2886,7 @@ class Tensor:
     """
     if self.requires_grad: raise RuntimeError("can't backprop through bitcast")
     return F.Cast.apply(self, dtype=dtype, bitcast=True) if self.dtype != dtype else self
+
   def float(self) -> Tensor:
     """
     Convenience method to cast `self` to a `float32` Tensor.
@@ -2888,6 +2901,7 @@ class Tensor:
     ```
     """
     return self.cast(dtypes.float32)
+
   def half(self) -> Tensor:
     """
     Convenience method to cast `self` to a `float16` Tensor.
@@ -2903,15 +2917,35 @@ class Tensor:
     """
     return self.cast(dtypes.float16)
 
-  # ***** convenience stuff *****
+  def int(self) -> Tensor:
+    """
+    Convenience method to cast `self` to a `int32` Tensor.
 
-  @property
-  def ndim(self) -> int: return len(self.shape)
-  def numel(self) -> sint: return prod(self.shape)
-  def element_size(self) -> int: return self.dtype.itemsize
-  def nbytes(self) -> int: return self.numel() * self.element_size()
-  def is_floating_point(self) -> bool: return dtypes.is_float(self.dtype)
-  def size(self, dim=None) -> Union[sint, Tuple[sint, ...]]: return self.shape if dim is None else self.shape[dim]
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([-1.5, -0.5, 0.0, 0.5, 1.5])
+    print(t.dtype, t.numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = t.int()
+    print(t.dtype, t.numpy())
+    ```
+    """
+    return self.cast(dtypes.int32)
+
+  def bool(self) -> Tensor:
+    """
+    Convenience method to cast `self` to a `bool` Tensor.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([-1, 0, 1])
+    print(t.dtype, t.numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = t.bool()
+    print(t.dtype, t.numpy())
+    ```
+    """
+    return self.cast(dtypes.bool)
 
   # *** image Tensor function replacements ***