fix gradient of pow(t, int)

semi revert some pow logic back to tensor. added direct gradient check because the backward in test_ops passed by luck

test/test_ops.py

@@ -629,6 +629,23 @@ def test_pow_const(self):
     helper_test_op(None, lambda x: 0**x, vals=[[-2.,-1,0,1,2,3]])
     helper_test_op(None, lambda x: (-2)**x, vals=[[-2.,-1,0,1,2,3]])
 
+  def test_pow_const_direct(self):
+    # x ** c
+    def get_tiny_gradient(x, c):
+      t = Tensor([x], dtype=dtypes.float)
+      return (t ** c)[0].gradient(t)[0].item()
+    def get_torch_gradient(x, c):
+      t = torch.tensor([x], dtype=torch.float, requires_grad=True)
+      return torch.autograd.grad(t ** c, t)[0].item()
+    for x in [0, 1]:
+      for c in [-1, 0, 0.3, 1, 2]:
+        tiny_out = get_tiny_gradient(x, c)
+        torch_out = get_torch_gradient(x, c)
+        if math.isnan(tiny_out):
+          assert math.isnan(torch_out)
+        else:
+          self.assertAlmostEqual(tiny_out, torch_out, msg=f"{x}, {c}")
+
   def test_pow_zero_tensor(self):
     helper_test_op(None, lambda x,y: x**y, vals=[[0.0], [0.3]])
     helper_test_op(None, lambda x,y: x**y, vals=[[0.0], [0.0]])

tinygrad/tensor.py

@@ -3373,6 +3373,12 @@ def pow(self, x:Union[Tensor, ConstType], reverse=False) -> Tensor:
     print((2.0 ** Tensor([-1, 2, 3])).numpy())
     ```
     """
+    # TODO: combine this with gradient
+    if not reverse and isinstance(x, get_args(ConstType)) and int(x) == x:
+      if x < 0: return self.reciprocal().pow(-x)
+      if x == 0: return self*0+1
+      return self.pow(int(x)//2).square() * (self if x%2 == 1 else 1)
+
     base, exponent = self._broadcasted(x, reverse=reverse)
     # TODO: int pow
     if not base.is_floating_point(): raise RuntimeError("base needs to be float")