diff --git a/test/test_ops.py b/test/test_ops.py
index 49b873f4b5..cf7b0f5307 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -69,7 +69,7 @@ class TestOps(unittest.TestCase):
                                   (torch.div, Tensor.div), (torch.pow, Tensor.pow)]:
       for shapes in [((5,13,24,16), (5,1,24,1)), ((1,3,1,7,1), (2,1,5,1,8))]:
         with self.subTest(op=torch_op.__name__, shapes=shapes):
-          helper_test_op(shapes, torch_op, tinygrad_op, gpu=self.gpu, forward_only=True)
+          helper_test_op(shapes, torch_op, tinygrad_op, gpu=self.gpu, forward_only=self.gpu)
 
 
   def test_broadcast_partial(self):
diff --git a/tinygrad/ops.py b/tinygrad/ops.py
index 62203df291..f825441818 100644
--- a/tinygrad/ops.py
+++ b/tinygrad/ops.py
@@ -4,25 +4,31 @@ import numpy as np
 from .tensor import Function, register
 
 # ************* basic ops *************
+def adBC(out, in_sh): #adjoint operation to broadcast is sum. Need to sum all axis with 1 = in_sh[i] < out.shape[i] 
+  return out.sum(axis=tuple([i for i in range(len(in_sh)) if in_sh[i]==1 and out.shape[i]>1])).reshape(in_sh)
 
 class Add(Function):
   @staticmethod
   def forward(ctx, x, y):
+    ctx.save_for_backward(x.shape,y.shape)
     return x+y
 
   @staticmethod
   def backward(ctx, grad_output):
-    return grad_output, grad_output
+    shape_x, shape_y = ctx.saved_tensors
+    return adBC(grad_output, shape_x), adBC(grad_output, shape_y)
 register('add', Add)
 
 class Sub(Function):
   @staticmethod
   def forward(ctx, x, y):
+    ctx.save_for_backward(x.shape,y.shape)
     return x-y
 
   @staticmethod
   def backward(ctx, grad_output):
-    return grad_output, -grad_output
+    shape_x, shape_y = ctx.saved_tensors
+    return adBC(grad_output, shape_x), adBC( -grad_output, shape_y)
 register('sub', Sub)
 
 class Mul(Function):
@@ -34,7 +40,7 @@ class Mul(Function):
   @staticmethod
   def backward(ctx, grad_output):
     x,y = ctx.saved_tensors
-    return y*grad_output, x*grad_output
+    return adBC(y*grad_output, x.shape), adBC(x*grad_output, y.shape)
 register('mul', Mul)
 
 class Div(Function):
@@ -46,7 +52,7 @@ class Div(Function):
   @staticmethod
   def backward(ctx, grad_output):
     x,y = ctx.saved_tensors
-    return grad_output / y, -x * grad_output / y**2
+    return adBC(grad_output / y, x.shape), adBC(-x * grad_output / y**2, y.shape)
 # TODO: registering this breaks the default div on the GPU
 #register('div', Div)
 
@@ -59,7 +65,7 @@ class Pow(Function):
   @staticmethod
   def backward(ctx, grad_output):
     x,y = ctx.saved_tensors
-    return y * (x**(y-1.0)) * grad_output, (x**y) * np.log(x) * grad_output
+    return adBC(y * (x**(y-1.0)) * grad_output,x.shape), adBC((x**y) * np.log(x) * grad_output,y.shape)
 register('pow', Pow)
 
 class Sum(Function):
@@ -206,7 +212,6 @@ class Conv2D(Function):
       ret[:,g] += np.tensordot(tx[:,g], tw[g], ((1,4,5),(1,2,3)))
     return np.moveaxis(ret,4,2).reshape(bs, cout, oy, ox)
 
-
   @staticmethod
   def backward(ctx, grad_output):
     bs,_,oy,ox = grad_output.shape