Torch.eq VS == (equality operator)

1

If a is some tensor and b is a tensor or a number, is there a difference between torch.eq(a, b) and a == b ?
If not, why does Pytorch API specify the torch.eq method?
Should I avoid using the == operator and use torch.eq instead?

1 Like
2

I think both approaches should yield the same result:

a = torch.arange(10)
b = 2

print(a == b)
print(torch.eq(a, b))

b = torch.arange(10)
print(a == b)
print(torch.eq(a, b))

I guess:

  • convenience if you prefer to use the explicit torch.* methods, e.g. such as torch.add instead of +
  • to allow users to pass the out tensor
  • I guess to map it easily to the internal aten::eq operator (but unsure if adding torch.eq actually makes it easier)
1 Like
3

OK, I marked ptrblck’s answer as solution. Maybe adding a small hint about the equivalence of == and torch.eq would be something worth adding into the documentation?

1 Like
4

Actually, ==(equality operator) and torch.eq are different!
You can try

print(b == a) 
print(torch.eq(b, a))

with

a = torch.arange(5)
b = 2

Then you will find*(in Python3.11 and PyTorch 2.2)* the equality operator can run while the torch.eq will give TypeError.

However, this is not the end of the story. as b is int in the above example, if we pass PyTorch tensor instead, both comparison will give the desired result.

a = torch.arange(5)
b = torch.arange(2,3)
print(b == a) 
# the output is tensor([False, False,  True, False, False])
print(torch.eq(b, a))
# the output is tensor([False, False,  True, False, False])

One final note, you can treat a == b as trying to run both torch.eq(a, b) and torch.eq(b, a).

5

No difference in normal usage.

But there is a huge difference when you try to do model conversion using torchscript torch.jit.script() or onnx torch.onnx.export()

using signs like >, <, == will result in model conversion failture for torch.onnx.export(), and also somtetimes affect torch.jit.script()
@ptrblck