Use expm1 instead of exp - 1 (#2660)

Summary:
Similarly to #2540 and #2541: use more numerically stable `expm1`.

Found with TorchFix https://github.com/pytorch-labs/torchfix/.  This should be the last instance to replace in the current codebase.

Pull Request resolved: #2660

Reviewed By: saitcakmak

Differential Revision: D67613867

Pulled By: kit1980

fbshipit-source-id: a6305f0f3ec4a3e6ec6f6f6b9c9361a61eaf06e9

Author: kit1980

botorch/test_functions/multi_objective.py

@@ -772,7 +772,7 @@ def penicillin_vectorized(cls, X_input: Tensor) -> Tensor:
             F_loss = (
                 V[active]
                 * cls.lambd
-                * (torch.exp(5 * ((T[active] - cls.T_o) / (cls.T_v - cls.T_o))) - 1)
+                * torch.special.expm1(5 * ((T[active] - cls.T_o) / (cls.T_v - cls.T_o)))
             )
             dV_dt = F[active] - F_loss
             mu = (

test/models/transforms/test_utils.py

@@ -86,7 +86,7 @@ def test_norm_to_lognorm(self):
             mu_ln_expected = torch.tensor(
                 [1.0, 2.0, 3.0], device=self.device, dtype=dtype
             )
-            var_ln_expected = (torch.exp(var) - 1) * mu_ln_expected**2
+            var_ln_expected = torch.special.expm1(var) * mu_ln_expected**2
             self.assertAllClose(mu_ln, mu_ln_expected)
             self.assertAllClose(var_ln, var_ln_expected)