Reduce number of operations in Gelu() by one Mul.

About 5% faster Gen.Activation.

PiperOrigin-RevId: 684035719

ops/ops-inl.h

@@ -65,16 +65,19 @@ StaticCast(From from) noexcept {
   }
 }
 
+// We use the tanh approximation for gelu (also used in training).
+// gelu(x) = 0.5 * x * (1 + tanh(sqrt(2/π) * (x + 0.044715 * x^3)))
+//         = 0.5 * x * (1 + tanh(x * (sqrt(2/π) + sqrt(2/π) * 0.044715 * x^2)))
+//         = 0.5 * x * (1 + tanh(x * (0.79788 + 0.035677 * x^2)))
+//         = x * (0.5 + 0.5 * tanh(x * (0.79788 + 0.035677 * x^2))))
 template <class D, HWY_IF_F32_D(D)>
 HWY_INLINE hn::Vec<D> Gelu(D d, hn::Vec<D> v) {
-  const hn::Vec<D> kMul = hn::Set(d, 0.044715f);
+  const hn::Vec<D> kMul = hn::Set(d, 0.03567740813636141f);
   const hn::Vec<D> kSqrt2OverPi = hn::Set(d, 0.797884560804236f);
   const hn::Vec<D> kHalf = hn::Set(d, 0.5f);
 
-  // tanh approximation matches training.
-  const hn::Vec<D> v3 = hn::Mul(hn::Mul(v, v), v);
-  const hn::Vec<D> arg = hn::Mul(kSqrt2OverPi, hn::MulAdd(kMul, v3, v));
-  // 0.5 * (1 + tan) = MulAdd(0.5, tan, 0.5).
+  const hn::Vec<D> v2 = hn::Mul(v, v);
+  const hn::Vec<D> arg = hn::Mul(v, hn::MulAdd(kMul, v2, kSqrt2OverPi));
   const hn::Vec<D> cdf = hn::MulAdd(kHalf, hn::Tanh(d, arg), kHalf);
   return hn::Mul(v, cdf);
 }