Precise sigmoid (#2659)

* bump patch

* Sigmoid matches PyTorch and is more precise on tails

mlx/backend/cpu/matmul.cpp

@@ -131,10 +131,6 @@ void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
 }
 
 void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
-  if (out.dtype() != float32) {
-    throw std::runtime_error(
-        "[AddMM::eval_cpu] Currently only supports float32.");
-  }
   if (out.size() == 0) {
     out.set_data(allocator::malloc(out.nbytes()));
     return;

mlx/backend/cpu/unary_ops.h

@@ -77,7 +77,8 @@ struct Real {
 struct Sigmoid {
   template <int N, typename T>
   Simd<T, N> operator()(Simd<T, N> x) {
-    return 1.0f / (1.0f + simd::exp(-x));
+    auto y = 1.0f / (1.0f + simd::exp(simd::abs(x)));
+    return simd::select(x < Simd<T, N>{0}, y, Simd<T, N>{1} - y);
   }
   SINGLE()
 };

mlx/backend/cuda/device/unary_ops.cuh

@@ -257,8 +257,8 @@ struct Round {
 struct Sigmoid {
   template <typename T>
   __device__ T operator()(T x) {
-    T y = 1 / (1 + exp(-abs(x)));
-    return (x < 0) ? 1 - y : y;
+    T y = 1 / (1 + exp(abs(x)));
+    return (x < 0) ? y : 1 - y;
   }
 };
 

mlx/backend/metal/kernels/unary_ops.h

@@ -309,8 +309,8 @@ struct Round {
 struct Sigmoid {
   template <typename T>
   T operator()(T x) {
-    auto y = 1 / (1 + metal::exp(-metal::abs(x)));
-    return (x < 0) ? 1 - y : y;
+    auto y = 1 / (1 + metal::exp(metal::abs(x)));
+    return (x < 0) ? y : 1 - y;
   }
 };
 

mlx/version.h

@@ -4,7 +4,7 @@
 
 #define MLX_VERSION_MAJOR 0
 #define MLX_VERSION_MINOR 29
-#define MLX_VERSION_PATCH 2
+#define MLX_VERSION_PATCH 3
 #define MLX_VERSION_NUMERIC \
   (100000 * MLX_VERSION_MAJOR + 1000 * MLX_VERSION_MINOR + MLX_VERSION_PATCH)
 

python/tests/test_blas.py

@@ -712,6 +712,15 @@ class TestBlas(mlx_tests.MLXTestCase):
             expected = beta * c + alpha * (a @ b)
             self.assertTrue(mx.allclose(expected, out))
 
+        # Test half precision
+        for t, tol in [(mx.float16, 1e-3), (mx.bfloat16, 1e-2)]:
+            c = mx.ones((32, 32)).astype(t)
+            a = mx.random.uniform(shape=(32, 32)).astype(t)
+            b = mx.random.uniform(shape=(32, 32)).astype(t)
+            out = mx.addmm(c, a, b)
+            expected = a @ b + c
+            self.assertTrue(mx.allclose(out, expected, rtol=tol, atol=tol))
+
     def test_addmm_grad(self):
         def make_ref_addmm(alpha, beta):
             return lambda c, a, b: alpha * (a @ b) + beta * c

python/tests/test_ops.py

@@ -1041,6 +1041,12 @@ class TestOps(mlx_tests.MLXTestCase):
         expected = 1 / (1 + np.exp(-a, dtype=np.float32))
         self.assertTrue(np.allclose(result, expected))
 
+        # Low precision
+        a = mx.array(-8.0).astype(mx.float16)
+        self.assertNotEqual(mx.sigmoid(a).item(), 0.0)
+        a = mx.array(8.0).astype(mx.float16)
+        self.assertNotEqual(mx.sigmoid(a).item(), 1.0)
+
     def test_allclose(self):
         a = mx.array(1.0)
         b = mx.array(1.0)