diff --git a/docs/tensor/elementwise.md b/docs/tensor/elementwise.md
index 4893658953..0bf376747e 100644
--- a/docs/tensor/elementwise.md
+++ b/docs/tensor/elementwise.md
@@ -35,6 +35,7 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 
 ::: tinygrad.Tensor.relu
 ::: tinygrad.Tensor.sigmoid
+::: tinygrad.Tensor.logsigmoid
 ::: tinygrad.Tensor.hardsigmoid
 ::: tinygrad.Tensor.elu
 ::: tinygrad.Tensor.celu
diff --git a/extra/torch_backend/backend.py b/extra/torch_backend/backend.py
index 17c8083f83..cf428eaede 100644
--- a/extra/torch_backend/backend.py
+++ b/extra/torch_backend/backend.py
@@ -376,6 +376,7 @@ decomps = [
   aten.softshrink,
   aten.hardshrink,
   aten.log_sigmoid_forward,
+  aten.log_sigmoid_backward,
   aten.isneginf,
   aten.isposinf,
   aten.nan_to_num,
diff --git a/test/test_ops.py b/test/test_ops.py
index 59062df383..2c76d7f01d 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -946,6 +946,11 @@ class TestOps(unittest.TestCase):
     self.assertAlmostEqual(sigmoid(x)[0].gradient(x)[0].item(), 0.0)
     x = Tensor([-300.0])
     self.assertAlmostEqual(sigmoid(x)[0].gradient(x)[0].item(), 0.0)
+
+  def test_logsigmoid(self):
+    helper_test_op([(45,65)], torch.nn.functional.logsigmoid, Tensor.logsigmoid)
+    helper_test_op([()], torch.nn.functional.logsigmoid, Tensor.logsigmoid)
+
   def test_hardsigmoid(self):
     helper_test_op([(45,65)], torch.nn.functional.hardsigmoid, Tensor.hardsigmoid)
     helper_test_op([()], torch.nn.functional.hardsigmoid, Tensor.hardsigmoid)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 278b3ba114..76989d3c1d 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -2957,6 +2957,18 @@ class Tensor(MathTrait):
     """
     return (1 + (self * (-1/math.log(2))).exp2()).reciprocal()
 
+  def logsigmoid(self) -> Tensor:
+    """
+    Applies the LogSigmoid function element-wise.
+
+    - See: https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.logsigmoid.html
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).logsigmoid().numpy())
+    ```
+    """
+    return -(-self).softplus()
+
   def hardsigmoid(self, alpha:float=1/6, beta:float=0.5) -> Tensor:
     """
     Applies the Hardsigmoid function element-wise.