diff --git a/mlx/ops.cpp b/mlx/ops.cpp
index 387f8e814..2012613e0 100644
--- a/mlx/ops.cpp
+++ b/mlx/ops.cpp
@@ -2656,9 +2656,40 @@ inline std::vector<int> conv_out_shape(
   std::vector<int> out_shape(in_shape.size());
   int i = 0;
   out_shape[i++] = N;
+
   for (; i < in_shape.size() - 1; i++) {
+    if (pads[i - 1] < 0) {
+      std::ostringstream msg;
+      msg << "[conv] Padding sizes must be non-negative."
+          << " Got padding " << pads << ".";
+      throw std::invalid_argument(msg.str());
+    }
+
+    if (strides[i - 1] <= 0) {
+      std::ostringstream msg;
+      msg << "[conv] Stride sizes must be positive."
+          << " Got strides " << strides << ".";
+      throw std::invalid_argument(msg.str());
+    }
+
+    if (dilation[i - 1] <= 0) {
+      std::ostringstream msg;
+      msg << "[conv] Dilation sizes must be positive."
+          << " Got dilation " << dilation << ".";
+      throw std::invalid_argument(msg.str());
+    }
+
     out_shape[i] = conv_out_axis_size(
         in_shape[i], wt_shape[i], strides[i - 1], pads[i - 1], dilation[i - 1]);
+
+    if (out_shape[i] <= 0) {
+      std::ostringstream msg;
+      msg << "[conv] Spatial dimensions of input after padding "
+          << " cannot be smaller than weight spatial dimensions."
+          << " Got input with shape " << in_shape << " and padding " << pads
+          << " for weight of shape " << wt_shape << ".";
+      throw std::invalid_argument(msg.str());
+    }
   }
   out_shape[i] = O;
 
diff --git a/python/mlx/nn/layers/positional_encoding.py b/python/mlx/nn/layers/positional_encoding.py
index 1935792a4..a8024f0a4 100644
--- a/python/mlx/nn/layers/positional_encoding.py
+++ b/python/mlx/nn/layers/positional_encoding.py
@@ -106,7 +106,9 @@ class RoPE(Module):
         if (N, D, offset, base, scale, dtype) != cls._cos_sin_theta_key:
             half_D = D // 2
             positions = mx.arange(offset, N, dtype=dtype) * scale
-            freqs = mx.exp(-mx.arange(0.0, half_D, dtype=dtype) * (math.log(base) / half_D))
+            freqs = mx.exp(
+                -mx.arange(0.0, half_D, dtype=dtype) * (math.log(base) / half_D)
+            )
             theta = mx.reshape(positions, (-1, 1)) * mx.reshape(freqs, (1, -1))
             cls._cos_sin_theta_key = (N, D, offset, base, scale, dtype)
             cls._cos_sin_theta_value = (mx.cos(theta), mx.sin(theta))
diff --git a/python/src/ops.cpp b/python/src/ops.cpp
index 432b98d76..d622dcdf1 100644
--- a/python/src/ops.cpp
+++ b/python/src/ops.cpp
@@ -2930,6 +2930,10 @@ void init_ops(py::module_& m) {
           throw std::invalid_argument("[convolve] Inputs must be 1D.");
         }
 
+        if (a.size() == 0 || v.size() == 0) {
+          throw std::invalid_argument("[convolve] Inputs cannot be empty.");
+        }
+
         array in = a.size() < v.size() ? v : a;
         array wt = a.size() < v.size() ? a : v;
         wt = slice(wt, {wt.shape(0) - 1}, {-wt.shape(0) - 1}, {-1}, s);