diff --git a/python/tvm/relay/op/nn/nn.py b/python/tvm/relay/op/nn/nn.py
index 42009be7cee89..257644994ffe8 100644
--- a/python/tvm/relay/op/nn/nn.py
+++ b/python/tvm/relay/op/nn/nn.py
@@ -3789,7 +3789,7 @@ def conv2d_backward_weight(
     r"""The gradient of conv2d with respect to weight.
 
     This operator takes the output gradient `grad` as the convolution kernel
-    and convolves it with `data` to produce the gradeint with respect to weight.
+    and convolves it with `data` to produce the gradient with respect to weight.
     Depending on an implementation, the roles of `data` and `grad` can be swapped
     (For example, in CUTLASS `data` acts as the filter).
 
diff --git a/src/relay/op/nn/convolution.cc b/src/relay/op/nn/convolution.cc
index 481a8f90ab8b1..1d87a455e442b 100644
--- a/src/relay/op/nn/convolution.cc
+++ b/src/relay/op/nn/convolution.cc
@@ -651,7 +651,7 @@ RELAY_REGISTER_OP("nn.conv2d_backward_weight")
     .describe(R"code(The gradient of the 2D convolution layer with respect to the weight.
 
 This layer computes the gradient of the conv2d op with respect to weight,
-given the origial input data and the output gradient.
+given the original input data and the output gradient.
 
 - **data**: This depends on the `layout` parameter. Input is 4D array of shape
             (batch_size, in_channels, height, width) if `layout` is `NCHW`.