diff --git a/oneflow/core/autograd/gradient_funcs/layer_norm.cpp b/oneflow/core/autograd/gradient_funcs/layer_norm.cpp
index b4081334b74..99134794afc 100644
--- a/oneflow/core/autograd/gradient_funcs/layer_norm.cpp
+++ b/oneflow/core/autograd/gradient_funcs/layer_norm.cpp
@@ -107,13 +107,13 @@ Maybe<void> LayerNorm::Apply(const LayerNormCaptureState* ctx, const TensorTuple
   int64_t begin_norm_axis = ctx->begin_norm_axis;
   if (begin_norm_axis < 0) { begin_norm_axis += dy->shape()->NumAxes(); }
 
-  std::shared_ptr<Tensor> gamma = saved_tensors.at(ctx->gamma_index);
   if (!ctx->has_affine) {
     // Use LayerNormParamGrad(Tensor dy, Tensor gamma, Int64 begin_params_axis, Double epsilon).
     dy = JUST(functional::LayerNormParamGrad(dy, begin_params_axis, ctx->epsilon));
   } else {
     // Use LayerNormAffineParamGrad(Tensor dy, Tensor gamma, Tensor normalized, Int64
     // begin_params_axis, Double epsilon).
+    std::shared_ptr<Tensor> gamma = saved_tensors.at(ctx->gamma_index);
     std::shared_ptr<Tensor> normalized = saved_tensors.at(ctx->normalized_index);
     const auto& results = JUST(functional::LayerNormAffineParamGrad(
         dy, gamma, normalized, begin_params_axis, ctx->epsilon));
diff --git a/python/oneflow/test/modules/test_layernorm.py b/python/oneflow/test/modules/test_layernorm.py
index 6022a07354f..07afe3e48a8 100644
--- a/python/oneflow/test/modules/test_layernorm.py
+++ b/python/oneflow/test/modules/test_layernorm.py
@@ -203,6 +203,24 @@ def get_random_norm_shape():
         y = m(x)
         return y
 
+    @autotest(n=20, auto_backward=True, rtol=1e-3, atol=1e-3)
+    def test_layernorm_without_affine(test_case):
+        device = random_device()
+        channel = random(1, 200).to(int)
+        height = random(1, 2).to(int)
+        width = random(8192, 32768).to(int)
+
+        def get_random_norm_shape():
+            begin_axis = random(1, 3).to(int).value()
+            return tuple((channel.value(), height.value(), width.value())[begin_axis:])
+
+        m = torch.nn.LayerNorm(normalized_shape=get_random_norm_shape()).to(device)
+        x = random_pytorch_tensor(ndim=4, dim1=channel, dim2=height, dim3=width).to(
+            device
+        )
+        y = m(x)
+        return y
+
 
 if __name__ == "__main__":
     unittest.main()