diff --git a/burn-autodiff/src/tests/maxpool2d.rs b/burn-autodiff/src/tests/maxpool2d.rs
index 25a7c332c..ae7a43b7b 100644
--- a/burn-autodiff/src/tests/maxpool2d.rs
+++ b/burn-autodiff/src/tests/maxpool2d.rs
@@ -4,9 +4,45 @@ mod tests {
     use burn_tensor::{module::max_pool2d, Data};
 
     #[test]
-    fn test_max_pool2d_simple() {
-        let batch_size = 1;
-        let channels_in = 1;
+    fn test_max_pool2d_simple_1() {
+        let kernel_size_1 = 3;
+        let kernel_size_2 = 3;
+        let padding_1 = 0;
+        let padding_2 = 0;
+        let stride_1 = 1;
+        let stride_2 = 1;
+
+        let x = TestADTensor::from_floats([[[
+            [0.2479, 0.6386, 0.3166, 0.5742],
+            [0.7065, 0.1940, 0.6305, 0.8959],
+            [0.5416, 0.8602, 0.8129, 0.1662],
+            [0.3358, 0.3059, 0.8293, 0.0990],
+        ]]])
+        .require_grad();
+        let x_grad_expected = TestADTensor::from_floats([[[
+            [0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 2.0],
+            [0.0, 2.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0],
+        ]]]);
+
+        let output = max_pool2d(
+            x.clone(),
+            [kernel_size_1, kernel_size_2],
+            [stride_1, stride_2],
+            [padding_1, padding_2],
+        );
+        let grads = output.backward();
+
+        // Asserts
+        let x_grad_actual = x.grad(&grads).unwrap();
+        x_grad_expected
+            .to_data()
+            .assert_approx_eq(&x_grad_actual.to_data(), 3);
+    }
+
+    #[test]
+    fn test_max_pool2d_simple_2() {
         let kernel_size_1 = 2;
         let kernel_size_2 = 2;
         let padding_1 = 1;
@@ -45,8 +81,6 @@ mod tests {
 
     #[test]
     fn test_max_pool2d_complex() {
-        let batch_size = 1;
-        let channels_in = 1;
         let kernel_size_1 = 4;
         let kernel_size_2 = 2;
         let padding_1 = 2;
diff --git a/burn-wgpu/src/kernel/pool/max_pool2d.rs b/burn-wgpu/src/kernel/pool/max_pool2d.rs
index 7ac1beca7..7bec89639 100644
--- a/burn-wgpu/src/kernel/pool/max_pool2d.rs
+++ b/burn-wgpu/src/kernel/pool/max_pool2d.rs
@@ -1,6 +1,10 @@
 use crate::{
     element::WgpuElement,
-    kernel::{self, elemwise_workgroup, pool::build_output_and_info_pool2d, KernelSettings},
+    kernel::{
+        self, elemwise_workgroup,
+        pool::{build_output_and_info_pool2d, build_pool2d_info},
+        KernelSettings,
+    },
     kernel_wgsl,
     tensor::WgpuTensor,
 };
@@ -87,31 +91,7 @@ pub(crate) fn max_pool2d_with_indices_backward<E: WgpuElement, I: WgpuElement>(
         .create_buffer(num_elems * core::mem::size_of::<E>());
     let output = WgpuTensor::new(x.context.clone(), x.shape.clone(), buffer);
 
-    let mut info: [u32; 18] = [0; 18];
-    info[0] = x.strides[0] as u32;
-    info[1] = x.strides[1] as u32;
-    info[2] = x.strides[2] as u32;
-    info[3] = x.strides[3] as u32;
-    info[4] = x.shape.dims[0] as u32;
-    info[5] = x.shape.dims[1] as u32;
-    info[6] = x.shape.dims[2] as u32;
-    info[7] = x.shape.dims[3] as u32;
-
-    info[8] = grad.strides[0] as u32;
-    info[9] = grad.strides[1] as u32;
-    info[10] = grad.strides[2] as u32;
-    info[11] = grad.strides[3] as u32;
-
-    info[12] = kernel_size[0] as u32;
-    info[13] = kernel_size[1] as u32;
-    info[14] = stride[0] as u32;
-    info[15] = stride[1] as u32;
-    info[16] = padding[0] as u32;
-    info[17] = padding[1] as u32;
-
-    let info_buffer = x
-        .context
-        .create_buffer_with_data(bytemuck::cast_slice(&info));
+    let info_buffer = build_pool2d_info(&x, &grad, kernel_size, stride, padding);
 
     let kernel = x.context.compile_static::<KernelSettings<
         MaxPool2dWithIndicesBackward,
diff --git a/burn-wgpu/src/template/pool/max_pool2d_with_indices_backward.wgsl b/burn-wgpu/src/template/pool/max_pool2d_with_indices_backward.wgsl
index a35689144..bd891bc23 100644
--- a/burn-wgpu/src/template/pool/max_pool2d_with_indices_backward.wgsl
+++ b/burn-wgpu/src/template/pool/max_pool2d_with_indices_backward.wgsl
@@ -13,7 +13,7 @@ var<storage, read_write> output: array<{{ elem }}>;
 
 @group(0)
 @binding(3)
-var<storage, read> info: array<u32, 18>;
+var<storage, read> info: array<u32, 22>;
 
 const WORKGROUP_SIZE_X = {{ workgroup_size_x }}u;
 
@@ -38,13 +38,17 @@ fn main(
     let grad_stride_1 = info[9];
     let grad_stride_2 = info[10];
     let grad_stride_3 = info[11];
+    let grad_shape_0 = info[12];
+    let grad_shape_1 = info[13];
+    let grad_shape_2 = info[14];
+    let grad_shape_3 = info[15];
 
-    let kernel_size_0 = info[12];
-    let kernel_size_1 = info[13];
-    let pool_stride_0 = info[14];
-    let pool_stride_1 = info[15];
-    let padding_0 = info[16];
-    let padding_1 = info[17];
+    let kernel_size_0 = info[16];
+    let kernel_size_1 = info[17];
+    let pool_stride_0 = info[18];
+    let pool_stride_1 = info[19];
+    let padding_0 = info[20];
+    let padding_1 = info[21];
 
     let b = id / input_stride_0 % input_shape_0;
     let c = id / input_stride_1 % input_shape_1;
@@ -61,8 +65,8 @@ fn main(
     let oh_start = u32(max(oh_start_tmp, 0));
     let ow_start = u32(max(ow_start_tmp, 0));
 
-    let oh_end = u32(max(kms_0, 0)) + oh_start;
-    let ow_end = u32(max(kms_1, 0)) + ow_start;
+    let oh_end = min(u32(max(kms_0, 0)) + oh_start, grad_shape_2 - 1u);
+    let ow_end = min(u32(max(kms_1, 0)) + ow_start, grad_shape_3 - 1u);
 
     let index_current = ih * input_stride_2 + iw * input_stride_3;
     var grad_acc = 0.0;