diff --git a/ivy/functional/frontends/torch/nn/functional/pooling_functions.py b/ivy/functional/frontends/torch/nn/functional/pooling_functions.py
index b1bacd44fa103..aba9ac22fd619 100644
--- a/ivy/functional/frontends/torch/nn/functional/pooling_functions.py
+++ b/ivy/functional/frontends/torch/nn/functional/pooling_functions.py
@@ -1,5 +1,5 @@
 # global
-
+from functools import reduce
 # local
 import ivy
 from ivy import with_unsupported_dtypes
@@ -207,56 +207,49 @@ def adaptive_avg_pool2d(input, output_size):
     "torch",
 )
 @to_ivy_arrays_and_back
+@to_ivy_arrays_and_back
 def lp_pool1d(input, norm_type, kernel_size, stride=None, ceil_mode=False):
     data_format = "NCW"
     padding = "VALID"
-    if stride is not None:
-        out = ivy.avg_pool1d(
-            ivy.pow(input, norm_type),
-            kernel_size,
-            stride,
-            padding,
-            data_format=data_format,
-            ceil_mode=ceil_mode,
-        )
+    if stride is None:
+        stride = kernel_size
+    if not isinstance(kernel_size, int):
+        kernel_mul = reduce(lambda x, y: x * y, kernel_size)
     else:
-        out = ivy.avg_pool1d(
-            ivy.pow(input, norm_type),
-            kernel_size,
-            kernel_size,
-            padding,
-            data_format=data_format,
-            ceil_mode=ceil_mode,
-        )
+        kernel_mul = kernel_size
 
-    return ivy.pow(ivy.multiply(out, kernel_size), ivy.divide(1.0, norm_type))
+    out = ivy.avg_pool1d(
+        ivy.pow(input, norm_type),
+        kernel_size,
+        stride,
+        padding,
+        data_format=data_format,
+        ceil_mode=ceil_mode,
+    )
+    p = 1.0 / norm_type if norm_type != 0 else 1.0
+    return ivy.pow(ivy.multiply(out, kernel_mul), p)
 
 
 @to_ivy_arrays_and_back
 def lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False):
     data_format = "NCHW"
     padding = "VALID"
-    if stride is not None:
-        out = ivy.avg_pool2d(
-            ivy.pow(input, norm_type),
-            kernel_size,
-            stride,
-            padding,
-            data_format=data_format,
-            ceil_mode=ceil_mode,
-        )
-    else:
-        out = ivy.avg_pool2d(
-            ivy.pow(input, norm_type),
-            kernel_size,
-            kernel_size,
-            padding,
-            data_format=data_format,
-            ceil_mode=ceil_mode,
-        )
+    if stride is None:
+        stride = kernel_size
+    out = ivy.avg_pool2d(
+        ivy.pow(input, norm_type),
+        kernel_size,
+        stride,
+        padding,
+        data_format=data_format,
+        ceil_mode=ceil_mode,
+    )
     if not isinstance(kernel_size, int):
-        kernel_size = kernel_size[0] * kernel_size[1]
-    return ivy.pow(ivy.multiply(out, kernel_size), ivy.divide(1.0, norm_type))
+        kernel_mul = reduce(lambda x, y: x * y, kernel_size)
+    else:
+        kernel_mul = kernel_size
+    p = ivy.divide(1.0, norm_type) if norm_type != 0 else 1.0
+    return ivy.pow(ivy.multiply(out, kernel_mul), p).astype(input.dtype)
 
 
 @to_ivy_arrays_and_back
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_pooling_functions.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_pooling_functions.py
index e0e5530b5f5da..c59055f4f4a54 100644
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_pooling_functions.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_pooling_functions.py
@@ -428,8 +428,9 @@ def test_torch_adaptive_max_pool2d(
         max_dims=3,
         min_side=1,
         max_side=3,
+        data_format="channel_first"
     ),
-    norm_type=helpers.number(min_value=0.1, max_value=6),
+    norm_type=helpers.ints(min_value=1, max_value=6),
     test_with_out=st.just(False),
 )
 def test_torch_lp_pool1d(
@@ -444,10 +445,6 @@ def test_torch_lp_pool1d(
 ):
     input_dtype, x, kernel_size, stride, _ = dtype_x_k_s
 
-    # Torch ground truth func expects input to be consistent
-    # with a channels first format i.e. NCW
-    x[0] = x[0].reshape((x[0].shape[0], x[0].shape[-1], x[0].shape[1]))
-
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
         backend_to_test=backend_fw,
@@ -471,8 +468,9 @@ def test_torch_lp_pool1d(
         max_dims=4,
         min_side=1,
         max_side=4,
+        data_format="channel_first",
     ),
-    norm_type=helpers.number(min_value=0.1, max_value=6),
+    norm_type=helpers.ints(min_value=1, max_value=6),
     test_with_out=st.just(False),
 )
 def test_torch_lp_pool2d(
@@ -486,10 +484,6 @@ def test_torch_lp_pool2d(
     on_device,
 ):
     input_dtype, x, kernel_size, stride, _ = dtype_x_k_s
-    # Torch ground truth func expects input to be consistent
-    # with a channels first format i.e. NCW
-    x[0] = x[0].transpose((0, 3, 1, 2))
-
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
         backend_to_test=backend_fw,