diff --git a/ivy/functional/frontends/torch/reduction_ops.py b/ivy/functional/frontends/torch/reduction_ops.py
index e69de29bb2d1d..eb700ee099942 100644
--- a/ivy/functional/frontends/torch/reduction_ops.py
+++ b/ivy/functional/frontends/torch/reduction_ops.py
@@ -0,0 +1,33 @@
+import ivy
+
+
+def argmax(input, dim=None, keepdim=False):
+    return ivy.argmax(input, axis=dim, keepdims=keepdim)
+
+
+def argmin(input, dim=None, keepdim=False):
+    return ivy.argmin(input, axis=dim, keepdims=keepdim)
+
+
+def amax(input, dim=None, keepdim=False, *, out=None):
+    return ivy.max(input, axis=dim, keepdims=keepdim, out=out)
+
+
+def amin(input, dim=None, keepdim=False, *, out=None):
+    return ivy.min(input, axis=dim, keepdims=keepdim, out=out)
+
+
+def all(input, dim=None, keepdim=False, *, out=None):
+    input_dtype = ivy.as_ivy_dtype(input.dtype)
+    ret = ivy.all(input, axis=dim, keepdims=keepdim, out=out)
+    if ivy.is_uint_dtype(input_dtype):
+        ret = ivy.astype(ret, input_dtype, out=out)
+    return ret
+
+
+def any(input, dim=None, keepdim=False, *, out=None):
+    input_dtype = ivy.as_ivy_dtype(input.dtype)
+    ret = ivy.any(input, axis=dim, keepdims=keepdim, out=out)
+    if ivy.is_uint_dtype(input_dtype):
+        ret = ivy.astype(ret, input_dtype, out=out)
+    return ret
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py
index e69de29bb2d1d..2ac3786d4acf1 100644
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py
@@ -0,0 +1,241 @@
+# global
+import numpy as np
+from hypothesis import given, strategies as st
+
+# local
+import ivy_tests.test_ivy.helpers as helpers
+from ivy_tests.test_ivy.helpers import handle_cmd_line_args
+
+
+@handle_cmd_line_args
+@given(
+    dtype_input_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        force_int_axis=True,
+        min_num_dims=1,
+        min_axis=-1,
+        max_axis=0,
+    ),
+    num_positional_args=helpers.num_positional_args(
+        fn_name="ivy.functional.frontends.torch.argmax"
+    ),
+    keepdims=st.booleans(),
+)
+def test_torch_argmax(
+    dtype_input_axis,
+    as_variable,
+    num_positional_args,
+    native_array,
+    keepdims,
+    fw,
+):
+    input_dtype, x, axis = dtype_input_axis
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        as_variable_flags=as_variable,
+        with_out=False,
+        num_positional_args=num_positional_args,
+        native_array_flags=native_array,
+        fw=fw,
+        frontend="torch",
+        fn_tree="argmax",
+        input=np.asarray(x, dtype=input_dtype),
+        dim=axis,
+        keepdim=keepdims,
+    )
+
+
+@handle_cmd_line_args
+@given(
+    dtype_input_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        force_int_axis=True,
+        min_num_dims=1,
+        min_axis=-1,
+        max_axis=0,
+    ),
+    num_positional_args=helpers.num_positional_args(
+        fn_name="ivy.functional.frontends.torch.argmin"
+    ),
+    keepdims=st.booleans(),
+)
+def test_torch_argmin(
+    dtype_input_axis,
+    as_variable,
+    num_positional_args,
+    native_array,
+    keepdims,
+    fw,
+):
+    input_dtype, x, axis = dtype_input_axis
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        as_variable_flags=as_variable,
+        with_out=False,
+        num_positional_args=num_positional_args,
+        native_array_flags=native_array,
+        fw=fw,
+        frontend="torch",
+        fn_tree="argmin",
+        input=np.asarray(x, dtype=input_dtype),
+        dim=axis,
+        keepdim=keepdims,
+    )
+
+
+@handle_cmd_line_args
+@given(
+    dtype_input_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        min_num_dims=1,
+        min_axis=-1,
+        max_axis=0,
+    ),
+    num_positional_args=helpers.num_positional_args(
+        fn_name="ivy.functional.frontends.torch.amax"
+    ),
+    keepdims=st.booleans(),
+)
+def test_torch_amax(
+    dtype_input_axis,
+    as_variable,
+    num_positional_args,
+    native_array,
+    keepdims,
+    with_out,
+    fw,
+):
+    input_dtype, x, axis = dtype_input_axis
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        as_variable_flags=as_variable,
+        with_out=with_out,
+        num_positional_args=num_positional_args,
+        native_array_flags=native_array,
+        fw=fw,
+        frontend="torch",
+        fn_tree="amax",
+        input=np.asarray(x, dtype=input_dtype),
+        dim=axis,
+        keepdim=keepdims,
+        out=None,
+    )
+
+
+@handle_cmd_line_args
+@given(
+    dtype_input_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        min_num_dims=1,
+        min_axis=-1,
+        max_axis=0,
+    ),
+    num_positional_args=helpers.num_positional_args(
+        fn_name="ivy.functional.frontends.torch.amin"
+    ),
+    keepdims=st.booleans(),
+)
+def test_torch_amin(
+    dtype_input_axis,
+    as_variable,
+    num_positional_args,
+    native_array,
+    keepdims,
+    with_out,
+    fw,
+):
+    input_dtype, x, axis = dtype_input_axis
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        as_variable_flags=as_variable,
+        with_out=with_out,
+        num_positional_args=num_positional_args,
+        native_array_flags=native_array,
+        fw=fw,
+        frontend="torch",
+        fn_tree="amin",
+        input=np.asarray(x, dtype=input_dtype),
+        dim=axis,
+        keepdim=keepdims,
+        out=None,
+    )
+
+
+@handle_cmd_line_args
+@given(
+    dtype_input_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_axis=-1,
+        max_axis=0,
+        min_num_dims=1,
+        allow_inf=False,
+    ),
+    num_positional_args=helpers.num_positional_args(
+        fn_name="ivy.functional.frontends.torch.all"
+    ),
+    keepdims=st.booleans(),
+)
+def test_torch_all(
+    dtype_input_axis,
+    as_variable,
+    num_positional_args,
+    native_array,
+    keepdims,
+    with_out,
+    fw,
+):
+    input_dtype, x, axis = dtype_input_axis
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        as_variable_flags=as_variable,
+        with_out=with_out,
+        num_positional_args=num_positional_args,
+        native_array_flags=native_array,
+        fw=fw,
+        frontend="torch",
+        fn_tree="all",
+        input=np.asarray(x, dtype=input_dtype),
+        dim=axis,
+        keepdim=keepdims,
+        out=None,
+    )
+
+
+@handle_cmd_line_args
+@given(
+    dtype_input_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_axis=-1,
+        max_axis=0,
+        min_num_dims=1,
+        allow_inf=False,
+    ),
+    num_positional_args=helpers.num_positional_args(
+        fn_name="ivy.functional.frontends.torch.any"
+    ),
+    keepdims=st.booleans(),
+)
+def test_torch_any(
+    dtype_input_axis,
+    as_variable,
+    num_positional_args,
+    native_array,
+    keepdims,
+    with_out,
+    fw,
+):
+    input_dtype, x, axis = dtype_input_axis
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        as_variable_flags=as_variable,
+        with_out=with_out,
+        num_positional_args=num_positional_args,
+        native_array_flags=native_array,
+        fw=fw,
+        frontend="torch",
+        fn_tree="any",
+        input=np.asarray(x, dtype=input_dtype),
+        dim=axis,
+        keepdim=keepdims,
+        out=None,
+    )