diff --git a/tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_log2.py b/tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_log2.py
index cd11a1b67dd..d44e4b3e5c0 100644
--- a/tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_log2.py
+++ b/tests/tt_eager/python_api_testing/unit_testing/backward_ops/test_backward_log2.py
@@ -5,7 +5,10 @@
 import torch
 import pytest
 import tt_lib
-from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import compare_results, data_gen_pt_tt
+from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import (
+    compare_pcc,
+    data_gen_with_range,
+)
 
 
 @pytest.mark.parametrize(
@@ -17,8 +20,9 @@
     ),
 )
 def test_bw_log2(input_shapes, device):
-    in_data, input_tensor = data_gen_pt_tt(input_shapes, device, True)
-    grad_data, grad_tensor = data_gen_pt_tt(input_shapes, device)
+    in_data, input_tensor = data_gen_with_range(input_shapes, -100, 100, device, True)
+    grad_data, grad_tensor = data_gen_with_range(input_shapes, -10, 10, device)
+
     tt_output_tensor_on_device = tt_lib.tensor.log2_bw(grad_tensor, input_tensor)
 
     in_data.retain_grad()
@@ -26,7 +30,6 @@ def test_bw_log2(input_shapes, device):
     pyt_y = torch.log2(in_data)
 
     pyt_y.backward(gradient=grad_data)
-
     golden_tensor = [in_data.grad]
-    comp_pass = compare_results(tt_output_tensor_on_device, golden_tensor)
+    comp_pass = compare_pcc(tt_output_tensor_on_device, golden_tensor)
     assert comp_pass
diff --git a/tt_eager/tt_dnn/op_library/backward/backward_ops.cpp b/tt_eager/tt_dnn/op_library/backward/backward_ops.cpp
index 0317852a97e..fd9b3a2ddd5 100644
--- a/tt_eager/tt_dnn/op_library/backward/backward_ops.cpp
+++ b/tt_eager/tt_dnn/op_library/backward/backward_ops.cpp
@@ -1652,7 +1652,10 @@ std::vector<Tensor> ceil_bw(const Tensor& grad, const Tensor& input, const Memor
 // bw(log2(in)) = grad/(in * 0.69314718055994530942)
 std::vector<Tensor> _log2_bw(const Tensor& grad, const Tensor& input, const MemoryConfig& output_mem_config) {
     std::vector<Tensor> grad_tensor;
+    Tensor t_inf = where(ltz(grad, output_mem_config), -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), output_mem_config);
     Tensor grad_a = mul(grad, recip(mul_unary(input, M_LN2, output_mem_config), output_mem_config), std::nullopt, output_mem_config);
+    grad_a = where(logical_and(eqz(input, output_mem_config), eqz(grad, output_mem_config), std::nullopt, output_mem_config), std::nanf(" "),
+             where(eqz(input, output_mem_config), t_inf, grad_a, output_mem_config), output_mem_config);
     grad_tensor.emplace_back(grad_a);
     return grad_tensor;
 }