diff --git a/tests/tt_eager/profiling/ops_for_profiling.py b/tests/tt_eager/profiling/ops_for_profiling.py
index 363cae70dba..b4bc238168f 100644
--- a/tests/tt_eager/profiling/ops_for_profiling.py
+++ b/tests/tt_eager/profiling/ops_for_profiling.py
@@ -2485,15 +2485,15 @@ def addalpha_bw(x, y, z):
 
 
 def addcmul_bw(x, y, z):
-    tt_lib.tensor.addcmul_bw(x, x, y, z, value=5)
+    ttnn.addcmul_bw(x, x, y, z, value=5)
 
 
 def addcdiv_bw(x, y, z):
-    tt_lib.tensor.addcdiv_bw(x, x, y, z, value=5)
+    ttnn.addcdiv_bw(x, x, y, z, alpha=5)
 
 
 def where_bw(x, y, z):
-    tt_lib.tensor.where_bw(x, y, z, z)
+    ttnn.where_bw(x, y, z, z)
 
 
 def bias_gelu_bw_none(x, y, z):
@@ -2644,11 +2644,11 @@ def fused_linear_shape_func(input_shape):
     },
     {
         "op": addcmul_bw,
-        "name": "tt_lib.tensor.addcmul_bw",
+        "name": "ttnn.addcmul_bw",
     },
     {
         "op": addcdiv_bw,
-        "name": "tt_lib.tensor.addcdiv_bw",
+        "name": "ttnn.addcdiv_bw",
     },
     {
         "op": ttnn.binary_assign_bw,
@@ -2701,7 +2701,7 @@ def fused_linear_shape_func(input_shape):
     # },
     {
         "op": where_bw,
-        "name": "tt_lib.tensor.where_bw",
+        "name": "ttnn.where_bw",
     },
     {
         "op": ttnn.sub_bw,
diff --git a/tests/tt_eager/profiling/profile_host_overhead.py b/tests/tt_eager/profiling/profile_host_overhead.py
index 73035499b09..6ed9d4b72d1 100644
--- a/tests/tt_eager/profiling/profile_host_overhead.py
+++ b/tests/tt_eager/profiling/profile_host_overhead.py
@@ -10,7 +10,7 @@
 import statistics
 from loguru import logger
 
-# from models.utility_functions import torch2tt_tensor
+from models.utility_functions import torch2tt_tensor
 from tests.tt_eager.profiling import ops_for_profiling
 from tracy import signpost
 
@@ -36,8 +36,8 @@
 NUM_REPEATS = 15
 
 
-def torch2tt_tensor(x, device, dlayout, in_mem_config, dtype):
-    return tt_lib.tensor.Tensor(x, dtype).pad_to_tile(float("nan")).to(dlayout).to(device, in_mem_config)
+# def torch2tt_tensor(x, device, dlayout, in_mem_config, dtype):
+#     return tt_lib.tensor.Tensor(x, dtype).pad_to_tile(float("nan")).to(dlayout).to(device, in_mem_config)
 
 
 def measure_host_overhead(op_func, op_name, device, num_call_to_stack, is_warmup):
diff --git a/tests/tt_eager/profiling/reference.txt b/tests/tt_eager/profiling/reference.txt
index 0505a3ee4d4..70767c62cae 100644
--- a/tests/tt_eager/profiling/reference.txt
+++ b/tests/tt_eager/profiling/reference.txt
@@ -51,9 +51,9 @@ tt_lib.tensor.add_unary,40,0.89,1.0,1.3,1.03
 tt_lib.tensor.addalpha,40,1.71,1.9,2.36,1.94
 ttnn.addalpha_bw,40,1.01,1.42,1.69,1.46
 tt_lib.tensor.addcdiv,40,17.67,19.69,20.7,19.42
-tt_lib.tensor.addcdiv_bw,40,31.79,34.53,34.99,33.88
+ttnn.addcdiv_bw,40,31.79,34.53,34.99,33.88
 tt_lib.tensor.addcmul,40,2.5,2.66,2.99,2.65
-tt_lib.tensor.addcmul_bw,40,3.68,4.31,4.62,4.13
+ttnn.addcmul_bw,40,3.68,4.31,4.62,4.13
 tt_lib.tensor.angle_bw,40,0.95,1.04,1.29,1.05
 tt_lib.tensor.arange,40,0.02,0.03,0.12,0.0
 tt_lib.tensor.argmax_all,40,113.57,118.43,119.39,39.46
@@ -98,7 +98,6 @@ ttnn.binary_gt_bw,40,1.78,2.16,2.43,2.24
 ttnn.binary_le_bw,40,1.98,2.06,2.44,4.37
 ttnn.binary_lt_bw,40,1.9,2.27,2.56,2.18
 ttnn.binary_ne_bw,40,1.88,2.17,2.57,2.11
-tt_lib.tensor.bmm,40,0.9,0.96,3.05,1.0
 tt_lib.tensor.cbrt,40,4.93,5.56,5.86,5.32
 tt_lib.tensor.ceil_bw,40,0.96,1.06,1.35,1.09
 tt_lib.tensor.celu_bw,40,10.62,11.46,12.0,11.09
@@ -249,7 +248,7 @@ ttnn.lt,40,0.9,1.19,1.24,1.12
 tt_lib.tensor.ltz,40,0.81,0.9,1.12,0.93
 tt_lib.tensor.mac,40,1.69,1.79,2.13,1.83
 tt_lib.tensor.maeloss,40,11.89,13.2,14.13,12.75
-tt_lib.tensor.matmul,40,0.88,1.78,3.89,1.5
+ttnn.matmul,40,0.88,1.78,3.89,1.5
 ttnn.max_bw,40,9.93,10.57,11.04,10.39
 tt_lib.tensor.mean_hw,40,1.17,1.44,1.88,1.62
 ttnn.min_bw,40,9.15,10.14,10.67,10.13
@@ -388,7 +387,7 @@ tt_lib.tensor.var_hw,40,3.69,4.05,4.4,4.17
 tt_lib.tensor.where,40,3.57,4.16,4.47,4.19
 tt_lib.tensor.where_binary_x_const_y,40,4.46,5.09,5.27,4.84
 tt_lib.tensor.where_binary_x_y_const,40,4.1,4.33,4.65,4.25
-tt_lib.tensor.where_bw,40,8.32,9.01,10.22,9.05
+ttnn.where_bw,40,8.32,9.01,10.22,9.05
 tt_lib.tensor.where_unary_x_const_const,40,3.96,4.18,4.58,4.17
 tt_lib.tensor.xlogy,40,10.34,10.84,11.43,10.71
 ttnn.xlogy_bw,40,34.64,38.33,40.12,38.53