diff --git a/extra/torch_backend/test_kernel_fusion.py b/extra/torch_backend/test_kernel_fusion.py
index 0546323366..dffcfe067f 100644
--- a/extra/torch_backend/test_kernel_fusion.py
+++ b/extra/torch_backend/test_kernel_fusion.py
@@ -1,7 +1,6 @@
 # simple tests
 import unittest
 import torch
-import warnings
 from tinygrad.helpers import getenv, GlobalCounters
 if getenv("TINY_BACKEND2"):
   import extra.torch_backend.backend2
@@ -18,9 +17,7 @@ class TestKernelFusionRegression(unittest.TestCase):
     torch.manual_seed(42)
     GlobalCounters.reset()
     fn().detach().cpu().numpy()
-    expectation = f"{GlobalCounters.kernel_count} vs {expected_kernels} expected."
-    if GlobalCounters.kernel_count < expected_kernels: warnings.warn(f"{expectation} Expectation can be lowered.", UserWarning)
-    self.assertLessEqual(GlobalCounters.kernel_count, expected_kernels, f"{expectation}")
+    self.assertEqual(GlobalCounters.kernel_count, expected_kernels)
 
   def test_elementwise_fusion(self):
     def fn():
@@ -34,7 +31,7 @@ class TestKernelFusionRegression(unittest.TestCase):
       conv = torch.nn.Conv2d(3, 16, 3, padding=1).to(device)
       with torch.no_grad():
         return torch.nn.functional.relu(conv(x))
-    self._check_kernel_count(fn, 8)
+    self._check_kernel_count(fn, 6)
 
   def test_batchnorm_fusion(self):
     def fn():
@@ -44,7 +41,7 @@ class TestKernelFusionRegression(unittest.TestCase):
       bn.eval()
       with torch.no_grad():
         return torch.nn.functional.relu(bn(conv(x)))
-    self._check_kernel_count(fn, 16)
+    self._check_kernel_count(fn, 10)
 
   def test_reduce_fusion(self):
     def fn():
@@ -92,7 +89,7 @@ class TestKernelFusionRegression(unittest.TestCase):
         out = bn(conv(x))
         out += identity
         return torch.nn.functional.relu(out)
-    self._check_kernel_count(fn, 17)
+    self._check_kernel_count(fn, 12)
 
   def test_multiple_inplace_ops_fusion(self):
     def fn():
@@ -117,7 +114,7 @@ class TestKernelFusionRegression(unittest.TestCase):
       bn.train()
       with torch.no_grad():
         return bn(x)
-    self._check_kernel_count(fn, 10)
+    self._check_kernel_count(fn, 8)
 
   # this is a minimal extra/other_mnist/beautiful_mnist_torch.py to cover fusion for training with optimizer
   def test_mnist_training_fusion(self):
@@ -138,7 +135,7 @@ class TestKernelFusionRegression(unittest.TestCase):
       loss.backward()
       optimizer.step()
       return loss
-    self._check_kernel_count(fn, 28)
+    self._check_kernel_count(fn, 24)
 
 if __name__ == "__main__":
   unittest.main()