#6991: Update ttnn unit test files for non-nan

tests/ttnn/unit_tests/operations/test_mean.py

@@ -18,9 +18,6 @@
 def test_mean(device, batch_size, h, w, dim):
     torch.manual_seed(0)
 
-    if is_wormhole_b0() and dim == -2:
-        pytest.skip("Issue #6991: Wormhole B0: mean operation fails for dim=-2")
-
     torch_input_tensor = torch_random((batch_size, h, w), -1, 1, dtype=torch.bfloat16)
     torch_output_tensor = torch.mean(torch_input_tensor, dim=dim, keepdim=True, dtype=torch.bfloat16)
 

tests/ttnn/unit_tests/operations/test_min.py

@@ -16,8 +16,6 @@
 @pytest.mark.parametrize("w", [32, 64])
 @pytest.mark.parametrize("dim", [-1, -2])
 def test_min(device, batch_size, h, w, dim):
-    if is_wormhole_b0() and dim == -2:
-        pytest.skip("Issue #6991: PCC mismatch for dim=-2")
     torch.manual_seed(0)
 
     torch_input_tensor = torch_random((batch_size, h, w), -100, 100, dtype=torch.bfloat16)
@@ -37,8 +35,6 @@ def test_min(device, batch_size, h, w, dim):
 @pytest.mark.parametrize("h", [32, 64])
 @pytest.mark.parametrize("w", [32, 64])
 def test_min_global(device, batch_size, h, w):
-    if is_wormhole_b0():
-        pytest.skip("Issue #6991: PCC mismatch")
     torch.manual_seed(0)
 
     torch_input_tensor = torch_random((batch_size, h, w), -100, 100, dtype=torch.bfloat16)

tests/ttnn/unit_tests/operations/test_reduction.py

@@ -17,8 +17,6 @@
 @pytest.mark.parametrize("dim", [-1, -2])
 def test_std(device, batch_size, h, w, dim):
     torch.manual_seed(0)
-    if is_wormhole_b0() and dim == -2:
-        pytest.skip("Issue #6991: PCC mismatch for dim=-2")
 
     torch_input_tensor = torch.randn((batch_size, h, w), dtype=torch.bfloat16)
     torch_output_tensor = torch.std(torch_input_tensor, dim=dim, keepdim=True)
@@ -39,8 +37,6 @@ def test_std(device, batch_size, h, w, dim):
 @pytest.mark.parametrize("dim", [-1, -2])
 def test_var(device, batch_size, h, w, dim):
     torch.manual_seed(0)
-    if is_wormhole_b0() and dim == -2:
-        pytest.skip("Issue #6991: PCC mismatch for dim=-2")
 
     torch_input_tensor = torch.randn((batch_size, h, w), dtype=torch.bfloat16)
     torch_output_tensor = torch.var(torch_input_tensor, dim=dim, keepdim=True)

tests/ttnn/unit_tests/operations/test_sum.py

@@ -17,8 +17,6 @@
 @pytest.mark.parametrize("dim", [-1, -2, (2, 1)])
 def test_sum(device, batch_size, h, w, dim):
     torch.manual_seed(0)
-    if is_wormhole_b0():
-        pytest.skip("Issue #6991: PCC mismatch")
 
     torch_input_tensor = torch_random((batch_size, h, w), -100, 100, dtype=torch.bfloat16)
     torch_output_tensor = torch.sum(torch_input_tensor, dim=dim, keepdim=True)
@@ -38,8 +36,6 @@ def test_sum(device, batch_size, h, w, dim):
 @pytest.mark.parametrize("w", [32, 64])
 def test_sum_global(device, batch_size, h, w):
     torch.manual_seed(0)
-    if is_wormhole_b0():
-        pytest.skip("Issue #6991: PCC mismatch")
 
     torch_input_tensor = torch_random((batch_size, h, w), -100, 100, dtype=torch.bfloat16)
     torch_output_tensor = torch.sum(torch_input_tensor)

tests/ttnn/unit_tests/operations/test_unary.py

@@ -157,9 +157,8 @@ def test_cosh(device, h, w):
 
 @pytest.mark.parametrize("h", [64])
 @pytest.mark.parametrize("w", [128])
-@skip_for_wormhole_b0("Issue #6991: Failing on wormhole_b0 PCC issue")
 def test_acosh(device, h, w):
-    run_unary_test(device, h, w, ttnn.acosh, torch.acosh)
+    run_unary_test_with_range(device, h, w, 1, 100, ttnn.acosh, torch.acosh)
 
 
 @pytest.mark.parametrize("h", [64])
@@ -212,9 +211,21 @@ def run_unary_test_with_float(device, h, w, scalar, ttnn_function, torch_functio
     assert_with_pcc(torch_output_tensor, output_tensor, pcc)
 
 
-@pytest.mark.parametrize("scalar", [1, 2])
+def run_unary_test_with_range(device, h, w, scalar, low, high, ttnn_function, torch_function, pcc=0.9999):
+    torch_input_tensor = torch.rand((h, w), dtype=torch.bfloat16) * (high - low) + low
+    torch_output_tensor = torch_function(torch_input_tensor, scalar)
+
+    input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)
+    output_tensor = ttnn_function(input_tensor, scalar)
+    output_tensor = ttnn.to_layout(output_tensor, ttnn.ROW_MAJOR_LAYOUT)
+    output_tensor = ttnn.from_device(output_tensor)
+    output_tensor = ttnn.to_torch(output_tensor)
+
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc)
+
+
+@pytest.mark.parametrize("scalar", [1, 1e-6])
 @pytest.mark.parametrize("h", [64])
 @pytest.mark.parametrize("w", [128])
-@skip_for_wormhole_b0("Issue #6991: Failing on wormhole_b0 PCC issue")
 def test_logit(device, h, w, scalar):
-    run_unary_test_with_float(device, h, w, scalar, ttnn.logit, torch.logit)
+    run_unary_test_with_range(device, h, w, scalar, ttnn.logit, torch.logit)