#0: add tests and remove asserts to enable greater RM slice support

tests/ttnn/unit_tests/operations/test_slice.py

@@ -752,8 +752,37 @@ def test_slice_adversarial_fixed(input_shape, dim, start, end, step, layout, dev
 @pytest.mark.parametrize(
     "input_shape, dim, start, end, step, layout",
     (
-        ([8732, 4], 1, 0, -1, 4, ttnn.TILE_LAYOUT),  # Need tensor for this or a padding aware tiled kernel
         ([1, 7], 0, 0, -1, 1, ttnn.ROW_MAJOR_LAYOUT),  # page size must equal buffer size
+        ([1, 8, 2, 2], 2, -1, -1, 1, ttnn.TILE_LAYOUT),  # Buffer size and page size should be larger than 0 bytes
+        ([3], 0, 0, -1, 1, ttnn.TILE_LAYOUT),  # Difference in expected shape as it's a 1D tensor
+    ),
+)
+def test_slice_adversarial(input_shape, dim, start, end, step, layout, device):
+    pytest.skip("These tests are known to fail")
+    torch_input = torch.randn(input_shape, dtype=torch.bfloat16)
+
+    slice_obj = slice(start, end, step)
+
+    # Prepare indices for slicing in the specified dimension
+    indices = [slice(None)] * len(input_shape)  # By default, select all elements along every dimension
+    indices[dim] = slice_obj  # Apply slicing to the target dimension
+    indices = tuple(indices)
+
+    # Apply slicing to the input_tensor
+    torch_output_tensor = torch_input[indices]
+
+    ttnn_tensor = ttnn.from_torch(torch_input, device=device, layout=layout, dtype=ttnn.bfloat16)
+    ttnn_output = ttnn_tensor[indices]
+
+    ttnn_output_tensor = ttnn.to_torch(ttnn_output)
+
+    assert_with_pcc(torch_output_tensor, ttnn_output_tensor, 0.999)
+
+
+@pytest.mark.parametrize(
+    "input_shape, dim, start, end, step, layout",
+    (
+        ([8732, 4], 1, 0, -1, 4, ttnn.TILE_LAYOUT),  # Need tensor for this or a padding aware tiled kernel
         (
             [1, 7, 71, 64],
             3,
@@ -762,12 +791,9 @@ def test_slice_adversarial_fixed(input_shape, dim, start, end, step, layout, dev
             1,
             ttnn.ROW_MAJOR_LAYOUT,
         ),  # An unpadding slice operations for a RowMajor layout on the output tensor requires the last dimension to be on a 32 bit boundary
-        ([1, 8, 2, 2], 2, -1, -1, 1, ttnn.TILE_LAYOUT),  # Buffer size and page size should be larger than 0 bytes
-        ([3], 0, 0, -1, 1, ttnn.TILE_LAYOUT),  # Difference in expected shape as it's a 1D tensor
     ),
 )
-def test_slice_adversarial(input_shape, dim, start, end, step, layout, device):
-    pytest.skip("These tests are expected to fail at the moment")
+def test_slice_adversarial_fixed(input_shape, dim, start, end, step, layout, device):
     torch_input = torch.randn(input_shape, dtype=torch.bfloat16)
 
     slice_obj = slice(start, end, step)

ttnn/cpp/ttnn/operations/data_movement/slice/device/slice_op.cpp

@@ -102,18 +102,11 @@ void SliceDeviceOperation::validate_with_output_tensors(
             (output_tensor_shape[-1] % TILE_WIDTH == 0) && (this->slice_start[-1] % TILE_WIDTH == 0),
             "Can only unpad tilized tensor with full tiles");
     } else if (input_tensor_a.get_layout() == Layout::ROW_MAJOR) {
-        TT_FATAL(
-            (output_tensor_shape[-1] * input_tensor_a.element_size() % sizeof(uint32_t) == 0),
-            "An unpadding slice operations for a RowMajor layout on the output tensor requires the last dimension to be on a 32 bit boundary. For example, the final dimension needs to be divisible by 2 for bfloat16. The resulting tensor shape is {}, which is not 4B aligned as the last dimension is {}",
-                        output_tensor_shape[-1], input_tensor_a.element_size());
         if (has_step) {
             for (uint32_t i = 0; i < input_tensor_a.get_legacy_shape().rank(); i++) {
                 TT_FATAL(step[i] > 0, "Step({}) = {} should be positive", i, step[i]);
             }
         }
-        else {
-            TT_FATAL(this->slice_start[-1] * input_tensor_a.element_size() % sizeof(uint32_t) == 0, "Slice needs to start at an aligned position");
-        }
     }
 }