#14915: re-ordering code in test_embedding after rebase

tests/ttnn/unit_tests/operations/test_embedding.py

@@ -206,6 +206,32 @@ def reverse_embedding_output(output_tensor, weights_tensor):
     return reversed_indices
 
 
+def create_tile_tensor(height, width, tile_size):
+    """
+    Creates a 2D tensor where each element represents the tile it belongs to.
+
+    Parameters:
+        height (int): The height of the tensor (number of rows).
+        width (int): The width of the tensor (number of columns).
+        tile_size (int): The size of each square tile (tile_size x tile_size).
+
+    Returns:
+        torch.Tensor: A 2D tensor with tile indices.
+    """
+    # Calculate the number of tiles in each dimension
+    tiles_per_row = (width + tile_size - 1) // tile_size
+    tiles_per_col = (height + tile_size - 1) // tile_size
+
+    # Create row and column indices
+    row_indices = torch.arange(height).unsqueeze(1) // tile_size
+    col_indices = torch.arange(width).unsqueeze(0) // tile_size
+
+    # Calculate tile indices
+    tile_tensor = row_indices * tiles_per_row + col_indices
+    print(tile_tensor.shape)
+    return tile_tensor
+
+
 @pytest.mark.parametrize("batch_size", [1, 8, 9])
 @pytest.mark.parametrize("sentence_size", [32, 256, 512])
 @pytest.mark.parametrize("hidden_embedding_dim", [768, 4096])  # Bert_Num_Cols_768, Llama_Num_Cols
@@ -401,47 +427,3 @@ def test_tg_llama_sharded_embedding(
     )
     output_tensor = ttnn.to_torch(output_tensor)
     assert_with_pcc(output_tensor, torch_output_tensor[:, 0, :].unsqueeze(1))
-
-def get_random_factors(N):
-    if N == 0:
-        raise ValueError("Input number must be non-zero.")
-
-    # Get all divisors of N
-    divisors = [i for i in range(1, abs(N) + 1) if N % i == 0]
-
-    # Randomly select a divisor (excluding N itself to ensure two factors)
-    A = random.choice(divisors[:-1])
-
-    # Compute the second factor
-    B = N // A
-
-    return A, B
-
-
-import torch
-
-
-def create_tile_tensor(height, width, tile_size):
-    """
-    Creates a 2D tensor where each element represents the tile it belongs to.
-
-    Parameters:
-        height (int): The height of the tensor (number of rows).
-        width (int): The width of the tensor (number of columns).
-        tile_size (int): The size of each square tile (tile_size x tile_size).
-
-    Returns:
-        torch.Tensor: A 2D tensor with tile indices.
-    """
-    # Calculate the number of tiles in each dimension
-    tiles_per_row = (width + tile_size - 1) // tile_size
-    tiles_per_col = (height + tile_size - 1) // tile_size
-
-    # Create row and column indices
-    row_indices = torch.arange(height).unsqueeze(1) // tile_size
-    col_indices = torch.arange(width).unsqueeze(0) // tile_size
-
-    # Calculate tile indices
-    tile_tensor = row_indices * tiles_per_row + col_indices
-    print(tile_tensor.shape)
-    return tile_tensor