diff --git a/tt_metal/common/test_tiles.hpp b/tt_metal/common/test_tiles.hpp
index 4633e1a990bc..e828e07a7b72 100644
--- a/tt_metal/common/test_tiles.hpp
+++ b/tt_metal/common/test_tiles.hpp
@@ -348,7 +348,8 @@ inline std::vector<T> convert_layout(
     ZoneScoped;
 
     TT_ASSERT(shape.size() >= 2, "Shape size {} must be at least rank 2!", shape.size());
-    uint32_t H = shape[shape.size() - 2], W = shape[shape.size() - 1];
+    uint32_t H = shape[shape.size() - 2];
+    uint32_t W = shape[shape.size() - 1];
     for (int i = 0; i < shape.size() - 2; i++) {
         H *= shape[i];
     }
diff --git a/ttnn/cpp/pybind11/pytensor.cpp b/ttnn/cpp/pybind11/pytensor.cpp
index 82eaabcca8aa..09de90a74cd6 100644
--- a/ttnn/cpp/pybind11/pytensor.cpp
+++ b/ttnn/cpp/pybind11/pytensor.cpp
@@ -18,6 +18,7 @@
 #include "ttnn/tensor/host_buffer/types.hpp"
 #include "ttnn/tensor/tensor.hpp"
 #include "ttnn/tensor/tensor_impl.hpp"
+#include "ttnn/tensor/tensor_ops.hpp"
 
 namespace py = pybind11;
 
@@ -107,8 +108,17 @@ Tensor convert_float_vector_to_tt_tensor(
             log_warning(tt::LogAlways, "Tensor layout must be Layout::TILE for bfloat8_b or bfloat4_b! Tensor layout will be {} instead of the requested {}!", Layout::TILE, layout);
         }
         auto owned_buffer = create_owned_buffer_from_vector_of_floats(std::move(data), DataType::FLOAT32);
-        auto float_tensor = Tensor(OwnedStorage{owned_buffer}, shape, DataType::FLOAT32, Layout::ROW_MAJOR, tile).to(Layout::TILE);
-        auto output_float_data = owned_buffer::get_as<float>(float_tensor).get();
+        auto float_tensor = Tensor(OwnedStorage{owned_buffer}, shape, DataType::FLOAT32, Layout::ROW_MAJOR, tile);
+        auto tile_val = tile.value_or(Tile());
+        if (shape[2] % tile_val.get_height() != 0 || shape[3] % tile_val.get_width() != 0) {
+            auto padded_shape = shape;
+            padded_shape[2] = tt::round_up(shape[2], tile_val.get_height());
+            padded_shape[3] = tt::round_up(shape[3], tile_val.get_width());
+
+            float_tensor = tensor_ops::tensor_pad(float_tensor, LegacyShape(shape, padded_shape), ttnn::SimpleShape{0, 0, 0, 0}, 0);
+        }
+        std::cout << float_tensor.shape() << std::endl;
+        auto output_float_data = owned_buffer::get_as<float>(float_tensor.to(Layout::TILE)).get();
         auto output_packed_data =
             data_type == DataType::BFLOAT8_B
                 ? pack_fp32_vec_as_bfp8_tiles(output_float_data, /*row_major_input=*/false, /*is_exp_a=*/false, tile)
diff --git a/ttnn/cpp/ttnn/tensor/tensor_ops.cpp b/ttnn/cpp/ttnn/tensor/tensor_ops.cpp
index bd06cf3370c7..09aa043e49fe 100644
--- a/ttnn/cpp/ttnn/tensor/tensor_ops.cpp
+++ b/ttnn/cpp/ttnn/tensor/tensor_ops.cpp
@@ -237,7 +237,10 @@ Tensor tensor_pad(const Tensor& input_tensor, const tt::tt_metal::LegacyShape& o
     TT_ASSERT(
         input_tensor.storage_type() == StorageType::OWNED or input_tensor.storage_type() == StorageType::MULTI_DEVICE_HOST or
         input_tensor.storage_type() == StorageType::BORROWED && "Tensor must be on host for padding");
-    TT_ASSERT(input_tensor.get_layout() == Layout::ROW_MAJOR && "Tensor layout must be ROW_MAJOR for padding");
+    if (input_tensor.get_layout() != Layout::ROW_MAJOR) {
+        log_warning(tt::LogOp, "Tensor layout {} must be ROW_MAJOR for padding! Returning original tensor!", input_tensor.get_layout());
+        return input_tensor;
+    }
 
     auto input_shape = input_tensor.get_legacy_shape();
     auto dimensions_pads = std::vector<Padding::PadDimension>();