checkInBoundsForStorage Class — pytorch Architecture

Source Code

aten/src/ATen/native/Resize.h lines 82–124

template <typename T>
inline void checkInBoundsForStorage(
    ArrayRef<T> size,
    ArrayRef<T> stride,
    T storage_offset,
    const caffe2::TypeMeta& data_type,
    const Storage& new_storage) {
  T storage_size_bytes, storage_size_plus_offset_bytes;
  if (stride.data()) {
    storage_size_bytes =
        at::detail::computeStorageNbytes(size, stride, data_type.itemsize());
    storage_size_plus_offset_bytes = at::detail::computeStorageNbytes(
        size, stride, data_type.itemsize(), storage_offset);
  } else {
    storage_size_bytes =
        at::detail::computeStorageNbytesContiguous(size, data_type.itemsize());
    storage_size_plus_offset_bytes = at::detail::computeStorageNbytesContiguous(
        size, data_type.itemsize(), storage_offset);
  }
  // It's ok to always evaluate to False for this early return for SymInts because
  // (1) maybe_convert_symint below only installs guard for int64_t case
  // (2) we check for this condition in the TORCH_MAYBE_SYM_CHECK below
  if (TORCH_GUARD_OR_FALSE(sym_eq(storage_size_bytes, 0))) {
    // NB: (a tensor with arbitrary 0 dims)'s storage can have any numel.
    return;
  }
  T new_storage_size_bytes = maybe_convert_symint<T>(new_storage.sym_nbytes());
  TORCH_MAYBE_SYM_CHECK(
      sym_eq(storage_size_bytes, 0) || sym_le(storage_size_plus_offset_bytes, new_storage_size_bytes),
      "setStorage: sizes ",
      size,
      ", strides ",
      stride,
      ","
      " storage offset ",
      storage_offset,
      ", and itemsize ",
      data_type.itemsize(),
      " requiring a storage size of ",
      storage_size_plus_offset_bytes,
      " are out of bounds for storage of size ",
      new_storage_size_bytes);
}