# Buggy code
def execute(cls, ctx, op):
    def _base_concat(chunk, inputs):
        # auto generated concat when executing a DataFrame, Series or Index
        if chunk.op.output_types[0] == OutputType.dataframe:
            return _auto_concat_dataframe_chunks(chunk, inputs)
        elif chunk.op.output_types[0] == OutputType.series:
            return _auto_concat_series_chunks(chunk, inputs)
        elif chunk.op.output_types[0] == OutputType.index:
            return _auto_concat_index_chunks(chunk, inputs)
        elif chunk.op.output_types[0] == OutputType.categorical:
            return _auto_concat_categorical_chunks(chunk, inputs)
        else:  # pragma: no cover
            raise TypeError(
                "Only DataFrameChunk, SeriesChunk, IndexChunk, "
                "and CategoricalChunk can be automatically concatenated"
            )

    def _auto_concat_dataframe_chunks(chunk, inputs):
        if chunk.op.axis is not None:
            return pd.concat(inputs, axis=op.axis)

        # auto generated concat when executing a DataFrame
        if len(inputs) == 1:
            ret = inputs[0]
        else:
            max_rows = max(inp.index[0] for inp in chunk.inputs)
            min_rows = min(inp.index[0] for inp in chunk.inputs)
            n_rows = max_rows - min_rows + 1
            n_cols = int(len(inputs) // n_rows)
            assert n_rows * n_cols == len(inputs)

            xdf = pd if isinstance(inputs[0], (pd.DataFrame, pd.Series)) else cudf

            concats = []
            for i in range(n_rows):
                if n_cols == 1:
                    concats.append(inputs[i])
                else:
                    concat = xdf.concat(
                        [inputs[i * n_cols + j] for j in range(n_cols)], axis=1
                    )
                    concats.append(concat)

            if xdf is pd:
                # The `sort=False` is to suppress a `FutureWarning` of pandas,
                # when the index or column of chunks to concatenate is not aligned,
                # which may happens for certain ops.
                #
                # See also Note [Columns of Left Join] in test_merge_execution.py.
                ret = xdf.concat(concats, sort=False)
            else:
                ret = xdf.concat(concats)
                # cuDF will lost index name when concat two seriess.
                ret.index.name = concats[0].index.name

        if getattr(chunk.index_value, "should_be_monotonic", False):
            ret.sort_index(inplace=True)
        if getattr(chunk.columns_value, "should_be_monotonic", False):
            ret.sort_index(axis=1, inplace=True)
        return ret

    def _auto_concat_series_chunks(chunk, inputs):
        # auto generated concat when executing a Series
        if all(np.isscalar(inp) for inp in inputs):
            return pd.Series(inputs)
        else:
            if len(inputs) == 1:
                concat = inputs[0]
            else:
                xdf = pd if isinstance(inputs[0], pd.Series) else cudf
                if chunk.op.axis is not None:
                    concat = xdf.concat(inputs, axis=chunk.op.axis)
                else:
                    concat = xdf.concat(inputs)
            if getattr(chunk.index_value, "should_be_monotonic", False):
                concat.sort_index(inplace=True)
            return concat

    def _auto_concat_index_chunks(chunk, inputs):
        if len(inputs) == 1:
            xdf = pd if isinstance(inputs[0], pd.Index) else cudf
            concat_df = xdf.DataFrame(index=inputs[0])
        else:
            xdf = pd if isinstance(inputs[0], pd.Index) else cudf
            empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
            concat_df = xdf.concat(empty_dfs, axis=0)
        if getattr(chunk.index_value, "should_be_monotonic", False):
            concat_df.sort_index(inplace=True)
        return concat_df.index

    def _auto_concat_categorical_chunks(_, inputs):
        if len(inputs) == 1:  # pragma: no cover
            return inputs[0]
        else:
            # convert categorical into array
            arrays = [np.asarray(inp) for inp in inputs]
            array = np.concatenate(arrays)
            return pd.Categorical(
                array, categories=inputs[0].categories, ordered=inputs[0].ordered
            )

    chunk = op.outputs[0]
    inputs = [ctx[input.key] for input in op.inputs]

    if isinstance(inputs[0], tuple):
        ctx[chunk.key] = tuple(
            _base_concat(chunk, [input[i] for input in inputs])
            for i in range(len(inputs[0]))
        )
    else:
        ctx[chunk.key] = _base_concat(chunk, inputs)


# Traceback (AttributeError)
AttributeError                            Traceback (most recent call last)
<ipython-input-3-a85925f048d0> in <module>
1 start=time.time()
2 df_mars=df_mars.to_gpu()
----> 3 print(df_mars.sum().to_frame(name="sum").execute())

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/core.py in execute(self, session, **kw)
580
581     def execute(self, session=None, **kw):
--> 582         self._data.execute(session, **kw)
583         return self
584

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/core.py in execute(self, session, **kw)
368
369         # no more fetch, thus just fire run
--> 370         session.run(self, **kw)
371         # return Tileable or ExecutableTuple itself
372         return self

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/session.py in run(self, *tileables, **kw)
460         tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
461                           for t in tileables)
--> 462         result = self._sess.run(*tileables, **kw)
463
464         for t in tileables:

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/session.py in run(self, *tileables, **kw)
105             # set number of running cores
106             self.context.set_ncores(kw['n_parallel'])
--> 107             res = self._executor.execute_tileables(tileables, **kw)
108             return res
109

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/utils.py in _wrapped(*args, **kwargs)
406                 _kernel_mode.eager = False
407             _kernel_mode.eager_count = enter_eager_count + 1
--> 408             return func(*args, **kwargs)
409         finally:
410             _kernel_mode.eager_count -= 1

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/utils.py in inner(*args, **kwargs)
500     def inner(*args, **kwargs):
501         with build_mode():
--> 502             return func(*args, **kwargs)
503     return inner
504

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
878                                    n_parallel=n_parallel or n_thread,
879                                    print_progress=print_progress, mock=mock,
--> 880                                    chunk_result=chunk_result)
881
882                 # update shape of tileable and its chunks whatever it's successful or not

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
688             print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
689             fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 690         res = graph_execution.execute(retval)
691         self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
692         if mock:

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/executor.py in execute(self, retval)
569         # wait until all the futures completed
570         for future in executed_futures:
--> 571             future.result()
572
573         if retval:

/opt/conda/envs/rapids/lib/python3.6/concurrent/futures/_base.py in result(self, timeout)
423                 raise CancelledError()
424             elif self._state == FINISHED:
--> 425                 return self.__get_result()
426
427             self._condition.wait(timeout)

/opt/conda/envs/rapids/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
382     def __get_result(self):
383         if self._exception:
--> 384             raise self._exception
385         else:
386             return self._result

/opt/conda/envs/rapids/lib/python3.6/concurrent/futures/thread.py in run(self)
54
55         try:
---> 56             result = self.fn(*self.args, **self.kwargs)
57         except BaseException as exc:
58             self.future.set_exception(exc)

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/executor.py in _execute_operand(self, op)
441             # so we pass the first operand's first output to Executor.handle
442             first_op = ops[0]
--> 443             Executor.handle(first_op, results, self._mock)
444
445             # update maximal memory usage during execution

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/executor.py in handle(cls, op, results, mock)
639                 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
640                 try:
--> 641                     return runner(results, op)
642                 except UFuncTypeError as e:
643                     raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/dataframe/reduction/core.py in execute(cls, ctx, op)
405                 cls._execute_agg(ctx, op)
406             elif op.stage == OperandStage.map:
--> 407                 cls._execute_map(ctx, op)
408             else:
409                 in_data = ctx[op.inputs[0].key]

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/dataframe/reduction/core.py in _execute_map(cls, ctx, op)
380             cls._execute_map_with_count(ctx, op)
381         else:
--> 382             cls._execute_without_count(ctx, op)
383
384     @classmethod

/opt/conda/envs/rapids/lib/python3.6/site-packages/mars/dataframe/reduction/core.py in _execute_without_count(cls, ctx, op, reduction_func)
370                 # cannot just do xdf.DataFrame(r).T
371                 # cuz the dtype will be object since pandas 1.0
--> 372                 df = xdf.DataFrame(OrderedDict((d, [v]) for d, v in r.iteritems()))
373             else:
374                 df = xdf.DataFrame(r)

AttributeError: 'Series' object has no attribute 'iteritems'

# Fixed code
def execute(cls, ctx, op):
    def _base_concat(chunk, inputs):
        # auto generated concat when executing a DataFrame, Series or Index
        if chunk.op.output_types[0] == OutputType.dataframe:
            return _auto_concat_dataframe_chunks(chunk, inputs)
        elif chunk.op.output_types[0] == OutputType.series:
            return _auto_concat_series_chunks(chunk, inputs)
        elif chunk.op.output_types[0] == OutputType.index:
            return _auto_concat_index_chunks(chunk, inputs)
        elif chunk.op.output_types[0] == OutputType.categorical:
            return _auto_concat_categorical_chunks(chunk, inputs)
        else:  # pragma: no cover
            raise TypeError(
                "Only DataFrameChunk, SeriesChunk, IndexChunk, "
                "and CategoricalChunk can be automatically concatenated"
            )

    def _auto_concat_dataframe_chunks(chunk, inputs):
        xdf = pd if isinstance(inputs[0], (pd.DataFrame, pd.Series)) else cudf

        if chunk.op.axis is not None:
            return xdf.concat(inputs, axis=op.axis)

        # auto generated concat when executing a DataFrame
        if len(inputs) == 1:
            ret = inputs[0]
        else:
            max_rows = max(inp.index[0] for inp in chunk.inputs)
            min_rows = min(inp.index[0] for inp in chunk.inputs)
            n_rows = max_rows - min_rows + 1
            n_cols = int(len(inputs) // n_rows)
            assert n_rows * n_cols == len(inputs)

            concats = []
            for i in range(n_rows):
                if n_cols == 1:
                    concats.append(inputs[i])
                else:
                    concat = xdf.concat(
                        [inputs[i * n_cols + j] for j in range(n_cols)], axis=1
                    )
                    concats.append(concat)

            if xdf is pd:
                # The `sort=False` is to suppress a `FutureWarning` of pandas,
                # when the index or column of chunks to concatenate is not aligned,
                # which may happens for certain ops.
                #
                # See also Note [Columns of Left Join] in test_merge_execution.py.
                ret = xdf.concat(concats, sort=False)
            else:
                ret = xdf.concat(concats)
                # cuDF will lost index name when concat two seriess.
                ret.index.name = concats[0].index.name

        if getattr(chunk.index_value, "should_be_monotonic", False):
            ret.sort_index(inplace=True)
        if getattr(chunk.columns_value, "should_be_monotonic", False):
            ret.sort_index(axis=1, inplace=True)
        return ret

    def _auto_concat_series_chunks(chunk, inputs):
        # auto generated concat when executing a Series
        if all(np.isscalar(inp) for inp in inputs):
            return pd.Series(inputs)
        else:
            if len(inputs) == 1:
                concat = inputs[0]
            else:
                xdf = pd if isinstance(inputs[0], pd.Series) else cudf
                if chunk.op.axis is not None:
                    concat = xdf.concat(inputs, axis=chunk.op.axis)
                else:
                    concat = xdf.concat(inputs)
            if getattr(chunk.index_value, "should_be_monotonic", False):
                concat.sort_index(inplace=True)
            return concat

    def _auto_concat_index_chunks(chunk, inputs):
        if len(inputs) == 1:
            xdf = pd if isinstance(inputs[0], pd.Index) else cudf
            concat_df = xdf.DataFrame(index=inputs[0])
        else:
            xdf = pd if isinstance(inputs[0], pd.Index) else cudf
            empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
            concat_df = xdf.concat(empty_dfs, axis=0)
        if getattr(chunk.index_value, "should_be_monotonic", False):
            concat_df.sort_index(inplace=True)
        return concat_df.index

    def _auto_concat_categorical_chunks(_, inputs):
        if len(inputs) == 1:  # pragma: no cover
            return inputs[0]
        else:
            # convert categorical into array
            arrays = [np.asarray(inp) for inp in inputs]
            array = np.concatenate(arrays)
            return pd.Categorical(
                array, categories=inputs[0].categories, ordered=inputs[0].ordered
            )

    chunk = op.outputs[0]
    inputs = [ctx[input.key] for input in op.inputs]

    if isinstance(inputs[0], tuple):
        ctx[chunk.key] = tuple(
            _base_concat(chunk, [input[i] for input in inputs])
            for i in range(len(inputs[0]))
        )
    else:
        ctx[chunk.key] = _base_concat(chunk, inputs)