[BUG][GPU Error Bug] "SELECT <number> FROM <table> HAVING EVERY(<boolean>)" brings Error

Open qwebug opened this issue 2 years ago • 0 comments

What happened:

"SELECT <number> FROM <table> HAVING EVERY(<boolean>)" brings error, when using GPU.

However it is able to output result, when using CPU.

What you expected to happen:

It will not bring error, when using GPU.

Minimal Complete Verifiable Example:

import pandas as pd
import dask.dataframe as dd
from dask_sql import Context

c = Context()

df0 = pd.DataFrame({
    'c0': ['A'],
    'c1': ['B'],
})
t0 = dd.from_pandas(df0, npartitions=1)

c.create_table('t0', t0, gpu=False)
c.create_table('t0_gpu', t0, gpu=True)

print('CPU Result:')
result1= c.sql("SELECT 1 FROM t0 HAVING EVERY(true)").compute()
print(result1)

print('GPU Result:')
result2= c.sql("SELECT 1 FROM t0_gpu HAVING EVERY(true)").compute()
print(result2)

Result:

INFO:numba.cuda.cudadrv.driver:init
CPU Result:
   Int64(1)
0         1
GPU Result:
Traceback (most recent call last):
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/utils.py", line 193, in raise_on_meta_error
    yield
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/core.py", line 6793, in _emulate
    return func(*_extract_meta(args, True), **_extract_meta(kwargs, True))
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 1203, in _groupby_apply_funcs
    r = func(grouped, **func_kwargs)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 1249, in _apply_func_to_column
    return func(df_like[column])
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/logical/aggregate.py", line 152, in <lambda>
    dd.Aggregation("every", lambda s: s.all(), lambda s0: s0.all())
AttributeError: 'SeriesGroupBy' object has no attribute 'all'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/tmp/bug.py", line 21, in <module>
    result2= c.sql("SELECT 1 FROM t0_gpu HAVING EVERY(true)").compute()
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/context.py", line 513, in sql
    return self._compute_table_from_rel(rel, return_futures)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/context.py", line 839, in _compute_table_from_rel
    dc = RelConverter.convert(rel, context=self)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/convert.py", line 61, in convert
    df = plugin_instance.convert(rel, context=context)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/logical/project.py", line 28, in convert
    (dc,) = self.assert_inputs(rel, 1, context)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/base.py", line 84, in assert_inputs
    return [RelConverter.convert(input_rel, context) for input_rel in input_rels]
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/base.py", line 84, in <listcomp>
    return [RelConverter.convert(input_rel, context) for input_rel in input_rels]
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/convert.py", line 61, in convert
    df = plugin_instance.convert(rel, context=context)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/logical/filter.py", line 56, in convert
    (dc,) = self.assert_inputs(rel, 1, context)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/base.py", line 84, in assert_inputs
    return [RelConverter.convert(input_rel, context) for input_rel in input_rels]
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/base.py", line 84, in <listcomp>
    return [RelConverter.convert(input_rel, context) for input_rel in input_rels]
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/convert.py", line 61, in convert
    df = plugin_instance.convert(rel, context=context)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/logical/aggregate.py", line 231, in convert
    df_agg, output_column_order, cc = self._do_aggregations(
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/logical/aggregate.py", line 312, in _do_aggregations
    df_result = self._perform_aggregation(
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/logical/aggregate.py", line 551, in _perform_aggregation
    agg_result = grouped_df.agg(aggregations_dict, **groupby_agg_options)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 374, in wrapper
    return func(self, *args, **kwargs)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 2884, in agg
    return self.aggregate(
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/nvtx/nvtx.py", line 101, in inner
    result = func(*args, **kwargs)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_cudf/groupby.py", line 218, in aggregate
    return super().aggregate(
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 2873, in aggregate
    return super().aggregate(
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 2369, in aggregate
    result = aca(
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/core.py", line 6746, in apply_concat_apply
    meta_chunk = _emulate(chunk, *args, udf=True, **chunk_kwargs)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/core.py", line 6792, in _emulate
    with raise_on_meta_error(funcname(func), udf=udf), check_numeric_only_deprecation():
  File "/opt/conda/envs/rapids/lib/python3.10/contextlib.py", line 153, in __exit__
    self.gen.throw(typ, value, traceback)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/utils.py", line 214, in raise_on_meta_error
    raise ValueError(msg) from e
ValueError: Metadata inference failed in `_groupby_apply_funcs`.

You have supplied a custom function and Dask is unable to 
determine the type of output that that function returns. 

To resolve this please provide a meta= keyword.
The docstring of the Dask function you ran should have more information.

Original error is below:
------------------------
AttributeError("'SeriesGroupBy' object has no attribute 'all'")

Traceback:
---------
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/utils.py", line 193, in raise_on_meta_error
    yield
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/core.py", line 6793, in _emulate
    return func(*_extract_meta(args, True), **_extract_meta(kwargs, True))
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 1203, in _groupby_apply_funcs
    r = func(grouped, **func_kwargs)
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask/dataframe/groupby.py", line 1249, in _apply_func_to_column
    return func(df_like[column])
  File "/opt/conda/envs/rapids/lib/python3.10/site-packages/dask_sql/physical/rel/logical/aggregate.py", line 152, in <lambda>
    dd.Aggregation("every", lambda s: s.all(), lambda s0: s0.all())

Anything else we need to know?:

Environment:

dask-sql version: 2023.6.0
Python version: Python 3.10.11
Operating System: Ubuntu22.04
Install method (conda, pip, source): Docker deploy by https://hub.docker.com/layers/rapidsai/rapidsai-dev/23.06-cuda11.8-devel-ubuntu22.04-py3.10/images/sha256-cfbb61fdf7227b090a435a2e758114f3f1c31872ed8dbd96e5e564bb5fd184a7?context=explore

Sep 20 '23 08:09 qwebug