[jira] [Created] (MADLIB-1345) DL: Performance improvement in DL functions

["Ekta Khanna (JIRA)" <ji...@apache.org>]

Ekta Khanna created MADLIB-1345:
-----------------------------------

             Summary: DL: Performance improvement in DL functions
                 Key: MADLIB-1345
                 URL: https://issues.apache.org/jira/browse/MADLIB-1345
             Project: Apache MADlib
          Issue Type: Improvement
            Reporter: Ekta Khanna


Currently, we pass around model_data, model_arch, etc.. for each buffer/image for fit(), predict() and evaluate(). This causes a lot of overhead and slows down the query considerable.
We tried to set model_data and model_arch using GD for predict. Following were the runtimes:

with GD
~707 sec(with CPU) - 50K places10_20seg
without GD
~1650 sec(with CPU) - 50K places10_20seg

Below is the patch for GD changes:
{code}
def set_predict_GD(model_architecture, model_data,
                           is_response, normalizing_const, seg_ids,
                           images_per_seg, gpus_per_host, segments_per_host,
                           **kwargs):
    GD = kwargs['GD']
    GD['model_architecture'] = model_architecture
    GD['model_data'] = model_data
    GD['is_response'] = is_response
    GD['normalizing_const'] = normalizing_const
    #GD['current_seg_id'] = current_seg_id
    GD['seg_ids'] = seg_ids
    GD['images_per_seg'] = images_per_seg
    GD['gpus_per_host'] = gpus_per_host
    GD['segments_per_host'] = segments_per_host

def predict()
....
set_gd_query=plpy.prepare("""
           SELECT set_predict_GD
            ($MAD${model_arch}$MAD$,
            $1,
            {is_response},
            {normalizing_const},
            -- gp_segment_id,
            ARRAY{seg_ids_test},
            ARRAY{images_per_seg_test},
            {gpus_per_host},
            {segments_per_host}
            ) from gp_dist_random('gp_id')
            """.format(**locals()), ["bytea"]) #Using gp_dist_random('gp_id')  in the query makes the UDF run on each segment
plpy.execute(set_gd_query, [model_data])

predict_query = plpy.execute("""
    CREATE TABLE {output_table} AS
    SELECT {id_col}, {prediction_select_clause}
    FROM (
        SELECT {test_table}.{id_col},
               ({schema_madlib}.internal_keras_predict
                   ({independent_varname}, {gp_segment_id_col})
               ) AS {intermediate_col}
    FROM {test_table}
    ) q distributed by ({id_col})
    """.format(**locals()))


def internal_keras_predict(independent_var, current_seg_id, **kwargs):
    start = time.time()
    SD = kwargs['SD']
    GD = kwargs['GD']
    is_response = GD['is_response']
    normalizing_const = GD['normalizing_const']
    #current_seg_id = GD['current_seg_id']
    seg_ids = GD['seg_ids']
    images_per_seg = GD['images_per_seg']
    gpus_per_host = GD['gpus_per_host']
    segments_per_host = GD['segments_per_host']


    device_name = get_device_name_and_set_cuda_env(gpus_per_host,
                                                   current_seg_id)

    ...
{code}

With the above changes , we found out that GD is not reliable for GPDB because of the following reasons:
Consider a single node gpdb cluster with 3 segments

Calling set_gd using gp_dist_random(), creates 1 process per seg and sets GD on each of these processes.
seg1 - pid 100 - gd is set here for seg1
seg2 - pid 200 - gd is set here for seg2
seg3 - pid300- gd is set here for seg3
Now, CREATE TABLE.. in predict(), spins up 2 processes per seg, (the old processes where GD was set) + 1 new process per seg.
seg1 - pid 100 - gd is set here for seg1 (reused from before)
seg1 - pid 101 - gd is read here for seg1
seg2 - pid 200 - gd is set here for seg2 (reused from before)
seg1 - pid 201 - gd is read here for seg2
seg3 - pid300 - gd is set here for seg3 (reused from before)
seg1 - pid 301- gd is read here for seg3
This causes problems because , the processes where GD is read from is not same as the process where it was set.
Couple of ways to avoid this problem

# Change predict code to run two plpy execute queries, the first one being the internal predict query and the second one being the create table query.
# Distribute the source table by the id column and while creating the predict output table use that id column as the distribution key.
We are not sure if this is good enough for all use cases like what if the source table has an index which might do the same thing as the create table command. Our goal is to avoid the query from creating multiple processes.

# Explore the GD option
# Explore alternatives so that we don't have to pass the model data for every row/buffer/image in the transition function/udf



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