[QST]ensemble schema changes after upgrading merlin version and refuses to take array as input

[wwelder]

❓ Questions & Help

Details

I am not sure if it is a bug, it bacially breaks my workflow and I can't upgrade to Merlin version 23.

I used merlin tensorflow 22.12 and my exported ensemble has all features as arrays and the config.pbtxt looks like

platform: "ensemble"
input {
  name: "userid"
  data_type: TYPE_INT64
  dims: -1
  dims: 1
}
input {
  name: "contentid"
  data_type: TYPE_INT64
  dims: -1
  dims: 1
}

However, after upgrading to 23.06(or 23.05), all inputs has one dim only and my grpc triton inference server refused to get a batch array as input and the config.pbtxt became following:

platform: "merlin_executor"
input {
  name: "userid"
  data_type: TYPE_INT64
  dims: -1
}
input {
  name: "contentid"
  data_type: TYPE_INT64
  dims: -1
}

Here is my train_and_export script, I ran this same script in merlin 22 and 23 and got the different results above

from merlin.systems.dag.ensemble import Ensemble
from merlin.systems.dag.ops.tensorflow import PredictTensorflow
from merlin.systems.dag.ops.workflow import TransformWorkflow
from nvtabular.workflow import Workflow
from merlin.models.tf import BinaryOutput
import tensorflow as tf
from merlin.io.dataset import Dataset
from merlin.schema.tags import Tags
from merlin.models.tf import Loader
import merlin.models.tf as mm
import argparse
import json
import os
import numpy as np
from nvtabular.loader.tf_utils import configure_tensorflow
import nvtabular as nvt
from nvtabular.ops import *
from merlin.schema.tags import Tags

import tensorflow as tf
import shutil
import nvtabular as nvt
from merlin.io import Shuffle
from merlin.core.utils import device_mem_size
import shutil
from pathlib import Path
from typing import List, Optional, Union

from merlin.io import Dataset

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0" 
configure_tensorflow()

targets = ["watchfull", "liked", "commented", "scrolledtonext", "profileclicked", "followed"] >> FillMissing(
    fill_val=0) >> AddMetadata(tags=[Tags.BINARY_CLASSIFICATION, "target"])

DATA_FOLDER = "./data/"
train = Dataset("./data/trains/", engine="parquet", part_size="128MB")
valid = Dataset("./data/valids/", engine="parquet", part_size="128MB")

output_path = os.path.join(DATA_FOLDER, "processed")

category_temp_directory = os.path.join(DATA_FOLDER, "categories")

user_id = ["userid"] >> Categorify(
    freq_threshold=5, out_path=category_temp_directory) >> TagAsUserID()
item_id = ["contentid"] >> Categorify(
    freq_threshold=5, out_path=category_temp_directory) >> TagAsItemID()

add_feat = [
            "width",
            "height",
            "durationinseconds"] >> Categorify(out_path=category_temp_directory)

outputs = user_id + item_id + targets + add_feat

workflow = nvt.Workflow(outputs)

workflow_stored_path = os.path.join("./data/processed", "workflow")

workflow.fit(train)
workflow.transform(train).to_parquet(output_path=output_path + "/train/")
workflow.transform(valid).to_parquet(output_path=output_path + "/valid/")

# workflow_fit_transform(outputs, train, valid, output_path)
workflow.save(workflow_stored_path)

parser = argparse.ArgumentParser(
    description='Hyperparameters for model training'
)
args = parser.parse_args()

train = Dataset(os.path.join("./data/processed/train", "*.parquet"), part_size="150MB")
valid = Dataset(os.path.join("./data/processed/valid", "*.parquet"), part_size="150MB")

# define schema object
schema = train.schema

batch_size = 512
LR = 0.03
MODEL = 'MULTI'
inputs = mm.InputBlock(schema)
prediction_tasks = mm.PredictionTasks(schema)

block = mm.MLPBlock([256, 128, 64, 32])
cgc = mm.CGCBlock(prediction_tasks, expert_block=block, num_task_experts=5,
                  num_shared_experts=3)
model = mm.Model(inputs, block, cgc, prediction_tasks)
opt = tf.keras.optimizers.Adagrad(learning_rate=LR)
model.compile(optimizer=opt, run_eagerly=False)
model.fit(train, validation_data=valid, batch_size=batch_size)

model.save("model_multi")

# export ensemble
os.environ["TF_GPU_ALLOCATOR"]="cuda_malloc_async"

label_columns = workflow.output_schema.select_by_tag(Tags.TARGET).column_names
workflow.remove_inputs(label_columns)
tf_model_path = os.path.join('./', 'model_multi')

serving_operators = workflow.input_schema.column_names >> TransformWorkflow(workflow) >> PredictTensorflow(model)

ensemble = Ensemble(serving_operators, workflow.input_schema)

export_path = os.path.join('./data/ensembles', 'multimodel')

ens_conf, node_confs = ensemble.export(export_path, name='multimodel')

[rnyak]

@gopricy is there a toy data you can share with us to repro the issue? the changes in the config file is not a bug, we have changed the way we were dealing with list/ragged input features and fix-length input features.