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Remove tests for sparse tensors in dataloader #1783

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Mar 16, 2023
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Remove tests for sparse tensors in dataloader #1783

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60 changes: 0 additions & 60 deletions tests/unit/loader/test_tf_dataloader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,6 @@
from sklearn.metrics import roc_auc_score

import nvtabular as nvt
import nvtabular.tools.data_gen as datagen
from nvtabular import ops

tf = pytest.importorskip("tensorflow")
Expand Down Expand Up @@ -469,65 +468,6 @@ def test_multigpu_partitioning(datasets, engine, batch_size, global_rank):
assert indices == [global_rank]


@pytest.mark.parametrize("sparse_dense", [False, True])
def test_sparse_tensors(tmpdir, sparse_dense):
# create small dataset, add values to sparse_list
json_sample = {
"conts": {},
"cats": {
"spar1": {
"dtype": None,
"cardinality": 50,
"min_entry_size": 1,
"max_entry_size": 5,
"multi_min": 2,
"multi_max": 4,
"multi_avg": 3,
},
"spar2": {
"dtype": None,
"cardinality": 50,
"min_entry_size": 1,
"max_entry_size": 5,
"multi_min": 3,
"multi_max": 5,
"multi_avg": 4,
},
# "": {"dtype": None, "cardinality": 500, "min_entry_size": 1, "max_entry_size": 5},
},
"labels": {"rating": {"dtype": None, "cardinality": 2}},
}
cols = datagen._get_cols_from_schema(json_sample)
df_gen = datagen.DatasetGen(datagen.UniformDistro(), gpu_frac=0.0001)
target_path = os.path.join(tmpdir, "input/")
os.mkdir(target_path)
df_files = df_gen.full_df_create(10000, cols, output=target_path)
spa_lst = ["spar1", "spar2"]
spa_mx = {"spar1": 5, "spar2": 6}
batch_size = 10
data_itr = tf_dataloader.KerasSequenceLoader(
df_files,
cat_names=spa_lst,
cont_names=[],
label_names=["rating"],
batch_size=batch_size,
buffer_size=0.1,
sparse_names=spa_lst,
sparse_max=spa_mx,
sparse_as_dense=sparse_dense,
)
for batch in data_itr:
feats, labs = batch
for col in spa_lst:
feature_tensor = feats[f"{col}"]
if not sparse_dense:
assert list(feature_tensor.shape) == [batch_size, spa_mx[col]]
assert isinstance(feature_tensor, tf.sparse.SparseTensor)
else:
assert feature_tensor.shape[1] == spa_mx[col]
assert not isinstance(feature_tensor, tf.sparse.SparseTensor)


@pytest.mark.parametrize("batch_size", [1000])
@pytest.mark.parametrize("engine", ["parquet"])
@pytest.mark.parametrize("device", [None, 0])
Expand Down
38 changes: 0 additions & 38 deletions tests/unit/loader/test_torch_dataloader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -491,44 +491,6 @@ def test_mh_support(tmpdir):
assert idx > 0


@pytest.mark.parametrize("sparse_dense", [False, True])
def test_sparse_tensors(sparse_dense):
# create small dataset, add values to sparse_list
df = make_df(
{
"spar1": [[1, 2, 3, 4], [4, 2, 4, 4], [1, 3, 4, 3], [1, 1, 3, 3]],
"spar2": [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14], [15, 16]],
}
)
spa_lst = ["spar1", "spar2"]
spa_mx = {"spar1": 5, "spar2": 6}
batch_size = 2
data_itr = torch_dataloader.TorchAsyncItr(
nvt.Dataset(df),
cats=spa_lst,
conts=[],
labels=[],
batch_size=batch_size,
sparse_names=spa_lst,
sparse_max=spa_mx,
sparse_as_dense=sparse_dense,
)
for batch in data_itr:
feats, labs = batch
for col in spa_lst:
feature_tensor = feats[col]
if not sparse_dense:
assert list(feature_tensor.shape) == [batch_size, spa_mx[col]]
assert feature_tensor.is_sparse
else:
assert feature_tensor.shape[1] == spa_mx[col]
assert not feature_tensor.is_sparse

# add dict sparse_max entry for each target
# iterate dataloader grab sparse columns
# ensure they are correct structurally


@pytest.mark.parametrize("batch_size", [1000])
@pytest.mark.parametrize("engine", ["parquet"])
@pytest.mark.parametrize("device", [None, 0])
Expand Down