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Fix CI test based on the requirements of the new merlin loader #536

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merged 3 commits into from
Nov 16, 2022
Merged

Fix CI test based on the requirements of the new merlin loader #536

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d246d62
cast targets to int64 dtype required by torch.scatter_
sararb Nov 15, 2022
bcaa0a1
update the dataset schema to use the same as the one defined in the m…
sararb Nov 15, 2022
ef96142
update exomrees46 data used in ci with hex feature converted to numer…
sararb Nov 15, 2022
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2 changes: 1 addition & 1 deletion ci/test_integration.sh
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Expand Up @@ -35,7 +35,7 @@ pip install -r requirements.txt
cd t4r_paper_repro
FEATURE_SCHEMA_PATH=../datasets_configs/ecom_rees46/rees46_schema.pbtxt
pip install gdown
gdown https://drive.google.com/uc?id=1payLwuwfa_QG6GvFVg4KT1w7dSkO-jPZ
gdown https://drive.google.com/uc?id=1NCFZ5ya3zyxPsrmupEoc9UEm4sslAddV
apt-get update -y
apt-get install unzip -y
DATA_PATH=/transformers4rec/examples/t4rec_paper_experiments/t4r_paper_repro/
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2 changes: 1 addition & 1 deletion transformers4rec/torch/losses.py
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Expand Up @@ -31,7 +31,7 @@ def _smooth_one_hot(targets: torch.Tensor, n_classes: int, smoothing: float = 0.
targets = (
torch.empty(size=(targets.size(0), n_classes), device=targets.device)
.fill_(smoothing / (n_classes - 1))
.scatter_(1, targets.data.unsqueeze(1), 1.0 - smoothing)
.scatter_(1, targets.data.unsqueeze(1).to(torch.int64), 1.0 - smoothing)
)
return targets

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1 change: 1 addition & 0 deletions transformers4rec/torch/utils/data_utils.py
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Expand Up @@ -225,6 +225,7 @@ def __init__(
self.drop_last = drop_last

self.set_dataset(buffer_size, engine, reader_kwargs)
self.dataset.schema = self.dataset.schema.select_by_name(conts + cats + labels)

if (global_rank is not None) and (self.dataset.npartitions < global_size):
logger.warning(
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2 changes: 1 addition & 1 deletion transformers4rec/torch/utils/torch_utils.py
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Expand Up @@ -199,7 +199,7 @@ def create_output_placeholder(scores, ks):


def tranform_label_to_onehot(labels, vocab_size):
return one_hot_1d(labels.reshape(-1), vocab_size, dtype=torch.float32).detach()
return one_hot_1d(labels.reshape(-1).to(torch.int64), vocab_size, dtype=torch.float32).detach()


def one_hot_1d(
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