jdb78
fitting a single series causing torch to throw 'float' object has no attribute 'shape'
- PyTorch-Forecasting version: 0.10.3
- PyTorch version: 1.13.1
- Python version: 3.8.16
- Operating System: Linux 5.10.147
Expected behavior
I'm evaluating with DeepAR following the example using a single series.
Actual behavior
Getting AttributeError: 'float' object has no attribute 'shape' as I try to fit a simple time series.
Code to reproduce the problem
My timeseries pandas dataframe
value timestamp series
1700 801.0 1700 0
1701 650.0 1701 0
...
value float64
timestamp int64
series object
dtype: object
training = TimeSeriesDataSet(
df_trips[lambda x: x.index <= train_cutoff],
time_idx="timestamp",
target="value",
# categorical_encoders={"series": NaNLabelEncoder(add_nan=True).fit(df_trips.series)},
group_ids=['series'],
# static_categoricals=[
# "series"
# ],
min_encoder_length=max_encoder_length // 2,
max_encoder_length=max_encoder_length,
max_prediction_length=max_prediction_length,
time_varying_unknown_reals=["value"],
allow_missing_timesteps=True
)
train_dataloader = training.to_dataloader(
train=True, batch_size=batch_size, num_workers=0, batch_sampler="synchronized"
)
validation = TimeSeriesDataSet.from_dataset(
training,
data[1700:],
predict=True,
min_encoder_length=max_encoder_length // 2
)
Following the example, here is how training step is defined:
early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min")
trainer = pl.Trainer(
max_epochs=30,
gpus=0,
enable_model_summary=True,
gradient_clip_val=0.1,
callbacks=[early_stop_callback],
limit_train_batches=10,
enable_checkpointing=True,
)
net = DeepAR.from_dataset(
training,
learning_rate=0.1,
log_interval=10,
log_val_interval=1,
hidden_size=30,
rnn_layers=2,
loss=MultivariateNormalDistributionLoss(rank=30),
)
trainer.fit(
net,
train_dataloaders=train_dataloader,
val_dataloaders=val_dataloader
)
I get this error, omitting some of the trace frames:
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:441: LightningDeprecationWarning: Setting `Trainer(gpus=0)` is deprecated in v1.7 and will be removed in v2.0. Please use `Trainer(accelerator='gpu', devices=0)` instead.
rank_zero_deprecation(
INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False
INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores
INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs
INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/parsing.py:262: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.
rank_zero_warn(
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/parsing.py:262: UserWarning: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.
rank_zero_warn(
INFO:pytorch_lightning.callbacks.model_summary:
| Name | Type | Params
------------------------------------------------------------------------------
0 | loss | MultivariateNormalDistributionLoss | 0
1 | logging_metrics | ModuleList | 0
2 | embeddings | MultiEmbedding | 1
3 | rnn | LSTM | 11.6 K
4 | distribution_projector | Linear | 992
------------------------------------------------------------------------------
12.6 K Trainable params
0 Non-trainable params
12.6 K Total params
0.051 Total estimated model params size (MB)
Sanity Checking DataLoader 0: 0%
0/1 [10:39<?, ?it/s]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
[<ipython-input-44-9c3d1dad2a4c>](https://localhost:8080/#) in <module>
20 )
21
---> 22 trainer.fit(
23 net,
24 train_dataloaders=train_dataloader,
[/usr/local/lib/python3.8/dist-packages/torch/distributions/transformed_distribution.py](https://localhost:8080/#) in log_prob(self, value)
144 x = transform.inv(y)
145 event_dim += transform.domain.event_dim - transform.codomain.event_dim
--> 146 log_prob = log_prob - _sum_rightmost(transform.log_abs_det_jacobian(x, y),
147 event_dim - transform.domain.event_dim)
148 y = x
[/usr/local/lib/python3.8/dist-packages/torch/distributions/utils.py](https://localhost:8080/#) in _sum_rightmost(value, dim)
59 if dim == 0:
60 return value
---> 61 required_shape = value.shape[:-dim] + (-1,)
62 return value.reshape(required_shape).sum(-1)
@gunewar I believe specifying the normalizer when creating the TimeSeriesDataSet fixed it for me
training = TimeSeriesDataSet(
...
target_normalizer=TorchNormalizer(method='identity', center=True, transformation=None, method_kwargs={}),
add_target_scales=True,
...
)
@gunewar I believe specifying the normalizer when creating the
TimeSeriesDataSetfixed it for metraining = TimeSeriesDataSet( ... target_normalizer=TorchNormalizer(method='identity', center=True, transformation=None, method_kwargs={}), add_target_scales=True, ... )
This fixed the error for me too..I still don't understand why
@gunewar I believe specifying the normalizer when creating the
TimeSeriesDataSetfixed it for metraining = TimeSeriesDataSet( ... target_normalizer=TorchNormalizer(method='identity', center=True, transformation=None, method_kwargs={}), add_target_scales=True, ... )This fixed the error for me too..I still don't understand why
I guess the error happens when we only have one series...
I think I found the root of the issue - there's a bug in the log_abs_det_jacobian function (which is used by MultivariateDistributionLoss) for the ReLU target normalizer. I changed line 127 (and 108) in the snippet below from return 0.0 to return torch.tensor(0.0).to(x) so that it returns a tensor instead of a float, and that seemed to solve the problem.
https://github.com/jdb78/pytorch-forecasting/blob/4045054377f71bcf606852c46520c5d7fdf4d0d2/pytorch_forecasting/data/encoders.py#L111-L127
