[BUG] TypeError: model must be a LightningModule or torch._dynamo.OptimizedModule, got TemporalFusionTransformer

[hagersalehahmed]

what is main reason of this error? 1 frames
/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/compile.py in _maybe_unwrap_optimized(model)
123 if isinstance(model, pl.LightningModule):
124 return model
--> 125 raise TypeError(
126 f"model must be a LightningModule or torch._dynamo.OptimizedModule, got {type(model).__qualname__}"
127 )

There is one subject_ID is 12345, I split data into 80% training and 20% testing

data['time_idx'] = range(1, len(data) + 1)
split_index = int(0.8 * len(data))
train_df = data[:split_index]
test_df = data[split_index:]
print(len(train_df))


scaler_rr = MinMaxScaler()
scaler_spo2 = MinMaxScaler()
train_df["RR"] = scaler_rr.fit_transform(train_df[["RR"]])
test_df["RR"] = scaler_rr.transform(test_df[["RR"]])

train_df["SpO2"] = scaler_spo2.fit_transform(train_df[["SpO2"]])
test_df["SpO2"] = scaler_spo2.transform(test_df[["SpO2"]])
test_df.head()


max_encoder_length = 5  # past
max_prediction_length = 3 #Future 


training_cutoff = train_df['time_idx'].max()-max_prediction_length 

train_df["time_idx"] = pd.factorize(train_df["time_idx"])[0]
test_df["time_idx"] = pd.factorize(test_df["time_idx"])[0]


max_encoder_length = 5
max_prediction_length = 3


training = TimeSeriesDataSet(
    train_df,
    time_idx="time_idx",
    target=["RR", "SpO2"],
    group_ids=["subject_id"],
    min_encoder_length=max_encoder_length // 2,
    max_encoder_length=max_encoder_length,
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,
    static_categoricals=[],
    time_varying_known_reals=["time_idx"],
    time_varying_unknown_reals=["RR", "SpO2"],
    target_normalizer=MultiNormalizer(
        [
            GroupNormalizer(groups=["subject_id"], scale_by_group=True)
            for _ in ["RR", "SpO2"]
        ]
    ),
    add_relative_time_idx=True,
    add_target_scales=True,
    add_encoder_length=True,
)


testing = TimeSeriesDataSet.from_dataset(
    training,
    test_df,
    predict=True,
    stop_randomization=True,
)


batch_size = 16

train_dataloader = training.to_dataloader(
    train=True, batch_size=batch_size, num_workers=0
)

test_dataloader = testing.to_dataloader(
    train=False,  batch_size=batch_size*10, num_workers=0
)



trainer = pl.Trainer(
    accelerator="cpu",gradient_clip_val=0.1,
)


tft = TemporalFusionTransformer.from_dataset(
    training,
    # not meaningful for finding the learning rate but otherwise very important
    learning_rate=0.03,
    hidden_size=8,  # most important hyperparameter apart from learning rate
    # number of attention heads. Set to up to 4 for large datasets
    attention_head_size=1,
    dropout=0.1,  # between 0.1 and 0.3 are good values
    hidden_continuous_size=8,  # set to <= hidden_size
    loss=QuantileLoss(),
    optimizer="adam",
    
)
print(f"Number of parameters in network: {tft.size() / 1e3:.1f}k")

trainer.fit(
    tft,
    train_dataloaders=train_dataloader,
    val_dataloaders=test_dataloader,
)

[fnhirwa]

hey @hagersalehahmed would you please provide the full code example you were running for me to be able to reproduce the bug?
Thanks