Backports v0.15.1 #3187
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Backports v0.15.1 #3187
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Fixes awslabs#3185 *Description of changes:* There is currently a bug where the model inputs may be routed incorrect by the forecast generator. This effectively results in `past_feat_dynamic_real` and `past_feat_dynamic_cat` being ignored by the TFT model. MWE: ```python from unittest import mock import numpy as np import pandas as pd from gluonts.torch.model.tft import TemporalFusionTransformerEstimator freq = "D" N = 50 data = [ {"target": np.arange(N), "past_feat_dynamic_real": np.random.rand(1, N).astype("float32"), "start": pd.Period("2020-01-01", freq=freq)} ] predictor = TemporalFusionTransformerEstimator(prediction_length=1, freq=freq, past_dynamic_dims=[1], trainer_kwargs={"max_epochs": 1}).train(data) with mock.patch("gluonts.torch.model.tft.module.TemporalFusionTransformerModel._preprocess") as mock_fwd: try: fcst = list(predictor.predict(data)) except: pass call_kwargs = mock_fwd.call_args[1] call_kwargs["feat_dynamic_cat"] # tensor([[[0.8073]]]) call_kwargs["past_feat_dynamic_real"] # None ``` The bug occurs because model inputs are passed as positional arguments instead of keyword arguments. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. **Please tag this pr with at least one of these labels to make our release process faster:** BREAKING, new feature, bug fix, other change, dev setup
shchur
approved these changes
May 31, 2024
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Description of changes: backporting fixes
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.
Please tag this pr with at least one of these labels to make our release process faster: BREAKING, new feature, bug fix, other change, dev setup