Feat/test optional dep (onnx, ray, optuna)

.github/workflows/develop.yml

@@ -51,7 +51,7 @@ jobs:
           if [ "${{ matrix.os }}" == "macos-13" ]; then
             source $HOME/.local/bin/env
           fi
-          uv pip compile requirements/dev-all.txt > requirements-latest.txt
+          uv pip compile requirements/dev-all.txt requirements/optional.txt -o requirements-latest.txt
 
       - name: "Cache python environment"
         uses: actions/cache@v4
@@ -67,7 +67,7 @@ jobs:
       - name: "Install Dependencies"
         run: |
           # install latest dependencies (potentially updating cached dependencies)
-          pip install -U -r requirements/dev-all.txt
+          pip install -U -r requirements/dev-all.txt -r requirements/optional.txt
 
       - name: "Install libomp (for LightGBM)"
         run: |

.github/workflows/merge.yml

@@ -54,7 +54,7 @@ jobs:
           elif [ "${{ matrix.flavour }}" == "torch" ]; then
             pip install -r requirements/core.txt -r requirements/torch.txt -r requirements/dev.txt
           elif [ "${{ matrix.flavour }}" == "all" ]; then
-            pip install -r requirements/core.txt -r requirements/torch.txt -r requirements/notorch.txt -r requirements/dev.txt
+            pip install -r requirements/core.txt -r requirements/torch.txt -r requirements/notorch.txt -r requirements/optional.txt -r requirements/dev.txt
           fi
 
       - name: "Install libomp (for LightGBM)"
@@ -94,7 +94,7 @@ jobs:
       - name: "Compile Dependency Versions"
         run: |
           curl -LsSf https://astral.sh/uv/install.sh | sh
-          uv pip compile requirements/dev-all.txt > requirements-latest.txt
+          uv pip compile requirements/dev-all.txt requirements/optional.txt -o requirements-latest.txt
 
       # only restore cache but do not upload
       - name: "Restore cached python environment"
@@ -141,7 +141,7 @@ jobs:
       - name: "Compile Dependency Versions"
         run: |
           curl -LsSf https://astral.sh/uv/install.sh | sh
-          uv pip compile requirements/dev-all.txt > requirements-latest.txt
+          uv pip compile requirements/dev-all.txt -o requirements-latest.txt
 
       # only restore cache but do not upload
       - name: "Restore cached python environment"

.github/workflows/update-cache.yml

@@ -31,7 +31,7 @@ jobs:
           if [ "${{ matrix.os }}" == "macos-13" ]; then
             source $HOME/.local/bin/env
           fi
-          uv pip compile requirements/dev-all.txt > requirements-latest.txt
+          uv pip compile requirements/dev-all.txt -r requirements/optional.txt -o requirements-latest.txt
 
       - name: "Cache python environment"
         uses: actions/cache@v4
@@ -47,4 +47,4 @@ jobs:
       - name: "Install Latest Dependencies"
         run: |
           # install latest dependencies (potentially updating cached dependencies)
-          pip install -U -r requirements/dev-all.txt
+          pip install -U -r requirements/dev-all.txt -r requirements/optional.txt

darts/models/forecasting/tft_model.py

@@ -726,7 +726,7 @@ def __init__(
             If ``False``, only attends to previous time steps in the decoder. If ``True`` attends to previous,
             current, and future time steps. Defaults to ``False``.
         feed_forward
-            A feedforward network is a fully-connected layer with an activation. TFT Can be one of the glu variant's
+            A feedforward network is a fully-connected layer with an activation. Can be one of the glu variant's
             FeedForward Network (FFN)[2]. The glu variant's FeedForward Network are a series of FFNs designed to work
             better with Transformer based models. Defaults to ``"GatedResidualNetwork"``. ["GLU", "Bilinear", "ReGLU",
             "GEGLU", "SwiGLU", "ReLU", "GELU"] or the TFT original FeedForward Network ["GatedResidualNetwork"].

darts/models/forecasting/torch_forecasting_model.py

@@ -646,6 +646,12 @@ def _verify_past_future_covariates(self, past_covariates, future_covariates):
                 logger=logger,
             )
 
+    @abstractmethod
+    def _update_covariates_use(self):
+        """Based on the Forecasting class and the training_sample attribute, update the
+        uses_[past/future/static]_covariates attributes."""
+        pass
+
     def to_onnx(self, path: Optional[str] = None, **kwargs):
         """Export model to ONNX format for optimized inference, wrapping around PyTorch Lightning's
         :func:`torch.onnx.export` method (`official documentation <https://lightning.ai/docs/pytorch/
@@ -677,6 +683,8 @@ def to_onnx(self, path: Optional[str] = None, **kwargs):
             ``input_sample``, ``input_name``). For more information, read the `official documentation
             <https://pytorch.org/docs/master/onnx.html#torch.onnx.export>`_.
         """
+        # TODO: LSTM model should be exported with a batch size of 1
+        # TODO: predictions with TFT and TCN models is incorrect, might be caused by helper function to process inputs
         if not self._fit_called:
             raise_log(
                 ValueError("`fit()` needs to be called before `to_onnx()`."), logger
@@ -1774,7 +1782,7 @@ def save(
             self.trainer.save_checkpoint(path_ptl_ckpt, weights_only=clean)
 
         # TODO: keep track of PyTorch Lightning to see if they implement model checkpoint saving
-        #  without having to call fit/predict/validate/test before
+        # without having to call fit/predict/validate/test before
         # try to recover original automatic PL checkpoint
         elif self.load_ckpt_path:
             if os.path.exists(self.load_ckpt_path):
@@ -2133,6 +2141,9 @@ def load_weights_from_checkpoint(
         self.model.load_state_dict(ckpt["state_dict"], strict=strict)
         # update the fit_called attribute to allow for direct inference
         self._fit_called = True
+        # based on the shape of train_sample, figure out with covariates are used by the model
+        # (usually set in the Darts model prior to fitting it)
+        self._update_covariates_use()
 
     def load_weights(
         self, path: str, load_encoders: bool = True, skip_checks: bool = False, **kwargs
@@ -2683,6 +2694,13 @@ def extreme_lags(
             None,
         )
 
+    def _update_covariates_use(self):
+        """The model is expected to rely on the `PastCovariatesTrainingDataset`"""
+        _, past_covs, static_covs, _ = self.train_sample
+        self._uses_past_covariates = past_covs is not None
+        self._uses_future_covariates = False
+        self._uses_static_covariates = static_covs is not None
+
 
 class FutureCovariatesTorchModel(TorchForecastingModel, ABC):
     supports_past_covariates = False
@@ -2776,6 +2794,13 @@ def extreme_lags(
             None,
         )
 
+    def _update_covariates_use(self):
+        """The model is expected to rely on the `FutureCovariatesTrainingDataset`"""
+        _, future_covs, static_covs, _ = self.train_sample
+        self._uses_past_covariates = False
+        self._uses_future_covariates = future_covs is not None
+        self._uses_static_covariates = static_covs is not None
+
 
 class DualCovariatesTorchModel(TorchForecastingModel, ABC):
     supports_past_covariates = False
@@ -2870,6 +2895,15 @@ def extreme_lags(
             None,
         )
 
+    def _update_covariates_use(self):
+        """The model is expected to rely on the `DualCovariatesTrainingDataset`"""
+        _, historic_future_covs, future_covs, static_covs, _ = self.train_sample
+        self._uses_past_covariates = False
+        self._uses_future_covariates = (
+            historic_future_covs is not None or future_covs is not None
+        )
+        self._uses_static_covariates = static_covs is not None
+
 
 class MixedCovariatesTorchModel(TorchForecastingModel, ABC):
     def _build_train_dataset(
@@ -2964,6 +2998,17 @@ def extreme_lags(
             None,
         )
 
+    def _update_covariates_use(self):
+        """The model is expected to rely on the `MixedCovariatesTrainingDataset`"""
+        _, past_covs, historic_future_covs, future_covs, static_covs, _ = (
+            self.train_sample
+        )
+        self._uses_past_covariates = past_covs is not None
+        self._uses_future_covariates = (
+            historic_future_covs is not None or future_covs is not None
+        )
+        self._uses_static_covariates = static_covs is not None
+
 
 class SplitCovariatesTorchModel(TorchForecastingModel, ABC):
     def _build_train_dataset(
@@ -3058,3 +3103,12 @@ def extreme_lags(
             self.output_chunk_shift,
             None,
         )
+
+    def _update_covariates_use(self):
+        """The model is expected to rely on the `SplitCovariatesTrainingDataset`"""
+        _, past_covs, historic_future_covs, future_covs, static_covs, _ = (
+            self.train_sample
+        )
+        self._uses_past_covariates = past_covs is not None
+        self._uses_future_covariates = future_covs is not None
+        self._uses_static_covariates = static_covs is not None

darts/tests/conftest.py

@@ -17,6 +17,31 @@
     logger.warning("Torch not installed - Some tests will be skipped.")
     TORCH_AVAILABLE = False
 
+try:
+    import onnx  # noqa: F401
+    import onnxruntime  # noqa: F401
+
+    ONNX_AVAILABLE = True
+except ImportError:
+    logger.warning("Onnx not installed - Some tests will be skipped.")
+    ONNX_AVAILABLE = False
+
+try:
+    import optuna  # noqa: F401
+
+    OPTUNA_AVAILABLE = True
+except ImportError:
+    logger.warning("Optuna not installed - Some tests will be skipped.")
+    OPTUNA_AVAILABLE = False
+
+try:
+    import ray  # noqa: F401
+
+    RAY_AVAILABLE = True
+except ImportError:
+    logger.warning("Ray not installed - Some tests will be skipped.")
+    RAY_AVAILABLE = False
+
 tfm_kwargs = {
     "pl_trainer_kwargs": {
         "accelerator": "cpu",

darts/tests/models/forecasting/test_torch_forecasting_model.py

@@ -429,6 +429,63 @@ def create_model(**kwargs):
             model_new = create_model(**kwargs_)
             model_new.load_weights(model_path_manual)
 
+    @pytest.mark.parametrize(
+        "params",
+        itertools.product(
+            [DLinearModel, NBEATSModel, RNNModel],  # model_cls
+            [True, False],  # past_covs
+            [True, False],  # future_covs
+            [True, False],  # static covs
+        ),
+    )
+    def test_save_and_load_weights_covs_usage_attributes(self, tmpdir_fn, params):
+        """
+        Verify that save/load correctly preserve the use_[past/future/static]_covariates attribute.
+        """
+        model_cls, use_pc, use_fc, use_sc = params
+        model = model_cls(
+            input_chunk_length=4,
+            output_chunk_length=1,
+            n_epochs=1,
+            **tfm_kwargs,
+        )
+        # skip test if the combination of covariates is not supported by the model
+        if (
+            (use_pc and not model.supports_past_covariates)
+            or (use_fc and not model.supports_future_covariates)
+            or (use_sc and not model.supports_static_covariates)
+        ):
+            return
+
+        model.fit(
+            series=self.series
+            if not use_sc
+            else self.series.with_static_covariates(pd.Series([12], ["loc"])),
+            past_covariates=self.series + 10 if use_pc else None,
+            future_covariates=self.series - 5 if use_fc else None,
+        )
+        # save and load the model
+        filename_ckpt = f"{model.model_name}.pt"
+        model.save(filename_ckpt)
+        model_loaded = model_cls(
+            input_chunk_length=4,
+            output_chunk_length=1,
+            **tfm_kwargs,
+        )
+        model_loaded.load_weights(filename_ckpt)
+
+        assert model.uses_past_covariates == model_loaded.uses_past_covariates == use_pc
+        assert (
+            model.uses_future_covariates
+            == model_loaded.uses_future_covariates
+            == use_fc
+        )
+        assert (
+            model.uses_static_covariates
+            == model_loaded.uses_static_covariates
+            == use_sc
+        )
+
     def test_save_and_load_weights_w_encoders(self, tmpdir_fn):
         """
         Verify that save/load does not break encoders.