diff --git a/pina/_src/solver/base_solver.py b/pina/_src/solver/base_solver.py index ade70363c..da6f5a60a 100644 --- a/pina/_src/solver/base_solver.py +++ b/pina/_src/solver/base_solver.py @@ -287,6 +287,9 @@ def _compute_condition_loss(self, condition, data, batch_idx): data = dict(data) data["input"] = data["input"].clone() + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + # Compute and store the residual tensor for the condition self.residual_tensor = condition.evaluate(data, self) @@ -296,11 +299,55 @@ def _compute_condition_loss(self, condition, data, batch_idx): # Compute the tensor loss from the residual tensor condition_tensor_loss = self._loss_from_residual(condition_name) + # Optional regularization hook, e.g gradient-enhanced or residual-based + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + # Compute the scalar loss from the tensor loss and return it condition_scalar_loss = self._apply_reduction(condition_tensor_loss) return condition_scalar_loss + def _prepare_condition_data(self, data): + """ + Prepare the condition data for loss computation. This method can be + overridden by mixins to implement specific data preparation steps, such + as enabling gradient tracking for inputs in gradient-enhanced solvers. + + :param dict data: The original condition data. + :return: The prepared condition data. + :rtype: dict + """ + return data + + def _regularize_condition_loss( + self, + condition_tensor_loss, + condition_name, + data, + batch_idx, + ): + """ + Regularize the condition loss if needed. This method can be overridden + by mixins to implement specific regularization strategies, such as + adding a gradient penalty in gradient-enhanced solvers or applying + residual-based attention. + + :param condition_tensor_loss: The original tensor loss for the + condition. + :type condition_tensor_loss: torch.Tensor | LabelTensor + :param str condition_name: The name of the condition. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The regularized tensor loss for the condition. + :rtype: torch.Tensor | LabelTensor + """ + return condition_tensor_loss + def _loss_from_residual(self, condition_name=None): """ Compute the tensor loss from the residual tensor. diff --git a/pina/_src/solver/causal_physics_informed_single_model_solver.py b/pina/_src/solver/causal_physics_informed_single_model_solver.py index 4cf89a2fe..db243e020 100644 --- a/pina/_src/solver/causal_physics_informed_single_model_solver.py +++ b/pina/_src/solver/causal_physics_informed_single_model_solver.py @@ -200,6 +200,9 @@ def _compute_condition_loss(self, condition, data, batch_idx): data = dict(data) data["input"] = data["input"].clone() + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + # Extract the temporal domain time_domain = self.problem.temporal_domain @@ -251,6 +254,14 @@ def _compute_condition_loss(self, condition, data, batch_idx): # Compute the tensor loss from the residual tensor condition_tensor_loss = self._loss_from_residual(condition_name) + # Optional regularization hook + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + # Append the loss for the current time step to the list time_loss.append(condition_tensor_loss) diff --git a/pina/_src/solver/competitive_physics_informed_solver.py b/pina/_src/solver/competitive_physics_informed_solver.py index 9c1fe24ce..70ed77030 100644 --- a/pina/_src/solver/competitive_physics_informed_solver.py +++ b/pina/_src/solver/competitive_physics_informed_solver.py @@ -213,6 +213,9 @@ def _compute_condition_loss(self, condition, data, batch_idx): data = dict(data) data["input"] = data["input"].clone() + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + # Compute and store the residual tensor for the condition self.residual_tensor = condition.evaluate(data, self) @@ -229,6 +232,14 @@ def _compute_condition_loss(self, condition, data, batch_idx): # Compute the tensor loss from the residual tensor condition_tensor_loss = self._loss_from_residual(condition_name) + # Optional regularization hook, e.g gradient-enhanced or residual-based + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + # Compute the scalar loss from the tensor loss and return it condition_scalar_loss = self._apply_reduction(condition_tensor_loss) diff --git a/pina/_src/solver/mixin/ensemble_mixin.py b/pina/_src/solver/mixin/ensemble_mixin.py index 1682669e4..17757fc96 100644 --- a/pina/_src/solver/mixin/ensemble_mixin.py +++ b/pina/_src/solver/mixin/ensemble_mixin.py @@ -1,6 +1,7 @@ """Module for the ensemble mixin class.""" import torch +from pina._src.solver.base_solver import BaseSolver from pina._src.solver.mixin.multi_model_mixin import MultiModelMixin @@ -16,14 +17,65 @@ class EnsembleMixin(MultiModelMixin): def forward(self, x): """ - The forward pass implementation that evaluates all models and returns - the average of their outputs. + Forward pass for ensemble solvers. If an active model index is set, only + that model is evaluated. Otherwise, all models are evaluated and their + outputs are stacked together. :param x: The input data. :type x: torch.Tensor | LabelTensor | Data | Graph :return: The output of all models stacked together. :rtype: torch.Tensor | LabelTensor | Data | Graph """ + # Retrieve the index of the active model if set + active_idx = getattr(self, "_active_model_idx", None) + + # If an active model index is set, evaluate only that model + if active_idx is not None: + return self.models[active_idx](x) + + # Otherwise, evaluate all models and stack outputs return torch.stack( [self.models[idx](x) for idx in range(self.num_models)] - ).mean(dim=0) + ) + + def _compute_condition_loss(self, condition, data, batch_idx): + """ + Compute the scalar loss for a given condition and its data. + + :param BaseCondition condition: The condition for which to compute the + loss. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The scalar loss for the condition. + :rtype: torch.Tensor + """ + # Initialize model losses for the current condition + model_losses = [] + + # Restore the active model index if it was set, else set it to None + previous_active_model_idx = getattr(self, "_active_model_idx", None) + + # Try - finally to ensure active model index is always restored + try: + + # Iterate over all ensemble models to compute individual losses + for model_idx in range(self.num_models): + + # Set the active model index for the current iteration + self._active_model_idx = model_idx + + # Compute the scalar loss for the current model and condition + condition_scalar_loss = BaseSolver._compute_condition_loss( + self, condition, data, batch_idx + ) + + # Store the computed loss for the current model + model_losses.append(condition_scalar_loss) + + # Ensure that the active model index is always restored + finally: + + # Restore the previous active model index after computation + self._active_model_idx = previous_active_model_idx + + return torch.stack(model_losses).mean() diff --git a/pina/_src/solver/mixin/gradient_enhanced_mixin.py b/pina/_src/solver/mixin/gradient_enhanced_mixin.py index cd62e1705..8a492dc1e 100644 --- a/pina/_src/solver/mixin/gradient_enhanced_mixin.py +++ b/pina/_src/solver/mixin/gradient_enhanced_mixin.py @@ -81,41 +81,52 @@ def _init_gradient_enhanced_components( self.regularization_weight = regularization_weight self.regularized_conditions = regularized_conditions - def _compute_condition_loss(self, condition, data, batch_idx): + def _prepare_condition_data(self, data): """ - Compute the scalar loss for a given condition and its data. + Prepare the condition data for loss computation. This method can be + overridden by mixins to implement specific data preparation steps, such + as enabling gradient tracking for inputs in gradient-enhanced solvers. - :param BaseCondition condition: The condition for which to compute the - loss. - :param dict data: The data corresponding to the condition. - :param int batch_idx: The index of the current batch. - :return: The scalar loss for the condition. - :rtype: torch.Tensor + :param dict data: The original condition data. + :return: The prepared condition data. + :rtype: dict """ - # Clone the input tensor if it exists to avoid in-place modifications - if "input" in data and hasattr(data["input"], "clone"): - data = dict(data) - data["input"] = data["input"].clone() - # If data does not require grad, force requires_grad to True if "input" in data and not data["input"].requires_grad: data["input"].requires_grad_(True) - # Compute and store the residual tensor for the condition - self.residual_tensor = condition.evaluate(data, self) - self.residual_tensor.labels = [ - f"res_{i}" for i in range(self.residual_tensor.shape[1]) - ] - - # Retrieve condition name for more complex weighting schemes - condition_name = condition.name if hasattr(condition, "name") else None - - # Compute the tensor loss from the residual tensor - condition_tensor_loss = self._loss_from_residual(condition_name) + return data + def _regularize_condition_loss( + self, + condition_tensor_loss, + condition_name, + data, + batch_idx, + ): + """ + Regularize the condition loss if needed. This method can be overridden + by mixins to implement specific regularization strategies, such as + adding a gradient penalty in gradient-enhanced solvers or applying + residual-based attention. + + :param condition_tensor_loss: The original tensor loss for the + condition. + :type condition_tensor_loss: torch.Tensor | LabelTensor + :param str condition_name: The name of the condition. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The regularized tensor loss for the condition. + :rtype: torch.Tensor | LabelTensor + """ # Regularize the loss with the gradient penalty if needed if condition_name in self.regularized_conditions: + # Apply labels to the residual tensor for gradient computation + self.residual_tensor.labels = [ + f"res_{i}" for i in range(self.residual_tensor.shape[1]) + ] + # Compute the gradient of the residual with respect to spatial input residual_gradient = grad( output_=self.residual_tensor, @@ -134,7 +145,4 @@ def _compute_condition_loss(self, condition, data, batch_idx): # Add the gradient penalty to the original condition tensor loss condition_tensor_loss = condition_tensor_loss + penalty - # Compute the scalar loss from the tensor loss and return it - condition_scalar_loss = self._apply_reduction(condition_tensor_loss) - - return condition_scalar_loss + return condition_tensor_loss diff --git a/pina/_src/solver/mixin/residual_based_attention_mixin.py b/pina/_src/solver/mixin/residual_based_attention_mixin.py index bfd3589f3..04b72fa4e 100644 --- a/pina/_src/solver/mixin/residual_based_attention_mixin.py +++ b/pina/_src/solver/mixin/residual_based_attention_mixin.py @@ -94,31 +94,28 @@ def _init_residual_attention_components( self.register_buffer(f"weight_{cond}", torch.zeros((n_pts, 1))) self.weight_buffers[cond] = f"weight_{cond}" - def _compute_condition_loss(self, condition, data, batch_idx): + def _regularize_condition_loss( + self, + condition_tensor_loss, + condition_name, + data, + batch_idx, + ): """ - Compute the scalar loss for a given condition and its data. - - :param BaseCondition condition: The condition for which to compute the - loss. + Regularize the condition loss if needed. This method can be overridden + by mixins to implement specific regularization strategies, such as + adding a gradient penalty in gradient-enhanced solvers or applying + residual-based attention. + + :param condition_tensor_loss: The original tensor loss for the + condition. + :type condition_tensor_loss: torch.Tensor | LabelTensor + :param str condition_name: The name of the condition. :param dict data: The data corresponding to the condition. :param int batch_idx: The index of the current batch. - :return: The scalar loss for the condition. - :rtype: torch.Tensor + :return: The regularized tensor loss for the condition. + :rtype: torch.Tensor | LabelTensor """ - # Clone the input tensor if it exists to avoid in-place modifications - if "input" in data and hasattr(data["input"], "clone"): - data = dict(data) - data["input"] = data["input"].clone() - - # Compute and store the residual tensor for the condition - self.residual_tensor = condition.evaluate(data, self) - - # Retrieve condition name for more complex weighting schemes - condition_name = condition.name - - # Compute the tensor loss from the residual tensor - condition_tensor_loss = self._loss_from_residual(condition_name) - # Apply residual-based attention mechanism if needed if condition_name in self.regularized_conditions: @@ -150,7 +147,4 @@ def _compute_condition_loss(self, condition, data, batch_idx): # Weight the condition tensor loss with attention weights condition_tensor_loss = condition_tensor_loss * weights[idx] - # Compute the scalar loss from the tensor loss and return it - condition_scalar_loss = self._apply_reduction(condition_tensor_loss) - - return condition_scalar_loss + return condition_tensor_loss diff --git a/pina/_src/solver/self_adaptive_physics_informed_solver.py b/pina/_src/solver/self_adaptive_physics_informed_solver.py index db71a5257..7f2b4032a 100644 --- a/pina/_src/solver/self_adaptive_physics_informed_solver.py +++ b/pina/_src/solver/self_adaptive_physics_informed_solver.py @@ -240,6 +240,9 @@ def _compute_condition_loss(self, condition, data, batch_idx): data = dict(data) data["input"] = data["input"].clone() + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + # Compute and store the residual tensor for the condition self.residual_tensor = condition.evaluate(data, self) @@ -253,6 +256,14 @@ def _compute_condition_loss(self, condition, data, batch_idx): # Compute the tensor loss from the residual tensor condition_tensor_loss = self._loss_from_residual(condition_name) + # Optional regularization hook, e.g gradient-enhanced or residual-based + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + # Get the correct indices to retrieve the weights for the current batch len_residuals = self.residual_tensor.shape[0] diff --git a/tests/test_solver/test_autoregressive_ensemble_solver.py b/tests/test_solver/test_autoregressive_ensemble_solver.py index 9af4e5170..0ca9a86bd 100644 --- a/tests/test_solver/test_autoregressive_ensemble_solver.py +++ b/tests/test_solver/test_autoregressive_ensemble_solver.py @@ -234,7 +234,12 @@ def test_train_load_restore(clean_tmp_dir, use_lt): ) # Assert that the predictions from the loaded solver match original ones - assert new_solver.forward(test_pts).shape == (n_traj, t_steps, n_feats) + assert new_solver.forward(test_pts).shape == ( + n_models, + n_traj, + t_steps, + n_feats, + ) assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape torch.testing.assert_close( new_solver.forward(test_pts), solver.forward(test_pts) diff --git a/tutorials/tutorial14/tutorial.ipynb b/tutorials/tutorial14/tutorial.ipynb index a03d664ea..291ac8e24 100644 --- a/tutorials/tutorial14/tutorial.ipynb +++ b/tutorials/tutorial14/tutorial.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -162,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -190,25 +190,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's visualize the networks output before strated training" + "Let's visualize the networks output before training" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot solution\n", "with torch.no_grad():\n", @@ -238,41 +227,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n", - "GPU available: True (cuda), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ace96905471f4d2e9e6a471d5c1f5f08", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "with torch.no_grad():\n", " metrics = torch.stack(trainer.callbacks[0].store, dim=0)\n", @@ -363,20 +309,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot solution\n", "with torch.no_grad():\n",