feat: add recoverer algorithms

pyproject.toml

@@ -136,7 +136,10 @@ dependencies = [
     "aenum",
     "vbench-pruna; sys_platform != 'darwin'",
     "imageio-ffmpeg",
-    "jaxtyping"
+    "jaxtyping",
+    "peft>=0.18.0",
+    "trl<=0.21.0",
+    "termcolor==2.3.0",
 ]
 
 [project.optional-dependencies]

src/pruna/algorithms/base/tags.py

@@ -64,6 +64,14 @@ class AlgorithmTag(Enum):
         "batcher",
         "Batching groups multiple inputs together to be processed simultaneously, improving computational efficiency and reducing overall processing time.",
     )
+    RECOVERER = (
+        "recoverer",
+        "Recovery restores the performance of a model after compression.",
+    )
+    DISTILLER = (
+    "distiller",
+    "Distillation trains a smaller, simpler model to mimic a larger, more complex model.",
+    )
 
     def __init__(self, name: str, description: str):
         """

src/pruna/algorithms/distillation_perp.py

@@ -0,0 +1,73 @@
+# Copyright 2025 - Pruna AI GmbH. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import Iterable
+
+from pruna.algorithms.base.tags import AlgorithmTag
+from pruna.algorithms.global_utils.recovery.perp_recoverer import PERPRecoverer
+
+
+class TextToImagePERPDistillation(PERPRecoverer):
+    """
+    PERP distillation recoverer for text-to-image models.
+
+    This recoverer is a general purpose PERP recoverer for text-to-image models using norm and bias finetuning
+    as well as LoRA layers.
+
+    Parameters
+    ----------
+    use_lora : bool
+        Whether to use LoRA adapters.
+    use_in_place : bool
+        Whether to use norm and bias finetuning which will modify the model in place.
+    """
+
+    group_tags: list[AlgorithmTag] = [AlgorithmTag.DISTILLER, AlgorithmTag.RECOVERER]  # type: ignore[attr-defined]
+    algorithm_name = "text_to_image_distillation_perp"
+    tokenizer_required = False
+    compatible_before: Iterable[str | AlgorithmTag] = ["quanto", "torch_dynamic", "deepcache"]
+    compatible_after: Iterable[str | AlgorithmTag] = ["torch_compile"]
+    runs_on: list[str] = ["cuda"]
+
+    def __init__(self, use_lora: bool = True, use_in_place: bool = True) -> None:
+        super().__init__(task_name="text_to_image", use_lora=use_lora, use_in_place=use_in_place, is_distillation=True)
+
+
+class TextToImageInPlacePERPDistillation(TextToImagePERPDistillation):
+    """
+    PERP distillation recoverer for text-to-image models without LoRA adapters.
+
+    This is the same as ``text_to_image_distillation_perp``, but without LoRA layers which add extra computations and
+    thus slow down the inference of the final model.
+    """
+
+    algorithm_name = "text_to_image_distillation_inplace_perp"
+
+    def __init__(self) -> None:
+        super().__init__(use_lora=False, use_in_place=True)
+
+
+class TextToImageLoraDistillation(TextToImagePERPDistillation):
+    """
+    LoRA distillation recoverer for text-to-image models.
+
+    This recoverer attaches LoRA adapters to the model and uses them for distillation.
+    """
+
+    algorithm_name = "text_to_image_distillation_lora"
+
+    def __init__(self) -> None:
+        super().__init__(use_lora=True, use_in_place=False)

src/pruna/algorithms/global_utils/recovery/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2025 - Pruna AI GmbH. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

src/pruna/algorithms/global_utils/recovery/adapters/__init__.py

@@ -0,0 +1,101 @@
+# Copyright 2025 - Pruna AI GmbH. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any
+
+import torch
+
+from pruna.config.smash_config import SmashConfigPrefixWrapper
+
+
+class PrunaAdapter(ABC):
+    """Base class for adapters, defining which parameters to finetune for recovery."""
+
+    @property
+    @abstractmethod
+    def adapter_prefix(self) -> str:
+        """The prefix of the adapter to use in the config."""
+        pass
+
+    @classmethod
+    @abstractmethod
+    def get_hyperparameters(cls, task_name: str, **override_defaults: Any) -> list:
+        """
+        Configure all algorithm-specific hyperparameters with ConfigSpace.
+
+        Parameters
+        ----------
+        task_name : str
+            The name of the task, e.g. "text-to-image" or "text-to-text".
+        **override_defaults : Any
+            Values used to override the default hyperparameters when using multiple finetuners together.
+
+        Returns
+        -------
+        list
+            The hyperparameters.
+        """
+        pass
+
+    @classmethod
+    @abstractmethod
+    def activate(
+        cls,
+        model: torch.nn.Module,
+        smash_config: SmashConfigPrefixWrapper,
+        seed: int | None = None,
+    ) -> tuple[torch.nn.Module, int, int]:
+        """
+        Activate or create the parameters in the model corresponding to the adapter.
+
+        Parameters
+        ----------
+        model : torch.nn.Module
+            The model to apply the component to.
+        smash_config : SmashConfigPrefixWrapper
+            The configuration for the component.
+        seed : int
+            The seed to use for the adapter if it requires initialization.
+
+        Returns
+        -------
+        torch.nn.Module
+            The model with the adapter activated.
+        int
+            The number of trainable parameters.
+        int
+            The number of skipped parameters.
+        """
+        pass
+
+    @classmethod
+    def pre_smash_hook(
+        cls, model: torch.nn.Module, smash_config: SmashConfigPrefixWrapper, seed: int | None = None
+    ) -> None:
+        """
+        Optional hook to prepare the model/config before smashing.
+
+        Parameters
+        ----------
+        model : torch.nn.Module
+            The model to prepare.
+        smash_config : SmashConfigPrefixWrapper
+            Configuration scoped to this adapter.
+        seed : int | None
+            Optional seed for deterministic initialization.
+        """
+        pass

src/pruna/algorithms/global_utils/recovery/adapters/bias.py

@@ -0,0 +1,68 @@
+# Copyright 2025 - Pruna AI GmbH. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+
+from pruna.algorithms.global_utils.recovery.adapters import PrunaAdapter, utils
+
+
+class BiasAdapter(PrunaAdapter):
+    """Adapter for bias finetuning."""
+
+    adapter_prefix = "bias"
+
+    @classmethod
+    def get_hyperparameters(cls, *args, **kwargs) -> list:
+        """
+        Configure all method-specific hyperparameters with ConfigSpace.
+
+        Parameters
+        ----------
+        *args : Any
+            Unused arguments.
+        **kwargs : Any
+            Unused keyword arguments.
+
+        Returns
+        -------
+        list
+            The hyperparameters.
+        """
+        return []
+
+    @classmethod
+    def activate(cls, model: torch.nn.Module, *args, **kwargs) -> tuple[torch.nn.Module, int, int]:
+        """
+        Activate all biases for training.
+
+        Parameters
+        ----------
+        model : torch.nn.Module
+            The model containing the biases.
+        *args : Any
+            Unused additional arguments.
+        **kwargs : Any
+            Unused additional keyword arguments.
+
+        Returns
+        -------
+        torch.nn.Module
+            The model with the biases activated.
+        int
+            The number of trainable bias parameters.
+        int
+            The number of skipped bias parameters.
+        """
+        num_activ_param, num_skip_param = utils.unfreeze_parameters_by_name(model, target_modules=("bias",))
+        return model, num_activ_param, num_skip_param

src/pruna/algorithms/global_utils/recovery/adapters/head.py

@@ -0,0 +1,95 @@
+# Copyright 2025 - Pruna AI GmbH. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+
+import torch
+
+from pruna.algorithms.global_utils.recovery.adapters import PrunaAdapter, utils
+from pruna.logging.logger import pruna_logger
+
+
+class HeadAdapter(PrunaAdapter):
+    """Adapter for finetuning the model's head while keeping the backbone as is."""
+
+    adapter_prefix = "head"
+
+    @classmethod
+    def get_hyperparameters(cls, *args, **kwargs) -> list:
+        """
+        Configure all method-specific hyperparameters with ConfigSpace.
+
+        Parameters
+        ----------
+        *args : tuple
+            The arguments for the adapter.
+        **kwargs : dict
+            The hyperparameters for the adapter.
+
+        Returns
+        -------
+        list
+            The hyperparameters.
+        """
+        return []
+
+    @classmethod
+    def activate(cls, model: torch.nn.Module, *args, **kwargs) -> tuple[torch.nn.Module, int, int]:
+        """
+        Activate the model's head for training.
+
+        Parameters
+        ----------
+        model : torch.nn.Module
+            The model containing the head.
+        *args : tuple
+            The arguments for the adapter.
+        **kwargs : dict
+            The hyperparameters for the adapter.
+
+        Returns
+        -------
+        torch.nn.Module
+            The model with the head activated.
+        int
+            The number of trainable head parameters.
+        int
+            The number of skipped head parameters.
+        """
+        # find head from type and name
+        model_heads = [
+            component
+            for comp_name, component in inspect.getmembers(model)
+            if isinstance(component, torch.nn.Linear) and "head" in comp_name.lower()
+        ]
+        if len(model_heads) != 1:
+            # = 0: model with no head, e.g. diffusers
+            # > 1: model with multiple heads, e.g. for localization, not currently supported
+            model_head_names = [h[0] for h in model_heads]  # type: ignore[index]
+            pruna_logger.warning(
+                f"Found multiple heads but expected only one: {model_head_names}. Skipping head finetuning."
+            )
+            return model, 0, 0
+        model_head = model_heads[0]
+
+        # unfreeze head parameters, recording the number of trainable and skipped parameters
+        num_activ_param, num_skip_param = 0, 0
+        for param in model_head.parameters():
+            if utils.is_trainable(param):
+                param.requires_grad = True
+                num_activ_param += int(param.numel())
+            else:
+                num_skip_param += int(param.numel())
+
+        return model, num_activ_param, num_skip_param