Add model
Browse files- README.md +199 -0
- acip_model.py +179 -0
- config.json +99 -0
- generation_config.json +4 -0
- model-00001-of-00005.safetensors +3 -0
- model-00002-of-00005.safetensors +3 -0
- model-00003-of-00005.safetensors +3 -0
- model-00004-of-00005.safetensors +3 -0
- model-00005-of-00005.safetensors +3 -0
- model.safetensors.index.json +0 -0
- parametrized_layer.py +211 -0
- parametrized_model.py +747 -0
- projected_layer.py +308 -0
- utils.py +83 -0
README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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acip_model.py
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from typing import Any
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import torch
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from transformers import PreTrainedModel
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from .parametrized_model import ParametrizedModel, ParametrizedModelConfig
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class ACIPModelConfig(ParametrizedModelConfig):
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"""
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Configuration for `ACIPModel`. Same functionality as `ParametrizedModelConfig`.
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See Also:
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- `ParametrizedModelConfig`
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- `ACIPModel`
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"""
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model_type = "acip_model"
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class ACIPModel(ParametrizedModel):
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"""
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This class extends `ParametrizedModel` by additional functionality required for ACIP.
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It manages a `score_map` that stores the scores of the parametrized modules' target parameters,
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which are updated during tuning by the ACIP method.
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Moreover, it provides `prune_model_by_score` that prunes the target parameters of the model according to
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their scores to achieve any given compression ratio.
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Notes: The `score_map` is managed in float32 internally because a lower precision may lead to unexpected numerical
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inaccuracies in the resulting parameter ranking. Fortunately, the memory consumption is negligible compared to
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the model weights itself.
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See Also:
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- `ParametrizedModel`
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- `ACIPModelConfig`
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"""
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config_class = ACIPModelConfig
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def __init__(self, config: ACIPModelConfig, base_model: PreTrainedModel | None = None, **_: Any):
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super().__init__(config, base_model)
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self.config = config # redundant but enables type hinting for ACIPModelConfig
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self._score_map: dict[str, torch.Tensor] | None = None
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# Register and initialize score map buffers
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# Important: don't run _update_score_map here because load_state_dict might still override the buffers
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self._init_score_map_buffers()
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def _init_score_map_buffers(self):
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"""
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Register and initialize score map buffers in parametrized modules (with random numbers).
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Each target parameter "p_name" is associated with a buffer "p_name_score" that stores its score vector.
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"""
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for m_name, module in self.parametrized_modules.items():
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for p_name, param in module.parametrization.get_target_params().items():
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module.parametrization.register_buffer(p_name + "_score", torch.ones_like(param.data).float())
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def _update_score_map(self):
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"""Render `score_map` from the parametrized modules' score buffers."""
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self._score_map = {}
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for m_name, module in self.parametrized_modules.items():
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for p_name in module.parametrization.get_target_params().keys():
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self._score_map[f"{m_name}.parametrization.{p_name}"] = module.parametrization.get_buffer(
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p_name + "_score"
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)
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@property
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def score_map(self) -> dict[str, torch.Tensor]:
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"""Returns the score map as Tensor dictionary whose keys match those of `self.get_target_params`."""
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if self._score_map is None:
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self._update_score_map()
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return self._score_map
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@score_map.setter
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def score_map(self, score_map: dict[str, torch.Tensor]) -> None:
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"""
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Updates `score_map` and the corresponding parametrized modules' score buffers.
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Args:
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score_map: Dictionary whose keys should match (a subset of) `self.get_target_params`.
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"""
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if self._score_map is None:
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self._update_score_map()
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# score_map.keys() can be a subset of self.get_target_params().keys()
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for p_name, score in score_map.items():
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buffer = self.model.get_buffer(p_name + "_score")
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if buffer.shape != score.shape:
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raise ValueError(
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f"Score map for '{p_name}' has incorrect shape: expected {buffer.shape}, got {score.shape}"
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)
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# cast to float32 to avoid numerical instabilities
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buffer.copy_(score.detach().float())
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self._score_map[p_name] = buffer
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def _predict_compression_ratio_by_score(self, k: int, full: bool = False) -> tuple[float, dict[str, torch.Tensor]]:
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"""
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Helper function that checks what would happen if the k smallest target parameters are pruned
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according to the global score map ranking. It returns the resulting compression ratio
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and the corresponding parameter masks.
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Args:
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k: Number of target parameters to prune.
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full: Whether to count the number of parameters of the entire model or only the parametrized modules.
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See also `ParametrizedModel.get_num_params`.
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Returns: Tuple of compression ratio and parameter masks. The masks indicate which parameters to keep.
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"""
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# Find the threshold value for the k smallest entries according to the global score map ranking.
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109 |
+
score_map_cat = torch.cat([param.flatten() for param in self.score_map.values()])
|
110 |
+
threshold = torch.kthvalue(score_map_cat, k).values.item()
|
111 |
+
|
112 |
+
# Create a set of parameter masks marking which values to keep.
|
113 |
+
param_masks = {}
|
114 |
+
for p_name, score in self.score_map.items():
|
115 |
+
param_masks[p_name] = (score > threshold).to(dtype=score.dtype)
|
116 |
+
|
117 |
+
# Compute hypothetical compression ratio if param_masks would be used as masks for the target parameters.
|
118 |
+
compression_ratio = self.get_compression_ratio(full=full, target_params=param_masks)
|
119 |
+
return compression_ratio, param_masks
|
120 |
+
|
121 |
+
def _get_param_masks(self, compression_ratio: float, full: bool = False) -> dict[str, torch.Tensor]:
|
122 |
+
"""
|
123 |
+
Helper function that determines which parameters to keep to reach a target compression ratio.
|
124 |
+
Instead of looping over `k -> _predict_compression_ratio_by_score(k)`, a binary search can be used because
|
125 |
+
the compression ratio is monotonically increasing in k.
|
126 |
+
|
127 |
+
Args:
|
128 |
+
compression_ratio: Target compression ratio.
|
129 |
+
full: Whether to count the number of parameters of the entire model or only the parametrized modules.
|
130 |
+
See also `ParametrizedModel.get_num_params`.
|
131 |
+
|
132 |
+
Returns: Parameter masks indicating which parameters to keep to reach the target compression ratio.
|
133 |
+
"""
|
134 |
+
if compression_ratio == 1.0:
|
135 |
+
return {p_name: torch.ones_like(score) for p_name, score in self.score_map.items()}
|
136 |
+
|
137 |
+
# Perform a binary search to find the smallest k such that the compression ratio is at least compression_ratio.
|
138 |
+
# Here, k_lo and k_hi are the lower and upper bound of the search interval.
|
139 |
+
k_lo, k_hi = 1, sum(score.numel() for score in self.score_map.values())
|
140 |
+
while k_lo < k_hi:
|
141 |
+
k_mid = (k_lo + k_hi + 1) // 2 # round up to ensure low <= mid
|
142 |
+
ratio, _ = self._predict_compression_ratio_by_score(k=k_mid, full=full)
|
143 |
+
if ratio > compression_ratio:
|
144 |
+
k_lo = k_mid
|
145 |
+
else:
|
146 |
+
k_hi = k_mid - 1
|
147 |
+
k = k_lo
|
148 |
+
# TODO: handle tie-breaks
|
149 |
+
return self._predict_compression_ratio_by_score(k=k, full=full)[1]
|
150 |
+
|
151 |
+
def prune_model_by_score(self, compression_ratio: float, full: bool = False) -> None:
|
152 |
+
"""
|
153 |
+
This method prunes the target parameters of the model according to their scores to achieve
|
154 |
+
a given compression ratio.
|
155 |
+
|
156 |
+
This can be efficiently implemented by a simple binary search strategy:
|
157 |
+
We find the smallest number of parameters to be pruned according to the score map ranking
|
158 |
+
such that the resulting compression ratio is at least the target `compression_ratio`.
|
159 |
+
|
160 |
+
Args:
|
161 |
+
compression_ratio: The target compression ratio.
|
162 |
+
full: Whether to count the number of parameters of the entire model or only the parametrized modules.
|
163 |
+
See also `ParametrizedModel.get_num_params`.
|
164 |
+
"""
|
165 |
+
param_masks = self._get_param_masks(compression_ratio=compression_ratio, full=full)
|
166 |
+
|
167 |
+
# Reset the target parameters according to the parameter masks
|
168 |
+
for p_name, param in self.get_target_params().items():
|
169 |
+
param.data[param_masks[p_name] > 0.0] = 1.0 # dummy value, will be rescaled by reset_target_params
|
170 |
+
param.data[param_masks[p_name] == 0.0] = 0.0
|
171 |
+
for m_name, module in self.parametrized_modules.items():
|
172 |
+
if any(p_name.startswith(m_name) for p_name in param_masks.keys()):
|
173 |
+
module.parametrization.reset_target_params(mode="nonzero")
|
174 |
+
|
175 |
+
|
176 |
+
# Register ACIPModelConfig and ACIPModel for AutoModel
|
177 |
+
# Required to push custom model to Huggingface Hub (see https://huggingface.co/docs/transformers/en/custom_models)
|
178 |
+
ACIPModelConfig.register_for_auto_class()
|
179 |
+
ACIPModel.register_for_auto_class("AutoModel")
|
config.json
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/rwthfs/rz/cluster/home/cp343770/p-res-mwl-llmcompression/artifacts/runs/paper_v3/compress__llama1_7b/model",
|
3 |
+
"adapter_config": {
|
4 |
+
"peft_config": {
|
5 |
+
"default": {
|
6 |
+
"alpha_pattern": {},
|
7 |
+
"auto_mapping": null,
|
8 |
+
"base_model_name_or_path": "jeffwan/llama-7b-hf",
|
9 |
+
"bias": "none",
|
10 |
+
"eva_config": null,
|
11 |
+
"exclude_modules": [
|
12 |
+
"base",
|
13 |
+
"parametrization",
|
14 |
+
"ortho"
|
15 |
+
],
|
16 |
+
"fan_in_fan_out": false,
|
17 |
+
"inference_mode": false,
|
18 |
+
"init_lora_weights": true,
|
19 |
+
"layer_replication": null,
|
20 |
+
"layers_pattern": null,
|
21 |
+
"layers_to_transform": null,
|
22 |
+
"loftq_config": {},
|
23 |
+
"lora_alpha": 16,
|
24 |
+
"lora_bias": false,
|
25 |
+
"lora_dropout": 0.05,
|
26 |
+
"megatron_config": null,
|
27 |
+
"megatron_core": "megatron.core",
|
28 |
+
"modules_to_save": null,
|
29 |
+
"peft_type": "LORA",
|
30 |
+
"r": 32,
|
31 |
+
"rank_pattern": {},
|
32 |
+
"revision": null,
|
33 |
+
"target_modules": [
|
34 |
+
"q_proj",
|
35 |
+
"base",
|
36 |
+
"o_proj",
|
37 |
+
"gate_proj",
|
38 |
+
"down_proj",
|
39 |
+
"up_proj",
|
40 |
+
"v_proj",
|
41 |
+
"k_proj",
|
42 |
+
"ortho"
|
43 |
+
],
|
44 |
+
"task_type": "CAUSAL_LM",
|
45 |
+
"use_dora": false,
|
46 |
+
"use_rslora": false
|
47 |
+
}
|
48 |
+
}
|
49 |
+
},
|
50 |
+
"architectures": [
|
51 |
+
"ACIPModel"
|
52 |
+
],
|
53 |
+
"auto_map": {
|
54 |
+
"AutoConfig": "acip_model.ACIPModelConfig",
|
55 |
+
"AutoModel": "acip_model.ACIPModel"
|
56 |
+
},
|
57 |
+
"base_model_config": {
|
58 |
+
"pretrained_config": null,
|
59 |
+
"pretrained_model_cls": "transformers.models.auto.modeling_auto.AutoModelForCausalLM",
|
60 |
+
"pretrained_model_kwargs": {
|
61 |
+
"pretrained_model_name_or_path": "jeffwan/llama-7b-hf",
|
62 |
+
"torch_dtype": "bfloat16"
|
63 |
+
}
|
64 |
+
},
|
65 |
+
"model_mode": "train",
|
66 |
+
"model_type": "acip_model",
|
67 |
+
"parametrization_config": {
|
68 |
+
"exclude_modules": null,
|
69 |
+
"module_factory_cls": "svd",
|
70 |
+
"module_factory_kwargs": {
|
71 |
+
"mask_func": "ste",
|
72 |
+
"mask_scaling_factor": 0.02
|
73 |
+
},
|
74 |
+
"target_modules": [
|
75 |
+
"k_proj",
|
76 |
+
"down_proj",
|
77 |
+
"o_proj",
|
78 |
+
"v_proj",
|
79 |
+
"gate_proj",
|
80 |
+
"q_proj",
|
81 |
+
"up_proj"
|
82 |
+
]
|
83 |
+
},
|
84 |
+
"torch_dtype": "bfloat16",
|
85 |
+
"transformers_version": "4.46.3",
|
86 |
+
"weight_quantization_config": {
|
87 |
+
"exclude_modules": null,
|
88 |
+
"module_factory_cls": "bitsandbytes.nn.Linear4bit",
|
89 |
+
"module_factory_kwargs": {
|
90 |
+
"compute_dtype": "torch.bfloat16",
|
91 |
+
"quant_type": "fp4"
|
92 |
+
},
|
93 |
+
"target_modules": [
|
94 |
+
"ortho",
|
95 |
+
"base",
|
96 |
+
"base_layer"
|
97 |
+
]
|
98 |
+
}
|
99 |
+
}
|
generation_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"transformers_version": "4.46.3"
|
4 |
+
}
|
model-00001-of-00005.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0ef5fdccfa838748228160684b44f4c37a99a944cd330ae6af63d94188e9c8e3
|
3 |
+
size 4979348184
|
model-00002-of-00005.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d6182e14726f23f7f25faa53bab676e79acf5d3c3197624a2d15f7d458755db6
|
3 |
+
size 4989605584
|
model-00003-of-00005.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:dda70e06b09128d1b3309f98651a4fe9fb111bc360716f3d5e10e327a606a847
|
3 |
+
size 5000010480
|
model-00004-of-00005.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fbf327dd596abbea5916f36ae9b76eea6f45440e8c1fce3430fa8feaa153e203
|
3 |
+
size 4910324992
|
model-00005-of-00005.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:392b87946099ac53941f45818bb40be4af393d694f23bb7b7465674ba3503104
|
3 |
+
size 1281214432
|
model.safetensors.index.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
parametrized_layer.py
ADDED
@@ -0,0 +1,211 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from abc import ABC, abstractmethod
|
2 |
+
from typing import ClassVar, Literal, Protocol, runtime_checkable, Type
|
3 |
+
|
4 |
+
import torch
|
5 |
+
from torch import nn
|
6 |
+
|
7 |
+
|
8 |
+
class Parametrization(nn.Module, ABC):
|
9 |
+
"""
|
10 |
+
Abstract base class for parametrizations.
|
11 |
+
A parametrization can be injected into any torch module of type `base_class` by `parametrize_module`.
|
12 |
+
A parametrized module will follow the `ParametrizedModule` interface.
|
13 |
+
|
14 |
+
This will overload the weight, bias, and forward of the module so that they play together with
|
15 |
+
the parametrization. The external behavior of the parametrized module remains unchanged, for instance,
|
16 |
+
a parametrized `Linear` module will still work as expected.
|
17 |
+
|
18 |
+
Attributes:
|
19 |
+
base_class: The base class of the module that can be parametrized.
|
20 |
+
initialized: A flag that indicates whether the parametrization has been initialized.
|
21 |
+
"""
|
22 |
+
|
23 |
+
initialized: bool = False
|
24 |
+
base_class: ClassVar[Type[nn.Module]]
|
25 |
+
|
26 |
+
def initialize(self, base_module: "Parametrization.base_class") -> None:
|
27 |
+
self._initialize(base_module)
|
28 |
+
self.initialized = True
|
29 |
+
|
30 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
31 |
+
"""
|
32 |
+
Compute the forward pass of the parametrization.
|
33 |
+
This is particularly important when a standard forward pass based on `weight` would be inefficient.
|
34 |
+
"""
|
35 |
+
assert self.initialized
|
36 |
+
x = self._forward(x)
|
37 |
+
return x
|
38 |
+
|
39 |
+
@property
|
40 |
+
def weight(self) -> torch.Tensor:
|
41 |
+
"""Compute the weight tensor of the parametrization."""
|
42 |
+
return self._weight()
|
43 |
+
|
44 |
+
@property
|
45 |
+
def bias(self) -> torch.Tensor | None:
|
46 |
+
"""Compute the bias tensor of the parametrization."""
|
47 |
+
return self._bias()
|
48 |
+
|
49 |
+
@abstractmethod
|
50 |
+
def _forward(self, x: torch.Tensor) -> torch.Tensor:
|
51 |
+
raise NotImplementedError
|
52 |
+
|
53 |
+
@abstractmethod
|
54 |
+
def _initialize(self, base_module: "Parametrization.base_class") -> None:
|
55 |
+
"""
|
56 |
+
Initialize the parametrization based on a given base module.
|
57 |
+
This method should build the internal representation the module's weight and bias,
|
58 |
+
registering all required buffers and parameters in `self`.
|
59 |
+
"""
|
60 |
+
raise NotImplementedError
|
61 |
+
|
62 |
+
@abstractmethod
|
63 |
+
def _weight(self) -> torch.Tensor:
|
64 |
+
raise NotImplementedError
|
65 |
+
|
66 |
+
@abstractmethod
|
67 |
+
def _bias(self) -> torch.Tensor | None:
|
68 |
+
raise NotImplementedError
|
69 |
+
|
70 |
+
@abstractmethod
|
71 |
+
def get_target_params(self) -> dict[str, torch.nn.Parameter]:
|
72 |
+
"""
|
73 |
+
Return the (tunable) target parameters of the parametrization.
|
74 |
+
Here, "target parameters" means that they can be tuned and potentially compressed
|
75 |
+
by `self.reset_target_params(mode="compress")`.
|
76 |
+
Other torch parameters of the module could be tuned as well, but should not returned here.
|
77 |
+
The returned dictionary should be compatible with `self.named_parameters()`.
|
78 |
+
|
79 |
+
See Also:
|
80 |
+
- `ParametrizedModel.get_target_params`
|
81 |
+
- `ParametrizedModel.compress`
|
82 |
+
"""
|
83 |
+
raise NotImplementedError
|
84 |
+
|
85 |
+
@abstractmethod
|
86 |
+
def reset_target_params(self, mode: Literal["full", "nonzero", "compress"] = "full") -> None:
|
87 |
+
"""
|
88 |
+
Reset the target parameters of the parametrization according to a given mode.
|
89 |
+
|
90 |
+
Args:
|
91 |
+
mode: The reset mode.
|
92 |
+
"full" means reset to original value at initialization.
|
93 |
+
"nonzero" means reset all non-zero values to original value at initialization.
|
94 |
+
"compress" means the all zero values are removed and the the parameters are compressed accordingly.
|
95 |
+
"""
|
96 |
+
raise NotImplementedError
|
97 |
+
|
98 |
+
@abstractmethod
|
99 |
+
def get_num_params(self, compressed: bool = False, target_params: dict[str, torch.Tensor] | None = None) -> int:
|
100 |
+
"""
|
101 |
+
Computes the (effective) number of parameters of the parametrization.
|
102 |
+
|
103 |
+
Args:
|
104 |
+
compressed: Whether to count the number of parameters as if the module was actually compressed.
|
105 |
+
If `False`, the number of parameters is the same as in the original module.
|
106 |
+
target_params: Count the number of parameters as if `target_params` were used instead of
|
107 |
+
`self.get_target_params()`. This "what if" feature is important when pruning
|
108 |
+
a full `ParametrizedModel` to a certain target ratio.
|
109 |
+
"""
|
110 |
+
raise NotImplementedError
|
111 |
+
|
112 |
+
|
113 |
+
@runtime_checkable
|
114 |
+
class ParametrizedModule(Protocol):
|
115 |
+
"""
|
116 |
+
Interface for a parametrized `nn.Module`.
|
117 |
+
It ensures that `weight` and `bias` are forwarded to the `Parametrization` instance.
|
118 |
+
|
119 |
+
Attributes:
|
120 |
+
parametrization: The `Parametrization` instance of the module.
|
121 |
+
_forward: The original forward function of the module.
|
122 |
+
__old_class__: The original class of the module.
|
123 |
+
|
124 |
+
Notes:
|
125 |
+
`_forward` and `__old_class__` are used by `parametrize_module` and `unparametrize_module`
|
126 |
+
to allow restoring the original behavior of the module.
|
127 |
+
"""
|
128 |
+
|
129 |
+
parametrization: Parametrization
|
130 |
+
_forward: callable
|
131 |
+
__old_class__: type[nn.Module]
|
132 |
+
|
133 |
+
@property
|
134 |
+
def weight(self):
|
135 |
+
return self.parametrization.weight
|
136 |
+
|
137 |
+
@property
|
138 |
+
def bias(self):
|
139 |
+
return self.parametrization.bias
|
140 |
+
|
141 |
+
|
142 |
+
def parametrize_module(module: nn.Module, parametrization: Parametrization) -> ParametrizedModule and nn.Module:
|
143 |
+
"""
|
144 |
+
Parametrize a module using a `Parametrization` instance.
|
145 |
+
|
146 |
+
Args:
|
147 |
+
module: The module to be parametrized.
|
148 |
+
parametrization: The `Parametrization` instance to be applied to the module.
|
149 |
+
|
150 |
+
Returns: The parametrized module using the `ParametrizedModule` interface.
|
151 |
+
|
152 |
+
Notes:
|
153 |
+
Adopted from https://stackoverflow.com/a/31075641
|
154 |
+
"""
|
155 |
+
|
156 |
+
assert isinstance(module, parametrization.base_class)
|
157 |
+
module.__old_class__ = module.__class__
|
158 |
+
|
159 |
+
# Initializes the parametrization and adds it to the module
|
160 |
+
module.add_module("parametrization", parametrization)
|
161 |
+
module.parametrization.initialize(module)
|
162 |
+
|
163 |
+
# Save the original forward in case we want to remove the parametrization again
|
164 |
+
module._forward = module.forward
|
165 |
+
|
166 |
+
# Cast to new parametrized object class type
|
167 |
+
del module.weight
|
168 |
+
del module.bias
|
169 |
+
module.__class__ = type("Parametrized" + module.__class__.__name__, (module.__class__, ParametrizedModule), {})
|
170 |
+
# Make sure that we utilize the forward function of the parametrization
|
171 |
+
module.forward = module.parametrization.forward
|
172 |
+
|
173 |
+
return module
|
174 |
+
|
175 |
+
|
176 |
+
def unparametrize_module(module: ParametrizedModule) -> nn.Module:
|
177 |
+
"""
|
178 |
+
Revert the parametrization of a module.
|
179 |
+
|
180 |
+
Args:
|
181 |
+
module: A module that has been parametrized by `parametrize_module`.
|
182 |
+
|
183 |
+
Returns: The original module.
|
184 |
+
|
185 |
+
Notes:
|
186 |
+
Adopted from https://stackoverflow.com/a/31075641
|
187 |
+
"""
|
188 |
+
|
189 |
+
# Make sure to save weight and bias in intermediate variables
|
190 |
+
weight = module.weight
|
191 |
+
bias = module.bias
|
192 |
+
|
193 |
+
assert isinstance(module, nn.Module)
|
194 |
+
|
195 |
+
# This line will remove properties module.weight and module.bias
|
196 |
+
module.__class__ = type(module.__old_class__.__name__, (module.__old_class__,), {})
|
197 |
+
delattr(module, "__old_class__")
|
198 |
+
|
199 |
+
# Add weight and bias as native parameters to the module again
|
200 |
+
module.register_parameter("weight", nn.Parameter(weight, weight.requires_grad))
|
201 |
+
if bias is not None:
|
202 |
+
module.register_parameter("bias", nn.Parameter(bias, bias.requires_grad))
|
203 |
+
else:
|
204 |
+
module.register_parameter("bias", None)
|
205 |
+
|
206 |
+
# Recover the original forward pass and get rid of the parametrization
|
207 |
+
del module.parametrization
|
208 |
+
module.forward = module._forward
|
209 |
+
delattr(module, "_forward")
|
210 |
+
|
211 |
+
return module
|
parametrized_model.py
ADDED
@@ -0,0 +1,747 @@
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|
|
|
|
|
|
1 |
+
import logging
|
2 |
+
import os
|
3 |
+
from dataclasses import asdict, dataclass, field
|
4 |
+
from typing import Any, Literal, Type
|
5 |
+
|
6 |
+
import torch
|
7 |
+
from peft import PeftConfig
|
8 |
+
from peft.tuners.tuners_utils import _maybe_include_all_linear_layers, check_target_module_exists
|
9 |
+
from torch import nn
|
10 |
+
from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
|
11 |
+
|
12 |
+
from .parametrized_layer import Parametrization, parametrize_module, ParametrizedModule, unparametrize_module
|
13 |
+
from .projected_layer import SVDLinearParametrization
|
14 |
+
from .utils import get_class_from_str, get_str_from_class, init_empty_weights
|
15 |
+
|
16 |
+
logger = logging.getLogger(__name__)
|
17 |
+
|
18 |
+
|
19 |
+
@dataclass
|
20 |
+
class BaseModelConfig:
|
21 |
+
"""
|
22 |
+
Configuration for the base model to be parametrized by `ParametrizedModel`.
|
23 |
+
|
24 |
+
Attributes:
|
25 |
+
pretrained_model_cls: The class of the base model. Child class of `PreTrainedModel`.
|
26 |
+
pretrained_model_kwargs: Keyword arguments used when creating the base model in the constructor
|
27 |
+
of `ParametrizedModel` via `from_pretrained`.
|
28 |
+
pretrained_config: Optional config used when creating the base model in the constructor
|
29 |
+
of `ParametrizedModel` via `from_pretrained`.
|
30 |
+
|
31 |
+
See Also:
|
32 |
+
`ParametrizedModelConfig`
|
33 |
+
"""
|
34 |
+
|
35 |
+
pretrained_model_cls: Type[PreTrainedModel]
|
36 |
+
pretrained_model_kwargs: dict[str, Any] = field(default_factory=dict)
|
37 |
+
pretrained_config: PretrainedConfig | None = None
|
38 |
+
|
39 |
+
def __post_init__(self):
|
40 |
+
# if pretrained_model_cls is a string, convert it to a class (required for deserialization from JSON config)
|
41 |
+
if isinstance(self.pretrained_model_cls, str):
|
42 |
+
self.pretrained_model_cls = get_class_from_str(self.pretrained_model_cls) # noqa
|
43 |
+
else:
|
44 |
+
self.pretrained_model_cls = self.pretrained_model_cls
|
45 |
+
|
46 |
+
def to_dict(self) -> dict[str, Any]:
|
47 |
+
config_dict = asdict(self) # type: ignore
|
48 |
+
# make sure that pretrained_model_cls and pretrained_config are JSON serializable
|
49 |
+
config_dict["pretrained_model_cls"] = get_str_from_class(self.pretrained_model_cls)
|
50 |
+
if self.pretrained_config is not None:
|
51 |
+
config_dict["pretrained_config"] = self.pretrained_config.to_dict()
|
52 |
+
return config_dict
|
53 |
+
|
54 |
+
@classmethod
|
55 |
+
def from_dict(cls, config_dict: dict[str, Any]) -> "BaseModelConfig":
|
56 |
+
# try to deserialize pretrained_config with AutoConfig otherwise fall back to PretrainedConfig
|
57 |
+
try:
|
58 |
+
if config_dict["pretrained_config"] is not None:
|
59 |
+
# try AutoConfig to find the right model config class
|
60 |
+
config_dict["pretrained_config"] = AutoConfig.for_model(**config_dict["pretrained_config"])
|
61 |
+
except ValueError:
|
62 |
+
logger.warning("Unrecognized model identifier in AutoConfig, using PretrainedConfig instead.")
|
63 |
+
config_dict["pretrained_config"] = PretrainedConfig.from_dict(config_dict["pretrained_config"])
|
64 |
+
return cls(**config_dict)
|
65 |
+
|
66 |
+
|
67 |
+
# Predefined parametrization classes for `ParametrizationConfig.module_factory_cls` (avoids absolute package imports)
|
68 |
+
PARAMETRIZATION_FACTORY_REGISTRY: dict[str, Type[Parametrization]] = {
|
69 |
+
"svd": SVDLinearParametrization,
|
70 |
+
}
|
71 |
+
|
72 |
+
|
73 |
+
@dataclass
|
74 |
+
class ParametrizationConfig:
|
75 |
+
"""
|
76 |
+
Configuration for the parametrization to be applied to the linear layers of the base model in `ParametrizedModel`.
|
77 |
+
|
78 |
+
Attributes:
|
79 |
+
module_factory_cls: The class name of the parametrization to be applied to linear layers.
|
80 |
+
Can be a string representing a class name (with absolute module path) or a predefined key
|
81 |
+
from `PARAMETRIZATION_FACTORY_REGISTRY`.
|
82 |
+
Use `parse_module_factory_cls` to get the actual class when creating the parametrization.
|
83 |
+
module_factory_kwargs: Keyword arguments used when creating the parametrization with `module_factory_cls`.
|
84 |
+
target_modules: A (list of) string(s) specifying the names of the linear layers to be parametrized.
|
85 |
+
Follows the same semantics as Huggingface's `PeftConfig`, see also `check_target_module_exists`.
|
86 |
+
If a string, a regex match will be performed; if a list, a module will be parametrized if its name ends
|
87 |
+
with any of the strings in `target_modules`.
|
88 |
+
exclude_modules: A list of strings specifying the names of the linear layers to be excluded from
|
89 |
+
parametrization. A module will be excluded if any of the strings in `exclude_modules` is in its name.
|
90 |
+
|
91 |
+
See Also:
|
92 |
+
`ParametrizedModelConfig`
|
93 |
+
"""
|
94 |
+
|
95 |
+
module_factory_cls: str
|
96 |
+
module_factory_kwargs: dict[str, Any] = field(default_factory=dict)
|
97 |
+
target_modules: str | list[str] | None = None
|
98 |
+
exclude_modules: list[str] | None = None
|
99 |
+
|
100 |
+
def parse_module_factory_cls(self) -> Type[Parametrization]:
|
101 |
+
"""Returns the class of the parametrization to be applied to linear layers."""
|
102 |
+
try:
|
103 |
+
if self.module_factory_cls in PARAMETRIZATION_FACTORY_REGISTRY:
|
104 |
+
module_factory_cls = PARAMETRIZATION_FACTORY_REGISTRY[self.module_factory_cls]
|
105 |
+
else:
|
106 |
+
module_factory_cls = get_class_from_str(self.module_factory_cls)
|
107 |
+
except Exception:
|
108 |
+
raise ValueError(f"Unrecognized parametrization class: {self.module_factory_cls}")
|
109 |
+
return module_factory_cls
|
110 |
+
|
111 |
+
def to_dict(self) -> dict[str, Any]:
|
112 |
+
config_dict = asdict(self) # type: ignore
|
113 |
+
# _maybe_include_all_linear_layers creates sets which does not work with JSON serialization, so cast to list
|
114 |
+
for key, value in config_dict.items():
|
115 |
+
if isinstance(value, set):
|
116 |
+
config_dict[key] = list(value)
|
117 |
+
return config_dict
|
118 |
+
|
119 |
+
@classmethod
|
120 |
+
def from_dict(cls, config_dict: dict[str, Any]) -> "ParametrizationConfig":
|
121 |
+
return cls(**config_dict)
|
122 |
+
|
123 |
+
|
124 |
+
@dataclass
|
125 |
+
class AdapterConfig:
|
126 |
+
"""
|
127 |
+
Configuration for the Huggingface Peft adapters to be applied to the base model.
|
128 |
+
|
129 |
+
Attributes:
|
130 |
+
peft_config: One or more adapter `PeftConfig`s to be applied to the base model.
|
131 |
+
If a single `PeftConfig` is provided, it will wrapped by a dict with key "default".
|
132 |
+
The dictionary keys will be used as adapter names in `PretrainedModel.add_adapter`.
|
133 |
+
|
134 |
+
See Also:
|
135 |
+
`ParametrizedModelConfig`
|
136 |
+
"""
|
137 |
+
|
138 |
+
peft_config: PeftConfig | dict[str, PeftConfig]
|
139 |
+
|
140 |
+
def __post_init__(self):
|
141 |
+
if isinstance(self.peft_config, PeftConfig):
|
142 |
+
self.peft_config = {"default": self.peft_config}
|
143 |
+
|
144 |
+
def to_dict(self) -> dict[str, Any]:
|
145 |
+
config_dict = asdict(self) # type: ignore
|
146 |
+
# Make each PeftConfig JSON serializable
|
147 |
+
for adapter_name, peft_config in self.peft_config.items():
|
148 |
+
peft_config_dict = peft_config.to_dict()
|
149 |
+
# Peft casts lists to sets, which are not JSON serializable, so cast to list manually
|
150 |
+
for key, value in peft_config_dict.items():
|
151 |
+
if isinstance(value, set):
|
152 |
+
peft_config_dict[key] = list(value)
|
153 |
+
config_dict["peft_config"][adapter_name] = peft_config_dict
|
154 |
+
return config_dict
|
155 |
+
|
156 |
+
@classmethod
|
157 |
+
def from_dict(cls, config_dict: dict[str, Any]) -> "AdapterConfig":
|
158 |
+
# Deserialize each PeftConfig automatically with from_peft_type
|
159 |
+
for key, peft_config in config_dict["peft_config"].items():
|
160 |
+
config_dict["peft_config"][key] = PeftConfig.from_peft_type(**peft_config)
|
161 |
+
return cls(**config_dict)
|
162 |
+
|
163 |
+
|
164 |
+
try:
|
165 |
+
# Prevent import errors because for some systems like macOS, bitsandbytes cannot be installed directly
|
166 |
+
import bitsandbytes
|
167 |
+
|
168 |
+
# Predefined quantization classes for `WeightQuantizationConfig.module_factory_cls`
|
169 |
+
# (avoids absolute package imports)
|
170 |
+
QUANTIZATION_FACTORY_REGISTRY: dict[str, Type[nn.Linear]] = {
|
171 |
+
"bnb4bit": bitsandbytes.nn.Linear4bit,
|
172 |
+
}
|
173 |
+
except ImportError:
|
174 |
+
logger.warning("bitsandbytes is not installed, skipping quantization.")
|
175 |
+
QUANTIZATION_FACTORY_REGISTRY: dict[str, Type[nn.Linear]] = {}
|
176 |
+
|
177 |
+
|
178 |
+
@dataclass
|
179 |
+
class WeightQuantizationConfig:
|
180 |
+
"""
|
181 |
+
Configuration for an (optional) weight quantization to be applied to the base model.
|
182 |
+
So far, only fp4 quantization with bitsandbytes has been tested, but analogous bitsandbytes
|
183 |
+
quantizations should work as well. `module_factory_cls` might also use a different quantization library,
|
184 |
+
as long as it is compatible with the module replacement strategy in `ParametrizedModule.quantize`.
|
185 |
+
|
186 |
+
Attributes:
|
187 |
+
module_factory_cls: The class name of the quantization to be applied to linear layers.
|
188 |
+
Can be a string representing a class name (with absolute module path) or a predefined key
|
189 |
+
from `QUANTIZATION_FACTORY_REGISTRY`.
|
190 |
+
Use `parse_module_factory_cls` to get the actual class when creating the quantization.
|
191 |
+
module_factory_kwargs: Keyword arguments used when creating the quantization with `module_factory_cls`.
|
192 |
+
target_modules: A (list of) string(s) specifying the names of the linear layers to be quantized.
|
193 |
+
Follows the same semantics as Huggingface's `PeftConfig`, see also `check_target_module_exists`.
|
194 |
+
If a string, a regex match will be performed; if a list, a module will be quantized if its name ends
|
195 |
+
with any of the strings in `target_modules`.
|
196 |
+
exclude_modules: A list of strings specifying the names of the linear layers to be excluded from
|
197 |
+
quantization. A module will be excluded if any of the strings in `exclude_modules` is in its name.
|
198 |
+
|
199 |
+
See Also:
|
200 |
+
`ParametrizedModelConfig`
|
201 |
+
"""
|
202 |
+
|
203 |
+
module_factory_cls: str
|
204 |
+
module_factory_kwargs: dict[str, Any] = field(default_factory=dict)
|
205 |
+
target_modules: str | list[str] | None = None
|
206 |
+
exclude_modules: list[str] | None = None
|
207 |
+
|
208 |
+
def parse_module_factory_cls(self) -> Type[nn.Linear]:
|
209 |
+
"""Returns the class of the quantization to be applied to linear layers."""
|
210 |
+
try:
|
211 |
+
if self.module_factory_cls in QUANTIZATION_FACTORY_REGISTRY:
|
212 |
+
module_factory_cls = QUANTIZATION_FACTORY_REGISTRY[self.module_factory_cls]
|
213 |
+
else:
|
214 |
+
module_factory_cls = get_class_from_str(self.module_factory_cls)
|
215 |
+
except Exception:
|
216 |
+
raise ValueError(f"Unrecognized quantization class: {self.module_factory_cls}")
|
217 |
+
return module_factory_cls
|
218 |
+
|
219 |
+
def to_dict(self) -> dict[str, Any]:
|
220 |
+
config_dict = asdict(self) # type: ignore
|
221 |
+
# Make torch.dtype fields JSON serializable
|
222 |
+
for key, value in config_dict["module_factory_kwargs"].items():
|
223 |
+
if isinstance(value, torch.dtype):
|
224 |
+
config_dict["module_factory_kwargs"][key] = str(value)
|
225 |
+
# _maybe_include_all_linear_layers creates sets which does not work with JSON serialization, so cast to list
|
226 |
+
for key, value in config_dict.items():
|
227 |
+
if isinstance(value, set):
|
228 |
+
config_dict[key] = list(value)
|
229 |
+
return config_dict
|
230 |
+
|
231 |
+
@classmethod
|
232 |
+
def from_dict(cls, config_dict: dict[str, Any]) -> "WeightQuantizationConfig":
|
233 |
+
# Deserialize torch.dtype fields
|
234 |
+
for key, value in config_dict["module_factory_kwargs"].items():
|
235 |
+
if isinstance(value, str) and value.startswith("torch."):
|
236 |
+
dtype_name = value.split(".")[-1]
|
237 |
+
config_dict["module_factory_kwargs"][key] = getattr(torch, dtype_name)
|
238 |
+
return cls(**config_dict)
|
239 |
+
|
240 |
+
|
241 |
+
class ParametrizedModelConfig(PretrainedConfig):
|
242 |
+
"""
|
243 |
+
Configuration for `ParametrizedModel` implementing a `PretrainedConfig` to be fully compatible with
|
244 |
+
Huggingface's `PreTrainedModel` framework.
|
245 |
+
|
246 |
+
See Also:
|
247 |
+
- `BaseModelConfig`
|
248 |
+
- `ParametrizationConfig`
|
249 |
+
- `AdapterConfig`
|
250 |
+
- `WeightQuantizationConfig`
|
251 |
+
- `ParametrizedModel`
|
252 |
+
"""
|
253 |
+
|
254 |
+
model_type = "parametrized_model"
|
255 |
+
|
256 |
+
def __init__(
|
257 |
+
self,
|
258 |
+
base_model_config: BaseModelConfig | None = None,
|
259 |
+
parametrization_config: ParametrizationConfig | None = None,
|
260 |
+
adapter_config: AdapterConfig | None = None,
|
261 |
+
weight_quantization_config: WeightQuantizationConfig | None = None,
|
262 |
+
model_mode: Literal["train", "eval"] = "train",
|
263 |
+
**kwargs: Any,
|
264 |
+
):
|
265 |
+
"""
|
266 |
+
Initializes a `ParametrizedModelConfig`, serving as a container for `BaseModelConfig`, `ParametrizationConfig`,
|
267 |
+
`AdapterConfig`, and `WeightQuantizationConfig`.
|
268 |
+
|
269 |
+
Args:
|
270 |
+
base_model_config: `BaseModelConfig`
|
271 |
+
parametrization_config: `ParametrizationConfig`
|
272 |
+
adapter_config: `AdapterConfig`
|
273 |
+
weight_quantization_config: `WeightQuantizationConfig`
|
274 |
+
model_mode: Whether to initialize the model in train or eval mode.
|
275 |
+
**kwargs: Keyword arguments forwarded to `PretrainedConfig`.
|
276 |
+
"""
|
277 |
+
self.base_model_config = base_model_config
|
278 |
+
self.parametrization_config = parametrization_config
|
279 |
+
self.adapter_config = adapter_config
|
280 |
+
self.weight_quantization_config = weight_quantization_config
|
281 |
+
self.model_mode = model_mode
|
282 |
+
super().__init__(**kwargs)
|
283 |
+
|
284 |
+
def _convert_to_dict(self, config_dict: dict[str, Any]) -> dict[str, Any]:
|
285 |
+
if self.base_model_config is not None:
|
286 |
+
config_dict["base_model_config"] = self.base_model_config.to_dict()
|
287 |
+
if self.parametrization_config is not None:
|
288 |
+
config_dict["parametrization_config"] = self.parametrization_config.to_dict()
|
289 |
+
if self.adapter_config is not None:
|
290 |
+
config_dict["adapter_config"] = self.adapter_config.to_dict()
|
291 |
+
if self.weight_quantization_config is not None:
|
292 |
+
config_dict["weight_quantization_config"] = self.weight_quantization_config.to_dict()
|
293 |
+
return config_dict
|
294 |
+
|
295 |
+
def to_diff_dict(self):
|
296 |
+
# Override PretrainedConfig to_diff_dict to make subconfigs JSON serializable.
|
297 |
+
config_dict = super().to_diff_dict()
|
298 |
+
return self._convert_to_dict(config_dict)
|
299 |
+
|
300 |
+
def to_dict(self):
|
301 |
+
# Override PretrainedConfig to_diff to make subconfigs JSON serializable.
|
302 |
+
config_dict = super().to_dict()
|
303 |
+
return self._convert_to_dict(config_dict)
|
304 |
+
|
305 |
+
@classmethod
|
306 |
+
def from_dict(cls, config_dict: dict[str, Any], **kwargs: Any) -> PretrainedConfig:
|
307 |
+
# Deserialize BaseModelConfig
|
308 |
+
base_model_config_dict: dict[str, Any] | None = config_dict.pop("base_model_config", None)
|
309 |
+
if base_model_config_dict is not None:
|
310 |
+
base_model_config = BaseModelConfig.from_dict(base_model_config_dict)
|
311 |
+
else:
|
312 |
+
base_model_config = None
|
313 |
+
# Deserialize ParametrizationConfig
|
314 |
+
parametrization_config_dict: dict[str, Any] | None = config_dict.pop("parametrization_config", None)
|
315 |
+
if parametrization_config_dict is not None:
|
316 |
+
parametrization_config = ParametrizationConfig.from_dict(parametrization_config_dict)
|
317 |
+
else:
|
318 |
+
parametrization_config = None
|
319 |
+
# Deserialize AdapterConfig
|
320 |
+
adapter_config_dict: dict[str, Any] | None = config_dict.pop("adapter_config", None)
|
321 |
+
if adapter_config_dict is not None:
|
322 |
+
adapter_config = AdapterConfig.from_dict(adapter_config_dict)
|
323 |
+
else:
|
324 |
+
adapter_config = None
|
325 |
+
# Deserialize WeightQuantizationConfig
|
326 |
+
weight_quantization_config_dict: dict[str, Any] | None = config_dict.pop("weight_quantization_config", None)
|
327 |
+
if weight_quantization_config_dict is not None:
|
328 |
+
weight_quantization_config = WeightQuantizationConfig.from_dict(weight_quantization_config_dict)
|
329 |
+
else:
|
330 |
+
weight_quantization_config = None
|
331 |
+
|
332 |
+
config = super().from_dict(config_dict, **kwargs)
|
333 |
+
|
334 |
+
# Handle special case when return_unused_kwargs is True
|
335 |
+
if "return_unused_kwargs" in kwargs and kwargs["return_unused_kwargs"] is True:
|
336 |
+
config[0].base_model_config = base_model_config
|
337 |
+
config[0].parametrization_config = parametrization_config
|
338 |
+
config[0].adapter_config = adapter_config
|
339 |
+
config[0].weight_quantization_config = weight_quantization_config
|
340 |
+
else:
|
341 |
+
config.base_model_config = base_model_config
|
342 |
+
config.parametrization_config = parametrization_config
|
343 |
+
config.adapter_config = adapter_config
|
344 |
+
config.weight_quantization_config = weight_quantization_config
|
345 |
+
return config
|
346 |
+
|
347 |
+
|
348 |
+
class ParametrizedModel(PreTrainedModel):
|
349 |
+
"""
|
350 |
+
Base class for parametrized models implemented as a custom Huggingface `PreTrainedModel`.
|
351 |
+
It wraps any base model of type `PreTrainedModel` in `self.model`, whose linear layers can be
|
352 |
+
parametrized (`parametrize`), equipped with adapters (`inject_adapters`), and quantized (`quantize`).
|
353 |
+
The corresponding modules are accessed via `parametrized_modules`, `adapter_modules`,
|
354 |
+
and `quantized_modules`, respectively.
|
355 |
+
The class also provides several convenience methods to manage the parametrization: `get_target_params`,
|
356 |
+
`get_num_params`, `get_compression_ratio`, `reset_target_params`, `compress`.
|
357 |
+
|
358 |
+
Standard functionality (`forward`, `generate`, `save_pretrained`, `from_pretrained`) is essentially forwarded
|
359 |
+
to the wrapped model.
|
360 |
+
|
361 |
+
See Also:
|
362 |
+
`ParametrizedModelConfig`
|
363 |
+
"""
|
364 |
+
|
365 |
+
config_class = ParametrizedModelConfig
|
366 |
+
|
367 |
+
def __init__(self, config: ParametrizedModelConfig, base_model: PreTrainedModel | None = None, **_: Any):
|
368 |
+
"""
|
369 |
+
Initialize the `ParametrizedModel` from a given configuration or an existing base model.
|
370 |
+
|
371 |
+
Args:
|
372 |
+
config: `ParametrizedModelConfig` to be used.
|
373 |
+
base_model: If provided, this base model is used instead of creating it from `config.base_model_config`.
|
374 |
+
**_: Ignored keyword arguments to prevent unexpected keyword errors.
|
375 |
+
|
376 |
+
See Also: `BaseModelConfig`
|
377 |
+
"""
|
378 |
+
super().__init__(config)
|
379 |
+
self.config = config # redundant but enables type hinting for ParametrizedModelConfig
|
380 |
+
|
381 |
+
# Either use an existing base model or create a new one from config.base_model_config
|
382 |
+
if base_model is None:
|
383 |
+
if self.config.base_model_config is None:
|
384 |
+
raise ValueError("Either base_model or base_model_config must be provided.")
|
385 |
+
self.model = self.config.base_model_config.pretrained_model_cls.from_pretrained(
|
386 |
+
config=self.config.base_model_config.pretrained_config,
|
387 |
+
**self.config.base_model_config.pretrained_model_kwargs,
|
388 |
+
)
|
389 |
+
else:
|
390 |
+
self.model = base_model
|
391 |
+
|
392 |
+
# Set base model to train or eval mode.
|
393 |
+
self.train(self.config.model_mode == "train")
|
394 |
+
logger.info(f"Base model {self.model.__class__} created.")
|
395 |
+
|
396 |
+
# Perform parametrization.
|
397 |
+
self._parametrized_modules: dict[str, ParametrizedModule] | None = None
|
398 |
+
self.parametrize()
|
399 |
+
|
400 |
+
# Inject adapters.
|
401 |
+
self._adapter_modules: dict[str, nn.Module] | None = None
|
402 |
+
self.inject_adapters()
|
403 |
+
|
404 |
+
# Quantization needs to be performed manually via `quantize` because this is fully optional.
|
405 |
+
self._quantized_modules: dict[str, nn.Linear] | None = None
|
406 |
+
|
407 |
+
# Modified modules are initalized after parametrize and inject_adapters because they may alter the nested
|
408 |
+
# module and parameter structure of the model.
|
409 |
+
_ = self.parametrized_modules
|
410 |
+
_ = self.adapter_modules
|
411 |
+
_ = self.quantized_modules
|
412 |
+
|
413 |
+
# Initially disable all tunable parameters to avoid unexpected behavior.
|
414 |
+
# Tunable parameter selection should be handled by the optimizer factory in `BaseLitModule`.
|
415 |
+
for param in self.parameters():
|
416 |
+
param.requires_grad = False
|
417 |
+
|
418 |
+
@property
|
419 |
+
def base_model_name_or_path(self) -> str:
|
420 |
+
"""Convenience method to return the name or path of the base model."""
|
421 |
+
return self.model.name_or_path # type: ignore
|
422 |
+
|
423 |
+
def forward(self, *args, **kwargs) -> Any:
|
424 |
+
return self.model(*args, **kwargs)
|
425 |
+
|
426 |
+
def generate(self, *args, **kwargs) -> Any:
|
427 |
+
return self.model.generate(*args, **kwargs)
|
428 |
+
|
429 |
+
def save_pretrained(
|
430 |
+
self,
|
431 |
+
save_directory: str | os.PathLike,
|
432 |
+
state_dict: dict | None = None,
|
433 |
+
include_filter: list[str] | None = None,
|
434 |
+
exclude_filter: list[str] | None = None,
|
435 |
+
**kwargs: Any,
|
436 |
+
) -> None:
|
437 |
+
"""
|
438 |
+
Override of the default `save_pretrained` method to allow filtering of the saved state dict.
|
439 |
+
|
440 |
+
Args:
|
441 |
+
save_directory: Directory to save the model to.
|
442 |
+
state_dict: Manuel override of the state dict to be saved.
|
443 |
+
If None, `include_filter` and `exclude_filter` are applied to `self.state_dict()`.
|
444 |
+
include_filter: List of state dict keys to include from the state dict.
|
445 |
+
Match when the key ends with any of the strings in the list.
|
446 |
+
If None, all keys are included.
|
447 |
+
exclude_filter: List of state dict keys to exclude from in the state dict.
|
448 |
+
Match when the key ends with any of the strings in the list.
|
449 |
+
If None, no keys are excluded.
|
450 |
+
**kwargs: Keyword arguments to be passed to the default `save_pretrained` method.
|
451 |
+
|
452 |
+
See Also:
|
453 |
+
`PreTrainedModel.save_pretrained`
|
454 |
+
"""
|
455 |
+
if state_dict is None:
|
456 |
+
state_dict = self.state_dict()
|
457 |
+
if include_filter is not None:
|
458 |
+
state_dict = {k: v for k, v in state_dict.items() if any(k.endswith(f) for f in include_filter)}
|
459 |
+
if exclude_filter is not None:
|
460 |
+
state_dict = {k: v for k, v in state_dict.items() if not any(k.endswith(f) for f in exclude_filter)}
|
461 |
+
|
462 |
+
super().save_pretrained(save_directory=save_directory, state_dict=state_dict, **kwargs)
|
463 |
+
|
464 |
+
@classmethod
|
465 |
+
def from_pretrained(
|
466 |
+
cls,
|
467 |
+
pretrained_model_name_or_path: str | os.PathLike | None,
|
468 |
+
*model_args: Any,
|
469 |
+
with_init_empty_weights: bool = True,
|
470 |
+
**kwargs: Any,
|
471 |
+
) -> PreTrainedModel:
|
472 |
+
"""
|
473 |
+
Override of the default `from_pretrained` method to allow initialization with empty weights.
|
474 |
+
|
475 |
+
Args:
|
476 |
+
pretrained_model_name_or_path: Model name or path.
|
477 |
+
*model_args: Arguments to be passed to the default `from_pretrained` method.
|
478 |
+
with_init_empty_weights: Whether to initialize the model with empty weights or not.
|
479 |
+
**kwargs: Keyword arguments to be passed to the default `from_pretrained` method.
|
480 |
+
"""
|
481 |
+
with init_empty_weights(with_init_empty_weights):
|
482 |
+
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
483 |
+
|
484 |
+
@property
|
485 |
+
def parametrized_modules(self) -> dict[str, ParametrizedModule]:
|
486 |
+
"""
|
487 |
+
Returns a dictionary of all parametrized modules in the model.
|
488 |
+
The returned dictionary is compatible with `self.model.named_modules()`.
|
489 |
+
"""
|
490 |
+
if self._parametrized_modules is None:
|
491 |
+
self._parametrized_modules = {}
|
492 |
+
if self.config.parametrization_config is None:
|
493 |
+
return self._parametrized_modules
|
494 |
+
for m_name, module in self.model.named_modules():
|
495 |
+
if isinstance(module, ParametrizedModule):
|
496 |
+
self._parametrized_modules[m_name] = module
|
497 |
+
return self._parametrized_modules
|
498 |
+
|
499 |
+
@property
|
500 |
+
def adapter_modules(self) -> dict[str, nn.Module]:
|
501 |
+
"""
|
502 |
+
Returns a dictionary of all adapter modules in the model.
|
503 |
+
The returned dictionary is compatible with `self.model.named_modules()`.
|
504 |
+
"""
|
505 |
+
if self._adapter_modules is None:
|
506 |
+
self._adapter_modules = {}
|
507 |
+
if self.config.adapter_config is None:
|
508 |
+
return self._adapter_modules
|
509 |
+
try:
|
510 |
+
# Use the adapter management of `PreTrainedModel` to retrieve the adapter modules.
|
511 |
+
for adapter_name in self.model.active_adapters():
|
512 |
+
for m_name in self.model.get_adapter_state_dict(adapter_name).keys():
|
513 |
+
adapter_m_name = f"{m_name.rsplit('.', 1)[0]}.{adapter_name}"
|
514 |
+
self._adapter_modules[adapter_m_name] = self.model.get_submodule(adapter_m_name)
|
515 |
+
except ValueError as e:
|
516 |
+
logger.warning(e)
|
517 |
+
return self._adapter_modules
|
518 |
+
|
519 |
+
@property
|
520 |
+
def quantized_modules(self) -> dict[str, nn.Linear]:
|
521 |
+
"""
|
522 |
+
Returns a dictionary of all quantized modules in the model.
|
523 |
+
The returned dictionary is compatible with `self.model.named_modules()`.
|
524 |
+
"""
|
525 |
+
if self._quantized_modules is None:
|
526 |
+
self._quantized_modules = {}
|
527 |
+
if self.config.weight_quantization_config is None:
|
528 |
+
return self._quantized_modules
|
529 |
+
try:
|
530 |
+
module_factory_cls = self.config.weight_quantization_config.parse_module_factory_cls()
|
531 |
+
except Exception as e:
|
532 |
+
logger.warning(f"Could not parse weight quantization config, quantization not available.\nError: {e}")
|
533 |
+
return self._quantized_modules
|
534 |
+
for m_name, module in self.model.named_modules():
|
535 |
+
if isinstance(module, module_factory_cls):
|
536 |
+
self._quantized_modules[m_name] = module
|
537 |
+
return self._quantized_modules
|
538 |
+
|
539 |
+
def parametrize(self) -> None:
|
540 |
+
"""
|
541 |
+
Parametrize the `target_modules` from `ParametrizationConfig` using `parametrized_layer.parametrize_module`.
|
542 |
+
|
543 |
+
See Also: `ParametrizationConfig`
|
544 |
+
"""
|
545 |
+
if self.config.parametrization_config is None:
|
546 |
+
logger.debug("Model parametrization is disabled.")
|
547 |
+
return
|
548 |
+
|
549 |
+
# Use peft semantics, e.g, "all-linear" to include all linear layers
|
550 |
+
# TODO: Replace by own helper function to avoid unnecessary dependencies
|
551 |
+
config: ParametrizationConfig = _maybe_include_all_linear_layers( # type: ignore
|
552 |
+
self.config.parametrization_config, # type: ignore
|
553 |
+
self.model,
|
554 |
+
)
|
555 |
+
module_factory_cls = config.parse_module_factory_cls()
|
556 |
+
|
557 |
+
for m_name, module in self.model.named_modules():
|
558 |
+
# Only modify the modules that are targeted
|
559 |
+
if config.exclude_modules is not None and any(key in m_name for key in config.exclude_modules):
|
560 |
+
continue
|
561 |
+
if not check_target_module_exists(config, m_name):
|
562 |
+
continue
|
563 |
+
|
564 |
+
parametrization = module_factory_cls(**config.module_factory_kwargs)
|
565 |
+
parametrize_module(module=module, parametrization=parametrization)
|
566 |
+
logger.debug(f"Parametrized {module.__class__} module {m_name} as {parametrization.__class__}")
|
567 |
+
|
568 |
+
self._parametrized_modules = None # reset parametrized modules
|
569 |
+
logger.info("Parametrization completed.")
|
570 |
+
|
571 |
+
def inject_adapters(self) -> None:
|
572 |
+
"""
|
573 |
+
Inject adapters according to `AdapterConfig` using the adapter management of `PreTrainedModel`.
|
574 |
+
|
575 |
+
See Also: `AdapterConfig`
|
576 |
+
"""
|
577 |
+
if self.config.adapter_config is None:
|
578 |
+
logger.debug("Adapter injection is disabled.")
|
579 |
+
return
|
580 |
+
|
581 |
+
for adapter_name, peft_config in self.config.adapter_config.peft_config.items():
|
582 |
+
self.model.add_adapter(peft_config, adapter_name=adapter_name)
|
583 |
+
self.model.set_adapter(list(self.config.adapter_config.peft_config.keys()))
|
584 |
+
|
585 |
+
self._adapter_modules = None # reset adapter modules
|
586 |
+
logger.info("Adapters injected.")
|
587 |
+
|
588 |
+
def quantize(self) -> None:
|
589 |
+
"""
|
590 |
+
Quantize the `target_modules` from `WeightQuantizationConfig`.
|
591 |
+
|
592 |
+
See Also: `WeightQuantizationConfig`
|
593 |
+
"""
|
594 |
+
if self.config.weight_quantization_config is None:
|
595 |
+
logger.debug("Weight quantization is disabled.")
|
596 |
+
return
|
597 |
+
|
598 |
+
# Use peft semantics e.g "all-linear" to include all linear layers
|
599 |
+
# TODO: Replace by own helper function to avoid unnecessary dependencies
|
600 |
+
config: WeightQuantizationConfig = _maybe_include_all_linear_layers( # type: ignore
|
601 |
+
self.config.weight_quantization_config, # type: ignore
|
602 |
+
self.model,
|
603 |
+
)
|
604 |
+
module_factory_cls = config.parse_module_factory_cls()
|
605 |
+
|
606 |
+
for m_name, module in self.model.named_modules():
|
607 |
+
# Only modify the modules that are targeted
|
608 |
+
if config.exclude_modules is not None and any(key in m_name for key in config.exclude_modules):
|
609 |
+
continue
|
610 |
+
if not check_target_module_exists(config, m_name) or isinstance(module, ParametrizedModule):
|
611 |
+
continue
|
612 |
+
if not isinstance(module, nn.Linear):
|
613 |
+
continue
|
614 |
+
|
615 |
+
# Important: This module must NOT be created in a device context like with_init_device("cuda")
|
616 |
+
quantized_module = module_factory_cls(
|
617 |
+
module.in_features,
|
618 |
+
module.out_features,
|
619 |
+
bias=module.bias is not None,
|
620 |
+
device=module.weight.device,
|
621 |
+
**config.module_factory_kwargs,
|
622 |
+
)
|
623 |
+
# cf. https://huggingface.co/docs/bitsandbytes/reference/nn/linear4bit#bitsandbytes.nn.Linear4bit.example
|
624 |
+
quantized_module.load_state_dict(module.state_dict())
|
625 |
+
quantized_module = quantized_module.to(module.weight.device)
|
626 |
+
quantized_module.weight.requires_grad = False
|
627 |
+
logger.debug(f"Quantized {module.__class__} module {m_name} to {quantized_module.__class__}")
|
628 |
+
|
629 |
+
# Replace the target module by the quantized module
|
630 |
+
parent_name, child_name = m_name.rsplit(".", 1)
|
631 |
+
parent_module = self.model.get_submodule(parent_name)
|
632 |
+
parent_module.add_module(child_name, quantized_module)
|
633 |
+
|
634 |
+
self._quantized_modules = None # reset quantized modules
|
635 |
+
logger.info("Quantization completed.")
|
636 |
+
|
637 |
+
def get_target_params(self) -> dict[str, nn.Parameter]:
|
638 |
+
"""
|
639 |
+
Lifts `Parametrization.get_target_params` to the model scope.
|
640 |
+
The returned dictionary should be compatible with `self.model.named_parameters()`.
|
641 |
+
|
642 |
+
See Also:
|
643 |
+
`Parametrization.get_target_params`
|
644 |
+
"""
|
645 |
+
target_params = {}
|
646 |
+
for m_name, module in self.parametrized_modules.items():
|
647 |
+
for p_name, param in module.parametrization.get_target_params().items():
|
648 |
+
target_params[f"{m_name}.parametrization.{p_name}"] = param
|
649 |
+
return target_params
|
650 |
+
|
651 |
+
def get_num_params(
|
652 |
+
self, compressed: bool = False, full: bool = False, target_params: dict[str, torch.Tensor] | None = None
|
653 |
+
) -> int:
|
654 |
+
"""
|
655 |
+
Lifts `Parametrization.get_num_params` to the model scope.
|
656 |
+
Computes the (effective) number of parameters of the entire model.
|
657 |
+
|
658 |
+
Args:
|
659 |
+
compressed: Whether to count the number of parameters as if the parametrized modules were actually
|
660 |
+
compressed. If `False`, the number of parameters is the same as in the original module.
|
661 |
+
full: If `True`, all parameters of the model are counted, if `False` only those of parametrized modules.
|
662 |
+
Default is `False`, which follows the most common convention in the compression literature.
|
663 |
+
target_params: Count the number of parameters as if `target_params` were used instead of
|
664 |
+
the parametrized modules' target parameters. The dictionary keys should be compatible with those of
|
665 |
+
`self.get_target_params`.
|
666 |
+
|
667 |
+
See Also:
|
668 |
+
`Parametrization.get_num_params`
|
669 |
+
"""
|
670 |
+
num_params_full = 0
|
671 |
+
if full:
|
672 |
+
for name, param in self.model.named_parameters():
|
673 |
+
if "parametrization" not in name: # exclude parametrized modules here (counted below)
|
674 |
+
if hasattr(param, "quant_state"): # HOTFIX: special case for bitsandbytes-quantized parameters
|
675 |
+
num_params_full += param.numel() * 2
|
676 |
+
else:
|
677 |
+
num_params_full += param.numel()
|
678 |
+
|
679 |
+
num_params = 0
|
680 |
+
for module_name, module in self.parametrized_modules.items():
|
681 |
+
module_target_params = None
|
682 |
+
if compressed and target_params is not None:
|
683 |
+
# Make target_params' keys those of parametrized models, i.e., trim f"{module_name}.parametrization."
|
684 |
+
prefix = f"{module_name}.parametrization."
|
685 |
+
# Filter and re-map keys for the current module
|
686 |
+
module_target_params = {
|
687 |
+
key[len(prefix) :]: value for key, value in target_params.items() if key.startswith(prefix)
|
688 |
+
}
|
689 |
+
if not module_target_params:
|
690 |
+
module_target_params = None
|
691 |
+
|
692 |
+
num_params += module.parametrization.get_num_params(
|
693 |
+
compressed=compressed, target_params=module_target_params
|
694 |
+
)
|
695 |
+
num_params = num_params + num_params_full
|
696 |
+
if num_params == 0:
|
697 |
+
# dummy to avoid division by zero (e.g., if there are no parametrized_modules and full=False)
|
698 |
+
num_params = 1e-6
|
699 |
+
return num_params
|
700 |
+
|
701 |
+
def get_compression_ratio(self, full: bool = False, target_params: dict[str, torch.Tensor] | None = None) -> float:
|
702 |
+
"""
|
703 |
+
Convenience function to compute the compression ratio of the present model.
|
704 |
+
|
705 |
+
See Also:
|
706 |
+
`get_num_params`
|
707 |
+
"""
|
708 |
+
return self.get_num_params(compressed=True, full=full, target_params=target_params) / self.get_num_params(
|
709 |
+
full=full
|
710 |
+
)
|
711 |
+
|
712 |
+
def reset_target_params(self, mode: Literal["full", "nonzero", "compress"] = "full") -> None:
|
713 |
+
"""
|
714 |
+
Lifts `Parametrization.reset_target_params` to the model scope.
|
715 |
+
|
716 |
+
Args:
|
717 |
+
mode: The reset mode, see `Parametrization.reset_target_params`.
|
718 |
+
|
719 |
+
See Also:
|
720 |
+
`Parametrization.reset_target_params`
|
721 |
+
"""
|
722 |
+
for m_name, module in self.parametrized_modules.items():
|
723 |
+
module.parametrization.reset_target_params(mode=mode)
|
724 |
+
|
725 |
+
def compress(self) -> None:
|
726 |
+
"""
|
727 |
+
Compresses all parametrized modules using `Parametrization.reset_target_params(mode="compress")`.
|
728 |
+
If no compression is possible, the module is unparametrized and removed from `parametrized_modules`.
|
729 |
+
"""
|
730 |
+
removed_parametrized_modules = []
|
731 |
+
for m_name, module in self.parametrized_modules.items():
|
732 |
+
if module.parametrization.get_num_params(compressed=True) / module.parametrization.get_num_params() >= 1.0:
|
733 |
+
unparametrize_module(module)
|
734 |
+
removed_parametrized_modules.append(m_name)
|
735 |
+
logger.debug(f"Unparametrizing {module.__class__} module {m_name}")
|
736 |
+
else:
|
737 |
+
module.parametrization.reset_target_params(mode="compress")
|
738 |
+
logger.debug(f"Compressing {module.__class__} module {m_name}")
|
739 |
+
for m_name in removed_parametrized_modules:
|
740 |
+
self.parametrized_modules.pop(m_name)
|
741 |
+
logger.info("Compression completed.")
|
742 |
+
|
743 |
+
|
744 |
+
# Register ParametrizedModelConfig and ParametrizedModel for AutoModel
|
745 |
+
# Required to push custom model to Huggingface Hub (see https://huggingface.co/docs/transformers/en/custom_models)
|
746 |
+
ParametrizedModelConfig.register_for_auto_class()
|
747 |
+
ParametrizedModel.register_for_auto_class("AutoModel")
|
projected_layer.py
ADDED
@@ -0,0 +1,308 @@
|
|
|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
1 |
+
import math
|
2 |
+
from abc import ABC, abstractmethod
|
3 |
+
from logging import getLogger
|
4 |
+
from typing import Literal
|
5 |
+
|
6 |
+
import torch
|
7 |
+
from torch import nn
|
8 |
+
from torch.nn import functional as F
|
9 |
+
|
10 |
+
from .parametrized_layer import Parametrization
|
11 |
+
from .utils import use_init_empty_weights
|
12 |
+
|
13 |
+
logger = getLogger(__name__)
|
14 |
+
|
15 |
+
|
16 |
+
class CompressionCriterion(ABC):
|
17 |
+
"""
|
18 |
+
Abstract class for compression criterion of a (target) parameter of a parametrized module.
|
19 |
+
"""
|
20 |
+
|
21 |
+
@abstractmethod
|
22 |
+
def __call__(self, x: torch.Tensor) -> torch.Tensor:
|
23 |
+
"""
|
24 |
+
Args:
|
25 |
+
x: A tensor of any shape
|
26 |
+
|
27 |
+
Returns: A boolean mask of the same shape as `x` where `False` indicates that the entry can be removed.
|
28 |
+
"""
|
29 |
+
raise NotImplementedError
|
30 |
+
|
31 |
+
|
32 |
+
class ThresholdCriterion(CompressionCriterion):
|
33 |
+
"""
|
34 |
+
Compression criterion based on a threshold. All entries below `self.threshold` can be removed.
|
35 |
+
"""
|
36 |
+
|
37 |
+
def __init__(self, threshold: float = 0.0):
|
38 |
+
self.threshold = threshold
|
39 |
+
|
40 |
+
def __call__(self, x: torch.Tensor) -> torch.Tensor:
|
41 |
+
return x > self.threshold
|
42 |
+
|
43 |
+
|
44 |
+
class ProjectedLinearParametrization(Parametrization, ABC):
|
45 |
+
"""
|
46 |
+
Implementation of a linear layer parametrization, factorizing the weight matrix as
|
47 |
+
`weight = ortho.weight @ torch.diag(mask) @ base.weight`.
|
48 |
+
Here, `ortho` is a linear layer with orthogonal columns, `mask` represents a (binary) diagonal matrix
|
49 |
+
that can be pruned, and `base` is a linear layer (determined by the choice of `ortho`).
|
50 |
+
Any child class needs to implement `_ortho_init` which creates `ortho`. Based on this, `mask` and `base` are
|
51 |
+
initialized such that the original weight matrix is obtained at initialization.
|
52 |
+
|
53 |
+
`mask` corresponds to the only target parameter of this parametrization. Pruning it will result in
|
54 |
+
a low-rank matrix representation of the parametrized linear module.
|
55 |
+
"""
|
56 |
+
|
57 |
+
base_class = nn.Linear
|
58 |
+
|
59 |
+
def __init__(
|
60 |
+
self,
|
61 |
+
mask_func: Literal["ste", "relu", "none"] = "ste",
|
62 |
+
mask_scaling_factor: float | str = "norm",
|
63 |
+
compression_criterion: CompressionCriterion = ThresholdCriterion(),
|
64 |
+
):
|
65 |
+
"""
|
66 |
+
Args:
|
67 |
+
mask_func: A function applied to the mask parameter in each forward pass implementing
|
68 |
+
custom functionalities. Available options: ["ste", "relu", "none"].
|
69 |
+
"ste" means using a straight-through estimator, i.e., in the forward pass, `mask` is binarized, which
|
70 |
+
is ignored in the backward pass. Before `mask` passed through a ReLU activation.
|
71 |
+
"relu" means that `mask` is passed through a ReLU activation.
|
72 |
+
"none" means that `mask` is not modified.
|
73 |
+
mask_scaling_factor: Conceptually, `mask` is initialized with ones, but rescaling to a smaller value
|
74 |
+
can vastly improve the training speed. `mask_scaling_factor` specifies this rescaling factor.
|
75 |
+
The rescaling should be compensated by scaling `ortho` accordingly in `self._ortho_init`.
|
76 |
+
If `mask_scaling_factor='norm'`, the scaling factor is chosen such that `mask` has unit L2 norm
|
77 |
+
(note that this can lead to a different behavior in model tuning than for a fixed factor
|
78 |
+
when some target parameters have different number of elements).
|
79 |
+
compression_criterion: `CompressionCriterion` to be used in `self.reset_target_params(mode="compress")`.
|
80 |
+
"""
|
81 |
+
super().__init__()
|
82 |
+
self.mask_func = {
|
83 |
+
"ste": mask_func_ste,
|
84 |
+
"relu": mask_func_relu,
|
85 |
+
"none": mask_func_none,
|
86 |
+
}[mask_func]
|
87 |
+
self._mask_scaling_factor = mask_scaling_factor
|
88 |
+
self.compression_criterion = compression_criterion
|
89 |
+
|
90 |
+
def _forward(self, x: torch.Tensor) -> torch.Tensor:
|
91 |
+
# This implementation avoids an explicit materalization of `weight`.
|
92 |
+
x = self.base(x)
|
93 |
+
x = self.mask_func(self.mask, self.mask_scaling_factor) * x
|
94 |
+
x = self.ortho(x)
|
95 |
+
return x
|
96 |
+
|
97 |
+
def _weight(self) -> torch.Tensor:
|
98 |
+
# Compute the original weight matrix, don't use this in forward pass for efficiency reasons
|
99 |
+
mask = self.mask_func(self.mask, self.mask_scaling_factor)
|
100 |
+
return self.ortho.weight @ torch.diag(mask) @ self.base.weight
|
101 |
+
|
102 |
+
def _bias(self) -> torch.Tensor | None:
|
103 |
+
return self.ortho.bias
|
104 |
+
|
105 |
+
def _initialize(self, base_module: base_class) -> None:
|
106 |
+
factory_kwargs = {"device": base_module.weight.device, "dtype": base_module.weight.dtype}
|
107 |
+
in_dim, out_dim = base_module.in_features, base_module.out_features
|
108 |
+
proj_dim = min(in_dim, out_dim) # infer mask (bottleneck) dimension
|
109 |
+
|
110 |
+
# Initialize ortho layer ....
|
111 |
+
self.add_module(
|
112 |
+
"ortho",
|
113 |
+
nn.Linear(in_features=proj_dim, out_features=out_dim, bias=base_module.bias is not None, **factory_kwargs),
|
114 |
+
)
|
115 |
+
self._ortho_init(base_module.weight)
|
116 |
+
if base_module.bias is not None:
|
117 |
+
# It is important that ortho carries the bias (and not base) because ortho is used to compute the final
|
118 |
+
# output of the forward pass
|
119 |
+
self.ortho.bias.data.copy_(base_module.bias.data)
|
120 |
+
|
121 |
+
# ... and compute the base layer based on the choice of ortho (this only works of ortho has orthogonal columns)
|
122 |
+
base = base_module.__class__(in_features=in_dim, out_features=proj_dim, bias=False, **factory_kwargs)
|
123 |
+
base.weight.data.copy_(self.ortho.weight.data.T @ base_module.weight.data)
|
124 |
+
self.add_module("base", base)
|
125 |
+
|
126 |
+
# Creating (tunable) mask parameter ...
|
127 |
+
self.register_parameter("mask", torch.nn.Parameter(torch.ones(proj_dim, **factory_kwargs)))
|
128 |
+
# ... and rescale mask properly in a separate step
|
129 |
+
# (because reset_target_params calls mask_scaling_factor, which in turn may require mask to already exist)
|
130 |
+
self.reset_target_params()
|
131 |
+
|
132 |
+
@abstractmethod
|
133 |
+
def _ortho_init(self, weight: torch.Tensor) -> None:
|
134 |
+
"""
|
135 |
+
Initialize ortho layer. Must be implemented by child class.
|
136 |
+
|
137 |
+
Args:
|
138 |
+
weight: Weight matrix of the original linear layer module.
|
139 |
+
"""
|
140 |
+
raise NotImplementedError
|
141 |
+
|
142 |
+
def get_target_params(self) -> dict[str, torch.nn.Parameter]:
|
143 |
+
return {"mask": self.mask}
|
144 |
+
|
145 |
+
@property
|
146 |
+
def mask_scaling_factor(self) -> float:
|
147 |
+
if self._mask_scaling_factor == "norm":
|
148 |
+
# Choose scaling factor such that mask has unit L2 norm.
|
149 |
+
# Note: mask already needs to exist at this point to infer its shape.
|
150 |
+
self._mask_scaling_factor = 1 / math.sqrt(self.mask.numel())
|
151 |
+
return self._mask_scaling_factor
|
152 |
+
elif isinstance(self._mask_scaling_factor, float):
|
153 |
+
return self._mask_scaling_factor
|
154 |
+
else:
|
155 |
+
raise ValueError(f"Invalid mask_scaling_factor: {self._mask_scaling_factor}")
|
156 |
+
|
157 |
+
@property
|
158 |
+
def in_features(self) -> int:
|
159 |
+
return self.base.in_features
|
160 |
+
|
161 |
+
@property
|
162 |
+
def out_features(self) -> int:
|
163 |
+
return self.ortho.out_features
|
164 |
+
|
165 |
+
def reset_target_params(self, mode: Literal["full", "nonzero", "compress"] = "full") -> None:
|
166 |
+
with torch.no_grad():
|
167 |
+
if mode == "full":
|
168 |
+
# Scale mask values properly by self.mask_scaling_factor
|
169 |
+
self.mask.data = torch.ones_like(self.mask.data) * self.mask_scaling_factor
|
170 |
+
elif mode == "nonzero":
|
171 |
+
# Scale mask values properly by self.mask_scaling_factor
|
172 |
+
self.mask.data[self.mask.data > 0] = 1.0 * self.mask_scaling_factor
|
173 |
+
self.mask.data[self.mask.data < 0] = 0.0
|
174 |
+
elif mode == "compress":
|
175 |
+
if self.compression_criterion is None:
|
176 |
+
logger.warning("Compression criterion is not set. No op...")
|
177 |
+
return
|
178 |
+
# Select entries of parameter mask that should be kept
|
179 |
+
dim_select = self.compression_criterion(self.mask)
|
180 |
+
# Create and register compressed layers and mask
|
181 |
+
new_base = new_linear_from_mask(self.base, dim_select, column_select=False)
|
182 |
+
new_ortho = new_linear_from_mask(self.ortho, dim_select, column_select=True)
|
183 |
+
new_mask = self.mask[dim_select].clone().detach()
|
184 |
+
del self.mask, self.base, self.ortho
|
185 |
+
self.register_module("base", new_base)
|
186 |
+
self.register_module("ortho", new_ortho)
|
187 |
+
self.register_parameter("mask", nn.Parameter(new_mask))
|
188 |
+
else:
|
189 |
+
raise ValueError(f"Invalid mode: {mode}")
|
190 |
+
|
191 |
+
def get_num_params(self, compressed: bool = False, target_params: dict[str, torch.Tensor] | None = None) -> int:
|
192 |
+
if not compressed:
|
193 |
+
# Compute number of parameters for full linear layer
|
194 |
+
num_params = self.in_features * self.out_features
|
195 |
+
if self.bias is not None:
|
196 |
+
num_params += self.out_features
|
197 |
+
return num_params
|
198 |
+
else:
|
199 |
+
# Compute number of mask values that could be discarded by self.reset_target_params(mode="compress") ...
|
200 |
+
if target_params is not None:
|
201 |
+
sparsity = mask_sparsity(target_params["mask"] != 0.0, threshold=0.0)
|
202 |
+
else:
|
203 |
+
sparsity = mask_sparsity(self.mask)
|
204 |
+
# ... and compute the (hypothetical) number of parameters for a compressed module.
|
205 |
+
num_params = self.in_features * sparsity + sparsity * self.out_features
|
206 |
+
if self.bias is not None:
|
207 |
+
num_params += self.out_features
|
208 |
+
# If the number of parameters for the compressed module would be larger than the number of parameters
|
209 |
+
# for the full module, return the latter because we can always unparametrize to the original module if
|
210 |
+
# compression would not be effective.
|
211 |
+
num_params = min(self.get_num_params(compressed=False), num_params)
|
212 |
+
return num_params
|
213 |
+
|
214 |
+
|
215 |
+
class SVDLinearParametrization(ProjectedLinearParametrization):
|
216 |
+
"""
|
217 |
+
Implementation of a linear layer parametrization using SVD decomposition.
|
218 |
+
If the SVD of weight is U * S * V^T, then `ortho.weight = U` and `base.weight = S * V^T`.
|
219 |
+
As base is computed automatically by `_initialize`, `_ortho_init` only needs to compute U and
|
220 |
+
scale it properly with `mask_scaling_factor`. The singular values S are buffered just in case they are needed
|
221 |
+
in the tuning process.
|
222 |
+
"""
|
223 |
+
|
224 |
+
def _ortho_init(self, weight: torch.Tensor) -> None:
|
225 |
+
k = min(weight.shape[0], weight.shape[1])
|
226 |
+
if use_init_empty_weights.get():
|
227 |
+
# Check if the init_empty_weights context is active which avoids a (costly) SVD computation and just
|
228 |
+
# initializes U and S as empty tensors. They are loaded later from a pretrained model.
|
229 |
+
logger.debug("Parametrizing with empty weights.")
|
230 |
+
U = torch.empty(weight.shape[0], k)
|
231 |
+
S = torch.empty(k, 1)
|
232 |
+
else:
|
233 |
+
# Detaching is important to avoid memory leaks. torch.linalg.svd only works with float32.
|
234 |
+
U, S, _ = torch.linalg.svd(weight.detach().float(), full_matrices=False)
|
235 |
+
# Rescaling U based on mask_scaling_factor
|
236 |
+
# This step is somewhat manual because calling mask_scaling_factor requires the mask to already exist
|
237 |
+
if self._mask_scaling_factor == "norm":
|
238 |
+
U = math.pow(k, 1 / 4) * U
|
239 |
+
else:
|
240 |
+
U = math.sqrt(1 / self._mask_scaling_factor) * U
|
241 |
+
factory_kwargs = {"device": weight.device, "dtype": weight.dtype}
|
242 |
+
self.ortho.weight.data.copy_(U.detach().to(**factory_kwargs))
|
243 |
+
self.register_buffer("S", S.detach().flatten().to(**factory_kwargs))
|
244 |
+
|
245 |
+
|
246 |
+
def mask_func_ste(mask: torch.Tensor, mask_scaling_factor: float) -> torch.Tensor:
|
247 |
+
# See ProjectedLinearParametrization.__init__ for more details.
|
248 |
+
mask = F.relu(mask)
|
249 |
+
return (mask > 0).to(mask.dtype).detach() * mask_scaling_factor + mask - mask.detach()
|
250 |
+
|
251 |
+
|
252 |
+
def mask_func_relu(mask: torch.Tensor, mask_scaling_factor: float) -> torch.Tensor:
|
253 |
+
# See ProjectedLinearParametrization.__init__ for more details.
|
254 |
+
return F.relu(mask)
|
255 |
+
|
256 |
+
|
257 |
+
def mask_func_none(mask: torch.Tensor, mask_scaling_factor: float) -> torch.Tensor:
|
258 |
+
# See ProjectedLinearParametrization.__init__ for more details.
|
259 |
+
return mask
|
260 |
+
|
261 |
+
|
262 |
+
def mask_sparsity(mask: torch.Tensor, threshold: float = 0.0) -> int:
|
263 |
+
"""Simple util function to compute the number of non-zero elements of a mask, where an element is considered
|
264 |
+
non-zero if its value is strictly greater than `threshold`."""
|
265 |
+
return torch.count_nonzero(mask > threshold).item()
|
266 |
+
|
267 |
+
|
268 |
+
def new_linear_from_mask(module: nn.Linear, dim_select: torch.Tensor, column_select=True) -> nn.Linear:
|
269 |
+
"""
|
270 |
+
Creates a new linear layer from an existing one based on a mask indicating which columns/rows to keep.
|
271 |
+
|
272 |
+
Args:
|
273 |
+
module: Module to be pruned.
|
274 |
+
dim_select: Boolean tensor mask indicating which columns/rows to keep.
|
275 |
+
column_select: Whether to prune columns (True) or rows (False) according to `dim_select`.
|
276 |
+
|
277 |
+
Returns: Pruned module.
|
278 |
+
"""
|
279 |
+
assert dim_select.dtype == torch.bool, "dim_select must be boolean"
|
280 |
+
|
281 |
+
in_features, out_features = module.in_features, module.out_features
|
282 |
+
sparsity = dim_select.sum().item()
|
283 |
+
if column_select:
|
284 |
+
in_features = sparsity
|
285 |
+
else:
|
286 |
+
out_features = sparsity
|
287 |
+
new_module = module.__class__(
|
288 |
+
in_features=in_features,
|
289 |
+
out_features=out_features,
|
290 |
+
bias=module.bias is not None,
|
291 |
+
device=module.weight.device,
|
292 |
+
dtype=module.weight.dtype,
|
293 |
+
)
|
294 |
+
weight = module.weight.data
|
295 |
+
if column_select:
|
296 |
+
weight = weight[:, dim_select]
|
297 |
+
else:
|
298 |
+
weight = weight[dim_select, :]
|
299 |
+
new_module.weight.data.copy_(weight.detach())
|
300 |
+
|
301 |
+
if new_module.bias is not None:
|
302 |
+
if column_select:
|
303 |
+
new_module.bias.data.copy_(module.bias.detach())
|
304 |
+
else:
|
305 |
+
# If rows are pruned, the bias needs to be pruned as well
|
306 |
+
new_module.bias.data.copy_(module.bias[dim_select].detach())
|
307 |
+
|
308 |
+
return new_module
|
utils.py
ADDED
@@ -0,0 +1,83 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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1 |
+
import contextvars
|
2 |
+
import importlib
|
3 |
+
from contextlib import contextmanager
|
4 |
+
from typing import Any, Type
|
5 |
+
|
6 |
+
|
7 |
+
def get_class_from_str(class_str: str, package: str | None = None) -> Type[Any]:
|
8 |
+
"""
|
9 |
+
Converts a string to the corresponding class object, supporting relative imports.
|
10 |
+
For relative module paths (starting with '.'), a package must be provided.
|
11 |
+
|
12 |
+
Args:
|
13 |
+
class_str: String representation of the class, either absolute or relative.
|
14 |
+
package: Package context, only required for relative imports.
|
15 |
+
|
16 |
+
Returns: Class object corresponding to the provided string.
|
17 |
+
"""
|
18 |
+
if not isinstance(class_str, str) and isinstance(class_str, type):
|
19 |
+
return class_str
|
20 |
+
|
21 |
+
module_path, _, class_name = class_str.rpartition(".")
|
22 |
+
if not module_path and class_str.startswith("."):
|
23 |
+
module_path = "."
|
24 |
+
if module_path.startswith("."):
|
25 |
+
if not package:
|
26 |
+
raise ValueError("Relative module path provided without a package context.")
|
27 |
+
module = importlib.import_module(module_path, package=package)
|
28 |
+
else:
|
29 |
+
module = importlib.import_module(module_path)
|
30 |
+
return getattr(module, class_name)
|
31 |
+
|
32 |
+
|
33 |
+
def get_str_from_class(cls: Type[Any], package: str | None = None) -> str:
|
34 |
+
"""
|
35 |
+
Converts a class object to its string representation.
|
36 |
+
If a package is provided and the class's module is a submodule of the package,
|
37 |
+
the returned string will use a relative import.
|
38 |
+
Otherwise, an absolute import string is returned.
|
39 |
+
|
40 |
+
Args:
|
41 |
+
cls: Class object to convert.
|
42 |
+
package: Package context, only required for relative imports.
|
43 |
+
|
44 |
+
Returns: String representation of the class.
|
45 |
+
"""
|
46 |
+
if isinstance(cls, str):
|
47 |
+
return cls
|
48 |
+
|
49 |
+
module_path = cls.__module__
|
50 |
+
class_name = cls.__name__
|
51 |
+
|
52 |
+
if package:
|
53 |
+
# When class is defined directly in the package's __init__.py
|
54 |
+
if module_path == package:
|
55 |
+
return f".{class_name}"
|
56 |
+
# When class is in a submodule of the package
|
57 |
+
elif module_path.startswith(package + "."):
|
58 |
+
# Get the relative part (including the dot)
|
59 |
+
relative = module_path[len(package) :]
|
60 |
+
if not relative.startswith("."):
|
61 |
+
relative = "." + relative
|
62 |
+
return f"{relative}.{class_name}"
|
63 |
+
return f"{module_path}.{class_name}"
|
64 |
+
|
65 |
+
|
66 |
+
use_init_empty_weights = contextvars.ContextVar("init_empty_weights", default=False)
|
67 |
+
|
68 |
+
|
69 |
+
@contextmanager
|
70 |
+
def init_empty_weights(value: bool):
|
71 |
+
"""
|
72 |
+
Context manager to indicate that a (parametrized) model should be initialized with empty weights or not.
|
73 |
+
If active, `use_init_empty_weights` will be set to `True` otherwise to `False`.
|
74 |
+
To check if the context is active, import and check `use_init_empty_weights.get()`.
|
75 |
+
|
76 |
+
Args:
|
77 |
+
value: Indicates whether the model should be initialized with empty weights or not.
|
78 |
+
"""
|
79 |
+
token = use_init_empty_weights.set(value)
|
80 |
+
try:
|
81 |
+
yield
|
82 |
+
finally:
|
83 |
+
use_init_empty_weights.reset(token)
|