https://github.com/EleutherAI/delphi
https://github.com/EleutherAI/delphi
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/EleutherAI/delphi
- Owner: EleutherAI
- License: apache-2.0
- Created: 2024-06-18T15:26:29.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-26T20:33:32.000Z (over 1 year ago)
- Last Synced: 2025-01-27T16:39:36.388Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage: https://blog.eleuther.ai/autointerp/
- Size: 589 MB
- Stars: 138
- Watchers: 2
- Forks: 16
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Introduction
Delphi was the home of a temple to Phoebus Apollo, which famously had the inscription, 'Know Thyself.' This library lets language models know themselves through automated interpretability.
This library provides utilities for generating and scoring text explanations of sparse autoencoder (SAE) and transcoder features. The explainer and scorer models can be run locally or accessed using API calls via OpenRouter.
The branch used for the article [Automatically Interpreting Millions of Features in Large Language Models](https://arxiv.org/pdf/2410.13928) is the legacy branch [article_version](https://github.com/EleutherAI/delphi/tree/article_version), that branch contains the scripts to reproduce our experiments. Note that we're still actively improving the codebase and that the newest version on the main branch could require slightly different usage.
# Installation
Install this library as a local editable installation. Run the following command from the `delphi` directory.
```pip install -e .```
# Getting Started
To run the default pipeline from the command line, use the following command:
`python -m delphi EleutherAI/pythia-160m EleutherAI/Pythia-160m-SST-k32-32k --n_tokens 10_000_000 --max_latents 100 --hookpoints layers.5 --scorers detection --filter_bos --name llama-3-8B`
This command will:
1. Cache activations for the first 10 million tokens of the default dataset, `EleutherAI/SmolLM2-135M-10B`.
2. Generate explanations for the first 100 features of layer 5 using the default explainer model, `hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4`.
3. Score the explanations using the detection scorer.
4. Log summary metrics including per-scorer F1 scores and confusion matrices, and produce histograms of the scorer classification accuracies.
The pipeline is highly configurable and can also be called programmatically (see the [end-to-end test](https://github.com/EleutherAI/delphi/blob/main/delphi/tests/e2e.py) for an example).
To use experimental features, create a custom pipeline. You can take inspiration from the main pipeline in [delphi.\_\_main\_\_](https://github.com/EleutherAI/delphi/blob/main/delphi/__main__.py).
## Caching
The first step to generate explanations is to cache sparse model activations. To do so, load your sparse models into the base model, load the tokens you want to cache the activations from, create a `LatentCache` object and run it. We recommend caching over at least 10M tokens.
```python
from sparsify.data import chunk_and_tokenize
from delphi.latents import LatentCache
data = load_dataset("EleutherAI/SmolLM2-135M-10B", split="train[:1%]")
tokens = chunk_and_tokenize(data, tokenizer, max_seq_len=256, text_key="raw_content")["input_ids"]
cache = LatentCache(
model,
submodule_dict,
batch_size = 8
)
cache.run(n_tokens = 10_000_000, tokens=tokens)
```
(See `populate_cache` in `delphi.__main__` for a full example.)
Caching saves `.safetensors` of `dict["activations", "locations", "tokens"]`.
```python
cache.save_splits(
n_splits=5,
save_dir="raw_latents"
)
```
Safetensors are split into shards over the width of the autoencoder.
## Loading Latent Records
The `.latents` module provides utilities for reconstructing and sampling various statistics for sparse features. The `LatentDataset` will construct lazy loaded buffers that load activations into memory when called as an iterator object. For ease of use with the autointerp pipeline, we have a *constructor* and *sampler*: the constructor defines builds the context windows from the cached activations and tokens, and the sampler divides these contexts into a training and testing set, used to generate explanations and evaluate them.
```python
from delphi.latents import LatentDataset
from delphi.config import SamplerConfig, ConstructorConfig
latent_dict = {
".model.layer.0": torch.arange(0, 131072)
}
sampler_cfg = SamplerConfig()
constructor_cfg = ConstructorConfig()
dataset = LatentDataset(
raw_dir="feature_folder",
modules=[".model.layer.0"], # This a list of the different caches to load from
sampler_cfg=sampler_cfg,
constructor_cfg=constructor_cfg,
latents=latent_dict,
tokenizer=tokenizer
)
```
### FAISS Index for Hard Negatives
When constructing features for explanation, you can use FAISS (semantic similarity search) to create hard negative examples. Hard negatives are non-activating examples that are semantically similar to activating examples. This approach:
1. Creates embeddings for both activating and non-activating examples using the specified embedding model
2. Builds a FAISS index for efficient similarity search
3. Finds non-activating examples that are semantically similar to activating examples
4. Optionally caches embeddings to speed up future runs
To use FAISS for hard negatives, set the `non_activating_source` parameter to "FAISS" in your `ConstructorConfig`:
```python
from delphi.config import ConstructorConfig
constructor_cfg = ConstructorConfig(
non_activating_source="FAISS",
faiss_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
faiss_embedding_cache_enabled=True,
faiss_embedding_cache_dir=".embedding_cache"
)
```
### Contrastive Explainer
The `ContrastiveExplainer` adds both positive (activating) and negative (non-activating) examples to a single explainer prompt so the explainer model is less likely to label features that are not exclusive to the feature activations (ie we are more likely to provide non activating tokens which are semantically similar). This explainer is automatically used when the `non_activating_source` is set to "FAISS".
```python
from delphi.explainers import ContrastiveExplainer
explainer = ContrastiveExplainer(
client,
threshold=0.3,
max_examples=15,
max_non_activating=5,
verbose=True
)
```
## Generating Explanations
We currently support using OpenRouter's OpenAI compatible API or running locally with VLLM. Define the client you want to use, then create an explainer from the `.explainers` module.
```python
from delphi.explainers import DefaultExplainer
from delphi.clients import Offline,OpenRouter
# Run locally with VLLM
client = Offline("meta-llama/Meta-Llama-3.1-8B-Instruct", max_memory=0.8, max_model_len=5120, num_gpus=1)
# Run with OpenRouter
client = OpenRouter("meta-llama/Meta-Llama-3.1-8B-Instruct", api_key=key)
explainer = DefaultExplainer(
client,
tokenizer = dataset.tokenizer,
)
```
The explainer should be added to a pipe, which will send the explanation requests to the client. The pipe should have a function that happens after the request is completed, to e.g. save the data, and could also have a function that happens before the request is sent, e.g to transform some of the data.
```python
from delphi.pipeline import process_wrapper
def explainer_postprocess(result):
with open(f"{explanation_dir}/{result.record.latent}.txt", "wb") as f:
f.write(orjson.dumps(result.explanation))
return result
explainer_pipe = process_wrapper(explainer,
postprocess=explainer_postprocess,
)
```
The pipe should then be used in a pipeline. Running the pipeline will send requests to the client in batches of paralel requests.
```python
from delphi.pipeline import Pipeline
import asyncio
pipeline = Pipeline(
loader,
explainer_pipe,
)
asyncio.run(pipeline.run(n_processes))
```
## Scoring Explanations
The process of running a scorer is similar to that of an explainer. You need to have a client running, and you need to create a Scorer from the '.scorer' module. You can either load the explanations you generated earlier, or generate new ones using the explainer pipe.
```python
RecallScorer(
client,
tokenizer=tokenizer,
batch_size=cfg.batch_size
)
```
You can then create a pipe to run the scorer. The pipe should have a pre-processer, that takes the results from the previous pipe and a post processor, that saves the scores. An scorer should always be run after a explainer pipe, but the explainer pipe can be used to load saved explanations.
```python
from delphi.scorers import FuzzingScorer, RecallScorer
from delphi.explainers import explanation_loader,random_explanation_loader
# Because we are running the explainer and scorer separately, we need to add the explanation and extra examples back to the record
def scorer_preprocess(result):
record = result.record
record.explanation = result.explanation
record.extra_examples = record.not_active
return record
def scorer_postprocess(result, score_dir):
with open(f"{score_dir}/{result.record.feature}.txt", "wb") as f:
f.write(orjson.dumps(result.score))
# If one wants to load the explanations they generated earlier
# explainer_pipe = partial(explanation_loader, explanation_dir=EXPLAINER_OUT_DIR)
scorer_pipe = process_wrapper(
RecallScorer(client, tokenizer=dataset.tokenizer, batch_size=cfg.batch_size),
preprocess=scorer_preprocess,
postprocess=partial(scorer_postprocess, score_dir=recall_dir),
)
```
It is possible to have more than one scorer per pipe. One could use that to run fuzzing and detection together:
```python
scorer_pipe = Pipe(
process_wrapper(
RecallScorer(client, tokenizer=tokenizer, batch_size=cfg.batch_size),
preprocess=scorer_preprocess,
postprocess=partial(scorer_postprocess, score_dir=recall_dir),
),
process_wrapper(
FuzzingScorer(client, tokenizer=tokenizer, batch_size=cfg.batch_size),
preprocess=scorer_preprocess,
postprocess=partial(scorer_postprocess, score_dir=fuzz_dir),
),
)
```
Then the pipe should be sent to the pipeline and run:
```python
pipeline = Pipeline(
loader.load,
explainer_pipe,
scorer_pipe,
)
asyncio.run(pipeline.run())
```
### Simulation
To do simulation scoring we forked and modified OpenAIs neuron explainer. The name of the scorer is `OpenAISimulator`, and it can be run with the same setup as described above.
### Surprisal
Surprisal scoring computes the loss over some examples and uses a base model. We don't use VLLM but run the model using the `AutoModelForCausalLM` wrapper from HuggingFace. The setup is similar as above but for a example check `surprisal.py` in the experiments folder.
### Embedding
Embedding scoring uses a small embedding model through `sentence_transformers` to embed the examples do retrival. It also does not use VLLM but run the model directly. The setup is similar as above but for a example check `embedding.py` in the experiments folder.
## Scripts
Example scripts can be found in `demos`. Some of these scripts can be called from the CLI, as seen in examples found in `scripts`. These baseline scripts should allow anyone to start generating and scoring explanations in any SAE they are interested in. One always needs to first cache the activations of the features of any given SAE, and then generating explanations and scoring them can be done at the same time.
## Experiments
The experiments discussed in [the blog post](https://blog.eleuther.ai/autointerp/) were mostly run in a legacy version of this code, which can be found in the [Experiments](https://github.com/EleutherAI/delphi/tree/Experiments) branch.
## Development
Set up the pre-commit lint and run the unit tests:
```bash
pip install pre-commit pytest
pre-commit install
pytest .
```
Run an end-to-end test:
```python -m delphi.tests.e2e```
We use [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/) for releases.
## License
Copyright 2024 the EleutherAI Institute
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.