From 2937c21bb299c4270aea3c8a8cfe43940decd1bc Mon Sep 17 00:00:00 2001 From: Gabriele Sarti Date: Sun, 2 Jul 2023 10:53:58 +0200 Subject: [PATCH] Add tutorial draft (#196) --- docs/source/images/cat.png | Bin 0 -> 130680 bytes docs/source/images/cat_example_plot.png | Bin 0 -> 58168 bytes .../source/images/contrastive_attribution.png | Bin 0 -> 254009 bytes .../source/images/lm_gradient_attribution.png | Bin 0 -> 154833 bytes .../images/lm_perturbation_attribution.png | Bin 0 -> 142180 bytes examples/inseq_tutorial.ipynb | 2031 +++++++++++++++++ 6 files changed, 2031 insertions(+) create mode 100644 docs/source/images/cat.png create mode 100644 docs/source/images/cat_example_plot.png create mode 100644 docs/source/images/contrastive_attribution.png create mode 100644 docs/source/images/lm_gradient_attribution.png create mode 100644 docs/source/images/lm_perturbation_attribution.png create mode 100644 examples/inseq_tutorial.ipynb diff --git a/docs/source/images/cat.png 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zXW4idtO3jCe5?8czo@^C{AvZ#*Lh4%{;C4f|JtAS`{m5_|8KLan8#nSZ3L*emo5PG z1G&1pptJx$rg)(Uu^1OK1m<%E%jcwR{L*U+Pqn{~f`WC5K!i%KDjnP(U(3%E2wReO zcWFPzXJ)j}wdNcY>_=xbmgyM!uax?e_x!vlqs{^8BLy-*SoO@~N-i V`$B0}iQMPM%*66awXsX|e*g=5BV_;p literal 0 HcmV?d00001 diff --git a/examples/inseq_tutorial.ipynb b/examples/inseq_tutorial.ipynb new file mode 100644 index 00000000..18fcba4c --- /dev/null +++ b/examples/inseq_tutorial.ipynb @@ -0,0 +1,2031 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/inseq-team/inseq/blob/main/examples/inseq_tutorial.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "# Run in Colab to install local packages\n", + "%pip install bitsandbytes accelerate\n", + "%pip install git+https://github.com/huggingface/transformers.git\n", + "%pip install git+https://github.com/inseq-team/inseq.git" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial showcases how to use the [inseq](https://github.com/inseq-team/inseq) library for various interpretability analyses, with a focus on advanced use cases such as aggregation and contrastive attribution. The tutorial was adapted from the [LCL'23 tutorial on Interpretability for NLP](https://github.com/gsarti/lcl23-xnlm-lab) with the goal of updating it whenever new functionalities or breaking changes are introduced.\n", + "\n", + "⚠️ **IMPORTANT** ⚠️ : `inseq` is a very new library and under active development. Current results were obtained using the latest development versions on June 30, 2023. If you find any issue, or you are not able to reproduce the results shown here, we'd love if you could open an issue on [the inseq Github repository](https://github.com/inseq-team/inseq) so that we could update the tutorial accordingly! 🙂\n", + "\n", + "# Introduction: Feature Attribution for NLP\n", + "\n", + "*See [Madsen et al. 2022](https://dl.acm.org/doi/10.1145/3546577) for a survey on feature attribution methods for NLP applications.*\n", + "\n", + "**Feature attribution methods** leverage the internal information (e.g. gradients, attention) or predictions of a model to quantify the relationship between its inputs and its predictions.\n", + "\n", + "Attribution methods produce **importance scores** (or *saliency scores*) for every element of the input, reflecting the importance of every input element in driving the model prediction. These scores are often presented using highlights (or *attribution maps*) to facilitate comprehension, although recent research showed the risks of textual highlights misinterpretation caused by human cognitive biases ([Jacovi et al. 2022](https://dl.acm.org/doi/abs/10.1145/3531146.3533127), [Jacovi et al. 2023](https://arxiv.org/abs/2305.02679))\n", + "\n", + "\"Example\n", + "\n", + "*Hypothetical example of attribution map for sentiment analysis from [Madsen et al. 2022](https://dl.acm.org/doi/10.1145/3546577). c represents the explained (predicted) class, while y is the correct label for the example.*\n", + "\n", + "We can categorize feature attribution approaches in three major families:\n", + "\n", + "- **Gradient-based methods** such as [Integrated gradients]() use gradients as a natural source of information to motivate model predictions. Gradients for model parameters computed in relation to a loss function are commonly used during training to update model parameters, since they represent *the magnitude of change needed for a parameter such that the prediction matches the target label*. In the case of feature attribution, gradients computed with respect to a model prediction logit or probability are instead taken as *how much the parameter is contributing towards the prediction*.\n", + "\n", + "\"How\n", + "\n", + "*Overview of feature attribution using gradient information. Adapted from Jay Alammar blog post \"[Interfaces for Explaining Transformer Language Models](https://jalammar.github.io/explaining-transformers/)\"*\n", + "\n", + "- **Perturbation-based methods** such as [Occlusion](https://captum.ai/api/occlusion.html) estimate importance of inputs by introducing noise in the prediction process, usually by masking or removing either input features or network components, and verifying the downstream effect on model predictions. Intuitively, these can also be used to determine the importance of layers in the neural network.\n", + "\n", + "\"How\n", + "\n", + "- **Internals-based methods** use quantities computed naturally by the network during their predictions to motivate its internal computations. For Transformers-based model, [attention weights](https://aclanthology.org/D19-1002/) are commonly used, by themselves or multiplied with other quantities, as indications of feature importance.\n", + "\n", + "\"Aligning\n", + "\n", + "*Example of attention weights aligning source and target words in a translation task. From Bahdanau et al. 2015 \"[Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473)\"*\n", + "\n", + "\n", + "
\n", + " Additional details about feature attribution methods\n", + "
    \n", + "
  1. We distinguish gradient and internals methods since gradient methods require a prediction target (e.g. the predicted class label, the next generated word) to compute importance scores, while internals-based one generally do not rely on the predicted output to determine importance.
  2. \n", + "
    \n", + "
  3. Feature importance is generally computed for input tokens, but most methods can also be used to compute importance of intermediate representations (or activations) computed by the model (e.g. stopping at an intermediate layer when backpropagating gradients).
  4. \n", + "
    \n", + "
  5. Methods using layer-specific quantities like attention weights usually aggregate those across layers to obtain a proxy importance for input features, either averaging naively or with more reasonable methods such as rollout and flow.
  6. \n", + "
    \n", + "
  7. The granularity of importance scores depend on the attribution method that is being used. For example, the attention mechanism operates at a token level, so attention weights used as importance scores will be one per token in the input sequence. Gradients, on the contrary, are computed for all the dimensions of each token embedding, so an aggregation strategy should be used to obtain a single score per token.
  8. \n", + "
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" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attributing Generative Language Models with Inseq\n", + "\n", + "*Adapted in part from the [Inseq documentation](https://inseq.readthedocs.io/)*\n", + "\n", + "[Inseq](https://github.com/inseq-team/inseq) ([Sarti et al., 2023](https://arxiv.org/abs/2302.13942)) is a toolkit based on the [🤗 Transformers](https//github.com/huggingface/transformers) and [Captum](https://captum.ai/docs/introduction) libraries for intepreting language generation models using feature attribution methods. Inseq allows you to analyze the behavior of a language generation model by computing the importance of each input token for each token in the generated output using the various categories of attribution methods like those described in the previous section (use `inseq.list_feature_attribution_methods()` to list all available methods). You can refer to the [Inseq paper](https://arxiv.org/abs/2302.13942) for an overview of the tool and some usage examples.\n", + "\n", + "The current version of the library (v0.5.0, June 2023) supports all [`AutoModelForSeq2SeqLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSeq2SeqLM) (among others, [T5](https://huggingface.co/docs/transformers/model_doc/t5), [Bart](https://huggingface.co/docs/transformers/model_doc/bart) and all >1000 [MarianNMT](https://huggingface.co/docs/transformers/model_doc/marian) MT models) and [AutoModelForCausalLM](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) (among others, [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [Bloom](https://huggingface.co/docs/transformers/model_doc/bloom) and [LLaMa](https://huggingface.co/docs/transformers/model_doc/llama)), including advanced tools for custom attribution targets and post-processing of attribution matrices.\n", + "\n", + "The following code is a \"Hello world\" equivalent in Inseq: in three lines of code, an English-to-Italian machine translation model is loaded alongside an attribution method, attribution is performed, and results are visualized:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gsarti/.cache/pypoetry/virtualenvs/inseq-PzwjmCYf-py3.9/lib/python3.9/site-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n", + " warnings.warn(\"Recommended: pip install sacremoses.\")\n", + "/home/gsarti/.cache/pypoetry/virtualenvs/inseq-PzwjmCYf-py3.9/lib/python3.9/site-packages/transformers/generation/utils.py:1255: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n", + " warnings.warn(\n", + "/home/gsarti/.cache/pypoetry/virtualenvs/inseq-PzwjmCYf-py3.9/lib/python3.9/site-packages/transformers/generation/utils.py:1349: UserWarning: Using `max_length`'s default (512) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", + " warnings.warn(\n", + "Attributing with input_x_gradient...: 100%|██████████| 20/20 [00:00<00:00, 24.44it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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'\n", + " ],\n", + " target: list with 19 elements of type TokenWithId:[\n", + " '▁Ci', 'a', 'o', '▁a', '▁tutti', ',', '▁spero', '▁che', '▁vi', '▁stia', 'te', '▁gode', 'ndo', '▁il', '▁tu', 'tori', 'al', '!', '
'\n", + " ],\n", + " source_attributions: torch.float32 tensor of shape [14, 19, 512] on cpu,\n", + " target_attributions: torch.float32 tensor of shape [19, 19, 512] on cpu,\n", + " step_scores: {\n", + " probability: torch.float32 tensor of shape [19] on cpu: [\n", + " 0.47, 0.91, 0.91, 0.87, 0.91, 0.74, 0.51, 0.49, 0.67, 0.71, 0.65, 0.74, 0.95, 0.80, 0.88, 0.94, 0.96, 0.87, 0.89\n", + " ],\n", + " },\n", + " sequence_scores: {},\n", + " attr_pos_start: 0,\n", + " attr_pos_end: 19,\n", + " })\n", + " ],\n", + " step_attributions: None,\n", + " info: {\n", + " attribution_method: input_x_gradient,\n", + " attr_pos_start: 1,\n", + " attr_pos_end: 20,\n", + " output_step_attributions: False,\n", + " attribute_target: True,\n", + " step_scores: list with 1 elements of type str:[\n", + " 'probability'\n", + " ],\n", + " exec_time: 0.777145,\n", + " model_name: Helsinki-NLP/opus-mt-en-it,\n", + " model_class: MarianMTModel,\n", + " tokenizer_name: None,\n", + " tokenizer_class: MarianTokenizer,\n", + " include_eos_baseline: False,\n", + " attributed_fn: probability_fn,\n", + " attribution_args: {},\n", + " attributed_fn_args: {},\n", + " step_scores_args: {},\n", + " input_texts: list with 1 elements of type str:[\n", + " 'Hello everyone, hope you're enjoying the tutorial!'\n", + " ],\n", + " generated_texts: list with 1 elements of type str:[\n", + " 'Ciao a tutti, spero che vi stiate godendo il tutorial!'\n", + " ],\n", + " generation_args: {},\n", + " constrained_decoding: False,\n", + " generate_from_target_prefix: False,\n", + " },\n", + "})" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is an explanation of the various fields:\n", + "\n", + "- `sequence_attributions` is a list containing a `FeatureAttributionSequenceOutput` per attributed sequence (in this case just one). These object contain the main information derived from the attribution process, including `source_attributions` and `target_attributions`, the tokenized `source` and `target` sequences and all `step_scores` that were computed for the generation.\n", + "\n", + "- `step_scores` are usually empty, but can be filled with `FeatureAttributionStepOutput` (per-step attributions across all examples instead of per-example attributions across all-steps, like `sequence_attributions`) by passing the parameter `output_step_attributions=True` to `model.attribute()`.\n", + "\n", + "- `info` is a dictionary containing a lot of information about the overall process, including attributed model and attribution methods, execution time and all parameters that were used for generation and attribution.\n", + "\n", + "Both the `FeatureAttributionSequenceOutput` class and the visualization can be easily serialized as follows, to be reloaded later on for analysis:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "out.save(\"marian_en_it_attribution.json\")\n", + "\n", + "# Reload the saved output\n", + "reloaded_out = inseq.FeatureAttributionOutput.load(\"marian_en_it_attribution.json\")\n", + "\n", + "html = reloaded_out.show(display=False, return_html=True)\n", + "\n", + "with open(\"marian_en_it_attribution.html\", \"w\") as f:\n", + " f.write(html)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Post-processing Attributions with Aggregators\n", + "\n", + "You might have noticed that the shape of the `source_attributions` tensor in `out` is [12, 17, 512] (source length x target length x hidden size) because we are using a gradient-based attribution method returning one importance score per hidden dimension, but when calling `out.show()` the output is a matrix of shape [12, 17].\n", + "\n", + "This is because every attribution method is automatically assigned an appropriate default `Aggregator`, which is called implicitly on `out.show()` to ensure that attributions can be visualized out-of-the-box. In this case the default aggregation involves taking the L2 norm of the hidden size (i.e. the *magnitude* of the importance score vector, strictly positive) and then normalize scores to ensure that all importance scores for every generated token (columns) across `source_attributions` and `target_attributions` sum to one. The normalization step is included since it allows us to express the *relative importance* of input features when generating the target token at every step.\n", + "\n", + "We can see now another example using the `attention` attribution method, returning simply attention weights for the model (note that we do not need to reload the model, but it is sufficient to pass a new `method` identifier to `model.attribute`):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Attributing with attention...: 100%|██████████| 20/20 [00:00<00:00, 98.32it/s] \n" + ] + }, + { + "data": { + "text/plain": [ + "torch.Size([14, 19, 6, 8])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = model.attribute(\n", + " input_texts=\"Hello everyone, hope you're enjoying the tutorial!\",\n", + " attribute_target=True,\n", + " method=\"attention\"\n", + ")\n", + "# out[0] is a shortcut for out.sequence_attributions[0]\n", + "out[0].source_attributions.shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The attribution matrix produced by `attention` has dimensions (source length, sequence length, # layers, # attention heads), so `out[0].source_attributions[:, 2, 3, 4]` are attention weights associated to source tokens by the 4th attention head of the 3rd layer, produced when generating the 2nd target token. We can see the aggregation strategy employed by default as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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▁Ciao▁a▁tutti,▁spero▁che▁vi▁stiate▁godendo▁il▁tutorial!</s>
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ĠsheĠwasĠsick.
The0.270.2410.1520.158
Ġmanager0.4720.1480.180.143
Ġwent0.0770.1350.1220.153
Ġhome0.1020.0950.2760.16
Ġbecause0.0790.1540.1090.076
Ġshe0.2270.0720.068
Ġwas0.0890.057
Ġsick0.186
.
probability0.080.2810.0710.29
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Ġhe → ĠsheĠwasĠsick.
The0.2480.1440.1310.09
Ġmanager0.4620.2130.1650.175
Ġwent0.0950.0530.1270.095
Ġhome0.1140.0770.2290.094
Ġbecause0.0810.1730.0940.077
Ġhe → Ġshe0.340.1860.175
Ġwas0.0680.057
Ġsick0.237
.
probability-0.350.030.0040.008
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\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Note: this takes the per-hidden dimension diff between the two options and then performs L2 aggregation at token level + normalization\n", + "out[0].aggregate(\"pair\", paired_attr=out[1], do_post_aggregation_checks=False).show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the example, we can see that the model is 35% more likely to generate the masculine pronoun `he` when referring to the noun `the manager` compared to its feminine counterpart `she`, while probabilities of all subsequent tokens are much less affected. The pattern of the previous attributions is mostly preserved, with `manager` influencing the generation of the pronoun and `home` influencing the choice of word `sick`." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Understanding Inseq utilities with `explain`\n", + "\n", + "Like Hugging Face, Inseq uses strings to identify many of its components (attribution methods, aggregators, aggregation functions, ...). \n", + "\n", + "Besides the `list_XXX` functions mentioned above that can be used to show available values, `inseq` also provides an `explain` utility to visualize the docstring of the class associated to the string identifier: " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Integrated Gradients attribution method.\n", + "\n", + " Reference implementation:\n", + " `https://captum.ai/api/integrated_gradients.html `__.\n", + " \n" + ] + } + ], + "source": [ + "import inseq\n", + "\n", + "inseq.explain(\"integrated_gradients\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregates two FeatureAttributionSequenceOutput object into a single one containing the diff.\n", + "\n", + " Args:\n", + " attr (:class:`~inseq.data.FeatureAttributionSequenceOutput`): The starting attribution object.\n", + " paired_attr (:class:`~inseq.data.FeatureAttributionSequenceOutput`): The attribution object with whom\n", + " the diff is computed, representing a change from `attr_start` (e.g. minimal pair edit).\n", + " aggregate_fn (:obj:`Callable`, optional): Function to aggregate elementwise values of the pair.\n", + " Defaults to the difference between the two elements.\n", + " \n" + ] + } + ], + "source": [ + "inseq.explain(\"pair\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contrastive Explanations using Inseq\n", + "\n", + "Gradient-based feature attribution methods commonly compute gradients of feature vectors with respect to a prediction target (logits or probabilities), and use those as proxies of an explanation answering the question **\"How is this feature X influencing this model prediction P?\"**. However, in some cases we might want to compute attributions with respect to a different target to explain other properties of the prediction, e.g. the entropy (i.e. flatness/sharpness) of the probability distribution over the vocabulary.\n", + "\n", + "While `PairAggregator` can be used to visualize the difference between two attribution outputs, using the difference in probability between an option A (e.g. `he` in the previous example) and option B (e.g. `she`) as a target for gradient-based attribution methods is a more principled way to obtain **contrastive explanations** answering the question **\"How is this feature X contributing to the prediction of A rather than B?\"**.\n", + "\n", + "The importance scores produced by this method, introduced by [Yin and Neubig, (2022)](https://aclanthology.org/2022.emnlp-main.14/), is better to disentagle different factors that might play a role in the prediction, and can be used to explain the model behavior in a more nuanced way:\n", + "\n", + "\"Contrastive\n", + "\n", + "Inseq allows users to specify custom attribution targets using the `attributed_fn` parameter. Attributed functions are simply step scores like `probability` values we extracted above (default attribution target). Custom scores can be registered using `inseq.register_step_function` for more advanced use cases (tutorial [in the documentation](https://inseq.readthedocs.io/en/latest/examples/custom_attribute_target.html)), but many including contrastive explanations (`contrast_prob_diff`) are already available out-of-the-box in the library:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Attributing with saliency...: 100%|██████████| 7/7 [00:00<00:00, 13.78it/s]\n", + "/home/gsarti/projects/inseq/inseq/data/attribution.py:304: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3571.)\n", + " Args:\n" + ] + }, + { + "data": { + "text/html": [ + "
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▁Ho▁salutato▁la → ▁il▁manager</s>
▁I0.00.00.00.0380.0-0.001
▁said0.00.00.00.0480.001-0.001
▁hi0.00.00.00.0560.001-0.002
▁to0.00.00.00.0260.0-0.001
▁the0.00.00.00.0660.001-0.002
▁manager0.00.00.00.3250.005-0.007
</s>0.00.00.00.070.001-0.002
contrast_prob_diff0.00.00.00.7070.013-0.043
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▁Ho▁salutato▁la → ▁il▁manager</s>
▁Ho0.00.00.0120.0-0.005
▁saluta0.00.0470.002-0.008
to0.0190.0-0.003
▁la → ▁il0.001-0.003
▁manager-0.008
</s>
contrast_prob_diff0.00.00.00.7070.013-0.043
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it_IT▁Sai▁già▁quando▁tornerai?</s>
pcxmi-8.0932.096-0.2140.0070.1580.044-0.068-0.02
probability0.00.020.8340.9270.4750.930.6450.897
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\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import inseq\n", + "\n", + "# Use \"dummy\" to extract only step scores without attributions\n", + "model = inseq.load_model(\n", + " \"facebook/mbart-large-50-one-to-many-mmt\",\n", + " \"dummy\",\n", + " # These arguments are used by Huggingface to specify the source and target languages used by the model\n", + " tokenizer_kwargs={\"src_lang\": \"en_XX\", \"tgt_lang\": \"it_IT\"},\n", + ")\n", + "\n", + "source_without_context = \"Do you already know when you'll be back?\"\n", + "source_with_context = \"Thank you for your help, my friend, you really saved my life. Do you already know when you'll be back?\"\n", + "\n", + "print(\"Generation without context:\", model.generate(source_without_context, forced_bos_token_id=model.tokenizer.lang_code_to_id[\"it_IT\"]))\n", + "print(\"Generation with context:\", model.generate(source_with_context, forced_bos_token_id=model.tokenizer.lang_code_to_id[\"it_IT\"]))\n", + "\n", + "out = model.attribute(\n", + " source_without_context,\n", + " \"Sai già quando tornerai?\",\n", + " contrast_sources=source_with_context,\n", + " contrast_target_prefixes=\"Grazie per il tuo aiuto, mi hai davvero salvato la vita.\",\n", + " attribute_target=True,\n", + " # We also visualize the score used as target using the same function as step score\n", + " step_scores=[\"pcxmi\", \"probability\"]\n", + ")\n", + "\n", + "out.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The high value of P-CXMI step score for `Sai` here points to the fact that given the full context, the model is twice as likely to predict `Sai` rather than other options.\n", + "\n", + "### Providing custom alignments for contrastive targets\n", + "\n", + "When providing `contrast_targets` to the `attribute` function, the original target tokens are normally matched with contrast tokens at the same position. While this could not pose a problem for minimal pair with slight changes, it can lead to unusable alignments for more extensive updates, limiting the applicability of such methods for real-world contrastive pairs in which differences are not necessarily minimal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gsarti/.cache/pypoetry/virtualenvs/inseq-PzwjmCYf-py3.9/lib/python3.9/site-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n", + " warnings.warn(\"Recommended: pip install sacremoses.\")\n", + "Attributing with saliency...: 100%|██████████| 8/8 [00:01<00:00, 5.91it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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▁Le → ▁Les▁forces → ▁soldats▁militaires → ▁de▁de → ▁la▁paix▁des → ▁ONU▁Nations → </s>
▁UN0.090.0570.070.0810.0750.2530.241
▁peacekeepers0.5680.6320.6290.6150.6180.4840.47
</s>0.3420.3110.3010.3040.3070.2630.289
contrast_prob_diff0.0310.1380.8860.1560.954-0.434-0.039
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')]\n", + "[(0, ''), (1, '▁Le'), (2, '▁forces'), (3, '▁militaires'), (4, '▁de'), (5, '▁paix'), (6, '▁des'), (7, '▁Nations'), (8, '▁Unies'), (9, '')]\n" + ] + } + ], + "source": [ + "# Print tokens to get token indices\n", + "print([(i, x) for i, x in enumerate(model.encode(mt_target, as_targets=True).input_tokens[0])])\n", + "print([(i, x) for i, x in enumerate(model.encode(pe_target, as_targets=True).input_tokens[0])])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Attributing with saliency...: 100%|██████████| 8/8 [00:00<00:00, 14.78it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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▁Le → ▁Les▁forces → ▁soldats▁de▁de → ▁la▁paix▁Nations → ▁ONU</s>
▁UN0.090.0570.0820.0810.0750.2570.092
▁peacekeepers0.5680.6320.6160.6150.6180.4540.589
</s>0.3420.3110.3020.3040.3070.2890.319
contrast_prob_diff0.0310.1380.1420.1560.954-0.920.015
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▁Le → ▁Les▁forces → ▁soldats▁de▁de → ▁la▁paix▁Nations → ▁ONU</s>
▁UN0.090.0570.0820.0810.0750.2570.092
▁peacekeepers0.5680.6320.6160.6150.6180.4540.589
</s>0.3420.3110.3020.3040.3070.2890.319
contrast_prob_diff0.0310.1380.1420.1560.954-0.920.015
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\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "out = model.attribute(\n", + " source,\n", + " mt_target,\n", + " attributed_fn=\"contrast_prob_diff\",\n", + " step_scores=[\"contrast_prob_diff\"],\n", + " contrast_targets=pe_target,\n", + " contrast_targets_alignments=\"auto\",\n", + ")\n", + "\n", + "# Reasonable alignments produced automatically\n", + "out.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Localizing Factual Knowledge in GPT-2 with Contrastive Attribution Tracing\n", + "\n", + "In this final section, you'll see how to use contrastive attribution to localize factual knowledge in GPT-2, using the **Contrastive Attribution Tracing** method we introduced in [(Sarti et al., 2023)](https://arxiv.org/abs/2302.13942). \n", + "\n", + "The method works by propagating importance scores (e.g. gradients) computed with contrastive attribution to intermediate representations of the model. This is different from regular contrastive attribution, which propagates importance all the way to input token embeddings as a proxy for token importance, and can be interpreted as a **\"How important is the representation of token T formed by the model at layer L in driving the prediction of prediction P?\"**.\n", + "\n", + "\"Contrastive\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next code block, we reproduce the procedure adopted for the case study introducing Contrastive Attribution Tracing using Inseq and `bitsandbytes` to use the GPT-2 XL model more efficiently (tested on a 6GB GPU):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import inseq\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "\n", + "# The model is loaded in 8-bit on available GPUs using the bitsandbytes library integrated in HF Transformers\n", + "# This will make the model much smaller for inference purposes, but attributions are not guaranteed to match those\n", + "# of the full-precision model.\n", + "model = AutoModelForCausalLM.from_pretrained(\"gpt2-xl\", load_in_8bit=True, device_map=\"auto\")\n", + "tokenizer = AutoTokenizer.from_pretrained(\"gpt2-xl\")\n", + "\n", + "# Important: adding a space after the prompt would cause problems since the final space would be tokenized separately\n", + "prompt = \"The Eiffel Tower is located in the city of\"\n", + "true_answer = \"The Eiffel Tower is located in the city of Paris\"\n", + "false_answer = \"The Eiffel Tower is located in the city of Rome\"\n", + "\n", + "# GPT-2 XL is a Transformer model with 48 layers\n", + "for layer in range(48):\n", + " attrib_model = inseq.load_model(\n", + " model,\n", + " \"layer_gradient_x_activation\",\n", + " tokenizer=\"gpt2-xl\",\n", + " target_layer=model.transformer.h[layer].mlp,\n", + " )\n", + " # Contrastive attribution of true vs false answer\n", + " out = attrib_model.attribute(\n", + " prompt,\n", + " true_answer,\n", + " attributed_fn=\"contrast_prob_diff\",\n", + " contrast_targets=false_answer,\n", + " step_scores=[\"contrast_prob_diff\"],\n", + " show_progress=False,\n", + " )\n", + " # Save aggregated attributions to disk\n", + " out = out.aggregate()\n", + " out.save(f\"../data/cat_outputs/layer_{layer}.json\", overwrite=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we saved attrbutions at every layer of the model, we load them and use the `get_scores_dicts` utility function to extract dictionaries of scores for every layer.\n", + "We convert those into a single Pandas dataframe for convenience of analysis:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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el0.1464650.1330300.1343020.1361650.1536910.1509280.1867200.1784930.2280010.243314...0.0937430.0714720.0711150.0515870.0147770.0175630.0000000.0000000.0000000.0
ĠTower0.1405870.1374750.1470580.1411950.1494500.1622610.1294860.1371380.1191400.121860...0.0340610.0338700.0293590.0350420.0226480.0051570.0000000.0000000.0000000.0
Ġis0.0349330.0364810.0319050.0282720.0329800.0349670.0328640.0385850.0316860.028574...0.0043900.0060130.0051110.0037950.0000000.0000000.0000000.0000000.0000000.0
Ġlocated0.0973110.0991460.1016570.1029710.0973990.0807220.0902080.0932880.0896230.081699...0.0048090.0051280.0059930.0084870.0022540.0023060.0000000.0000000.0000000.0
Ġin0.0562760.0679800.0554510.0531520.0564960.0638590.0673010.0907640.0990420.105749...0.1669780.1799030.1871510.1744600.1425610.1382370.0490520.0276080.0102320.0
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Ġcity0.0767850.0830720.0842110.0893550.0790660.0831210.0806900.0961550.0828090.066747...0.0019630.0000000.0000000.0018980.0000000.0000000.0000000.0000000.0000000.0
Ġof0.0554020.0563980.0406870.0512880.0515080.0687260.0692540.0881210.1009930.109671...0.5927690.6075610.6077350.6208190.7033570.7061210.8315200.8710000.8811911.0
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0.155769 ... 0.042697 0.035851 \n", + "el 0.186720 0.178493 0.228001 0.243314 ... 0.093743 0.071472 \n", + "ĠTower 0.129486 0.137138 0.119140 0.121860 ... 0.034061 0.033870 \n", + "Ġis 0.032864 0.038585 0.031686 0.028574 ... 0.004390 0.006013 \n", + "Ġlocated 0.090208 0.093288 0.089623 0.081699 ... 0.004809 0.005128 \n", + "Ġin 0.067301 0.090764 0.099042 0.105749 ... 0.166978 0.179903 \n", + "Ġthe 0.029469 0.035213 0.033358 0.026387 ... 0.031717 0.032127 \n", + "Ġcity 0.080690 0.096155 0.082809 0.066747 ... 0.001963 0.000000 \n", + "Ġof 0.069254 0.088121 0.100993 0.109671 ... 0.592769 0.607561 \n", + "\n", + " 40 41 42 43 44 45 \n", + "The 0.004040 0.003287 0.003187 0.003994 0.008052 0.010903 \\\n", + "ĠE 0.022277 0.022929 0.030234 0.029892 0.008052 0.011526 \n", + "iff 0.034897 0.044016 0.045351 0.047375 0.051627 0.040190 \n", + "el 0.071115 0.051587 0.014777 0.017563 0.000000 0.000000 \n", + "ĠTower 0.029359 0.035042 0.022648 0.005157 0.000000 0.000000 \n", + "Ġis 0.005111 0.003795 0.000000 0.000000 0.000000 0.000000 \n", + "Ġlocated 0.005993 0.008487 0.002254 0.002306 0.000000 0.000000 \n", + "Ġin 0.187151 0.174460 0.142561 0.138237 0.049052 0.027608 \n", + "Ġthe 0.032323 0.033680 0.035631 0.049355 0.051697 0.038773 \n", + "Ġcity 0.000000 0.001898 0.000000 0.000000 0.000000 0.000000 \n", + "Ġof 0.607735 0.620819 0.703357 0.706121 0.831520 0.871000 \n", + "\n", + " 46 47 \n", + "The 0.031432 0.0 \n", + "ĠE 0.008484 0.0 \n", + "iff 0.025707 0.0 \n", + "el 0.000000 0.0 \n", + "ĠTower 0.000000 0.0 \n", + "Ġis 0.000000 0.0 \n", + "Ġlocated 0.000000 0.0 \n", + "Ġin 0.010232 0.0 \n", + "Ġthe 0.042955 0.0 \n", + "Ġcity 0.000000 0.0 \n", + "Ġof 0.881191 1.0 \n", + "\n", + "[11 rows x 48 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from inseq import FeatureAttributionOutput\n", + "import pandas as pd\n", + "\n", + "scores = {}\n", + "\n", + "for layer_idx in range(48):\n", + " curr_out = FeatureAttributionOutput.load(f\"../data/cat_outputs/layer_{layer_idx}.json\")\n", + " out_dict = curr_out.get_scores_dicts(do_aggregation=False)[0]\n", + " scores[layer_idx] = [score for score in out_dict[\"target_attributions\"][\"ĠParis\"].values()][:-1]\n", + "\n", + "prefix_tokens = list(out_dict[\"target_attributions\"][\"ĠParis\"].keys())\n", + "attributions_df = pd.DataFrame(scores, index=prefix_tokens[:-1])\n", + "\n", + "# Attributed prefix tokens x layers\n", + "attributions_df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can conclude by visualizing the attribution scores using `matplotlib`:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "filename = \"../img/cat_example_plot.png\"\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 3), dpi=200)\n", + "plt.subplots_adjust(bottom=0.15, left=0.275)\n", + "h = ax.pcolor(\n", + " attributions_df.values,\n", + " cmap=\"Reds\",\n", + " vmin=0,\n", + " vmax=0.75,\n", + ")\n", + "ax.invert_yaxis()\n", + "ax.set_yticks([0.5 + i for i in range(len(attributions_df.values))])\n", + "ax.set_xticks([0.5 + i for i in range(0, attributions_df.values.shape[1], 4)])\n", + "ax.set_xticklabels(list(range(0, 48, 4)))\n", + "ax.set_yticklabels(attributions_df.index)\n", + "cb = plt.colorbar(h, ticks=[0, .15, .3, .45, .6, .75])\n", + "fig.suptitle(\"What activations are contributing to predicting 'Paris' over 'Rome'?\")\n", + "plt.savefig(filename)\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can make three observations about this result:\n", + "\n", + "- The last token of the prefix `of` plays an increasingly relevant role in predicting the next token as we go deeper in the model. This is not surprising, as the model needs to rely heavily on local context to preserve fluency.\n", + "\n", + "- The representations formed by the model in its intermediate layers for some subwords composing the subject of the factual association (`iff`, `el`, `Tower`) are influencing the prediction of `Paris` over `Rome`. This can be interpreted as the emebddings formed at these layers encoding some knowledge about the association between `Eiffel Tower` and `Paris`, which is then used to correctly predict the next token.\n", + "\n", + "- The word `in`, expressing the type of relation between subject and target, is also influencing the prediction of `Paris` over `Rome` in the middle to last layers.\n", + "\n", + "In our case study, we conduct an analysis over a full corpus of factual associations, finding that **these patterns are consistent across different examples** and correspond to importance patterns shown by seminal work such as [Meng et al. (2022)](https://rome.baulab.info/) using causal interventions to obtain the same insights.\n", + "\n", + "❗ **Key takeaway**: While feature attributions have been historically used to explain local importance of input features, recent methods like our CAT approach show their potential to **explain where information is encoded in the model**, similarly to probing classifiers, but without the noise introduced by training external probes.\n", + "\n", + "## Conclusion and Let's Build Together!\n", + "\n", + "In this tutorial, we learned how to use modern 🤗-based tools to compute feature attribution for NLP models and evaluate their results. We also learned how to use contrastive attribution to analyze model behavior in more nuanced ways, and how to use contrastive attribution tracing to localize factual knowledge in GPT-2.\n", + "\n", + "We hope this tutorial will be helpful for your research! 😃\n", + "\n", + "**A final note:** the Inseq library is relatively new and born out of a concerted effort of several PhD student and interpretability researchers. Our goal is to build a toolbox that is robust, flexible and easy to use to improve the reproducibility of research in interpretability for NLP. If you want to use Inseq in your research, or even better contribute to its developments, please reach out to us! 🙏 We would be more than happy to help you out and discuss your ideas! 😃" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}