From fd85bf1e80d38dad8ea5a42aa0010737cedc3118 Mon Sep 17 00:00:00 2001 From: Cibi Arjun Date: Fri, 28 Jun 2024 13:06:19 -0700 Subject: [PATCH 01/86] Internal Change PiperOrigin-RevId: 647775232 --- docs/demos/coref.html | 5 + docs/demos/images.html | 5 + docs/demos/index.html | 55 ++++ docs/demos/is_eval.html | 5 + docs/demos/lm.html | 5 + docs/demos/t5.html | 5 + docs/documentation/_images/attention.png | Bin 0 -> 188417 bytes docs/documentation/_images/lit-attention.png | Bin 0 -> 91620 bytes .../_images/lit-datapoint-compare.png | Bin 368329 -> 459728 bytes .../documentation/_images/lit-s2s-journey.png | Bin 0 -> 147695 bytes .../_images/lit-winogender-metrics.png | Bin 0 -> 160995 bytes docs/documentation/_images/lit-winogender.png | Bin 0 -> 254066 bytes docs/documentation/_images/pair-selection.png | Bin 159071 -> 34353 bytes docs/documentation/_sources/api.md.txt | 60 +++-- docs/documentation/_sources/components.md.txt | 89 +++++-- docs/documentation/_sources/demos.md.txt | 99 ++++++- docs/documentation/_sources/docker.md.txt | 34 +-- docs/documentation/_sources/faq.md.txt | 22 +- .../_sources/frontend_development.md.txt | 16 +- .../_sources/getting_started.md.txt | 5 +- docs/documentation/_sources/ui_guide.md.txt | 114 +++++--- docs/documentation/_static/basic.css | 2 +- docs/documentation/_static/doctools.js | 2 +- docs/documentation/_static/language_data.js | 4 +- docs/documentation/_static/pygments.css | 12 +- docs/documentation/_static/scripts/furo.js | 2 +- .../documentation/_static/scripts/furo.js.map | 2 +- docs/documentation/_static/searchtools.js | 165 +++++------- docs/documentation/_static/styles/furo.css | 2 +- .../documentation/_static/styles/furo.css.map | 2 +- docs/documentation/api.html | 193 +++++--------- docs/documentation/components.html | 250 ++++++++---------- docs/documentation/demos.html | 219 ++++++++------- docs/documentation/docker.html | 127 +++------ docs/documentation/faq.html | 136 +++------- docs/documentation/frontend_development.html | 129 +++------ docs/documentation/genindex.html | 78 +----- docs/documentation/getting_started.html | 106 ++------ docs/documentation/glossary.html | 87 +----- .../includes/highlight_demos.html | 85 +----- docs/documentation/index.html | 94 ++----- docs/documentation/search.html | 83 +----- docs/documentation/searchindex.js | 2 +- docs/documentation/ui_guide.html | 248 ++++++++--------- docs/index.html | 3 +- docs/tutorials/coref/index.html | 187 +++++++++++++ docs/tutorials/generation/index.html | 149 +++++++++++ docs/tutorials/index.html | 8 + docs/tutorials/sentiment/index.html | 2 +- docs/tutorials/sequence-salience/index.html | 17 +- docs/tutorials/tab-feat-attr/index.html | 2 +- docs/tutorials/text-salience/index.html | 2 +- 52 files changed, 1436 insertions(+), 1483 deletions(-) create mode 100644 docs/demos/coref.html create mode 100644 docs/demos/images.html create mode 100644 docs/demos/is_eval.html create mode 100644 docs/demos/lm.html create mode 100644 docs/demos/t5.html create mode 100644 docs/documentation/_images/attention.png create mode 100644 docs/documentation/_images/lit-attention.png create mode 100644 docs/documentation/_images/lit-s2s-journey.png create mode 100644 docs/documentation/_images/lit-winogender-metrics.png create mode 100644 docs/documentation/_images/lit-winogender.png create mode 100644 docs/tutorials/coref/index.html create mode 100644 docs/tutorials/generation/index.html diff --git a/docs/demos/coref.html b/docs/demos/coref.html new file mode 100644 index 00000000..1de185ef --- /dev/null +++ b/docs/demos/coref.html @@ -0,0 +1,5 @@ + + + \ No newline at end of file diff --git a/docs/demos/images.html b/docs/demos/images.html new file mode 100644 index 00000000..ce72cef5 --- /dev/null +++ b/docs/demos/images.html @@ -0,0 +1,5 @@ + + + \ No newline at end of file diff --git a/docs/demos/index.html b/docs/demos/index.html index 3a33dc86..546221f4 100644 --- a/docs/demos/index.html +++ b/docs/demos/index.html @@ -98,6 +98,17 @@
Analyze a tabular data model with LIT, including exploring partial dependence plots and automatically finding counterfactuals.
+
+
Image classification
+
images multiclass classification +
+
DATA SOURCES
+
+ Imagenette +
+
Analyze an image classification model with LIT, including multiple image salience techniques.
+
+
Classification and regression models
BERT binary classification multi-class classification regression @@ -119,6 +130,50 @@
Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.
+
+
+
Gender bias in coreference systems
+
BERT coreference fairness Winogender +
+
DATA SOURCES
+
+ Winogender schemas +
+
Use LIT to explore gendered associations in a coreference system, which matches pronouns to their antecedents. This demo highlights how LIT can work with structured prediction models (edge classification), and its capability for disaggregated analysis.
+
+
+
+
Fill in the blanks
+
BERT masked language model +
+
DATA SOURCES
+
+ Stanford Sentiment Treebank, Movie Reviews +
+
Explore a BERT-based masked-language model. See what tokens the model predicts should fill in the blank when any token from an example sentence is masked out.
+
+
+
+
Text generation
+
T5 generation +
+
DATA SOURCES
+
+ CNN / Daily Mail +
+
Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.
+
+
+
+
Evaluating input salience methods
+
BERT salience evaluation +
+
DATA SOURCES
+
+ Stanford Sentiment Treebank, Toxicity +
+
Explore the faithfulness of input salience methods on a BERT-base model across different datasets and artificial shortcuts.
+
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z;35{R`-kr3@5DD4#?m)!klqsvPOyAqQ`7DkT7ZO16K*ntETU&P-@5x{^8m~Bv6t565(HjztOdoqLQtRdiBolCn`QYFOFxz5$N z6X@I>_kB9N_;K1DAGUK22F47)(HgTD9F!A`1EddEEmmU9!FIg`fVd%OAq>;UYyaJu zlJvX)Zf&mlr+eQL19`Im&lw%#zocUSF;sN{Zp|@A2dlszk=ywve{vHv08(B;!aexUMfEyht8J44ub*R~=3$BX0%f^CryU{ek941PmvXN5Ctbmkwb2P16t*$4|5>ZQ-yL)H?z^8)|5SUM(9ebh zWt#DNTWRxEqjmFpY??4M*Q_4bpSJqzwtuyZHdgP$^g1p7ES`ToK;lMZbJW3u)i6b+ zdN<-HA3y%7*8eQP-)s=4s}?)p`grUA|C_(RX>GddJC7<0Ca(X{l7EWzR~c(JfnICb nd__<6kB|Syg`daQ*2|kmL?%Z)nzJF-fWPN5%F@M>#_#_hKv7;q diff --git a/docs/documentation/_sources/api.md.txt b/docs/documentation/_sources/api.md.txt index 97ea5b64..a174bf39 100644 --- a/docs/documentation/_sources/api.md.txt +++ b/docs/documentation/_sources/api.md.txt @@ -1,6 +1,6 @@ # LIT Python API - + @@ -349,7 +349,7 @@ list of scores for each token. The Integrated Gradients saliency method additionally requires a `TokenEmbeddings` input and corresponding output, as well as a label field `Target` to pin the gradient target to the same class as an input and corresponding output. See the -[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) +[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py) for an example of these spec requirements. The core API involves implementing the `run()` method: @@ -675,7 +675,7 @@ Each `LitType` subclass encapsulates its own semantics (see * A field that appears in _both_ the model's input and output specs is assumed to represent the same value. This pattern is used for model-based input manipulation. For example, a - [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) + [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py) might output `'tokens': lit_types.Tokens(...)`, and accept as (optional) input `'tokens': lit_types.Tokens(required=False, ...)`. An interpretability component could take output from the former, swap one or more tokens (e.g. @@ -712,9 +712,11 @@ this can cause jitter (UI modules appearing, disappearing, reordering, resizing, etc.) when switching between models or datasets with heterogeneous `Spec`s. When implementing your own LIT components and modules, you can use -[`utils.find_spec_keys()`][utils-lib-py] (Python) and -[`findSpecKeys()`][utils-lib] (TypeScript) to identify fields of interest in a -`Spec`. These methods recognize and respect subclasses. For example, +[`utils.find_spec_keys()`][utils-lib] +(Python) and +[`findSpecKeys()`][utils-lib] +(TypeScript) to identify fields of interest in a `Spec`. These methods recognize +and respect subclasses. For example, `utils.find_spec_keys(spec, Scalar)` will also match any `RegressionScore` fields, but `utils.find_spec_keys(spec, RegressionScore)` will not return all `Scalar` fields in the `Spec`. @@ -805,13 +807,8 @@ _See the [examples](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples) ### Available types -The full set of `LitType`s is defined in -[types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types -such as `Integer` and `Scalar` have predefined ranges that can be overridden -using corresponding `min_val` and `max_val` attributes as seen in -[penguin data](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py) -`INPUT_SPEC`. The different types available in LIT are summarized in the table -below. +The full set of `LitType`s is defined in [types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types such as `Integer` and `Scalar` have predefined ranges that can be overridden using corresponding `min_val` and `max_val` attributes as seen [here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438). The different types available in LIT are summarized +in the table below. Note: Bracket syntax, such as `[num_tokens]`, refers to the shapes of NumPy arrays where each element inside the brackets is an integer. @@ -862,7 +859,7 @@ naming collisions with protected TypeScript keywords.* Some properties of the LIT frontend can be configured from Python as **arguments to `dev_server.Server()`**. These include: -* `page_title`: set a custom page title. +* `page_title`: set a custom page title, such as "Coreference Demo". * `canonical_url`: set a "canonical" URL (such as a shortlink) that will be used as the base when copying links from the LIT UI. * `default_layout`: set the default UI layout, by name. See `layout.ts` and @@ -889,16 +886,22 @@ You can specify custom web app layouts from Python via the `layouts=` attribute. The value should be a `Mapping[str, LitCanonicalLayout]`, such as: ```python -PENGUIN_LAYOUT = layout.LitCanonicalLayout( +LM_LAYOUT = layout.LitCanonicalLayout( upper={ - 'Main': [ - modules.DiveModule, + "Main": [ + modules.EmbeddingsModule, modules.DataTableModule, modules.DatapointEditorModule, ] }, - lower=layout.STANDARD_LAYOUT.lower, - description='Custom layout for the Palmer Penguins demo.', + lower={ + "Predictions": [ + modules.LanguageModelPredictionModule, + modules.ConfusionMatrixModule, + ], + "Counterfactuals": [modules.GeneratorModule], + }, + description="Custom layout for language models.", ) ``` @@ -909,12 +912,14 @@ lit_demo = dev_server.Server( models, datasets, # other args... - layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT}, - default_layout='penguins', + layouts={"lm": LM_LAYOUT}, **server_flags.get_flags()) return lit_demo.serve() ``` +For a full example, see +[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). + You can see the pre-configured layouts provided by LIT, as well as the list of modules that can be included in your custom layout in [`layout.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py). A @@ -984,15 +989,15 @@ needing to reload the server or click the UI. For example, to view examples in a dataset: ```python -from lit_nlp.examples.glue import data as glue_data -dataset = glue_data.SST2Data('validation') +from lit_nlp.examples.datasets import glue +dataset = glue.SST2Data('validation') print(dataset.examples) # list of records {"sentence": ..., "label": ...} ``` And to run inference on a few of them: ```python -from lit_nlp.examples.glue import models as glue_models +from lit_nlp.examples.models import glue_models model = glue_models.SST2Model("/path/to/model/files") preds = list(model.predict(dataset.examples[:5])) @@ -1016,19 +1021,16 @@ For a full working example in Colab, see [LIT_components_example.ipynb](https:// [build-metadata]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/app.py -[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py +[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py [curves-interp]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/curves.py [dataset-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py [grad-maps]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/gradient_maps.py [json]: https://www.json.org [mnli-dataset]: https://cims.nyu.edu/~sbowman/multinli/ - [mnli-demo]: https://pair-code.github.io/lit/demos/glue.html - -[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py +[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py [should_display_module]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts [types_py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py [types_ts]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts [utils-lib]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts -[utils-lib-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py [word-replacer]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py diff --git a/docs/documentation/_sources/components.md.txt b/docs/documentation/_sources/components.md.txt index 90b99dbd..f2366d7c 100644 --- a/docs/documentation/_sources/components.md.txt +++ b/docs/documentation/_sources/components.md.txt @@ -1,6 +1,6 @@ # Components and Features - + @@ -44,9 +44,10 @@ every invocation.) Generally, you'll need to: directly. Alternatively, you can export to a `SavedModel` and load this in an eager mode -runtime. This leads to much simpler code, but may require changes to your -`SavedModel` exporter in order to access model internals like embeddings, -gradients, or attention. +runtime. This leads to much simpler code (see +[this example](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&rcl=378713625)), +but may require changes to your `SavedModel` exporter in order to access model +internals like embeddings, gradients, or attention. ### Remote or hosted models @@ -104,6 +105,8 @@ LIT supports many features for classification tasks, including common metrics, confusion matrices, and custom thresholding via the UI. Classification is implemented with the `MulticlassPreds` and `CategoryLabel` types. +* For a basic example on a binary sentiment task, see + [examples/simple_tf2_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py). * Models should define a `MulticlassPreds` field in their output spec with the `vocab=` attribute as the set of class labels, and for each example should return a vector of probabilities for each class. @@ -113,7 +116,9 @@ implemented with the `MulticlassPreds` and `CategoryLabel` types. * A negative class can be designated using the `null_idx` attribute of `MulticlassPreds` (most commonly, `null_idx=0`), and metrics such as precision, recall, F1 will be computed for the remaining classes. AUC and - AUCPR will be computed for binary classification tasks. + AUCPR will be computed for binary classification tasks. For an example, see + the + [comment toxicity model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&rcl=386779180). * If `null_idx` is set and there is only one other class, the other class (often, class `1`) is treated as a positive class, and the LIT UI can be used to change the classification threshold. If `null_idx` is set and there @@ -134,7 +139,7 @@ faceting, and scatterplots of scalar output. Regression is implemented with the which contains numerical targets, and the model's `RegressionScore` field should set `parent=` to the name of this field. * For an example, see the STS-B textual similarity task in - [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). + [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py). ### Multi-label classification @@ -142,6 +147,8 @@ LIT supports multi-label tasks, when a model can label a single example with more than one label. Multi-label classification is implemented with the `SparseMultilabelPreds` and `SparseMultilabel` types. +* For a basic example on an image labeling task, see + [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py). * Models should define a `SparseMultilabelPreds` field in their output spec with the`vocab=` attribute as the set of class labels, and for each example should return a list of class score tuples. Each tuple contains two @@ -172,6 +179,8 @@ or decoder. the model can also output a `ReferenceScores` field (with values as `list[float]`) with `parent=` set to reference a `TextSegment` or `ReferenceTexts` field from the input. +* For modeling examples, see + [examples/models/t5.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py) ![Generated Text Module](images/components/generation-results.png){w=600px align=center} @@ -206,7 +215,7 @@ and otherwise to different parts of the input. [Dataset class documentation](./api.md#datasets) and the corresponding [Model](./api.md#models). * For a more involved code example including per-token gradients, see - [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). + [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py). ### Tabular data @@ -224,7 +233,7 @@ these cases, LIT validates model compatibility by checking that: (`SparseMultilabel`) field. For a demo using a penguin stats dataset/binary classification task, see -[examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py). +[examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py). ### Images @@ -237,6 +246,12 @@ NOTE: We may transition images away from encoded strings, moving to individual pixel color values. We will ensure we don't break existing checked-in code with such a change. +* See [examples/datasets/open_images.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py) for a + dataset containing images, including converting images to base64 encoded + strings. +* For a demo of an image classifier, see + [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py). + ## Token-based Salience LIT supports several methods for token-based input salience, including @@ -286,8 +301,8 @@ $$S(i) \propto x_i \cdot \nabla_{x_i} \hat{y}$$ Compared to grad-norm, this gives directional scores: a positive score is can be interpreted as that token having a positive influence on the prediction -$\hat{y}$, while a negative score suggests that the prediction would be -stronger if that token was removed. +$\hat{y}$, while a negative score suggests that the prediction would be stronger +if that token was removed. To enable this method, your model should, as part of the [output spec and `predict()` implementation](./api.md#models): @@ -351,7 +366,7 @@ An example spec would look like: For a more concrete example that also supports multiple segments with separate gradients, see our -[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py), +[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py), or contact the LIT team for assistance. ### LIME @@ -433,7 +448,7 @@ left-to-right ("causal") language models: The UI supports multiple options for analysis, including: -* Select from predefined target sequences, or explain generations from the +* Select from pre-defined target sequences, or explain generations from the model. * Different salience methods, including [Gradient Norm](#gradient-norm) and [Gradient-dot-Input](#gradient-dot-input). @@ -441,7 +456,7 @@ The UI supports multiple options for analysis, including: to words, sentences, lines, or paragraphs. Quickly switch between different views to refine your analysis to different parts of a prompt. * Display density options to enable working with longer sequences, such as - document text, few-shot examples, or chain-of-thought prompts. + document text, few-shot eaxmples, or chain-of-thought prompts. For a walkthrough of how to use sequence salience to debug LLMs, check out the Responsible Generative AI Toolkit at @@ -467,11 +482,13 @@ using either KerasNLP or Transformers. * LIT-for-Gemma Colab: [`lit_gemma.ipynb`](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb) * Demo binary: - https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py + [`lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py) * KerasNLP model wrappers: - https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py + [`instrumented_keras_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py) * Transformers model wrappers: - https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py + [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py) + + ## Salience Clustering @@ -523,7 +540,9 @@ your model should, as part of the input image. The model should be able to accept input images as numpy arrays in addition - to accepting base64 URL encoded format. + to accepting base64 URL encoded format. See + [mobilenet.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py) + for an example. A variety of image saliency techniques are implemented for models that return image gradients, through use of the @@ -534,6 +553,36 @@ and XRAI. Each of these techniques returns a saliency map image as a base64-encoded string through the `ImageSalience` type. +## Attention + +LIT can display a visualization of attention heads from transformers and other +models: + +![Attention Visualization](./images/components/attention.png){w=400px align=center} + +To enable this, your model should return one or more fields of the type +`AttentionHeads`, with values as arrays of shape `[num_heads, num_tokens, +num_tokens]`. Each field represents a set of heads at a single layer of the +model, so models will often have more than one: + +```python + def output_spec(self) -> types.Spec: + return { + # ... + "tokens": lit_types.Tokens(parent="input_text"), + "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), + "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), + "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), + # ... + } +``` + +The `align_in` and `align_out` attributes are the names of `Tokens` fields for +the source and target tokens for that layer. For self-attention (such as in +BERT) these would name the same field, but for encoder-decoder attention they +could reference different token sets (such as "input_tokens" and +"target_tokens"). + ## Embedding Projector LIT includes a version of the @@ -565,7 +614,7 @@ example, we could facet by class label: ![Metrics Table](./images/components/metrics-table.png) -To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab. +To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab. To enable metrics, your model should set the `parent` attribute on one or more output fields, pointing to the name of the input field that it should be @@ -604,7 +653,7 @@ analysis. ![Confusion Matrix](./images/components/confusion-matrix.png){w=600px align=center} -To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab. +To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab. The confusion matrix is supported for classification models, or if the input data includes any categorical features (`CategoryLabel`). @@ -690,8 +739,6 @@ datapoints, giving a global view of feature effects. ![Partial Dependence Plots Module](./images/components/lit-pdps.png){w=400px align=center} -To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the "Predictions" tab. - ### Dive Dive is a visualization module, inspired by our prior work on diff --git a/docs/documentation/_sources/demos.md.txt b/docs/documentation/_sources/demos.md.txt index 448c0d23..306d3139 100644 --- a/docs/documentation/_sources/demos.md.txt +++ b/docs/documentation/_sources/demos.md.txt @@ -1,6 +1,6 @@ # Demos - + @@ -17,7 +17,7 @@ https://pair-code.github.io/lit/demos/. ### Sentiment and NLI **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html \ -**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) +**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) * Multi-task demo: * Sentiment analysis as a binary classification task @@ -36,12 +36,26 @@ https://pair-code.github.io/lit/demos/. the margin. * Embedding projector to find clusters in representation space. * Integrated Gradients, LIME, and other salience methods. + * Attention visualization. * Counterfactual generators, including HotFlip for targeted adversarial perturbations. Tip: check out a case study for this demo on the public LIT website: https://pair-code.github.io/lit/tutorials/sentiment +### Multilingual (XNLI) + +**Code:** [examples/xnli_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) + +* [XNLI](https://cims.nyu.edu/~sbowman/xnli/) dataset translates a subset of + MultiNLI into 14 different languages. +* Specify `--languages=en,jp,hi,...` flag to select which languages to load. +* NLI as a three-way classification task with two-segment input (premise, + hypothesis). +* Fine-tuned multilingual BERT model. +* Salience methods work with non-whitespace-delimited text, by using the + model's wordpiece tokenization. + -------------------------------------------------------------------------------- ## Regression / Scoring @@ -49,7 +63,7 @@ https://pair-code.github.io/lit/tutorials/sentiment ### Textual Similarity (STS-B) **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html?models=stsb&dataset=stsb_dev \ -**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) +**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) * STS-B textual similarity task, predicting scores on a range from 0 (unrelated) to 5 (very similar). @@ -60,6 +74,7 @@ https://pair-code.github.io/lit/tutorials/sentiment quick filtering. * Embedding projector to find clusters in representation space. * Integrated Gradients, LIME, and other salience methods. + * Attention visualization. -------------------------------------------------------------------------------- @@ -67,20 +82,76 @@ https://pair-code.github.io/lit/tutorials/sentiment ### Gemma -**Code:** -[examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py) +**Code:** [examples/lm_salience_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py) -* Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch) - and Transformers (with PyTorch). +* Supports Gemma 2B and 7B models using KerasNLP and TensorFlow. * Interactively debug LLM prompts using [sequence salience](./components.md#sequence-salience). * Multiple salience methods (grad-l2 and grad-dot-input), at multiple - granularities: token-, word-, line-, sentence-, and paragraph-level. + granularities: token-, word-, sentence-, and paragraph-level. Tip: check out the in-depth walkthrough at https://ai.google.dev/responsible/model_behavior, part of the Responsible Generative AI Toolkit. +### T5 + +**Hosted instance:** https://pair-code.github.io/lit/demos/t5.html \ +**Code:** [examples/t5_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) + +* Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel + formats. +* Visualize beam candidates and highlight diffs against references. +* Visualize per-token decoder hypotheses to see where the model veers away + from desired output. +* Filter examples by ROUGE score against reference. +* Embeddings from last layer of model, visualized with UMAP or PCA. +* Task wrappers to handle pre- and post-processing for summarization and + machine translation tasks. +* Pre-loaded eval sets for CNNDM and WMT. + +Tip: check out a case study for this demo on the public LIT website: +https://pair-code.github.io/lit/tutorials/generation + +-------------------------------------------------------------------------------- + +## Language Modeling + +### BERT and GPT-2 + +**Hosted instance:** https://pair-code.github.io/lit/demos/lm.html \ +**Code:** [examples/lm_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py) + +* Compare multiple BERT and GPT-2 models side-by-side on a variety of + plain-text corpora. +* LM visualization supports different modes: + * BERT masked language model: click-to-mask, and query model at that + position. + * GPT-2 shows left-to-right hypotheses for each target token. +* Embedding projector to show latent space of the model. + +-------------------------------------------------------------------------------- + +## Structured Prediction + +### Gender Bias in Coreference + +**Hosted instance:** https://pair-code.github.io/lit/demos/coref.html \ +**Code:** [examples/coref/coref_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py) + +* Gold-mention coreference model, trained on + [OntoNotes](https://catalog.ldc.upenn.edu/LDC2013T19). +* Evaluate on the Winogender schemas + ([Rudinger et al. 2018](https://arxiv.org/abs/1804.09301)) which test for + gendered associations with profession names. +* Visualizations of coreference edges, as well as binary classification + between two candidate referents. +* Stratified metrics for quantifying model bias as a function of pronoun + gender or Bureau of Labor Statistics profession data. + +Tip: check out a case study for this demo on the public LIT website: +https://pair-code.github.io/lit/tutorials/coref + -------------------------------------------------------------------------------- ## Multimodal @@ -88,7 +159,7 @@ Generative AI Toolkit. ### Tabular Data: Penguin Classification **Hosted instance:** https://pair-code.github.io/lit/demos/penguins.html \ -**Code:** [examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) +**Code:** [examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) * Binary classification on [penguin dataset](https://www.tensorflow.org/datasets/catalog/penguins). @@ -98,3 +169,13 @@ Generative AI Toolkit. * Use binary classifier threshold setters to find best thresholds for slices of examples to achieve specific fairness constraints, such as demographic parity. + +### Image Classification with MobileNet + +**Hosted instance:** https://pair-code.github.io/lit/demos/images.html \ +**Code:** [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) + +* Classification on ImageNet labels using a MobileNet model. +* Showing using of LIT on image data. +* Explore results of multiple gradient-based image saliency techniques in the + Salience Maps module. diff --git a/docs/documentation/_sources/docker.md.txt b/docs/documentation/_sources/docker.md.txt index ebf7eb7c..c9cad085 100644 --- a/docs/documentation/_sources/docker.md.txt +++ b/docs/documentation/_sources/docker.md.txt @@ -1,6 +1,6 @@ # Running LIT in a Docker container - + Users might want to deploy LIT onto servers for public-facing, long-running instances. This is how we host the LIT demos found on @@ -13,21 +13,22 @@ LIT can be run as a containerized app using [Docker](https://www.docker.com/) or your preferred engine. This is how we run our [hosted demos](https://pair-code.github.io/lit/demos/). -We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the `lit_nlp/examples` directory. -The `Dockerfile` installs all necessary dependencies for LIT and builds the +We provide a basic +[`Dockerfile`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile) that you can +use to build and run any of the demos in the `lit_nlp/examples` directory. The +`Dockerfile` installs all necessary dependencies for LIT and builds the front-end code from source. Then it runs [gunicorn](https://gunicorn.org/) as the HTTP server, invoking the `get_wsgi_app()` method from our demo file to get the WSGI app to serve. The options provided to gunicorn for our use-case can be found in [`gunicorn_config.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py). You can find a reference implementation in -[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). +[`glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) or +[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). -Use the following shell -https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the -default Docker image for LIT from the provided `Dockerfile`, and then run a -container from that image. Comments are provided in-line to help explain what -each step does. +Use the following shell commands to build the default Docker image for LIT from +the provided `Dockerfile`, and then run a container from that image. Comments +are provided in-line to help explain what each step does. ```shell # Build the docker image using the -t argument to name the image. Remember to @@ -47,11 +48,14 @@ below. ```shell # DEMO_NAME is used to complete the Python module path # -# "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()" +# "lit_nlp.examples.$DEMO_NAME" # # Therefore, valid values for DEMO_NAME are Python module paths in the -# lit_nlp/examples directory, such as glue, penguin, tydi, etc. -docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp +# lit_nlp/examples directory, such as +# +# * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc. +# * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc. +docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp # Use the DEMO_PORT environment variable as to change the port that LIT uses in # the container. Be sure to also change the -p option to map the container's @@ -62,15 +66,15 @@ docker run --rm -p 2345:2345 -e DEMO_PORT=2345 lit-nlp # containers on your machine using the combination of the DEMO_NAME and # DEMO_PORT arguments, and docker run with the -d flag to run the container in # the background. -docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp -docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp +docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp +docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp ``` ## Integrating Custom LIT Instances with the Default Docker Image Many LIT users create their own custom LIT server script to demo or serve, which involves creating an executable Python module with a `main()` method, as -described in the [Python API docs](api.md#adding-models-and-data). +described in the [Python API docs](https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data). These custom server scripts can be easily integrated with LIT's default image as long as your server meets two requirements: diff --git a/docs/documentation/_sources/faq.md.txt b/docs/documentation/_sources/faq.md.txt index aa8bd03d..de6a5b57 100644 --- a/docs/documentation/_sources/faq.md.txt +++ b/docs/documentation/_sources/faq.md.txt @@ -1,6 +1,6 @@ # Frequently Asked Questions - + @@ -17,7 +17,9 @@ works with any modern ML framework. For more information, see In addition to text, LIT has good support for different modalities, including images and tabular data. For examples, see: -* [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) - +* [Image demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) - + image classification, using a Mobilenet model. +* [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) - multi-class classification on tabular (numeric and categorical string) data, using the [Palmer Penguins](https://www.tensorflow.org/datasets/catalog/penguins) @@ -30,7 +32,13 @@ For more details, see All strings in LIT are unicode and most components use model-provided tokenization if available, so in most cases non-English languages and non-Latin -scripts should work without any modifications. +scripts should work without any modifications. For examples, see: + +* [XNLI demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) - + cross-lingual NLI, with up to 15 languages supported via a multilingual BERT + model. +* [T5 demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) - + includes WMT data for machine translation ## Scale @@ -91,8 +99,8 @@ manually entered in the web UI) are stored in server memory, and if `--data_dir` is specified, may be cached to disk. LIT has the ability to create or edit datapoints in the UI and then save them to -disk. If you do not want the tool to be able to write edited datapoints to disk, -then pass the `--demo_mode` runtime flag to the LIT server. +disk. If you do not want the tool to be able to write edited datapoints to +disk, then pass the `--demo_mode` runtime flag to the LIT server. ### I have proprietary data. Is LIT secure for my team to use? @@ -169,5 +177,7 @@ official training APIs. However, to facilitate code reuse you can easily add training methods to your model class. In fact, several of our demos do exactly this, using LIT's `Dataset` objects to manage training data along with standard training APIs (such as Keras' `model.fit()`). See -[`glue/models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) +[`quickstart_sst_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py) +and/or +[`glue_models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py) for examples. diff --git a/docs/documentation/_sources/frontend_development.md.txt b/docs/documentation/_sources/frontend_development.md.txt index fe8a82bb..a51aed81 100644 --- a/docs/documentation/_sources/frontend_development.md.txt +++ b/docs/documentation/_sources/frontend_development.md.txt @@ -1,6 +1,6 @@ # Frontend Developer Guide - + @@ -46,9 +46,9 @@ and rendering the various `LitModule` components, a process about which we'll go into greater detail later. The JS bundle entry point is -[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts), which first -imports the loaded, the `` web component is declared, and attaches -itself to the DOM, waiting for the app to be initialized. +[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts), which +first imports the loaded, the `` web component is declared, and +attaches itself to the DOM, waiting for the app to be initialized. The second step is kicking off app initialization. The [`LitApp`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts) singleton @@ -67,7 +67,7 @@ pre-configured layouts in * `default`: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and -* `three_panel`: A three-panel layout that puts exploratory data +* `experimental`: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -76,15 +76,15 @@ pre-configured layouts in You can also add [custom layouts](./api.md#customizing-the-layout) to your LIT instance by defining one or more `LitCanonicalLayout` instances and passing them -to the server. For an example, see -[`prompt_debugging/layouts.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py). +to the server. For an example, see `CUSTOM_LAYOUTS` in +[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). Note: The pre-configured layouts are added to every `LitApp` instance using [dictionary updates](https://docs.python.org/3/library/stdtypes.html#dict) where the Mapping passed to the `LitApp` constructor overrides the pre-configured layouts `Mapping`. Thus, you can remove or change these pre-configured layouts as you like by passing a `Mapping` where the values of `simple`, `default`, -and/or `three_panel` is `None` (to remove) or a `LitCanonicalLayout` instance +and/or `experimental` is `None` (to remove) or a `LitCanonicalLayout` instance (to override) as you desire. The actual layout of components in the LIT UI, see diff --git a/docs/documentation/_sources/getting_started.md.txt b/docs/documentation/_sources/getting_started.md.txt index 0ca605bf..80e4eae9 100644 --- a/docs/documentation/_sources/getting_started.md.txt +++ b/docs/documentation/_sources/getting_started.md.txt @@ -1,6 +1,6 @@ # Getting Started with LIT - + @@ -72,7 +72,8 @@ variety of model types. The code for these is under [examples](https://github.co ; each is a small script that loads one or more models and starts a LIT server. -Most demos can be run with a single command. To run the default one, you can do: +Most demos can be run with a single blaze command. To run the default one, you +can do: ```sh python -m lit_nlp.examples.glue.demo \ diff --git a/docs/documentation/_sources/ui_guide.md.txt b/docs/documentation/_sources/ui_guide.md.txt index bb5028ae..4a38a88c 100644 --- a/docs/documentation/_sources/ui_guide.md.txt +++ b/docs/documentation/_sources/ui_guide.md.txt @@ -1,6 +1,6 @@ # UI Guide - + This is a user guide for the Learning Interpretability Tool (LIT) UI. @@ -42,7 +42,7 @@ LIT provides three pre-configured layouts: * `default`: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and -* `three_panel`: A three-panel layout that puts exploratory data +* `experimental`: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -130,26 +130,19 @@ controls and information. The left side of the toolbar contains a set of menus for quickly controlling datapoint selection and coloring. This includes the following controls: -* The **"Select datapoint"** menu provides a drop-down of several options: - * the **"Random"** option selects a random datapoint, - * the **"All related"** option adds any datapoints "related" to the - current selection. In LIT, "related" is defined as datapoints created - from some source datapoint (through manual editing or a datapoint - generator), or a source datapoint that a selected datapoint was created - from, - * the **"Parents"** option adds the source datapoints that the selected - datapoints were created from, - * the **"Children"** option adds the datapoints created from the selected - datapoints (through manual editing or a datapoint generator), - * the **Slices** option allows quick selection of an already-created slice - of datapoints, - * the **"Clear selection"** button deselects all selected datapoints. -* The **"Color by"** menu enables setting of the color of each datapoint in - the modules that visualize all datapoints (such as the *Embeddings* and - *Scalars* modules) by any number of datapoint features or model outputs on - those datapoints (such as coloring by some categorical input feature, or by - prediction error for a regression task). -* The **Slices** menu allows adding/selecting/removing slices of datapoints. +* The **"Select related"** option adds any datapoints "related" to the current + selection. In LIT, "related" is defined as datapoints created from some + source datapoint (through manual editing or a datapoint generator), or a + source datapoint that a selected datapoint was created from. +* The **"Clear selection"** button deselects all selected datapoints. The + dropdown also contains a color legend for the current color setting. +* The **Slices** option allows quick selection of an already-created slice of + datapoints. +* The **"Datapoint color"** menu enables setting of the color of each + datapoint in the modules that visualize all datapoints (such as the + *Embeddings* and *Scalars* modules) by any number of datapoint features or + model outputs on those datapoints (such as coloring by some categorical + input feature, or by prediction error for a regression task). Next to the menus is a button for pinning/unpinning a datapoint. Pinning a datapoint puts LIT into datapoint comparison mode, where two datapoints can be @@ -329,9 +322,9 @@ such as model predictions, enable them from the "Columns" dropdown. The datapoint editor shows the details of the primary selected datapoint, if one is selected. Any field can be edited, and a new datapoint created with those -edits through the **"Add"** button. Any edit to an existing datapoint must be -saved as a new datapoint to be explored, to keep datapoints immutable for -simplicity of use. +edits through the **"Make new datapoint"** button. Any edit to an existing +datapoint must be saved as a new datapoint to be explored, to keep datapoints +immutable for simplicity of use. When no datapoint is selected, the editor shows a blank datapoint that can be filled out by hand to create a completely new datapoint. @@ -480,15 +473,24 @@ background of each text piece is colored by the salience of that piece on the prediction, and hovering on any piece will display the exact value calculated for that piece. -There is an **"autorun"** button by each methodology on the right side of the -bar (the methodoloy name is on the left side). If it is checked, then that -calculation is made when a new primary datapoint is selected. If it is +There is an **"autorun"** button by each methodology. If it is checked, then +that calculation is made when a new primary datapoint is selected. If it is unchecked, the calculation isn't made until it is checked. This can be valuable so that expensive, long-running saliency calculations (such as LIME) aren't performed on every datapoint selection, but only when explicitly asked for. ![LIT saliency maps](./images/lit-salience.png "LIT saliency maps") +### Attention + +For models that return attention head information along with predictions, the +attention module shows which tokens are attended to between layers of a model. +Dropdowns allow you to select which layer and attention head is being +visualized. Line opacity is controlled by the magnitude of the attention between +those tokens. + +![LIT attention](./images/lit-attention.png "LIT attention"){w=500px align=center} + ## User Journeys In this section, we explore some example user journeys and how LIT enables them. @@ -513,11 +515,51 @@ model. ![Sentiment analysis](./images/lit-sentiment-analysis.png "Sentiment analysis") -### Sequence salience - -Sequence salience generalizes token-based salience to text-to-text models, -allowing you to explain the impact of the prompt tokens on parts of the model -output. - -Check out [here](components.md#sequence-salience) for more details on how to -navigate the Sequence Salience UI module. +### Gender Bias in Coreference + +Does a system encode gendered associations, which might lead to incorrect +predictions? We load a coreference model trained on OntoNotes, and load the +Winogender dataset into LIT for evaluation. Each Winogender example has a +pronoun and two candidate referents, one a occupation term like (“technician”) +and one is an “other participant” (like “customer”). Our model predicts +coreference probabilities for each candidate. We can explore the model’s +sensitivity to pronouns by comparing two examples side-by-side (see structured +predictions in screenshot). We can see how commonly the model makes similar +errors by paging through the dataset, or by selecting specific slices of +interest. + +![Winogender analysis](./images/lit-winogender.png "Winogender analysis") + +In the *Metrics* table, we can slice this selection by pronoun type and by the +true referent. On the set of male-dominated occupations (< 25% female by BLS), +we see the model performs well when the ground-truth agrees with the +stereotype - e.g. when the answer is the occupation term, male pronouns are +correctly resolved 83% of the time, compared to female pronouns only 37.5% of +the time (screenshot section (c)). + +![Winogender analysis](./images/lit-winogender-metrics.png "Winogender analysis") + +### Debugging Text Generation + + + +Does the training data explain a particular error in text generation? We analyze +an older T5 model on the CNN-DM summarization task. LIT’s *Scalars* module +allows us to look at per-example ROUGE scores, and quickly select an example +with middling performance (screenshot section (a)). We find the generated text +(screenshot section (b)) contains an erroneous constituent: “alastair cook was +replaced as captain by former captain ...”. We can dig deeper, using LIT’s +language modeling module (screenshot section (c)) to see that the token “by” is +predicted with high probability (28.7%). + +To find out how T5 arrived at this prediction, we utilize the “similarity +searcher” component through the datapoint generator (screenshot section (d)). +This performs a fast approximate nearest-neighbor lookup from a pre-built index +over the training corpus, using embeddings from the T5 decoder. With one click, +we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection. +We see that the words “captain” and “former” appear 34 and 16 times in these +examples–along with 3 occurrences of “replaced by” (screenshot section (e)), +suggesting a strong prior toward our erroneous phrase. + +![LIT sequence-to-sequence analysis](./images/lit-s2s-journey.png "LIT sequence-to-sequence analysis"){w=500px align=center} diff --git a/docs/documentation/_static/basic.css b/docs/documentation/_static/basic.css index f316efcb..30fee9d0 100644 --- a/docs/documentation/_static/basic.css +++ b/docs/documentation/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ diff --git a/docs/documentation/_static/doctools.js b/docs/documentation/_static/doctools.js index 4d67807d..d06a71d7 100644 --- a/docs/documentation/_static/doctools.js +++ b/docs/documentation/_static/doctools.js @@ -4,7 +4,7 @@ * * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ diff --git a/docs/documentation/_static/language_data.js b/docs/documentation/_static/language_data.js index 367b8ed8..250f5665 100644 --- a/docs/documentation/_static/language_data.js +++ b/docs/documentation/_static/language_data.js @@ -5,7 +5,7 @@ * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -13,7 +13,7 @@ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; -/* Non-minified version is copied as a separate JS file, if available */ +/* Non-minified version is copied as a separate JS file, is available */ /** * Porter Stemmer diff --git a/docs/documentation/_static/pygments.css b/docs/documentation/_static/pygments.css index 02b4b128..c2e07c71 100644 --- a/docs/documentation/_static/pygments.css +++ b/docs/documentation/_static/pygments.css @@ -106,17 +106,17 @@ body[data-theme="dark"] .highlight .cp { color: #ff3a3a; font-weight: bold } /* body[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body[data-theme="dark"] .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ +body[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */ body[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body[data-theme="dark"] .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ -body[data-theme="dark"] .highlight .gr { color: #ff3a3a } /* Generic.Error */ +body[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */ body[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */ body[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */ body[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */ body[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */ body[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */ body[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */ -body[data-theme="dark"] .highlight .gt { color: #ff3a3a } /* Generic.Traceback */ +body[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */ body[data-theme="dark"] .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */ body[data-theme="dark"] .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */ body[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */ @@ -192,17 +192,17 @@ body:not([data-theme="light"]) .highlight .cp { color: #ff3a3a; font-weight: bol body:not([data-theme="light"]) .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body:not([data-theme="light"]) .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body:not([data-theme="light"]) .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body:not([data-theme="light"]) .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ +body:not([data-theme="light"]) .highlight .gd { color: #d22323 } /* Generic.Deleted */ body:not([data-theme="light"]) .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body:not([data-theme="light"]) .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ -body:not([data-theme="light"]) .highlight .gr { color: #ff3a3a } /* Generic.Error */ +body:not([data-theme="light"]) .highlight .gr { color: #d22323 } /* Generic.Error */ body:not([data-theme="light"]) .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */ body:not([data-theme="light"]) .highlight .gi { color: #589819 } /* Generic.Inserted */ body:not([data-theme="light"]) .highlight .go { color: #cccccc } /* Generic.Output */ body:not([data-theme="light"]) .highlight .gp { color: #aaaaaa } /* Generic.Prompt */ body:not([data-theme="light"]) .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */ body:not([data-theme="light"]) .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */ -body:not([data-theme="light"]) .highlight .gt { color: #ff3a3a } /* Generic.Traceback */ +body:not([data-theme="light"]) .highlight .gt { color: #d22323 } /* Generic.Traceback */ body:not([data-theme="light"]) .highlight .kc { color: #6ebf26; 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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the 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emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n var bounds = elem.getBoundingClientRect();\n var offset = getOffset(settings);\n if (bottom) {\n return (\n parseInt(bounds.bottom, 10) <\n (window.innerHeight || document.documentElement.clientHeight)\n );\n }\n return parseInt(bounds.top, 10) <= offset;\n };\n\n /**\n * Check if at the bottom of the viewport\n * @return {Boolean} If true, page is at the bottom of the viewport\n */\n var isAtBottom = function () {\n if (\n Math.ceil(window.innerHeight + window.pageYOffset) >=\n getDocumentHeight()\n )\n return true;\n return false;\n };\n\n /**\n * Check if the last item should be used (even if not at the top of the page)\n * @param {Object} item The last item\n * @param {Object} settings The settings for this instantiation\n * @return {Boolean} If true, use the last item\n */\n var useLastItem = function (item, settings) {\n if (isAtBottom() && isInView(item.content, settings, true)) return true;\n return false;\n };\n\n /**\n * Get the active content\n * @param {Array} contents The content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n contents.push({\n nav: item,\n content: content,\n });\n });\n\n // Sort contents by the order they appear in the DOM\n sortContents(contents);\n };\n\n /**\n * Detect which content is currently active\n */\n publicAPIs.detect = function () {\n // Get the active content\n var active = getActive(contents, settings);\n\n // if there's no active content, deactivate and bail\n if (!active) {\n if (current) {\n deactivate(current, settings);\n current = null;\n }\n return;\n }\n\n // If the active content is the one currently active, do nothing\n if (current && active.content === current.content) return;\n\n // Deactivate the current content and activate the new content\n deactivate(current, settings);\n activate(active, settings);\n\n // Update the currently active content\n current = active;\n };\n\n /**\n * Detect the active content on scroll\n * Debounced for performance\n */\n var scrollHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the current instantiation\n */\n var init = function () {\n // Merge user options into defaults\n settings = extend(defaults, options || {});\n\n // Setup variables based on the current DOM\n publicAPIs.setup();\n\n // Find the currently active content\n publicAPIs.detect();\n\n // Setup event listeners\n window.addEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.addEventListener(\"resize\", resizeHandler, false);\n }\n };\n\n //\n // Initialize and return the public APIs\n //\n\n init();\n return publicAPIs;\n };\n\n //\n // Return the Constructor\n //\n\n return Constructor;\n },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n if (Math.floor(header.getBoundingClientRect().top) == 0) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n } else {\n if (positionY < lastScrollTop) {\n document.documentElement.classList.add(\"show-back-to-top\");\n } else if (positionY > lastScrollTop) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n }\n }\n lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n if (tocScroll === null) {\n return;\n }\n\n // top of page.\n if (positionY == 0) {\n tocScroll.scrollTo(0, 0);\n } else if (\n // bottom of page.\n Math.ceil(positionY) >=\n Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n ) {\n tocScroll.scrollTo(0, tocScroll.scrollHeight);\n } else {\n // somewhere in the middle.\n const current = document.querySelector(\".scroll-current\");\n if (current == null) {\n return;\n }\n\n // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n // // scroll the currently \"active\" heading in toc, into view.\n // const rect = current.getBoundingClientRect();\n // if (0 > rect.top) {\n // current.scrollIntoView(true); // the argument is \"alignTop\"\n // } else if (rect.bottom > window.innerHeight) {\n // current.scrollIntoView(false);\n // }\n }\n}\n\nfunction scrollHandler(positionY) {\n scrollHandlerForHeader();\n scrollHandlerForBackToTop(positionY);\n scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n mode = \"auto\";\n }\n\n document.body.dataset.theme = mode;\n localStorage.setItem(\"theme\", mode);\n console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n if (prefersDark) {\n // Auto (dark) -> Light -> Dark\n if (currentTheme === \"auto\") {\n setTheme(\"light\");\n } else if (currentTheme == \"light\") {\n setTheme(\"dark\");\n } else {\n setTheme(\"auto\");\n }\n } else {\n // Auto (light) -> Dark -> Light\n if (currentTheme === \"auto\") {\n setTheme(\"dark\");\n } else if (currentTheme == \"dark\") {\n setTheme(\"light\");\n } else {\n setTheme(\"auto\");\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n let last_known_scroll_position = 0;\n let ticking = false;\n\n window.addEventListener(\"scroll\", function (e) {\n last_known_scroll_position = window.scrollY;\n\n if (!ticking) {\n window.requestAnimationFrame(function () {\n scrollHandler(last_known_scroll_position);\n ticking = false;\n });\n\n ticking = true;\n }\n });\n window.scroll();\n}\n\nfunction setupScrollSpy() {\n if (tocScroll === null) {\n return;\n }\n\n // Scrollspy -- highlight table on contents, based on scroll\n new Gumshoe(\".toc-tree a\", {\n reflow: true,\n recursive: true,\n navClass: \"scroll-current\",\n offset: () => {\n let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n return header.getBoundingClientRect().height + 0.5 * rem + 1;\n },\n });\n}\n\nfunction setupTheme() {\n // Attach event handlers for toggling themes\n const buttons = document.getElementsByClassName(\"theme-toggle\");\n Array.from(buttons).forEach((btn) => {\n btn.addEventListener(\"click\", cycleThemeOnce);\n });\n}\n\nfunction setup() {\n setupTheme();\n setupScrollHandler();\n setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n document.body.parentNode.classList.remove(\"no-js\");\n\n header = document.querySelector(\"header\");\n tocScroll = document.querySelector(\".toc-scroll\");\n\n setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""} \ No newline at end of file diff --git a/docs/documentation/_static/searchtools.js b/docs/documentation/_static/searchtools.js index 92da3f8b..7918c3fa 100644 --- a/docs/documentation/_static/searchtools.js +++ b/docs/documentation/_static/searchtools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for the full-text search. * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -99,7 +99,7 @@ const _displayItem = (item, searchTerms, highlightTerms) => { .then((data) => { if (data) listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, anchor) + Search.makeSearchSummary(data, searchTerms) ); // highlight search terms in the summary if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js @@ -116,8 +116,8 @@ const _finishSearch = (resultCount) => { ); else Search.status.innerText = _( - "Search finished, found ${resultCount} page(s) matching the search query." - ).replace('${resultCount}', resultCount); + `Search finished, found ${resultCount} page(s) matching the search query.` + ); }; const _displayNextItem = ( results, @@ -137,22 +137,6 @@ const _displayNextItem = ( // search finished, update title and status message else _finishSearch(resultCount); }; -// Helper function used by query() to order search results. -// Each input is an array of [docname, title, anchor, descr, score, filename]. -// Order the results by score (in opposite order of appearance, since the -// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. -const _orderResultsByScoreThenName = (a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; -}; /** * Default splitQuery function. Can be overridden in ``sphinx.search`` with a @@ -176,26 +160,13 @@ const Search = { _queued_query: null, _pulse_status: -1, - htmlToText: (htmlString, anchor) => { + htmlToText: (htmlString) => { const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - for (const removalQuery of [".headerlinks", "script", "style"]) { - htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); - } - if (anchor) { - const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); - if (anchorContent) return anchorContent.textContent; - - console.warn( - `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` - ); - } - - // if anchor not specified or not found, fall back to main content + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent) return docContent.textContent; - + if (docContent !== undefined) return docContent.textContent; console.warn( - "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." ); return ""; }, @@ -268,7 +239,16 @@ const Search = { else Search.deferQuery(query); }, - _parseQuery: (query) => { + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + // stem the search terms and add them to the correct list const stemmer = new Stemmer(); const searchTerms = new Set(); @@ -304,32 +284,16 @@ const Search = { // console.info("required: ", [...searchTerms]); // console.info("excluded: ", [...excludedTerms]); - return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; - }, - - /** - * execute search (requires search index to be loaded) - */ - _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // Collect multiple result groups to be sorted separately and then ordered. - // Each is an array of [docname, title, anchor, descr, score, filename]. - const normalResults = []; - const nonMainIndexResults = []; - + // array of [docname, title, anchor, descr, score, filename] + let results = []; _removeChildren(document.getElementById("search-progress")); - const queryLower = query.toLowerCase().trim(); + const queryLower = query.toLowerCase(); for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { for (const [file, id] of foundTitles) { let score = Math.round(100 * queryLower.length / title.length) - normalResults.push([ + results.push([ docNames[file], titles[file] !== title ? `${titles[file]} > ${title}` : title, id !== null ? "#" + id : "", @@ -344,47 +308,46 @@ const Search = { // search for explicit entries in index directives for (const [entry, foundEntries] of Object.entries(indexEntries)) { if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id, isMain] of foundEntries) { - const score = Math.round(100 * queryLower.length / entry.length); - const result = [ + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ docNames[file], titles[file], id ? "#" + id : "", null, score, filenames[file], - ]; - if (isMain) { - normalResults.push(result); - } else { - nonMainIndexResults.push(result); - } + ]); } } } // lookup as object objectTerms.forEach((term) => - normalResults.push(...Search.performObjectSearch(term, objectTerms)) + results.push(...Search.performObjectSearch(term, objectTerms)) ); // lookup as search terms in fulltext - normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); // let the scorer override scores with a custom scoring function - if (Scorer.score) { - normalResults.forEach((item) => (item[4] = Scorer.score(item))); - nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); - } - - // Sort each group of results by score and then alphabetically by name. - normalResults.sort(_orderResultsByScoreThenName); - nonMainIndexResults.sort(_orderResultsByScoreThenName); - - // Combine the result groups in (reverse) order. - // Non-main index entries are typically arbitrary cross-references, - // so display them after other results. - let results = [...nonMainIndexResults, ...normalResults]; + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); // remove duplicate search results // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept @@ -398,12 +361,7 @@ const Search = { return acc; }, []); - return results.reverse(); - }, - - query: (query) => { - const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); - const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + results = results.reverse(); // for debugging //Search.lastresults = results.slice(); // a copy @@ -508,18 +466,14 @@ const Search = { // add support for partial matches if (word.length > 2) { const escapedWord = _escapeRegExp(word); - if (!terms.hasOwnProperty(word)) { - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - } - if (!titleTerms.hasOwnProperty(word)) { - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); - }); - } + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); } // no match but word was a required one @@ -542,8 +496,9 @@ const Search = { // create the mapping files.forEach((file) => { - if (!fileMap.has(file)) fileMap.set(file, [word]); - else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); }); }); @@ -594,8 +549,8 @@ const Search = { * search summary for a given text. keywords is a list * of stemmed words. */ - makeSearchSummary: (htmlText, keywords, anchor) => { - const text = Search.htmlToText(htmlText, anchor); + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); if (text === "") return null; 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\n$icon-size: 1.25rem\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: $icon-size\n width: $icon-size\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: $icon-size\n width: $icon-size\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: $icon-size\n width: $icon-size\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: $icon-size\n width: $icon-size\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$full-width: $content-width + 2 * ($content-padding + $sidebar-width);\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 4.25rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n border-left: 0.1875rem solid\n border-radius: 0.125rem\n\n padding-left: 0.75rem\n\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\ndiv.versionadded\n border-color: var(--color-api-added-border)\n .versionmodified\n color: var(--color-api-added)\n\ndiv.versionchanged\n border-color: var(--color-api-changed-border)\n .versionmodified\n color: var(--color-api-changed)\n\ndiv.deprecated\n border-color: var(--color-api-deprecated-border)\n .versionmodified\n color: var(--color-api-deprecated)\n\ndiv.versionremoved\n border-color: var(--color-api-removed-border)\n .versionmodified\n color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(2.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n color: var(--color-sidebar-link-text)\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the

- -
@@ -186,8 +130,7 @@
@@ -243,17 +186,11 @@ Back to top
-
- - - - -
+
@@ -263,13 +200,13 @@
-
+
-

LIT Python API

- +

LIT Python API#

+
-

Design Overview

+

Design Overview#

LIT is a modular system, comprising a collection of backend components (written in Python) and frontend modules (written in TypeScript). Most users will develop against the Python API, which is documented below and allows LIT to be extended @@ -294,7 +231,7 @@

Design Overviewfrontend developer guide.

-

Adding Models and Data

+

Adding Models and Data#

To run LIT with your own models and data, you can create a custom demo.py script that passes these to the LIT server. For example:

def main(_):
@@ -324,7 +261,7 @@ 
Adding Models and DataFor pre-built demo.py examples, check out
 https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples

 

-
Validating Models and Data

+
Validating Models and Data#

 
Datasets and models can optionally be validated by LIT to ensure that dataset
 examples match their spec and that model output values match their spec.
 This can be very helpful during development of new model and dataset wrappers
@@ -342,7 +279,7 @@ 
Validating Models and Data
-
Datasets

+
Datasets#

 
Datasets (Dataset) are
 just a list of examples, with associated type information following LIT’s
 type system.

@@ -404,7 +341,7 @@ 
Datasets
 these features for slicing, binning, etc., and they will also be available to
 interpretation components such as custom metrics.

 

-
Transformations

+
Transformations#

 
The Dataset class also supports a limited set of transformations, similar to
 TensorFlow’s
 tf.data.Dataset
@@ -423,7 +360,7 @@ 
Transformations
-
Models

+
Models#

 
Models (Model) are
 functions which take inputs and produce outputs, with associated type
 information following LIT’s type system. The core API consists
@@ -485,7 +422,7 @@ 
ModelsIf your model is on a remote server, consider using the BatchedRemoteModel
 base class, which implements parallel batched requests using a thread pool.

 

-
Adding more outputs

+
Adding more outputs#

 
The above example defined a black-box model, with predictions but no access to
 internals. If we want a richer view into the model’s behavior, we can add
 additional return fields corresponding to hidden-state activations, gradients,
@@ -534,7 +471,7 @@ 
Adding more outputs
-
Optional inputs

+
Optional inputs#

 
By default, LIT treats input_spec fields as required. However, this can be set
 to false if you wish to define optional model inputs. For example, a model that
 can accept pre-tokenized inputs might have the following spec:

@@ -558,7 +495,7 @@ 
Optional inputs
-
Interpretation Components

+
Interpretation Components#

 
Backend interpretation components include metrics, salience maps, visualization
 aids like UMAP, and
 counterfactual generator plug-ins.

@@ -572,7 +509,7 @@ 
Interpretation ComponentsTokenEmbeddings input and corresponding output, as
 well as a label field Target to pin the gradient target to the same class as
 an input and corresponding output. See the
-GLUE models class
+GLUE models class
 for an example of these spec requirements.

 
The core API involves implementing the run() method:

 
  def run(self,
@@ -631,7 +568,7 @@ 
Interpretation Components
-
Metrics

+
Metrics#

 
For metrics, the
 SimpleMetrics
 class implements the spec-matching and input-unpacking logic to satisfy the
@@ -665,7 +602,7 @@ 
Metrics¶
 on the unpacked values.

 

 

-
Generators

+
Generators#

 
Conceptually, a generator is just an interpreter that returns new input
 examples. These may depend on the input only, as for techniques such as back-
 translation, or can involve feedback from the model, such as for adversarial
@@ -701,7 +638,7 @@ 
Generatorsconfig, such as the list of substitutions for the
 word replacer.

 

-
Backtranslator Generator

+
Backtranslator Generator#

 
The backtranslator
 generator translates text segment inputs into foreign languages and back to the
 source language in order to create paraphrases.
@@ -715,7 +652,7 @@ 
Backtranslator Generator
-
Configuration UI

+
Configuration UI#

 
Interpreter components support an optional config option to specify run-time
 options, such as the number of samples for LIME or the pivot languages for
 back-translation. LIT provides a simple DSL to define these options, which will
@@ -771,7 +708,7 @@ 
Configuration UI
-
Type System

+
Type System#

 
LIT passes data around (e.g., between the server and the web app) as flat
 records with string keys. In Python types these are Mapping[str, ...] and in
 TypeScript types these are {[key: string]: unknown}. LIT serializes these
@@ -794,7 +731,7 @@ 
Type Systemtypes.py, with
 parallel implementations in lit_types.ts.

 

-
Conventions

+
Conventions#

 
LIT supports several different “kinds” of Specs (input vs output vs meta,
 etc.), and their use in context has specific implications, described
 per base class below.

@@ -810,7 +747,7 @@ 
Conventionslit_nlp.api.model.Model

+
  • lit_nlp.api.model.Model
    
 
      
 
    • .input_spec() -> Spec describes the shape required of all records
 passed into the Model.predict() function via the inputs argument. LIT
@@ -825,7 +762,7 @@ 
      Conventionslit_nlp.api.components.[Interpreter | Generator]
      
+
    • lit_nlp.api.components.[Interpreter | Generator]
      
 
        
 
      • .config_spec() -> Spec describes the user-configurable parameters
 for running this component. Returning an empty Spec means that this
@@ -841,7 +778,7 @@ 
        Conventionslit_nlp.api.components.Metrics
        
+
      • lit_nlp.api.components.Metrics
        
 
          
 
        • .config_spec() -> Spec describes the user-configurable parameters
 for running this component. Returning an empty Spec means that this
@@ -881,7 +818,7 @@ 
          Conventionslanguage model
+language model
 might output 'tokens': lit_types.Tokens(...), and accept as (optional)
 input 'tokens': lit_types.Tokens(required=False, ...). An interpretability
 component could take output from the former, swap one or more tokens (e.g.
@@ -890,17 +827,17 @@ 
          Conventions
-
          Compatibility Checks
          
+
          Compatibility Checks#
          
 
          LIT’s type system plays a critical role in ensuring reliability of and
 interoperability between the Model, Dataset, Interpreter, Generator, and
 Metrics classes:
          
 
            
 
          • The Model-Dataset compatibility check ensures that the
 Model.input_spec() is a subset of the Dataset.spec(). The base
-Model class provides a robust and universal implementation of
+Model class provides a robust and universal implementation of
 this check in the is_compatible_with_dataset() API, but you can override
 this method in your Model subclass if you so choose.
            • 
-
          • All lit_nlp.api.components classes provide an
+
          • All lit_nlp.api.components classes provide an
 is_compatible API to check their compatibility against Models and
 Datasets, as appropriate. For example, the
 WordReplacer generator only checks against the Dataset
@@ -918,9 +855,11 @@ 
            Compatibility ChecksSpecs.
            
 
            When implementing your own LIT components and modules, you can use
-utils.find_spec_keys() (Python) and
-findSpecKeys() (TypeScript) to identify fields of interest in a
-Spec. These methods recognize and respect subclasses. For example,
+utils.find_spec_keys()
+(Python) and
+findSpecKeys()
+(TypeScript) to identify fields of interest in a Spec. These methods recognize
+and respect subclasses. For example,
 utils.find_spec_keys(spec, Scalar) will also match any RegressionScore
 fields, but utils.find_spec_keys(spec, RegressionScore) will not return all
 Scalar fields in the Spec.
            
@@ -933,7 +872,7 @@ 
            Compatibility Checks
 
    • 
 
    
-
    An In-Depth Example
    
+
    An In-Depth Example#
    
 
    Consider the following example from the MNLI demo. The
 MultiNLI dataset might define the following Spec.
    
 
    # Dataset.spec()
@@ -999,14 +938,9 @@ 
    An In-Depth Exampleexamples for more.
    
 
    
 
    
-
    Available types
    
-
    The full set of LitTypes is defined in
-types.py. Numeric types
-such as Integer and Scalar have predefined ranges that can be overridden
-using corresponding min_val and max_val attributes as seen in
-penguin data
-INPUT_SPEC. The different types available in LIT are summarized in the table
-below.
    
+
    Available types#
    
+
    The full set of LitTypes is defined in types.py. Numeric types such as Integer and Scalar have predefined ranges that can be overridden using corresponding min_val and max_val attributes as seen here. The different types available in LIT are summarized
+in the table below.
    
 
    Note: Bracket syntax, such as <float>[num_tokens], refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.
    
 
    
@@ -1150,11 +1084,11 @@ 
    Available types
-
    Server Configuration
    
+
    Server Configuration#
    
 
    Some properties of the LIT frontend can be configured from Python as
 arguments to dev_server.Server(). These include:
    
 
 
    
 
    
-
    Multi-label classification
    
+
    Multi-label classification#
    
 
    LIT supports multi-label tasks, when a model can label a single example with
 more than one label. Multi-label classification is implemented with the
 SparseMultilabelPreds and SparseMultilabel types.
    
 
 
    Generated Text Module
    
 
    
 
    
-
    Span Labeling and Structured Prediction
    
+
    Span Labeling and Structured Prediction#
    
 
    LIT can support a variety of structured prediction types, and provides rich,
 interactive visualizations.
    
 
      
@@ -458,7 +404,7 @@ 
      Span Labeling and Structured PredictionStructured Predictions Module
      
 
    
 
    
-
    Multiple input segments
    
+
    Multiple input segments#
    
 
    LIT can easily handle multiple text fields, or a mix of text, categorical,
 scalar, and other input features. LIT does not explicitly “privilege” one input
 field, and metadata in the model spec can be used to align gradients, attention,
@@ -468,11 +414,11 @@ 
    Multiple input segmentsDataset class documentation and the corresponding
 Model.
    
 
  • For a more involved code example including per-token gradients, see
-examples/glue/demo.py.
    • 
+examples/glue_demo.py.
    
 
 
    
 
    
-
    Tabular data
    
+
    Tabular data#
    
 
    LIT can be used as a replacement for the What-If Tool
 but with more extensibility, when working with predictions over tabular data.
    
 
    Some interpreters, such as Kernel SHAP, require models that use tabular data. In
@@ -486,10 +432,10 @@ 
    Tabular dataSparseMultilabel) field.
    
 
 
    For a demo using a penguin stats dataset/binary classification task, see
-examples/penguin/demo.py.
    
+examples/penguin_demo.py.
    
 
    
 
    
-
    Images
    
+
    Images#
    
 
    LIT also contains support for models with images as input features or generated
 images as model output. The LIT type ImageBytes can be used as a feature in
 datasets and as part of an input spec or output spec for a model. That feature’s
@@ -497,10 +443,17 @@ 
    ImagesNOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don’t break existing checked-in code with
 such a change.
    
+    
 
    
 
    
 
    
-
    Token-based Salience
    
+
    Token-based Salience#
    
 
    LIT supports several methods for token-based input salience, including
 gradient-based methods as well as black-box techniques like LIME that don’t
 require any access to model internals. Output is rendered in the Salience Maps
@@ -512,7 +465,7 @@ 
    Token-based SalienceRegressionScore) and generation (GeneratedText or
 GeneratedTextCandidates) outputs.
    
 
    
-
    Gradient Norm
    
+
    Gradient Norm#
    
 
    This is a simple method, in which salience scores are proportional to the L2
 norm of the gradient, i.e. the score for token \(i\) is:
    
 
    
@@ -537,7 +490,7 @@ 
    Gradient Norm
-
    Gradient-dot-Input
    
+
    Gradient-dot-Input#
    
 
    In this method, salience scores are proportional to the dot product of the input
 embeddings and their gradients, i.e. for token \(i\) we compute:
    
 
    
@@ -546,8 +499,8 @@ 
    Gradient-dot-Input\(\hat{y}\), while a negative score suggests that the prediction would be
-stronger if that token was removed.
    
+\(\hat{y}\), while a negative score suggests that the prediction would be stronger
+if that token was removed.
    
 
    To enable this method, your model should, as part of the
 output spec and predict() implementation:
    
 
    
 
    
-
    LIME
    
+
    LIME#
    
 
    LIME is a black-box salience method that
 does not require access to any model internals. It works by generating a set of
 perturbed inputs - generally, by dropping out or masking tokens - and training a
@@ -625,7 +578,7 @@ 
    LIMERegressionScore) model.
    
 
    
 
    
-
    Target Selection on Classification Output
    
+
    Target Selection on Classification Output#
    
 
    For all salience methods, we require that the class to explain is given as a
 label field in the input. For example, if the input example is:
    
 
    {"text": "this movie was terrible!", "label": "0"}
@@ -662,7 +615,7 @@ 
    Target Selection on Classification Output
-
    Sequence Salience
    
+
    Sequence Salience#
    
 
    Sequence salience generalizes token-based salience to text-to-text models,
 allowing you to explain the impact of the prompt tokens on parts of the model
 output.
    
@@ -672,7 +625,7 @@ 
    Sequence SalienceSequence salience - visualization
    
 
    The UI supports multiple options for analysis, including:
    
 
 
    
 
    
-
    Salience Clustering
    
+
    Salience Clustering#
    
 
    LIT includes a basic implementation of the salience clustering method from
 Ebert et al. 2022, which uses k-means on a
 salience-weighted bag-of-words representation to find patterns in model
@@ -723,7 +676,7 @@ 
    Salience Clusteringthe paper.
    
 
    
 
    
-
    Tabular Feature Attribution
    
+
    Tabular Feature Attribution#
    
 
    Tabular feature attribution seeks to understand the importance of a column of
 data on a model’s predictions. LIT’s tabular feature attribution module supports
 this analysis using the SHAP interpreter.
@@ -734,7 +687,7 @@ 
    Tabular Feature AttributionTabular feature attribution module module
    
 
    
 
    
-
    Pixel-based Salience
    
+
    Pixel-based Salience#
    
 
    LIT also supports pixel-based salience methods, for models that take images as
 inputs. Output is rendered in the Salience Maps module in the LIT UI, which
 allows for comparison of multiple methods at once.
    
@@ -754,7 +707,9 @@ 
    Pixel-based Salience
 
    The model should be able to accept input images as numpy arrays in addition
-to accepting base64 URL encoded format.
    
+to accepting base64 URL encoded format. See
+    mobilenet.py
+for an example.
    
 
 
 
    A variety of image saliency techniques are implemented for models that return
@@ -765,8 +720,33 @@ 
    Pixel-based SalienceEach of these techniques returns a saliency map image as a base64-encoded string
 through the ImageSalience type.
    
 
    
+
    
+
    Attention#
    
+
    LIT can display a visualization of attention heads from transformers and other
+models:
    
+
    Attention Visualization
    
+
    To enable this, your model should return one or more fields of the type
+AttentionHeads, with values as arrays of shape <float>[num_heads, num_tokens, num_tokens]. Each field represents a set of heads at a single layer of the
+model, so models will often have more than one:
    
+
       def output_spec(self) -> types.Spec:
+     return {
+         # ...
+         "tokens": lit_types.Tokens(parent="input_text"),
+         "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
+         "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
+         "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
+         # ...
+     }
+
    
+
    
+
    The align_in and align_out attributes are the names of Tokens fields for
+the source and target tokens for that layer. For self-attention (such as in
+BERT) these would name the same field, but for encoder-decoder attention they
+could reference different token sets (such as “input_tokens” and
+“target_tokens”).
    
+
    
 
    
-
    Embedding Projector
    
+
    Embedding Projector#
    
 
    LIT includes a version of the
 embedding projector which can be used to
 visualize the latent space of your model, in order to find clusters or patterns
@@ -782,16 +762,16 @@ 
    Embedding Projector<float>[emb_dim] for each example.
    
 
    
 
    
-
    Aggregate Analysis
    
+
    Aggregate Analysis#
    
 
    
-
    Metrics
    
+
    Metrics#
    
 
    LIT includes common metrics for classification, regression, and seq2seq (BLEU)
 by default, which will appear in the table when the appropriate types are
 present in the model output and input data. Metrics can be computed on the whole
 dataset, a selected subset, or on facets defined by particular features. For
 example, we could facet by class label:
    
 
    Metrics Table
    
-
    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Metrics” tab.
    
+
    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Performance” tab.
    
 
    To enable metrics, your model should set the parent attribute on one or more
 output fields, pointing to the name of the input field that it should be
 evaluated against. For example, for classification, the data spec might have:
    
@@ -816,19 +796,19 @@ 
    MetricsAPI documentation for more.
    
 
    
 
    
-
    Confusion Matrix
    
+
    Confusion Matrix#
    
 
    LIT includes a powerful and flexible confusion matrix, which can be used to
 compare predictions to gold labels as well as to compare between two models or
 between different categorical features. You can click cells or row/column
 headers to select a subset of examples, which is useful for intersectional
 analysis.
    
 
    Confusion Matrix
    
-
    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Metrics” tab.
    
+
    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Performance” tab.
    
 
    The confusion matrix is supported for classification models, or if the input
 data includes any categorical features (CategoryLabel).
    
 
    
 
    
-
    Scalar Plots
    
+
    Scalar Plots#
    
 
    LIT includes scatterplots for scalar features, including plain scalars (Scalar
 or RegressionScore) as well as per-class probabilities from classification
 output (MulticlassPreds).
    
@@ -840,7 +820,7 @@ 
    Scalar Plotshttps://pair-code.github.io/lit/demos/glue.html and navigate to the “Predictions” tab.
    
 
    
 
    
-
    Binary Classification Thresholds
    
+
    Binary Classification Thresholds#
    
 
    For binary classification models, LIT contains a module for setting
 classification thresholds, which determine at what score for the positive class
 the model determines that an example should be classified as belonging to the
@@ -876,7 +856,7 @@ 
    Binary Classification Thresholds
    
 
    
 
    
-
    Partial Dependence Plots
    
+
    Partial Dependence Plots#
    
 
    For classification or regression models with CategoryLabel or Scalar input
 features, the Partial Dependence Plots module shows plots indicating the effect
 that changing those individual features has on model output.
    
@@ -894,10 +874,9 @@ 
    Partial Dependence PlotsPartial Dependence Plots Module
    
-
    To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the “Predictions” tab.
    
 
    
 
    
-
    Dive
    
+
    Dive#
    
 
    Dive is a visualization module, inspired by our prior work on
 Facets Dive and its use in the
 What-If Tool, that enables
@@ -921,7 +900,7 @@ 
    Dive
 
    
 
    
-
    TCAV
    
+
    TCAV#
    
 
    Many interpretability methods provide importance values per input feature (e.g,
 token). By contrast, TCAV shows the
 importance of high-level concepts (e.g., color, gender, race) for a prediction
@@ -939,7 +918,7 @@ 
    TCAV
 
    
-
    Example
    
+
    Example#
    
 
    1.) To use TCAV, begin by creating one or more ‘concept’ slices.
    
 
    Every dataset/model is different, but for images, as low as 15 data points are
 shown to be sufficient. Start by adding at least 3 data points and add more as
@@ -965,7 +944,7 @@ 
    Example
    TCAV2
    
 
    
 
    
-
    Statistical Significance
    
+
    Statistical Significance#
    
 
    One of the pitfalls with the TCAV method is the potential generating meaningless
 CAVs, since any randomly chosen set of images will still produce a CAV (even if
 it is not meaningful).
    
@@ -989,13 +968,13 @@ 
    Statistical Significance
-
    Sorting by Cosine Similarity
    
+
    Sorting by Cosine Similarity#
    
 
    The option to sort examples by cosine similarity to a CAV will be available in
 an upcoming release.
    
 
    
 
    
 
    
-
    Counterfactual Analysis
    
+
    Counterfactual Analysis#
    
 
    While aggregate metrics can give a picture of overall behavior, and salience
 maps can give quick insight into a model’s local behavior, many questions about
 model behavior are best answered in a counterfactual setting: “How does my model
@@ -1006,7 +985,7 @@ 
    Counterfactual Analysis
 
    
-
    Manual Editing
    
+
    Manual Editing#
    
 
    Examples can be edited manually in the Datapoint Editor module:
    
 
    Manual Edit in the Datapoint Editor
    
 
    The “Add and Compare” button can be used to enter comparison mode, which will
@@ -1020,7 +999,7 @@ 
    Manual EditingPair Selection Controls
    
 
    
 
    
-
    Generators
    
+
    Generators#
    
 
    The Generator Module supports automatic generation of counterfactuals
 through a variety of plug-in components:
    
 
    Generator Module
    
@@ -1143,6 +1122,7 @@ 
    GeneratorsSalience Clustering
 
  • Tabular Feature Attribution
    • 
 
  • Pixel-based Salience
    • 
+
  • Attention
    • 
 
  • Embedding Projector
    • 
 
  • Aggregate Analysis
@@ -335,31 +288,91 @@ 
    Textual Similarity (STS-B) 
 
    
 
    
-
    Sequence-to-Sequence 
    
+
    Sequence-to-Sequence #
    
 
    
-
    Gemma 
    
-
    Code:
-examples/prompt_debugging/server.py
    
+
    Gemma #
    
+
    Code: examples/lm_salience_demo.py
    
 
      
-
    • Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
-and Transformers (with PyTorch).
      • 
+
    • Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.
      • 
 
    • Interactively debug LLM prompts using
 sequence salience.
      • 
 
    • Multiple salience methods (grad-l2 and grad-dot-input), at multiple
-granularities: token-, word-, line-, sentence-, and paragraph-level.
      • 
+granularities: token-, word-, sentence-, and paragraph-level.
      
 
    
 
    Tip: check out the in-depth walkthrough at
 https://ai.google.dev/responsible/model_behavior, part of the Responsible
 Generative AI Toolkit.
    
 
    
+
    
+
    T5 #
    
+
    Hosted instance: https://pair-code.github.io/lit/demos/t5.html 
    
+Code: examples/t5_demo.py
    
+
      
+
    • Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel
+formats.
      • 
+
    • Visualize beam candidates and highlight diffs against references.
      • 
+
    • Visualize per-token decoder hypotheses to see where the model veers away
+from desired output.
      • 
+
    • Filter examples by ROUGE score against reference.
      • 
+
    • Embeddings from last layer of model, visualized with UMAP or PCA.
      • 
+
    • Task wrappers to handle pre- and post-processing for summarization and
+machine translation tasks.
      • 
+
    • Pre-loaded eval sets for CNNDM and WMT.
      • 
+
    
+
    Tip: check out a case study for this demo on the public LIT website:
+https://pair-code.github.io/lit/tutorials/generation
    
+
    
+
    
+
    
+
    
+
    Language Modeling #
    
+
    
+
    BERT and GPT-2 #
    
+
    Hosted instance: https://pair-code.github.io/lit/demos/lm.html 
    
+Code: examples/lm_demo.py
    
+
      
+
    • Compare multiple BERT and GPT-2 models side-by-side on a variety of
+plain-text corpora.
      • 
+
    • LM visualization supports different modes:
      
+
        
+
      • BERT masked language model: click-to-mask, and query model at that
+position.
        • 
+
      • GPT-2 shows left-to-right hypotheses for each target token.
        • 
+
      
+
      • 
+
    • Embedding projector to show latent space of the model.
      • 
+
    
+
    
+
    
+
    
+
    
+
    Structured Prediction #
    
+
    
+
    Gender Bias in Coreference #
    
+
    Hosted instance: https://pair-code.github.io/lit/demos/coref.html 
    
+Code: examples/coref/coref_demo.py
    
+
      
+
    • Gold-mention coreference model, trained on
+OntoNotes.
      • 
+
    • Evaluate on the Winogender schemas
+(Rudinger et al. 2018) which test for
+gendered associations with profession names.
      • 
+
    • Visualizations of coreference edges, as well as binary classification
+between two candidate referents.
      • 
+
    • Stratified metrics for quantifying model bias as a function of pronoun
+gender or Bureau of Labor Statistics profession data.
      • 
+
    
+
    Tip: check out a case study for this demo on the public LIT website:
+https://pair-code.github.io/lit/tutorials/coref
    
+
    
 
    
 
    
 
    
-
    Multimodal 
    
+
    Multimodal #
    
 
    
-
    Tabular Data: Penguin Classification 
    
+
    Tabular Data: Penguin Classification #
    
 
    Hosted instance: https://pair-code.github.io/lit/demos/penguins.html 
    
-Code: examples/penguin/demo.py
    
+Code: examples/penguin_demo.py
    
 
    
 
    
 
    • 
 
@@ -432,6 +456,7 @@ 
    Tabular Data: Penguin Classification 
 
 
  • Regression / Scoring 
@@ -458,8 +493,8 @@ 
    Tabular Data: Penguin Classification 
+    
         Running LIT in a Docker container - 🔥LIT 1.0 documentation
       
-    
+    
     
     
     
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+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       
       
       
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+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       
       
     
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     Auto light/dark mode
     
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       
       
       
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           Back to top
         
         
    
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+          
 
    
             
@@ -263,33 +200,34 @@
             
           
         
    
-        
    
+        
    
           
    
-
    Running LIT in a Docker container
    
-
+
    Running LIT in a Docker container#
    
+
 
    Users might want to deploy LIT onto servers for public-facing, long-running
 instances. This is how we host the LIT demos found on
 https://pair-code.github.io/lit/demos/. This doc describes the basic usage of
 LIT’s built-in demos, how to integrate your custom demo into this
    
 
    
-
    Basic Usage
    
+
    Basic Usage#
    
 
    LIT can be run as a containerized app using Docker or
 your preferred engine. This is how we run our
 hosted demos.
    
-
    We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the lit_nlp/examples directory.
-The Dockerfile installs all necessary dependencies for LIT and builds the
+
    We provide a basic
+Dockerfile that you can
+use to build and run any of the demos in the lit_nlp/examples directory. The
+Dockerfile installs all necessary dependencies for LIT and builds the
 front-end code from source. Then it runs gunicorn as
 the HTTP server, invoking the get_wsgi_app() method from our demo file to get
 the WSGI app to serve. The options provided to gunicorn for our use-case can be
 found in
 gunicorn_config.py.
 You can find a reference implementation in
-glue/demo.py.
    
-
    Use the following shell
-https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the
-default Docker image for LIT from the provided Dockerfile, and then run a
-container from that image. Comments are provided in-line to help explain what
-each step does.
    
+glue_demo.py or
+lm_demo.py.
    
+
    Use the following shell commands to build the default Docker image for LIT from
+the provided Dockerfile, and then run a container from that image. Comments
+are provided in-line to help explain what each step does.
    
 
    # Build the docker image using the -t argument to name the image. Remember to
 # include the trailing . so Docker knows where to look for the Dockerfile.
 docker build --file Dockerfile --tag lit-nlp .
@@ -305,11 +243,14 @@ 
    Basic Usage
    # DEMO_NAME is used to complete the Python module path
 #
-#     "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()"
+#     "lit_nlp.examples.$DEMO_NAME"
 #
 # Therefore, valid values for DEMO_NAME are Python module paths in the
-# lit_nlp/examples directory, such as glue, penguin, tydi, etc.
-docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
+# lit_nlp/examples directory, such as
+#
+#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.
+#   * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc.
+docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp
 
 # Use the DEMO_PORT environment variable as to change the port that LIT uses in
 # the container. Be sure to also change the -p option to map the container's
@@ -320,16 +261,16 @@ 
    Basic Usage# containers on your machine using the combination of the DEMO_NAME and
 # DEMO_PORT arguments, and docker run with the -d flag to run the container in
 # the background.
-docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
-docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp
+docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp
+docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp
 
    
 
    
 
    
 
    
-
    Integrating Custom LIT Instances with the Default Docker Image
    
+
    Integrating Custom LIT Instances with the Default Docker Image#
    
 
    Many LIT users create their own custom LIT server script to demo or serve, which
 involves creating an executable Python module with a main() method, as
-described in the Python API docs.
    
+described in the Python API docs.
    
 
    These custom server scripts can be easily integrated with LIT’s default image as
 long as your server meets two requirements:
    
 
      
@@ -392,7 +333,7 @@ 
      Integrating Custom LIT Instances with the Default Docker Image
-
      Building Your Own Image
      
+
      Building Your Own Image#
      
 
      Coming soon.
      
 
    
 
    
@@ -468,8 +409,8 @@ 
    Building Your Own Image
   
    
 
    
-    
+    
     
-    
+    
     
 
\ No newline at end of file
diff --git a/docs/documentation/faq.html b/docs/documentation/faq.html
index 32bd2624..18038110 100644
--- a/docs/documentation/faq.html
+++ b/docs/documentation/faq.html
@@ -5,10 +5,10 @@
     
 
 
-    
+    
         Frequently Asked Questions - 🔥LIT 1.0 documentation
       
-    
+    
     
     
     
@@ -69,7 +69,7 @@
   
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+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       
       
       
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+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       
       
     
@@ -92,7 +92,7 @@
   
     Auto light/dark mode
     
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       
       
       
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-          
    
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+          
 
    
             
@@ -263,23 +200,25 @@
             
           
         
    
-        
    
+        
    
           
    
-
    Frequently Asked Questions
    
-
+
    Frequently Asked Questions#
    
+
 
 
    Looking for help? Submit bugs, ask questions, suggest content, and request
 features on our
 Github issues list.
    
 
    
-
    Model and Data Types
    
+
    Model and Data Types#
    
 
    LIT can handle a variety of models with different input and output types, and
 works with any modern ML framework. For more information, see
 Framework & Model Support.
    
 
    In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:
    
 
    
 
    
-
    Languages
    
+
    Languages#
    
 
    All strings in LIT are unicode and most components use model-provided
 tokenization if available, so in most cases non-English languages and non-Latin
-scripts should work without any modifications.
    
+scripts should work without any modifications. For examples, see:
    
+
      
+
    • XNLI demo -
+cross-lingual NLI, with up to 15 languages supported via a multilingual BERT
+model.
      • 
+
    • T5 demo -
+includes WMT data for machine translation
      • 
+
    
 
    
 
    
-
    Scale
    
+
    Scale#
    
 
    
-
    Dataset Size
    
+
    Dataset Size#
    
 
    LIT can comfortably handle 10k-100k datapoints, depending on the speed of the
 server (for hosting the model) and your local machine (for viewing the UI). When
 working with large datasets, there are a couple caveats:
    
@@ -323,7 +269,7 @@ 
    Dataset Size
-
    Large Models
    
+
    Large Models#
    
 
    LIT can work with large or slow models, as long as you can wrap them into the
 model API. If you have more than a few preloaded datapoints, however, you’ll
 probably want to use warm_start=1 (or pass --warm_start=1 as a flag) to
@@ -342,17 +288,17 @@ 
    Large Models
-
    Privacy and Security
    
+
    Privacy and Security#
    
 
    LIT allows users to query the model, as well as to view the loaded evaluation
 data. The LIT UI state is ephemeral and exists only in the browser window;
 however, model predictions and any newly-generated examples (including as
 manually entered in the web UI) are stored in server memory, and if --data_dir
 is specified, may be cached to disk.
    
 
    LIT has the ability to create or edit datapoints in the UI and then save them to
-disk. If you do not want the tool to be able to write edited datapoints to disk,
-then pass the --demo_mode runtime flag to the LIT server.
    
+disk. If you do not want the tool to be able to write edited datapoints to
+disk, then pass the --demo_mode runtime flag to the LIT server.
    
 
    
-
    I have proprietary data. Is LIT secure for my team to use?
    
+
    I have proprietary data. Is LIT secure for my team to use?#
    
 
    We don’t store, collect or share datasets, models or any other information
 loaded into LIT. When you run a LIT server, anyone with access to the web
 address of the server will be able to see data from the loaded datasets and
@@ -366,9 +312,9 @@ 
    I have proprietary data. Is LIT secure for my team to use?
-
    Workflow and Integrations
    
+
    Workflow and Integrations#
    
 
    
-
    Sending examples from another tool
    
+
    Sending examples from another tool#
    
 
    LIT can read input fields directly from the URL; they should be encoded as
 data_<fieldname>=<value>, and field names should match those in the (default)
 dataset.
    
@@ -377,7 +323,7 @@ 
    Sending examples from another tooldata0, data1, data2, e.g. data0_<fieldname>=<value>.
    
 
    
 
    
-
    Downloading or exporting data
    
+
    Downloading or exporting data#
    
 
    Currently, there are three ways to export data from the LIT UI:
    
 
      
 
    • In the Data Table, you can copy or download the current view in CSV format -
@@ -391,7 +337,7 @@ 
      Downloading or exporting data
-
      Loading data from the UI
      
+
      Loading data from the UI#
      
 
      There is limited support for this via the settings (⚙️) menu. Select a dataset,
 and enter a path to load from:
      
 
      Load data from the UI
      
@@ -400,7 +346,7 @@ 
      Loading data from the UI
-
      Using components outside the LIT UI
      
+
      Using components outside the LIT UI#
      
 
      Python components such as models, datasets, and generators are designed to
 support standalone use. These don’t depend on the LIT serving framework, and you
 can treat them as any other Python class and use from Colab, regular scripts,
@@ -416,13 +362,15 @@ 
      Using components outside the LIT UI
-
      Training models with LIT
      
+
      Training models with LIT#
      
 
      LIT is primarily an evaluation/inference-time tool, so we don’t provide any
 official training APIs. However, to facilitate code reuse you can easily add
 training methods to your model class. In fact, several of our demos do exactly
 this, using LIT’s Dataset objects to manage training data along with standard
 training APIs (such as Keras’ model.fit()). See
-glue/models.py
+quickstart_sst_demo.py
+and/or
+glue_models.py
 for examples.
      
 
    
 
    
@@ -507,8 +455,8 @@ 
    Training models with LIT
-    
+    
     
-    
+    
     
 
\ No newline at end of file
diff --git a/docs/documentation/frontend_development.html b/docs/documentation/frontend_development.html
index de68d9af..f3b9bbea 100644
--- a/docs/documentation/frontend_development.html
+++ b/docs/documentation/frontend_development.html
@@ -5,10 +5,10 @@
     
 
 
-    
+    
         Frontend Developer Guide - 🔥LIT 1.0 documentation
       
-    
+    
     
     
     
@@ -69,7 +69,7 @@
   
     Light mode
     
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
       
       
       
@@ -84,7 +84,7 @@
   
     Dark mode
     
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
       
       
     
@@ -92,7 +92,7 @@
   
     Auto light/dark mode
     
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
       
       
       
@@ -102,60 +102,6 @@
       
     
   
-  
-    Auto light/dark, in light mode
-    
-      
-      
-      
-      
-      
-      
-      
-      
-      
-      
-    
-  
-  
-    Auto light/dark, in dark mode
-    
-      
-      
-      
-      
-      
-      
-      
-      
-      
-      
-    
-  
-  
-    
-      
-      
-      
-      
-  
-  
-    
-      
-      
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-      
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-    
-  
 
 
 
@@ -167,8 +113,6 @@
   
 
 
-
-
 
 
 
    
@@ -186,8 +130,7 @@
       
    
         
@@ -243,17 +186,11 @@
           Back to top
         
         
    
-          
    
-  
-    
-    
-  
-
    
+          
 
    
             
@@ -263,15 +200,15 @@
             
           
         
    
-        
    
+        
    
           
    
-
    Frontend Developer Guide
    
-
+
    Frontend Developer Guide#
    
+
 
 
    This document aims to describe the current LIT frontend system, including
 conventions, best practices, and gotchas.
    
 
    
-
    High Level Overview
    
+
    High Level Overview#
    
 
    LIT is powered by two central pieces of tech -
 lit-element for components and HTML
 rendering, and mobx for observable-oriented
@@ -286,13 +223,13 @@ 
    High Level Overview
-
    Application Architecture
    
+
    Application Architecture#
    
 
    The LIT client frontend is roughly divided into three conceptual groups -
 Modules (which render visualizations), Services (which manage data), and
 the App itself (which coordinates initialization of services and determines
 which modules to render).
    
 
    
-
    Bootstrapping
    
+
    Bootstrapping#
    
 
    The LIT app bootstrapping takes place in two steps: First, the served
 index.html
 page contains a single web component for the
@@ -304,16 +241,16 @@ 
    BootstrappingLitModule components, a process about which we’ll go
 into greater detail later.
    
 
    The JS bundle entry point is
-main.ts, which first
-imports the loaded, the <lit-app> web component is declared, and attaches
-itself to the DOM, waiting for the app to be initialized.
    
+main.ts, which
+first imports the loaded, the <lit-app> web component is declared, and
+attaches itself to the DOM, waiting for the app to be initialized.
    
 
    The second step is kicking off app initialization. The
 LitApp singleton
 class is provided with a layout declaring which LitModule components to use,
 then builds the app services and kicks off app initialization and loading data.
    
 
    
 
    
-
    Layout
    
+
    Layout#
    
 
    A layout defines the arraignment of LitModule classes in the UI. Layouts are
 specified in Python as LitCanonicalLayout instances, and LIT includes three
 pre-configured layouts in
@@ -324,7 +261,7 @@ 
    Layout
    default: The original LIT layout with a single group of modules on top for
 exploring and selecting data, and a collection of tabs supporting different
 analytical tasks on the bottom; and
    • 
-
  • three_panel: A three-panel layout that puts exploratory data
+
  • experimental: A three-panel layout that puts exploratory data
 visualizations at full-page height on the left, tools for inspecting and
 manipulating examples and their associated predictions in the upper right,
 and a collection of tabs supporting different analytical tasks in the lower
@@ -333,14 +270,14 @@ 
    Layout
 
    You can also add custom layouts to your LIT
 instance by defining one or more LitCanonicalLayout instances and passing them
-to the server. For an example, see
-prompt_debugging/layouts.py.
    
+to the server. For an example, see CUSTOM_LAYOUTS in
+lm_demo.py.
    
 
    Note: The pre-configured layouts are added to every LitApp instance using
 dictionary updates where
 the Mapping passed to the LitApp constructor overrides the pre-configured
 layouts Mapping. Thus, you can remove or change these pre-configured layouts
 as you like by passing a Mapping where the values of simple, default,
-and/or three_panel is None (to remove) or a LitCanonicalLayout instance
+and/or experimental is None (to remove) or a LitCanonicalLayout instance
 (to override) as you desire.
    
 
    The actual layout of components in the LIT UI, see
 <lit-modules>,
@@ -352,7 +289,7 @@ 
    Layoutmodules_service.
    
 
    • 
 
    
-
    Initialization
    
+
    Initialization#
    
 
    Finally, the LIT App initializes by building the various service classes and
 starting the initial load of data from the server. This process consists of:
    
 
      
@@ -364,7 +301,7 @@ 
      Initialization
-
      Modules (LitModule)
      
+
      Modules (LitModule)#
      
 
      The
 LitModule
 is the base class from which all module components derive. It provides a number
@@ -436,7 +373,7 @@ 
      Modules (LitModule)
-
      Setup
      
+
      Setup#
      
 
      First, a LitModule must declare a static title string (1) and template
 function (2). The template function determines how the modules layout renders
 the component template and passes in module properties, such as the name of the
@@ -469,7 +406,7 @@ 
      Setup
 other TypeScript files in different build units.
      
 
    
 
    
-
    Functionality
    
+
    Functionality#
    
 
    The above module has a very simple task - When the user selects input data, it
 makes a request to an API service to fetch and display a pig latin translation
 of the data. Since we’re using mobx observables to store and compute our state,
@@ -508,7 +445,7 @@ 
    Functionality
-
    Escape Hatches
    
+
    Escape Hatches#
    
 
    Finally, it’s worth noting that the declarative template-based rendering setup,
 while effective for handling most component render logic, is sometimes
 inadequate for more advanced visualizations. In particular, the template
@@ -538,7 +475,7 @@ 
    Escape Hatches
-
    Stateful Child Elements
    
+
    Stateful Child Elements#
    
 
    Some modules may contain stateful child elements, where the element has some
 internal state that can have an effect on the module that contains it. Examples
 of this include any modules that contain the
@@ -577,7 +514,7 @@ 
    Stateful Child Elements
 
    
 
    
-
    Style Guide
    
+
    Style Guide#
    
 
 
@@ -323,6 +262,7 @@ 
    Research
 
  • Salience Clustering
    • 
 
  • Tabular Feature Attribution
    • 
 
  • Pixel-based Salience
    • 
+
  • Attention
    • 
 
  • Embedding Projector
    • 
 
  • Aggregate Analysis
    • 
 
  • TCAV
    • 
@@ -430,10 +370,8 @@ 
    Research
    - + - - - + \ No newline at end of file diff --git a/docs/documentation/search.html b/docs/documentation/search.html index 3c64769b..1e5289dc 100644 --- a/docs/documentation/search.html +++ b/docs/documentation/search.html @@ -1,14 +1,11 @@ - - + - - -Search - 🔥LIT 1.0 documentation - + Search - 🔥LIT 1.0 documentation + @@ -69,7 +66,7 @@ Light mode + stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,7 +81,7 @@ Dark mode + stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> @@ -92,7 +89,7 @@ Auto light/dark mode + stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow"> @@ -102,60 +99,6 @@ - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -167,8 +110,6 @@ - -
    @@ -186,8 +127,7 @@
    @@ -246,8 +186,7 @@
    @@ -257,7 +196,7 @@
    -
    +
    - + - + diff --git a/docs/documentation/searchindex.js b/docs/documentation/searchindex.js index b0ae447b..75ece15f 100644 --- a/docs/documentation/searchindex.js +++ b/docs/documentation/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"Accessing the LIT UI in Notebooks": [[0, "accessing-the-lit-ui-in-notebooks"]], "Adding Models and Data": [[0, "adding-models-and-data"]], "Adding more outputs": [[0, "adding-more-outputs"]], "Aggregate Analysis": [[1, "aggregate-analysis"]], "An In-Depth Example": [[0, "an-in-depth-example"]], "Application Architecture": [[5, "application-architecture"]], "Available types": [[0, "available-types"]], "Backtranslator Generator": [[0, "backtranslator-generator"]], "Basic Usage": [[3, "basic-usage"]], "Binary Classification Thresholds": [[1, "binary-classification-thresholds"]], "Bootstrapping": [[5, "bootstrapping"]], "Building Your Own Image": [[3, "building-your-own-image"]], "Classification": [[1, "classification"]], "Classification ": [[2, "classification"]], "Comparing Datapoints": [[10, "comparing-datapoints"]], "Comparing Models": [[10, "comparing-models"]], "Compatibility Checks": [[0, "compatibility-checks"]], "Components and Features": [[1, "components-and-features"]], "Configuration UI": [[0, "configuration-ui"]], "Confusion Matrix": [[1, "confusion-matrix"], [10, "confusion-matrix"]], "Conventions": [[0, "conventions"]], "Counterfactual Analysis": [[1, "counterfactual-analysis"]], "Custom Client / Modules": [[5, "custom-client-modules"]], "Customizing the Layout": [[0, "customizing-the-layout"]], "Data Table": [[10, "data-table"]], "Data loading": [[1, "data-loading"]], "Datapoint Editor": [[10, "datapoint-editor"]], "Datapoint Generator": [[10, "datapoint-generator"]], "Datapoint Selections": [[10, "datapoint-selections"]], "Dataset Size": [[4, "dataset-size"]], "Datasets": [[0, "datasets"]], "Demos": [[2, "demos"]], "Design Overview": [[0, "design-overview"]], "Development Tips (open-source)": [[5, "development-tips-open-source"]], "Dive": [[1, "dive"]], "Downloading or exporting data": [[4, "downloading-or-exporting-data"]], "Embedding Projector": [[1, "embedding-projector"], [10, "embedding-projector"]], "Escape Hatches": [[5, "escape-hatches"]], "Example": [[1, "example"]], "Framework and Model Support": [[1, "framework-and-model-support"]], "Frequently Asked Questions": [[4, "frequently-asked-questions"]], "Frontend Developer Guide": [[5, "frontend-developer-guide"]], "Functionality": [[5, "functionality"]], "Gemma ": [[2, "gemma"]], "General Layout": [[10, "general-layout"]], "Generators": [[0, "generators"], [1, "generators"]], "Getting Started with LIT": [[6, "getting-started-with-lit"]], "Global Settings": [[10, "global-settings"]], "Glossary": [[7, "glossary"]], "Gradient Norm": [[1, "gradient-norm"]], "Gradient-dot-Input": [[1, "gradient-dot-input"]], "High Level Overview": [[5, "high-level-overview"]], "Hosted demos": [[6, "hosted-demos"]], "HuggingFace Transformers": [[1, "huggingface-transformers"]], "I have proprietary data. 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[[1, "manual-editing"]], "Metrics": [[0, "metrics"], [1, "metrics"]], "Metrics Table": [[10, "metrics-table"]], "Model Output": [[10, "model-output"]], "Model and Data Types": [[4, "model-and-data-types"]], "Models": [[0, "models"]], "Module Details": [[10, "module-details"]], "Modules (LitModule)": [[5, "modules-litmodule"]], "Multi-label classification": [[1, "multi-label-classification"]], "Multimodal ": [[2, "multimodal"]], "Multiple input segments": [[1, "multiple-input-segments"]], "Optional inputs": [[0, "optional-inputs"]], "Partial Dependence Plots": [[1, "partial-dependence-plots"]], "Pixel-based Salience": [[1, "pixel-based-salience"]], "Privacy and Security": [[4, "privacy-and-security"]], "Regression / Scoring": [[1, "regression-scoring"]], "Regression / Scoring ": [[2, "regression-scoring"]], "Remote or hosted models": [[1, "remote-or-hosted-models"]], "Research": [[9, "research"]], "Run an existing example ": [[6, "run-an-existing-example"]], "Running LIT in a Docker 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[[1, "manual-editing"]], "Demos": [[2, "demos"]], "Classification ": [[2, "classification"]], "Sentiment and NLI ": [[2, "sentiment-and-nli"]], "Multilingual (XNLI) ": [[2, "multilingual-xnli"]], "Regression / Scoring ": [[2, "regression-scoring"]], "Textual Similarity (STS-B) ": [[2, "textual-similarity-sts-b"]], "Sequence-to-Sequence ": [[2, "sequence-to-sequence"]], "Gemma ": [[2, "gemma"]], "T5 ": [[2, "t5"]], "Language Modeling ": [[2, "language-modeling"]], "BERT and GPT-2 ": [[2, "bert-and-gpt-2"]], "Structured Prediction ": [[2, "structured-prediction"]], "Gender Bias in Coreference ": [[2, "gender-bias-in-coreference"]], "Multimodal ": [[2, "multimodal"]], "Tabular Data: Penguin Classification ": [[2, "tabular-data-penguin-classification"]], "Image Classification with MobileNet ": [[2, "image-classification-with-mobilenet"]], "Running LIT in a Docker container": [[3, "running-lit-in-a-docker-container"]], "Basic Usage": [[3, "basic-usage"]], "Integrating Custom LIT Instances with the Default Docker Image": [[3, "integrating-custom-lit-instances-with-the-default-docker-image"]], "Building Your Own Image": [[3, "building-your-own-image"]], "Frequently Asked Questions": [[4, "frequently-asked-questions"]], "Model and Data Types": [[4, "model-and-data-types"]], "Languages": [[4, "languages"]], "Scale": [[4, "scale"]], "Dataset Size": [[4, "dataset-size"]], "Large Models": [[4, "large-models"]], "Privacy and Security": [[4, "privacy-and-security"]], "I have proprietary data. Is LIT secure for my team to use?": [[4, "i-have-proprietary-data-is-lit-secure-for-my-team-to-use"]], "Workflow and Integrations": [[4, "workflow-and-integrations"]], "Sending examples from another tool": [[4, "sending-examples-from-another-tool"]], "Downloading or exporting data": [[4, "downloading-or-exporting-data"]], "Loading data from the UI": [[4, "loading-data-from-the-ui"]], "Using components outside the LIT UI": [[4, "using-components-outside-the-lit-ui"]], "Training models with LIT": [[4, "training-models-with-lit"]], "Frontend Developer Guide": [[5, "frontend-developer-guide"]], "High Level Overview": [[5, "high-level-overview"]], "Application Architecture": [[5, "application-architecture"]], "Bootstrapping": [[5, "bootstrapping"]], "Layout": [[5, "layout"]], "Initialization": [[5, "initialization"]], "Modules (LitModule)": [[5, "modules-litmodule"]], "Setup": [[5, "setup"]], "Functionality": [[5, "functionality"]], "Escape Hatches": [[5, "escape-hatches"]], "Stateful Child Elements": [[5, "stateful-child-elements"]], "Style Guide": [[5, "style-guide"]], "Development Tips (open-source)": [[5, "development-tips-open-source"]], "Custom Client / Modules": [[5, "custom-client-modules"]], "Getting Started with LIT": [[6, "getting-started-with-lit"]], "Installation": [[6, "installation"]], "Hosted demos": [[6, "hosted-demos"]], "LIT with your model ": [[6, "lit-with-your-model"]], "Using LIT in notebooks ": [[6, "using-lit-in-notebooks"]], "Stand-alone components ": [[6, "stand-alone-components"]], "Run an existing example ": [[6, "run-an-existing-example"]], "Glossary": [[7, "glossary"]], "Learning Interpretability Tool (LIT)": [[9, "learning-interpretability-tool-lit"]], "Research": [[9, "research"]], "UI Guide": [[10, "ui-guide"]], "General Layout": [[10, "general-layout"]], "Layout Options": [[10, "layout-options"]], "Datapoint Selections": [[10, "datapoint-selections"]], "Toolbars": [[10, "toolbars"]], "Top Bar": [[10, "top-bar"]], "Global Settings": [[10, "global-settings"]], "URL Sharing": [[10, "url-sharing"]], "Main Toolbar": [[10, "main-toolbar"]], "Status Bar": [[10, "status-bar"]], "Comparing Models": [[10, "comparing-models"]], "Comparing Datapoints": [[10, "comparing-datapoints"]], "Slices": [[10, "slices"]], "Module Details": [[10, "module-details"]], "Data Table": [[10, "data-table"]], "Datapoint Editor": [[10, "datapoint-editor"]], "Datapoint Generator": [[10, "datapoint-generator"]], "Metrics Table": [[10, "metrics-table"]], "Scalars": [[10, "scalars"]], "Model Output": [[10, "model-output"]], "Salience Maps": [[10, "salience-maps"]], "User Journeys": [[10, "user-journeys"]], "Sentiment Analysis": [[10, "sentiment-analysis"]], "Gender Bias in Coreference": [[10, "gender-bias-in-coreference"]], "Debugging Text Generation": [[10, "debugging-text-generation"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/docs/documentation/ui_guide.html b/docs/documentation/ui_guide.html index 2f5108ca..0945b12d 100644 --- a/docs/documentation/ui_guide.html +++ b/docs/documentation/ui_guide.html @@ -5,10 +5,10 @@ - + UI Guide - 🔥LIT 1.0 documentation - + @@ -69,7 +69,7 @@ Light mode + stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,7 +84,7 @@ Dark mode + stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> @@ -92,7 +92,7 @@ Auto light/dark mode + stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow"> @@ -102,60 +102,6 @@ - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -167,8 +113,6 @@ - -
    @@ -186,8 +130,7 @@
    @@ -243,17 +186,11 @@ Back to top
    -
    - - - - -
    +
    @@ -263,16 +200,16 @@
    -
    +
    -

    UI Guide

    - +

    UI Guide#

    +

    This is a user guide for the Learning Interpretability Tool (LIT) UI.

    For a quick video tour of LIT, check out this video.

    -

    General Layout

    +

    General Layout#

    LIT lives inside a single page web application, comprised of multiple toolbars and a main section consisting of individual modules. Modules will automatically display if they are applicable to the current model and dataset; for example, @@ -290,7 +227,7 @@

    General Layout -

    Layout Options

    +

    Layout Options#

    @@ -301,7 +238,7 @@

    Layout Optionsdefault: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and

    -

  • three_panel: A three-panel layout that puts exploratory data +

  • experimental: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -311,7 +248,7 @@

    Layout Options -

    Datapoint Selections

    +

    Datapoint Selections#

    LIT displays a loaded dataset and its model results across the set of selected models. Users can dive into detailed results by selecting datapoints from the dataset.

    @@ -341,16 +278,16 @@

    Datapoint SelectionsMetrics Module section.

    • -

    Toolbars

    +

    Toolbars#

    There are three toolbars provided in LIT. The top bar includes the selected model(s) and dataset, a settings button, and URL sharing functionality. Below that is the main toolbar with the menus and controls for navigation and selection. At the bottom of the page is a status bar.

    LIT toolbars

    -

    Top Bar

    +

    Top Bar#

    -

    Global Settings

    +

    Global Settings#

    The global settings dialog is accessible through the “Configure” button in the top bar.

    LIT can be launched with a set of models and datasets. The settings screen @@ -365,7 +302,7 @@

    Global Settings

    -

    URL Sharing

    +

    URL Sharing#

    Much of the LIT app’s state — the loaded models and datasets, selected datapoints, minimized and/or full-screen modules — is stored in URL parameters. The “Copy Link” button in the top bar allows a user to share @@ -376,35 +313,25 @@

    URL Sharing -

    Main Toolbar

    +

    Main Toolbar#

    The main toolbar is right below the top bar and contains a number of different controls and information. The left side of the toolbar contains a set of menus for quickly controlling datapoint selection and coloring. This includes the following controls:

      -

    • The “Select datapoint” menu provides a drop-down of several options:

      -
        -

      • the “Random” option selects a random datapoint,

        • -

      • the “All related” option adds any datapoints “related” to the -current selection. In LIT, “related” is defined as datapoints created -from some source datapoint (through manual editing or a datapoint -generator), or a source datapoint that a selected datapoint was created -from,

        • -

      • the “Parents” option adds the source datapoints that the selected -datapoints were created from,

        • -

      • the “Children” option adds the datapoints created from the selected -datapoints (through manual editing or a datapoint generator),

        • -

      • the Slices option allows quick selection of an already-created slice -of datapoints,

        • -

      • the “Clear selection” button deselects all selected datapoints.

        • -
      -
      • -

    • The “Color by” menu enables setting of the color of each datapoint in -the modules that visualize all datapoints (such as the Embeddings and -Scalars modules) by any number of datapoint features or model outputs on -those datapoints (such as coloring by some categorical input feature, or by -prediction error for a regression task).

      • -

    • The Slices menu allows adding/selecting/removing slices of datapoints.

      • +

    • The “Select related” option adds any datapoints “related” to the current +selection. In LIT, “related” is defined as datapoints created from some +source datapoint (through manual editing or a datapoint generator), or a +source datapoint that a selected datapoint was created from.

      • +

    • The “Clear selection” button deselects all selected datapoints. The +dropdown also contains a color legend for the current color setting.

      • +

    • The Slices option allows quick selection of an already-created slice of +datapoints.

      • +

    • The “Datapoint color” menu enables setting of the color of each +datapoint in the modules that visualize all datapoints (such as the +Embeddings and Scalars modules) by any number of datapoint features or +model outputs on those datapoints (such as coloring by some categorical +input feature, or by prediction error for a regression task).

    Next to the menus is a button for pinning/unpinning a datapoint. Pinning a datapoint puts LIT into datapoint comparison mode, where two datapoints can be @@ -422,7 +349,7 @@

    Main Toolbar -

    Status Bar

    +

    Status Bar#

    The status bar at the bottom of the tool contains a text area on the left side. If the tool is currently waiting on the results of a call to the backend (such as for running predictions or getting embeddings), this information will be @@ -435,7 +362,7 @@

    Status Bar -

    Comparing Models

    +

    Comparing Models#

    By loading more than one model in the global settings controls, LIT can compare multiple models. A subset of modules that show per-model information are then duplicated to allow easy comparison across two models. Other modules, such the @@ -444,7 +371,7 @@

    Comparing Models

    -

    Comparing Datapoints

    +

    Comparing Datapoints#

    Pinning a datapoint, through either the toolbar button or controls in modules (e.g., the pin icons in Data Table rows), puts LIT into datapoint comparison mode. In this mode, the pinned datapoint is used as a reference to compare the @@ -459,7 +386,7 @@

    Comparing DatapointsLIT datapoint comparison

    -

    Slices

    +

    Slices#

    The Slice Editor allow users to create, edit, select, and delete slices. The current selection can be saved as a slice by giving it a name and clicking “Create slice”. The slice list allows you to select any of the previously-saved @@ -478,14 +405,14 @@

    SlicesLIT slice controls

    -

    Module Details

    +

    Module Details#

    This section contains details on using and interacting with individual modules that are built into LIT. Note that this list may not be complete and additional modules can be created and used in LIT by clients.

    All modules can be toggled to be shown full-screen through use of the full-screen button in the top-right of each module.

    -

    Embedding Projector

    +

    Embedding Projector#

    When using LIT with a model that returns embeddings (or activations) in addition to predictions, the embedding projector will show all datapoints by their embeddings projected down to 3 dimensions. This is useful for exploring and @@ -503,7 +430,7 @@

    Embedding Projector -

    Data Table

    +

    Data Table#

    The data table shows all datapoints in a simple table. Datapoints can be selected or unselected through a click. Shift+click allows selecting a set of consecutive datapoints, and control+click allows selecting a set of individual @@ -556,12 +483,12 @@

    Data Table -

    Datapoint Editor

    +

    Datapoint Editor#

    The datapoint editor shows the details of the primary selected datapoint, if one is selected. Any field can be edited, and a new datapoint created with those -edits through the “Add” button. Any edit to an existing datapoint must be -saved as a new datapoint to be explored, to keep datapoints immutable for -simplicity of use.

    +edits through the “Make new datapoint” button. Any edit to an existing +datapoint must be saved as a new datapoint to be explored, to keep datapoints +immutable for simplicity of use.

    When no datapoint is selected, the editor shows a blank datapoint that can be filled out by hand to create a completely new datapoint.

    Features shown with a “(*)” next to their name are required as model input and @@ -569,7 +496,7 @@

    Datapoint EditorLIT datapoint editor

    -

    Datapoint Generator

    +

    Datapoint Generator#

    The datapoint generator module allows creation of new datapoints from all currently-selected datapoints (or the entire dataset if no datapoints are selected) through a set of counterfactual datapoint generators. These generators @@ -604,7 +531,7 @@

    Datapoint Generator

    -

    Metrics Table

    +

    Metrics Table#

    The metrics table shows model metrics for each model in a table format. The exact metric types are determined by the python metrics component that calculates metrics given the model types being evaluated. These can include @@ -626,7 +553,7 @@

    Metrics Table

    -

    Confusion Matrix

    +

    Confusion Matrix#

    The confusion matrix buckets all datapoints from the dataset (or the current selection, if one is made) into buckets in a 2D matrix. This is normally used to compare classification predictions on a model versus the ground truth classes of @@ -644,7 +571,7 @@

    Confusion Matrix

    -

    Scalars

    +

    Scalars#

    The scalars module shows a set of scatter or jitter plots, one for each scalar output of a loaded model (such as a regression score, or a classification score for a specific class). Each of them contains all datapoints in the dataset, laid @@ -668,7 +595,7 @@

    Scalars

    LIT prediction scores

    -

    Model Output

    +

    Model Output#

    Model output modules show the result of a model on the primary selected datapoint. The visuals of these modules depend on the model task being performed. For a simple classification task, it will show the class scores from @@ -681,7 +608,7 @@

    Model OutputLIT structured prediction

    -

    Salience Maps

    +

    Salience Maps#

    Salience maps show the influence of different parts of inputs features on a model’s prediction on the primary selection. This module can contain multiple methodologies for calculating this salience, depending on the capabilities of @@ -691,20 +618,28 @@

    Salience Maps

    +
    +

    Attention#

    +

    For models that return attention head information along with predictions, the +attention module shows which tokens are attended to between layers of a model. +Dropdowns allow you to select which layer and attention head is being +visualized. Line opacity is controlled by the magnitude of the attention between +those tokens.

    +

    LIT attention

    +
    -

    User Journeys

    +

    User Journeys#

    In this section, we explore some example user journeys and how LIT enables them.

    -

    Sentiment Analysis

    +

    Sentiment Analysis#

    How well does a sentiment classifier handle negation? We load the development set of the Stanford Sentiment Treebank, and use the search function in LIT’s data table to find the 56 datapoints containing the word “not”. Looking at the @@ -721,13 +656,48 @@

    Sentiment Analysis

    -
    -

    Sequence salience

    -

    Sequence salience generalizes token-based salience to text-to-text models, -allowing you to explain the impact of the prompt tokens on parts of the model -output.

    -

    Check out here for more details on how to -navigate the Sequence Salience UI module.

    +
    +

    Gender Bias in Coreference#

    +

    Does a system encode gendered associations, which might lead to incorrect +predictions? We load a coreference model trained on OntoNotes, and load the +Winogender dataset into LIT for evaluation. Each Winogender example has a +pronoun and two candidate referents, one a occupation term like (“technician”) +and one is an “other participant” (like “customer”). Our model predicts +coreference probabilities for each candidate. We can explore the model’s +sensitivity to pronouns by comparing two examples side-by-side (see structured +predictions in screenshot). We can see how commonly the model makes similar +errors by paging through the dataset, or by selecting specific slices of +interest.

    +

    Winogender analysis

    +

    In the Metrics table, we can slice this selection by pronoun type and by the +true referent. On the set of male-dominated occupations (< 25% female by BLS), +we see the model performs well when the ground-truth agrees with the +stereotype - e.g. when the answer is the occupation term, male pronouns are +correctly resolved 83% of the time, compared to female pronouns only 37.5% of +the time (screenshot section (c)).

    +

    Winogender analysis

    +
    +
    +

    Debugging Text Generation#

    + +

    Does the training data explain a particular error in text generation? We analyze +an older T5 model on the CNN-DM summarization task. LIT’s Scalars module +allows us to look at per-example ROUGE scores, and quickly select an example +with middling performance (screenshot section (a)). We find the generated text +(screenshot section (b)) contains an erroneous constituent: “alastair cook was +replaced as captain by former captain …”. We can dig deeper, using LIT’s +language modeling module (screenshot section (c)) to see that the token “by” is +predicted with high probability (28.7%).

    +

    To find out how T5 arrived at this prediction, we utilize the “similarity +searcher” component through the datapoint generator (screenshot section (d)). +This performs a fast approximate nearest-neighbor lookup from a pre-built index +over the training corpus, using embeddings from the T5 decoder. With one click, +we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection. +We see that the words “captain” and “former” appear 34 and 16 times in these +examples–along with 3 occurrences of “replaced by” (screenshot section (e)), +suggesting a strong prior toward our erroneous phrase.

    +

    LIT sequence-to-sequence analysis

    @@ -816,11 +786,13 @@

    Sequence salienceScalars
  • Model Output
  • Salience Maps
    • +
  • Attention
  • User Journeys
    • @@ -835,8 +807,8 @@

    Sequence salience - + - + \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index 65c7ceaa..d6b44ac4 100644 --- a/docs/index.html +++ b/docs/index.html @@ -110,6 +110,7 @@

    Built-in capabilities

    Salience maps

    +

    Attention visualization

    Metrics calculations

    Counterfactual generation

    Model and datapoint comparison

    @@ -150,7 +151,7 @@

    What's the latest

    UPDATES
    -

    Version 1.2

    +

    Version 1.1

    Input salience for text-to-text LLMs, with wrappers for HuggingFace Transformers and KerasNLP models.
    See release notes
    diff --git a/docs/tutorials/coref/index.html b/docs/tutorials/coref/index.html new file mode 100644 index 00000000..ea57ac9d --- /dev/null +++ b/docs/tutorials/coref/index.html @@ -0,0 +1,187 @@ + + + + + + + + + + + Gender Bias in Coreference + + + + + + + + + + + + + + + +
    + +
    +
    + + + + + Learning Interpretability Tool + + + +
    + + +
    +
    +
    + Learning Interpretability Tool + +
    + + +
    +
    +
    +
    + Tutorials > Analysis > Coreference +
    +

    Gender Bias in Coreference

    +
    Explore this demo yourself.
    +

    Or, run your own with examples/coref/coref_demo.py

    +

    Does a system encode gendered associations, which might lead to incorrect predictions? We explore this for coreference, the task of identifying whether two mentions refer to the same (real-world) entity. For example, in the sentence "The technician told the customer that they could pay with cash.", we understand from the context that "they" refers to "the customer", the one paying.

    +

    The Winogender dataset introduced by Rudinger et al. 2018 presents a set of challenging coreference examples designed to explore gender bias. It consists of 120 templates, each with semantic context that makes it easy for humans to tell the answer. Each template is instantiated with different pronouns, in order to give a minimal pair:

    +
      +
    • "The technician told the customer that he could pay with cash."
      • +
    • "The technician told the customer that she could pay with cash."
      • +
    +

    In both cases, the pronoun should refer to the customer - but does our model agree? Or does it fall back on stereotypes about who can be a technician, or a customer? We can use LIT to explore this interactively, making use of the side-by-side functionality, structured prediction visualization, and powerful features for aggregate analysis to validate our findings.

    +

    We load our coreference model into LIT, along with a copy of the Winogender dataset. Our model predicts probabilities for each mention pair - in this case the (occupation, pronoun) and (participant, pronoun) pairs - and LIT renders this as a pair of edges:

    +
    + +
    + +
    +
    Above: A coreference prediction.
    +
    +

    We can select an example by clicking the row in the data table in the top left of the UI; the predictions will display automatically in the "Predictions" tab below. To look at two predictions side-by-side, we can enable "Compare datapoints" mode in the toolbar, which will pin our first selection as a "reference" and allow us to select another point to compare:

    +
    + +
    + +
    +
    Above: Selecting two datapoints to compare.
    +
    +

    We see that LIT automatically replicates the predictions view, allowing us to see how our model handles "he" and "she" differently on these two sentences:

    +
    + +
    + +
    +
    Above: Comparing coreference predictions of two datapoints.
    +
    +

    To see why this might be, we can make use of some additional information from the U.S. Bureau of Labor Statistics (BLS), which tabulates the gender percentages in different occupations. Our example loads this along with the dataset, and LIT shows this as a column in the data table:

    +
    + +
    + +
    +
    Above: Datapoints with extra informational feature columns.
    +
    +

    We see that "technician" is only 40% female, suggesting that our model might be picking up on social biases with its eagerness to identify "he" as the technician in the example above.

    +

    Is this a pattern? In addition to individual instances, we can use LIT to see if this holds on larger slices of the data. Turning to the "Performance" tab, we see that our model gets around 63% accuracy overall.

    +
    + +
    + +
    +
    Above: Overall model accuracy in the metrics table.
    +
    +

    Let's see how this breaks down. On the right, the Scalars module lets us select data based on scalar values, such as the percent female of each profession according to BLS. Let's select the points on the left, with professions that are stereotypically male (< 25% female). Additionally, we'll stratify our metrics based on the pronoun group, and whether the answer should be the occupation term or the other, neutral, participant:

    +
    + +
    + +
    +
    Above: Metrics faceted into sub-groups and scalar results plots.
    +
    +

    We can see that on this slice, our model performs very well when the ground truth agrees with the stereotype - i.e. when the answer is the occupation term, our model resolves male pronouns correctly 91% of the time, while only matching female pronouns 37% of the time in exactly the same contexts.

    + +
    +
    +
    +
    time to read
    +
    10 minutes
    +
    takeaways
    +
    Learn about how to explore fairness using datapoint comparison and metrics comparisons.
    +
    +
    +
    + The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our Github. +
    +
    + +
    +
    +
    + Google Research - Language +
    +
    Github
    +
    + + + +
    +
    + +
    + +
    + + + + + + + diff --git a/docs/tutorials/generation/index.html b/docs/tutorials/generation/index.html new file mode 100644 index 00000000..6647a8b1 --- /dev/null +++ b/docs/tutorials/generation/index.html @@ -0,0 +1,149 @@ + + + + + + + + + + + Debugging a Text Generator + + + + + + + + + + + + + + + +
    + +
    +
    + + + + + Learning Interpretability Tool + + + +
    + + +
    +
    +
    + Learning Interpretability Tool + +
    + + +
    +
    +
    +
    + Tutorials > Analysis > Generator +
    +

    Debugging a Text Generator

    +
    Explore this demo yourself.
    +

    Or, run your own with examples/t5_demo.py

    +

    Does the training data explain a particular error in text generation? We can use LIT to analyze a T5 model on the CNN-DM summarization task. LIT’s scalar plot module allows us to look at per-example ROUGE scores, and quickly select an example with middling performance.

    +
    + +
    + +
    +
    Above: The datapoint of interest, with its inaccurate summarization.
    +
    +

    We find the generated text contains an erroneous subject: "[A]lastair [C]ook was replaced as captain by former captain ...". In reading the input text, we can see that he was replaced as captain by Eoin Morgan, and not by a former captain. Another former captain does have a quote in the input text, and the model seemed to confuse these two subjects when generating its summary.

    +
    + +
    + +
    +
    Above: Finding the most similar datapoints from the training set.
    +
    +

    To investigate how T5 arrived at this prediction, we utilize the “similarity searcher” component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings +from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our datapoint of interest from the training set and add them to the LIT UI for inspection. We can see through the search capability in the data table that the words “captain” and “former” appear 34 and 16 times in these examples–along with 3 occurrences of “replaced by” and two occurrences of “by former”. This suggests a strong prior toward our erroneous phrase from the training data most related to our datapoint of interest.

    +
    + +
    + +
    +
    Above: An example of the existence of "by former" in a similar datapoint from the training set.
    +
    + +
    +
    +
    +
    time to read
    +
    3 minutes
    +
    takeaways
    +
    Learn about how to debug a prediction on a datapoint by searching for similar datapoints from the training set.
    +
    +
    +
    + The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our Github. +
    +
    + +
    +
    +
    + Google Research - Language +
    +
    Github
    +
    + + + +
    +
    + +
    + +
    + + + + + + + diff --git a/docs/tutorials/index.html b/docs/tutorials/index.html index c415c6ce..d1d43d44 100644 --- a/docs/tutorials/index.html +++ b/docs/tutorials/index.html @@ -115,6 +115,14 @@

    Global Model Analy

    Exploring a Sentiment Classifier

    Learn about how we used LIT to analyze a sentiment classifier.

    +
    +

    Debugging a Text Generator

    +
    Learn about how we used LIT to debug summarization by a text generation model.
    +
    +
    +

    Gender Bias in Coreference

    +
    Learn how we used LIT to explore gendered associations in a pronoun resolution model.
    +

    diff --git a/docs/tutorials/sentiment/index.html b/docs/tutorials/sentiment/index.html index 1ee96c27..017f1694 100644 --- a/docs/tutorials/sentiment/index.html +++ b/docs/tutorials/sentiment/index.html @@ -77,7 +77,7 @@

    Exploring a Sentiment Classifier

    Explore this demo yourself.
    -

    Or, run your own with examples/glue/demo.py

    +

    Or, run your own with examples/glue_demo.py

    How well does a sentiment classifier handle negation? We can use LIT to interactively ask this question and get answers. We loaded up LIT the development set of the Stanford Sentiment Treebank (SST), which contains sentences from movie reviews that have been human-labeled as having a negative sentiment (0), or a positive sentiment (1). For a model, we are using a BERT-based binary classifier that has been trained to classify sentiment.

    Using the search function in LIT’s data table, we find the 67 datapoints containing the word “not”. By selecting these datapoints and looking at the Metrics Table, we find that our BERT model gets 91% of these correct, which is slightly higher than the accuracy across the entire dataset.

    diff --git a/docs/tutorials/sequence-salience/index.html b/docs/tutorials/sequence-salience/index.html index e499ef9a..9e76d614 100644 --- a/docs/tutorials/sequence-salience/index.html +++ b/docs/tutorials/sequence-salience/index.html @@ -14,7 +14,7 @@ gtag('config', 'G-Q74F5RJLXB'); - Prompt Debugging with Sequence Salience + Prompt Engineering with Sequence Salience @@ -73,11 +73,11 @@
    - Tutorials > Analysis > Prompt Debugging with Sequence Salience + Tutorials > Analysis > Prompt Engineering with Sequence Salience
    -

    Prompt Debugging with Sequence Salience

    +

    Prompt Engineering with Sequence Salience

    Follow along in Google Colab.
    -

    Or, run this locally with examples/prompt_debugging/server.py

    +

    Or, run this locally with examples/lm_salience_demo.py

    Large language models (LLMs), such as Gemini and GPT-4, have become ubiquitous. Recent releases of "open weights" models, including Llama 2, Mistral, and Gemma, have made it easier @@ -294,7 +294,8 @@

    Case Study 2: Assessing Constitutional Principles in Prompts

    them in prompt engineering use cases.

    Case Study 3: Side-by-Side Behavior Comparisons

    LIT support a side-by-side (SxS) mode that can be used to compare two -models, or here, compare model behavior on two related examples. +models, or here, compare model behavior on two related examples. (Check out the +gender bias tutorial for another case study in SxS analysis.) Let's see how we can use this to understand differences in prompt designs with Sequence Salience.

    GSM8K is a benchmark dataset of grade school math problems commonly @@ -440,8 +441,8 @@

    Conclusion

    research area. The LIT team has provided reference implementations for computing gradient-based salience— Grad L2 Norm and Grad · Input—for LLMs in two -popular frameworks: KerasNLP and -HuggingFace Transformers.

    +popular frameworks: KerasNLP and +HuggingFace Transformers.

    There is considerable opportunity to research how the model analysis foundations described in this tutorial can support richer workflows, particularly as they relate to aggregate analysis of salience results over many examples, and the @@ -468,7 +469,7 @@

    Further Reading

    time to read
    20 minutes
    takeaways
    -
    Learn to use LIT's Sequence Salience module for prompt debugging.
    +
    Learn to use LIT's Sequence Salience module for prompt engineering.
    diff --git a/docs/tutorials/tab-feat-attr/index.html b/docs/tutorials/tab-feat-attr/index.html index 777b5603..5a6d8e6a 100644 --- a/docs/tutorials/tab-feat-attr/index.html +++ b/docs/tutorials/tab-feat-attr/index.html @@ -78,7 +78,7 @@

    Tabular Feature Attribution

    Explore this demo yourself.

    Or, run your own with -examples/penguin/demo.py

    +examples/penguin_demo.py

    LIT supports many techniques like salience maps and counterfactual generators for text data. But what if you have a tabular dataset? You might want to find out which features (columns) are most relevant to the model’s predictions. LIT's diff --git a/docs/tutorials/text-salience/index.html b/docs/tutorials/text-salience/index.html index 58e807a8..aa5cabf7 100644 --- a/docs/tutorials/text-salience/index.html +++ b/docs/tutorials/text-salience/index.html @@ -77,7 +77,7 @@

    Salience Maps for Text

    Explore this demo yourself.
    -

    Or, run your own with examples/glue/demo.py

    +

    Or, run your own with examples/glue_demo.py

    LIT enables users to analyze individual predictions for text input using salience maps, for which gradient-based and/or blackbox methods are available. In this tutorial, we will explore how to use salience maps to analyze a text From bf72d0efc4fcaa0a94d9c6c5549813778b8edeb9 Mon Sep 17 00:00:00 2001 From: Cibi Arjun Date: Wed, 3 Jul 2024 11:23:39 -0700 Subject: [PATCH 02/86] Internal Change PiperOrigin-RevId: 649141517 --- lit_nlp/examples/glue/README.md | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 lit_nlp/examples/glue/README.md diff --git a/lit_nlp/examples/glue/README.md b/lit_nlp/examples/glue/README.md new file mode 100644 index 00000000..fcc733d2 --- /dev/null +++ b/lit_nlp/examples/glue/README.md @@ -0,0 +1,4 @@ +Glue Demo + +* LIT team hosts a glue demo which can be accessible at https://pair-code.github.io/lit/demos/glue.html. +* For more details, check out the documentations at https://pair-code.github.io/lit/demos/glue.htmls#glue and https://pair-code.github.io/lit/demos/glue.htmls#stsb. \ No newline at end of file From cac1bddb70111ce2c8227cf641739ac61fd9b782 Mon Sep 17 00:00:00 2001 From: Cibi Arjun Date: Wed, 3 Jul 2024 13:08:26 -0700 Subject: [PATCH 03/86] Fix broken links to lit demos and documentation. PiperOrigin-RevId: 649175778 --- lit_nlp/examples/glue/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/glue/README.md b/lit_nlp/examples/glue/README.md index fcc733d2..65ea36fe 100644 --- a/lit_nlp/examples/glue/README.md +++ b/lit_nlp/examples/glue/README.md @@ -1,4 +1,4 @@ Glue Demo * LIT team hosts a glue demo which can be accessible at https://pair-code.github.io/lit/demos/glue.html. -* For more details, check out the documentations at https://pair-code.github.io/lit/demos/glue.htmls#glue and https://pair-code.github.io/lit/demos/glue.htmls#stsb. \ No newline at end of file +* For more details, check out the documentations for Sentiment and NLI / Textual Similarity at https://pair-code.github.io/lit/documentation/demos.html. \ No newline at end of file From 25b16fecb1ac5c7f657c5044677f0324188809b6 Mon Sep 17 00:00:00 2001 From: Bin Du Date: Tue, 9 Jul 2024 10:55:28 -0700 Subject: [PATCH 04/86] Internal change. PiperOrigin-RevId: 650687128 --- website/sphinx_src/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/sphinx_src/index.md b/website/sphinx_src/index.md index 211c7321..84608f66 100644 --- a/website/sphinx_src/index.md +++ b/website/sphinx_src/index.md @@ -1,6 +1,6 @@ # Learning Interpretability Tool (LIT) - + From c5c09ec42f74fa982e008136123e59108e52b7ff Mon Sep 17 00:00:00 2001 From: Cibi Arjun Date: Wed, 10 Jul 2024 10:13:06 -0700 Subject: [PATCH 05/86] Internal Change PiperOrigin-RevId: 651064476 --- lit_nlp/examples/penguin/README.md | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 lit_nlp/examples/penguin/README.md diff --git a/lit_nlp/examples/penguin/README.md b/lit_nlp/examples/penguin/README.md new file mode 100644 index 00000000..1a827468 --- /dev/null +++ b/lit_nlp/examples/penguin/README.md @@ -0,0 +1,4 @@ +Penguin Demo + +* LIT team hosts a penguin demo which can be accessible at https://pair-code.github.io/lit/demos/penguins.html. +* For more details, check out the documented example for tabular data at https://pair-code.github.io/lit/documentation/demos.html. \ No newline at end of file From e9edab6639146833ecf8c94182931b8bead460bd Mon Sep 17 00:00:00 2001 From: Googler Date: Wed, 24 Jul 2024 07:42:59 -0700 Subject: [PATCH 06/86] No public description PiperOrigin-RevId: 655562589 --- lit_nlp/examples/gcp/models.py | 93 +++++++++++++++++++++++++++++ lit_nlp/examples/gcp/models_test.py | 62 +++++++++++++++++++ pyproject.toml | 1 + requirements_examples.txt | 1 + 4 files changed, 157 insertions(+) create mode 100644 lit_nlp/examples/gcp/models.py create mode 100644 lit_nlp/examples/gcp/models_test.py diff --git a/lit_nlp/examples/gcp/models.py b/lit_nlp/examples/gcp/models.py new file mode 100644 index 00000000..436e6ccb --- /dev/null +++ b/lit_nlp/examples/gcp/models.py @@ -0,0 +1,93 @@ +"""Model Wrapper for generative models.""" + +from collections.abc import Iterable +import logging +import time +from typing import Optional, Union +from vertexai import generative_models +from lit_nlp.api import model as lit_model +from lit_nlp.api import types as lit_types + +_MAX_NUM_RETRIES = 5 + +_DEFAULT_CANDIDATE_COUNT = 1 + +_DEFAULT_MAX_OUTPUT_TOKENS = 256 + + +class VertexModelGardenModel(lit_model.BatchedRemoteModel): + """VertexModelGardenModel is a wrapper for Vertex AI Model Garden model. + + Attributes: + model_name: The name of the model to load. + max_concurrent_requests: The maximum number of concurrent requests to the + model. + max_qps: The maximum number of queries per second to the model. + temperature: The temperature to use for the model. + candidate_count: The number of candidates to generate. + max_output_tokens: The maximum number of tokens to generate. + + Please note the model will predict all examples at a fixed temperature. + """ + + def __init__( + self, + model_name: str, + max_concurrent_requests: int = 4, + max_qps: Union[int, float] = 25, + temperature: Optional[float] = None, + candidate_count: Optional[int] = _DEFAULT_CANDIDATE_COUNT, + max_output_tokens: Optional[int] = _DEFAULT_MAX_OUTPUT_TOKENS, + ): + super().__init__(max_concurrent_requests, max_qps) + # Connect to the remote model. + self._generation_config = generative_models.GenerationConfig( + temperature=temperature, + candidate_count=candidate_count, + max_output_tokens=max_output_tokens, + ) + self._model = generative_models.GenerativeModel(model_name) + + # TODO(fanyeycourage): Enable query_model to take a list of input_text, and + # return a list of predictions. + def query_model(self, input_text: str) -> lit_types.ScoredTextCandidates: + num_attempts = 0 + predictions = None + + while num_attempts < _MAX_NUM_RETRIES and predictions is None: + num_attempts += 1 + + try: + predictions = self._model.generate_content( + input_text, + generation_config=self._generation_config, + ) + except Exception as e: # pylint: disable=broad-except + wait_time = 2**num_attempts + logging.warning('Waiting %ds to retry... (%s)', wait_time, e) + time.sleep(2**num_attempts) + + if predictions is None: + raise ValueError('Failed to get predictions.') + + if not isinstance(predictions, Iterable): + predictions = [predictions] + + return [(prediction.text, None) for prediction in predictions] + + def predict_minibatch( + self, inputs: list[lit_types.JsonDict] + ) -> list[lit_types.JsonDict]: + res = [ + {'response': self.query_model(input_dict['prompt'])} + for input_dict in inputs + ] + return res + + def input_spec(self) -> lit_types.Spec: + return { + 'prompt': lit_types.TextSegment(), + } + + def output_spec(self) -> lit_types.Spec: + return {'response': lit_types.GeneratedTextCandidates(parent='prompt')} diff --git a/lit_nlp/examples/gcp/models_test.py b/lit_nlp/examples/gcp/models_test.py new file mode 100644 index 00000000..c9042f2a --- /dev/null +++ b/lit_nlp/examples/gcp/models_test.py @@ -0,0 +1,62 @@ +"""Tests for lit_nlp.examples.gcp.models.""" + +from unittest import mock +from absl.testing import absltest +from vertexai import generative_models +from lit_nlp.examples.gcp import models + + +class ModelsTest(absltest.TestCase): + + @mock.patch( + "vertexai.generative_models.GenerativeModel.generate_content" + ) + @mock.patch( + "vertexai.generative_models.GenerativeModel.__init__", + return_value=None, + ) + def test_query_model(self, mock_init, mock_generate_content): + response1 = generative_models.GenerationResponse.from_dict({ + "candidates": [{ + "content": { + "parts": [ + {"text": "I say yes you say no"}, + ], + "role": "model", + } + }] + }) + response2 = generative_models.GenerationResponse.from_dict({ + "candidates": [{ + "content": { + "parts": [ + {"text": "I have a dog"}, + ], + "role": "model", + } + }] + }) + mock_generate_content.side_effect = [response1, response2] + + model = models.VertexModelGardenModel(model_name="gemini-pro") + model._model = mock.MagicMock() + model._model.generate_content.side_effect = [response1, response2] + + output = model.predict( + inputs=[{"prompt": "I say yes you say no"}, {"prompt": "I have a dog"}] + ) + result = list(output) + self.assertLen(result, 2) + self.assertEqual( + result, + [ + {"response": [("I say yes you say no", None)]}, + {"response": [("I have a dog", None)]}, + ], + ) + + mock_init.assert_called_once_with("gemini-pro") + + +if __name__ == "__main__": + absltest.main() diff --git a/pyproject.toml b/pyproject.toml index e2c1d89e..02376ff8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,6 +86,7 @@ examples = [ "tensorflow-text>=2.10.0,<2.16.0", "torch>=2.0.0", "transformers>=4.27.1", + "vertexai>=1.49.0", ] # LINT.ThenChange(./requirements_examples.txt) # LINT.IfChange diff --git a/requirements_examples.txt b/requirements_examples.txt index 3015bfcb..6dd36176 100644 --- a/requirements_examples.txt +++ b/requirements_examples.txt @@ -20,4 +20,5 @@ tensorflow-datasets>=4.9.0 tensorflow-text>=2.10.0,<2.16.0 torch>=2.0.0 transformers>=4.27.1 +vertexai>=1.49.0 # LINT.ThenChange(./pyproject.toml) From 8e74960442f57df1f860dbb02a3364ba9a17b6e2 Mon Sep 17 00:00:00 2001 From: Bin Du Date: Tue, 6 Aug 2024 10:07:46 -0700 Subject: [PATCH 07/86] Make sequence length a flag when launching the prompt debugging demo. PiperOrigin-RevId: 660000091 --- lit_nlp/examples/prompt_debugging/models.py | 7 +- lit_nlp/examples/prompt_debugging/server.py | 7 + .../prompt_debugging/transformers_lms.py | 89 +++++++++---- .../transformers_lms_int_test.py | 120 ++++++++++++++++-- 4 files changed, 183 insertions(+), 40 deletions(-) diff --git a/lit_nlp/examples/prompt_debugging/models.py b/lit_nlp/examples/prompt_debugging/models.py index 9518cf70..681b643d 100644 --- a/lit_nlp/examples/prompt_debugging/models.py +++ b/lit_nlp/examples/prompt_debugging/models.py @@ -14,6 +14,7 @@ DEFAULT_DL_RUNTIME = "tensorflow" DEFAULT_MODELS = ["gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en"] DEFAULT_PRECISION = "bfloat16" +DEFAULT_SEQUENCE_LENGTH = 512 def _initialize_modeling_environment( @@ -47,6 +48,7 @@ def get_models( dl_runtime: str = DEFAULT_DL_RUNTIME, precision: str = DEFAULT_PRECISION, batch_size: int = DEFAULT_BATCH_SIZE, + sequence_length: int = DEFAULT_SEQUENCE_LENGTH, ) -> Mapping[str, lit_model.Model]: """Loads models from the given configs. @@ -62,6 +64,7 @@ def get_models( precision: Floating point precision for the models, either `bfloat16` or `float32`. batch_size: The number of examples to process per batch. + sequence_length: The maximum sequence length of the input. Returns: A mapping from model name to initialized LIT model. @@ -92,7 +95,7 @@ def get_models( models |= keras_lms.initialize_model_group_for_salience( model_name, model_name_or_path=path, - max_length=512, + max_length=sequence_length, batch_size=batch_size, ) else: @@ -103,7 +106,7 @@ def get_models( path, batch_size=batch_size, framework=dl_runtime, - max_new_tokens=512, + max_length=sequence_length, ) return models diff --git a/lit_nlp/examples/prompt_debugging/server.py b/lit_nlp/examples/prompt_debugging/server.py index 55ef7420..e3293fdb 100644 --- a/lit_nlp/examples/prompt_debugging/server.py +++ b/lit_nlp/examples/prompt_debugging/server.py @@ -119,6 +119,12 @@ " supported at this time.", ) +_SEQUENCE_LENGTH = flags.DEFINE_integer( + "sequence_length", + models.DEFAULT_SEQUENCE_LENGTH, + "The maximum sequence length of the input prompt + generated text", +) + _FLAGS = flags.FLAGS _FLAGS.set_default("development_demo", True) _FLAGS.set_default("page_title", "LM Prompt Debugging") @@ -158,6 +164,7 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: dl_runtime=_DL_RUNTIME.value, precision=_PRECISION.value, batch_size=_BATCH_SIZE.value, + sequence_length=_SEQUENCE_LENGTH.value, ), datasets=datasets.get_datasets( datasets_config=_DATASETS.value, max_examples=_MAX_EXAMPLES.value diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index 6ba20581..fbe5229d 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -9,6 +9,7 @@ from collections.abc import Sequence import enum import functools +from typing import Any, Mapping from absl import logging from lit_nlp.api import model as lit_model @@ -33,7 +34,6 @@ # pylint: enable=g-import-not-at-top -_DEFAULT_MAX_LENGTH = 1024 _PYTORCH = "torch" _TENSORFLOW = "tensorflow" # HuggingFace uses two letter abbreviations for pytorch and tensorflow. @@ -123,7 +123,6 @@ def __init__( model_name_or_path, use_fast=False, padding_side="left", - model_max_length=_DEFAULT_MAX_LENGTH, ) # Set this after init, as if pad_token= is passed to # AutoTokenizer.from_pretrained() above it will create a new token with @@ -199,41 +198,65 @@ class HFGenerativeModel(HFBaseModel): @classmethod def init_spec(cls) -> lit_model.Spec: return super().init_spec() | { - "max_new_tokens": lit_types.Integer(default=50, min_val=1, max_val=500) + "max_length": lit_types.Integer(default=512, min_val=1, max_val=2048) } - def __init__(self, *args, max_new_tokens=50, **kw): + def __init__(self, *args, max_length=512, **kw): """Constructor for HFGenerativeModel. Args: *args: as to HFBaseModel.__init__ - max_new_tokens: the maximum number of new tokens to generate. + max_length: the maximum length the generated tokens can have. Corresponds + to the length of the input prompt + max_new_tokens. **kw: as to HFBaseModel.__init__ """ super().__init__(*args, **kw) - self.max_new_tokens = max_new_tokens + self.max_length = max_length - def _postprocess(self, preds): - """Post-process single-example preds. Operates on numpy arrays.""" + def _postprocess(self, preds: Mapping[str, Any]) -> Mapping[str, Any]: + """Post-process single-example preds. Operates on numpy arrays. + + Args: + preds: a dict of the model outputs, including the response text, number of + input and output tokens, and the embeddings of the input and output + tokens (merged into a single array). + + Returns: + a dict of the processed model outputs, including the response texts and + embeddings of the input and output tokens (separated into two arrays). + """ # TODO(b/324957491): return actual decoder scores for each generation. # GeneratedTextCandidates should be a list[(text, score)] - preds["response"] = [(preds["response"], 1.0)] - ntok_in = preds.pop("ntok_in") - embs = preds.pop("embs") + processed_preds = {} + processed_preds["response"] = [(preds["response"], 1.0)] + ntok_in = preds["ntok_in"] + ntok_out = preds["ntok_out"] + embs = preds["embs"] + assert embs.shape[0] >= ntok_in + ntok_out # Mean-pool over input tokens. - preds["prompt_embeddings"] = np.mean( - embs[-(self.max_new_tokens + ntok_in) : -self.max_new_tokens], axis=0 + processed_preds["prompt_embeddings"] = np.mean( + embs[-(ntok_out + ntok_in) : -ntok_out], axis=0 ) # Mean-pool over output (generated) tokens. # TODO(b/324957491): slice this to only "real" output tokens, # if generation length < max generation length. - preds["response_embeddings"] = np.mean(embs[-self.max_new_tokens :], axis=0) + processed_preds["response_embeddings"] = np.mean(embs[-ntok_out:], axis=0) - return preds + return processed_preds - ## - # LIT API implementations - def predict_minibatch(self, inputs): + def _get_batched_outputs( + self, inputs: Sequence[Mapping[str, lit_types.TextSegment]] + ) -> Mapping[str, Any]: + """Returns the batched outputs generated by the model for the given inputs. + + Args: + inputs: model inputs containing text prompts. + + Returns: + a dict of the model outputs, including the generated texts and auxiliary + data in numpy arrays (could come from torch or tensorflow, depending on + the transformer backend). + """ prompts = [ex["prompt"] for ex in inputs] encoded_inputs = self.tokenizer( prompts, @@ -250,11 +273,12 @@ def predict_minibatch(self, inputs): outputs = self.model.generate( encoded_inputs["input_ids"], attention_mask=encoded_inputs["attention_mask"], - max_new_tokens=self.max_new_tokens, + max_length=self.max_length, ) + ntok_out = self.max_length - encoded_inputs["input_ids"].shape[1] responses = self.tokenizer.batch_decode( - outputs[:, -self.max_new_tokens :], skip_special_tokens=True + outputs[:, -ntok_out:], skip_special_tokens=True ) if self.framework == MLFramework.PT: @@ -269,20 +293,35 @@ def predict_minibatch(self, inputs): .cpu() .to(torch.int) ), + "ntok_out": torch.full( + (encoded_inputs["input_ids"].shape[0],), ntok_out + ), } else: embeddings = self.embedding_table(outputs) batched_outputs = { "embs": embeddings, "ntok_in": tf.reduce_sum(encoded_inputs["attention_mask"], axis=1), - # TODO(b/324957491): compute ntok_out if < max_output_tokens ? + "ntok_out": tf.fill( + [ + encoded_inputs["input_ids"].shape[0], + ], + ntok_out, + ), } # Convert to numpy for post-processing. detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()} detached_outputs["response"] = responses + return detached_outputs + + ## + # LIT API implementations + def predict_minibatch(self, inputs): + batched_outputs = self._get_batched_outputs(inputs) + # Split up batched outputs, then post-process each example. - unbatched_outputs = utils.unbatch_preds(detached_outputs) + unbatched_outputs = utils.unbatch_preds(batched_outputs) return map(self._postprocess, unbatched_outputs) def output_spec(self) -> lit_types.Spec: @@ -557,12 +596,10 @@ def output_spec(self) -> lit_types.Spec: def initialize_model_group_for_salience( - name, *args, max_new_tokens=512, **kw + name, *args, max_length=512, **kw ) -> dict[str, lit_model.Model]: """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'.""" - generation_model = HFGenerativeModel( - *args, **kw, max_new_tokens=max_new_tokens - ) + generation_model = HFGenerativeModel(*args, **kw, max_length=max_length) salience_model = HFSalienceModel.from_loaded(generation_model) tokenizer_model = HFTokenizerModel.from_loaded(generation_model) return { diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py b/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py index a00ed981..6022cfa9 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py @@ -1,6 +1,38 @@ from absl.testing import absltest from absl.testing import parameterized from lit_nlp.examples.prompt_debugging import transformers_lms +import numpy as np +from transformers import tokenization_utils + +_MAX_LENGTH = 32 + + +def _tokenize_text( + text: str, tokenizer: tokenization_utils.PreTrainedTokenizer, framework: str +) -> tokenization_utils.BatchEncoding: + return_tensors_type = ( + transformers_lms._HF_PYTORCH + if framework == transformers_lms.MLFramework.PT.value + else transformers_lms._HF_TENSORFLOW + ) + return tokenizer( + text, + return_tensors=return_tensors_type, + add_special_tokens=True, + ) + + +def _get_text_mean_embeddings( + text: str, model: transformers_lms.HFBaseModel, framework: str +) -> np.ndarray: + tokens = _tokenize_text( + text=text, tokenizer=model.tokenizer, framework=framework + ) + embeddings = model.embedding_table(tokens["input_ids"]) + if framework == transformers_lms.MLFramework.PT.value: + embeddings = embeddings.detach() + mean_embeddings = np.mean(embeddings.numpy()[0], axis=0) + return mean_embeddings class TransformersLMSGeneration(parameterized.TestCase): @@ -8,32 +40,96 @@ class TransformersLMSGeneration(parameterized.TestCase): @parameterized.named_parameters( dict( - testcase_name="tensorflow", + testcase_name="tensorflow_framework", framework=transformers_lms.MLFramework.TF.value, model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2.tar.gz", ), dict( - testcase_name="pytorch", + testcase_name="pytorch_framework", framework=transformers_lms.MLFramework.PT.value, model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2-pt.tar.gz", ), ) - def test_gpt2_generation(self, framework, model_path): + def test_gpt2_generation_output(self, framework, model_path): model = transformers_lms.HFGenerativeModel( - model_name_or_path=model_path, framework=framework + model_name_or_path=model_path, + framework=framework, + max_length=_MAX_LENGTH, ) model_in = [{"prompt": "Today is"}, {"prompt": "What is the color of"}] model_out = list(model.predict(model_in)) - # Sanity-check output vs output spec. - self.assertLen(model_out, 2) - for key in model.output_spec().keys(): - self.assertIn(key, model_out[0].keys()) + with self.subTest(name="model_input_length_matches_output_length"): + self.assertLen(model_out, 2) + + with self.subTest(name="model_output_has_expected_spec_keys"): + expected_output_keys = sorted(model.output_spec().keys()) + for cur_output in model_out: + self.assertSequenceEqual( + sorted(cur_output.keys()), expected_output_keys + ) + + with self.subTest( + name="model_output_prompt_and_response_embeddings_match_those_computed_from_embedding_table" + ): + for cur_input, cur_output in zip(model_in, model_out): + expected_input_embeddings = _get_text_mean_embeddings( + text=cur_input["prompt"], model=model, framework=framework + ) + expected_output_embeddings = _get_text_mean_embeddings( + text=cur_output["response"][0][0], model=model, framework=framework + ) + np.testing.assert_array_almost_equal( + expected_input_embeddings, + cur_output["prompt_embeddings"], + ) + np.testing.assert_array_almost_equal( + expected_output_embeddings, + cur_output["response_embeddings"], + ) - # Check that the embedding dimension is the same for prompt and response. - self.assertEqual( - model_out[0]["prompt_embeddings"].shape, - model_out[0]["response_embeddings"].shape, + @parameterized.named_parameters( + dict( + testcase_name="tensorflow_framework", + framework=transformers_lms.MLFramework.TF.value, + model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2.tar.gz", + ), + dict( + testcase_name="pytorch_framework", + framework=transformers_lms.MLFramework.PT.value, + model_path="https://storage.googleapis.com/what-if-tool-resources/lit-models/gpt2-pt.tar.gz", + ), + ) + def test_gpt2_batched_generation_has_correct_input_and_output_token_lengths( + self, framework, model_path + ): + model = transformers_lms.HFGenerativeModel( + model_name_or_path=model_path, + framework=framework, + max_length=_MAX_LENGTH, + ) + model_in = [{"prompt": "Today is"}, {"prompt": "What is the color of"}] + batched_outputs = model._get_batched_outputs(model_in) + tokenized_inputs = [ + _tokenize_text( + text=input_dict["prompt"], + tokenizer=model.tokenizer, + framework=framework, + ) + for input_dict in model_in + ] + expected_input_token_len = np.array([ + tokenized_input["input_ids"].shape[1] + for tokenized_input in tokenized_inputs + ]) + expected_output_token_len = np.full( + (len(model_in),), _MAX_LENGTH - np.max(expected_input_token_len) + ) + np.testing.assert_array_equal( + expected_input_token_len, batched_outputs["ntok_in"] + ) + np.testing.assert_array_equal( + expected_output_token_len, batched_outputs["ntok_out"] ) From 4e5e8e25c2abb658dc141f0d9c6059dd41e14535 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 7 Aug 2024 08:14:03 -0700 Subject: [PATCH 08/86] feat: Add initial version of LIT demos for generative models. PiperOrigin-RevId: 660395674 --- lit_nlp/examples/gcp/demo.py | 118 +++++++++++++++++++++++++++++++++ lit_nlp/examples/gcp/models.py | 17 ++++- 2 files changed, 134 insertions(+), 1 deletion(-) create mode 100644 lit_nlp/examples/gcp/demo.py diff --git a/lit_nlp/examples/gcp/demo.py b/lit_nlp/examples/gcp/demo.py new file mode 100644 index 00000000..2c1929d1 --- /dev/null +++ b/lit_nlp/examples/gcp/demo.py @@ -0,0 +1,118 @@ +r"""A blank demo ready to load generative models and datasets. + +To use with VertexAI Model Garden models, you must install the following packages: + + pip install vertexai>=1.49.0 + +To run the demo, you must set you GCP project location and project id. Also, the +credential must be set using the VertexAI API key. +(https://ai.google.dev/gemini-api/docs/quickstart?lang=python#set_up_your_api_key). + +You can also configure the datasets and max_examples to load. If datasets and +max_examples are not provided, the default datasets and max_examples will be used. + +The following command can be used to run the demo: + python -m lit_nlp.examples.gcp.demo \ + --project_id=$GCP_PROJECT_ID \ + --project_location=$GCP_PROJECT_LOCATION \ + --credential=$VERTEX_AI_API_KEY \ + --datasets=$DATASETS \ + --max_examples=$MAX_EXAMPLES \ + --alsologtostderr + +Then navigate to localhost:5432 to access the demo UI. +""" + +from collections.abc import Sequence +import sys +from typing import Optional +from absl import app +from absl import flags +from absl import logging +from google.cloud.aiplatform import vertexai +from lit_nlp import app as lit_app +from lit_nlp import dev_server +from lit_nlp import server_flags +from lit_nlp.examples.gcp import models as gcp_models +from lit_nlp.examples.prompt_debugging import datasets as prompt_debugging_datasets + +FLAGS = flags.FLAGS + +# Define GCP project information and vertex AI API key. +LOCATION = flags.DEFINE_string( + 'project_location', + None, + 'Please enter your GCP project location', + required=True, +) +PROJECT_ID = flags.DEFINE_string( + 'project_id', + None, + 'Please enter your project id', + required=True, +) + +# Define dataset information. +_DATASETS = flags.DEFINE_list( + 'datasets', + prompt_debugging_datasets.DEFAULT_DATASETS, + 'Datasets to load, as :. Format should be either .jsonl where' + " each record contains 'prompt' and optional 'target' and 'source' fields," + ' or a plain text file with one prompt per line.', +) + +_MAX_EXAMPLES = flags.DEFINE_integer( + 'max_examples', + prompt_debugging_datasets.DEFAULT_MAX_EXAMPLES, + ( + 'Maximum number of examples to load from each evaluation set. Set to' + ' None to load the full set.' + ), +) + + +def get_wsgi_app() -> Optional[dev_server.LitServerType]: + """Return WSGI app for container-hosted demos.""" + FLAGS.set_default('server_type', 'external') + FLAGS.set_default('demo_mode', True) + # Parse flags without calling app.run(main), to avoid conflict with + # gunicorn command line flags. + unused = flags.FLAGS(sys.argv, known_only=True) + if unused: + logging.info( + 'generateive_demo:get_wsgi_app() called with unused args: %s', unused + ) + return main([]) + + +def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: + if len(argv) > 1: + raise app.UsageError('Too many command-line arguments.') + + vertexai.init(project=PROJECT_ID.value, location=LOCATION.value) + + models = {} + model_loaders: lit_app.ModelLoadersMap = {} + model_loaders['gemini'] = ( + gcp_models.VertexModelGardenModel, + gcp_models.VertexModelGardenModel.init_spec(), + ) + + datasets = prompt_debugging_datasets.get_datasets( + datasets_config=_DATASETS.value, max_examples=_MAX_EXAMPLES.value + ) + dataset_loaders = prompt_debugging_datasets.get_dataset_loaders() + + # TODO(faneycourage): Design and add a layout for generative demos. + lit_demo = dev_server.Server( + models=models, + model_loaders=model_loaders, + datasets=datasets, + dataset_loaders=dataset_loaders, + **server_flags.get_flags() + ) + return lit_demo.serve() + + +if __name__ == '__main__': + app.run(main) diff --git a/lit_nlp/examples/gcp/models.py b/lit_nlp/examples/gcp/models.py index 436e6ccb..1bd7ce20 100644 --- a/lit_nlp/examples/gcp/models.py +++ b/lit_nlp/examples/gcp/models.py @@ -53,6 +53,7 @@ def __init__( def query_model(self, input_text: str) -> lit_types.ScoredTextCandidates: num_attempts = 0 predictions = None + exception = None while num_attempts < _MAX_NUM_RETRIES and predictions is None: num_attempts += 1 @@ -64,11 +65,14 @@ def query_model(self, input_text: str) -> lit_types.ScoredTextCandidates: ) except Exception as e: # pylint: disable=broad-except wait_time = 2**num_attempts + exception = e logging.warning('Waiting %ds to retry... (%s)', wait_time, e) time.sleep(2**num_attempts) if predictions is None: - raise ValueError('Failed to get predictions.') + raise ValueError( + f'Failed to get predictions. ({exception})' + ) from exception if not isinstance(predictions, Iterable): predictions = [predictions] @@ -84,6 +88,17 @@ def predict_minibatch( ] return res + @classmethod + def init_spec(cls) -> lit_types.Spec: + return { + 'model_name': lit_types.String(default='gemini-1.0-pro', required=True), + 'max_concurrent_requests': lit_types.Integer(default=4, required=False), + 'max_qps': lit_types.Integer(default=25, required=False), + 'temperature': lit_types.Scalar(default=0.7, required=False), + 'candidate_count': lit_types.Integer(default=1, required=False), + 'max_output_tokens': lit_types.Integer(default=256, required=False), + } + def input_spec(self) -> lit_types.Spec: return { 'prompt': lit_types.TextSegment(), From c1f5055eb7ee8b3671484c863a0967c05fa58338 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 8 Aug 2024 06:08:14 -0700 Subject: [PATCH 09/86] Upgrade LIT to MobX v6. PiperOrigin-RevId: 660806702 --- lit_nlp/client/core/app_statusbar.ts | 7 +- lit_nlp/client/core/documentation.ts | 6 +- lit_nlp/client/core/faceting_control.ts | 7 +- lit_nlp/client/core/global_settings.ts | 58 +++++--- lit_nlp/client/core/lit_module.ts | 23 +-- lit_nlp/client/core/main_toolbar.ts | 7 +- lit_nlp/client/core/modules.ts | 7 +- lit_nlp/client/core/slice_module.ts | 3 +- lit_nlp/client/core/widget_group.ts | 18 ++- lit_nlp/client/elements/annotated_text_vis.ts | 9 +- lit_nlp/client/elements/color_legend.ts | 9 +- lit_nlp/client/elements/data_matrix.ts | 10 +- lit_nlp/client/elements/generated_text_vis.ts | 7 +- .../client/elements/interpreter_controls.ts | 7 +- lit_nlp/client/elements/menu.ts | 11 +- lit_nlp/client/elements/table.ts | 24 ++-- lit_nlp/client/elements/tooltip.ts | 4 +- lit_nlp/client/lib/elements.ts | 12 +- lit_nlp/client/lib/generated_text_utils.ts | 7 +- lit_nlp/client/lib/utils.ts | 2 +- .../client/modules/annotated_text_module.ts | 10 +- .../client/modules/classification_module.ts | 7 +- .../client/modules/confusion_matrix_module.ts | 16 ++- lit_nlp/client/modules/curves_module.ts | 5 +- lit_nlp/client/modules/data_table_module.ts | 7 +- .../client/modules/datapoint_editor_module.ts | 9 +- lit_nlp/client/modules/dive_module.ts | 7 +- .../client/modules/documentation_module.ts | 7 +- lit_nlp/client/modules/embeddings_module.ts | 15 +- .../modules/feature_attribution_module.ts | 8 +- .../client/modules/generated_image_module.ts | 11 +- .../client/modules/generated_text_module.ts | 13 +- lit_nlp/client/modules/generator_module.ts | 34 +++-- .../legacy_sequence_salience_module.ts | 25 ++-- lit_nlp/client/modules/metrics_module.ts | 10 +- lit_nlp/client/modules/multilabel_module.ts | 9 +- lit_nlp/client/modules/pdp_module.ts | 7 +- lit_nlp/client/modules/regression_module.ts | 22 +-- .../modules/salience_clustering_module.ts | 48 ++++--- lit_nlp/client/modules/salience_map_module.ts | 15 +- lit_nlp/client/modules/scalar_module.ts | 8 +- .../modules/sequence_salience_module.ts | 80 ++++++----- lit_nlp/client/modules/tcav_module.ts | 33 ++--- lit_nlp/client/modules/tda_module.ts | 19 ++- lit_nlp/client/modules/thresholder_module.ts | 3 +- .../client/services/classification_service.ts | 30 ++-- lit_nlp/client/services/color_service.ts | 36 ++--- lit_nlp/client/services/color_service_test.ts | 8 +- lit_nlp/client/services/data_service.ts | 24 ++-- lit_nlp/client/services/data_service_test.ts | 132 +++++++++--------- lit_nlp/client/services/focus_service.ts | 12 +- lit_nlp/client/services/group_service.ts | 3 +- lit_nlp/client/services/group_service_test.ts | 126 +++++++++-------- lit_nlp/client/services/modules_service.ts | 14 +- lit_nlp/client/services/selection_service.ts | 13 +- lit_nlp/client/services/settings_service.ts | 9 +- lit_nlp/client/services/slice_service.ts | 6 +- lit_nlp/client/services/state_service.ts | 46 +++--- lit_nlp/client/services/status_service.ts | 9 +- lit_nlp/package.json | 2 +- 60 files changed, 678 insertions(+), 458 deletions(-) diff --git a/lit_nlp/client/core/app_statusbar.ts b/lit_nlp/client/core/app_statusbar.ts index bf45e86e..c884961c 100644 --- a/lit_nlp/client/core/app_statusbar.ts +++ b/lit_nlp/client/core/app_statusbar.ts @@ -28,7 +28,7 @@ import {MobxLitElement} from '@adobe/lit-mobx'; import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {styles as sharedStyles} from '../lib/shared_styles.css'; import {AppState, StatusService} from '../services/services'; @@ -49,6 +49,11 @@ export class StatusbarComponent extends MobxLitElement { private readonly statusService = app.getService(StatusService); @observable private renderFullMessages = false; + constructor() { + super(); + makeObservable(this); + } + override render() { const progressClass = classMap({ 'progress-line': this.statusService.isLoading, diff --git a/lit_nlp/client/core/documentation.ts b/lit_nlp/client/core/documentation.ts index ea0cf7c8..9966bf32 100644 --- a/lit_nlp/client/core/documentation.ts +++ b/lit_nlp/client/core/documentation.ts @@ -25,7 +25,7 @@ import '../elements/checkbox'; import {html} from 'lit'; import {customElement, query} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {ReactiveElement} from '../lib/elements'; import {styles as sharedStyles} from '../lib/shared_styles.css'; @@ -48,7 +48,6 @@ export class DocumentationComponent extends ReactiveElement { } // Markdown of splash screen text to render. - @observable private readonly markdownPages = [ `![](static/onboarding_1_welcome.gif)\n# Welcome to LIT!\nLIT is a ` + `visual, interactive tool to help ML researchers, engineers, product ` + @@ -96,6 +95,7 @@ export class DocumentationComponent extends ReactiveElement { constructor(private readonly appState = app.getService(AppState)) { super(); + makeObservable(this); } override firstUpdated() { @@ -162,7 +162,7 @@ export class DocumentationComponent extends ReactiveElement { } override render() { - const hiddenClassMap = classMap({hide: !this.isOpen}); + const hiddenClassMap = classMap({'hide': !this.isOpen}); const docToDisplay = this.pagesToRender[this.currentPage]; const onCloseClick = () => { this.isOpen = false; diff --git a/lit_nlp/client/core/faceting_control.ts b/lit_nlp/client/core/faceting_control.ts index 91dbfd77..33a670ab 100644 --- a/lit_nlp/client/core/faceting_control.ts +++ b/lit_nlp/client/core/faceting_control.ts @@ -23,7 +23,7 @@ import {html, TemplateResult} from 'lit'; // tslint:disable:no-new-decorators import {customElement, property} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {ReactiveElement} from '../lib/elements'; @@ -60,8 +60,8 @@ export class FacetingControl extends ReactiveElement { private readonly discreteCount = new Map(); @observable private hasExcessBins = false; - @observable private features: string[] = []; - @observable private bins: NumericFeatureBins = {}; + @observable.ref private features: string[] = []; + @observable.ref private bins: NumericFeatureBins = {}; @property({type: Boolean, reflect: true}) disabled = false; @property({type: String}) contextName?: string; @@ -74,6 +74,7 @@ export class FacetingControl extends ReactiveElement { constructor(private readonly groupService = app.getService(GroupService)) { super(); + makeObservable(this); this.initFeatureConfigs(); } diff --git a/lit_nlp/client/core/global_settings.ts b/lit_nlp/client/core/global_settings.ts index 923d2f23..9837d0c5 100644 --- a/lit_nlp/client/core/global_settings.ts +++ b/lit_nlp/client/core/global_settings.ts @@ -32,11 +32,11 @@ import {MobxLitElement} from '@adobe/lit-mobx'; import {html, TemplateResult} from 'lit'; import {customElement, property} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; -import {action, computed, observable} from 'mobx'; +import {action, computed, makeObservable, observable} from 'mobx'; import {styles as sharedStyles} from '../lib/shared_styles.css'; import {StringLitType} from '../lib/lit_types'; -import {type CallConfig, datasetDisplayName, LitTabGroupLayout, NONE_DS_DICT_KEY, Spec} from '../lib/types'; +import {type CallConfig, datasetDisplayName, LitTabGroupLayout, NONE_DS_DICT_KEY, type Spec} from '../lib/types'; import {getTemplateStringFromMarkdown, validateCallConfig} from '../lib/utils'; import {LitInputField} from '../elements/lit_input_field'; import {resolveModuleConfig} from '../services/modules_service'; @@ -61,6 +61,7 @@ const COMPATIBLE_TXT = 'Compatible'; const INCOMPATIBLE_TXT = 'Incompatible'; const NEW_NAME_FIELD = 'new_name'; +const NEW_NAME_SPEC: Spec = {[NEW_NAME_FIELD]: new StringLitType()}; const LOAD_DISABLED_TXT = 'Provide a value for "new_name" to load'; function initializeCallConfig(spec: Spec): CallConfig { @@ -89,7 +90,7 @@ export class GlobalSettingsComponent extends MobxLitElement { @observable private selectedLayout = ''; @observable private readonly modelCheckboxValues = new Map(); @observable selectedTab: TabName = 'Models'; - @observable private status?: string; + @observable private status?: string = undefined; // TODO(b/207137261): Determine if datapointsStatus, modelStatus, // pathForDatapoints, and pathForModel are still necessary/how to use convey @@ -104,24 +105,24 @@ export class GlobalSettingsComponent extends MobxLitElement { @observable private readonly openDatasetKeys: Set = new Set(); @observable private readonly openLayoutKeys: Set = new Set(); - @observable private datasetToLoad?: string; - @observable private modelToLoad?: string; + @observable private datasetToLoad?: string = undefined; + @observable private modelToLoad?: string = undefined; @observable private loadingCallConfig: CallConfig = {}; @observable private missingCallConfigFields: string[] = []; @computed get loadableDatasets(): string[] { const {datasets} = this.appState.metadata.initSpecs; const loadable = Object.entries(datasets) - .filter(([unused, spec]) => spec != null) - .map(([name, unused]) => name); + .filter(([, spec]) => spec != null) + .map(([name, ]) => name); return loadable; } @computed get loadableModels(): string[] { const {models} = this.appState.metadata.initSpecs; const loadable = Object.entries(models) - .filter(([unused, spec]) => spec != null) - .map(([name, unused]) => name); + .filter(([, spec]) => spec != null) + .map(([name, ]) => name); return loadable; } @@ -131,6 +132,11 @@ export class GlobalSettingsComponent extends MobxLitElement { .map(([modelName,]) => modelName); } + constructor() { + super(); + makeObservable(this); + } + // TODO(b/207137261): Determine where and how dataset saving happens after the // load from init_spec() refactor. // @computed get saveDatapointButtonDisabled() { @@ -684,23 +690,27 @@ export class GlobalSettingsComponent extends MobxLitElement { * @param load A callback function for telling the LIT server to load a new * instance of the selected component given the configured parameters. */ - private renderLoader(panel: string, options: string[], selectedOption: string, - spec: Spec, select: EventHandler, load: EventHandler) { + private renderLoader( + panel: string, + options: string[], + selectedOption: string, + spec: Spec, + select: EventHandler, + load: EventHandler + ) { const disableReset = Object.entries(this.loadingCallConfig) .map(([name, value]) => name === NEW_NAME_FIELD ? !value : spec[name]?.default === value) .reduce((a, b) => a && b, true); const disableSubmit = !this.loadingCallConfig[NEW_NAME_FIELD]; - const specEntries = Object.entries(spec); + const aggregateSpec: Spec = {...NEW_NAME_SPEC, ...spec}; + const specEntries = Object.entries(aggregateSpec); const reset = () => {this.resetLoadingCallConfig();}; const selectionChanged = (e: Event) => {select(e);}; const configInputs = specEntries.length ? - Object.entries({ - [NEW_NAME_FIELD]: new StringLitType(), - ...spec - }).map(([fieldName, fieldType]) => { + Object.entries(aggregateSpec).map(([fieldName, fieldType]) => { const value = this.loadingCallConfig[fieldName]; const updateConfig = (e: Event) => { const {value} = e.target as LitInputField; @@ -859,10 +869,16 @@ export class GlobalSettingsComponent extends MobxLitElement { } private renderLine( - name: string, selectorHtml: TemplateResult, selected: boolean, - disabled: boolean, expanderOpen: boolean, onExpanderClick: () => void, - renderStatus: boolean, expandedInfoHtml: TemplateResult, - description = '') { + name: string, + selectorHtml: TemplateResult, + selected: boolean, + disabled: boolean, + expanderOpen: boolean, + onExpanderClick: () => void, + renderStatus: boolean, + expandedInfoHtml: TemplateResult, + description = '' + ) { const expanderIcon = expanderOpen ? 'expand_less' : 'expand_more'; // Icons for arrows. @@ -882,7 +898,7 @@ export class GlobalSettingsComponent extends MobxLitElement { classMap({'expanded-info': true, 'open': expanderOpen}); const status = renderStatus ? this.renderStatus(selected, disabled) : ''; return html` -

    +
    ${selectorHtml}
    diff --git a/lit_nlp/client/core/lit_module.ts b/lit_nlp/client/core/lit_module.ts index 277439d2..ce42806e 100644 --- a/lit_nlp/client/core/lit_module.ts +++ b/lit_nlp/client/core/lit_module.ts @@ -18,7 +18,7 @@ // tslint:disable:no-new-decorators import {html, TemplateResult} from 'lit'; import {property} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {ReactiveElement} from '../lib/elements'; import {LitModuleClass, ModelInfoMap, SCROLL_SYNC_CSS_CLASS, Spec} from '../lib/types'; @@ -57,7 +57,7 @@ export abstract class LitModule extends ReactiveElement { @property({type: Object}) onSyncScroll: OnScrollFn|null = null; // Name of this module, to show in the UI. - static title: string = ''; + static title = ''; /** * Information about this module that displays on hover. @@ -65,19 +65,19 @@ export abstract class LitModule extends ReactiveElement { static infoMarkdown = ''; // Number of columns of the 12 column horizontal layout. - static numCols: number = 4; + static numCols = 4; // Whether to collapse this module by default. - static collapseByDefault: boolean = false; + static collapseByDefault = false; // If true, duplicate this module in example comparison mode. - static duplicateForExampleComparison: boolean = false; + static duplicateForExampleComparison = false; // If true, duplicate this module when running with more than one model. - static duplicateForModelComparison: boolean = true; + static duplicateForModelComparison = true; // If true, duplicate this module as rows, instead of columns. - static duplicateAsRow: boolean = false; + static duplicateAsRow = false; // Template function. Should return HTML to create this element in the DOM. static template: @@ -88,8 +88,8 @@ export abstract class LitModule extends ReactiveElement { @observable @property({type: Number}) selectionServiceIndex = 0; // tslint:disable:no-any - @observable - protected readonly latestLoadPromises = new Map>(); + protected readonly latestLoadPromises = + observable.map(new Map>()); // tslint:enable:no-any protected readonly apiService = app.getService(ApiService); @@ -100,6 +100,11 @@ export abstract class LitModule extends ReactiveElement { return app.getServiceArray(SelectionService)[this.selectionServiceIndex]; } + constructor () { + super(); + makeObservable(this); + } + override updated() { // If the class defined by SCROLL_SYNC_CSS_CLASS is used in the module then // set its onscroll callback to propagate to the parent widget. diff --git a/lit_nlp/client/core/main_toolbar.ts b/lit_nlp/client/core/main_toolbar.ts index 2d7724a9..6c2008e3 100644 --- a/lit_nlp/client/core/main_toolbar.ts +++ b/lit_nlp/client/core/main_toolbar.ts @@ -28,7 +28,7 @@ import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed} from 'mobx'; +import {makeObservable, computed} from 'mobx'; import {MenuItem} from '../elements/menu'; import {styles as sharedStyles} from '../lib/shared_styles.css'; @@ -233,6 +233,11 @@ export class LitMainToolbar extends MobxLitElement { .map(d => [d.id, d.meta['parentId']!]); } + constructor() { + super(); + makeObservable(this); + } + /** * Button to select a random example. */ diff --git a/lit_nlp/client/core/modules.ts b/lit_nlp/client/core/modules.ts index ffb53b85..6d73a86f 100644 --- a/lit_nlp/client/core/modules.ts +++ b/lit_nlp/client/core/modules.ts @@ -25,7 +25,7 @@ import {html} from 'lit'; import {customElement, property} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {ReactiveElement} from '../lib/elements'; import {styles as sharedStyles} from '../lib/shared_styles.css'; @@ -137,6 +137,11 @@ export class LitModules extends ReactiveElement { return [sharedStyles, styles]; } + constructor() { + super(); + makeObservable(this); + } + override connectedCallback() { super.connectedCallback(); // We set up a callback in the modulesService to allow it to explicitly diff --git a/lit_nlp/client/core/slice_module.ts b/lit_nlp/client/core/slice_module.ts index efd607a3..674e652a 100644 --- a/lit_nlp/client/core/slice_module.ts +++ b/lit_nlp/client/core/slice_module.ts @@ -20,7 +20,7 @@ import '../elements/export_controls'; import {customElement} from 'lit/decorators.js'; import {html} from 'lit'; import {classMap} from 'lit/directives/class-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from './app'; import {LitModule} from './lit_module'; @@ -74,6 +74,7 @@ export class SliceModule extends LitModule { constructor() { super(); + makeObservable(this); const facetsChange = (event: CustomEvent) => { this.sliceByFeatures = event.detail.features; diff --git a/lit_nlp/client/core/widget_group.ts b/lit_nlp/client/core/widget_group.ts index 23210608..362cd5fd 100644 --- a/lit_nlp/client/core/widget_group.ts +++ b/lit_nlp/client/core/widget_group.ts @@ -210,7 +210,7 @@ export class WidgetGroup extends ReactiveElement { }); // Set sub-component dimensions based on the container. - const widgetStyle = {width: '100%', height: '100%'}; + const widgetStyle = {'width': '100%', 'height': '100%'}; if (this.duplicateAsRow) { widgetStyle.width = `${100 / configGroup.length}%`; } else { @@ -237,7 +237,9 @@ export class WidgetGroup extends ReactiveElement {
    ${this.renderHeader(configGroup)}
    - ${configGroup.map(config => this.renderModule(config, widgetStyle, showSubtitle))} + ${configGroup.map( + (config) => this.renderModule(config, widgetStyle, showSubtitle) + )}
    @@ -247,9 +249,16 @@ export class WidgetGroup extends ReactiveElement { renderModule( - config: RenderConfig, styles: {[key: string]: string}, - showSubtitle: boolean) { + config: RenderConfig, + styles: {[key: string]: string}, + showSubtitle: boolean + ) { const moduleType = config.moduleType; + if (typeof moduleType.template !== 'function') { + console.log(typeof config, typeof moduleType, typeof moduleType.template); + return; + } + const modelName = config.modelName || ''; const selectionServiceIndex = config.selectionServiceIndex || 0; const shouldReact = this.visible && !this.minimized; @@ -320,6 +329,7 @@ export class WidgetGroup extends ReactiveElement { * A wrapper for a LIT module that renders the contents in a box with * expand/contract capabilities. */ +// TODO(b/319249148): Determine if this inherit from LitElement instead. @customElement('lit-widget') export class LitWidget extends MobxLitElement { @property({type: String}) displayTitle = ''; diff --git a/lit_nlp/client/elements/annotated_text_vis.ts b/lit_nlp/client/elements/annotated_text_vis.ts index ea680630..db8b84b0 100644 --- a/lit_nlp/client/elements/annotated_text_vis.ts +++ b/lit_nlp/client/elements/annotated_text_vis.ts @@ -5,11 +5,11 @@ // tslint:disable:no-new-decorators import {property} from 'lit/decorators.js'; import {customElement} from 'lit/decorators.js'; -import { html, LitElement} from 'lit'; +import {html, LitElement} from 'lit'; import {TemplateResult} from 'lit'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {getVizColor} from '../lib/colors'; import {ReactiveElement} from '../lib/elements'; @@ -171,6 +171,11 @@ export class AnnotatedTextVis extends ReactiveElement { return [sharedStyles, styles]; } + constructor() { + super(); + makeObservable(this); + } + override connectedCallback() { super.connectedCallback(); this.reactImmediately(() => this.annotations, annotations => { diff --git a/lit_nlp/client/elements/color_legend.ts b/lit_nlp/client/elements/color_legend.ts index 9c48beca..fe3ed8cb 100644 --- a/lit_nlp/client/elements/color_legend.ts +++ b/lit_nlp/client/elements/color_legend.ts @@ -23,7 +23,7 @@ import {html} from 'lit'; import {customElement, property} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {DEFAULT} from '../lib/colors'; import {ReactiveElement} from '../lib/elements'; @@ -59,7 +59,7 @@ function stylePropToNumber(styles: CSSStyleDeclaration, */ @customElement('color-legend') export class ColorLegend extends ReactiveElement { - @observable @property({type: Object}) scale: D3Scale = + @observable.ref @property({type: Object}) scale: D3Scale = d3.scaleOrdinal([DEFAULT]).domain(['all']) as D3Scale; @property({type: String}) legendType = LegendType.CATEGORICAL; @property({type: String}) label = ''; @@ -89,6 +89,11 @@ export class ColorLegend extends ReactiveElement { return [sharedStyles, styles]; } + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { const {host} = this.shadowRoot!; if (host) { diff --git a/lit_nlp/client/elements/data_matrix.ts b/lit_nlp/client/elements/data_matrix.ts index ce833d35..6c8742b1 100644 --- a/lit_nlp/client/elements/data_matrix.ts +++ b/lit_nlp/client/elements/data_matrix.ts @@ -23,7 +23,7 @@ import {html, LitElement} from 'lit'; import {customElement, property} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {MAJOR_TONAL_COLORS, ramp} from '../lib/colors'; import {styles as sharedStyles} from '../lib/shared_styles.css'; @@ -32,7 +32,8 @@ import {styles} from './data_matrix.css'; // Custom color ramp for the Data Matrix -const LOW = 0, HIGH = 8; // 0: -50, 1: -100, 2: -200, etc., HIGH gets excluded +const LOW = 0; +const HIGH = 8; // 0: -50, 1: -100, 2: -200, etc., HIGH gets excluded // Text color flips (black => white) above -600, calc the % where that happens const COLOR_FLIP_PCT = Math.floor((6 - LOW) / (HIGH - 1 - LOW) * 100); const COLOR_RAMP = ramp([...MAJOR_TONAL_COLORS.primary.slice(LOW, HIGH) @@ -88,6 +89,11 @@ export class DataMatrix extends LitElement { return d3.scaleSequential(COLOR_RAMP).domain([0, this.totalIds]); } + constructor() { + super(); + makeObservable(this); + } + private updateSelection() { const selectedCells: Array<[number, number]> = []; for (let i = 0; i < this.matrixCells.length; i++) { diff --git a/lit_nlp/client/elements/generated_text_vis.ts b/lit_nlp/client/elements/generated_text_vis.ts index 23dbdea3..f678b617 100644 --- a/lit_nlp/client/elements/generated_text_vis.ts +++ b/lit_nlp/client/elements/generated_text_vis.ts @@ -19,7 +19,7 @@ import {html} from 'lit'; import {customElement, property} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {ReactiveElement} from '../lib/elements'; import {DiffMode, getTextDiff, type TextDiff} from '../lib/generated_text_utils'; @@ -79,6 +79,11 @@ export class GeneratedTextVis extends ReactiveElement { return getTextDiff(referenceText, outputText, byWord); } + constructor() { + super(); + makeObservable(this); + } + renderDiffString(strings: string[], equal: boolean[]) { let displayStrings = strings; diff --git a/lit_nlp/client/elements/interpreter_controls.ts b/lit_nlp/client/elements/interpreter_controls.ts index c3b4e110..bf090956 100644 --- a/lit_nlp/client/elements/interpreter_controls.ts +++ b/lit_nlp/client/elements/interpreter_controls.ts @@ -21,7 +21,7 @@ import '../elements/numeric_input'; import {html} from 'lit'; import {customElement, property} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {ReactiveElement} from '../lib/elements'; import {BooleanLitType, CategoryLabel, LitType, LitTypeWithVocab, MultiFieldMatcher, Scalar, SingleFieldMatcher, SparseMultilabel, Tokens} from '../lib/lit_types'; @@ -90,6 +90,11 @@ export class InterpreterControls extends ReactiveElement { return settings; } + constructor() { + super(); + makeObservable(this); + } + override connectedCallback() { super.connectedCallback(); this.settings = structuredClone(this.defaultSettings); diff --git a/lit_nlp/client/elements/menu.ts b/lit_nlp/client/elements/menu.ts index 3b49096e..e98a536f 100644 --- a/lit_nlp/client/elements/menu.ts +++ b/lit_nlp/client/elements/menu.ts @@ -20,13 +20,11 @@ import '@material/mwc-list/mwc-list-item'; import {MobxLitElement} from '@adobe/lit-mobx'; import {Menu} from '@material/mwc-menu'; -import {query} from 'lit/decorators.js'; -import {property} from 'lit/decorators.js'; -import {customElement} from 'lit/decorators.js'; +import {customElement, property, query} from 'lit/decorators.js'; import { html, LitElement, TemplateResult} from 'lit'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed} from 'mobx'; +import {computed, makeObservable} from 'mobx'; import {styles} from './menu.css'; @@ -59,6 +57,11 @@ export class MenuToolbar extends MobxLitElement { return Array.from(this.menuData.keys()); } + constructor() { + super(); + makeObservable(this); + } + override render() { // clang-format off diff --git a/lit_nlp/client/elements/table.ts b/lit_nlp/client/elements/table.ts index 2645b472..85dc8a7a 100644 --- a/lit_nlp/client/elements/table.ts +++ b/lit_nlp/client/elements/table.ts @@ -32,7 +32,7 @@ import {customElement, property, queryAll} from 'lit/decorators.js'; import {isTemplateResult} from 'lit/directive-helpers.js'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {action, computed, observable} from 'mobx'; +import {action, computed, makeObservable, observable} from 'mobx'; import * as papa from 'papaparse'; import {ReactiveElement} from '../lib/elements'; @@ -142,7 +142,7 @@ export class DataTable extends ReactiveElement { } // Sort order precedence: 1) sortName, 2) input order - @observable private sortName?: string; + @observable private sortName?: string = undefined; @observable private sortAscending = true; @observable private showColumnMenu = false; @observable private columnMenuName = ''; @@ -168,6 +168,11 @@ export class DataTable extends ReactiveElement { private readonly HOVER_TIMEOUT_MS = 3; private hoverTimeoutId: number|null = null; + constructor() { + super(); + makeObservable(this); + } + override connectedCallback() { super.connectedCallback(); // If the inputs change, re-sort data based on the new inputs. @@ -842,21 +847,20 @@ export class DataTable extends ReactiveElement { const headerId = this.columnNameToId(title); const toggleSort = (e: Event) => { + e.preventDefault(); e.stopPropagation(); // Table supports three sort states/transitions after a click: if (this.sortName !== title) { - // 1. If title !== sortName, sort by that title in ascending order + // 1. If title !== sortName, sort by that title in ascending order this.sortName = title; this.sortAscending = true; + } else if (this.sortAscending) { + // 2. If title === sortName && ascending, switch to descending + this.sortAscending = false; } else { - if (this.sortAscending) { - // 2. If title === sortName && ascending, switch to descending - this.sortAscending = false; - } else { - // 3. If title === sortName && descending, turn off sort - this.sortName = undefined; - } + // 3. If title === sortName && descending, turn off sort + this.sortName = undefined; } }; diff --git a/lit_nlp/client/elements/tooltip.ts b/lit_nlp/client/elements/tooltip.ts index 62749cd3..c0472205 100644 --- a/lit_nlp/client/elements/tooltip.ts +++ b/lit_nlp/client/elements/tooltip.ts @@ -74,12 +74,12 @@ export class LitTooltip extends ReactiveElement { // prettier-ignore return html`
    - ${this.content === '' ? '' : html` + ${!this.content ? '' : html` help_outline `} - ${this.content === '' ? '' : html` + ${!this.content ? '' : html` ${getTemplateStringFromMarkdown(this.content)} `} diff --git a/lit_nlp/client/lib/elements.ts b/lit_nlp/client/lib/elements.ts index 69ed7b64..bb891640 100644 --- a/lit_nlp/client/lib/elements.ts +++ b/lit_nlp/client/lib/elements.ts @@ -22,7 +22,7 @@ // tslint:disable:no-new-decorators import {MobxLitElement} from '@adobe/lit-mobx'; import {property} from 'lit/decorators.js'; -import {IReactionDisposer, IReactionOptions, IReactionPublic, observable, reaction} from 'mobx'; +import {IReactionDisposer, IReactionOptions, IReactionPublic, makeObservable, observable, reaction} from 'mobx'; type ReactionInputFn = (r: IReactionPublic) => T; // tslint:disable:no-any @@ -62,6 +62,8 @@ export abstract class ReactiveElement extends MobxLitElement { constructor() { super(); + makeObservable(this); + reaction(() => this.shouldReact, shouldReact => { if (shouldReact === 0) { return; @@ -94,13 +96,15 @@ export abstract class ReactiveElement extends MobxLitElement { * disconnected. */ protected react( - fn: ReactionInputFn, effect: (arg: T, r: IReactionPublic) => void, - opts: IReactionOptions = {}) { + fn: ReactionInputFn, + effect: (arg: T, r: IReactionPublic) => void, + opts: IReactionOptions = {} + ) { // Wrapper function to pass to the mobx reaction call. If the element is // set to react, then the effect happens immediately. If not, the arguments // for the effect are stored in the map, keyed by the effect, so that // the latest reaction of each type is stored for later use. - const reactWrapper = (arg: T, r: IReactionPublic) => { + const reactWrapper = (arg: T, prev: T, r: IReactionPublic) => { if (this.shouldReact !== 0) { effect.apply(window, [arg, r]); } else { diff --git a/lit_nlp/client/lib/generated_text_utils.ts b/lit_nlp/client/lib/generated_text_utils.ts index cab456b0..f32b0862 100644 --- a/lit_nlp/client/lib/generated_text_utils.ts +++ b/lit_nlp/client/lib/generated_text_utils.ts @@ -131,8 +131,11 @@ export interface TargetOption { * Get all possible target strings from reference and model output. */ export function getAllTargetOptions( - dataSpec: Spec, outputSpec: Spec, input?: IndexedInput|null, - preds?: GeneratedTextResult|null): TargetOption[] { + dataSpec: Spec, + outputSpec: Spec, + input?: IndexedInput|null, + preds?: GeneratedTextResult|null +): TargetOption[] { const ret: TargetOption[] = []; for (const text of getAllReferenceTexts(dataSpec, outputSpec, input)) { ret.push({text, source: TargetSource.REFERENCE}); diff --git a/lit_nlp/client/lib/utils.ts b/lit_nlp/client/lib/utils.ts index b35d782c..6a925074 100644 --- a/lit_nlp/client/lib/utils.ts +++ b/lit_nlp/client/lib/utils.ts @@ -45,7 +45,7 @@ export function mean(values: number[]): number { /** Calculates the median for a list of numbers. */ export function median(values: number[]): number { - const sorted = [...values].sort(); + const sorted = [...values].sort((a, b) => a - b); const medIdx = Math.floor(sorted.length / 2); let median: number; diff --git a/lit_nlp/client/modules/annotated_text_module.ts b/lit_nlp/client/modules/annotated_text_module.ts index 7165cb12..3009b954 100644 --- a/lit_nlp/client/modules/annotated_text_module.ts +++ b/lit_nlp/client/modules/annotated_text_module.ts @@ -16,7 +16,7 @@ import '../elements/annotated_text_vis'; import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {LitModule} from '../core/lit_module'; import {type AnnotationGroups, TextSegments} from '../elements/annotated_text_vis'; @@ -104,9 +104,15 @@ export class AnnotatedTextModule extends LitModule { return sharedStyles; } - @observable private currentData?: IndexedInput; + @observable private currentData?: IndexedInput = undefined; @observable private currentPreds: AnnotationGroups = {}; + constructor() { + super(); + // TODO(b/319249148): Assess whether these need to be @observable vs @state. + makeObservable(this); + } + override firstUpdated() { const getPrimarySelectedInputData = () => this.selectionService.primarySelectedInputData; diff --git a/lit_nlp/client/modules/classification_module.ts b/lit_nlp/client/modules/classification_module.ts index ecb85219..70bf2f40 100644 --- a/lit_nlp/client/modules/classification_module.ts +++ b/lit_nlp/client/modules/classification_module.ts @@ -21,7 +21,7 @@ import '../elements/switch'; import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -88,6 +88,11 @@ export class ClassificationModule extends LitModule { @observable private sparseMode = false; @observable private labeledPredictions: LabeledPredictions = {}; + constructor() { + super(); + makeObservable(this); + } + override connectedCallback() { super.connectedCallback(); const getSelectionChanges = () => [ diff --git a/lit_nlp/client/modules/confusion_matrix_module.ts b/lit_nlp/client/modules/confusion_matrix_module.ts index 1c8fcea6..cb6b32ce 100644 --- a/lit_nlp/client/modules/confusion_matrix_module.ts +++ b/lit_nlp/client/modules/confusion_matrix_module.ts @@ -18,7 +18,7 @@ // tslint:disable:no-new-decorators import {html} from 'lit'; import {customElement, property} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {FacetsChange} from '../core/faceting_control'; @@ -93,6 +93,7 @@ export class ConfusionMatrixModule extends LitModule { constructor() { super(); + makeObservable(this); this.setInitialOptions(); const facetsChange = (event: CustomEvent) => { @@ -312,17 +313,17 @@ class ConfusionMatrix extends ReactiveElement { } /** Feature to use for the rows of the matrix */ - @observable @property({type: Object}) row?: CmatOption; + @observable @property({type: Object}) row?: CmatOption = undefined; /** Feature to use for the columns of the matrix */ - @observable @property({type: Object}) col?: CmatOption; + @observable @property({type: Object}) col?: CmatOption = undefined; /** Dataset to map into the cells of the matrix */ - @observable @property({type: Array}) data?: IndexedInput[]; + @observable @property({type: Array}) data?: IndexedInput[] = undefined; /** If true, don't show rows or columns containing only empty cells */ @property({type: Boolean}) hideEmptyLabels = false; /** Label for the matrix */ @property({type: String}) label = 'Confusion Matrix'; - @observable private cells: MatrixCell[][] = []; + @observable.ref private cells: MatrixCell[][] = []; // tslint:disable-next-line:no-any @@ -398,6 +399,11 @@ class ConfusionMatrix extends ReactiveElement { })); } + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { const configChange = () => [this.row, this.col, this.data]; this.reactImmediately(configChange, () => {this.calculateMatrix();}); diff --git a/lit_nlp/client/modules/curves_module.ts b/lit_nlp/client/modules/curves_module.ts index 93246468..8d4c4d1d 100644 --- a/lit_nlp/client/modules/curves_module.ts +++ b/lit_nlp/client/modules/curves_module.ts @@ -21,7 +21,7 @@ import '../elements/line_chart'; import {html, TemplateResult} from 'lit'; import {customElement, state} from 'lit/decorators.js'; -import {action, computed, observable} from 'mobx'; +import {action, computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {FacetsChange} from '../core/faceting_control'; @@ -79,7 +79,7 @@ export class CurvesModule extends LitModule { @state() private showSlices = false; @observable private readonly isPanelCollapsed = new Map(); - @observable private datasetCurves?: CurvesData[]; + @observable private datasetCurves?: CurvesData[] = undefined; @observable private sliceCurves: CurvesDataMap = {}; @observable private groupedCurves: CurvesDataMap = {}; @observable private selectedPredKeyIndex = 0; @@ -101,6 +101,7 @@ export class CurvesModule extends LitModule { constructor() { super(); + makeObservable(this); const facetsChange = (event: CustomEvent) => { this.setFacetInfo(event); diff --git a/lit_nlp/client/modules/data_table_module.ts b/lit_nlp/client/modules/data_table_module.ts index 7f6a1a43..0e5cac67 100644 --- a/lit_nlp/client/modules/data_table_module.ts +++ b/lit_nlp/client/modules/data_table_module.ts @@ -23,7 +23,7 @@ import {html} from 'lit'; import {customElement, query} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -396,6 +396,11 @@ export class DataTableModule extends LitModule { }); } + constructor() { + super(); + makeObservable(this); + } + override connectedCallback() { super.connectedCallback(); const updateColsChange = () => diff --git a/lit_nlp/client/modules/datapoint_editor_module.ts b/lit_nlp/client/modules/datapoint_editor_module.ts index 0017004e..98c3e658 100644 --- a/lit_nlp/client/modules/datapoint_editor_module.ts +++ b/lit_nlp/client/modules/datapoint_editor_module.ts @@ -23,7 +23,7 @@ import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable, when} from 'mobx'; +import {computed, makeObservable, observable, when} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -148,7 +148,7 @@ export class DatapointEditorModule extends LitModule { @observable inputHeights: {[name: string]: string} = {}; @observable maximizedImageFields = new Set(); @observable editingTokenIndex = -1; - @observable editingTokenField?: string; + @observable editingTokenField?: string = undefined; @observable editingTokenWidth = 0; @computed @@ -233,6 +233,11 @@ export class DatapointEditorModule extends LitModule { this.sparseMultilabelInputKeys); } + constructor() { + super(); + makeObservable(this); + } + override connectedCallback() { super.connectedCallback(); this.reactImmediately( diff --git a/lit_nlp/client/modules/dive_module.ts b/lit_nlp/client/modules/dive_module.ts index 065bfbbd..ae51c663 100644 --- a/lit_nlp/client/modules/dive_module.ts +++ b/lit_nlp/client/modules/dive_module.ts @@ -23,7 +23,7 @@ import {css, html} from 'lit'; import {customElement, query} from 'lit/decorators.js'; import {Scene, Selection, SpriteView, TextSelection} from 'megaplot'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -236,6 +236,11 @@ export class DiveModule extends LitModule { points: undefined }); + constructor() { + super(); + makeObservable(this); + } + /** * Configures the Megaplot Selections for each facet in `this.groupedExamples` * using the grouped facets method, which produces a 2D matrix of dot groups, diff --git a/lit_nlp/client/modules/documentation_module.ts b/lit_nlp/client/modules/documentation_module.ts index 56562437..c8730275 100644 --- a/lit_nlp/client/modules/documentation_module.ts +++ b/lit_nlp/client/modules/documentation_module.ts @@ -22,7 +22,7 @@ // tslint:disable:no-new-decorators import {customElement} from 'lit/decorators.js'; import {css, html} from 'lit'; -import {computed} from 'mobx'; +import {computed, makeObservable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; import {ModelInfoMap, Spec} from '../lib/types'; @@ -60,6 +60,11 @@ export class DocumentationModule extends LitModule { return getTemplateStringFromMarkdown(this.appState.metadata.inlineDoc!); } + constructor() { + super(); + makeObservable(this); + } + override renderImpl() { return html`
    ${this.markdownString}
    `; } diff --git a/lit_nlp/client/modules/embeddings_module.ts b/lit_nlp/client/modules/embeddings_module.ts index 48d54234..369c5f71 100644 --- a/lit_nlp/client/modules/embeddings_module.ts +++ b/lit_nlp/client/modules/embeddings_module.ts @@ -24,7 +24,7 @@ import * as d3 from 'd3'; import {Dataset, Point3D, ScatterGL} from 'scatter-gl'; import {html, TemplateResult} from 'lit'; import {customElement} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -103,20 +103,18 @@ export class EmbeddingsModule extends LitModule { static override numCols = 3; - @observable - private isLoading = false; + @observable private isLoading = false; // Selection of one of the above configs. - @observable private projectorName: string; - @computed - get projector(): ProjectorOptions { + @observable private projectorName = ''; + @computed get projector(): ProjectorOptions { return this.projectorChoices[this.projectorName]; } // Actual projected points. - @observable private projectedPoints: Point3D[] = []; + @observable.ref private projectedPoints: Point3D[] = []; - @observable private spriteImage?: HTMLImageElement|string; + @observable private spriteImage?: HTMLImageElement|string = undefined; private readonly colorService = app.getService(ColorService); private readonly focusService = app.getService(FocusService); @@ -287,6 +285,7 @@ export class EmbeddingsModule extends LitModule { constructor() { super(); + makeObservable(this); // Filter to only available projectors. // TODO(b/272281218): configure this from metadata instead of using a diff --git a/lit_nlp/client/modules/feature_attribution_module.ts b/lit_nlp/client/modules/feature_attribution_module.ts index ae07f221..6cadb0a4 100644 --- a/lit_nlp/client/modules/feature_attribution_module.ts +++ b/lit_nlp/client/modules/feature_attribution_module.ts @@ -21,7 +21,7 @@ import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {FacetsChange} from '../core/faceting_control'; @@ -120,8 +120,8 @@ export class FeatureAttributionModule extends LitModule { private readonly colorMap = new SignedSalienceCmap(); private readonly facetingControl = document.createElement('faceting-control'); - @observable private startsOpen?: string; - @observable private isColored = false; + @observable private startsOpen?: string = undefined; + @observable private isColored = true; @observable private features: string[] = []; @observable private bins: NumericFeatureBins = {}; @observable private readonly settings = @@ -134,6 +134,7 @@ export class FeatureAttributionModule extends LitModule { constructor() { super(); + makeObservable(this); const facetsChange = (event: CustomEvent) => { this.features = event.detail.features; @@ -464,7 +465,6 @@ export class FeatureAttributionModule extends LitModule { selections is not supported.
    `: null} ${Object.entries(this.summaries) - .sort() .map(([facet, summary]) => html`
    `; -static supportedTypes = [ImageBytes, ImageBytesList]; + static supportedTypes = [ImageBytes, ImageBytesList]; static override get styles() { const styles = css` @@ -61,7 +61,12 @@ static supportedTypes = [ImageBytes, ImageBytesList]; return [sharedStyles, styles]; } - @observable private generatedImages: {[key: string]: string} = {}; + @observable.ref private generatedImages: {[key: string]: string} = {}; + + constructor() { + super(); + makeObservable(this); + } override firstUpdated() { this.reactImmediately( diff --git a/lit_nlp/client/modules/generated_text_module.ts b/lit_nlp/client/modules/generated_text_module.ts index 0c0ba927..176a8a35 100644 --- a/lit_nlp/client/modules/generated_text_module.ts +++ b/lit_nlp/client/modules/generated_text_module.ts @@ -21,7 +21,7 @@ import '../elements/switch'; import {css, html} from 'lit'; import {customElement} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {LitModule} from '../core/lit_module'; import {styles as visStyles} from '../elements/generated_text_vis.css'; @@ -85,10 +85,10 @@ export class GeneratedTextModule extends LitModule { } @observable private inputData: Input|null = null; - @observable private generatedText: GeneratedTextResult = {}; - @observable private referenceScores: ReferenceScoresResult = {}; + @observable.ref private generatedText: GeneratedTextResult = {}; + @observable.ref private referenceScores: ReferenceScoresResult = {}; @observable private diffMode: DiffMode = DiffMode.NONE; - @observable private invertDiffs: boolean = false; + @observable private invertDiffs = false; @computed get referenceFields(): Map { @@ -121,6 +121,11 @@ export class GeneratedTextModule extends LitModule { return refMap; } + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { this.reactImmediately( () => this.selectionService.primarySelectedInputData, data => { diff --git a/lit_nlp/client/modules/generator_module.ts b/lit_nlp/client/modules/generator_module.ts index 79af59bb..6c310ad6 100644 --- a/lit_nlp/client/modules/generator_module.ts +++ b/lit_nlp/client/modules/generator_module.ts @@ -23,7 +23,7 @@ import {MobxLitElement} from '@adobe/lit-mobx'; import {css, html, TemplateResult} from 'lit'; // tslint:disable:no-new-decorators import {customElement} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -37,6 +37,10 @@ import {SelectionService, SliceService} from '../services/services'; import {styles} from './generator_module.css'; +interface GlobalParameters { + [key: string]: string; +} + /** * Custom element for in-table add/remove controls. * We use a custom element here so we can encapsulate styles. @@ -107,12 +111,10 @@ export class GeneratorModule extends LitModule { return [sharedStyles, styles]; } - @observable editedData: Input = {}; @observable isGenerating = false; - @observable generated: IndexedInput[][] = []; @observable appliedGenerator: string|null = null; - @observable sliceName: string = ''; + @observable sliceName = ''; @computed get datasetName() { @@ -125,11 +127,11 @@ export class GeneratorModule extends LitModule { } @computed - get globalParams() { + get globalParams(): GlobalParameters { return { 'model_name': this.modelName, 'dataset_name': this.datasetName, - }; + } ; } @computed @@ -137,6 +139,11 @@ export class GeneratorModule extends LitModule { return this.generated.reduce((a, b) => a + b.length, 0); } + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { const getSelectedData = () => this.selectionService.primarySelectedInputData; @@ -170,11 +177,12 @@ export class GeneratorModule extends LitModule { private makeAutoSliceName(generator: string, config?: CallConfig) { const segments: string[] = [generator]; + const {globalParams} = this; if (config != null) { - for (const key of Object.keys(config)) { + for (const [key, value] of Object.entries(config)) { // Skip these since they don't come from the actual controls form. - if (this.globalParams.hasOwnProperty(key)) continue; - segments.push(`${key}=${config[key]}`); + if (Object.hasOwn(globalParams, key)) continue; + segments.push(`${key}=${value}`); } } return segments.join(':'); @@ -193,7 +201,7 @@ export class GeneratorModule extends LitModule { // parentId and source should already be set from the backend. for (const examples of generated) { for (const ex of examples) { - Object.assign(ex['meta'], {added: 1}); + Object.assign(ex.meta, {added: 1}); } } this.generated = generated; @@ -215,7 +223,7 @@ export class GeneratorModule extends LitModule { } const parentIds = - new Set(newExamples.map(ex => ex.meta['parentId']!)); + new Set(newExamples.map(ex => ex.meta.parentId!)); // Select parents and children, and set primary to the first child. this.selectionService.selectIds([...parentIds, ...newIds], this); @@ -229,7 +237,7 @@ export class GeneratorModule extends LitModule { referenceSelectionService.selectIds([...parentIds, ...newIds], this); // parentIds[0] is not necessarily the parent of newIds[0], if // generated[0] is []. - const parentId = newExamples[0].meta['parentId']!; + const parentId = newExamples[0].meta.parentId!; referenceSelectionService.setPrimarySelection(parentId, this); } } @@ -427,7 +435,7 @@ export class GeneratorModule extends LitModule { Add and compare
    `; diff --git a/lit_nlp/client/modules/legacy_sequence_salience_module.ts b/lit_nlp/client/modules/legacy_sequence_salience_module.ts index 8916075d..6670abe5 100644 --- a/lit_nlp/client/modules/legacy_sequence_salience_module.ts +++ b/lit_nlp/client/modules/legacy_sequence_salience_module.ts @@ -11,7 +11,7 @@ import '../elements/token_chips'; // tslint:disable:no-new-decorators import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {LitModule} from '../core/lit_module'; import {LegendType} from '../elements/color_legend'; @@ -68,19 +68,19 @@ export class LegacySequenceSalienceModule extends LitModule { } // Current data - @observable private currentData?: IndexedInput; - @observable private currentPreds?: GeneratedTextResult; - @observable private salienceTarget?: string; - @observable + @observable.ref private currentData?: IndexedInput = undefined; + @observable.ref private currentPreds?: GeneratedTextResult = undefined; + @observable private salienceTarget?: string = undefined; + @observable.ref private currentSalience: {[fieldName: string]: SequenceSalienceMap} = {}; @observable private selectedSalienceField?: string = undefined; - @observable private focusState?: TokenFocusState = undefined; + @observable.ref private focusState?: TokenFocusState = undefined; // Options - @observable private cmapGamma: number = 2.0; + @observable private cmapGamma = 2.0; @observable private cmapScalingMode: ColorScalingMode = ColorScalingMode.NORMALIZE; - @observable private denseView: boolean = false; + @observable private denseView = false; @computed get salienceSpecInfo(): Spec { @@ -107,6 +107,11 @@ export class LegacySequenceSalienceModule extends LitModule { dataSpec, outputSpec, this.currentData, this.currentPreds); } + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { if (this.selectedSalienceField === undefined) { this.selectedSalienceField = Object.keys(this.salienceSpecInfo)[0]; @@ -126,14 +131,14 @@ export class LegacySequenceSalienceModule extends LitModule { } private async updateToSelection(input: IndexedInput|null) { + this.currentPreds = undefined; + if (input == null) { this.currentData = undefined; - this.currentPreds = undefined; return; } // Before waiting for the backend call, update data and clear annotations. this.currentData = input; - this.currentPreds = undefined; const promise = this.apiService.getPreds( [input], this.model, this.appState.currentDataset, GENERATION_TYPES, [], diff --git a/lit_nlp/client/modules/metrics_module.ts b/lit_nlp/client/modules/metrics_module.ts index ebd6e0ce..d404de68 100644 --- a/lit_nlp/client/modules/metrics_module.ts +++ b/lit_nlp/client/modules/metrics_module.ts @@ -20,7 +20,7 @@ import {html} from 'lit'; import {customElement, query} from 'lit/decorators.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {FacetsChange} from '../core/faceting_control'; @@ -93,16 +93,17 @@ export class MetricsModule extends LitModule { app.getService(ClassificationService); private readonly facetingControl = document.createElement('faceting-control'); - @observable private selectedFacetBins: NumericFeatureBins = {}; - + @observable.ref private selectedFacetBins: NumericFeatureBins = {}; @observable private metricsMap: MetricsMap = {}; @observable private facetBySlice = false; @observable private selectedFacets: string[] = []; @observable private pendingCalls = 0; + @query('#metrics-table') private readonly table?: DataTable; constructor() { super(); + makeObservable(this); const facetsChange = (event: CustomEvent) => { this.selectedFacets = event.detail.features; @@ -305,8 +306,7 @@ export class MetricsModule extends LitModule { // TODO(b/254832560): Allow the user to configure which metrics component // are run via the UI and pass them in to this ApiService call. const metricsToRun = compatMetrics.length ? compatMetrics.join(',') : ''; - const config = - this.classificationService.marginSettings[model] as CallConfig || {}; + const config = this.classificationService.getMargins(model) as CallConfig; let metrics: MetricsResponse; if (selectedInputs.length) { diff --git a/lit_nlp/client/modules/multilabel_module.ts b/lit_nlp/client/modules/multilabel_module.ts index 3a41dd24..7916ac57 100644 --- a/lit_nlp/client/modules/multilabel_module.ts +++ b/lit_nlp/client/modules/multilabel_module.ts @@ -19,7 +19,7 @@ import '../elements/score_bar'; import {customElement} from 'lit/decorators.js'; import { html} from 'lit'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -59,11 +59,16 @@ export class MultilabelModule extends LitModule { return [sharedStyles, styles]; } - @observable private resultsInfo: AllResultsInfo = {}; + @observable.ref private resultsInfo: AllResultsInfo = {}; private datapoints: IndexedInput[] = []; private readonly groundTruthLabels = new Set(); private maxValues: {[predKey: string]: number} = {}; + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { const getSelectedInput = () => this.selectionService.primarySelectedInputData; diff --git a/lit_nlp/client/modules/pdp_module.ts b/lit_nlp/client/modules/pdp_module.ts index 4699fdc6..107bf866 100644 --- a/lit_nlp/client/modules/pdp_module.ts +++ b/lit_nlp/client/modules/pdp_module.ts @@ -21,7 +21,7 @@ import '../elements/bar_chart'; import {customElement} from 'lit/decorators.js'; import {html} from 'lit'; import {until} from 'lit/directives/until.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {LitModule} from '../core/lit_module'; import {ExpansionToggle} from '../elements/expansion_panel'; import {CategoryLabel, LitTypeWithVocab, MulticlassPreds, RegressionScore, Scalar} from '../lib/lit_types'; @@ -69,6 +69,11 @@ export class PdpModule extends LitModule { // is for the current selection before displaying it. private selectionSet = new Set(); + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { const getInputSpec = () => this.appState.getModelSpec(this.model).input; this.reactImmediately(getInputSpec, inputSpec => { diff --git a/lit_nlp/client/modules/regression_module.ts b/lit_nlp/client/modules/regression_module.ts index ad85efb0..fa49f780 100644 --- a/lit_nlp/client/modules/regression_module.ts +++ b/lit_nlp/client/modules/regression_module.ts @@ -18,7 +18,7 @@ // tslint:disable:no-new-decorators import {html} from 'lit'; import {customElement} from 'lit/decorators.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -51,7 +51,12 @@ export class RegressionModule extends LitModule { } private readonly dataService = app.getService(DataService); - @observable private result: RegressionResults|null = null; + @observable.ref private result: RegressionResults|null = null; + + constructor() { + super(); + makeObservable(this); + } override firstUpdated() { const getSelectionChanges = () => @@ -84,19 +89,16 @@ export class RegressionModule extends LitModule { } override renderImpl() { - if (this.result == null) { - return null; - } - const result = this.result; - const input = this.selectionService.primarySelectedInputData!; + const {result} = this; + if (result == null) {return null;} + const input = this.selectionService.primarySelectedInputData!; // Use the spec to find which fields we should display. const spec = this.appState.getModelSpec(this.model); - const scoreFields: string[] = findSpecKeys(spec.output, RegressionScore); - - + const scoreFields = findSpecKeys(spec.output, RegressionScore); const rows: TableData[] = []; let hasParent = false; + // Per output, display score, and parent field and error if available. for (const scoreField of scoreFields) { // Add new row for each output from the model. diff --git a/lit_nlp/client/modules/salience_clustering_module.ts b/lit_nlp/client/modules/salience_clustering_module.ts index e350a416..6f5ee29c 100644 --- a/lit_nlp/client/modules/salience_clustering_module.ts +++ b/lit_nlp/client/modules/salience_clustering_module.ts @@ -18,7 +18,7 @@ import {html, TemplateResult} from 'lit'; // tslint:disable:no-new-decorators import {customElement} from 'lit/decorators.js'; -import {observable} from 'mobx'; +import {makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -43,6 +43,7 @@ const TOP_K_TOKENS_KEY = const N_TOKENS_TO_DISPLAY_KEY = 'Number of tokens to display per cluster'; const SALIENCE_CLUSTERING_INTERPRETER_NAME = 'Salience Clustering'; const REUSE_CLUSTERING = 'reuse_clustering'; +const REUSE_CLUSTERING_CONFIG: CallConfig = {[REUSE_CLUSTERING]: true}; interface ModuleState { dataColumns: string[]; @@ -109,6 +110,11 @@ export class SalienceClusteringModule extends LitModule { }, }; + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { const state: ModuleState = { dataColumns: [], @@ -151,10 +157,10 @@ export class SalienceClusteringModule extends LitModule { // Function to get value for this new data column when new datapoints are // added. const getValueFn = async (gradKey: string, input: IndexedInput) => { - const config = { + const config: CallConfig = { ...this.state.clusteringConfig, ...this.state.salienceConfigs[salienceMapper], - [REUSE_CLUSTERING]: true, + ...REUSE_CLUSTERING_CONFIG }; const clusteringResult = await this.apiService.getInterpretations( [input], @@ -235,24 +241,34 @@ export class SalienceClusteringModule extends LitModule { // Render a table that lists clusters with their top tokens. private renderSingleGradKeyTopTokenInfos( - gradKey: string, clusterInfosByFields: ClusterInfos, - colorMap: SalienceCmap) { + gradKey: string, + clusterInfosByFields: ClusterInfos, + colorMap: SalienceCmap + ) { // TODO(b/268221058): Compute number of data points per cluster from backend // clang-format off const rowsByClusters: TableData[] = - Object.entries(clusterInfosByFields).map(([clusterId, clusterInfo]) => { - return [ - Number(clusterId), - clusterInfo.exampleIds.length, - html` - - ` - ]; - }); + // This lint rule doesn't treat numbers as the same as quoted properties + // in interface definitions, even though they functionally are, so + // disabling until that's fixed. + // tslint:disable-next-line:ban-unsafe-reflection + Object.entries(clusterInfosByFields).map(([id, info]) => { + return [ + Number(id), + info.exampleIds.length, + html` + + ` + ]; + }); // clang-format on const onSelectClusters = (clusterIdxs: number[]) => { + // This lint rule doesn't treat numbers as the same as quoted properties + // in interface definitions, even though they functionally are, so + // disabling until that's fixed. + // tslint:disable-next-line:ban-unsafe-reflection const dataPointIds = Object.entries(clusterInfosByFields) .flatMap(([k, v]) => clusterIdxs.includes(Number(k)) ? v.exampleIds : []); diff --git a/lit_nlp/client/modules/salience_map_module.ts b/lit_nlp/client/modules/salience_map_module.ts index c05cbae3..64f03d06 100644 --- a/lit_nlp/client/modules/salience_map_module.ts +++ b/lit_nlp/client/modules/salience_map_module.ts @@ -28,7 +28,7 @@ import {html} from 'lit'; // tslint:disable:no-new-decorators import {customElement} from 'lit/decorators.js'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -146,11 +146,11 @@ export class SalienceMapModule extends LitModule { @observable private state: {[name: string]: InterpreterState} = {}; - @observable private currentData?: IndexedInput; - @observable private currentPreds?: Preds; + @observable.ref private currentData?: IndexedInput = undefined; + @observable.ref private currentPreds?: Preds = undefined; // Index into possible labels. - @observable private targetField?: string; - @observable private salienceTarget?: number; + @observable private targetField?: string = undefined; + @observable private salienceTarget?: number = undefined; // Check that the target selector is available for gradient-based salience. // TODO(b/205996131): remove this and always show dropdown, once everything @@ -229,6 +229,11 @@ export class SalienceMapModule extends LitModule { input, {[this.inputLabelField]: newLabel}, 'SalienceMapModule'); } + constructor() { + super(); + makeObservable(this); + } + shouldRunInterpreter(name: string) { return this.state[name].autorun; } diff --git a/lit_nlp/client/modules/scalar_module.ts b/lit_nlp/client/modules/scalar_module.ts index 337fb8f5..b2a02ae7 100644 --- a/lit_nlp/client/modules/scalar_module.ts +++ b/lit_nlp/client/modules/scalar_module.ts @@ -23,7 +23,7 @@ import * as d3 from 'd3'; import {html, TemplateResult} from 'lit'; import {customElement} from 'lit/decorators.js'; import {Scene, Selection, SpriteView} from 'megaplot'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; // tslint:disable-next-line:ban-module-namespace-object-escape const seedrandom = require('seedrandom'); // from //third_party/javascript/typings/seedrandom:bundle @@ -126,7 +126,7 @@ export class ScalarModule extends LitModule { new ResizeObserver(() => {this.resizePlots();}); private numPlotsRendered = 0; - @observable private preds: IndexedScalars[] = []; + @observable.ref private preds: IndexedScalars[] = []; @computed get datasetScalarKeys(): string[] { return findSpecKeys(this.appState.currentDatasetSpec, Scalar); @@ -140,6 +140,10 @@ export class ScalarModule extends LitModule { return `div.scatterplot[data-id="${id}"]`; } + constructor() { + super(); + makeObservable(this); + } override connectedCallback() { super.connectedCallback(); diff --git a/lit_nlp/client/modules/sequence_salience_module.ts b/lit_nlp/client/modules/sequence_salience_module.ts index 68f38450..0afad530 100644 --- a/lit_nlp/client/modules/sequence_salience_module.ts +++ b/lit_nlp/client/modules/sequence_salience_module.ts @@ -12,7 +12,7 @@ import {css, html} from 'lit'; // tslint:disable:no-new-decorators import {customElement, property} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {LitModule} from '../core/lit_module'; import {LegendType} from '../elements/color_legend'; @@ -56,6 +56,8 @@ const LEGEND_INFO_TITLE_UNSIGNED = 'score (more purple) for a token means that token was more influential ' + 'on the model\'s prediction of the selected target.'; +const MODEL_PREDS_KEY = 'modelPreds'; + /** * A convenience implementation of LitModule for single model, single example * use. Implements some standard boilerplate to fetch model predictions. @@ -77,10 +79,15 @@ export class SingleExampleSingleModelModule extends LitModule { static override duplicateForModelComparison = true; // Override this to request only specific types. - protected predsTypes: LitTypeTypesList = [LitType]; + protected readonly predsTypes: LitTypeTypesList = [LitType]; + + @observable protected currentData?: IndexedInput = undefined; + @observable protected currentPreds?: Preds = undefined; - @observable protected currentData?: IndexedInput; - @observable protected currentPreds?: Preds; + constructor() { + super(); + makeObservable(this); + } // Override this for any post-processing. protected postprocessPreds(input: IndexedInput, preds: Preds): Preds { @@ -102,6 +109,8 @@ export class SingleExampleSingleModelModule extends LitModule { // currentPreds should already be cleared by the resetState() call above. this.currentData = input; + // TODO(b/270268760): Get the generations from the DataService and use its + // async callback to get the data from the API if it's missing. const promise = this.apiService.getPreds( [input], this.model, @@ -110,8 +119,8 @@ export class SingleExampleSingleModelModule extends LitModule { [], `Getting predictions from ${this.model}`, ); - const results = await this.loadLatest('modelPreds', promise); - if (results === null) return; + const results = await this.loadLatest(MODEL_PREDS_KEY, promise); + if (results == null) return; const preds = this.postprocessPreds(input, results[0]); @@ -122,9 +131,11 @@ export class SingleExampleSingleModelModule extends LitModule { override firstUpdated() { this.reactImmediately( - () => - [this.selectionService.primarySelectedInputData, this.model, - this.appState.currentDataset], + () => [ + this.selectionService.primarySelectedInputData, + this.model, + this.appState.currentDataset + ], () => { this.updateToSelection(); }, @@ -243,7 +254,7 @@ export class SequenceSalienceModule extends SingleExampleSingleModelModule { @observable.ref private currentTokens: string[] = []; @observable.ref private salienceTargetOptions: TargetOption[] = []; - @observable private salienceTargetOption?: number; // index into above + @observable private salienceTargetOption?: number = undefined; // index into above @observable.ref private targetSegmentSpan?: [number, number] = undefined; @@ -465,6 +476,11 @@ export class SequenceSalienceModule extends SingleExampleSingleModelModule { this.unsignedSalienceCmap; } + constructor() { + super(); + makeObservable(this); + } + spanToKey(span: number[]) { return `${span[0]}:${span[1]}`; } @@ -477,6 +493,9 @@ export class SequenceSalienceModule extends SingleExampleSingleModelModule { return; } + // TODO(b/270268760): Get tokenization for each the generation from the + // DataService and use its async callback to get the data from the API if + // it's missing. const promise = this.apiService.getPreds( [input], this.tokenizerModelName, @@ -549,16 +568,19 @@ export class SequenceSalienceModule extends SingleExampleSingleModelModule { // Update target options based on current data and preds. // TODO: could this just be @computed? // If we maintain explicit state, we can support custom target strings. - this.reactImmediately(() => [this.currentData, this.currentPreds], () => { - const dataSpec = this.appState.currentDatasetSpec; - const outputSpec = this.appState.getModelSpec(this.model).output; - this.salienceTargetOptions = getAllTargetOptions( - dataSpec, - outputSpec, - this.currentData, - this.currentPreds, - ); - }); + this.reactImmediately( + () => [this.currentData, this.currentPreds] as const, + ([currentData, currentPreds]) => { + const dataSpec = this.appState.currentDatasetSpec; + const outputSpec = this.appState.getModelSpec(this.model).output; + const targetOptions = getAllTargetOptions( + dataSpec, + outputSpec, + currentData, + currentPreds + ); + this.salienceTargetOptions = targetOptions; + }); // If selected example OR selected target string change. // NOTE: you may see a console warning: "Element sequence-salience-module @@ -762,7 +784,7 @@ export class SequenceSalienceModule extends SingleExampleSingleModelModule { const targetSelectorHelp = 'Select a (response) from the model or a pre-defined (target) sequence from the dataset.'; - const isLoadingPreds = this.latestLoadPromises.has('modelPreds'); + const isLoadingPreds = this.latestLoadPromises.has(MODEL_PREDS_KEY); const indicatorTextClass = classMap({ 'target-info-line': true, @@ -910,20 +932,14 @@ export class SequenceSalienceModule extends SingleExampleSingleModelModule { // the @click handler needs the original list index. const optionsFromDataset = this.salienceTargetOptions - .map((target, i) => { - if (target.source !== TargetSource.REFERENCE) return null; - return formatOption(target, i); - }) - .filter(val => val != null); + .filter((t) => t.source === TargetSource.REFERENCE) + .map((t, i) => formatOption(t, i)); const optionsFromModel = this.salienceTargetOptions - .map((target, i) => { - if (target.source !== TargetSource.MODEL_OUTPUT) return null; - return formatOption(target, i); - }) - .filter(val => val != null); + .filter((t) => t.source === TargetSource.MODEL_OUTPUT) + .map((t, i) => formatOption(t, i)); - const isLoadingPreds = this.latestLoadPromises.has('modelPreds'); + const isLoadingPreds = this.latestLoadPromises.has(MODEL_PREDS_KEY); // TODO(b/324959547): get field names 'target' and 'response' from spec // via generated_text_utils.ts, rather than hard-coding. diff --git a/lit_nlp/client/modules/tcav_module.ts b/lit_nlp/client/modules/tcav_module.ts index 22dbe35a..007a9b62 100644 --- a/lit_nlp/client/modules/tcav_module.ts +++ b/lit_nlp/client/modules/tcav_module.ts @@ -22,14 +22,14 @@ import '../elements/tcav_score_bar'; import {html, TemplateResult} from 'lit'; import {customElement} from 'lit/decorators.js'; import {classMap} from 'lit/directives/class-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; import {TableData} from '../elements/table'; import {Embeddings, Gradients, MulticlassPreds, Scalar} from '../lib/lit_types'; import {styles as sharedStyles} from '../lib/shared_styles.css'; -import {CallConfig, IndexedInput, ModelInfoMap, Spec} from '../lib/types'; +import {CallConfig, IndexedInput, ModelInfoMap} from '../lib/types'; import {createLitType, doesOutputSpecContain, findSpecKeys} from '../lib/utils'; import {ColumnData} from '../services/data_service'; import {DataService, SliceService} from '../services/services'; @@ -66,7 +66,6 @@ interface TcavResults { cav: number[]; } - /** * The TCAV module. */ @@ -100,26 +99,22 @@ export class TCAVModule extends LitModule { private resultsTableData: TableData[] = []; private cavCounter = 0; - @computed - get modelSpec() { + @computed get modelSpec() { return this.appState.getModelSpec(this.model); } - @computed - get gradKeys() { + @computed get gradKeys() { return findSpecKeys(this.modelSpec.output, Gradients); } - @computed - get TCAVSliceNames() { + @computed get TCAVSliceNames() { return this.sliceService.sliceNames.filter( name => name !== STARRED_SLICE_NAME); } // Returns pairs in the format [positive slice, negative slice (or null)] // for slices selected in the settings. - @computed - get slicePairs(): Array<[string, string|null]> { + @computed get slicePairs(): Array<[string, string|null]> { const positiveSlices: string[] = Array.from(this.selectedSlices.values()); const negativeSlices: string[] = Array.from(this.negativeSlices.values()); @@ -139,24 +134,25 @@ export class TCAVModule extends LitModule { return pairs; } - - - @computed - get predClasses() { + @computed get predClasses() { const [predKey] = findSpecKeys(this.modelSpec.output, MulticlassPreds); // TODO(lit-dev): Handle the multi-headed case with more than one pred key. return predKey == null ? [] : (this.modelSpec.output[predKey] as MulticlassPreds).vocab; } - @computed - get nullIndex() { + @computed get nullIndex() { const [predKey] = findSpecKeys(this.modelSpec.output, MulticlassPreds); // TODO(lit-dev): Handle the multi-headed case with more than one pred key. return predKey == null ? undefined : (this.modelSpec.output[predKey] as MulticlassPreds).null_idx; } + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { // Set the first grad key as default in selector. if (this.selectedLayers.size === 0 && this.gradKeys.length > 0) { @@ -482,8 +478,7 @@ export class TCAVModule extends LitModule { this.requestUpdate(); } - static override shouldDisplayModule( - modelSpecs: ModelInfoMap, datasetSpec: Spec) { + static override shouldDisplayModule(modelSpecs: ModelInfoMap) { // Ensure the models can support TCAV and that the TCAV interpreter is // loaded. if (!doesOutputSpecContain( diff --git a/lit_nlp/client/modules/tda_module.ts b/lit_nlp/client/modules/tda_module.ts index 461d24b3..547fcf4f 100644 --- a/lit_nlp/client/modules/tda_module.ts +++ b/lit_nlp/client/modules/tda_module.ts @@ -23,7 +23,7 @@ import {MobxLitElement} from '@adobe/lit-mobx'; import {css, html, TemplateResult} from 'lit'; // tslint:disable:no-new-decorators import {customElement, property} from 'lit/decorators.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -109,16 +109,16 @@ export class TrainingDataAttributionModule extends LitModule { return [sharedStyles, styles]; } - @observable private currentData?: IndexedInput; + @observable.ref private currentData?: IndexedInput = undefined; // Field overrides from label controls. @observable private customLabels: Input = {}; // Used to find target label options // TODO(b/224802615): generalize this to other label types (classification, // scoring, multilabel) - @observable private currentPreds?: GeneratedTextResult; + @observable.ref private currentPreds?: GeneratedTextResult = undefined; @observable isRunning = false; - @observable retrievedExamples: IndexedInput[][] = []; + @observable.ref retrievedExamples: IndexedInput[][] = []; @observable appliedGenerator: string|null = null; @computed @@ -210,6 +210,11 @@ export class TrainingDataAttributionModule extends LitModule { return this.retrievedExamples.reduce((a, b) => a + b.length, 0); } + constructor() { + super(); + makeObservable(this); + } + override firstUpdated() { const getSelectedData = () => this.selectionService.primarySelectedInputData; @@ -281,7 +286,7 @@ export class TrainingDataAttributionModule extends LitModule { // parentId and source should already be set from the backend. for (const examples of generated) { for (const ex of examples) { - Object.assign(ex['meta'], {added: 1}); + Object.assign(ex.meta, {added: 1}); } } this.retrievedExamples = generated; @@ -297,7 +302,7 @@ export class TrainingDataAttributionModule extends LitModule { if (newIds.length === 0) return; const parentIds = - new Set(newExamples.map(ex => ex.meta['parentId']!)); + new Set(newExamples.map(ex => ex.meta.parentId!)); // Select parents and children, and set primary to the first child. this.selectionService.selectIds([...parentIds, ...newIds], this); @@ -311,7 +316,7 @@ export class TrainingDataAttributionModule extends LitModule { referenceSelectionService.selectIds([...parentIds, ...newIds], this); // parentIds[0] is not necessarily the parent of newIds[0], if // generated[0] is []. - const parentId = newExamples[0].meta['parentId']!; + const parentId = newExamples[0].meta.parentId!; referenceSelectionService.setPrimarySelection(parentId, this); } } diff --git a/lit_nlp/client/modules/thresholder_module.ts b/lit_nlp/client/modules/thresholder_module.ts index bbd295f4..b7984f7c 100644 --- a/lit_nlp/client/modules/thresholder_module.ts +++ b/lit_nlp/client/modules/thresholder_module.ts @@ -20,7 +20,7 @@ import '../elements/threshold_slider'; import {customElement} from 'lit/decorators.js'; import {css, html} from 'lit'; import {styleMap} from 'lit/directives/style-map.js'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {FacetsChange} from '../core/faceting_control'; import {app} from '../core/app'; import {LitModule} from '../core/lit_module'; @@ -100,6 +100,7 @@ export class ThresholderModule extends LitModule { constructor() { super(); + makeObservable(this); const facetsChange = (event: CustomEvent) => { this.selectedFacets.length = 0; diff --git a/lit_nlp/client/services/classification_service.ts b/lit_nlp/client/services/classification_service.ts index 6c28a761..890f7956 100644 --- a/lit_nlp/client/services/classification_service.ts +++ b/lit_nlp/client/services/classification_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {action, computed, observable, reaction} from 'mobx'; +import {action, computed, makeObservable, observable, reaction} from 'mobx'; import {MulticlassPreds} from '../lib/lit_types'; import {type FacetedData, type GroupedExamples, type SpecMap} from '../lib/types'; @@ -69,6 +69,7 @@ export class ClassificationService extends LitService { constructor(private readonly appState: AppState) { super(); + makeObservable(this); // Reset classification margins when the models change. reaction( @@ -181,18 +182,19 @@ export class ClassificationService extends LitService { } } - getMargin(model: string, fieldName: string, facet?: FacetedData) { - if (this.marginSettings[model] == null || - this.marginSettings[model][fieldName] == null) { - return 0; - } - const fieldMargins = this.marginSettings[model][fieldName]; - if (facet == null) { - return fieldMargins[GLOBAL_FACET]?.margin || 0; - } else if (facet.displayName != null) { - return fieldMargins[facet.displayName]?.margin || 0; - } else { - return 0; - } + /** + * Returns the margins for a given model, possibly empty. + */ + getMargins(model: string): MarginsPerField { + return this.marginSettings[model] ?? {}; + } + + /** + * Returns the margin for a given model, field, and facet, or 0 if untracked. + */ + getMargin(model: string, fieldName: string, facet?: FacetedData): number { + const fieldMargins = this.getMargins(model)[fieldName]; + const facetString = facet?.displayName ?? GLOBAL_FACET; + return fieldMargins[facetString]?.margin ?? 0; } } diff --git a/lit_nlp/client/services/color_service.ts b/lit_nlp/client/services/color_service.ts index 304a8347..2ed69b1c 100644 --- a/lit_nlp/client/services/color_service.ts +++ b/lit_nlp/client/services/color_service.ts @@ -17,7 +17,7 @@ // tslint:disable:no-new-decorators import * as d3 from 'd3'; -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {CATEGORICAL_NORMAL, DEFAULT, MULTIHUE_CONTINUOUS, SalienceCmap, SignedSalienceCmap, UnsignedSalienceCmap} from '../lib/colors'; import {ColorOption, D3Scale, IndexedInput} from '../lib/types'; @@ -32,38 +32,31 @@ export {SalienceCmap, SignedSalienceCmap, UnsignedSalienceCmap}; /** * A singleton class that handles all coloring options. */ -export class ColorService extends LitService implements -ColorObservedByUrlService { +export class ColorService extends LitService + implements ColorObservedByUrlService { constructor( private readonly groupService: GroupService, private readonly dataService: DataService) { super(); + makeObservable(this); } private readonly defaultColor = DEFAULT; private readonly defaultOption: ColorOption = { name: 'None', - getValue: (input: IndexedInput) => 'all', + getValue: () => 'all', scale: d3.scaleOrdinal([this.defaultColor]).domain(['all']) as D3Scale, }; - // Name of selected feature to color datapoints by, or default not coloring by - // features. - @observable mySelectedColorOption = this.defaultOption; - // It's used for the url service. When urlService.syncStateToUrl is invoked, - // colorableOptions are not available. There, this variable is used to - // preserve the url param value entered by users. - @observable - selectedColorOptionName: string = ''; - - // All variables that affect color settings, so clients can listen for when - // they may need to rerender. - @computed get all() { - return [ - this.selectedColorOption, - ]; - } + /** + * Name of the selected coloring option. + * + * Coloring options depend on data retrieved asynchronously from the LIT + * server, so this feature is also used to preserve the value of the color_by + * URL parameter while those coloring options load. + */ + @observable selectedColorOptionName = ''; // Return the selectedColorOption based on the selectedColorOptionName @computed get selectedColorOption() { @@ -136,8 +129,7 @@ ColorObservedByUrlService { ]; } - @computed - get defaultClassificationColorOption() { + @computed get defaultClassificationColorOption() { return this.dataService.predictedClassFeatureName; } diff --git a/lit_nlp/client/services/color_service_test.ts b/lit_nlp/client/services/color_service_test.ts index 2130678a..05e3e5d4 100644 --- a/lit_nlp/client/services/color_service_test.ts +++ b/lit_nlp/client/services/color_service_test.ts @@ -28,15 +28,15 @@ import {mockMetadata} from '../lib/testing_utils'; import {ColorService, SignedSalienceCmap, UnsignedSalienceCmap} from './color_service'; import {DataService} from './data_service'; -import {GroupService} from './group_service'; +import {CategoricalFeatures, GroupService, NumericFeatures} from './group_service'; import {AppState} from './state_service'; describe('Color service test', () => { - const categoricalFeatures = { + const categoricalFeatures: CategoricalFeatures = { 'testFeat0': ['0', '1'], 'testFeat1': ['a', 'b', 'c'] }; - const numericalFeatureRanges = { + const numericalFeatureRanges: NumericFeatures = { 'testNumFeat0': [-5, 5], 'testNumFeat1': [0, 1], }; @@ -92,7 +92,7 @@ describe('Color service test', () => { const appState = app.getService(AppState); // tslint:disable-next-line:no-any (to spyOn a private, readonly property) - spyOnProperty(appState, 'inputData', 'get').and.returnValue(inputData); + spyOn(appState, 'inputData').and.returnValue(inputData); appState.metadata = mockMetadata; appState.setDatasetForTest('color_test', dataMap); diff --git a/lit_nlp/client/services/data_service.ts b/lit_nlp/client/services/data_service.ts index 354702bf..0661d2ce 100644 --- a/lit_nlp/client/services/data_service.ts +++ b/lit_nlp/client/services/data_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {action, computed, observable, reaction} from 'mobx'; +import {action, computed, makeObservable, observable, reaction} from 'mobx'; import {BINARY_NEG_POS, type ColorRange} from '../lib/colors'; import {BooleanLitType, CategoryLabel, GeneratedText, GeneratedTextCandidates, LitType, MulticlassPreds, RegressionScore, Scalar, SparseMultilabelPreds} from '../lib/lit_types'; @@ -88,8 +88,11 @@ export class DataService extends LitService { private readonly appState: AppState, private readonly classificationService: ClassificationService, private readonly apiService: ApiService, - private readonly settingsService: SettingsService) { + private readonly settingsService: SettingsService + ) { super(); + makeObservable(this); + reaction(() => appState.currentDataset, () => { this.columnHeaders.clear(); this.columnData.clear(); @@ -154,7 +157,7 @@ export class DataService extends LitService { const interpreterPromise = this.apiService.getInterpretations( data, model, this.appState.currentDataset, 'classification', - this.classificationService.marginSettings[model], + this.classificationService.getMargins(model), `Computing classification results`); const classificationResults = await interpreterPromise; @@ -198,8 +201,7 @@ export class DataService extends LitService { } } - @action - updatePredictedClassFeatureName(predClassFeatName: string) { + @action updatePredictedClassFeatureName(predClassFeatName: string) { this.predictedClassFeatureName = predClassFeatName; } @@ -333,8 +335,7 @@ export class DataService extends LitService { } } - @action - async setValuesForNewDatapoints(datapoints: IndexedInput[]) { + @action async setValuesForNewDatapoints(datapoints: IndexedInput[]) { // When new datapoints are created, set their data values for each // column stored in the data service. for (const input of datapoints) { @@ -346,8 +347,7 @@ export class DataService extends LitService { } } - @computed - get cols(): DataColumnHeader[] { + @computed get cols(): DataColumnHeader[] { return Array.from(this.columnHeaders.values()); } @@ -363,8 +363,7 @@ export class DataService extends LitService { /** Flattened list of values in data columns for reacting to data changes. **/ // TODO(b/156100081): Can we get observers to react to changes to columnData // without needing this computed list? - @computed - get dataVals() { + @computed get dataVals() { const vals: ValueType[] = []; for (const colVals of this.columnData.values()) { vals.push(...colVals.values()); @@ -400,8 +399,7 @@ export class DataService extends LitService { * @param {ColorRange=} colorRange a color range to associate with values from * this column. */ - @action - addColumn( + @action addColumn( columnVals: ColumnData, key: string, name: string, diff --git a/lit_nlp/client/services/data_service_test.ts b/lit_nlp/client/services/data_service_test.ts index a4c608f7..391320d9 100644 --- a/lit_nlp/client/services/data_service_test.ts +++ b/lit_nlp/client/services/data_service_test.ts @@ -30,77 +30,77 @@ import {ColumnData, DataService} from './data_service'; describe('DataService test', () => { const penguinData = new Map(); penguinData.set('a', { id:'a', meta:{}, data:{ - body_mass_g: 3098, - culmen_depth_mm: 22, - culmen_length_mm: 45, - flipper_length_mm: 172, - isAlive: true}}); + 'body_mass_g': 3098, + 'culmen_depth_mm': 22, + 'culmen_length_mm': 45, + 'flipper_length_mm': 172, + 'isAlive': true}}); penguinData.set('b', { id:'b', meta:{}, data:{ - body_mass_g: 3559, - culmen_depth_mm: 21, - culmen_length_mm: 32, - flipper_length_mm: 224, - isAlive: true}}); + 'body_mass_g': 3559, + 'culmen_depth_mm': 21, + 'culmen_length_mm': 32, + 'flipper_length_mm': 224, + 'isAlive': true}}); penguinData.set('c', { id:'c', meta:{}, data:{ - body_mass_g: 3217, - culmen_depth_mm: 19, - culmen_length_mm: 53, - flipper_length_mm: 214, - isAlive: true}}); + 'body_mass_g': 3217, + 'culmen_depth_mm': 19, + 'culmen_length_mm': 53, + 'flipper_length_mm': 214, + 'isAlive': true}}); penguinData.set('d', { id:'d', meta:{}, data:{ - body_mass_g: 2700, - culmen_depth_mm: 17, - culmen_length_mm: 34, - flipper_length_mm: 172, - isAlive: true}}); + 'body_mass_g': 2700, + 'culmen_depth_mm': 17, + 'culmen_length_mm': 34, + 'flipper_length_mm': 172, + 'isAlive': true}}); penguinData.set('e', { id:'e', meta:{}, data:{ - body_mass_g: 4301, - culmen_depth_mm: 14, - culmen_length_mm: 48, - flipper_length_mm: 206, - isAlive: true}}); + 'body_mass_g': 4301, + 'culmen_depth_mm': 14, + 'culmen_length_mm': 48, + 'flipper_length_mm': 206, + 'isAlive': true}}); penguinData.set('f', { id:'f', meta:{}, data:{ - body_mass_g: 5081, - culmen_depth_mm: 15, - culmen_length_mm: 60, - flipper_length_mm: 203, - isAlive: true}}); + 'body_mass_g': 5081, + 'culmen_depth_mm': 15, + 'culmen_length_mm': 60, + 'flipper_length_mm': 203, + 'isAlive': true}}); penguinData.set('g', { id:'g', meta:{}, data:{ - body_mass_g: 3085, - culmen_depth_mm: 13, - culmen_length_mm: 46, - flipper_length_mm: 177, - isAlive: true}}); + 'body_mass_g': 3085, + 'culmen_depth_mm': 13, + 'culmen_length_mm': 46, + 'flipper_length_mm': 177, + 'isAlive': true}}); penguinData.set('h', { id:'h', meta:{}, data:{ - body_mass_g: 4584, - culmen_depth_mm: 16, - culmen_length_mm: 57, - flipper_length_mm: 188, - isAlive: true}}); + 'body_mass_g': 4584, + 'culmen_depth_mm': 16, + 'culmen_length_mm': 57, + 'flipper_length_mm': 188, + 'isAlive': true}}); penguinData.set('i', { id:'i', meta:{}, data:{ - body_mass_g: 2804, - culmen_depth_mm: 18, - culmen_length_mm: 59, - flipper_length_mm: 221, - isAlive: true}}); + 'body_mass_g': 2804, + 'culmen_depth_mm': 18, + 'culmen_length_mm': 59, + 'flipper_length_mm': 221, + 'isAlive': true}}); penguinData.set('j', { id:'j', meta:{}, data:{ - body_mass_g: 3725, - culmen_depth_mm: 15, - culmen_length_mm: 47, - flipper_length_mm: 211, - isAlive: true}}); + 'body_mass_g': 3725, + 'culmen_depth_mm': 15, + 'culmen_length_mm': 47, + 'flipper_length_mm': 211, + 'isAlive': true}}); penguinData.set('k', { id:'k', meta:{}, data:{ - body_mass_g: 3615, - culmen_depth_mm: 13, - culmen_length_mm: 36, - flipper_length_mm: 213, - isAlive: true}}); + 'body_mass_g': 3615, + 'culmen_depth_mm': 13, + 'culmen_length_mm': 36, + 'flipper_length_mm': 213, + 'isAlive': true}}); penguinData.set('l', { id:'l', meta:{}, data:{ - body_mass_g: 6300, - culmen_depth_mm: 17, - culmen_length_mm: 43, - flipper_length_mm: 231, - isAlive: true}}); + 'body_mass_g': 6300, + 'culmen_depth_mm': 17, + 'culmen_length_mm': 43, + 'flipper_length_mm': 231, + 'isAlive': true}}); let appState: AppState; let classificationService: ClassificationService; @@ -116,7 +116,7 @@ describe('DataService test', () => { appState = app.getService(AppState); appState.metadata = mockMetadata; // tslint:disable-next-line:no-any (to spyOn a private, readonly property) - spyOnProperty(appState, 'inputData', 'get').and.returnValue(inputData); + spyOn(appState, 'inputData').and.returnValue(inputData); appState.setDatasetForTest('penguin_dev', penguinData); settingsService = app.getService(SettingsService); @@ -180,11 +180,11 @@ describe('DataService test', () => { id:'newDatapoint', meta:{}, data:{ - body_mass_g: 3725, - culmen_depth_mm: 15, - culmen_length_mm: 47, - flipper_length_mm: 211, - isAlive: true + 'body_mass_g': 3725, + 'culmen_depth_mm': 15, + 'culmen_length_mm': 47, + 'flipper_length_mm': 211, + 'isAlive': true } }; await dataService.setValuesForNewDatapoints([newDatapoint]); diff --git a/lit_nlp/client/services/focus_service.ts b/lit_nlp/client/services/focus_service.ts index a86abe75..db1d08eb 100644 --- a/lit_nlp/client/services/focus_service.ts +++ b/lit_nlp/client/services/focus_service.ts @@ -21,7 +21,7 @@ */ // tslint:disable:no-new-decorators -import {computed, observable, reaction} from 'mobx'; +import {computed, makeObservable, observable, reaction} from 'mobx'; import {LitService} from './lit_service'; import {SelectionService} from './selection_service'; @@ -47,17 +47,17 @@ export class FocusService extends LitService { /** * Gets the current focus data, or null if nothing is focused. */ - @computed - get focusData(): FocusData|null { + @computed get focusData(): FocusData|null { return this.focusDataInternal; } constructor(selectionService: SelectionService) { super(); + makeObservable(this); // If the primary selected input changes, reset the focus data. - reaction(() => selectionService.primarySelectedInputData, selectedInput => { - this.clearFocus(); - }); + reaction( + () => selectionService.primarySelectedInputData, + () => {this.clearFocus();}); } /** diff --git a/lit_nlp/client/services/group_service.ts b/lit_nlp/client/services/group_service.ts index 50f6dd34..c85a2650 100644 --- a/lit_nlp/client/services/group_service.ts +++ b/lit_nlp/client/services/group_service.ts @@ -22,7 +22,7 @@ // tslint:disable:no-new-decorators import * as d3 from 'd3'; // Used for creating bins, not visualization. -import {computed, reaction} from 'mobx'; +import {computed, makeObservable, reaction} from 'mobx'; import {BooleanLitType, CategoryLabel, LitTypeWithVocab, Scalar} from '../lib/lit_types'; import {FacetMap, GroupedExamples, IndexedInput} from '../lib/types'; @@ -125,6 +125,7 @@ export class GroupService extends LitService { constructor(private readonly appState: AppState, private readonly dataService: DataService) { super(); + makeObservable(this); // Reset stored numeric feature bins on dataset change. reaction(() => this.appState.currentInputData, () => { diff --git a/lit_nlp/client/services/group_service_test.ts b/lit_nlp/client/services/group_service_test.ts index 88c9b158..3b143d5c 100644 --- a/lit_nlp/client/services/group_service_test.ts +++ b/lit_nlp/client/services/group_service_test.ts @@ -66,79 +66,81 @@ describe('GroupService test', () => { const penguinData = new Map(); penguinData.set('a', { id:'a', meta:{}, data:{ - body_mass_g: 3098, - culmen_depth_mm: 22, - culmen_length_mm: 45, - flipper_length_mm: 172, - isAlive: true}}); + 'body_mass_g': 3098, + 'culmen_depth_mm': 22, + 'culmen_length_mm': 45, + 'flipper_length_mm': 172, + 'isAlive': true}}); penguinData.set('b', { id:'b', meta:{}, data:{ - body_mass_g: 3559, - culmen_depth_mm: 21, - culmen_length_mm: 32, - flipper_length_mm: 224, - isAlive: true}}); + 'body_mass_g': 3559, + 'culmen_depth_mm': 21, + 'culmen_length_mm': 32, + 'flipper_length_mm': 224, + 'isAlive': true}}); penguinData.set('c', { id:'c', meta:{}, data:{ - body_mass_g: 3217, - culmen_depth_mm: 19, - culmen_length_mm: 53, - flipper_length_mm: 214, - isAlive: true}}); + 'body_mass_g': 3217, + 'culmen_depth_mm': 19, + 'culmen_length_mm': 53, + 'flipper_length_mm': 214, + 'isAlive': true}}); penguinData.set('d', { id:'d', meta:{}, data:{ - body_mass_g: 2700, - culmen_depth_mm: 17, - culmen_length_mm: 34, - flipper_length_mm: 172, - isAlive: true}}); + 'body_mass_g': 2700, + 'culmen_depth_mm': 17, + 'culmen_length_mm': 34, + 'flipper_length_mm': 172, + 'isAlive': true}}); penguinData.set('e', { id:'e', meta:{}, data:{ - body_mass_g: 4301, - culmen_depth_mm: 14, - culmen_length_mm: 48, - flipper_length_mm: 206, - isAlive: true}}); + 'body_mass_g': 4301, + 'culmen_depth_mm': 14, + 'culmen_length_mm': 48, + 'flipper_length_mm': 206, + 'isAlive': true}}); penguinData.set('f', { id:'f', meta:{}, data:{ - body_mass_g: 5081, - culmen_depth_mm: 15, - culmen_length_mm: 60, - flipper_length_mm: 203, - isAlive: true}}); + 'body_mass_g': 5081, + 'culmen_depth_mm': 15, + 'culmen_length_mm': 60, + 'flipper_length_mm': 203, + 'isAlive': true}}); penguinData.set('g', { id:'g', meta:{}, data:{ - body_mass_g: 3085, - culmen_depth_mm: 13, - culmen_length_mm: 46, - flipper_length_mm: 177, - isAlive: true}}); + 'body_mass_g': 3085, + 'culmen_depth_mm': 13, + 'culmen_length_mm': 46, + 'flipper_length_mm': 177, + 'isAlive': true}}); penguinData.set('h', { id:'h', meta:{}, data:{ - body_mass_g: 4584, - culmen_depth_mm: 16, - culmen_length_mm: 57, - flipper_length_mm: 188, - isAlive: true}}); + 'body_mass_g': 4584, + 'culmen_depth_mm': 16, + 'culmen_length_mm': 57, + 'flipper_length_mm': 188, + 'isAlive': true}}); penguinData.set('i', { id:'i', meta:{}, data:{ - body_mass_g: 2804, - culmen_depth_mm: 18, - culmen_length_mm: 59, - flipper_length_mm: 221, - isAlive: true}}); + 'body_mass_g': 2804, + 'culmen_depth_mm': 18, + 'culmen_length_mm': 59, + 'flipper_length_mm': 221, + 'isAlive': true}}); penguinData.set('j', { id:'j', meta:{}, data:{ - body_mass_g: 3725, - culmen_depth_mm: 15, - culmen_length_mm: 47, - flipper_length_mm: 211, - isAlive: true}}); + 'body_mass_g': 3725, + 'culmen_depth_mm': 15, + 'culmen_length_mm': 47, + 'flipper_length_mm': 211, + 'isAlive': true}}); penguinData.set('k', { id:'k', meta:{}, data:{ - body_mass_g: 3615, - culmen_depth_mm: 13, - culmen_length_mm: 36, - flipper_length_mm: 213, - isAlive: true}}); + 'body_mass_g': 3615, + 'culmen_depth_mm': 13, + 'culmen_length_mm': 36, + 'flipper_length_mm': 213, + 'isAlive': true}}); penguinData.set('l', { id:'l', meta:{}, data:{ - body_mass_g: 6300, - culmen_depth_mm: 17, - culmen_length_mm: 43, - flipper_length_mm: 231, - isAlive: true}}); + 'body_mass_g': 6300, + 'culmen_depth_mm': 17, + 'culmen_length_mm': 43, + 'flipper_length_mm': 231, + 'isAlive': true}}); - let appState: AppState, groupService: GroupService, dataService: DataService; + let appState: AppState; + let groupService: GroupService; + let dataService: DataService; beforeEach(async () => { // Set up. @@ -150,7 +152,7 @@ describe('GroupService test', () => { dataService = app.getService(DataService); appState.metadata = mockMetadata; // tslint:disable-next-line:no-any (to spyOn a private, readonly property) - spyOnProperty(appState, 'inputData', 'get').and.returnValue(inputData); + spyOn(appState, 'inputData').and.returnValue(inputData); appState.setDatasetForTest('penguin_dev', penguinData); groupService = new GroupService(appState, dataService); diff --git a/lit_nlp/client/services/modules_service.ts b/lit_nlp/client/services/modules_service.ts index 6cdbc22b..70f2e8af 100644 --- a/lit_nlp/client/services/modules_service.ts +++ b/lit_nlp/client/services/modules_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {action, observable} from 'mobx'; +import {action, makeObservable, observable} from 'mobx'; import {LayoutSettings, type LitCanonicalLayout, LitComponentSpecifier, LitModuleClass, LitModuleConfig, LitTabGroupLayout, type ModelInfoMap, ResolvedModuleConfig, type Spec} from '../lib/types'; @@ -98,11 +98,11 @@ export function resolveModuleConfig( */ export class ModulesService extends LitService implements ModulesObservedByUrlService { - @observable declaredLayout: LitCanonicalLayout = { + declaredLayout: LitCanonicalLayout = { upper: {}, lower: {}, left: {}, layoutSettings: {}, description: '' }; @observable readonly selectedTabs = {upper: '', lower: '', left: ''}; - @observable private renderLayout: LitRenderConfig = { + @observable.ref private renderLayout: LitRenderConfig = { upper: {}, lower: {}, left: {} }; @observable hiddenModuleKeys = new Set(); @@ -110,6 +110,11 @@ export class ModulesService extends LitService implements allModuleKeys = new Set(); private renderModulesCallback: RenderModulesCallback = () => {}; + constructor() { + super(); + makeObservable(this); + } + // TODO(b/168201937): Remove imperative logic and use observables/reactions // for all module logic. @@ -252,9 +257,12 @@ export class ModulesService extends LitService implements this.declaredLayout.left, currentModelInfos, datasetSpec, compareExamples); + console.log('module_service.updateRenderLayout', {upper, lower, left}); + const renderLayout: LitRenderConfig = {upper, lower, left}; this.updateModuleKeys(renderLayout); + console.log('module_service.updateRenderLayout', {currentLayout: this.renderLayout, newLayout: renderLayout}, Date.now()); this.renderLayout = renderLayout; } diff --git a/lit_nlp/client/services/selection_service.ts b/lit_nlp/client/services/selection_service.ts index 91ae92db..8e9f7de6 100644 --- a/lit_nlp/client/services/selection_service.ts +++ b/lit_nlp/client/services/selection_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {action, computed, observable} from 'mobx'; +import {action, computed, makeObservable, observable} from 'mobx'; import {type IndexedInput, type ServiceUser} from '../lib/types'; @@ -36,10 +36,11 @@ export interface AppState { /** * A singleton service for managing App selections of input data. */ -export class SelectionService extends LitService implements - SelectionObservedByUrlService { +export class SelectionService extends LitService + implements SelectionObservedByUrlService { constructor(private readonly appState: AppState) { super(); + makeObservable(this); } @observable private readonly selectedIdsSet = new Set(); @@ -47,12 +48,12 @@ export class SelectionService extends LitService implements // Track the last user, so components can avoid resetting on selections they // triggered. - @observable private lastUserInternal?: ServiceUser; + @observable private lastUserInternal?: ServiceUser = undefined; // Tracks the last updated data indices to calculate selection. - @observable private shiftSelectionStartIndexInternal: number = 0; - @observable private shiftSelectionEndIndexInternal: number = 0; + @observable private shiftSelectionStartIndexInternal = 0; + @observable private shiftSelectionEndIndexInternal = 0; @computed get lastUser() { diff --git a/lit_nlp/client/services/settings_service.ts b/lit_nlp/client/services/settings_service.ts index 385d8bc4..de7adfe3 100644 --- a/lit_nlp/client/services/settings_service.ts +++ b/lit_nlp/client/services/settings_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {action, computed, reaction, runInAction} from 'mobx'; +import {action, computed, makeObservable, reaction, runInAction} from 'mobx'; import {arrayContainsSame} from '../lib/utils'; @@ -42,6 +42,7 @@ export class SettingsService extends LitService { private readonly modulesService: ModulesService, private readonly selectionService: SelectionService) { super(); + makeObservable(this); // If compare examples changes, update layout using the 'quick' path. reaction(() => appState.compareExamplesEnabled, compareExamplesEnabled => { this.modulesService.quickUpdateLayout( @@ -60,8 +61,7 @@ export class SettingsService extends LitService { return availableDatasets.includes(dataset); } - @computed - get isValidCurrentDataAndModels() { + @computed get isValidCurrentDataAndModels() { return this.isDatasetValidForModels( this.appState.currentDataset, this.appState.currentModels); } @@ -71,8 +71,7 @@ export class SettingsService extends LitService { * Use this if changing models or datasets to ensure that modules don't make * API calls with inconsistent state while the update is in progress. */ - @action - async updateSettings(updateParams: UpdateSettingsParams) { + @action async updateSettings(updateParams: UpdateSettingsParams) { const nextModels = updateParams.models ?? this.appState.currentModels; const nextDataset = updateParams.dataset ?? this.appState.currentDataset; const nextLayout = updateParams.layoutName ?? this.appState.layoutName; diff --git a/lit_nlp/client/services/slice_service.ts b/lit_nlp/client/services/slice_service.ts index 7c3fb200..b8943894 100644 --- a/lit_nlp/client/services/slice_service.ts +++ b/lit_nlp/client/services/slice_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {action, computed, observable, reaction} from 'mobx'; +import {action, computed, makeObservable, observable, reaction} from 'mobx'; import {IndexedInput, type ServiceUser} from '../lib/types'; import {arrayContainsSame} from '../lib/utils'; @@ -40,6 +40,7 @@ export class SliceService extends LitService { private readonly selectionService: SelectionService, private readonly appState: AppState) { super(); + makeObservable(this); reaction(() => selectionService.selectedInputData, selectedInputData => { // If selection doesn't match a slice, then reset selected slice to @@ -55,8 +56,7 @@ export class SliceService extends LitService { } // Initialize with an empty slice to hold favorited items. - @observable - namedSlices = + @observable namedSlices = new Map>([[STARRED_SLICE_NAME, new Set()]]); @observable private selectedSliceNameInternal: string|null = null; diff --git a/lit_nlp/client/services/state_service.ts b/lit_nlp/client/services/state_service.ts index ee55daa3..bc012693 100644 --- a/lit_nlp/client/services/state_service.ts +++ b/lit_nlp/client/services/state_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {action, computed, observable, toJS} from 'mobx'; +import {action, computed, makeObservable, observable, toJS} from 'mobx'; import {FieldMatcher, ImageBytes} from '../lib/lit_types'; import {defaultValueByField, IndexedInput, Input, type LitCanonicalLayout, type LitComponentLayouts, type LitMetadata, ModelInfo, type ModelInfoMap, ModelSpec, NONE_DS_DICT_KEY, type Spec} from '../lib/types'; @@ -46,22 +46,40 @@ export class AppState extends LitService implements StateObservedByUrlService { private readonly apiService: ApiService, private readonly statusService: StatusService) { super(); - } - + makeObservable(this); + } + + @observable.ref metadata: LitMetadata = { + models: {}, + datasets: {}, + generators: {}, + interpreters: {}, + metrics: {}, + layouts: {}, + demoMode: false, + defaultLayout: '', + syncState: false, + initSpecs: { + datasets: {}, + models: {}, + } + }; + @observable layoutName = ''; /** Set by urlService.syncStateToUrl */ private urlConfiguration!: UrlConfiguration; @observable initialized = false; - @observable documentationOpen = false; - // TODO(b/204677206): While cleaning up console warnings, find a better way to - // initialize the app so that we don't need this non-null assertion here - // https://www.typescriptlang.org/docs/handbook/release-notes/typescript-2-0.html#non-null-assertion-operator - @observable metadata!: LitMetadata; @observable currentModels: string[] = []; @observable compareExamplesEnabled = false; - @observable layoutName!: string; - @observable layouts: LitComponentLayouts = {}; + @observable.ref layouts: LitComponentLayouts = {}; + /** + * Enforce setting currentDataset through the setCurrentDataset method by + * making the currentDatasetInternal private... + */ + @observable private currentDatasetInternal = ''; + @observable private readonly inputData = + new Map(); private readonly newDatapointsCallbacks: NewDatapointsFn[] = []; @computed @@ -69,11 +87,6 @@ export class AppState extends LitService implements StateObservedByUrlService { return this.layouts[this.layoutName]; } - /** - * Enforce setting currentDataset through the setCurrentDataset method by - * making the currentDatasetInternal private... - */ - @observable private currentDatasetInternal = ''; @computed get currentDataset(): string { return this.currentDatasetInternal; @@ -112,9 +125,6 @@ export class AppState extends LitService implements StateObservedByUrlService { return findSpecKeys(this.currentDatasetSpec, ImageBytes).length > 0; } - @observable - private readonly inputData = new Map(); - private makeEmptyInputs(): IndexedInputMap { return new Map(); } diff --git a/lit_nlp/client/services/status_service.ts b/lit_nlp/client/services/status_service.ts index dcf3b52a..45003756 100644 --- a/lit_nlp/client/services/status_service.ts +++ b/lit_nlp/client/services/status_service.ts @@ -16,7 +16,7 @@ */ // tslint:disable:no-new-decorators -import {computed, observable} from 'mobx'; +import {computed, makeObservable, observable} from 'mobx'; import {hashCode} from '../lib/utils'; import {LitService} from './lit_service'; @@ -32,7 +32,7 @@ interface ErrorMessageHolder { * A singleton class that handles all API loading status messages. */ export class StatusService extends LitService { - private loadingId: number = 0; + private loadingId = 0; /** * An observable map of loading messages by loading id. @@ -90,6 +90,11 @@ export class StatusService extends LitService { return this.isLoading ? this.loadingMessages[0] : ''; } + constructor() { + super(); + makeObservable(this); + } + startLoading(message: string) { const id = this.loadingId; this.loadingEvents.set(id, message); diff --git a/lit_nlp/package.json b/lit_nlp/package.json index 928b9442..351f17a9 100644 --- a/lit_nlp/package.json +++ b/lit_nlp/package.json @@ -29,7 +29,7 @@ "lit": "2.7.6", "marked": "5.1.2", "megaplot": "github:PAIR-code/megaplot#ca8210d", - "mobx": "5.15.4", + "mobx": "6.1.8", "node-polyfill-webpack-plugin": "^2.0.1", "papaparse": "4.1.2", "scatter-gl": "0.0.13", From e7e11029b3d77d13acd9017fb4845ebb99b65cf2 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 8 Aug 2024 09:18:34 -0700 Subject: [PATCH 10/86] Fix Classification Results module coloring for correct class PiperOrigin-RevId: 660863933 --- .../client/modules/classification_module.ts | 61 +++++++++++-------- 1 file changed, 37 insertions(+), 24 deletions(-) diff --git a/lit_nlp/client/modules/classification_module.ts b/lit_nlp/client/modules/classification_module.ts index 70bf2f40..0bf6aa1b 100644 --- a/lit_nlp/client/modules/classification_module.ts +++ b/lit_nlp/client/modules/classification_module.ts @@ -75,8 +75,9 @@ export class ClassificationModule extends LitModule { return [sharedStyles, styles]; } - static override shouldDisplayModule(modelSpecs: ModelInfoMap, - datasetSpec: Spec) { + static override shouldDisplayModule( + modelSpecs: ModelInfoMap, datasetSpec: Spec + ) { return doesOutputSpecContain(modelSpecs, MulticlassPreds); } @@ -140,8 +141,9 @@ export class ClassificationModule extends LitModule { * and the values are dictionaries with a key for each class in the vocabulary * and arrays of DisplayInfo values for the pinned and selected datapoints. */ - private parseResult(model: string, inputs: IndexedInput[]): - LabeledPredictions { + private parseResult( + model: string, inputs: IndexedInput[] + ): LabeledPredictions { const {output} = this.appState.getModelSpec(model); const multiclassKeys = findSpecKeys(output, MulticlassPreds); const labeledPredictions: LabeledPredictions = {}; @@ -156,49 +158,60 @@ export class ClassificationModule extends LitModule { model, predKey, CalculatedColumnType.PREDICTED_CLASS); const predCorrectKey = this.dataService.getColumnName( model, predKey, CalculatedColumnType.CORRECT); - labeledPredictions[topLevelKey] = {}; const {parent, vocab} = output[predKey] as MulticlassPreds; - const scores = - inputs.map(input => this.dataService.getVal(input.id, topLevelKey)); - const predictedClasses = - inputs.map(input => this.dataService.getVal(input.id, predClassKey)); + + const inputData = inputs.map(i => ({ + predScores: this.dataService.getVal(i.id, topLevelKey), + predClass: this.dataService.getVal(i.id, predClassKey), + truth: parent != null ? this.dataService.getVal(i.id, parent) : null, + })); + // If no vocab provided, create a list of strings of the class indices. - const labels = - vocab || Array.from({length: scores[0].length}, (v, k) => `${k}`); + const labels = vocab || Array.from( + {length: inputData[0].predScores.length}, (v, k) => `${k}` + ); const colorableKeys = [predClassKey, predCorrectKey, parent]; const applyColor = colorableKeys.includes(colorOption.name); - // Iterate over the vocabulary for this prediction head - for (let i = 0; i < labels.length; i++) { - const label = labels[i]; - const color: string|undefined = applyColor ? colorRange[i] : undefined; + labeledPredictions[topLevelKey] = {}; + // Iterate over the vocabulary for this prediction head + labels.forEach((label, i) => { // Map the predctions for each example into DisplayInfo objects const rowPreds: DisplayInfo[] = []; - for (let j = 0; j < scores.length; j++) { - const score = scores[j]; + for (const {predScores, predClass, truth} of inputData) { // Only push null scores if not in sparseMode - if (score == null) { + if (predScores == null) { if (!this.sparseMode) { rowPreds.push({value: 0, isPredicted: false, isTruth: false}); } continue; } - const value = score[i]; - const isPredicted = label === predictedClasses[j]; - const {data} = inputs[j]; - const isTruth = (parent != null && data[parent] === labels[i]); + const value = predScores[i]; + const isPredicted = label === predClass; + const isTruth = truth === label; // Push values if not in sparseMode or if above threshold if (!this.sparseMode || value >= SPARSE_MODE_THRESHOLD) { - rowPreds.push({value, isPredicted, isTruth, color}); + const rowPred: DisplayInfo = {value, isPredicted, isTruth}; + + if (applyColor) { + if (colorOption.name === predCorrectKey) { + const colorIdx = isTruth ? 1 : 0; + rowPred.color = colorRange[colorIdx]; + } else { + rowPred.color = colorRange[i]; + } + } + + rowPreds.push(rowPred); } } if (rowPreds.length) labeledPredictions[topLevelKey][label] = rowPreds; - } + }); } return labeledPredictions; From acca9947cde2400ac0d4f7e032f6d622c0809c88 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 8 Aug 2024 09:36:30 -0700 Subject: [PATCH 11/86] Add Gemma model to VertexModelGardenModel class. PiperOrigin-RevId: 660870224 --- lit_nlp/examples/gcp/models.py | 107 +++++++++++++++++++++++++++- lit_nlp/examples/gcp/models_test.py | 42 ++++++++++- 2 files changed, 147 insertions(+), 2 deletions(-) diff --git a/lit_nlp/examples/gcp/models.py b/lit_nlp/examples/gcp/models.py index 1bd7ce20..265244e8 100644 --- a/lit_nlp/examples/gcp/models.py +++ b/lit_nlp/examples/gcp/models.py @@ -4,6 +4,7 @@ import logging import time from typing import Optional, Union +from google.cloud import aiplatform from vertexai import generative_models from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types @@ -15,6 +16,7 @@ _DEFAULT_MAX_OUTPUT_TOKENS = 256 +# TODO(fanyecourage): Rename this class. class VertexModelGardenModel(lit_model.BatchedRemoteModel): """VertexModelGardenModel is a wrapper for Vertex AI Model Garden model. @@ -24,7 +26,6 @@ class VertexModelGardenModel(lit_model.BatchedRemoteModel): model. max_qps: The maximum number of queries per second to the model. temperature: The temperature to use for the model. - candidate_count: The number of candidates to generate. max_output_tokens: The maximum number of tokens to generate. Please note the model will predict all examples at a fixed temperature. @@ -106,3 +107,107 @@ def input_spec(self) -> lit_types.Spec: def output_spec(self) -> lit_types.Spec: return {'response': lit_types.GeneratedTextCandidates(parent='prompt')} + + +class SelfHostedGenerativeModel(lit_model.BatchedRemoteModel): + """SelfHostedGenerativeModel is a wrapper for self-hosted generative models. + + This model wrapper is used for self-hosted generative models that require + self-deployment. + The model deployment process is managed by the user, and described in + https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component. It + is recommended deploy the model in Vertex AI. After the model is deployed, + an aip_endpoint_name will be provided, and can be used to query the + model. + + Attributes: + aip_endpoint_name: A fully-qualified GCP depolyed model endpoint resource + name or endpoint ID. + max_concurrent_requests: The maximum number of concurrent requests to the + model. + max_qps: The maximum number of queries per second to the model. + temperature: The temperature to use for the model. + candidate_count: The number of candidates to generate. + max_output_tokens: The maximum number of tokens to generate. + + Please note the model will predict all examples at a fixed temperature. + """ + + def __init__( + self, + aip_endpoint_name: str, + max_concurrent_requests: int = 4, + max_qps: Union[int, float] = 25, + temperature: Optional[float] = None, + max_output_tokens: Optional[int] = _DEFAULT_MAX_OUTPUT_TOKENS, + ): + super().__init__( + max_concurrent_requests=max_concurrent_requests, max_qps=max_qps + ) + self.temperature = temperature + self.max_output_tokens = max_output_tokens + self._endpoint = aiplatform.models.Endpoint(aip_endpoint_name) + + def query_model(self, input_text: str) -> lit_types.ScoredTextCandidates: + num_attempts = 0 + predictions = None + exception = None + + instances = [ + { + 'prompt': input_text, + 'max_tokens': self.max_output_tokens, + 'temperature': self.temperature, + }, + ] + + while num_attempts < _MAX_NUM_RETRIES and predictions is None: + num_attempts += 1 + + try: + predictions = self._endpoint.predict(instances).predictions + except Exception as e: # pylint: disable=broad-except + wait_time = 2**num_attempts + exception = e + logging.warning('Waiting %ds to retry... (%s)', wait_time, e) + time.sleep(2**num_attempts) + + if predictions is None: + raise ValueError( + 'Failed to get predictions with endpoint %s, after %d attempts.' + % (self._endpoint.name, _MAX_NUM_RETRIES) + ) from exception + + if not isinstance(predictions, Iterable): + predictions = [predictions] + + return [(prediction, None) for prediction in predictions] + + def predict_minibatch( + self, inputs: list[lit_types.JsonDict] + ) -> list[lit_types.JsonDict]: + res = [ + {'response': self.query_model(input_dict['prompt'])} + for input_dict in inputs + ] + return res + + @classmethod + def init_spec(cls, aip_endpoint_name: str) -> lit_types.Spec: + return { + 'aip_endpoint_name': lit_types.String( + default=aip_endpoint_name, required=True + ), + 'max_concurrent_requests': lit_types.Integer(default=4, required=False), + 'max_qps': lit_types.Integer(default=25, required=False), + 'temperature': lit_types.Scalar(default=0.7, required=False), + 'max_output_tokens': lit_types.Integer(default=256, required=False), + } + + def input_spec(self) -> lit_types.Spec: + return { + 'prompt': lit_types.TextSegment(), + } + + def output_spec(self) -> lit_types.Spec: + return {'response': lit_types.GeneratedTextCandidates(parent='prompt')} diff --git a/lit_nlp/examples/gcp/models_test.py b/lit_nlp/examples/gcp/models_test.py index c9042f2a..d68cec1f 100644 --- a/lit_nlp/examples/gcp/models_test.py +++ b/lit_nlp/examples/gcp/models_test.py @@ -2,6 +2,7 @@ from unittest import mock from absl.testing import absltest +from google.cloud import aiplatform from vertexai import generative_models from lit_nlp.examples.gcp import models @@ -15,7 +16,7 @@ class ModelsTest(absltest.TestCase): "vertexai.generative_models.GenerativeModel.__init__", return_value=None, ) - def test_query_model(self, mock_init, mock_generate_content): + def test_query_gemini_model(self, mock_init, mock_generate_content): response1 = generative_models.GenerationResponse.from_dict({ "candidates": [{ "content": { @@ -57,6 +58,45 @@ def test_query_model(self, mock_init, mock_generate_content): mock_init.assert_called_once_with("gemini-pro") + @mock.patch("google.cloud.aiplatform.models.Endpoint.predict") + @mock.patch( + "google.cloud.aiplatform.models.Endpoint.__init__", + return_value=None, + ) + def test_query_self_hosted_generative_model( + self, mock_init, mock_generate_content + ): + response1 = aiplatform.models.Prediction( + predictions=["I say yes you say no"], + deployed_model_id="", + ) + response2 = aiplatform.models.Prediction( + predictions=["I have a dog"], + deployed_model_id="", + ) + mock_generate_content.side_effect = [response1, response2] + + model = models.SelfHostedGenerativeModel( + aip_endpoint_name="endpoint_name" + ) + model._endpoint = mock.MagicMock() + model._endpoint.predict.side_effect = [response1, response2] + + output = model.predict( + inputs=[{"prompt": "I say yes you say no"}, {"prompt": "I have a dog"}] + ) + result = list(output) + self.assertLen(result, 2) + self.assertEqual( + result, + [ + {"response": [("I say yes you say no", None)]}, + {"response": [("I have a dog", None)]}, + ], + ) + + mock_init.assert_called_once_with("endpoint_name") + if __name__ == "__main__": absltest.main() From c8e74b2d42513c430de496a87d09df15ad970bc0 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 8 Aug 2024 09:56:45 -0700 Subject: [PATCH 12/86] Fix range access issue in Classification Module PiperOrigin-RevId: 660877258 --- lit_nlp/client/modules/classification_module.ts | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/lit_nlp/client/modules/classification_module.ts b/lit_nlp/client/modules/classification_module.ts index 0bf6aa1b..46548770 100644 --- a/lit_nlp/client/modules/classification_module.ts +++ b/lit_nlp/client/modules/classification_module.ts @@ -148,8 +148,6 @@ export class ClassificationModule extends LitModule { const multiclassKeys = findSpecKeys(output, MulticlassPreds); const labeledPredictions: LabeledPredictions = {}; const colorOption = this.colorService.selectedColorOption; - // tslint:disable-next-line:no-any - const colorRange = (colorOption.scale as any).range(); // Iterate over the multiclass prediction heads for (const predKey of multiclassKeys) { @@ -172,6 +170,13 @@ export class ClassificationModule extends LitModule { ); const colorableKeys = [predClassKey, predCorrectKey, parent]; const applyColor = colorableKeys.includes(colorOption.name); + // colorOption.scale can be a d3.scaleSequential (numeric features) or + // d3.scaleOrdinal (categorical or boolean features). In the version of D3 + // LIT uses, d3.scaleSequential does not have a .range() method, so we + // need to default to an empty array for numeric features. + // https://d3js.org/d3-scale/sequential#scaleSequential + const colorRange: string[] = // tslint:disable-next-line:no-any + applyColor ? (colorOption.scale as any).range() : []; labeledPredictions[topLevelKey] = {}; From 48253a173471474e9e96ed343092077a8207d1f1 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 8 Aug 2024 14:36:13 -0700 Subject: [PATCH 13/86] Make aip_endpoint_name optional for SelfHostedGenerativeModel, so that user can input the endpoint_name in LIT UI. PiperOrigin-RevId: 660985138 --- lit_nlp/examples/gcp/models.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/lit_nlp/examples/gcp/models.py b/lit_nlp/examples/gcp/models.py index 265244e8..829e4d71 100644 --- a/lit_nlp/examples/gcp/models.py +++ b/lit_nlp/examples/gcp/models.py @@ -193,11 +193,9 @@ def predict_minibatch( return res @classmethod - def init_spec(cls, aip_endpoint_name: str) -> lit_types.Spec: + def init_spec(cls) -> lit_types.Spec: return { - 'aip_endpoint_name': lit_types.String( - default=aip_endpoint_name, required=True - ), + 'aip_endpoint_name': lit_types.String(default='', required=True), 'max_concurrent_requests': lit_types.Integer(default=4, required=False), 'max_qps': lit_types.Integer(default=25, required=False), 'temperature': lit_types.Scalar(default=0.7, required=False), From b7d765970f8f96b582785c35821c406c639b8997 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Fri, 9 Aug 2024 06:24:35 -0700 Subject: [PATCH 14/86] Set default temperature to 0.7 for GCP models. PiperOrigin-RevId: 661239905 --- lit_nlp/examples/gcp/models.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/lit_nlp/examples/gcp/models.py b/lit_nlp/examples/gcp/models.py index 829e4d71..2eacc73d 100644 --- a/lit_nlp/examples/gcp/models.py +++ b/lit_nlp/examples/gcp/models.py @@ -15,6 +15,8 @@ _DEFAULT_MAX_OUTPUT_TOKENS = 256 +_DEFAULT_TEMPERATURE = 0.7 + # TODO(fanyecourage): Rename this class. class VertexModelGardenModel(lit_model.BatchedRemoteModel): @@ -36,7 +38,7 @@ def __init__( model_name: str, max_concurrent_requests: int = 4, max_qps: Union[int, float] = 25, - temperature: Optional[float] = None, + temperature: Optional[float] = _DEFAULT_TEMPERATURE, candidate_count: Optional[int] = _DEFAULT_CANDIDATE_COUNT, max_output_tokens: Optional[int] = _DEFAULT_MAX_OUTPUT_TOKENS, ): @@ -95,7 +97,9 @@ def init_spec(cls) -> lit_types.Spec: 'model_name': lit_types.String(default='gemini-1.0-pro', required=True), 'max_concurrent_requests': lit_types.Integer(default=4, required=False), 'max_qps': lit_types.Integer(default=25, required=False), - 'temperature': lit_types.Scalar(default=0.7, required=False), + 'temperature': lit_types.Scalar( + default=_DEFAULT_TEMPERATURE, required=False + ), 'candidate_count': lit_types.Integer(default=1, required=False), 'max_output_tokens': lit_types.Integer(default=256, required=False), } @@ -138,7 +142,7 @@ def __init__( aip_endpoint_name: str, max_concurrent_requests: int = 4, max_qps: Union[int, float] = 25, - temperature: Optional[float] = None, + temperature: Optional[float] = _DEFAULT_TEMPERATURE, max_output_tokens: Optional[int] = _DEFAULT_MAX_OUTPUT_TOKENS, ): super().__init__( @@ -198,7 +202,9 @@ def init_spec(cls) -> lit_types.Spec: 'aip_endpoint_name': lit_types.String(default='', required=True), 'max_concurrent_requests': lit_types.Integer(default=4, required=False), 'max_qps': lit_types.Integer(default=25, required=False), - 'temperature': lit_types.Scalar(default=0.7, required=False), + 'temperature': lit_types.Scalar( + default=_DEFAULT_TEMPERATURE, required=False + ), 'max_output_tokens': lit_types.Integer(default=256, required=False), } From 49b7975e325315378005b6e66f41d02957ee0c21 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Fri, 9 Aug 2024 06:45:19 -0700 Subject: [PATCH 15/86] Enable GCP demo use Gemma model. PiperOrigin-RevId: 661244373 --- lit_nlp/examples/gcp/demo.py | 55 +++++++++++++++++++++++++++++++++++- 1 file changed, 54 insertions(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/demo.py b/lit_nlp/examples/gcp/demo.py index 2c1929d1..907ee799 100644 --- a/lit_nlp/examples/gcp/demo.py +++ b/lit_nlp/examples/gcp/demo.py @@ -11,11 +11,29 @@ You can also configure the datasets and max_examples to load. If datasets and max_examples are not provided, the default datasets and max_examples will be used. +This LIT demo does not preload any model by default, and you can either load +them in the UI (using "Configure" menu) or preload them at demo launch time with +the optional "--gemini_model" or "--generative_model_endpoints" flags. + +The "--gemini_model" flag allows loading gemini models only and is a list of +strings with format as :. +The accepted Gemini version can be found at +https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference. + +The "--generative_model_endpoints" flag allows loading self-hosted generative +models, and is a list of strings with format as +:. The endpoint name is the +fully-qualified endpoint resource name or endpoint ID of aiplatform. The +endpoint will be available after the model is deployed on VertexAI in GCP. The +details of endpoint can be found at +https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component. + The following command can be used to run the demo: python -m lit_nlp.examples.gcp.demo \ --project_id=$GCP_PROJECT_ID \ --project_location=$GCP_PROJECT_LOCATION \ - --credential=$VERTEX_AI_API_KEY \ + --gemini_models=$GEMINI_MODEL_NAMES \ + --generative_model_endpoints=$GENERATIVE_MODEL_ENDPOINTS \ --datasets=$DATASETS \ --max_examples=$MAX_EXAMPLES \ --alsologtostderr @@ -51,6 +69,26 @@ 'Please enter your project id', required=True, ) +_GEMINI_MODELS = flags.DEFINE_list( + 'gemini_models', + None, + ' List of gemini models to load, as :.' + ' The accepted Gemini version can be found at' + ' https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference.', + required=False, +) +_GENERATIVE_MODEL_ENDPOINTS = flags.DEFINE_list( + 'generative_model_endpoints', + None, + ( + ' List of fully-qualified endpoint resource name or endpoint ID of' + ' aiplatform, as :.' + ' The endpoint will be provided after the model is deployed' + ' on VertexAI in GCP. The details of endpoint can be found at' + ' https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component.' + ), + required=False, +) # Define dataset information. _DATASETS = flags.DEFINE_list( @@ -92,11 +130,26 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: vertexai.init(project=PROJECT_ID.value, location=LOCATION.value) models = {} + if _GEMINI_MODELS.value: + for model_string in _GEMINI_MODELS.value: + name, gemini_model = model_string.split(':', 1) + models[name] = gcp_models.VertexModelGardenModel(gemini_model) + if _GENERATIVE_MODEL_ENDPOINTS.value: + for endpoint_string in _GENERATIVE_MODEL_ENDPOINTS.value: + name, endpoint_name = endpoint_string.split(':', 1) + models[name] = gcp_models.SelfHostedGenerativeModel( + aip_endpoint_name=endpoint_name, + ) + model_loaders: lit_app.ModelLoadersMap = {} model_loaders['gemini'] = ( gcp_models.VertexModelGardenModel, gcp_models.VertexModelGardenModel.init_spec(), ) + model_loaders['self_hosted_generative_model'] = ( + gcp_models.SelfHostedGenerativeModel, + gcp_models.SelfHostedGenerativeModel.init_spec(), + ) datasets = prompt_debugging_datasets.get_datasets( datasets_config=_DATASETS.value, max_examples=_MAX_EXAMPLES.value From 28c304b6b028eea3f24b80888d91a1c0907a14a8 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Tue, 13 Aug 2024 10:32:03 -0700 Subject: [PATCH 16/86] Remove a TODO that is infeasible. The generate_content function converts a list of input to a single content instead of a batch of inputs.(https://source.corp.google.com/piper///depot/google3/third_party/py/google/cloud/aiplatform/vertexai/generative_models/_generative_models.py;l=530-531;ws=codereview%2F858369486;rcl=1) PiperOrigin-RevId: 662568429 --- lit_nlp/examples/gcp/models.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/lit_nlp/examples/gcp/models.py b/lit_nlp/examples/gcp/models.py index 2eacc73d..8ece6676 100644 --- a/lit_nlp/examples/gcp/models.py +++ b/lit_nlp/examples/gcp/models.py @@ -51,8 +51,6 @@ def __init__( ) self._model = generative_models.GenerativeModel(model_name) - # TODO(fanyeycourage): Enable query_model to take a list of input_text, and - # return a list of predictions. def query_model(self, input_text: str) -> lit_types.ScoredTextCandidates: num_attempts = 0 predictions = None From c29ef59a1ea08987266c3ad5a57ab76de28fdeeb Mon Sep 17 00:00:00 2001 From: Bin Du Date: Fri, 16 Aug 2024 09:17:02 -0700 Subject: [PATCH 17/86] Internal change. PiperOrigin-RevId: 663751654 --- website/sphinx_src/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/sphinx_src/index.md b/website/sphinx_src/index.md index 84608f66..64921be0 100644 --- a/website/sphinx_src/index.md +++ b/website/sphinx_src/index.md @@ -1,6 +1,6 @@ # Learning Interpretability Tool (LIT) - + From c18e6807820294aadd77b78269ee19ceda3a98d4 Mon Sep 17 00:00:00 2001 From: Bin Du Date: Tue, 20 Aug 2024 09:12:28 -0700 Subject: [PATCH 18/86] Documentation update. PiperOrigin-RevId: 665391566 --- lit_nlp/package.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/package.json b/lit_nlp/package.json index 351f17a9..748ab3cc 100644 --- a/lit_nlp/package.json +++ b/lit_nlp/package.json @@ -1,6 +1,6 @@ { "name": "lit-client", - "version": "1.0.0-rc1", + "version": "1.2", "description": "Client app for LIT, the Learning Interpretability Tool", "engines": { "node": ">=18.0.0" From 7a1826f45ed134f9d1c95602e602bcd1d686d9b8 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Wed, 4 Sep 2024 13:30:39 -0700 Subject: [PATCH 19/86] Factor LLM model name generation into a utility function. PiperOrigin-RevId: 671080732 --- .../examples/prompt_debugging/keras_lms.py | 8 ++-- .../prompt_debugging/transformers_lms.py | 8 ++-- lit_nlp/examples/prompt_debugging/utils.py | 5 +++ .../examples/prompt_debugging/utils_test.py | 37 +++++++++++++++++++ 4 files changed, 52 insertions(+), 6 deletions(-) create mode 100644 lit_nlp/examples/prompt_debugging/utils.py create mode 100644 lit_nlp/examples/prompt_debugging/utils_test.py diff --git a/lit_nlp/examples/prompt_debugging/keras_lms.py b/lit_nlp/examples/prompt_debugging/keras_lms.py index 2f7025e9..d42fd9f9 100644 --- a/lit_nlp/examples/prompt_debugging/keras_lms.py +++ b/lit_nlp/examples/prompt_debugging/keras_lms.py @@ -11,6 +11,7 @@ from keras_nlp import models as keras_models from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types +from lit_nlp.examples.prompt_debugging import utils as pd_utils from lit_nlp.lib import utils as lit_utils @@ -534,9 +535,10 @@ def output_spec(self) -> lit_types.Spec: def initialize_model_group_for_salience( - name, *args, **kw + name: str, *args, **kw ) -> dict[str, lit_model.Model]: """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'.""" + salience_name, tokenizer_name = pd_utils.generate_model_group_names(name) generation_model = KerasGenerationModel(*args, **kw) salience_model = KerasSalienceModel(model=generation_model.model, *args, **kw) tokenizer_model = KerasTokenizerModel( @@ -544,6 +546,6 @@ def initialize_model_group_for_salience( ) return { name: generation_model, - f"_{name}_salience": salience_model, - f"_{name}_tokenizer": tokenizer_model, + salience_name: salience_model, + tokenizer_name: tokenizer_model, } diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index fbe5229d..ee60a432 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -14,6 +14,7 @@ from absl import logging from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types +from lit_nlp.examples.prompt_debugging import utils as pd_utils from lit_nlp.lib import file_cache from lit_nlp.lib import utils import numpy as np @@ -596,14 +597,15 @@ def output_spec(self) -> lit_types.Spec: def initialize_model_group_for_salience( - name, *args, max_length=512, **kw + name: str, *args, max_length: int = 512, **kw ) -> dict[str, lit_model.Model]: """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'.""" + salience_name, tokenizer_name = pd_utils.generate_model_group_names(name) generation_model = HFGenerativeModel(*args, **kw, max_length=max_length) salience_model = HFSalienceModel.from_loaded(generation_model) tokenizer_model = HFTokenizerModel.from_loaded(generation_model) return { name: generation_model, - f"_{name}_salience": salience_model, - f"_{name}_tokenizer": tokenizer_model, + salience_name: salience_model, + tokenizer_name: tokenizer_model, } diff --git a/lit_nlp/examples/prompt_debugging/utils.py b/lit_nlp/examples/prompt_debugging/utils.py new file mode 100644 index 00000000..4b990bab --- /dev/null +++ b/lit_nlp/examples/prompt_debugging/utils.py @@ -0,0 +1,5 @@ +"""Utility functions for Prompt Debugging use cases.""" + + +def generate_model_group_names(name: str) -> tuple[str, str]: + return f"_{name}_salience", f"_{name}_tokenizer" diff --git a/lit_nlp/examples/prompt_debugging/utils_test.py b/lit_nlp/examples/prompt_debugging/utils_test.py new file mode 100644 index 00000000..ae22ccb4 --- /dev/null +++ b/lit_nlp/examples/prompt_debugging/utils_test.py @@ -0,0 +1,37 @@ +from absl.testing import absltest +from absl.testing import parameterized +from lit_nlp.examples.prompt_debugging import utils + + +class UtilsTest(parameterized.TestCase): + + @parameterized.named_parameters( + dict( + testcase_name="empty_name", + name="", + expected_salience_name="__salience", + expected_tokenizer_name="__tokenizer", + ), + dict( + testcase_name="known_name", + name="gemma", + expected_salience_name="_gemma_salience", + expected_tokenizer_name="_gemma_tokenizer", + ), + dict( + testcase_name="custom_name_with_spaces", + name="my model", + expected_salience_name="_my model_salience", + expected_tokenizer_name="_my model_tokenizer", + ), + ) + def test_generate_model_group_names( + self, name, expected_salience_name, expected_tokenizer_name + ): + salience_name, tokenizer_name = utils.generate_model_group_names(name) + self.assertEqual(salience_name, expected_salience_name) + self.assertEqual(tokenizer_name, expected_tokenizer_name) + + +if __name__ == "__main__": + absltest.main() From 58b1d2b6d0d27c6dca086520cef45bf75466a101 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 5 Sep 2024 08:34:17 -0700 Subject: [PATCH 20/86] Fix indexing issue in Sequence Salience module. PiperOrigin-RevId: 671384008 --- .../modules/sequence_salience_module.ts | 27 ++++++++++--------- 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/lit_nlp/client/modules/sequence_salience_module.ts b/lit_nlp/client/modules/sequence_salience_module.ts index 0afad530..2a2a4763 100644 --- a/lit_nlp/client/modules/sequence_salience_module.ts +++ b/lit_nlp/client/modules/sequence_salience_module.ts @@ -922,22 +922,25 @@ export class SequenceSalienceModule extends SingleExampleSingleModelModule { this.salienceTargetOption = i; }; // prettier-ignore - return html` -
    -
    ${target.text}
    -
    `; + return { + source: target.source, + template: html`
    +
    ${target.text}
    +
    `, + }; }; // Slightly awkward, but we need to process and /then/ filter, because // the @click handler needs the original list index. - const optionsFromDataset = - this.salienceTargetOptions - .filter((t) => t.source === TargetSource.REFERENCE) - .map((t, i) => formatOption(t, i)); - const optionsFromModel = - this.salienceTargetOptions - .filter((t) => t.source === TargetSource.MODEL_OUTPUT) - .map((t, i) => formatOption(t, i)); + const formattedOptions = + this.salienceTargetOptions.map((t, i) => formatOption(t, i)); + const optionsFromDataset = formattedOptions + .filter((t) => t.source === TargetSource.REFERENCE) + .map((t) => t.template); + const optionsFromModel = formattedOptions + .filter((t) => t.source === TargetSource.MODEL_OUTPUT) + .map((t) => t.template); const isLoadingPreds = this.latestLoadPromises.has(MODEL_PREDS_KEY); From af1b72cb888baff38062996da8501eb0e4e5f717 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Fri, 6 Sep 2024 11:02:55 -0700 Subject: [PATCH 21/86] Refactor LIT Vertex AI demos to the lit.nlp.examples.vertexAI folder. PiperOrigin-RevId: 671827404 --- lit_nlp/examples/{gcp => vertexai}/demo.py | 16 ++++++++-------- lit_nlp/examples/{gcp => vertexai}/models.py | 4 ++-- .../examples/{gcp => vertexai}/models_test.py | 8 +++----- 3 files changed, 13 insertions(+), 15 deletions(-) rename lit_nlp/examples/{gcp => vertexai}/demo.py (92%) rename lit_nlp/examples/{gcp => vertexai}/models.py (98%) rename lit_nlp/examples/{gcp => vertexai}/models_test.py (93%) diff --git a/lit_nlp/examples/gcp/demo.py b/lit_nlp/examples/vertexai/demo.py similarity index 92% rename from lit_nlp/examples/gcp/demo.py rename to lit_nlp/examples/vertexai/demo.py index 907ee799..806bb312 100644 --- a/lit_nlp/examples/gcp/demo.py +++ b/lit_nlp/examples/vertexai/demo.py @@ -29,7 +29,7 @@ https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component. The following command can be used to run the demo: - python -m lit_nlp.examples.gcp.demo \ + python -m lit_nlp.examples.vertexai.demo \ --project_id=$GCP_PROJECT_ID \ --project_location=$GCP_PROJECT_LOCATION \ --gemini_models=$GEMINI_MODEL_NAMES \ @@ -51,8 +51,8 @@ from lit_nlp import app as lit_app from lit_nlp import dev_server from lit_nlp import server_flags -from lit_nlp.examples.gcp import models as gcp_models from lit_nlp.examples.prompt_debugging import datasets as prompt_debugging_datasets +from lit_nlp.examples.vertexai import models as vertexai_models FLAGS = flags.FLAGS @@ -133,22 +133,22 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: if _GEMINI_MODELS.value: for model_string in _GEMINI_MODELS.value: name, gemini_model = model_string.split(':', 1) - models[name] = gcp_models.VertexModelGardenModel(gemini_model) + models[name] = vertexai_models.VertexModelGardenModel(gemini_model) if _GENERATIVE_MODEL_ENDPOINTS.value: for endpoint_string in _GENERATIVE_MODEL_ENDPOINTS.value: name, endpoint_name = endpoint_string.split(':', 1) - models[name] = gcp_models.SelfHostedGenerativeModel( + models[name] = vertexai_models.SelfHostedGenerativeModel( aip_endpoint_name=endpoint_name, ) model_loaders: lit_app.ModelLoadersMap = {} model_loaders['gemini'] = ( - gcp_models.VertexModelGardenModel, - gcp_models.VertexModelGardenModel.init_spec(), + vertexai_models.VertexModelGardenModel, + vertexai_models.VertexModelGardenModel.init_spec(), ) model_loaders['self_hosted_generative_model'] = ( - gcp_models.SelfHostedGenerativeModel, - gcp_models.SelfHostedGenerativeModel.init_spec(), + vertexai_models.SelfHostedGenerativeModel, + vertexai_models.SelfHostedGenerativeModel.init_spec(), ) datasets = prompt_debugging_datasets.get_datasets( diff --git a/lit_nlp/examples/gcp/models.py b/lit_nlp/examples/vertexai/models.py similarity index 98% rename from lit_nlp/examples/gcp/models.py rename to lit_nlp/examples/vertexai/models.py index 8ece6676..7bf99ad7 100644 --- a/lit_nlp/examples/gcp/models.py +++ b/lit_nlp/examples/vertexai/models.py @@ -123,8 +123,8 @@ class SelfHostedGenerativeModel(lit_model.BatchedRemoteModel): model. Attributes: - aip_endpoint_name: A fully-qualified GCP depolyed model endpoint resource - name or endpoint ID. + aip_endpoint_name: A fully-qualified VertexAI depolyed model endpoint + resource name or endpoint ID. max_concurrent_requests: The maximum number of concurrent requests to the model. max_qps: The maximum number of queries per second to the model. diff --git a/lit_nlp/examples/gcp/models_test.py b/lit_nlp/examples/vertexai/models_test.py similarity index 93% rename from lit_nlp/examples/gcp/models_test.py rename to lit_nlp/examples/vertexai/models_test.py index d68cec1f..79ecdbb1 100644 --- a/lit_nlp/examples/gcp/models_test.py +++ b/lit_nlp/examples/vertexai/models_test.py @@ -1,10 +1,10 @@ -"""Tests for lit_nlp.examples.gcp.models.""" +"""Tests for lit_nlp.examples.vertexai.models.""" from unittest import mock from absl.testing import absltest from google.cloud import aiplatform from vertexai import generative_models -from lit_nlp.examples.gcp import models +from lit_nlp.examples.vertexai import models class ModelsTest(absltest.TestCase): @@ -76,9 +76,7 @@ def test_query_self_hosted_generative_model( ) mock_generate_content.side_effect = [response1, response2] - model = models.SelfHostedGenerativeModel( - aip_endpoint_name="endpoint_name" - ) + model = models.SelfHostedGenerativeModel(aip_endpoint_name="endpoint_name") model._endpoint = mock.MagicMock() model._endpoint.predict.side_effect = [response1, response2] From 75da3efb487b055e96fbb7a4920a6bdea4cc5111 Mon Sep 17 00:00:00 2001 From: Googler Date: Fri, 6 Sep 2024 11:38:21 -0700 Subject: [PATCH 22/86] Fix TS2869 compilation issues with TypeScript 5.6. PiperOrigin-RevId: 671839614 --- lit_nlp/client/modules/data_table_module.ts | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lit_nlp/client/modules/data_table_module.ts b/lit_nlp/client/modules/data_table_module.ts index 0e5cac67..116a7427 100644 --- a/lit_nlp/client/modules/data_table_module.ts +++ b/lit_nlp/client/modules/data_table_module.ts @@ -532,8 +532,8 @@ export class DataTableModule extends LitModule { Reset view
    `; From b6cf557c7311dac8b0adfd6769635ccb1a1bf814 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 10 Sep 2024 10:44:06 -0700 Subject: [PATCH 23/86] LIT: Refactor model field names and specs into a constants module. PiperOrigin-RevId: 673021601 --- .../examples/prompt_debugging/constants.py | 44 ++++++++++ .../examples/prompt_debugging/keras_lms.py | 85 +++++++------------ .../prompt_debugging/transformers_lms.py | 78 +++++++++-------- 3 files changed, 114 insertions(+), 93 deletions(-) create mode 100644 lit_nlp/examples/prompt_debugging/constants.py diff --git a/lit_nlp/examples/prompt_debugging/constants.py b/lit_nlp/examples/prompt_debugging/constants.py new file mode 100644 index 00000000..e0be4047 --- /dev/null +++ b/lit_nlp/examples/prompt_debugging/constants.py @@ -0,0 +1,44 @@ +"""Constants used across parallel classes in the Prompt Debugging example.""" + +import types +from lit_nlp.api import types as lit_types + + +class FieldNames(types.SimpleNamespace): + PROMPT = "prompt" + RESPONSE = "response" + PROMPT_EMBEDDINGS = "prompt_embeddings" + RESPONSE_EMBEDDINGS = "response_embeddings" + TARGET = "target" + TOKENS = "tokens" + TARGET_MASK = "target_mask" + GRAD_DOT_INPUT = "grad_dot_input" + GRAD_NORM = "grad_l2" + + +INPUT_SPEC: lit_types.Spec = { + FieldNames.PROMPT: lit_types.TextSegment(), + FieldNames.TARGET: lit_types.TextSegment(required=False), +} + +INPUT_SPEC_SALIENCE: lit_types.Spec = { + FieldNames.TARGET_MASK: lit_types.TokenScores(align="", required=False), +} + +OUTPUT_SPEC_GENERATION: lit_types.Spec = { + FieldNames.RESPONSE: lit_types.GeneratedText(parent=FieldNames.TARGET) +} + +OUTPUT_SPEC_GENERATION_EMBEDDINGS: lit_types.Spec = { + FieldNames.PROMPT_EMBEDDINGS: lit_types.Embeddings(required=False), + FieldNames.RESPONSE_EMBEDDINGS: lit_types.Embeddings(required=False), +} + +OUTPUT_SPEC_TOKENIZER: lit_types.Spec = { + FieldNames.TOKENS: lit_types.Tokens(parent=""), +} + +OUTPUT_SPEC_SALIENCE: lit_types.Spec = { + FieldNames.GRAD_DOT_INPUT: lit_types.TokenScores(align=FieldNames.TOKENS), + FieldNames.GRAD_NORM: lit_types.TokenScores(align=FieldNames.TOKENS), +} | OUTPUT_SPEC_TOKENIZER diff --git a/lit_nlp/examples/prompt_debugging/keras_lms.py b/lit_nlp/examples/prompt_debugging/keras_lms.py index d42fd9f9..9c546eb5 100644 --- a/lit_nlp/examples/prompt_debugging/keras_lms.py +++ b/lit_nlp/examples/prompt_debugging/keras_lms.py @@ -3,7 +3,6 @@ from collections.abc import Sequence import functools import inspect -import types from typing import Optional from absl import logging @@ -11,6 +10,7 @@ from keras_nlp import models as keras_models from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types +from lit_nlp.examples.prompt_debugging import constants as pd_constants from lit_nlp.examples.prompt_debugging import utils as pd_utils from lit_nlp.lib import utils as lit_utils @@ -35,19 +35,6 @@ _DEFAULT_MAX_LENGTH = 1024 -class FieldNames(types.SimpleNamespace): - PROMPT = "prompt" - RESPONSE = "response" - PROMPT_EMBEDDINGS = "prompt_embeddings" - RESPONSE_EMBEDDINGS = "response_embeddings" - TARGET = "target" - TOKENS = "tokens" - TARGET_MASK = "target_mask" - GRAD_DOT_INPUT = "grad_dot_input" - GRAD_NORM = "grad_l2" - TOKEN_LOSS = "token_loss" - - class _KerasBaseModel(lit_model.BatchedModel): """Base LIT model wrapper class for Keras on TensorFlow.""" @@ -183,10 +170,7 @@ def init_spec(cls): return None def input_spec(self): - return { - FieldNames.PROMPT: lit_types.TextSegment(), - FieldNames.TARGET: lit_types.TextSegment(required=False), - } + return pd_constants.INPUT_SPEC class KerasGenerationModel(_KerasBaseModel): @@ -240,7 +224,9 @@ def predict_minibatch( self, inputs: list[lit_types.JsonDict], ) -> list[lit_types.JsonDict]: - prompts: Sequence[str] = [ex[FieldNames.PROMPT] for ex in inputs] + prompts: Sequence[str] = [ + ex[pd_constants.FieldNames.PROMPT] for ex in inputs + ] # TODO(lit-dev): suppport loading cached responses here, since running # generation can be expensive. @@ -254,7 +240,9 @@ def predict_minibatch( for response, prompt in zip(full_responses, prompts) ] - outputs = [{FieldNames.RESPONSE: response} for response in responses] + outputs = [ + {pd_constants.FieldNames.RESPONSE: response} for response in responses + ] if self.output_embeddings: prompt_embeddings = self.embed_and_mean_pool(prompts) @@ -263,20 +251,19 @@ def predict_minibatch( response_embeddings = self.embed_and_mean_pool(responses) for o, p, r in zip(outputs, prompt_embeddings, response_embeddings): - o[FieldNames.PROMPT_EMBEDDINGS] = keras.ops.convert_to_numpy(p) - o[FieldNames.RESPONSE_EMBEDDINGS] = keras.ops.convert_to_numpy(r) + o[pd_constants.FieldNames.PROMPT_EMBEDDINGS] = ( + keras.ops.convert_to_numpy(p) + ) + o[pd_constants.FieldNames.RESPONSE_EMBEDDINGS] = ( + keras.ops.convert_to_numpy(r) + ) return outputs def output_spec(self) -> lit_types.Spec: - ret = { - FieldNames.RESPONSE: lit_types.GeneratedText(parent=FieldNames.TARGET) - } + ret = pd_constants.OUTPUT_SPEC_GENERATION if self.output_embeddings: - return ret | { - FieldNames.PROMPT_EMBEDDINGS: lit_types.Embeddings(), - FieldNames.RESPONSE_EMBEDDINGS: lit_types.Embeddings(), - } + return ret | pd_constants.OUTPUT_SPEC_GENERATION_EMBEDDINGS return ret @@ -355,11 +342,8 @@ def _pred(self, input_ids, padding_mask, target_masks): batched_outputs = { "input_ids": input_ids, "padding_mask": padding_mask, - # Gradients are already aligned to input tokens. - FieldNames.GRAD_NORM: grad_l2, - FieldNames.GRAD_DOT_INPUT: grad_dot_input, - # Shift token loss to align with (input) tokens. - # FieldNames.TOKEN_LOSS: tf.roll(per_token_loss, shift=1, axis=1), + pd_constants.FieldNames.GRAD_NORM: grad_l2, + pd_constants.FieldNames.GRAD_DOT_INPUT: grad_dot_input, } return batched_outputs @@ -447,7 +431,7 @@ def _postprocess(self, preds): """Post-process single-example preds. Operates on numpy arrays.""" mask = preds.pop("padding_mask").astype(bool) ids = preds.pop("input_ids")[mask] - preds[FieldNames.TOKENS] = self.ids_to_clean_tokens(ids) + preds[pd_constants.FieldNames.TOKENS] = self.ids_to_clean_tokens(ids) for key in lit_utils.find_spec_keys( self.output_spec(), lit_types.TokenScores ): @@ -460,13 +444,17 @@ def _postprocess(self, preds): def predict_minibatch(self, inputs): """Predict on a single minibatch of examples.""" texts: Sequence[str] = [ - ex[FieldNames.PROMPT] + ex.get(FieldNames.TARGET, "") for ex in inputs + ex[pd_constants.FieldNames.PROMPT] + + ex.get(pd_constants.FieldNames.TARGET, "") + for ex in inputs ] preprocessed_texts = self.encode_inputs(texts) sequence_ids = preprocessed_texts["token_ids"] padding_mask = preprocessed_texts["padding_mask"] - target_masks = [ex.get(FieldNames.TARGET_MASK, []) for ex in inputs] + target_masks = [ + ex.get(pd_constants.FieldNames.TARGET_MASK, []) for ex in inputs + ] # Get the predictions. batched_outputs = self._pred(sequence_ids, padding_mask, target_masks) @@ -479,19 +467,10 @@ def predict_minibatch(self, inputs): return map(self._postprocess, unbatched_outputs) def input_spec(self): - return super().input_spec() | { - FieldNames.TARGET_MASK: lit_types.TokenScores(align="", required=False), - } + return super().input_spec() | pd_constants.INPUT_SPEC_SALIENCE def output_spec(self) -> lit_types.Spec: - return { - FieldNames.TOKENS: lit_types.Tokens(parent=""), # All tokens. - FieldNames.GRAD_NORM: lit_types.TokenScores(align=FieldNames.TOKENS), - FieldNames.GRAD_DOT_INPUT: lit_types.TokenScores( - align=FieldNames.TOKENS - ), - # FieldNames.TOKEN_LOSS: lit_types.TokenScores(align=FieldNames.TOKENS), - } + return pd_constants.OUTPUT_SPEC_SALIENCE class KerasTokenizerModel(_KerasBaseModel): @@ -507,13 +486,15 @@ def _postprocess(self, preds): # rather than acting as a boolean mask. mask = preds.pop("padding_mask").astype(bool) ids = preds.pop("token_ids")[mask] - preds[FieldNames.TOKENS] = self.ids_to_clean_tokens(ids) + preds[pd_constants.FieldNames.TOKENS] = self.ids_to_clean_tokens(ids) return preds def predict_minibatch(self, inputs): """Tokenize a single minibatch of examples.""" texts: Sequence[str] = [ - ex[FieldNames.PROMPT] + ex.get(FieldNames.TARGET, "") for ex in inputs + ex[pd_constants.FieldNames.PROMPT] + + ex.get(pd_constants.FieldNames.TARGET, "") + for ex in inputs ] preprocessed_texts = self.encode_inputs(texts) batched_outputs = { @@ -529,9 +510,7 @@ def predict_minibatch(self, inputs): return map(self._postprocess, unbatched_outputs) def output_spec(self) -> lit_types.Spec: - return { - FieldNames.TOKENS: lit_types.Tokens(parent=""), # All tokens. - } + return pd_constants.OUTPUT_SPEC_TOKENIZER def initialize_model_group_for_salience( diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index ee60a432..8cdf603a 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -14,6 +14,7 @@ from absl import logging from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types +from lit_nlp.examples.prompt_debugging import constants as pd_constants from lit_nlp.examples.prompt_debugging import utils as pd_utils from lit_nlp.lib import file_cache from lit_nlp.lib import utils @@ -179,10 +180,7 @@ def max_minibatch_size(self) -> int: return self.batch_size def input_spec(self): - return { - "prompt": lit_types.TextSegment(), - "target": lit_types.TextSegment(required=False), - } + return pd_constants.INPUT_SPEC class HFGenerativeModel(HFBaseModel): @@ -229,19 +227,23 @@ def _postprocess(self, preds: Mapping[str, Any]) -> Mapping[str, Any]: # TODO(b/324957491): return actual decoder scores for each generation. # GeneratedTextCandidates should be a list[(text, score)] processed_preds = {} - processed_preds["response"] = [(preds["response"], 1.0)] + processed_preds[pd_constants.FieldNames.RESPONSE] = [ + (preds[pd_constants.FieldNames.RESPONSE], 1.0) + ] ntok_in = preds["ntok_in"] ntok_out = preds["ntok_out"] embs = preds["embs"] assert embs.shape[0] >= ntok_in + ntok_out # Mean-pool over input tokens. - processed_preds["prompt_embeddings"] = np.mean( + processed_preds[pd_constants.FieldNames.PROMPT_EMBEDDINGS] = np.mean( embs[-(ntok_out + ntok_in) : -ntok_out], axis=0 ) # Mean-pool over output (generated) tokens. # TODO(b/324957491): slice this to only "real" output tokens, # if generation length < max generation length. - processed_preds["response_embeddings"] = np.mean(embs[-ntok_out:], axis=0) + processed_preds[pd_constants.FieldNames.RESPONSE_EMBEDDINGS] = np.mean( + embs[-ntok_out:], axis=0 + ) return processed_preds @@ -313,7 +315,7 @@ def _get_batched_outputs( # Convert to numpy for post-processing. detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()} - detached_outputs["response"] = responses + detached_outputs[pd_constants.FieldNames.RESPONSE] = responses return detached_outputs ## @@ -326,11 +328,10 @@ def predict_minibatch(self, inputs): return map(self._postprocess, unbatched_outputs) def output_spec(self) -> lit_types.Spec: - return { - "response": lit_types.GeneratedTextCandidates(parent="target"), - "prompt_embeddings": lit_types.Embeddings(required=False), - "response_embeddings": lit_types.Embeddings(required=False), - } + return ( + pd_constants.OUTPUT_SPEC_GENERATION + | pd_constants.OUTPUT_SPEC_GENERATION_EMBEDDINGS + ) class HFSalienceModel(HFBaseModel): @@ -422,11 +423,8 @@ def _pred_tf(self, encoded_inputs, target_masks): batched_outputs = { "input_ids": input_ids, "attention_mask": encoded_inputs["attention_mask"], - # Gradients are already aligned to input tokens. - "grad_l2": grad_l2, - "grad_dot_input": grad_dot_input, - # Shift token loss to align with (input) tokens. - # "token_loss": tf.roll(per_token_loss, shift=1, axis=1), + pd_constants.FieldNames.GRAD_NORM: grad_l2, + pd_constants.FieldNames.GRAD_DOT_INPUT: grad_dot_input, } return batched_outputs @@ -486,11 +484,10 @@ def _pred_pt(self, encoded_inputs, target_masks): batched_outputs = { "input_ids": input_ids.cpu().to(torch.int), "attention_mask": attention_mask.cpu().to(torch.int), - # Gradients are already aligned to input tokens. - "grad_l2": grad_l2.cpu().to(torch.float), - "grad_dot_input": grad_dot_input.cpu().to(torch.float), - # Shift token loss to align with (input) tokens. - # "token_loss": torch.roll(per_token_loss, shifts=1, dims=1), + pd_constants.FieldNames.GRAD_NORM: grad_l2.cpu().to(torch.float), + pd_constants.FieldNames.GRAD_DOT_INPUT: grad_dot_input.cpu().to( + torch.float + ), } return batched_outputs @@ -501,7 +498,7 @@ def _postprocess(self, preds): # rather than acting as a boolean mask. mask = preds.pop("attention_mask").astype(bool) ids = preds.pop("input_ids")[mask] - preds["tokens"] = self.ids_to_clean_tokens(ids) + preds[pd_constants.FieldNames.TOKENS] = self.ids_to_clean_tokens(ids) for key in utils.find_spec_keys(self.output_spec(), lit_types.TokenScores): preds[key] = preds[key][mask] # First token (usually ) is not actually predicted, so return 0 for loss. @@ -513,7 +510,11 @@ def _postprocess(self, preds): def predict_minibatch(self, inputs): """Predict on a single minibatch of examples.""" # Preprocess inputs. - texts = [ex["prompt"] + ex.get("target", "") for ex in inputs] + texts = [ + ex[pd_constants.FieldNames.PROMPT] + + ex.get(pd_constants.FieldNames.TARGET, "") + for ex in inputs + ] encoded_inputs = self.tokenizer( texts, return_tensors=_HF_PYTORCH @@ -523,7 +524,9 @@ def predict_minibatch(self, inputs): padding="longest", truncation="longest_first", ) - target_masks = [ex.get("target_mask", []) for ex in inputs] + target_masks = [ + ex.get(pd_constants.FieldNames.TARGET_MASK, []) for ex in inputs + ] # Get the predictions. if self.framework == MLFramework.PT: @@ -538,17 +541,10 @@ def predict_minibatch(self, inputs): return map(self._postprocess, unbatched_outputs) def input_spec(self): - return super().input_spec() | { - "target_mask": lit_types.TokenScores(align="", required=False), - } + return super().input_spec() | pd_constants.INPUT_SPEC_SALIENCE def output_spec(self) -> lit_types.Spec: - return { - "tokens": lit_types.Tokens(parent=""), # all tokens - "grad_l2": lit_types.TokenScores(align="tokens"), - "grad_dot_input": lit_types.TokenScores(align="tokens"), - # "token_loss": lit_types.TokenScores(align="tokens"), - } + return pd_constants.OUTPUT_SPEC_SALIENCE class HFTokenizerModel(HFBaseModel): @@ -563,14 +559,18 @@ def _postprocess(self, preds): # rather than acting as a boolean mask. mask = preds.pop("attention_mask").astype(bool) ids = preds.pop("input_ids")[mask] - preds["tokens"] = self.ids_to_clean_tokens(ids) + preds[pd_constants.FieldNames.TOKENS] = self.ids_to_clean_tokens(ids) return preds # LIT API implementations def predict_minibatch(self, inputs): """Predict on a single minibatch of examples.""" # Preprocess inputs. - texts = [ex["prompt"] + ex.get("target", "") for ex in inputs] + texts = [ + ex[pd_constants.FieldNames.PROMPT] + + ex.get(pd_constants.FieldNames.TARGET, "") + for ex in inputs + ] encoded_inputs = self.tokenizer( texts, return_tensors=_HF_PYTORCH @@ -591,9 +591,7 @@ def predict_minibatch(self, inputs): return map(self._postprocess, unbatched_outputs) def output_spec(self) -> lit_types.Spec: - return { - "tokens": lit_types.Tokens(parent=""), # all tokens - } + return pd_constants.OUTPUT_SPEC_TOKENIZER def initialize_model_group_for_salience( From 8dbf16be3e9a867d4d5b6b27a25bebc548a7b8fc Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Tue, 10 Sep 2024 14:54:34 -0700 Subject: [PATCH 24/86] Add Dockerfile for LIT Vertex AI demo This Dockerfile can be used to build a container image for a LIT Vertex AI demo. The image includes all of the necessary dependencies for running a LIT demo, including the LIT Python package, the Vertex AI SDK, and the gunicorn web server. The Dockerfile also includes a script for starting the LIT demo. The script takes several arguments, including the name of the demo, the port on which the demo should listen, and the location of the data files for the demo. The Dockerfile can be used to build a container image for a LIT Vertex AI demo. The image can then be deployed to a Vertex AI endpoint. PiperOrigin-RevId: 673117000 --- lit_nlp/examples/vertexai/Dockerfile | 77 ++++++++++++++++++++ lit_nlp/examples/vertexai/demo.py | 42 +++++++++-- lit_nlp/examples/vertexai/gunicorn_config.py | 25 +++++++ lit_nlp/examples/vertexai/requirements.txt | 20 +++++ pyproject.toml | 12 ++- requirements_test.txt | 1 + 6 files changed, 170 insertions(+), 7 deletions(-) create mode 100644 lit_nlp/examples/vertexai/Dockerfile create mode 100644 lit_nlp/examples/vertexai/gunicorn_config.py create mode 100644 lit_nlp/examples/vertexai/requirements.txt diff --git a/lit_nlp/examples/vertexai/Dockerfile b/lit_nlp/examples/vertexai/Dockerfile new file mode 100644 index 00000000..04244d1d --- /dev/null +++ b/lit_nlp/examples/vertexai/Dockerfile @@ -0,0 +1,77 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== +# Use the official lightweight Python image. +# https://hub.docker.com/_/python + +# TODO(faneycourage): Add a readme file for the demo. + +# ---- LIT Base Container ---- + +FROM python:3.11-slim AS lit-nlp-base + +# Update Ubuntu packages and install basic utils +RUN apt-get update +RUN apt-get install -y wget curl gnupg2 gcc g++ git + +# Copy local code to the container image. +ENV APP_HOME /app +WORKDIR $APP_HOME + +COPY ./lit_nlp/examples/vertexai/gunicorn_config.py ./ + + + +# ---- LIT Container for Hosted Demos ---- + +FROM lit-nlp-base AS lit-nlp-prod + +RUN python -m pip install 'lit-nlp[vertexai]' + +WORKDIR $APP_HOME +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] + + + +# ---- LIT Container for Developing and Testing Hosted Demos ---- + +FROM lit-nlp-base AS lit-nlp-dev + +# Install yarn +RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | apt-key add - +RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ + tee /etc/apt/sources.list.d/yarn.list +RUN apt update && apt -y install yarn + +# Set up python environment with production dependencies +# This step is slow as it installs many packages. +COPY requirements_core.txt ./ +RUN python -m pip install -r requirements_core.txt + +COPY lit_nlp/examples/vertexai/requirements.txt lit_nlp/examples/vertexai/requirements.txt +RUN python -m pip install -r lit_nlp/examples/vertexai/requirements.txt + +# Copy the rest of the lit_nlp package +COPY . ./ + +# Build front-end with yarn +WORKDIR $APP_HOME/lit_nlp/client +ENV NODE_OPTIONS "--openssl-legacy-provider" +RUN yarn && yarn build && rm -rf node_modules/* + +# Run LIT server +# Note that the config file supports configuring the LIT demo that is launched +# via the DEMO_NAME and DEMO_PORT environment variables. +WORKDIR $APP_HOME +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] \ No newline at end of file diff --git a/lit_nlp/examples/vertexai/demo.py b/lit_nlp/examples/vertexai/demo.py index 806bb312..70c5cd46 100644 --- a/lit_nlp/examples/vertexai/demo.py +++ b/lit_nlp/examples/vertexai/demo.py @@ -39,9 +39,11 @@ --alsologtostderr Then navigate to localhost:5432 to access the demo UI. + """ from collections.abc import Sequence +import os import sys from typing import Optional from absl import app @@ -54,16 +56,16 @@ from lit_nlp.examples.prompt_debugging import datasets as prompt_debugging_datasets from lit_nlp.examples.vertexai import models as vertexai_models -FLAGS = flags.FLAGS +_FLAGS = flags.FLAGS # Define GCP project information and vertex AI API key. -LOCATION = flags.DEFINE_string( +_LOCATION = flags.DEFINE_string( 'project_location', None, 'Please enter your GCP project location', required=True, ) -PROJECT_ID = flags.DEFINE_string( +_PROJECT_ID = flags.DEFINE_string( 'project_id', None, 'Please enter your project id', @@ -111,8 +113,36 @@ def get_wsgi_app() -> Optional[dev_server.LitServerType]: """Return WSGI app for container-hosted demos.""" - FLAGS.set_default('server_type', 'external') - FLAGS.set_default('demo_mode', True) + _FLAGS.set_default('server_type', 'external') + _FLAGS.set_default('demo_mode', True) + + location = os.getenv('PROJECT_LOCATION', None) + _FLAGS['project_location'].value = location + + project_id = os.getenv('PROJECT_ID', None) + _FLAGS['project_id'].value = project_id + + gemini_models = os.getenv('GEMINI_MODELS', None) + if gemini_models: + gemini_model_list = gemini_models.split(',') + _FLAGS['gemini_models'].value = gemini_model_list + + generative_model_endpoints = os.getenv('GENERATIVE_MODEL_ENDPOINTS', None) + if generative_model_endpoints: + generative_model_endpoints_list = generative_model_endpoints.split(',') + _FLAGS['generative_model_endpoints'].value = ( + generative_model_endpoints_list + ) + + datasets = os.getenv('DATASETS', None) + if datasets: + datasets_list = datasets.split(',') + _FLAGS['datasets'].value = datasets_list + + max_examples = os.getenv('MAX_EXAMPLES', None) + if max_examples: + _FLAGS['max_examples'].value = int(max_examples) + # Parse flags without calling app.run(main), to avoid conflict with # gunicorn command line flags. unused = flags.FLAGS(sys.argv, known_only=True) @@ -127,7 +157,7 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') - vertexai.init(project=PROJECT_ID.value, location=LOCATION.value) + vertexai.init(project=_PROJECT_ID.value, location=_LOCATION.value) models = {} if _GEMINI_MODELS.value: diff --git a/lit_nlp/examples/vertexai/gunicorn_config.py b/lit_nlp/examples/vertexai/gunicorn_config.py new file mode 100644 index 00000000..92d87851 --- /dev/null +++ b/lit_nlp/examples/vertexai/gunicorn_config.py @@ -0,0 +1,25 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== +"""gunicorn configuration for cloud-hosted demos.""" + +import os + +_DEMO_PORT = os.getenv('DEMO_PORT', '5432') + +bind = f'0.0.0.0:{_DEMO_PORT}' +timeout = 3600 +threads = 8 +worker_class = 'gthread' +wsgi_app = 'lit_nlp.examples.vertexai.demo:get_wsgi_app()' diff --git a/lit_nlp/examples/vertexai/requirements.txt b/lit_nlp/examples/vertexai/requirements.txt new file mode 100644 index 00000000..e43911f7 --- /dev/null +++ b/lit_nlp/examples/vertexai/requirements.txt @@ -0,0 +1,20 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== + +-r ../../../requirements_core.txt + +google-cloud-aiplatform>=1.60.0 +gunicorn>=20.1.0 +vertexai>=1.49.0 diff --git a/pyproject.toml b/pyproject.toml index 02376ff8..2ec96196 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,9 +77,12 @@ keywords = [ ] [project.optional-dependencies] +examples-common = [ + "gunicorn>=20.1.0", +] # LINT.IfChange examples = [ - "gunicorn>=20.1.0", + "lit-nlp[examples-common]", "sentencepiece==0.1.99", "tensorflow>=2.10.0,<2.16.0", "tensorflow-datasets>=4.9.0", @@ -90,6 +93,13 @@ examples = [ ] # LINT.ThenChange(./requirements_examples.txt) # LINT.IfChange +vertexai = [ + "lit-nlp[examples-common]", + "google-cloud-aiplatform>=1.60.0", + "vertexai>=1.49.0", +] +# LINT.ThenChange(./lit_nlp/examples/vertexai/requirements.txt) +# LINT.IfChange test = [ "lime==0.2.0.1", "pytest>=7.4.0,<8.0.0", diff --git a/requirements_test.txt b/requirements_test.txt index 0f6cc418..d454942d 100644 --- a/requirements_test.txt +++ b/requirements_test.txt @@ -14,5 +14,6 @@ # ============================================================================== # LINT.IfChange lime==0.2.0.1 +google-cloud-aiplatform>=1.60.0 pytest>=7.4.0,<8.0.0 # LINT.ThenChange(./pyproject.toml) From f21556a19ecc421ab5fc37d2d5b8232299fac98e Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 12 Sep 2024 09:22:47 -0700 Subject: [PATCH 25/86] LIT: Use `ENV VAR=value` form in Dockerfiles PiperOrigin-RevId: 673878737 --- Dockerfile | 4 ++-- lit_nlp/examples/vertexai/Dockerfile | 11 +++++------ 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/Dockerfile b/Dockerfile index 11c1eeea..4dcf08ad 100644 --- a/Dockerfile +++ b/Dockerfile @@ -27,7 +27,7 @@ RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ RUN apt update && apt -y install yarn # Copy local code to the container image. -ENV APP_HOME /app +ENV APP_HOME=/app WORKDIR $APP_HOME # Set up python environment with production dependencies @@ -40,7 +40,7 @@ COPY . ./ # Build front-end with yarn WORKDIR $APP_HOME/lit_nlp/client -ENV NODE_OPTIONS "--openssl-legacy-provider" +ENV NODE_OPTIONS="--openssl-legacy-provider" RUN yarn && yarn build && rm -rf node_modules/* # Run LIT server diff --git a/lit_nlp/examples/vertexai/Dockerfile b/lit_nlp/examples/vertexai/Dockerfile index 04244d1d..03c7828c 100644 --- a/lit_nlp/examples/vertexai/Dockerfile +++ b/lit_nlp/examples/vertexai/Dockerfile @@ -26,7 +26,7 @@ RUN apt-get update RUN apt-get install -y wget curl gnupg2 gcc g++ git # Copy local code to the container image. -ENV APP_HOME /app +ENV APP_HOME=/app WORKDIR $APP_HOME COPY ./lit_nlp/examples/vertexai/gunicorn_config.py ./ @@ -57,9 +57,8 @@ RUN apt update && apt -y install yarn # Set up python environment with production dependencies # This step is slow as it installs many packages. COPY requirements_core.txt ./ -RUN python -m pip install -r requirements_core.txt - -COPY lit_nlp/examples/vertexai/requirements.txt lit_nlp/examples/vertexai/requirements.txt +COPY lit_nlp/examples/vertexai/requirements.txt \ + lit_nlp/examples/vertexai/requirements.txt RUN python -m pip install -r lit_nlp/examples/vertexai/requirements.txt # Copy the rest of the lit_nlp package @@ -67,11 +66,11 @@ COPY . ./ # Build front-end with yarn WORKDIR $APP_HOME/lit_nlp/client -ENV NODE_OPTIONS "--openssl-legacy-provider" +ENV NODE_OPTIONS="--openssl-legacy-provider" RUN yarn && yarn build && rm -rf node_modules/* # Run LIT server # Note that the config file supports configuring the LIT demo that is launched # via the DEMO_NAME and DEMO_PORT environment variables. WORKDIR $APP_HOME -ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] \ No newline at end of file +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] From 603919503ab59830eda88be96befee2c68c40cce Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 12 Sep 2024 10:00:50 -0700 Subject: [PATCH 26/86] LIT: Change the demo_mode flag to be False in Vertex AI demo. PiperOrigin-RevId: 673893499 --- lit_nlp/examples/vertexai/demo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/vertexai/demo.py b/lit_nlp/examples/vertexai/demo.py index 70c5cd46..f40ec5f8 100644 --- a/lit_nlp/examples/vertexai/demo.py +++ b/lit_nlp/examples/vertexai/demo.py @@ -114,7 +114,7 @@ def get_wsgi_app() -> Optional[dev_server.LitServerType]: """Return WSGI app for container-hosted demos.""" _FLAGS.set_default('server_type', 'external') - _FLAGS.set_default('demo_mode', True) + _FLAGS.set_default('demo_mode', False) location = os.getenv('PROJECT_LOCATION', None) _FLAGS['project_location'].value = location From f4436a26ed79f481e16e2c53c0551703e7ba8c4f Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 12 Sep 2024 14:50:24 -0700 Subject: [PATCH 27/86] LIT: Update numpy version to be smaller than 2.0.0. PiperOrigin-RevId: 674020174 --- pyproject.toml | 4 ++-- requirements_core.txt | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2ec96196..dec21486 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ dependencies = [ "Levenshtein>=0.21.1", "matplotlib>=3.7.1", "ml-collections>=0.1.1", - "numpy>=1.24.1", + "numpy>=1.24.1,<2.0.0", "pandas>=2.0.3", "Pillow>=10.0.0", "portpicker>=1.5.2", @@ -98,7 +98,7 @@ vertexai = [ "google-cloud-aiplatform>=1.60.0", "vertexai>=1.49.0", ] -# LINT.ThenChange(./lit_nlp/examples/vertexai/requirements.txt) +# LINT.ThenChange(./examples/vertexai/requirements.txt) # LINT.IfChange test = [ "lime==0.2.0.1", diff --git a/requirements_core.txt b/requirements_core.txt index c1bb2434..8bcd7847 100644 --- a/requirements_core.txt +++ b/requirements_core.txt @@ -23,7 +23,7 @@ ipython>=7.34.0 Levenshtein>=0.21.1 matplotlib>=3.6.0,<3.9.0 ml-collections>=0.1.1 -numpy>=1.24.1 +numpy>=1.24.1,<2.0.0 pandas>=2.0.3 Pillow>=10.0.0 portpicker>=1.5.2 From 362cf1600cfd8343f5b8f978d1783bb867f5575b Mon Sep 17 00:00:00 2001 From: Cibi Arjun Date: Fri, 13 Sep 2024 14:35:37 -0700 Subject: [PATCH 28/86] Internal Change PiperOrigin-RevId: 674437767 --- lit_nlp/app.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/lit_nlp/app.py b/lit_nlp/app.py index a83eabb7..9ae3a30a 100644 --- a/lit_nlp/app.py +++ b/lit_nlp/app.py @@ -1018,6 +1018,13 @@ def __init__( index_file='static/index.html', ) + def get_dataset_specs(self) -> dict[str, dict[str, str]]: + datasets_with_spec = collections.defaultdict(dict) + for name, ds in self._datasets.items(): + for field_name, lit_data_class in ds.spec().items(): + datasets_with_spec[name][field_name] = type(lit_data_class).__name__ + return datasets_with_spec + def save_cache(self): for m in self._models.values(): if isinstance(m, caching.CachingModelWrapper): From e190a710f6da512a2cd15aed30b60055b8cefa6f Mon Sep 17 00:00:00 2001 From: Bin Du Date: Fri, 13 Sep 2024 15:28:14 -0700 Subject: [PATCH 29/86] Fix dependency issue at OSS. PiperOrigin-RevId: 674454511 --- lit_nlp/app.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lit_nlp/app.py b/lit_nlp/app.py index 9ae3a30a..fb2296fa 100644 --- a/lit_nlp/app.py +++ b/lit_nlp/app.py @@ -14,6 +14,7 @@ # ============================================================================== """LIT backend, as a standard WSGI app.""" +import collections from collections.abc import Callable, Iterable, Mapping, Sequence import functools import glob @@ -1021,7 +1022,7 @@ def __init__( def get_dataset_specs(self) -> dict[str, dict[str, str]]: datasets_with_spec = collections.defaultdict(dict) for name, ds in self._datasets.items(): - for field_name, lit_data_class in ds.spec().items(): + for field_name, lit_data_class in ds.spec().items(): datasets_with_spec[name][field_name] = type(lit_data_class).__name__ return datasets_with_spec From ba4d975a90612b0c41a02b3dcb4dbb548261fdd7 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Mon, 16 Sep 2024 10:21:22 -0700 Subject: [PATCH 30/86] LIT: Load multiple model wrappers with shared model via the /create_model API PiperOrigin-RevId: 675199376 --- lit_nlp/api/model.py | 5 +- lit_nlp/api/types.py | 19 +-- lit_nlp/app.py | 116 ++++++++++++++---- lit_nlp/client/core/global_settings.ts | 11 +- lit_nlp/client/services/api_service.ts | 2 +- .../examples/prompt_debugging/keras_lms.py | 16 ++- lit_nlp/examples/prompt_debugging/models.py | 82 +++++++++++-- lit_nlp/examples/prompt_debugging/server.py | 11 +- .../prompt_debugging/transformers_lms.py | 20 +-- 9 files changed, 217 insertions(+), 65 deletions(-) diff --git a/lit_nlp/api/model.py b/lit_nlp/api/model.py index 49a68649..79558fce 100644 --- a/lit_nlp/api/model.py +++ b/lit_nlp/api/model.py @@ -14,7 +14,7 @@ # ============================================================================== """Base classes for LIT models.""" import abc -from collections.abc import Iterable, Iterator +from collections.abc import Iterable, Iterator, Mapping import inspect import itertools import multiprocessing.pool # for ThreadPool @@ -203,6 +203,9 @@ def predict(self, inputs: Iterable[JsonDict], **kw) -> Iterable[JsonDict]: pass +ModelMap = Mapping[str, Model] + + class ModelWrapper(Model): """Wrapper for a LIT model. diff --git a/lit_nlp/api/types.py b/lit_nlp/api/types.py index 2ba9d274..3e7dbfd7 100644 --- a/lit_nlp/api/types.py +++ b/lit_nlp/api/types.py @@ -1047,9 +1047,12 @@ def is_param_optional(parameter: inspect.Parameter) -> bool: # Otherwise, attempt to infer a type from the Paramater object. if param.annotation is param.empty and param.default is param.empty: - raise TypeError(f"Unable to infer a type for parameter '{param.name}' " - f"of '{func.__name__}'. Please add a type hint or " - "default value, or implement a Spec literal.") + fn_name = getattr(func, "__name__", repr(func)) + raise TypeError( + f"Unable to infer a type for parameter '{param.name}' of '{fn_name}'." + " Please add a type hint or default value, or implement a Spec" + " literal." + ) if param.annotation is param.empty: param_type = type(param.default) @@ -1065,9 +1068,11 @@ def is_param_optional(parameter: inspect.Parameter) -> bool: lit_type_params["default"] = param.default spec[param.name] = lit_type_cstr(**lit_type_params) else: - raise TypeError(f"Unsupported type '{param_type}' for parameter " - f"'{param.name}' of '{func.__name__}'. If possible " - "(e.g., this parameter is Optional), please implement a " - "spec literal instead of using inferencing.") + fn_name = getattr(func, "__name__", repr(func)) + raise TypeError( + f"Unsupported type '{param_type}' for parameter '{param.name}' of" + f" '{fn_name}'. If possible (e.g., this parameter is Optional)," + " please implement a spec literal instead of using inferencing." + ) return spec diff --git a/lit_nlp/app.py b/lit_nlp/app.py index fb2296fa..94343fe9 100644 --- a/lit_nlp/app.py +++ b/lit_nlp/app.py @@ -17,13 +17,11 @@ import collections from collections.abc import Callable, Iterable, Mapping, Sequence import functools -import glob import math import os import random import threading -import time -from typing import Any, Optional, TypedDict, Union +from typing import Any, Optional, TypedDict, Union, cast, get_type_hints from absl import logging from lit_nlp.api import components as lit_components @@ -53,7 +51,12 @@ DatasetLoader = tuple[Callable[..., lit_dataset.Dataset], Optional[types.Spec]] DatasetLoadersMap = dict[str, DatasetLoader] -ModelLoader = tuple[Callable[..., lit_model.Model], Optional[types.Spec]] +SingleModelLoader = Callable[..., lit_model.Model] +MultipleModelLoader = Callable[..., lit_model.ModelMap] +ModelLoader = tuple[ + Union[SingleModelLoader, MultipleModelLoader], + Optional[types.Spec], +] ModelLoadersMap = dict[str, ModelLoader] _EMPTY_DATASET_KEY = '_union_empty' @@ -435,10 +438,11 @@ def _create_dataset( dataset_cls, dataset_init_spec = loader_info if dataset_init_spec is not None: + initializer_name = getattr(dataset_cls, '__name__', repr(dataset_cls)) utils.validate_config_against_spec( config, dataset_init_spec, - f'{dataset_name} ({dataset_cls.__name__})', + f'{dataset_name} ({initializer_name})', raise_for_unsupported=True, ) @@ -450,49 +454,107 @@ def _create_dataset( self._info = self._build_metadata() return (self._info, new_name) - def _create_model(self, - data: types.JsonDict, - model_name: Optional[str] = None, - **unused_kw): - """Create a model, updating and returning the metadata.""" + def _create_model( + self, data: types.JsonDict, model_name: Optional[str] = None, **unused_kw + ): + """Create a model, updating and returning the metadata. + + LIT supports two types of model loaders: + + * Single-model loaders that return an instance of `lit_model.Model`; and + * Multiple-model loaders that return a `Mapping[str, lit_model.Model]`. + + Multiple-model loaders are primarily used for LLM use cases, such as the + Prompt Debugging example, where LIT needs to access the generation, + tokenization, and salience computation features of a model separately, and + thus initializes one lit_model.Model wrapper for each of these purposes. + Note that the `Callable` associated with a given Multiple-model + `ModelLoader` must take `new_name` parameter as it is assumed that this + `Callable` will initialize multiple LIT Model wrappers for different + functions performed by a shared model, such as the generate, tokenize, and + salience functions of an LLM for prompt debugging use cases. + + Single-model loaders are used in most other use cases, such as + classification and regression tasks where the prediction is more stable. + + Args: + data: the JSON payload provided in the request. + model_name: the model intializer to use, a key of LitApp._model_loaders. + + Returns: + A tuple containing the updated LitApp metadata and the name of the models + that were added. + + Raises: + ValueError: If any of the following are missing: model_name, the config, + or a value for new_name in the config; if there is not a model loader + configured for the provided model_name; or if there is a name collision + with one of the models returned by a multiple-model loader. + """ if model_name is None: raise ValueError('No base model specified.') + if (loader_info := self._model_loaders.get(model_name)) is None: + raise ValueError( + f'No loader information (Cls + init_spec) found for {model_name}' + ) + config: Optional[dict[str, Any]] = data.get('config') if config is None: raise ValueError('No config specified.') new_name: Optional[str] = config.pop('new_name', None) - if new_name is None: + if not new_name: raise ValueError('No name provided for the new model.') - elif new_name in self._models: - return (self._info, new_name) # Return the existing model - - if (loader_info := self._model_loaders.get(model_name)) is None: - raise ValueError( - f'No loader information (Cls + init_spec) found for {model_name}' - ) - model_cls, model_init_spec = loader_info + model_initializer, model_init_spec = loader_info if model_init_spec is not None: + initializer_name = getattr( + model_initializer, '__name__', repr(model_initializer) + ) utils.validate_config_against_spec( config, model_init_spec, - f'{model_name} ({model_cls.__name__})', + f'{model_name} ({initializer_name})', raise_for_unsupported=True, ) - new_model = model_cls(**config) - self._models[new_name] = caching.CachingModelWrapper( - new_model, new_name, **self._caching_model_wrapper_kw - ) + return_type = get_type_hints(model_initializer)['return'] + + if Mapping in return_type.__mro__: + model_initializer = cast(MultipleModelLoader, model_initializer) + new_models = model_initializer(new_name=new_name, **config) + new_model_names: list[str] = list(new_models.keys()) + model_name_collisions = [ + model_name + for model_name in new_model_names + if model_name in self._models + ] + if model_name_collisions: + raise ValueError(f'Model(s) already exist: {model_name_collisions}.') + + for model_name, model_instance in new_models.items(): + self._models[model_name] = caching.CachingModelWrapper( + model_instance, model_name, **self._caching_model_wrapper_kw + ) + else: + if new_name in self._models: + return (self._info, new_name) # Return the existing model + + new_model_names: list[str] = [new_name] + model_initializer = cast(SingleModelLoader, model_initializer) + new_model = model_initializer(**config) + self._models[new_name] = caching.CachingModelWrapper( + new_model, new_name, **self._caching_model_wrapper_kw + ) + empty_dataset = lit_dataset.NoneDataset(self._models) self._datasets[_EMPTY_DATASET_KEY] = lit_dataset.IndexedDataset( base=self._run_annotators(empty_dataset), id_fn=caching.input_hash ) self._info = self._build_metadata() - return (self._info, new_name) + return (self._info, new_model_names) def _get_generated( self, @@ -824,7 +886,7 @@ def _handler(app: wsgi_app.App, request, environ): if ( data and 'inputs' in data.keys() - and len(data.get('inputs')) + and data.get('inputs') and 'dataset_name' in kw ): data['inputs'] = self._reconstitute_inputs( @@ -849,7 +911,7 @@ def _handler(app: wsgi_app.App, request, environ): def __init__( self, - models: Mapping[str, lit_model.Model], + models: lit_model.ModelMap, datasets: Mapping[str, lit_dataset.Dataset], generators: Optional[Mapping[str, lit_components.Generator]] = None, interpreters: Optional[Mapping[str, lit_components.Interpreter]] = None, diff --git a/lit_nlp/client/core/global_settings.ts b/lit_nlp/client/core/global_settings.ts index 9837d0c5..a64c14b3 100644 --- a/lit_nlp/client/core/global_settings.ts +++ b/lit_nlp/client/core/global_settings.ts @@ -483,14 +483,19 @@ export class GlobalSettingsComponent extends MobxLitElement { if (newInfo == null) {return;} - const [metadata, modelName] = newInfo; + + // TODO(b/270268760): Adding a model via the UI is profoundly slow for + // LLM use cases due to adding modelNames to the AppState.currentModels, + // which fetches preds for the entire dataset at once. Doing this on + // demand would dramatically improve performance and allow adding + // modelNames to the AppState.currentModels + const [metadata, /* modelNames */] = newInfo; if (loaderSpec != null) { this.loadingCallConfig = initializeCallConfig(loaderSpec); } this.appState.metadata = metadata; - this.appState.currentModels.push(modelName); + this.resetLoadingCallConfig(); this.initializeLocalState(); - // this.status = 'New model initialized and added auccessfully.'; }; const hideLoadingControls = this.appState.metadata.demoMode || diff --git a/lit_nlp/client/services/api_service.ts b/lit_nlp/client/services/api_service.ts index 477c7b23..28568b96 100644 --- a/lit_nlp/client/services/api_service.ts +++ b/lit_nlp/client/services/api_service.ts @@ -96,7 +96,7 @@ export class ApiService extends LitService { * with and satisfy the requirements of the `Model.init_spec()`. */ async createModel(model: string, config: CallConfig): - Promise<[LitMetadata, string]> { + Promise<[LitMetadata, string[]]> { const loadMessage = 'Loading new model'; return this.queryServer( '/create_model', diff --git a/lit_nlp/examples/prompt_debugging/keras_lms.py b/lit_nlp/examples/prompt_debugging/keras_lms.py index 9c546eb5..4cf88c79 100644 --- a/lit_nlp/examples/prompt_debugging/keras_lms.py +++ b/lit_nlp/examples/prompt_debugging/keras_lms.py @@ -514,17 +514,15 @@ def output_spec(self) -> lit_types.Spec: def initialize_model_group_for_salience( - name: str, *args, **kw -) -> dict[str, lit_model.Model]: + new_name: str, **kw +) -> lit_model.ModelMap: """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'.""" - salience_name, tokenizer_name = pd_utils.generate_model_group_names(name) - generation_model = KerasGenerationModel(*args, **kw) - salience_model = KerasSalienceModel(model=generation_model.model, *args, **kw) - tokenizer_model = KerasTokenizerModel( - model=generation_model.model, *args, **kw - ) + salience_name, tokenizer_name = pd_utils.generate_model_group_names(new_name) + generation_model = KerasGenerationModel(**kw) + salience_model = KerasSalienceModel(model=generation_model.model, **kw) + tokenizer_model = KerasTokenizerModel(model=generation_model.model, **kw) return { - name: generation_model, + new_name: generation_model, salience_name: salience_model, tokenizer_name: tokenizer_model, } diff --git a/lit_nlp/examples/prompt_debugging/models.py b/lit_nlp/examples/prompt_debugging/models.py index 681b643d..7c046f0c 100644 --- a/lit_nlp/examples/prompt_debugging/models.py +++ b/lit_nlp/examples/prompt_debugging/models.py @@ -1,11 +1,13 @@ """Methods for configuring models for prompt debugging.""" -from collections.abc import Mapping, Sequence +from collections.abc import Sequence import os from typing import Optional from absl import logging +from lit_nlp import app as lit_app from lit_nlp.api import model as lit_model +from lit_nlp.api import types as lit_types from lit_nlp.lib import file_cache @@ -48,8 +50,8 @@ def get_models( dl_runtime: str = DEFAULT_DL_RUNTIME, precision: str = DEFAULT_PRECISION, batch_size: int = DEFAULT_BATCH_SIZE, - sequence_length: int = DEFAULT_SEQUENCE_LENGTH, -) -> Mapping[str, lit_model.Model]: + max_length: int = DEFAULT_SEQUENCE_LENGTH, +) -> lit_model.ModelMap: """Loads models from the given configs. Args: @@ -64,7 +66,7 @@ def get_models( precision: Floating point precision for the models, either `bfloat16` or `float32`. batch_size: The number of examples to process per batch. - sequence_length: The maximum sequence length of the input. + max_length: The maximum sequence length of the input. Returns: A mapping from model name to initialized LIT model. @@ -95,7 +97,7 @@ def get_models( models |= keras_lms.initialize_model_group_for_salience( model_name, model_name_or_path=path, - max_length=sequence_length, + max_length=max_length, batch_size=batch_size, ) else: @@ -103,10 +105,76 @@ def get_models( models |= transformers_lms.initialize_model_group_for_salience( model_name, - path, + model_name_or_path=path, batch_size=batch_size, framework=dl_runtime, - max_length=sequence_length, + max_length=max_length, ) return models + + +def get_model_loaders( + dl_framework: str = DEFAULT_DL_FRAMEWORK, + dl_runtime: str = DEFAULT_DL_RUNTIME, + batch_size: int = DEFAULT_BATCH_SIZE, + max_length: int = DEFAULT_SEQUENCE_LENGTH, +) -> lit_app.ModelLoadersMap: + """Get the model loader for the configured framework and runtime. + + Args: + dl_framework: The deep learning framework that loads and runs the model on + the runtime, all models are loaded with the same framework, + `model_name_or_path` incompatibilities will result in errors. + dl_runtime: The deep learning runtime that the model runs on, either + "tensorflow" or "torch". All loaded models will use the same runtime, + incompatibilities will result in errors. + batch_size: The default batch size. + max_length: The default maximum sequence length. + + Returns: + A mapping from model name to initialized LIT model. + """ + + common_init_spec: lit_types.Spec = { + "model_name_or_path": lit_types.String(), + "batch_size": lit_types.Integer( + default=batch_size, min_val=1, max_val=64, required=False + ), + "max_length": lit_types.Integer( + default=max_length, min_val=1, max_val=2048, required=False + ), + } + + if dl_framework == "kerasnlp": + from lit_nlp.examples.prompt_debugging import keras_lms # pylint: disable=g-import-not-at-top # pytype: disable=import-error + + keras_init_spec: lit_types.Spec = { + **common_init_spec, + "dynamic_sequence_length": lit_types.Boolean( + default=True, required=False + ), + } + + return { + "Keras LLM": ( + keras_lms.initialize_model_group_for_salience, + keras_init_spec, + ) + } + else: + from lit_nlp.examples.prompt_debugging import transformers_lms # pylint: disable=g-import-not-at-top # pytype: disable=import-error + + transformers_init_spec: lit_types.Spec = { + **common_init_spec, + "framework": lit_types.CategoryLabel( + vocab=transformers_lms.SUPPORTED_ML_RUNTIMES, default=dl_runtime + ), + } + + return { + "Transformers LLM": ( + transformers_lms.initialize_model_group_for_salience, + transformers_init_spec, + ) + } diff --git a/lit_nlp/examples/prompt_debugging/server.py b/lit_nlp/examples/prompt_debugging/server.py index e3293fdb..90104708 100644 --- a/lit_nlp/examples/prompt_debugging/server.py +++ b/lit_nlp/examples/prompt_debugging/server.py @@ -79,10 +79,11 @@ "The number of examples to process per batch.", ) +_SUPPORTED_FRAMEWORKS = ("kerasnlp", "transformers") _DL_FRAMEWORK = flags.DEFINE_enum( "dl_framework", models.DEFAULT_DL_FRAMEWORK, - ("kerasnlp", "transformers"), + _SUPPORTED_FRAMEWORKS, "The deep learning framework that loads and runs the model on the backend." " This server will attempt to load all models specified by the --models" " flag with the configured framework, incompatibilities will result in" @@ -164,12 +165,18 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: dl_runtime=_DL_RUNTIME.value, precision=_PRECISION.value, batch_size=_BATCH_SIZE.value, - sequence_length=_SEQUENCE_LENGTH.value, + max_length=_SEQUENCE_LENGTH.value, ), datasets=datasets.get_datasets( datasets_config=_DATASETS.value, max_examples=_MAX_EXAMPLES.value ), layouts=layouts.PROMPT_DEBUGGING_LAYOUTS, + model_loaders=models.get_model_loaders( + dl_framework=_DL_FRAMEWORK.value, + dl_runtime=_DL_RUNTIME.value, + batch_size=_BATCH_SIZE.value, + max_length=_SEQUENCE_LENGTH.value, + ), dataset_loaders=datasets.get_dataset_loaders(), onboard_start_doc=_SPLASH_SCREEN_DOC, **server_flags.get_flags(), diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index 8cdf603a..95446b27 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -51,11 +51,13 @@ class MLFramework(enum.Enum): TF = _TENSORFLOW +SUPPORTED_ML_RUNTIMES = [framework.value for framework in MLFramework] + + class HFBaseModel(lit_model.BatchedModel): """Base class for HF generative, salience, tokenizer model wrappers.""" # Enum str values for entries in MLFramework, used for init_spec and logging. - _ML_FRAMEWORK_VALUES = [framework.value for framework in MLFramework] @property def num_layers(self): @@ -66,7 +68,7 @@ def init_spec(cls) -> lit_model.Spec: return { "model_name_or_path": lit_types.String(default="gpt2"), "batch_size": lit_types.Integer(default=6, min_val=1, max_val=64), - "framework": lit_types.CategoryLabel(vocab=cls._ML_FRAMEWORK_VALUES), + "framework": lit_types.CategoryLabel(vocab=SUPPORTED_ML_RUNTIMES), } def __init__( @@ -76,6 +78,7 @@ def __init__( framework=_PYTORCH, model=None, tokenizer=None, + **unused_kw, ): """Constructor for HF base model wrappers. @@ -139,7 +142,7 @@ def __init__( else: raise ValueError( f"The provided value `{framework}` for arg `framework` is not" - f" supported, please choose from {self._ML_FRAMEWORK_VALUES}." + f" supported, please choose from {SUPPORTED_ML_RUNTIMES}." ) self.model = auto_model.from_pretrained( model_name_or_path, @@ -595,15 +598,16 @@ def output_spec(self) -> lit_types.Spec: def initialize_model_group_for_salience( - name: str, *args, max_length: int = 512, **kw -) -> dict[str, lit_model.Model]: + new_name: str, **kw +) -> lit_model.ModelMap: """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'.""" - salience_name, tokenizer_name = pd_utils.generate_model_group_names(name) - generation_model = HFGenerativeModel(*args, **kw, max_length=max_length) + max_length = kw.pop("max_length", 512) + salience_name, tokenizer_name = pd_utils.generate_model_group_names(new_name) + generation_model = HFGenerativeModel(max_length=max_length, **kw) salience_model = HFSalienceModel.from_loaded(generation_model) tokenizer_model = HFTokenizerModel.from_loaded(generation_model) return { - name: generation_model, + new_name: generation_model, salience_name: salience_model, tokenizer_name: tokenizer_model, } From 80d3a17271f2aa70ff6584542a8b38188fff9eb8 Mon Sep 17 00:00:00 2001 From: Bin Du Date: Wed, 18 Sep 2024 10:55:40 -0700 Subject: [PATCH 31/86] Move the file caching logic to within the model `__init__` function. This allows the model loaded from the UI to also be cached. PiperOrigin-RevId: 676052839 --- lit_nlp/examples/prompt_debugging/keras_lms.py | 8 ++++++++ lit_nlp/examples/prompt_debugging/models.py | 7 ------- lit_nlp/examples/prompt_debugging/transformers_lms.py | 7 +++++-- 3 files changed, 13 insertions(+), 9 deletions(-) diff --git a/lit_nlp/examples/prompt_debugging/keras_lms.py b/lit_nlp/examples/prompt_debugging/keras_lms.py index 4cf88c79..84755583 100644 --- a/lit_nlp/examples/prompt_debugging/keras_lms.py +++ b/lit_nlp/examples/prompt_debugging/keras_lms.py @@ -12,6 +12,7 @@ from lit_nlp.api import types as lit_types from lit_nlp.examples.prompt_debugging import constants as pd_constants from lit_nlp.examples.prompt_debugging import utils as pd_utils +from lit_nlp.lib import file_cache from lit_nlp.lib import utils as lit_utils @@ -74,6 +75,13 @@ def __init__( if model is not None: self.model = model elif model_name_or_path is not None: + if ( + is_tar_gz := model_name_or_path.endswith(".tar.gz") + ) or file_cache.is_remote(model_name_or_path): + model_name_or_path = file_cache.cached_path( + model_name_or_path, + extract_compressed_file=is_tar_gz, + ) self.model = keras_models.CausalLM.from_preset(model_name_or_path) else: raise ValueError("Must provide either model or model_name_or_path.") diff --git a/lit_nlp/examples/prompt_debugging/models.py b/lit_nlp/examples/prompt_debugging/models.py index 7c046f0c..ba733fe1 100644 --- a/lit_nlp/examples/prompt_debugging/models.py +++ b/lit_nlp/examples/prompt_debugging/models.py @@ -8,7 +8,6 @@ from lit_nlp import app as lit_app from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types -from lit_nlp.lib import file_cache DEFAULT_BATCH_SIZE = 1 @@ -85,12 +84,6 @@ def get_models( model_name, path = model_string.split(":", 1) logging.info("Loading model '%s' from '%s'", model_name, path) - if path.endswith(".tar.gz") or file_cache.is_remote(path): - path = file_cache.cached_path( - path, - extract_compressed_file=path.endswith(".tar.gz"), - ) - if dl_framework == "kerasnlp": from lit_nlp.examples.prompt_debugging import keras_lms # pylint: disable=g-import-not-at-top # pytype: disable=import-error diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index 95446b27..594ca9f1 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -116,9 +116,12 @@ def __init__( else: # Normally path is a directory; if it's an archive file, download and # extract to the transformers cache. - if model_name_or_path.endswith(".tar.gz"): + if ( + is_tar_gz := model_name_or_path.endswith(".tar.gz") + ) or file_cache.is_remote(model_name_or_path): model_name_or_path = file_cache.cached_path( - model_name_or_path, extract_compressed_file=True + model_name_or_path, + extract_compressed_file=is_tar_gz, ) # Note: we need to left-pad for generation to work properly. From 66b7c8a36de45f43eb2cd0f9c54bddf2ace742fb Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 18 Sep 2024 11:22:57 -0700 Subject: [PATCH 32/86] LIT: Update layout for LIT vertexai demo. PiperOrigin-RevId: 676063586 --- lit_nlp/examples/vertexai/demo.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/lit_nlp/examples/vertexai/demo.py b/lit_nlp/examples/vertexai/demo.py index f40ec5f8..350603b5 100644 --- a/lit_nlp/examples/vertexai/demo.py +++ b/lit_nlp/examples/vertexai/demo.py @@ -53,6 +53,7 @@ from lit_nlp import app as lit_app from lit_nlp import dev_server from lit_nlp import server_flags +from lit_nlp.components import word_replacer from lit_nlp.examples.prompt_debugging import datasets as prompt_debugging_datasets from lit_nlp.examples.vertexai import models as vertexai_models @@ -186,10 +187,12 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: ) dataset_loaders = prompt_debugging_datasets.get_dataset_loaders() - # TODO(faneycourage): Design and add a layout for generative demos. + generators = {'word_replacer': word_replacer.WordReplacer()} + lit_demo = dev_server.Server( models=models, model_loaders=model_loaders, + generators=generators, datasets=datasets, dataset_loaders=dataset_loaders, **server_flags.get_flags() From b87d3d49e89a885a50753c9d30ec08192b0288aa Mon Sep 17 00:00:00 2001 From: Bin Du Date: Wed, 18 Sep 2024 12:53:11 -0700 Subject: [PATCH 33/86] Make transformer lms HFGenerativeModel output generated texts. PiperOrigin-RevId: 676097135 --- lit_nlp/examples/prompt_debugging/transformers_lms.py | 8 ++++---- .../prompt_debugging/transformers_lms_int_test.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index 594ca9f1..f493926c 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -230,11 +230,11 @@ def _postprocess(self, preds: Mapping[str, Any]) -> Mapping[str, Any]: a dict of the processed model outputs, including the response texts and embeddings of the input and output tokens (separated into two arrays). """ - # TODO(b/324957491): return actual decoder scores for each generation. - # GeneratedTextCandidates should be a list[(text, score)] + # TODO(b/324957491): return actual decoder scores for each generation. For + # now, we only output GeneratedText. processed_preds = {} - processed_preds[pd_constants.FieldNames.RESPONSE] = [ - (preds[pd_constants.FieldNames.RESPONSE], 1.0) + processed_preds[pd_constants.FieldNames.RESPONSE] = preds[ + pd_constants.FieldNames.RESPONSE ] ntok_in = preds["ntok_in"] ntok_out = preds["ntok_out"] diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py b/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py index 6022cfa9..9c7cde6f 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms_int_test.py @@ -77,7 +77,7 @@ def test_gpt2_generation_output(self, framework, model_path): text=cur_input["prompt"], model=model, framework=framework ) expected_output_embeddings = _get_text_mean_embeddings( - text=cur_output["response"][0][0], model=model, framework=framework + text=cur_output["response"], model=model, framework=framework ) np.testing.assert_array_almost_equal( expected_input_embeddings, From 338c6b12de98b61287a25650ad2c6ad7f7bb80cd Mon Sep 17 00:00:00 2001 From: Bin Du Date: Wed, 18 Sep 2024 13:08:59 -0700 Subject: [PATCH 34/86] Add the custom model and dataset loaders to prompt debugging notebook. PiperOrigin-RevId: 676102850 --- lit_nlp/examples/prompt_debugging/notebook.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/lit_nlp/examples/prompt_debugging/notebook.py b/lit_nlp/examples/prompt_debugging/notebook.py index a7812fc2..df4d5bea 100644 --- a/lit_nlp/examples/prompt_debugging/notebook.py +++ b/lit_nlp/examples/prompt_debugging/notebook.py @@ -59,5 +59,12 @@ def make_notebook_widget( ), layouts=layouts.PROMPT_DEBUGGING_LAYOUTS, default_layout=layouts.LEFT_RIGHT, + model_loaders=models.get_model_loaders( + dl_framework=dl_framework, + dl_runtime=dl_runtime, + batch_size=batch_size, + max_length=models.DEFAULT_SEQUENCE_LENGTH, + ), + dataset_loaders=datasets.get_dataset_loaders(), **kwargs, ) From 3dc61a2e8a5e776fd09f24e4cdf7de345eaeb0de Mon Sep 17 00:00:00 2001 From: Bin Du Date: Fri, 20 Sep 2024 13:41:07 -0700 Subject: [PATCH 35/86] Add instructions on how to debug LIT UI in Colab. PiperOrigin-RevId: 676955724 --- website/sphinx_src/faq.md | 53 ++++++++++++++++++ .../images/lit-colab-server-address.png | Bin 0 -> 28883 bytes .../images/lit-ui-error-in-colab.png | Bin 0 -> 124899 bytes 3 files changed, 53 insertions(+) create mode 100644 website/sphinx_src/images/lit-colab-server-address.png create mode 100644 website/sphinx_src/images/lit-ui-error-in-colab.png diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md index aa8bd03d..8e334b24 100644 --- a/website/sphinx_src/faq.md +++ b/website/sphinx_src/faq.md @@ -171,3 +171,56 @@ this, using LIT's `Dataset` objects to manage training data along with standard training APIs (such as Keras' `model.fit()`). See [`glue/models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) for examples. + +### Debug LIT UI in Colab + +The LIT instance launched from CLI typically has helpful error messages in the +UI. However, this is not the case for the LIT UI in Colab and the error message +does not report any stacktrace, which makes debugging very difficult. + +![LIT UI error in colab](./images/lit-ui-error-in-colab.png "LIT UI error in colab") + +While in +[Chrome developer tools](https://support.google.com/campaignmanager/answer/2828688?hl=en), +you will be able to debug issues solely related to the frontend, but not so for +issues related to the backend or on the HTTP request path. + +Thus, to show the full stacktrace, you would need to find the HTTP request sent +from the frontend to the backend, compose the same request in colab and send it +to the server. + +1. When rendering the UI, display it in a separate tab to make things a bit + easier to work with, e.g. `lit_widget.render(open_in_new_tab=True)`. +2. Open + [Chrome developer tools](https://support.google.com/campaignmanager/answer/2828688?hl=en), + go to "Sources" tab and find the file + [client/services/api_service.ts](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/services/api_service.ts) and set a + breakpoint right after where the HTTP request is set up in the `queryServer` + method, e.g. after this line `const res = await fetch(url, {method: 'POST', + body});`. + * Note it is possible that the whole frontend source code is compiled into + a `main.js` file, and the code is not exactly the same as that in LIT + frontend source code. You might have to do a bit digging to find the + right line. +3. Go to the UI and trigger the behavior that causes the error. Now in Chrome + developer tools you will be able to see the variables and their values in + the `queryServer` method. Copy the values of the `url` and `body` variables + in the method. +4. Go back to Colab, compose your HTTP request method. Look for the main server + address printed out from `lit_widget.render(open_in_new_tab=True)`. + +![LIT colab server address](./images/lit-colab-server-address.png "LIT colab server address") + +Let's say the server address is "https://localhost:32943/?" as shown above, the +`body` variable obtained earlier has value "request_body_text" and the `url` +variable has value "./get_preds?param1=value1". Then your HTTP request will be +like this: + +```sh +! curl -H "Content-Type: application/json" \ + -d "request_body_text" \ + -X POST "http://localhost:32943/get_preds?param1=value1" +``` + +Run this in Colab and you should be able to retrieve the full stacktrace of the +error. diff --git a/website/sphinx_src/images/lit-colab-server-address.png b/website/sphinx_src/images/lit-colab-server-address.png new file mode 100644 index 0000000000000000000000000000000000000000..35b5643572b7066313e2b5939253aa15fa9fc7ad GIT binary patch literal 28883 zcmdqJ^l3z2;hTuDRwIV<+T`f)o}8DaNBmkFcaaODI2jgaStXPC$Ex z{G8XH=0koxc2JfQdsH?;zW3qpWOA5~rT4pN`Hs!87Tteoq8#*Pw2Yra}e#)mF= z=xcOCAp82WOEs{CSqPJ}3moee>f^oUtC#3+bLew;PFGyMvh6G{CVN=dQ&W3ZMUDz@ zroG?cao8Rb&?kv(_j^7o8v5$h6ERUVw8zx1pZb6M_kk~`|FcR2`&^0{&|2?{i|J{w3%C?db1k@@6C36Ut7KWn%tM7yiER z4Ja4;fA;RbPPLIpiuxarOzS8E{(H~=x{aFI>wg>d=hO(plQ*Ezg6*FJ7=K;)4N|9`jE(ik`iFEmrGRTPzs+_f3C|PR2a$D{81^vO1;Q z-v<9R;$wp7!BJLea3)3Cr+<(Azx!oc`&6Qj8=4pU9PhvN@L$b;P()ghY|=QOg8uKT 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zKTk21i(Ky+?tiDQ1bkEiVO032O(B+vS?rQ68ox$PPc#whtvEvaUrgh`&O|jrNAP4T zgwVyY*ov;V6Ii@d`L_Slt68NIG4U)#;coiLr890PpDGEyP?Znkb$zp`MV7WYc(~+R zDQp$lm$1@M)u`1db#@$cgKrtuGkH4Ls)#Aw**OUZ_*`xx*mUO`GN&X&`pO9QM2oy% z{L9?*BDSVMG^=1?igj6CM6yc!5mU0n7<1y-cH5uOrb)NA_P7U3_dieBJEGuZT`f3Q zO)P<0a~}n+H{1B)@`;PhqreQkzOdD=%yVCQkVqJ#?(S92C=i`P$iNSwt zjs^7qGPHv0F96)f#$a%th--VKS{zhEx_uGpvGefG{a0c~giOZuN!0r;njYt8$nV3ifh-+1p&R$G-EtE0!ZY z_rasMrM?TYyJ?nm`RU%CU;nSNc#l;7SBd9m8;hw4$&*B-RSGUs(|>^a^L1RgpZ1Gw zk9KJ_$vh4^#@rPM|`KtyErGQ_Mk;dYW1aAkZMzik0k3Im$vhN2-Z_DxMydROPC{Xuq~v)<-n z>ReNrKc>%xPOIpt0ty zR?shT4;jN1eqH5bhl|f;DMgZ^(R2-JSa9GU#VyL1X6X1hkh%&erHRW+{vN=&m2{S! zf&KCyBdm<+FCyqs7r*Tvh5z3pBIBQ9L|!35D%X^g{wVeTy~o^EW29eteq7M+lv~XE QpWn+s*W_Nkwqwly0Sv4G7XSbN literal 0 HcmV?d00001 From c86dd92c6ec3e51be55bebfdb0903b092c5aed17 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Wed, 2 Oct 2024 08:48:41 -0700 Subject: [PATCH 36/86] LIT: Fix assertion error when generation stops short of max_length PiperOrigin-RevId: 681469809 --- .../examples/prompt_debugging/transformers_lms.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index f493926c..e6c66deb 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -279,12 +279,12 @@ def _get_batched_outputs( if self.framework == MLFramework.PT: encoded_inputs = encoded_inputs.to(self.device) - outputs = self.model.generate( - encoded_inputs["input_ids"], - attention_mask=encoded_inputs["attention_mask"], - max_length=self.max_length, - ) - ntok_out = self.max_length - encoded_inputs["input_ids"].shape[1] + outputs = self.model.generate(**encoded_inputs, max_length=self.max_length) + + if isinstance(outputs, transformers.utils.ModelOutput): + outputs = outputs.sequences + + ntok_out = outputs.shape[1] - encoded_inputs["input_ids"].shape[1] responses = self.tokenizer.batch_decode( outputs[:, -ntok_out:], skip_special_tokens=True From bff43f5b0e66f372507a55df1fe4a945b819fa86 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Wed, 2 Oct 2024 12:43:16 -0700 Subject: [PATCH 37/86] LIT: Isolate DL runtime-specific generation logic for Transformers PiperOrigin-RevId: 681555357 --- .../prompt_debugging/transformers_lms.py | 47 ++++++------------- 1 file changed, 14 insertions(+), 33 deletions(-) diff --git a/lit_nlp/examples/prompt_debugging/transformers_lms.py b/lit_nlp/examples/prompt_debugging/transformers_lms.py index e6c66deb..7a226691 100644 --- a/lit_nlp/examples/prompt_debugging/transformers_lms.py +++ b/lit_nlp/examples/prompt_debugging/transformers_lms.py @@ -266,16 +266,17 @@ def _get_batched_outputs( data in numpy arrays (could come from torch or tensorflow, depending on the transformer backend). """ - prompts = [ex["prompt"] for ex in inputs] encoded_inputs = self.tokenizer( - prompts, - return_tensors=_HF_PYTORCH - if self.framework == MLFramework.PT - else _HF_TENSORFLOW, + [ex["prompt"] for ex in inputs], + return_tensors=( + _HF_PYTORCH if self.framework == MLFramework.PT else _HF_TENSORFLOW + ), add_special_tokens=True, padding="longest", truncation="longest_first", ) + batch_size, ntok_in = encoded_inputs["input_ids"].shape + if self.framework == MLFramework.PT: encoded_inputs = encoded_inputs.to(self.device) @@ -284,7 +285,7 @@ def _get_batched_outputs( if isinstance(outputs, transformers.utils.ModelOutput): outputs = outputs.sequences - ntok_out = outputs.shape[1] - encoded_inputs["input_ids"].shape[1] + ntok_out = outputs.shape[1] - ntok_in responses = self.tokenizer.batch_decode( outputs[:, -ntok_out:], skip_special_tokens=True @@ -293,36 +294,16 @@ def _get_batched_outputs( if self.framework == MLFramework.PT: with torch.no_grad(): # Input embeddings: [batch_size, num_tokens, emb_dim] - embeddings = self.embedding_table(outputs) - - batched_outputs = { - "embs": embeddings.cpu().to(torch.float), - "ntok_in": ( - torch.sum(encoded_inputs["attention_mask"], axis=1) - .cpu() - .to(torch.int) - ), - "ntok_out": torch.full( - (encoded_inputs["input_ids"].shape[0],), ntok_out - ), - } + embeddings = self.embedding_table(outputs).cpu().to(torch.float) else: embeddings = self.embedding_table(outputs) - batched_outputs = { - "embs": embeddings, - "ntok_in": tf.reduce_sum(encoded_inputs["attention_mask"], axis=1), - "ntok_out": tf.fill( - [ - encoded_inputs["input_ids"].shape[0], - ], - ntok_out, - ), - } - # Convert to numpy for post-processing. - detached_outputs = {k: v.numpy() for k, v in batched_outputs.items()} - detached_outputs[pd_constants.FieldNames.RESPONSE] = responses - return detached_outputs + return { + "embs": embeddings.numpy(), + "ntok_in": np.array((batch_size, ntok_in)), + "ntok_out": np.full((batch_size,), ntok_out), + pd_constants.FieldNames.RESPONSE: responses, + } ## # LIT API implementations From bc641d78b245d021527e094e8a65caf29ab0b42c Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 2 Oct 2024 18:37:10 -0700 Subject: [PATCH 38/86] LIT:Fix type hints for model initializers. PiperOrigin-RevId: 681670616 --- lit_nlp/app.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/lit_nlp/app.py b/lit_nlp/app.py index 94343fe9..7d8afba5 100644 --- a/lit_nlp/app.py +++ b/lit_nlp/app.py @@ -17,6 +17,7 @@ import collections from collections.abc import Callable, Iterable, Mapping, Sequence import functools +import inspect import math import os import random @@ -520,7 +521,10 @@ def _create_model( raise_for_unsupported=True, ) - return_type = get_type_hints(model_initializer)['return'] + return_type = lit_model.Model + + if inspect.isfunction(model_initializer): + return_type = get_type_hints(model_initializer)['return'] if Mapping in return_type.__mro__: model_initializer = cast(MultipleModelLoader, model_initializer) From e2513e87f5ef95fa001e2efe5a5f63605bcd8521 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 3 Oct 2024 08:45:10 -0700 Subject: [PATCH 39/86] LIT:Update default DL runtime to torch. PiperOrigin-RevId: 681896534 --- lit_nlp/examples/prompt_debugging/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/prompt_debugging/models.py b/lit_nlp/examples/prompt_debugging/models.py index ba733fe1..ca9c797e 100644 --- a/lit_nlp/examples/prompt_debugging/models.py +++ b/lit_nlp/examples/prompt_debugging/models.py @@ -12,7 +12,7 @@ DEFAULT_BATCH_SIZE = 1 DEFAULT_DL_FRAMEWORK = "kerasnlp" -DEFAULT_DL_RUNTIME = "tensorflow" +DEFAULT_DL_RUNTIME = "torch" DEFAULT_MODELS = ["gemma_1.1_instruct_2b_en:gemma_1.1_instruct_2b_en"] DEFAULT_PRECISION = "bfloat16" DEFAULT_SEQUENCE_LENGTH = 512 From 6a018457b7f7d901ed3365d02ed2e0a77abd1d50 Mon Sep 17 00:00:00 2001 From: Bin Du Date: Thu, 10 Oct 2024 08:59:51 -0700 Subject: [PATCH 40/86] Internal change. PiperOrigin-RevId: 684462773 --- lit_nlp/dev_server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/dev_server.py b/lit_nlp/dev_server.py index 4cb8c386..6256f66b 100644 --- a/lit_nlp/dev_server.py +++ b/lit_nlp/dev_server.py @@ -14,7 +14,7 @@ # ============================================================================== """Development wrapper for LIT server.""" import inspect -from typing import Optional, Union +from typing import Any, Optional, Union from absl import logging from lit_nlp import app as lit_app From 4bf1f81666fe546357f00c86a2315d2852346ebe Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Mon, 12 Aug 2024 15:52:37 +0000 Subject: [PATCH 41/86] Converting hosted demos images to multi-stage builds --- .github/workflows/ci.yml | 1 + Dockerfile | 37 ++++++++++++++++++++++++++++--------- 2 files changed, 29 insertions(+), 9 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2422e3b5..74014c8e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -73,4 +73,5 @@ jobs: - name: Build Docker image uses: docker/build-push-action@v4 with: + target: lit-nlp-prod tags: lit-nlp:ci-${{ github.sha }} diff --git a/Dockerfile b/Dockerfile index 4dcf08ad..677e4eaa 100644 --- a/Dockerfile +++ b/Dockerfile @@ -14,22 +14,44 @@ # ============================================================================== # Use the official lightweight Python image. # https://hub.docker.com/_/python -FROM python:3.10-slim + +# ---- LIT Base Container ---- + +FROM python:3.11-slim AS lit-nlp-base # Update Ubuntu packages and install basic utils RUN apt-get update RUN apt-get install -y wget curl gnupg2 gcc g++ git +# Copy local code to the container image. +ENV APP_HOME /app +WORKDIR $APP_HOME + +COPY ./lit_nlp/examples/gunicorn_config.py ./ + + + +# ---- LIT Container for Hosted Demos ---- + +FROM lit-nlp-base AS lit-nlp-prod + +RUN python -m pip install 'lit-nlp[examples]' + +WORKDIR $APP_HOME +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] + + + +# ---- LIT Container for Developing and Testing Hosted Demos ---- + +FROM lit-nlp-base AS lit-nlp-dev + # Install yarn RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | apt-key add - RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ tee /etc/apt/sources.list.d/yarn.list RUN apt update && apt -y install yarn -# Copy local code to the container image. -ENV APP_HOME=/app -WORKDIR $APP_HOME - # Set up python environment with production dependencies # This step is slow as it installs many packages. COPY ./requirements*.txt ./ @@ -47,7 +69,4 @@ RUN yarn && yarn build && rm -rf node_modules/* # Note that the config file supports configuring the LIT demo that is launched # via the DEMO_NAME and DEMO_PORT environment variables. WORKDIR $APP_HOME -ENTRYPOINT [ \ - "gunicorn", \ - "--config=lit_nlp/examples/gunicorn_config.py" \ -] +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] From 1075325c6a08d8fdef3bcf66f193b8d5aef673fb Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 13 Aug 2024 19:52:25 +0000 Subject: [PATCH 42/86] Adding initial dockerfile for GCP use case --- lit_nlp/examples/gcp/Dockerfile | 111 ++++++++++++++++++++++++++ lit_nlp/examples/gcp/requirements.txt | 2 + 2 files changed, 113 insertions(+) create mode 100644 lit_nlp/examples/gcp/Dockerfile create mode 100644 lit_nlp/examples/gcp/requirements.txt diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile new file mode 100644 index 00000000..e269af14 --- /dev/null +++ b/lit_nlp/examples/gcp/Dockerfile @@ -0,0 +1,111 @@ +# Copyright 2023 Google LLC +# +# 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. +# ============================================================================== +# Use the official lightweight Python image. +# https://hub.docker.com/_/python + +FROM nvidia/cuda:12.5.1-base-ubuntu22.04 AS base +ENV DEBIAN_FRONTEND=noninteractive +ENV LANG C.UTF-8 + +ARG PYTHON_VERSION=python3.11 + +RUN apt-get update + +# Install the CUDA Keyring package +# See https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#network-repo-installation-for-ubuntu +RUN apt-get install -y curl gnupg ca-certificates +RUN curl https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb \ + -o cuda-keyring_1.1-1_all.deb +RUN dpkg -i cuda-keyring_1.1-1_all.deb + +# Install system and CUDA packages +RUN apt-get install -y --no-install-recommends \ + cuda-command-line-tools-12-3 \ + cuda-cudart-dev-12-3 \ + cuda-nvcc-12-3 \ + cuda-cupti-12-3 \ + cuda-nvprune-12-3 \ + cuda-libraries-12-3 \ + cuda-nvrtc-12-3 \ + libcufft-12-3 \ + libcurand-12-3 \ + libcusolver-12-3 \ + libcusparse-12-3 \ + libcublas-12-3 \ + libcudnn8=8.9.6.50-1+cuda12.2 \ + libnvinfer-plugin8=8.6.1.6-1+cuda12.0 \ + libnvinfer8=8.6.1.6-1+cuda12.0 \ + build-essential \ + pkg-config \ + software-properties-common \ + unzip + +# Install Python 3.11 +RUN apt-get install -y --no-install-recommends \ + $PYTHON_VERSION \ + $PYTHON_VERSION-venv \ + $PYTHON_VERSION-distutils \ + $PYTHON_VERSION-dev +RUN ln -sf /usr/bin/$PYTHON_VERSION /usr/bin/python3 +RUN ln -sf /usr/bin/$PYTHON_VERSION /usr/bin/python + +# Install pip +RUN curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py +RUN python3 get-pip.py +RUN python3 -m pip install --no-cache-dir --upgrade pip + +RUN apt-get clean +RUN rm -rf /var/lib/apt/lists/* + + + +# ---- LIT on GCP from source ---- + +FROM base AS lit-gcp-dev +ENV APP_HOME /app +WORKDIR $APP_HOME + +# Install Node.js v18 (the base image ships with Node.js v12) +# See https://github.com/nodesource/distributions +RUN curl -fsSL https://deb.nodesource.com/setup_18.x -o nodesource_setup.sh +RUN bash nodesource_setup.sh +RUN apt-get install -y nodejs + +# Install yarn +RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | apt-key add - +RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ + tee /etc/apt/sources.list.d/yarn.list +RUN apt update && apt -y install yarn + +# TODO(b/353980272): Replace the default config with the GCP-specific config +COPY ./lit_nlp/examples/gunicorn_config.py ./ + +# TODO(b/353980272): Replace this with a requirements file specific to the GCP +# exmaple, this should include the core lit-nlp package. +COPY ./requirements*.txt ./ +RUN python -m pip install -r requirements.txt + +# Copy the rest of the lit_nlp package +COPY . ./ + +# Build front-end with yarn +WORKDIR $APP_HOME/lit_nlp/client +ENV NODE_OPTIONS "--openssl-legacy-provider" +RUN yarn && yarn build && rm -rf node_modules/* + +# TODO(b/353980272): Replace this with the GCP-specific config +# See https://github.com/PAIR-code/lit/blob/main/Dockerfile +WORKDIR $APP_HOME +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] diff --git a/lit_nlp/examples/gcp/requirements.txt b/lit_nlp/examples/gcp/requirements.txt new file mode 100644 index 00000000..d535c4ba --- /dev/null +++ b/lit_nlp/examples/gcp/requirements.txt @@ -0,0 +1,2 @@ +lit-nlp +vertexai>=1.49.0 \ No newline at end of file From e80c106efe2739bad6cca72ad89b449ebf190319 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 14 Aug 2024 15:23:48 +0000 Subject: [PATCH 43/86] Update lit-on-gcp requirements. --- lit_nlp/examples/gcp/requirements.txt | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/requirements.txt b/lit_nlp/examples/gcp/requirements.txt index d535c4ba..8ed90eb8 100644 --- a/lit_nlp/examples/gcp/requirements.txt +++ b/lit_nlp/examples/gcp/requirements.txt @@ -1,2 +1,21 @@ -lit-nlp +# Copyright 2023 Google LLC +# +# 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. +# ============================================================================== + +-r ../../../requirements_core.txt + +google-cloud-aiplatform>=1.60.0 +gunicorn>=20.1.0 +lit-nlp>=1.2 vertexai>=1.49.0 \ No newline at end of file From ba1fa385175d37a9aa36e101b3b7a710abfecadd Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Wed, 14 Aug 2024 15:34:29 +0000 Subject: [PATCH 44/86] Updating date on license declaration --- lit_nlp/examples/gcp/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/requirements.txt b/lit_nlp/examples/gcp/requirements.txt index 8ed90eb8..a1a77354 100644 --- a/lit_nlp/examples/gcp/requirements.txt +++ b/lit_nlp/examples/gcp/requirements.txt @@ -1,4 +1,4 @@ -# Copyright 2023 Google LLC +# Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. From 5d6cbce83235d5c7be6f22ea6a1f8fbeef358bab Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Wed, 14 Aug 2024 19:21:46 +0000 Subject: [PATCH 45/86] Updating dockerfile for LIT on GCP --- lit_nlp/examples/gcp/Dockerfile | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index e269af14..5325bc4c 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -94,8 +94,9 @@ COPY ./lit_nlp/examples/gunicorn_config.py ./ # TODO(b/353980272): Replace this with a requirements file specific to the GCP # exmaple, this should include the core lit-nlp package. -COPY ./requirements*.txt ./ -RUN python -m pip install -r requirements.txt +COPY requirements_core.txt ./ +COPY lit_nlp/examples/gcp/requirements.txt lit_nlp/examples/gcp/requirements.txt +RUN python -m pip install -r lit_nlp/examples/gcp/requirements.txt # Copy the rest of the lit_nlp package COPY . ./ From 1acc868d4a5fa0fd2a135f132f56bb4cb8ba3990 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Tue, 20 Aug 2024 19:02:48 +0000 Subject: [PATCH 46/86] Add model_server to lit-on-gcp, --- lit_nlp/examples/gcp/ model_server.py | 88 +++++++++++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 lit_nlp/examples/gcp/ model_server.py diff --git a/lit_nlp/examples/gcp/ model_server.py b/lit_nlp/examples/gcp/ model_server.py new file mode 100644 index 00000000..3267a94a --- /dev/null +++ b/lit_nlp/examples/gcp/ model_server.py @@ -0,0 +1,88 @@ +r"""A model server for serving models on GCP via Gunicorn.""" + +from collections.abc import Sequence +import functools +import os +from typing import Optional +from absl import app +from absl import flags +from lit_nlp import dev_server +from lit_nlp.examples.prompt_debugging import models as prompt_debugging_models +from lit_nlp.lib import serialize +from lit_nlp.lib import wsgi_app + +_FLAGS = flags.FLAGS + +DEFAULT_DL_FRAMEWORK = 'kerasnlp' +DEFAULT_DL_RUNTIME = 'tensorflow' +DEFAULT_PRECISION = 'bfloat16' +DEFAULT_SEQUENCE_LENGTH = 512 +DEFAULT_BATCH_SIZE = 1 +DEFAULT_MODELS = 'gemma_1.1_instruct_2b_en:/cns/je-d/home/mattdangerw/keras/gemma/gemma_1.1_instruct_2b_en/3/' + + +def get_wsgi_app() -> wsgi_app.App: + """Return WSGI app for container-hosted demos.""" + + def wrap_handler(predict_fn): + @functools.wraps(predict_fn) + def _handler(app, request, environ): + data = serialize.from_json(request.data) if len(request.data) else None + inputs = data['inputs'] + outputs = predict_fn(inputs) + response_body = serialize.to_json(outputs, simple=True) + return app.respond(request, response_body, 'application/json', 200) + + return _handler + + model_config = os.getenv('MODEL_CONFIG', DEFAULT_MODELS).split(',') + dl_framework = os.environ.get('DL_FRAMEWORK', DEFAULT_DL_FRAMEWORK) + dl_runtime = os.environ.get('DL_RUNTIME', DEFAULT_DL_RUNTIME) + precision = os.environ.get('PRECISION', DEFAULT_PRECISION) + batch_size = os.environ.get('BATCH_SIZE', DEFAULT_BATCH_SIZE) + sequence_length = os.environ.get('SEQUENCE_LENGTH', DEFAULT_SEQUENCE_LENGTH) + + # Parse flags without calling app.run(main), to avoid conflict with + # gunicorn command line flags. + models = prompt_debugging_models.get_models( + models_config=model_config, + dl_framework=dl_framework, + dl_runtime=dl_runtime, + precision=precision, + batch_size=batch_size, + sequence_length=sequence_length, + ) + + if len(DEFAULT_MODELS) < 1: + raise ValueError('No models specified in DEFAULT_MODELS') + model_name = DEFAULT_MODELS[0].split(':')[0] + + predict_model = models[model_name] + salience_model = models[f'{model_name}_salience'] + tokenize_model = models[f'{model_name}_tokenize'] + + handlers = { + '/predict': predict_model.predict, + '/salience': salience_model.predict, + '/tokenize': tokenize_model.predict, + } + + wrapped_handlers = { + endpoint: wrap_handler(endpoint_fn) + for endpoint, endpoint_fn in handlers.items() + } + + return wsgi_app.App( + wrapped_handlers, project_root='gcp', index_file='index.html' + ) + + +def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: + if len(argv) > 1: + raise app.UsageError('Too many command-line arguments.') + + return get_wsgi_app() + + +if __name__ == '__main__': + app.run(main) From 7d8904d5fdb9ce1bc00fa036316e7348b0d7026d Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Wed, 21 Aug 2024 18:39:28 +0000 Subject: [PATCH 47/86] Updating copyright year in Dockerfile --- lit_nlp/examples/gcp/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index 5325bc4c..dd4bd575 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -1,4 +1,4 @@ -# Copyright 2023 Google LLC +# Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. From c478f8e4be8e7a8898288cf325f99e77ffcc309d Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 21 Aug 2024 19:48:39 +0000 Subject: [PATCH 48/86] Add unit test for model_server. --- lit_nlp/examples/gcp/model_server_test.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 lit_nlp/examples/gcp/model_server_test.py diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py new file mode 100644 index 00000000..e69de29b From b9a0b8210263da9ee6d741e4e0f0444849e3a141 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 21 Aug 2024 19:50:26 +0000 Subject: [PATCH 49/86] Add webtest package to lit_on_gcp requirement file. --- lit_nlp/examples/gcp/requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/requirements.txt b/lit_nlp/examples/gcp/requirements.txt index a1a77354..d5ee763a 100644 --- a/lit_nlp/examples/gcp/requirements.txt +++ b/lit_nlp/examples/gcp/requirements.txt @@ -18,4 +18,5 @@ google-cloud-aiplatform>=1.60.0 gunicorn>=20.1.0 lit-nlp>=1.2 -vertexai>=1.49.0 \ No newline at end of file +vertexai>=1.49.0 +webtest>=2.0 \ No newline at end of file From 55bfc993cc27fd25ae5089d58ae822bfeca296a3 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 22 Aug 2024 00:33:10 +0000 Subject: [PATCH 50/86] Adding gunicorn config for model_server.py --- lit_nlp/examples/gcp/Dockerfile | 6 ++--- lit_nlp/examples/gcp/README.md | 7 ++++++ .../gcp/{ model_server.py => model_server.py} | 7 ++---- .../gcp/model_server_gunicorn_config.py | 25 +++++++++++++++++++ 4 files changed, 37 insertions(+), 8 deletions(-) create mode 100644 lit_nlp/examples/gcp/README.md rename lit_nlp/examples/gcp/{ model_server.py => model_server.py} (92%) create mode 100644 lit_nlp/examples/gcp/model_server_gunicorn_config.py diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index dd4bd575..3f6a5536 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -73,7 +73,7 @@ RUN rm -rf /var/lib/apt/lists/* # ---- LIT on GCP from source ---- -FROM base AS lit-gcp-dev +FROM base AS lit-gcp-model-server-dev ENV APP_HOME /app WORKDIR $APP_HOME @@ -90,7 +90,7 @@ RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ RUN apt update && apt -y install yarn # TODO(b/353980272): Replace the default config with the GCP-specific config -COPY ./lit_nlp/examples/gunicorn_config.py ./ +COPY ./lit_nlp/examples/gcp/model_server_gunicorn_config.py ./ # TODO(b/353980272): Replace this with a requirements file specific to the GCP # exmaple, this should include the core lit-nlp package. @@ -109,4 +109,4 @@ RUN yarn && yarn build && rm -rf node_modules/* # TODO(b/353980272): Replace this with the GCP-specific config # See https://github.com/PAIR-code/lit/blob/main/Dockerfile WORKDIR $APP_HOME -ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] +ENTRYPOINT ["gunicorn", "--config=model_server_gunicorn_config.py"] diff --git a/lit_nlp/examples/gcp/README.md b/lit_nlp/examples/gcp/README.md new file mode 100644 index 00000000..d2efba25 --- /dev/null +++ b/lit_nlp/examples/gcp/README.md @@ -0,0 +1,7 @@ +# Using LLMs in LIT on Google Cloud Platform + +Architectural Notes + +* The `LitApp` HTTP API assumes that inputs will be passed around as + identifiers and the reconsituted on the LitApp server before being sent to + the model. The `model_server.py` will not have direct access to the loaded Datasets, and thus the HTTP API assumes that the JSON data passed to its endpoints will be the complete, reconstituted examples from the `LitApp`. The `model_server.py` will send back predictions in full JSON format. \ No newline at end of file diff --git a/lit_nlp/examples/gcp/ model_server.py b/lit_nlp/examples/gcp/model_server.py similarity index 92% rename from lit_nlp/examples/gcp/ model_server.py rename to lit_nlp/examples/gcp/model_server.py index 3267a94a..3cc432e8 100644 --- a/lit_nlp/examples/gcp/ model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -5,24 +5,21 @@ import os from typing import Optional from absl import app -from absl import flags from lit_nlp import dev_server from lit_nlp.examples.prompt_debugging import models as prompt_debugging_models from lit_nlp.lib import serialize from lit_nlp.lib import wsgi_app -_FLAGS = flags.FLAGS - DEFAULT_DL_FRAMEWORK = 'kerasnlp' DEFAULT_DL_RUNTIME = 'tensorflow' DEFAULT_PRECISION = 'bfloat16' DEFAULT_SEQUENCE_LENGTH = 512 DEFAULT_BATCH_SIZE = 1 -DEFAULT_MODELS = 'gemma_1.1_instruct_2b_en:/cns/je-d/home/mattdangerw/keras/gemma/gemma_1.1_instruct_2b_en/3/' +DEFAULT_MODELS = 'gemma_1.1_2b_IT:gemma_1.1_instruct_2b_en' def get_wsgi_app() -> wsgi_app.App: - """Return WSGI app for container-hosted demos.""" + """Return WSGI app for an LLM server.""" def wrap_handler(predict_fn): @functools.wraps(predict_fn) diff --git a/lit_nlp/examples/gcp/model_server_gunicorn_config.py b/lit_nlp/examples/gcp/model_server_gunicorn_config.py new file mode 100644 index 00000000..647a275e --- /dev/null +++ b/lit_nlp/examples/gcp/model_server_gunicorn_config.py @@ -0,0 +1,25 @@ +# Copyright 2020 Google LLC +# +# 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. +# ============================================================================== +"""gunicorn configuration for cloud-hosted demos.""" + +import os + +_PORT = os.getenv('PORT', '5432') + +bind = f'0.0.0.0:{_PORT}' +timeout = 3600 +threads = 8 +worker_class = 'gthread' +wsgi_app = f'lit_nlp.examples.gcp.model_server:get_wsgi_app()' From 180f68ad3774f8b276e262c0dcb7307ad87e42a3 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 22 Aug 2024 13:04:36 +0000 Subject: [PATCH 51/86] Add unit test for model_server of lit on GCP. --- lit_nlp/examples/gcp/model_server_test.py | 55 +++++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index e69de29b..1874e72c 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -0,0 +1,55 @@ +from unittest import mock +from absl.testing import absltest +from lit_nlp.examples.gcp import model_server +import webtest + + +class TestWSGIApp(absltest.TestCase): + + @mock.patch('lit_nlp.examples.prompt_debugging.models.get_models') + def test_predict_endpoint(self, mock_get_models): + + mock_model = mock.MagicMock() + mock_model.predict.side_effect = [{'response': 'test output text'}] + + salience_model = mock.MagicMock() + salience_model.predict.side_effect = [{ + 'tokens': ['test', 'output', 'text'], + 'grad_l2': [0.1234, 0.3456, 0.5678], + 'grad_dot_input': [0.1234, -0.3456, 0.5678], + }] + + tokenize_model = mock.MagicMock() + tokenize_model.predict.side_effect = [ + {'tokens': ['test', 'output', 'text']} + ] + + mock_get_models.return_value = { + 'gemma_1.1_instruct_2b_en': mock_model, + 'gemma_1.1_instruct_2b_en_salience': salience_model, + 'gemma_1.1_instruct_2b_en_tokenize': tokenize_model, + } + app = webtest.TestApp(model_server.get_wsgi_app()) + + response = app.post_json('/predict', {'inputs': 'test_input'}) + self.assertEqual(response.status_code, 200) + self.assertEqual(response.json, {'response': 'test output text'}) + + response = app.post_json('/salience', {'inputs': 'test_input'}) + self.assertEqual(response.status_code, 200) + self.assertEqual( + response.json, + { + 'tokens': ['test', 'output', 'text'], + 'grad_l2': [0.1234, 0.3456, 0.5678], + 'grad_dot_input': [0.1234, -0.3456, 0.5678], + }, + ) + + response = app.post_json('/tokenize', {'inputs': 'test_input'}) + self.assertEqual(response.status_code, 200) + self.assertEqual(response.json, {'tokens': ['test', 'output', 'text']}) + + +if __name__ == '__main__': + absltest.main() From 424adce9cf8c9cbabdf5d89d485cdc5f3fd098ed Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 22 Aug 2024 13:20:00 +0000 Subject: [PATCH 52/86] Update pyproject.toml and requirment_test files. --- pyproject.toml | 1 + requirements_test.txt | 1 + 2 files changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index dec21486..1f727cd6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -103,6 +103,7 @@ vertexai = [ test = [ "lime==0.2.0.1", "pytest>=7.4.0,<8.0.0", + "webtest>=2.0", ] # LINT.ThenChange(./requirements_test.txt) diff --git a/requirements_test.txt b/requirements_test.txt index d454942d..e1c020e9 100644 --- a/requirements_test.txt +++ b/requirements_test.txt @@ -16,4 +16,5 @@ lime==0.2.0.1 google-cloud-aiplatform>=1.60.0 pytest>=7.4.0,<8.0.0 +webtest>=2.0 # LINT.ThenChange(./pyproject.toml) From ae5825a020a84b57677e7a273697733c8b9c5d61 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 22 Aug 2024 13:29:29 +0000 Subject: [PATCH 53/86] Fetch model_name from model_config for lit-on-gcp model server. --- lit_nlp/examples/gcp/model_server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 3cc432e8..5bae89a1 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -52,7 +52,7 @@ def _handler(app, request, environ): if len(DEFAULT_MODELS) < 1: raise ValueError('No models specified in DEFAULT_MODELS') - model_name = DEFAULT_MODELS[0].split(':')[0] + model_name = model_config[0].split(':')[0] predict_model = models[model_name] salience_model = models[f'{model_name}_salience'] From 64114d553ffd2c0ffd7bc674fb32a36e564ea0f4 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 22 Aug 2024 14:01:40 +0000 Subject: [PATCH 54/86] Fixing KeyError in tests related to default name change --- lit_nlp/examples/gcp/model_server_test.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index 1874e72c..666fec0f 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -25,9 +25,9 @@ def test_predict_endpoint(self, mock_get_models): ] mock_get_models.return_value = { - 'gemma_1.1_instruct_2b_en': mock_model, - 'gemma_1.1_instruct_2b_en_salience': salience_model, - 'gemma_1.1_instruct_2b_en_tokenize': tokenize_model, + 'gemma_1.1_2b_IT': mock_model, + 'gemma_1.1_2b_IT_salience': salience_model, + 'gemma_1.1_2b_IT_tokenize': tokenize_model, } app = webtest.TestApp(model_server.get_wsgi_app()) From 1d019c7a1bf5f135ea42104889167b79c3f795cd Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 22 Aug 2024 18:45:14 +0000 Subject: [PATCH 55/86] Fixing requirements and udpating README --- lit_nlp/examples/gcp/Dockerfile | 2 ++ lit_nlp/examples/gcp/README.md | 30 +++++++++++++++++++++++++++ lit_nlp/examples/gcp/requirements.txt | 1 + 3 files changed, 33 insertions(+) diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index 3f6a5536..3b29c337 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -95,6 +95,8 @@ COPY ./lit_nlp/examples/gcp/model_server_gunicorn_config.py ./ # TODO(b/353980272): Replace this with a requirements file specific to the GCP # exmaple, this should include the core lit-nlp package. COPY requirements_core.txt ./ +COPY lit_nlp/examples/prompt_debugging/requirements.txt \ + lit_nlp/examples/prompt_debugging/requirements.txt COPY lit_nlp/examples/gcp/requirements.txt lit_nlp/examples/gcp/requirements.txt RUN python -m pip install -r lit_nlp/examples/gcp/requirements.txt diff --git a/lit_nlp/examples/gcp/README.md b/lit_nlp/examples/gcp/README.md index d2efba25..7af966df 100644 --- a/lit_nlp/examples/gcp/README.md +++ b/lit_nlp/examples/gcp/README.md @@ -1,5 +1,35 @@ # Using LLMs in LIT on Google Cloud Platform +## Developing + +### Use a virtual environment + +```shell +# Create and activate the virtual environment +python3 -m venv ~/.venvs/lit-on-gcp +source ~/.venvs/lit-on-gcp/bin/activate + +# Install the requirements and LIT in editable mode +pip install -f ./lit_nlp/examples/gcp/requirements.txt +pip install -e . + +# Optionally, install tetsing requirements +pip install -f ./requirements_test.txt +``` + +### Build the Docker image + +```shell +docker build -f ./lit_nlp/examples/gcp/Dockerfile -t lit-app:gcp-dev . +``` + +### Run GPT-2 in a Docker container + +```shell +# Runs GPT-2 in Keras on Tensorflow +docker run --rm -p 5432:5432 -e MODEL_CONFIG=gpt2:gpt2_base_en lit-app:gcp-dev +``` + Architectural Notes * The `LitApp` HTTP API assumes that inputs will be passed around as diff --git a/lit_nlp/examples/gcp/requirements.txt b/lit_nlp/examples/gcp/requirements.txt index d5ee763a..9586f030 100644 --- a/lit_nlp/examples/gcp/requirements.txt +++ b/lit_nlp/examples/gcp/requirements.txt @@ -14,6 +14,7 @@ # ============================================================================== -r ../../../requirements_core.txt +-r ../prompt_debugging/requirements.txt google-cloud-aiplatform>=1.60.0 gunicorn>=20.1.0 From f24b841959f0402498a056a5164a86ecae6dbb94 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 22 Aug 2024 18:46:42 +0000 Subject: [PATCH 56/86] Adding testing instructions to README --- lit_nlp/examples/gcp/README.md | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/lit_nlp/examples/gcp/README.md b/lit_nlp/examples/gcp/README.md index 7af966df..376fcbbd 100644 --- a/lit_nlp/examples/gcp/README.md +++ b/lit_nlp/examples/gcp/README.md @@ -15,6 +15,7 @@ pip install -e . # Optionally, install tetsing requirements pip install -f ./requirements_test.txt +pytest pytest lit_nlp/examples/gcp ``` ### Build the Docker image @@ -29,9 +30,3 @@ docker build -f ./lit_nlp/examples/gcp/Dockerfile -t lit-app:gcp-dev . # Runs GPT-2 in Keras on Tensorflow docker run --rm -p 5432:5432 -e MODEL_CONFIG=gpt2:gpt2_base_en lit-app:gcp-dev ``` - -Architectural Notes - -* The `LitApp` HTTP API assumes that inputs will be passed around as - identifiers and the reconsituted on the LitApp server before being sent to - the model. The `model_server.py` will not have direct access to the loaded Datasets, and thus the HTTP API assumes that the JSON data passed to its endpoints will be the complete, reconstituted examples from the `LitApp`. The `model_server.py` will send back predictions in full JSON format. \ No newline at end of file From 32ec2737ccbff23c1dbdd02406e3ffa4b03ee87e Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 4 Sep 2024 23:41:52 +0000 Subject: [PATCH 57/86] Correct the model name of model_server. --- lit_nlp/examples/gcp/model_server.py | 7 +++---- lit_nlp/examples/gcp/model_server_test.py | 4 ++-- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 5bae89a1..ea7896cd 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -27,7 +27,7 @@ def _handler(app, request, environ): data = serialize.from_json(request.data) if len(request.data) else None inputs = data['inputs'] outputs = predict_fn(inputs) - response_body = serialize.to_json(outputs, simple=True) + response_body = serialize.to_json(outputs, simple=True) return app.respond(request, response_body, 'application/json', 200) return _handler @@ -55,8 +55,8 @@ def _handler(app, request, environ): model_name = model_config[0].split(':')[0] predict_model = models[model_name] - salience_model = models[f'{model_name}_salience'] - tokenize_model = models[f'{model_name}_tokenize'] + salience_model = models[f'_{model_name}_salience'] + tokenize_model = models[f'_{model_name}_tokenizer'] handlers = { '/predict': predict_model.predict, @@ -73,7 +73,6 @@ def _handler(app, request, environ): wrapped_handlers, project_root='gcp', index_file='index.html' ) - def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index 666fec0f..3586cc10 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -26,8 +26,8 @@ def test_predict_endpoint(self, mock_get_models): mock_get_models.return_value = { 'gemma_1.1_2b_IT': mock_model, - 'gemma_1.1_2b_IT_salience': salience_model, - 'gemma_1.1_2b_IT_tokenize': tokenize_model, + '_gemma_1.1_2b_IT_salience': salience_model, + '_gemma_1.1_2b_IT_tokenize': tokenize_model, } app = webtest.TestApp(model_server.get_wsgi_app()) From 0a4eb3f3ce2b3d7eac90d7cee2db8e63c47d233e Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 5 Sep 2024 00:33:25 +0000 Subject: [PATCH 58/86] Fix wsgi handler name. --- lit_nlp/examples/gcp/model_server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index ea7896cd..249502f3 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -56,7 +56,7 @@ def _handler(app, request, environ): predict_model = models[model_name] salience_model = models[f'_{model_name}_salience'] - tokenize_model = models[f'_{model_name}_tokenizer'] + tokenize_model = models[f'_{model_name}_tokenize'] handlers = { '/predict': predict_model.predict, From 811dc9df25c1c8bb1717d17384484f55427f4194 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 5 Sep 2024 14:14:05 +0000 Subject: [PATCH 59/86] Modify the output to be a list to ensure compatibility with JSON serialization. --- lit_nlp/examples/gcp/model_server.py | 2 +- lit_nlp/examples/gcp/model_server_test.py | 16 ++++++++-------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 249502f3..7845824c 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -27,7 +27,7 @@ def _handler(app, request, environ): data = serialize.from_json(request.data) if len(request.data) else None inputs = data['inputs'] outputs = predict_fn(inputs) - response_body = serialize.to_json(outputs, simple=True) + response_body = serialize.to_json(list(outputs), simple=True) return app.respond(request, response_body, 'application/json', 200) return _handler diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index 3586cc10..f50c2427 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -10,18 +10,18 @@ class TestWSGIApp(absltest.TestCase): def test_predict_endpoint(self, mock_get_models): mock_model = mock.MagicMock() - mock_model.predict.side_effect = [{'response': 'test output text'}] + mock_model.predict.side_effect = [[{'response': 'test output text'}]] salience_model = mock.MagicMock() - salience_model.predict.side_effect = [{ + salience_model.predict.side_effect = [[{ 'tokens': ['test', 'output', 'text'], 'grad_l2': [0.1234, 0.3456, 0.5678], 'grad_dot_input': [0.1234, -0.3456, 0.5678], - }] + }]] tokenize_model = mock.MagicMock() tokenize_model.predict.side_effect = [ - {'tokens': ['test', 'output', 'text']} + [{'tokens': ['test', 'output', 'text']}] ] mock_get_models.return_value = { @@ -33,22 +33,22 @@ def test_predict_endpoint(self, mock_get_models): response = app.post_json('/predict', {'inputs': 'test_input'}) self.assertEqual(response.status_code, 200) - self.assertEqual(response.json, {'response': 'test output text'}) + self.assertEqual(response.json, [{'response': 'test output text'}]) response = app.post_json('/salience', {'inputs': 'test_input'}) self.assertEqual(response.status_code, 200) self.assertEqual( response.json, - { + [{ 'tokens': ['test', 'output', 'text'], 'grad_l2': [0.1234, 0.3456, 0.5678], 'grad_dot_input': [0.1234, -0.3456, 0.5678], - }, + }], ) response = app.post_json('/tokenize', {'inputs': 'test_input'}) self.assertEqual(response.status_code, 200) - self.assertEqual(response.json, {'tokens': ['test', 'output', 'text']}) + self.assertEqual(response.json, [{'tokens': ['test', 'output', 'text']}]) if __name__ == '__main__': From d442048f2ad712e33e0d98c7423ca22a0bff8164 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 5 Sep 2024 15:37:56 +0000 Subject: [PATCH 60/86] Adopting the generate_model_group_names() API from PD Utils --- lit_nlp/examples/gcp/model_server.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 7845824c..210c9fb8 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -6,7 +6,8 @@ from typing import Optional from absl import app from lit_nlp import dev_server -from lit_nlp.examples.prompt_debugging import models as prompt_debugging_models +from lit_nlp.examples.prompt_debugging import models as pd_models +from lit_nlp.examples.prompt_debugging import utils as pd_utils from lit_nlp.lib import serialize from lit_nlp.lib import wsgi_app @@ -41,7 +42,7 @@ def _handler(app, request, environ): # Parse flags without calling app.run(main), to avoid conflict with # gunicorn command line flags. - models = prompt_debugging_models.get_models( + models = pd_models.get_models( models_config=model_config, dl_framework=dl_framework, dl_runtime=dl_runtime, @@ -53,13 +54,14 @@ def _handler(app, request, environ): if len(DEFAULT_MODELS) < 1: raise ValueError('No models specified in DEFAULT_MODELS') model_name = model_config[0].split(':')[0] + sal_name, tok_name = pd_utils.generate_model_group_names(model_name) - predict_model = models[model_name] - salience_model = models[f'_{model_name}_salience'] - tokenize_model = models[f'_{model_name}_tokenize'] + generation_model = models[model_name] + salience_model = models[sal_name] + tokenize_model = models[tok_name] handlers = { - '/predict': predict_model.predict, + '/predict': generation_model.predict, '/salience': salience_model.predict, '/tokenize': tokenize_model.predict, } From 2488aa7cf8f8a112607ca0c8b40870efde73ec24 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 5 Sep 2024 15:53:01 +0000 Subject: [PATCH 61/86] Using generate_model_group_names() API in model server test --- lit_nlp/examples/gcp/model_server_test.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index f50c2427..cdeb8242 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -1,6 +1,9 @@ +import os from unittest import mock + from absl.testing import absltest from lit_nlp.examples.gcp import model_server +from lit_nlp.examples.prompt_debugging import utils as pd_utils import webtest @@ -8,6 +11,9 @@ class TestWSGIApp(absltest.TestCase): @mock.patch('lit_nlp.examples.prompt_debugging.models.get_models') def test_predict_endpoint(self, mock_get_models): + test_model_name = 'lit_on_gcp_test_model' + test_model_config = f'{test_model_name}:test_model_path' + os.environ['MODEL_CONFIG'] = test_model_config mock_model = mock.MagicMock() mock_model.predict.side_effect = [[{'response': 'test output text'}]] @@ -24,10 +30,12 @@ def test_predict_endpoint(self, mock_get_models): [{'tokens': ['test', 'output', 'text']}] ] + sal_name, tok_name = pd_utils.generate_model_group_names(test_model_name) + mock_get_models.return_value = { - 'gemma_1.1_2b_IT': mock_model, - '_gemma_1.1_2b_IT_salience': salience_model, - '_gemma_1.1_2b_IT_tokenize': tokenize_model, + test_model_name: mock_model, + sal_name: salience_model, + tok_name: tokenize_model, } app = webtest.TestApp(model_server.get_wsgi_app()) From 9baac29b96970ef7fa64f2f36ce2c79ff73707b7 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Thu, 5 Sep 2024 16:15:39 +0000 Subject: [PATCH 62/86] Code health update on model server tests --- lit_nlp/examples/gcp/model_server_test.py | 53 ++++++++++++++--------- 1 file changed, 32 insertions(+), 21 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index cdeb8242..59f0d949 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -2,21 +2,23 @@ from unittest import mock from absl.testing import absltest +from absl.testing import parameterized from lit_nlp.examples.gcp import model_server from lit_nlp.examples.prompt_debugging import utils as pd_utils import webtest -class TestWSGIApp(absltest.TestCase): +class TestWSGIApp(parameterized.TestCase): - @mock.patch('lit_nlp.examples.prompt_debugging.models.get_models') - def test_predict_endpoint(self, mock_get_models): + @classmethod + def setUpClass(cls): test_model_name = 'lit_on_gcp_test_model' + sal_name, tok_name = pd_utils.generate_model_group_names(test_model_name) test_model_config = f'{test_model_name}:test_model_path' os.environ['MODEL_CONFIG'] = test_model_config - mock_model = mock.MagicMock() - mock_model.predict.side_effect = [[{'response': 'test output text'}]] + generation_model = mock.MagicMock() + generation_model.predict.side_effect = [[{'response': 'test output text'}]] salience_model = mock.MagicMock() salience_model.predict.side_effect = [[{ @@ -30,33 +32,42 @@ def test_predict_endpoint(self, mock_get_models): [{'tokens': ['test', 'output', 'text']}] ] - sal_name, tok_name = pd_utils.generate_model_group_names(test_model_name) - - mock_get_models.return_value = { - test_model_name: mock_model, + cls.mock_models = { + test_model_name: generation_model, sal_name: salience_model, tok_name: tokenize_model, } - app = webtest.TestApp(model_server.get_wsgi_app()) - response = app.post_json('/predict', {'inputs': 'test_input'}) - self.assertEqual(response.status_code, 200) - self.assertEqual(response.json, [{'response': 'test output text'}]) - response = app.post_json('/salience', {'inputs': 'test_input'}) - self.assertEqual(response.status_code, 200) - self.assertEqual( - response.json, - [{ + @parameterized.named_parameters( + dict( + testcase_name='predict', + endpoint='/predict', + expected=[{'response': 'test output text'}], + ), + dict( + testcase_name='salience', + endpoint='/salience', + expected=[{ 'tokens': ['test', 'output', 'text'], 'grad_l2': [0.1234, 0.3456, 0.5678], 'grad_dot_input': [0.1234, -0.3456, 0.5678], }], - ) + ), + dict( + testcase_name='tokenize', + endpoint='/tokenize', + expected=[{'tokens': ['test', 'output', 'text']}], + ), + ) + @mock.patch('lit_nlp.examples.prompt_debugging.models.get_models') + def test_endpoint(self, mock_get_models, endpoint, expected): + mock_get_models.return_value = self.mock_models + app = webtest.TestApp(model_server.get_wsgi_app()) - response = app.post_json('/tokenize', {'inputs': 'test_input'}) + response = app.post_json(endpoint, {'inputs': [{'prompt': 'test input'}]}) self.assertEqual(response.status_code, 200) - self.assertEqual(response.json, [{'tokens': ['test', 'output', 'text']}]) + self.assertEqual(response.json, expected) if __name__ == '__main__': From 60bdc7cf382bd0c5ead2576c119277230a6080c9 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Fri, 6 Sep 2024 17:00:07 +0000 Subject: [PATCH 63/86] Linting cleanup in the model server --- lit_nlp/examples/gcp/model_server.py | 39 ++++++++++++++-------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 210c9fb8..22f590cd 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -4,6 +4,7 @@ import functools import os from typing import Optional + from absl import app from lit_nlp import dev_server from lit_nlp.examples.prompt_debugging import models as pd_models @@ -24,7 +25,7 @@ def get_wsgi_app() -> wsgi_app.App: def wrap_handler(predict_fn): @functools.wraps(predict_fn) - def _handler(app, request, environ): + def _handler(app: wsgi_app.App, request, unused_environ): data = serialize.from_json(request.data) if len(request.data) else None inputs = data['inputs'] outputs = predict_fn(inputs) @@ -33,15 +34,19 @@ def _handler(app, request, environ): return _handler - model_config = os.getenv('MODEL_CONFIG', DEFAULT_MODELS).split(',') - dl_framework = os.environ.get('DL_FRAMEWORK', DEFAULT_DL_FRAMEWORK) - dl_runtime = os.environ.get('DL_RUNTIME', DEFAULT_DL_RUNTIME) - precision = os.environ.get('PRECISION', DEFAULT_PRECISION) - batch_size = os.environ.get('BATCH_SIZE', DEFAULT_BATCH_SIZE) - sequence_length = os.environ.get('SEQUENCE_LENGTH', DEFAULT_SEQUENCE_LENGTH) + if not (model_config := os.getenv('MODEL_CONFIG', DEFAULT_MODELS).split(',')): + raise ValueError('No model configuration was provided') + elif (num_configs := len(model_config)) > 1: + raise ValueError( + f'Only 1 model configuration can be provided, got {num_configs}' + ) + + dl_framework = os.getenv('DL_FRAMEWORK', DEFAULT_DL_FRAMEWORK) + dl_runtime = os.getenv('DL_RUNTIME', DEFAULT_DL_RUNTIME) + precision = os.getenv('PRECISION', DEFAULT_PRECISION) + batch_size = int(os.getenv('BATCH_SIZE', DEFAULT_BATCH_SIZE)) + sequence_length = int(os.getenv('SEQUENCE_LENGTH', DEFAULT_SEQUENCE_LENGTH)) - # Parse flags without calling app.run(main), to avoid conflict with - # gunicorn command line flags. models = pd_models.get_models( models_config=model_config, dl_framework=dl_framework, @@ -51,19 +56,13 @@ def _handler(app, request, environ): sequence_length=sequence_length, ) - if len(DEFAULT_MODELS) < 1: - raise ValueError('No models specified in DEFAULT_MODELS') - model_name = model_config[0].split(':')[0] - sal_name, tok_name = pd_utils.generate_model_group_names(model_name) - - generation_model = models[model_name] - salience_model = models[sal_name] - tokenize_model = models[tok_name] + gen_name = model_config[0].split(':')[0] + sal_name, tok_name = pd_utils.generate_model_group_names(gen_name) handlers = { - '/predict': generation_model.predict, - '/salience': salience_model.predict, - '/tokenize': tokenize_model.predict, + '/predict': models[gen_name].predict, + '/salience': models[sal_name].predict, + '/tokenize': models[tok_name].predict, } wrapped_handlers = { From 7681476d5056d927905f24333b890501a36df040 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 10 Sep 2024 19:52:52 +0000 Subject: [PATCH 64/86] Adds LIT app server code --- lit_nlp/examples/gcp/constants.py | 6 ++ lit_nlp/examples/gcp/model.py | 106 +++++++++++++++++++++++++++ lit_nlp/examples/gcp/model_server.py | 9 ++- lit_nlp/examples/gcp/server.py | 78 ++++++++++++++++++++ 4 files changed, 196 insertions(+), 3 deletions(-) create mode 100644 lit_nlp/examples/gcp/constants.py create mode 100644 lit_nlp/examples/gcp/model.py create mode 100644 lit_nlp/examples/gcp/server.py diff --git a/lit_nlp/examples/gcp/constants.py b/lit_nlp/examples/gcp/constants.py new file mode 100644 index 00000000..244c7637 --- /dev/null +++ b/lit_nlp/examples/gcp/constants.py @@ -0,0 +1,6 @@ +import enum + +class LlmHTTPEndpoints(enum.Enum): + GENERATE = 'predict' + SALIENCE = 'salience' + TOKENIZE = 'tokenize' \ No newline at end of file diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py new file mode 100644 index 00000000..67192acb --- /dev/null +++ b/lit_nlp/examples/gcp/model.py @@ -0,0 +1,106 @@ +"""Wrapper for connetecting to LLMs on GCP via the model_server HTTP API.""" + +import enum + +from lit_nlp.api import model as lit_model +from lit_nlp.api import types as lit_types +from lit_nlp.api.types import Spec +from lit_nlp.examples.gcp import constants as lit_gcp_constants +from lit_nlp.examples.prompt_debugging import constants as pd_constants +from lit_nlp.examples.prompt_debugging import utils as pd_utils +from lit_nlp.lib import serialize +import requests + +""" +Plan for this module: + +From GitHub: + +* Rebase to include cl/672527408 and the CL described above +* Define an enum to track HTTP endpoints across Python modules +* Adopt HTTP endpoint enum across model_server.py and LlmOverHTTP +* Adopt model_specs.py in LlmOverHTTP, using HTTP endpoint enum for + conditional additions + +""" + +_LlmHTTPEndpoints = lit_gcp_constants.LlmHTTPEndpoints + + +class LlmOverHTTP(lit_model.BatchedRemoteModel): + + def __init__( + self, + base_url: str, + endpoint: str | _LlmHTTPEndpoints, + max_concurrent_requests: int = 4, + max_qps: int | float = 25 + ): + super().__init__(max_concurrent_requests, max_qps) + self.endpoint = _LlmHTTPEndpoints(endpoint) + self.url = f'{base_url}/{self.endpoint.value}' + + def input_spec(self) -> lit_types.Spec: + input_spec = pd_constants.INPUT_SPEC + + if self.endpoint == _LlmHTTPEndpoints.SALIENCE: + input_spec |= pd_constants.INPUT_SPEC_SALIENCE + + return input_spec + + def output_spec(self) -> lit_types.Spec: + if self.endpoint == _LlmHTTPEndpoints.GENERATE: + return ( + pd_constants.OUTPUT_SPEC_GENERATION + | pd_constants.OUTPUT_SPEC_GENERATION_EMBEDDINGS + ) + elif self.endpoint == _LlmHTTPEndpoints.SALIENCE: + return pd_constants.OUTPUT_SPEC_SALIENCE + else: + return pd_constants.OUTPUT_SPEC_TOKENIZER + + def predict_minibatch( + self, inputs: list[lit_types.JsonDict] + ) -> list[lit_types.JsonDict]: + """Run prediction on a batch of inputs. + + Subclass should implement this. + + Args: + inputs: sequence of inputs, following model.input_spec() + + Returns: + list of outputs, following model.output_spec() + """ + response = requests.post( + self.url, data=serialize.to_json(list(inputs), simple=True) + ) + + if not (200 <= response.status_code < 300): + raise RuntimeError() + + outputs = serialize.from_json(response.text) + return outputs + + +def initialize_model_group_for_salience( + name: str, base_url: str, *args, **kw +) -> dict[str, lit_model.Model]: + """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'.""" + salience_name, tokenizer_name = pd_utils.generate_model_group_names(name) + + generation_model = LlmOverHTTP( + *args, base_url=base_url, endpoint=_LlmHTTPEndpoints.GENERATE, **kw + ) + salience_model = LlmOverHTTP( + *args, base_url=base_url, endpoint=_LlmHTTPEndpoints.SALIENCE, **kw + ) + tokenizer_model = LlmOverHTTP( + *args, base_url=base_url, endpoint=_LlmHTTPEndpoints.TOKENIZE, **kw + ) + + return { + name: generation_model, + salience_name: salience_model, + tokenizer_name: tokenizer_model, + } diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 22f590cd..d818c778 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -7,6 +7,7 @@ from absl import app from lit_nlp import dev_server +from lit_nlp.examples.gcp import constants as lit_gcp_constants from lit_nlp.examples.prompt_debugging import models as pd_models from lit_nlp.examples.prompt_debugging import utils as pd_utils from lit_nlp.lib import serialize @@ -19,6 +20,8 @@ DEFAULT_BATCH_SIZE = 1 DEFAULT_MODELS = 'gemma_1.1_2b_IT:gemma_1.1_instruct_2b_en' +_LlmHTTPEndpoints = lit_gcp_constants.LlmHTTPEndpoints + def get_wsgi_app() -> wsgi_app.App: """Return WSGI app for an LLM server.""" @@ -60,9 +63,9 @@ def _handler(app: wsgi_app.App, request, unused_environ): sal_name, tok_name = pd_utils.generate_model_group_names(gen_name) handlers = { - '/predict': models[gen_name].predict, - '/salience': models[sal_name].predict, - '/tokenize': models[tok_name].predict, + f'/{_LlmHTTPEndpoints.GENERATE.value}': models[gen_name].predict, + f'/{_LlmHTTPEndpoints.SALIENCE.value}': models[sal_name].predict, + f'/{_LlmHTTPEndpoints.TOKENIZE.value}': models[tok_name].predict, } wrapped_handlers = { diff --git a/lit_nlp/examples/gcp/server.py b/lit_nlp/examples/gcp/server.py new file mode 100644 index 00000000..437b5378 --- /dev/null +++ b/lit_nlp/examples/gcp/server.py @@ -0,0 +1,78 @@ +"""Server for sequence salience with a left-to-right language model.""" + +from collections.abc import Mapping, Sequence +import sys +from typing import Optional + +from absl import app +from absl import flags +from absl import logging +from lit_nlp import dev_server +from lit_nlp import server_flags +from lit_nlp.api import model as lit_model +from lit_nlp.api import types as lit_types +from lit_nlp.examples.gcp import model as lit_gcp_model +from lit_nlp.examples.prompt_debugging import datasets as pd_datasets +from lit_nlp.examples.prompt_debugging import layouts as pd_layouts + + +_FLAGS = flags.FLAGS + +_SPLASH_SCREEN_DOC = """ +# Language Model Salience + +To begin, select an example, then click the segment(s) (tokens, words, etc.) +of the output that you would like to explain. Preceding segments(s) will be +highlighted according to their importance to the selected target segment(s), +with darker colors indicating a greater influence (salience) of that segment on +the model's likelihood of the target segment. +""" + + +def init_llm_on_gcp( + name: str, base_url: str, *args, **kw +) -> Mapping[str, lit_model.Model]: + return lit_gcp_model.initialize_model_group_for_salience( + name=name, base_url=base_url, *args, **kw + ) + + +def get_wsgi_app() -> Optional[dev_server.LitServerType]: + """Return WSGI app for container-hosted demos.""" + _FLAGS.set_default("server_type", "external") + _FLAGS.set_default("demo_mode", True) + _FLAGS.set_default("page_title", "LM Prompt Debugging") + _FLAGS.set_default("default_layout", pd_layouts.THREE_PANEL) + # Parse flags without calling app.run(main), to avoid conflict with + # gunicorn command line flags. + unused = flags.FLAGS(sys.argv, known_only=True) + if unused: + logging.info("lm_demo:get_wsgi_app() called with unused args: %s", unused) + return main([]) + + +def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: + if len(argv) > 1: + raise app.UsageError("Too many command-line arguments.") + + lit_demo = dev_server.Server( + models={}, + datasets={}, + layouts=pd_layouts.PROMPT_DEBUGGING_LAYOUTS, + model_loaders={ + 'LLM on GCP': (init_llm_on_gcp, { + 'name': lit_types.String(), + 'base_url': lit_types.String(), + 'max_concurrent_requests': lit_types.Integer(default=1), + 'max_qps': lit_types.Scalar(default=25), + }) + }, + dataset_loaders=pd_datasets.get_dataset_loaders(), + onboard_start_doc=_SPLASH_SCREEN_DOC, + **server_flags.get_flags(), + ) + return lit_demo.serve() + + +if __name__ == "__main__": + app.run(main) From 4c81182a7db1fda7f8ba071a9542876f462a13fa Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 17 Sep 2024 09:04:27 +0000 Subject: [PATCH 65/86] Adding LIT App server for LIT LLMs on GCP --- lit_nlp/examples/gcp/Dockerfile | 59 +++++++++++++++++-- lit_nlp/examples/gcp/model.py | 38 +++++++----- lit_nlp/examples/gcp/model_server_test.py | 13 ++-- lit_nlp/examples/gcp/server.py | 23 +------- .../examples/gcp/server_gunicorn_config.py | 25 ++++++++ 5 files changed, 112 insertions(+), 46 deletions(-) create mode 100644 lit_nlp/examples/gcp/server_gunicorn_config.py diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index 3b29c337..14d3d0c4 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -15,7 +15,23 @@ # Use the official lightweight Python image. # https://hub.docker.com/_/python -FROM nvidia/cuda:12.5.1-base-ubuntu22.04 AS base +# ---- LIT on GCP Base Images ---- + +FROM python:3.11-slim AS lit-gcp-app-server-base + +# Update Ubuntu packages and install basic utils +RUN apt-get update +RUN apt-get install -y wget curl gnupg2 gcc g++ git + +# Copy local code to the container image. +ENV APP_HOME /app +WORKDIR $APP_HOME + +COPY ./lit_nlp/examples/gcp/server_gunicorn_config.py ./gunicorn_config.py + + + +FROM nvidia/cuda:12.5.1-base-ubuntu22.04 AS lit-gcp-model-server-base ENV DEBIAN_FRONTEND=noninteractive ENV LANG C.UTF-8 @@ -71,9 +87,42 @@ RUN rm -rf /var/lib/apt/lists/* -# ---- LIT on GCP from source ---- +# ---- LIT on GCP Development Images ---- + +FROM lit-gcp-app-server-base AS lit-gcp-app-server-dev + +# Install yarn +RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | apt-key add - +RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ + tee /etc/apt/sources.list.d/yarn.list +RUN apt update && apt -y install yarn + +# Set up python environment with production dependencies +# This step is slow as it installs many packages. +COPY requirements_core.txt ./ +COPY lit_nlp/examples/prompt_debugging/requirements.txt \ + lit_nlp/examples/prompt_debugging/requirements.txt +COPY lit_nlp/examples/gcp/requirements.txt \ + lit_nlp/examples/gcp/requirements.txt +RUN python -m pip install -r lit_nlp/examples/gcp/requirements.txt + +# Copy the rest of the lit_nlp package +COPY . ./ + +# Build front-end with yarn +WORKDIR $APP_HOME/lit_nlp/client +ENV NODE_OPTIONS "--openssl-legacy-provider" +RUN yarn && yarn build && rm -rf node_modules/* + +# Run LIT server +# Note that the config file supports configuring the LIT demo that is launched +# via the DEMO_NAME and DEMO_PORT environment variables. +WORKDIR $APP_HOME +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] + + -FROM base AS lit-gcp-model-server-dev +FROM lit-gcp-model-server-base AS lit-gcp-model-server-dev ENV APP_HOME /app WORKDIR $APP_HOME @@ -89,7 +138,6 @@ RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ tee /etc/apt/sources.list.d/yarn.list RUN apt update && apt -y install yarn -# TODO(b/353980272): Replace the default config with the GCP-specific config COPY ./lit_nlp/examples/gcp/model_server_gunicorn_config.py ./ # TODO(b/353980272): Replace this with a requirements file specific to the GCP @@ -97,7 +145,8 @@ COPY ./lit_nlp/examples/gcp/model_server_gunicorn_config.py ./ COPY requirements_core.txt ./ COPY lit_nlp/examples/prompt_debugging/requirements.txt \ lit_nlp/examples/prompt_debugging/requirements.txt -COPY lit_nlp/examples/gcp/requirements.txt lit_nlp/examples/gcp/requirements.txt +COPY lit_nlp/examples/gcp/requirements.txt \ + lit_nlp/examples/gcp/requirements.txt RUN python -m pip install -r lit_nlp/examples/gcp/requirements.txt # Copy the rest of the lit_nlp package diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index 67192acb..86f51204 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -1,10 +1,8 @@ """Wrapper for connetecting to LLMs on GCP via the model_server HTTP API.""" -import enum - +from lit_nlp import app as lit_app from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types -from lit_nlp.api.types import Spec from lit_nlp.examples.gcp import constants as lit_gcp_constants from lit_nlp.examples.prompt_debugging import constants as pd_constants from lit_nlp.examples.prompt_debugging import utils as pd_utils @@ -14,18 +12,19 @@ """ Plan for this module: -From GitHub: - -* Rebase to include cl/672527408 and the CL described above -* Define an enum to track HTTP endpoints across Python modules -* Adopt HTTP endpoint enum across model_server.py and LlmOverHTTP -* Adopt model_specs.py in LlmOverHTTP, using HTTP endpoint enum for - conditional additions - """ _LlmHTTPEndpoints = lit_gcp_constants.LlmHTTPEndpoints +LLM_ON_GCP_INIT_SPEC: lit_types.Spec = { + # Note that `new_name` is not actually passed to LlmOverHTTP but the + # `/create_model` API will validate the config with a `new_name` in it. + 'new_name': lit_types.String(required=False), + 'base_url': lit_types.String(), + 'max_concurrent_requests': lit_types.Integer(default=1), + 'max_qps': lit_types.Scalar(default=25), +} + class LlmOverHTTP(lit_model.BatchedRemoteModel): @@ -84,10 +83,10 @@ def predict_minibatch( def initialize_model_group_for_salience( - name: str, base_url: str, *args, **kw -) -> dict[str, lit_model.Model]: + new_name: str, base_url: str, *args, **kw +) -> lit_model.ModelMap: """Creates '{name}' and '_{name}_salience' and '_{name}_tokenizer'.""" - salience_name, tokenizer_name = pd_utils.generate_model_group_names(name) + salience_name, tokenizer_name = pd_utils.generate_model_group_names(new_name) generation_model = LlmOverHTTP( *args, base_url=base_url, endpoint=_LlmHTTPEndpoints.GENERATE, **kw @@ -100,7 +99,16 @@ def initialize_model_group_for_salience( ) return { - name: generation_model, + new_name: generation_model, salience_name: salience_model, tokenizer_name: tokenizer_model, } + + +def get_model_loaders() -> lit_app.ModelLoadersMap: + return { + 'LLM Over HTTP': ( + initialize_model_group_for_salience, + LLM_ON_GCP_INIT_SPEC + ) + } \ No newline at end of file diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index 59f0d949..8c43cc35 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -3,6 +3,7 @@ from absl.testing import absltest from absl.testing import parameterized +from lit_nlp.examples.gcp import constants as lit_gcp_constants from lit_nlp.examples.gcp import model_server from lit_nlp.examples.prompt_debugging import utils as pd_utils import webtest @@ -41,13 +42,13 @@ def setUpClass(cls): @parameterized.named_parameters( dict( - testcase_name='predict', - endpoint='/predict', + testcase_name=lit_gcp_constants.LlmHTTPEndpoints.GENERATE.value, + endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.GENERATE.value}', expected=[{'response': 'test output text'}], ), dict( - testcase_name='salience', - endpoint='/salience', + testcase_name=lit_gcp_constants.LlmHTTPEndpoints.SALIENCE.value, + endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.SALIENCE.value}', expected=[{ 'tokens': ['test', 'output', 'text'], 'grad_l2': [0.1234, 0.3456, 0.5678], @@ -55,8 +56,8 @@ def setUpClass(cls): }], ), dict( - testcase_name='tokenize', - endpoint='/tokenize', + testcase_name=lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE, + endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE}', expected=[{'tokens': ['test', 'output', 'text']}], ), ) diff --git a/lit_nlp/examples/gcp/server.py b/lit_nlp/examples/gcp/server.py index 437b5378..1db063ce 100644 --- a/lit_nlp/examples/gcp/server.py +++ b/lit_nlp/examples/gcp/server.py @@ -1,6 +1,6 @@ """Server for sequence salience with a left-to-right language model.""" -from collections.abc import Mapping, Sequence +from collections.abc import Sequence import sys from typing import Optional @@ -9,8 +9,6 @@ from absl import logging from lit_nlp import dev_server from lit_nlp import server_flags -from lit_nlp.api import model as lit_model -from lit_nlp.api import types as lit_types from lit_nlp.examples.gcp import model as lit_gcp_model from lit_nlp.examples.prompt_debugging import datasets as pd_datasets from lit_nlp.examples.prompt_debugging import layouts as pd_layouts @@ -29,18 +27,10 @@ """ -def init_llm_on_gcp( - name: str, base_url: str, *args, **kw -) -> Mapping[str, lit_model.Model]: - return lit_gcp_model.initialize_model_group_for_salience( - name=name, base_url=base_url, *args, **kw - ) - - def get_wsgi_app() -> Optional[dev_server.LitServerType]: """Return WSGI app for container-hosted demos.""" _FLAGS.set_default("server_type", "external") - _FLAGS.set_default("demo_mode", True) + _FLAGS.set_default("demo_mode", False) _FLAGS.set_default("page_title", "LM Prompt Debugging") _FLAGS.set_default("default_layout", pd_layouts.THREE_PANEL) # Parse flags without calling app.run(main), to avoid conflict with @@ -59,14 +49,7 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: models={}, datasets={}, layouts=pd_layouts.PROMPT_DEBUGGING_LAYOUTS, - model_loaders={ - 'LLM on GCP': (init_llm_on_gcp, { - 'name': lit_types.String(), - 'base_url': lit_types.String(), - 'max_concurrent_requests': lit_types.Integer(default=1), - 'max_qps': lit_types.Scalar(default=25), - }) - }, + model_loaders=lit_gcp_model.get_model_loaders(), dataset_loaders=pd_datasets.get_dataset_loaders(), onboard_start_doc=_SPLASH_SCREEN_DOC, **server_flags.get_flags(), diff --git a/lit_nlp/examples/gcp/server_gunicorn_config.py b/lit_nlp/examples/gcp/server_gunicorn_config.py new file mode 100644 index 00000000..01e0bc32 --- /dev/null +++ b/lit_nlp/examples/gcp/server_gunicorn_config.py @@ -0,0 +1,25 @@ +# Copyright 2020 Google LLC +# +# 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. +# ============================================================================== +"""gunicorn configuration for cloud-hosted demos.""" + +import os + +_PORT = os.getenv('PORT', '5432') + +bind = f'0.0.0.0:{_PORT}' +timeout = 3600 +threads = 8 +worker_class = 'gthread' +wsgi_app = f'lit_nlp.examples.gcp.server:get_wsgi_app()' From a486f4b015bdf3201bfe5624fdb335757199b2b3 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 17 Sep 2024 09:16:37 +0000 Subject: [PATCH 66/86] Fixing enum accessor in UTs --- lit_nlp/examples/gcp/model_server_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index 8c43cc35..6cd01fad 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -56,8 +56,8 @@ def setUpClass(cls): }], ), dict( - testcase_name=lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE, - endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE}', + testcase_name=lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE.value, + endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE.value}', expected=[{'tokens': ['test', 'output', 'text']}], ), ) From 1d1140c747a543e157615ff9f44023f4deac0961 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Mon, 23 Sep 2024 15:01:16 +0000 Subject: [PATCH 67/86] Migrate to updated pd_models.get_models kw args --- lit_nlp/examples/gcp/model_server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index d818c778..d4debaee 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -56,7 +56,7 @@ def _handler(app: wsgi_app.App, request, unused_environ): dl_runtime=dl_runtime, precision=precision, batch_size=batch_size, - sequence_length=sequence_length, + max_length=sequence_length, ) gen_name = model_config[0].split(':')[0] From 0d698b8cbd4ea8d9c7298a41679d008bf344efaa Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Tue, 24 Sep 2024 15:25:00 +0000 Subject: [PATCH 68/86] Fix errors in gcp model_server and models. --- lit_nlp/examples/gcp/model.py | 6 ++++-- lit_nlp/examples/gcp/model_server_gunicorn_config.py | 2 +- lit_nlp/examples/gcp/server.py | 7 ++++++- 3 files changed, 11 insertions(+), 4 deletions(-) diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index 86f51204..9e5e97cc 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -22,7 +22,7 @@ 'new_name': lit_types.String(required=False), 'base_url': lit_types.String(), 'max_concurrent_requests': lit_types.Integer(default=1), - 'max_qps': lit_types.Scalar(default=25), + 'max_qps': lit_types.Integer(default=25, required=False), } @@ -71,8 +71,10 @@ def predict_minibatch( Returns: list of outputs, following model.output_spec() """ + inputs = {'inputs': inputs} + response = requests.post( - self.url, data=serialize.to_json(list(inputs), simple=True) + self.url, data=serialize.to_json(inputs, simple=True) ) if not (200 <= response.status_code < 300): diff --git a/lit_nlp/examples/gcp/model_server_gunicorn_config.py b/lit_nlp/examples/gcp/model_server_gunicorn_config.py index 647a275e..b781c9b6 100644 --- a/lit_nlp/examples/gcp/model_server_gunicorn_config.py +++ b/lit_nlp/examples/gcp/model_server_gunicorn_config.py @@ -16,7 +16,7 @@ import os -_PORT = os.getenv('PORT', '5432') +_PORT = os.getenv('PORT', '8080') bind = f'0.0.0.0:{_PORT}' timeout = 3600 diff --git a/lit_nlp/examples/gcp/server.py b/lit_nlp/examples/gcp/server.py index 1db063ce..1250cc43 100644 --- a/lit_nlp/examples/gcp/server.py +++ b/lit_nlp/examples/gcp/server.py @@ -45,9 +45,14 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: if len(argv) > 1: raise app.UsageError("Too many command-line arguments.") + + datasets = pd_datasets.get_datasets( + datasets_config=pd_datasets.DEFAULT_DATASETS, max_examples=pd_datasets.DEFAULT_MAX_EXAMPLES + ) + lit_demo = dev_server.Server( models={}, - datasets={}, + datasets=datasets, layouts=pd_layouts.PROMPT_DEBUGGING_LAYOUTS, model_loaders=lit_gcp_model.get_model_loaders(), dataset_loaders=pd_datasets.get_dataset_loaders(), From 5a32a65bd9283b90c9be60bff539efb66644fa76 Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Wed, 25 Sep 2024 20:29:35 +0000 Subject: [PATCH 69/86] Combine vertexai docker image and gcp_server image. --- lit_nlp/examples/gcp/server.py | 15 +- .../models.py => gcp/vertexai_models.py} | 4 +- .../vertexai_models_test.py} | 6 +- lit_nlp/examples/vertexai/Dockerfile | 76 ------- lit_nlp/examples/vertexai/demo.py | 204 ------------------ lit_nlp/examples/vertexai/gunicorn_config.py | 25 --- lit_nlp/examples/vertexai/requirements.txt | 20 -- 7 files changed, 18 insertions(+), 332 deletions(-) rename lit_nlp/examples/{vertexai/models.py => gcp/vertexai_models.py} (97%) rename lit_nlp/examples/{vertexai/models_test.py => gcp/vertexai_models_test.py} (92%) delete mode 100644 lit_nlp/examples/vertexai/Dockerfile delete mode 100644 lit_nlp/examples/vertexai/demo.py delete mode 100644 lit_nlp/examples/vertexai/gunicorn_config.py delete mode 100644 lit_nlp/examples/vertexai/requirements.txt diff --git a/lit_nlp/examples/gcp/server.py b/lit_nlp/examples/gcp/server.py index 1250cc43..c75a6f7f 100644 --- a/lit_nlp/examples/gcp/server.py +++ b/lit_nlp/examples/gcp/server.py @@ -9,7 +9,9 @@ from absl import logging from lit_nlp import dev_server from lit_nlp import server_flags +from lit_nlp.components import word_replacer, scrambler from lit_nlp.examples.gcp import model as lit_gcp_model +from lit_nlp.examples.gcp import vertexai_models from lit_nlp.examples.prompt_debugging import datasets as pd_datasets from lit_nlp.examples.prompt_debugging import layouts as pd_layouts @@ -33,6 +35,7 @@ def get_wsgi_app() -> Optional[dev_server.LitServerType]: _FLAGS.set_default("demo_mode", False) _FLAGS.set_default("page_title", "LM Prompt Debugging") _FLAGS.set_default("default_layout", pd_layouts.THREE_PANEL) + # Parse flags without calling app.run(main), to avoid conflict with # gunicorn command line flags. unused = flags.FLAGS(sys.argv, known_only=True) @@ -45,16 +48,24 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: if len(argv) > 1: raise app.UsageError("Too many command-line arguments.") - datasets = pd_datasets.get_datasets( datasets_config=pd_datasets.DEFAULT_DATASETS, max_examples=pd_datasets.DEFAULT_MAX_EXAMPLES ) + model_loaders=lit_gcp_model.get_model_loaders() + model_loaders['gemini'] = ( + vertexai_models.GeminiFoundationalModel, + vertexai_models.GeminiFoundationalModel.init_spec(), + ) + + generators = {'word_replacer': word_replacer.WordReplacer(), 'scrambler':scrambler.Scrambler()} + lit_demo = dev_server.Server( models={}, datasets=datasets, layouts=pd_layouts.PROMPT_DEBUGGING_LAYOUTS, - model_loaders=lit_gcp_model.get_model_loaders(), + model_loaders=model_loaders, + generators=generators, dataset_loaders=pd_datasets.get_dataset_loaders(), onboard_start_doc=_SPLASH_SCREEN_DOC, **server_flags.get_flags(), diff --git a/lit_nlp/examples/vertexai/models.py b/lit_nlp/examples/gcp/vertexai_models.py similarity index 97% rename from lit_nlp/examples/vertexai/models.py rename to lit_nlp/examples/gcp/vertexai_models.py index 7bf99ad7..573063b3 100644 --- a/lit_nlp/examples/vertexai/models.py +++ b/lit_nlp/examples/gcp/vertexai_models.py @@ -19,8 +19,8 @@ # TODO(fanyecourage): Rename this class. -class VertexModelGardenModel(lit_model.BatchedRemoteModel): - """VertexModelGardenModel is a wrapper for Vertex AI Model Garden model. +class GeminiFoundationalModel(lit_model.BatchedRemoteModel): + """GeminiFoundationalModel is a wrapper for foundatoinal Gemini models in Vertex AI Model Garden model. Attributes: model_name: The name of the model to load. diff --git a/lit_nlp/examples/vertexai/models_test.py b/lit_nlp/examples/gcp/vertexai_models_test.py similarity index 92% rename from lit_nlp/examples/vertexai/models_test.py rename to lit_nlp/examples/gcp/vertexai_models_test.py index 79ecdbb1..1372e4ef 100644 --- a/lit_nlp/examples/vertexai/models_test.py +++ b/lit_nlp/examples/gcp/vertexai_models_test.py @@ -4,7 +4,7 @@ from absl.testing import absltest from google.cloud import aiplatform from vertexai import generative_models -from lit_nlp.examples.vertexai import models +from lit_nlp.examples.gcp import vertexai_models class ModelsTest(absltest.TestCase): @@ -39,7 +39,7 @@ def test_query_gemini_model(self, mock_init, mock_generate_content): }) mock_generate_content.side_effect = [response1, response2] - model = models.VertexModelGardenModel(model_name="gemini-pro") + model = vertexai_models.GeminiFoundationalModel(model_name="gemini-pro") model._model = mock.MagicMock() model._model.generate_content.side_effect = [response1, response2] @@ -76,7 +76,7 @@ def test_query_self_hosted_generative_model( ) mock_generate_content.side_effect = [response1, response2] - model = models.SelfHostedGenerativeModel(aip_endpoint_name="endpoint_name") + model = vertexai_models.SelfHostedGenerativeModel(aip_endpoint_name="endpoint_name") model._endpoint = mock.MagicMock() model._endpoint.predict.side_effect = [response1, response2] diff --git a/lit_nlp/examples/vertexai/Dockerfile b/lit_nlp/examples/vertexai/Dockerfile deleted file mode 100644 index 03c7828c..00000000 --- a/lit_nlp/examples/vertexai/Dockerfile +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright 2024 Google LLC -# -# 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. -# ============================================================================== -# Use the official lightweight Python image. -# https://hub.docker.com/_/python - -# TODO(faneycourage): Add a readme file for the demo. - -# ---- LIT Base Container ---- - -FROM python:3.11-slim AS lit-nlp-base - -# Update Ubuntu packages and install basic utils -RUN apt-get update -RUN apt-get install -y wget curl gnupg2 gcc g++ git - -# Copy local code to the container image. -ENV APP_HOME=/app -WORKDIR $APP_HOME - -COPY ./lit_nlp/examples/vertexai/gunicorn_config.py ./ - - - -# ---- LIT Container for Hosted Demos ---- - -FROM lit-nlp-base AS lit-nlp-prod - -RUN python -m pip install 'lit-nlp[vertexai]' - -WORKDIR $APP_HOME -ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] - - - -# ---- LIT Container for Developing and Testing Hosted Demos ---- - -FROM lit-nlp-base AS lit-nlp-dev - -# Install yarn -RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | apt-key add - -RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ - tee /etc/apt/sources.list.d/yarn.list -RUN apt update && apt -y install yarn - -# Set up python environment with production dependencies -# This step is slow as it installs many packages. -COPY requirements_core.txt ./ -COPY lit_nlp/examples/vertexai/requirements.txt \ - lit_nlp/examples/vertexai/requirements.txt -RUN python -m pip install -r lit_nlp/examples/vertexai/requirements.txt - -# Copy the rest of the lit_nlp package -COPY . ./ - -# Build front-end with yarn -WORKDIR $APP_HOME/lit_nlp/client -ENV NODE_OPTIONS="--openssl-legacy-provider" -RUN yarn && yarn build && rm -rf node_modules/* - -# Run LIT server -# Note that the config file supports configuring the LIT demo that is launched -# via the DEMO_NAME and DEMO_PORT environment variables. -WORKDIR $APP_HOME -ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] diff --git a/lit_nlp/examples/vertexai/demo.py b/lit_nlp/examples/vertexai/demo.py deleted file mode 100644 index 350603b5..00000000 --- a/lit_nlp/examples/vertexai/demo.py +++ /dev/null @@ -1,204 +0,0 @@ -r"""A blank demo ready to load generative models and datasets. - -To use with VertexAI Model Garden models, you must install the following packages: - - pip install vertexai>=1.49.0 - -To run the demo, you must set you GCP project location and project id. Also, the -credential must be set using the VertexAI API key. -(https://ai.google.dev/gemini-api/docs/quickstart?lang=python#set_up_your_api_key). - -You can also configure the datasets and max_examples to load. If datasets and -max_examples are not provided, the default datasets and max_examples will be used. - -This LIT demo does not preload any model by default, and you can either load -them in the UI (using "Configure" menu) or preload them at demo launch time with -the optional "--gemini_model" or "--generative_model_endpoints" flags. - -The "--gemini_model" flag allows loading gemini models only and is a list of -strings with format as :. -The accepted Gemini version can be found at -https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference. - -The "--generative_model_endpoints" flag allows loading self-hosted generative -models, and is a list of strings with format as -:. The endpoint name is the -fully-qualified endpoint resource name or endpoint ID of aiplatform. The -endpoint will be available after the model is deployed on VertexAI in GCP. The -details of endpoint can be found at -https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component. - -The following command can be used to run the demo: - python -m lit_nlp.examples.vertexai.demo \ - --project_id=$GCP_PROJECT_ID \ - --project_location=$GCP_PROJECT_LOCATION \ - --gemini_models=$GEMINI_MODEL_NAMES \ - --generative_model_endpoints=$GENERATIVE_MODEL_ENDPOINTS \ - --datasets=$DATASETS \ - --max_examples=$MAX_EXAMPLES \ - --alsologtostderr - -Then navigate to localhost:5432 to access the demo UI. - -""" - -from collections.abc import Sequence -import os -import sys -from typing import Optional -from absl import app -from absl import flags -from absl import logging -from google.cloud.aiplatform import vertexai -from lit_nlp import app as lit_app -from lit_nlp import dev_server -from lit_nlp import server_flags -from lit_nlp.components import word_replacer -from lit_nlp.examples.prompt_debugging import datasets as prompt_debugging_datasets -from lit_nlp.examples.vertexai import models as vertexai_models - -_FLAGS = flags.FLAGS - -# Define GCP project information and vertex AI API key. -_LOCATION = flags.DEFINE_string( - 'project_location', - None, - 'Please enter your GCP project location', - required=True, -) -_PROJECT_ID = flags.DEFINE_string( - 'project_id', - None, - 'Please enter your project id', - required=True, -) -_GEMINI_MODELS = flags.DEFINE_list( - 'gemini_models', - None, - ' List of gemini models to load, as :.' - ' The accepted Gemini version can be found at' - ' https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference.', - required=False, -) -_GENERATIVE_MODEL_ENDPOINTS = flags.DEFINE_list( - 'generative_model_endpoints', - None, - ( - ' List of fully-qualified endpoint resource name or endpoint ID of' - ' aiplatform, as :.' - ' The endpoint will be provided after the model is deployed' - ' on VertexAI in GCP. The details of endpoint can be found at' - ' https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component.' - ), - required=False, -) - -# Define dataset information. -_DATASETS = flags.DEFINE_list( - 'datasets', - prompt_debugging_datasets.DEFAULT_DATASETS, - 'Datasets to load, as :. Format should be either .jsonl where' - " each record contains 'prompt' and optional 'target' and 'source' fields," - ' or a plain text file with one prompt per line.', -) - -_MAX_EXAMPLES = flags.DEFINE_integer( - 'max_examples', - prompt_debugging_datasets.DEFAULT_MAX_EXAMPLES, - ( - 'Maximum number of examples to load from each evaluation set. Set to' - ' None to load the full set.' - ), -) - - -def get_wsgi_app() -> Optional[dev_server.LitServerType]: - """Return WSGI app for container-hosted demos.""" - _FLAGS.set_default('server_type', 'external') - _FLAGS.set_default('demo_mode', False) - - location = os.getenv('PROJECT_LOCATION', None) - _FLAGS['project_location'].value = location - - project_id = os.getenv('PROJECT_ID', None) - _FLAGS['project_id'].value = project_id - - gemini_models = os.getenv('GEMINI_MODELS', None) - if gemini_models: - gemini_model_list = gemini_models.split(',') - _FLAGS['gemini_models'].value = gemini_model_list - - generative_model_endpoints = os.getenv('GENERATIVE_MODEL_ENDPOINTS', None) - if generative_model_endpoints: - generative_model_endpoints_list = generative_model_endpoints.split(',') - _FLAGS['generative_model_endpoints'].value = ( - generative_model_endpoints_list - ) - - datasets = os.getenv('DATASETS', None) - if datasets: - datasets_list = datasets.split(',') - _FLAGS['datasets'].value = datasets_list - - max_examples = os.getenv('MAX_EXAMPLES', None) - if max_examples: - _FLAGS['max_examples'].value = int(max_examples) - - # Parse flags without calling app.run(main), to avoid conflict with - # gunicorn command line flags. - unused = flags.FLAGS(sys.argv, known_only=True) - if unused: - logging.info( - 'generateive_demo:get_wsgi_app() called with unused args: %s', unused - ) - return main([]) - - -def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: - if len(argv) > 1: - raise app.UsageError('Too many command-line arguments.') - - vertexai.init(project=_PROJECT_ID.value, location=_LOCATION.value) - - models = {} - if _GEMINI_MODELS.value: - for model_string in _GEMINI_MODELS.value: - name, gemini_model = model_string.split(':', 1) - models[name] = vertexai_models.VertexModelGardenModel(gemini_model) - if _GENERATIVE_MODEL_ENDPOINTS.value: - for endpoint_string in _GENERATIVE_MODEL_ENDPOINTS.value: - name, endpoint_name = endpoint_string.split(':', 1) - models[name] = vertexai_models.SelfHostedGenerativeModel( - aip_endpoint_name=endpoint_name, - ) - - model_loaders: lit_app.ModelLoadersMap = {} - model_loaders['gemini'] = ( - vertexai_models.VertexModelGardenModel, - vertexai_models.VertexModelGardenModel.init_spec(), - ) - model_loaders['self_hosted_generative_model'] = ( - vertexai_models.SelfHostedGenerativeModel, - vertexai_models.SelfHostedGenerativeModel.init_spec(), - ) - - datasets = prompt_debugging_datasets.get_datasets( - datasets_config=_DATASETS.value, max_examples=_MAX_EXAMPLES.value - ) - dataset_loaders = prompt_debugging_datasets.get_dataset_loaders() - - generators = {'word_replacer': word_replacer.WordReplacer()} - - lit_demo = dev_server.Server( - models=models, - model_loaders=model_loaders, - generators=generators, - datasets=datasets, - dataset_loaders=dataset_loaders, - **server_flags.get_flags() - ) - return lit_demo.serve() - - -if __name__ == '__main__': - app.run(main) diff --git a/lit_nlp/examples/vertexai/gunicorn_config.py b/lit_nlp/examples/vertexai/gunicorn_config.py deleted file mode 100644 index 92d87851..00000000 --- a/lit_nlp/examples/vertexai/gunicorn_config.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright 2024 Google LLC -# -# 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. -# ============================================================================== -"""gunicorn configuration for cloud-hosted demos.""" - -import os - -_DEMO_PORT = os.getenv('DEMO_PORT', '5432') - -bind = f'0.0.0.0:{_DEMO_PORT}' -timeout = 3600 -threads = 8 -worker_class = 'gthread' -wsgi_app = 'lit_nlp.examples.vertexai.demo:get_wsgi_app()' diff --git a/lit_nlp/examples/vertexai/requirements.txt b/lit_nlp/examples/vertexai/requirements.txt deleted file mode 100644 index e43911f7..00000000 --- a/lit_nlp/examples/vertexai/requirements.txt +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright 2024 Google LLC -# -# 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. -# ============================================================================== - --r ../../../requirements_core.txt - -google-cloud-aiplatform>=1.60.0 -gunicorn>=20.1.0 -vertexai>=1.49.0 From af3055b17d1dea9f5171da55f4401d83ccaebd6a Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 26 Sep 2024 01:06:12 +0000 Subject: [PATCH 70/86] Add identity_token as LLM_ON_GCP init spec. --- lit_nlp/examples/gcp/model.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index 9e5e97cc..05a4dc48 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -21,6 +21,7 @@ # `/create_model` API will validate the config with a `new_name` in it. 'new_name': lit_types.String(required=False), 'base_url': lit_types.String(), + 'identity_token': lit_types.String(default=""), 'max_concurrent_requests': lit_types.Integer(default=1), 'max_qps': lit_types.Integer(default=25, required=False), } @@ -31,6 +32,7 @@ class LlmOverHTTP(lit_model.BatchedRemoteModel): def __init__( self, base_url: str, + identity_token: str, endpoint: str | _LlmHTTPEndpoints, max_concurrent_requests: int = 4, max_qps: int | float = 25 @@ -38,6 +40,7 @@ def __init__( super().__init__(max_concurrent_requests, max_qps) self.endpoint = _LlmHTTPEndpoints(endpoint) self.url = f'{base_url}/{self.endpoint.value}' + self.identity_token = identity_token def input_spec(self) -> lit_types.Spec: input_spec = pd_constants.INPUT_SPEC @@ -72,9 +75,12 @@ def predict_minibatch( list of outputs, following model.output_spec() """ inputs = {'inputs': inputs} - + headers = { + 'Authorization': f'Bearer {self.identity_token}', + 'Content-Type': 'application/json' + } response = requests.post( - self.url, data=serialize.to_json(inputs, simple=True) + self.url, headers=headers,data=serialize.to_json(inputs, simple=True) ) if not (200 <= response.status_code < 300): From 82736831e5950496565bbcc3a7556d59e98186dd Mon Sep 17 00:00:00 2001 From: Fan Ye Date: Thu, 26 Sep 2024 01:07:51 +0000 Subject: [PATCH 71/86] Rename the model loader name. --- lit_nlp/examples/gcp/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index 05a4dc48..0d008e69 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -115,7 +115,7 @@ def initialize_model_group_for_salience( def get_model_loaders() -> lit_app.ModelLoadersMap: return { - 'LLM Over HTTP': ( + 'LLM (self hosted)': ( initialize_model_group_for_salience, LLM_ON_GCP_INIT_SPEC ) From 2e2e0df6e2754de0cf552b649278bc934cc448cc Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Fri, 11 Oct 2024 18:56:24 +0000 Subject: [PATCH 72/86] Updating requirements for TF 2.16 compatibility --- .github/workflows/ci.yml | 10 ++--- lit_nlp/examples/glue/models.py | 13 +++--- lit_nlp/examples/penguin/model.py | 8 +++- lit_nlp/examples/penguin/requirements.txt | 20 --------- .../prompt_debugging/requirements.txt | 22 ---------- lit_nlp/examples/tools/glue_trainer.py | 9 ++-- lit_nlp/examples/tydi/requirements.txt | 2 +- pyproject.toml | 26 +++++++----- requirements.txt | 31 ++++++++++++-- requirements_core.txt | 42 ------------------- ...ts.txt => requirements_examples_common.txt | 11 +++-- ...ts.txt => requirements_examples_descai.txt | 8 ++-- ...les.txt => requirements_examples_genai.txt | 11 ++--- requirements_test.txt | 4 +- 14 files changed, 86 insertions(+), 131 deletions(-) delete mode 100644 lit_nlp/examples/penguin/requirements.txt delete mode 100644 lit_nlp/examples/prompt_debugging/requirements.txt delete mode 100644 requirements_core.txt rename lit_nlp/examples/gcp/requirements.txt => requirements_examples_common.txt (81%) rename lit_nlp/examples/glue/requirements.txt => requirements_examples_descai.txt (86%) rename requirements_examples.txt => requirements_examples_genai.txt (85%) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 74014c8e..0432a655 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -45,14 +45,10 @@ jobs: uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - - name: Install Python dependencies - run: python -m pip install -r requirements.txt - - name: Install LIT package - run: python -m pip install -e . + - name: Install LIT package with testing dependencies + run: python -m pip install -e '.[test]' - name: Test LIT - run: | - python -m pip install pytest - pytest -v + run: pytest -v - name: Setup Node ${{ matrix.node-version }} uses: actions/setup-node@v2 with: diff --git a/lit_nlp/examples/glue/models.py b/lit_nlp/examples/glue/models.py index d8c3219b..7ea576a7 100644 --- a/lit_nlp/examples/glue/models.py +++ b/lit_nlp/examples/glue/models.py @@ -17,8 +17,11 @@ from lit_nlp.lib import utils import numpy as np import tensorflow as tf +import tf_keras as keras import transformers +os.environ["TF_USE_LEGACY_KERAS"] = "1" + JsonDict = lit_types.JsonDict Spec = lit_types.Spec TFSequenceClassifierOutput = ( @@ -203,13 +206,13 @@ def train( ) # Prepare model for training. - opt = tf.keras.optimizers.Adam(learning_rate=learning_rate, epsilon=1e-08) + opt = keras.optimizers.Adam(learning_rate=learning_rate, epsilon=1e-08) if self.is_regression: - loss = tf.keras.losses.MeanSquaredError() - metric = tf.keras.metrics.RootMeanSquaredError("rmse") + loss = keras.losses.MeanSquaredError() + metric = keras.metrics.RootMeanSquaredError("rmse") else: - loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) - metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy") + loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True) + metric = keras.metrics.SparseCategoricalAccuracy("accuracy") self.model.compile(optimizer=opt, loss=loss, metrics=[metric]) steps_per_epoch = len(train_inputs) // batch_size diff --git a/lit_nlp/examples/penguin/model.py b/lit_nlp/examples/penguin/model.py index 6aec9c11..625d2d9c 100644 --- a/lit_nlp/examples/penguin/model.py +++ b/lit_nlp/examples/penguin/model.py @@ -1,11 +1,15 @@ """🐧 TensorFlow Keras model for the Penguin dataset.""" +import os + from lit_nlp.api import model as lit_model from lit_nlp.api import types as lit_types from lit_nlp.examples.penguin import data as penguin_data from lit_nlp.lib import file_cache import numpy as np -import tensorflow as tf +import tf_keras as keras + +os.environ["TF_USE_LEGACY_KERAS"] = "1" _VOCABS = penguin_data.VOCABS @@ -15,7 +19,7 @@ class PenguinModel(lit_model.BatchedModel): def __init__(self, path: str): path = file_cache.cached_path(path) - self.model = tf.keras.models.load_model(path) + self.model = keras.models.load_model(path) # Feature column means and variance to normalize values before # prediction. self.means = np.array([ diff --git a/lit_nlp/examples/penguin/requirements.txt b/lit_nlp/examples/penguin/requirements.txt deleted file mode 100644 index 7bef3d18..00000000 --- a/lit_nlp/examples/penguin/requirements.txt +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright 2023 Google LLC -# -# 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. -# ============================================================================== - --r ../../../requirements_core.txt - -Jinja2>=3.1.4 -tensorflow>=2.10.0,<2.16.0 -tensorflow-datasets>=4.9.0 \ No newline at end of file diff --git a/lit_nlp/examples/prompt_debugging/requirements.txt b/lit_nlp/examples/prompt_debugging/requirements.txt deleted file mode 100644 index 5f76376e..00000000 --- a/lit_nlp/examples/prompt_debugging/requirements.txt +++ /dev/null @@ -1,22 +0,0 @@ -# Copyright 2023 Google LLC -# -# 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. -# ============================================================================== - --r ../../../requirements_core.txt - -keras>=3.1.0 -keras-nlp>=0.9.0 -tensorflow-text>=2.10.0 -torch>=2.0.0 -transformers>=4.27.1 \ No newline at end of file diff --git a/lit_nlp/examples/tools/glue_trainer.py b/lit_nlp/examples/tools/glue_trainer.py index 6e57af58..60c848dc 100644 --- a/lit_nlp/examples/tools/glue_trainer.py +++ b/lit_nlp/examples/tools/glue_trainer.py @@ -29,6 +29,9 @@ from lit_nlp.examples.glue import models as glue_models from lit_nlp.lib import serialize import tensorflow as tf +import tf_keras as keras + +os.environ["TF_USE_LEGACY_KERAS"] = "1" _ENCODER_NAME = flags.DEFINE_string( "encoder_name", "bert-base-uncased", @@ -55,10 +58,10 @@ def history_to_dict(keras_history): } -class EpochSaverCallback(tf.keras.callbacks.Callback): +class EpochSaverCallback(keras.callbacks.Callback): """Save model at the beginning of training and after every epoch. - Similar to tf.keras.callbacks.ModelCheckpoint, but this allows us to specify + Similar to keras.callbacks.ModelCheckpoint, but this allows us to specify a custom save fn to call, such as the HuggingFace model.save() which writes .h5 files and config information. """ @@ -87,7 +90,7 @@ def train_and_save(model, # Set up logging for TensorBoard. To view, run: # tensorboard --log_dir=/tensorboard keras_callbacks = [ - tf.keras.callbacks.TensorBoard( + keras.callbacks.TensorBoard( log_dir=os.path.join(train_path, "tensorboard")) ] if save_intermediates: diff --git a/lit_nlp/examples/tydi/requirements.txt b/lit_nlp/examples/tydi/requirements.txt index f04e568d..41c3762c 100644 --- a/lit_nlp/examples/tydi/requirements.txt +++ b/lit_nlp/examples/tydi/requirements.txt @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================== --r ../../../requirements_core.txt +-r ../../../requirements.txt flax>=0.5.3 jax==0.4.6 diff --git a/pyproject.toml b/pyproject.toml index 1f727cd6..70ebada8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,30 +77,36 @@ keywords = [ ] [project.optional-dependencies] +# LINT.IfChange examples-common = [ "gunicorn>=20.1.0", + "tensorflow>=2.16.0", + "transformers>=4.27.1", ] +# LINT.ThenChange(./requirements_examples_common.txt) # LINT.IfChange -examples = [ +examples-descai = [ "lit-nlp[examples-common]", - "sentencepiece==0.1.99", - "tensorflow>=2.10.0,<2.16.0", "tensorflow-datasets>=4.9.0", - "tensorflow-text>=2.10.0,<2.16.0", - "torch>=2.0.0", - "transformers>=4.27.1", - "vertexai>=1.49.0", + "tf-keras>=2.16", ] -# LINT.ThenChange(./requirements_examples.txt) +# LINT.ThenChange(./requirements_examples_descai.txt) # LINT.IfChange -vertexai = [ +examples-genai = [ "lit-nlp[examples-common]", "google-cloud-aiplatform>=1.60.0", + "keras>=3.0.0", + "keras-nlp>=0.14.0", + "sentencepiece==0.1.99", + "tensorflow-text>=2.16.0", + "torch>=2.0.0", "vertexai>=1.49.0", ] -# LINT.ThenChange(./examples/vertexai/requirements.txt) +# LINT.ThenChange(./requirements_examples_genai.txt) # LINT.IfChange test = [ + "lit-nlp[examples-gcp]", + "lit-nlp[examples-hosted]", "lime==0.2.0.1", "pytest>=7.4.0,<8.0.0", "webtest>=2.0", diff --git a/requirements.txt b/requirements.txt index 365c8d03..8bcd7847 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,31 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== --r requirements_core.txt --r requirements_examples.txt --r requirements_test.txt +# LINT.IfChange +absl-py>=1.4.0 +annoy>=1.17.3 +attrs>=22.1.0 +etils[epath]>=1.5.0 +filelock>=3.12.3 +google-cloud-translate>=3.11.1 +ipython>=7.34.0 +Levenshtein>=0.21.1 +matplotlib>=3.6.0,<3.9.0 +ml-collections>=0.1.1 +numpy>=1.24.1,<2.0.0 +pandas>=2.0.3 +Pillow>=10.0.0 +portpicker>=1.5.2 +requests>=2.31.0 +rouge-score>=0.1.2 +sacrebleu>=2.3.1 +saliency>=0.1.3 +scikit-learn>=1.0.2 +scipy>=1.10.1 +shap>=0.42.0,<0.46.0 +six>=1.16.0 +termcolor>=2.3.0 +tqdm>=4.64.0 +umap-learn>=0.5.1 +werkzeug>=2.2.3 +# LINT.ThenChange(./pyproject.toml) diff --git a/requirements_core.txt b/requirements_core.txt deleted file mode 100644 index 8bcd7847..00000000 --- a/requirements_core.txt +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright 2023 Google LLC -# -# 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. -# ============================================================================== -# LINT.IfChange -absl-py>=1.4.0 -annoy>=1.17.3 -attrs>=22.1.0 -etils[epath]>=1.5.0 -filelock>=3.12.3 -google-cloud-translate>=3.11.1 -ipython>=7.34.0 -Levenshtein>=0.21.1 -matplotlib>=3.6.0,<3.9.0 -ml-collections>=0.1.1 -numpy>=1.24.1,<2.0.0 -pandas>=2.0.3 -Pillow>=10.0.0 -portpicker>=1.5.2 -requests>=2.31.0 -rouge-score>=0.1.2 -sacrebleu>=2.3.1 -saliency>=0.1.3 -scikit-learn>=1.0.2 -scipy>=1.10.1 -shap>=0.42.0,<0.46.0 -six>=1.16.0 -termcolor>=2.3.0 -tqdm>=4.64.0 -umap-learn>=0.5.1 -werkzeug>=2.2.3 -# LINT.ThenChange(./pyproject.toml) diff --git a/lit_nlp/examples/gcp/requirements.txt b/requirements_examples_common.txt similarity index 81% rename from lit_nlp/examples/gcp/requirements.txt rename to requirements_examples_common.txt index 9586f030..cd2e943c 100644 --- a/lit_nlp/examples/gcp/requirements.txt +++ b/requirements_examples_common.txt @@ -13,11 +13,10 @@ # limitations under the License. # ============================================================================== --r ../../../requirements_core.txt --r ../prompt_debugging/requirements.txt +-r requirements.txt -google-cloud-aiplatform>=1.60.0 +# LINT.IfChange gunicorn>=20.1.0 -lit-nlp>=1.2 -vertexai>=1.49.0 -webtest>=2.0 \ No newline at end of file +tensorflow>=2.16.0 +transformers>=4.27.1 +# LINT.ThenChange(./pyproject.toml) diff --git a/lit_nlp/examples/glue/requirements.txt b/requirements_examples_descai.txt similarity index 86% rename from lit_nlp/examples/glue/requirements.txt rename to requirements_examples_descai.txt index 51f146be..d2a21abf 100644 --- a/lit_nlp/examples/glue/requirements.txt +++ b/requirements_examples_descai.txt @@ -12,9 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# LINT.IfChange +-r requirements_examples_common.txt --r ../../../requirements_core.txt - -tensorflow>=2.10.0,<2.16.0 tensorflow-datasets>=4.9.0 -transformers>=4.27.1 \ No newline at end of file +tf-keras>=2.16 +# LINT.ThenChange(./pyproject.toml) diff --git a/requirements_examples.txt b/requirements_examples_genai.txt similarity index 85% rename from requirements_examples.txt rename to requirements_examples_genai.txt index 6dd36176..ae81efe2 100644 --- a/requirements_examples.txt +++ b/requirements_examples_genai.txt @@ -13,12 +13,13 @@ # limitations under the License. # ============================================================================== # LINT.IfChange -gunicorn>=20.1.0 +-r requirements_examples_common.txt + +google-cloud-aiplatform>=1.60.0 +keras>=3.0.0 +keras-nlp>=0.14.0 sentencepiece==0.1.99 -tensorflow>=2.10.0,<2.16.0 -tensorflow-datasets>=4.9.0 -tensorflow-text>=2.10.0,<2.16.0 +tensorflow-text>=2.16.0 torch>=2.0.0 -transformers>=4.27.1 vertexai>=1.49.0 # LINT.ThenChange(./pyproject.toml) diff --git a/requirements_test.txt b/requirements_test.txt index e1c020e9..5ab86459 100644 --- a/requirements_test.txt +++ b/requirements_test.txt @@ -13,8 +13,10 @@ # limitations under the License. # ============================================================================== # LINT.IfChange +-r requirements_examples_descai.txt +-r requirements_examples_genai.txt + lime==0.2.0.1 -google-cloud-aiplatform>=1.60.0 pytest>=7.4.0,<8.0.0 webtest>=2.0 # LINT.ThenChange(./pyproject.toml) From 9b3cb1e62bbf9db2d612c63bd4a0cdd2b57784bc Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Mon, 14 Oct 2024 14:35:24 +0000 Subject: [PATCH 73/86] Fixing optional deps name skew --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 70ebada8..750a0d38 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -105,8 +105,8 @@ examples-genai = [ # LINT.ThenChange(./requirements_examples_genai.txt) # LINT.IfChange test = [ - "lit-nlp[examples-gcp]", - "lit-nlp[examples-hosted]", + "lit-nlp[examples-descai]", + "lit-nlp[examples-genai]", "lime==0.2.0.1", "pytest>=7.4.0,<8.0.0", "webtest>=2.0", From bc0271b9c5fddf160cb62e10e2795a2fdecd6275 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Mon, 14 Oct 2024 16:26:56 +0000 Subject: [PATCH 74/86] Dockerfile adjustments to separate prod/dev images --- Dockerfile | 9 ++++--- lit_nlp/examples/gcp/Dockerfile | 46 +++++++++++++++++++------------- requirements_examples_common.txt | 2 +- 3 files changed, 35 insertions(+), 22 deletions(-) diff --git a/Dockerfile b/Dockerfile index 677e4eaa..635d9281 100644 --- a/Dockerfile +++ b/Dockerfile @@ -35,7 +35,7 @@ COPY ./lit_nlp/examples/gunicorn_config.py ./ FROM lit-nlp-base AS lit-nlp-prod -RUN python -m pip install 'lit-nlp[examples]' +RUN python -m pip install 'lit-nlp[examples-descai]' WORKDIR $APP_HOME ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] @@ -54,8 +54,11 @@ RUN apt update && apt -y install yarn # Set up python environment with production dependencies # This step is slow as it installs many packages. -COPY ./requirements*.txt ./ -RUN python -m pip install -r requirements.txt +COPY requirements.txt \ + requirements_examples_common.txt \ + requirements_examples_descai.txt \ + ./ +RUN python -m pip install -r requirements_examples_descai.txt # Copy the rest of the lit_nlp package COPY . ./ diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index 14d3d0c4..fd685baf 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -85,6 +85,24 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip RUN apt-get clean RUN rm -rf /var/lib/apt/lists/* +COPY ./lit_nlp/examples/gcp/model_server_gunicorn_config.py ./ + + + +# ---- LIT on GCP Production Images ---- + +FROM lit-gcp-app-server-base AS lit-gcp-app-server + +RUN python -m pip install 'lit-nlp[examples-genai]' +ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] + + + +FROM lit-gcp-model-server-base AS lit-gcp-model-server + +RUN python -m pip install 'lit-nlp[examples-genai]' +ENTRYPOINT ["gunicorn", "--config=model_server_gunicorn_config.py"] + # ---- LIT on GCP Development Images ---- @@ -99,12 +117,11 @@ RUN apt update && apt -y install yarn # Set up python environment with production dependencies # This step is slow as it installs many packages. -COPY requirements_core.txt ./ -COPY lit_nlp/examples/prompt_debugging/requirements.txt \ - lit_nlp/examples/prompt_debugging/requirements.txt -COPY lit_nlp/examples/gcp/requirements.txt \ - lit_nlp/examples/gcp/requirements.txt -RUN python -m pip install -r lit_nlp/examples/gcp/requirements.txt +COPY requirements.txt \ + requirements_examples_common.txt \ + requirements_examples_genai.txt \ + ./ +RUN python -m pip install -r requirements_examples_genai.txt # Copy the rest of the lit_nlp package COPY . ./ @@ -138,16 +155,11 @@ RUN echo "deb https://dl.yarnpkg.com/debian/ stable main" | \ tee /etc/apt/sources.list.d/yarn.list RUN apt update && apt -y install yarn -COPY ./lit_nlp/examples/gcp/model_server_gunicorn_config.py ./ - -# TODO(b/353980272): Replace this with a requirements file specific to the GCP -# exmaple, this should include the core lit-nlp package. -COPY requirements_core.txt ./ -COPY lit_nlp/examples/prompt_debugging/requirements.txt \ - lit_nlp/examples/prompt_debugging/requirements.txt -COPY lit_nlp/examples/gcp/requirements.txt \ - lit_nlp/examples/gcp/requirements.txt -RUN python -m pip install -r lit_nlp/examples/gcp/requirements.txt +COPY requirements.txt \ + requirements_examples_common.txt \ + requirements_examples_genai.txt \ + ./ +RUN python -m pip install -r requirements_examples_genai.txt # Copy the rest of the lit_nlp package COPY . ./ @@ -157,7 +169,5 @@ WORKDIR $APP_HOME/lit_nlp/client ENV NODE_OPTIONS "--openssl-legacy-provider" RUN yarn && yarn build && rm -rf node_modules/* -# TODO(b/353980272): Replace this with the GCP-specific config -# See https://github.com/PAIR-code/lit/blob/main/Dockerfile WORKDIR $APP_HOME ENTRYPOINT ["gunicorn", "--config=model_server_gunicorn_config.py"] diff --git a/requirements_examples_common.txt b/requirements_examples_common.txt index cd2e943c..511505c9 100644 --- a/requirements_examples_common.txt +++ b/requirements_examples_common.txt @@ -13,9 +13,9 @@ # limitations under the License. # ============================================================================== +# LINT.IfChange -r requirements.txt -# LINT.IfChange gunicorn>=20.1.0 tensorflow>=2.16.0 transformers>=4.27.1 From e4dcc51692ae817421fa7f1b61d3f6d65eea2fe2 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Mon, 14 Oct 2024 16:47:29 +0000 Subject: [PATCH 75/86] Migrate to ENV name=value form in Dockerfiles --- Dockerfile | 2 +- lit_nlp/examples/gcp/Dockerfile | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/Dockerfile b/Dockerfile index 635d9281..e21d2342 100644 --- a/Dockerfile +++ b/Dockerfile @@ -24,7 +24,7 @@ RUN apt-get update RUN apt-get install -y wget curl gnupg2 gcc g++ git # Copy local code to the container image. -ENV APP_HOME /app +ENV APP_HOME=/app WORKDIR $APP_HOME COPY ./lit_nlp/examples/gunicorn_config.py ./ diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index fd685baf..854c9dd2 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -24,7 +24,7 @@ RUN apt-get update RUN apt-get install -y wget curl gnupg2 gcc g++ git # Copy local code to the container image. -ENV APP_HOME /app +ENV APP_HOME=/app WORKDIR $APP_HOME COPY ./lit_nlp/examples/gcp/server_gunicorn_config.py ./gunicorn_config.py @@ -33,7 +33,7 @@ COPY ./lit_nlp/examples/gcp/server_gunicorn_config.py ./gunicorn_config.py FROM nvidia/cuda:12.5.1-base-ubuntu22.04 AS lit-gcp-model-server-base ENV DEBIAN_FRONTEND=noninteractive -ENV LANG C.UTF-8 +ENV LANG=C.UTF-8 ARG PYTHON_VERSION=python3.11 @@ -128,7 +128,7 @@ COPY . ./ # Build front-end with yarn WORKDIR $APP_HOME/lit_nlp/client -ENV NODE_OPTIONS "--openssl-legacy-provider" +ENV NODE_OPTIONS="--openssl-legacy-provider" RUN yarn && yarn build && rm -rf node_modules/* # Run LIT server @@ -140,7 +140,7 @@ ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] FROM lit-gcp-model-server-base AS lit-gcp-model-server-dev -ENV APP_HOME /app +ENV APP_HOME=/app WORKDIR $APP_HOME # Install Node.js v18 (the base image ships with Node.js v12) @@ -166,7 +166,7 @@ COPY . ./ # Build front-end with yarn WORKDIR $APP_HOME/lit_nlp/client -ENV NODE_OPTIONS "--openssl-legacy-provider" +ENV NODE_OPTIONS="--openssl-legacy-provider" RUN yarn && yarn build && rm -rf node_modules/* WORKDIR $APP_HOME From 110c999f3250863275b6e4a172789ef66b9589ec Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Mon, 14 Oct 2024 19:13:21 +0000 Subject: [PATCH 76/86] Adding license declarations --- lit_nlp/examples/gcp/constants.py | 15 +++++++++++++++ lit_nlp/examples/gcp/model.py | 17 ++++++++++++++++- lit_nlp/examples/gcp/model_server.py | 15 +++++++++++++++ .../gcp/model_server_gunicorn_config.py | 2 +- lit_nlp/examples/gcp/model_server_test.py | 15 +++++++++++++++ lit_nlp/examples/gcp/server.py | 15 +++++++++++++++ lit_nlp/examples/gcp/server_gunicorn_config.py | 2 +- lit_nlp/examples/gcp/vertexai_models.py | 15 +++++++++++++++ lit_nlp/examples/gcp/vertexai_models_test.py | 15 +++++++++++++++ requirements_examples_descai.txt | 2 +- requirements_examples_genai.txt | 2 +- 11 files changed, 110 insertions(+), 5 deletions(-) diff --git a/lit_nlp/examples/gcp/constants.py b/lit_nlp/examples/gcp/constants.py index 244c7637..0bf90c58 100644 --- a/lit_nlp/examples/gcp/constants.py +++ b/lit_nlp/examples/gcp/constants.py @@ -1,3 +1,18 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + import enum class LlmHTTPEndpoints(enum.Enum): diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index 0d008e69..6cd2b0d1 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -1,3 +1,18 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== + """Wrapper for connetecting to LLMs on GCP via the model_server HTTP API.""" from lit_nlp import app as lit_app @@ -76,7 +91,7 @@ def predict_minibatch( """ inputs = {'inputs': inputs} headers = { - 'Authorization': f'Bearer {self.identity_token}', + 'Authorization': f'Bearer {self.identity_token}', 'Content-Type': 'application/json' } response = requests.post( diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index d4debaee..7b658b25 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -1,3 +1,18 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== + r"""A model server for serving models on GCP via Gunicorn.""" from collections.abc import Sequence diff --git a/lit_nlp/examples/gcp/model_server_gunicorn_config.py b/lit_nlp/examples/gcp/model_server_gunicorn_config.py index b781c9b6..78dbb00a 100644 --- a/lit_nlp/examples/gcp/model_server_gunicorn_config.py +++ b/lit_nlp/examples/gcp/model_server_gunicorn_config.py @@ -1,4 +1,4 @@ -# Copyright 2020 Google LLC +# Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index 6cd01fad..11d940fe 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -1,3 +1,18 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + import os from unittest import mock diff --git a/lit_nlp/examples/gcp/server.py b/lit_nlp/examples/gcp/server.py index c75a6f7f..1955022e 100644 --- a/lit_nlp/examples/gcp/server.py +++ b/lit_nlp/examples/gcp/server.py @@ -1,3 +1,18 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== + """Server for sequence salience with a left-to-right language model.""" from collections.abc import Sequence diff --git a/lit_nlp/examples/gcp/server_gunicorn_config.py b/lit_nlp/examples/gcp/server_gunicorn_config.py index 01e0bc32..a5f08c3b 100644 --- a/lit_nlp/examples/gcp/server_gunicorn_config.py +++ b/lit_nlp/examples/gcp/server_gunicorn_config.py @@ -1,4 +1,4 @@ -# Copyright 2020 Google LLC +# Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/lit_nlp/examples/gcp/vertexai_models.py b/lit_nlp/examples/gcp/vertexai_models.py index 573063b3..04f3ee81 100644 --- a/lit_nlp/examples/gcp/vertexai_models.py +++ b/lit_nlp/examples/gcp/vertexai_models.py @@ -1,3 +1,18 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== + """Model Wrapper for generative models.""" from collections.abc import Iterable diff --git a/lit_nlp/examples/gcp/vertexai_models_test.py b/lit_nlp/examples/gcp/vertexai_models_test.py index 1372e4ef..e5f0fa0b 100644 --- a/lit_nlp/examples/gcp/vertexai_models_test.py +++ b/lit_nlp/examples/gcp/vertexai_models_test.py @@ -1,3 +1,18 @@ +# Copyright 2024 Google LLC +# +# 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. +# ============================================================================== + """Tests for lit_nlp.examples.vertexai.models.""" from unittest import mock diff --git a/requirements_examples_descai.txt b/requirements_examples_descai.txt index d2a21abf..eba150dd 100644 --- a/requirements_examples_descai.txt +++ b/requirements_examples_descai.txt @@ -1,4 +1,4 @@ -# Copyright 2023 Google LLC +# Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/requirements_examples_genai.txt b/requirements_examples_genai.txt index ae81efe2..bb2d5066 100644 --- a/requirements_examples_genai.txt +++ b/requirements_examples_genai.txt @@ -1,4 +1,4 @@ -# Copyright 2023 Google LLC +# Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. From c778fe12da2c82b8262ed1a89cd6c5aaf7798e38 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 15 Oct 2024 12:36:03 +0000 Subject: [PATCH 77/86] Resolving review comments --- lit_nlp/examples/gcp/README.md | 2 +- lit_nlp/examples/gcp/model.py | 7 +----- lit_nlp/examples/gcp/model_server.py | 22 ++++++++----------- .../gcp/model_server_gunicorn_config.py | 2 +- .../examples/gcp/server_gunicorn_config.py | 2 +- lit_nlp/examples/gcp/vertexai_models.py | 4 ---- 6 files changed, 13 insertions(+), 26 deletions(-) diff --git a/lit_nlp/examples/gcp/README.md b/lit_nlp/examples/gcp/README.md index 376fcbbd..b27ed5e7 100644 --- a/lit_nlp/examples/gcp/README.md +++ b/lit_nlp/examples/gcp/README.md @@ -27,6 +27,6 @@ docker build -f ./lit_nlp/examples/gcp/Dockerfile -t lit-app:gcp-dev . ### Run GPT-2 in a Docker container ```shell -# Runs GPT-2 in Keras on Tensorflow +# Runs GPT-2 in Keras on PyTorch docker run --rm -p 5432:5432 -e MODEL_CONFIG=gpt2:gpt2_base_en lit-app:gcp-dev ``` diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index 6cd2b0d1..b20b4a43 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================== -"""Wrapper for connetecting to LLMs on GCP via the model_server HTTP API.""" +"""Wrapper for connecting to LLMs on GCP via the model_server HTTP API.""" from lit_nlp import app as lit_app from lit_nlp.api import model as lit_model @@ -24,11 +24,6 @@ from lit_nlp.lib import serialize import requests -""" -Plan for this module: - -""" - _LlmHTTPEndpoints = lit_gcp_constants.LlmHTTPEndpoints LLM_ON_GCP_INIT_SPEC: lit_types.Spec = { diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 7b658b25..6a3d25a3 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -18,21 +18,14 @@ from collections.abc import Sequence import functools import os -from typing import Optional from absl import app -from lit_nlp import dev_server from lit_nlp.examples.gcp import constants as lit_gcp_constants from lit_nlp.examples.prompt_debugging import models as pd_models from lit_nlp.examples.prompt_debugging import utils as pd_utils from lit_nlp.lib import serialize from lit_nlp.lib import wsgi_app -DEFAULT_DL_FRAMEWORK = 'kerasnlp' -DEFAULT_DL_RUNTIME = 'tensorflow' -DEFAULT_PRECISION = 'bfloat16' -DEFAULT_SEQUENCE_LENGTH = 512 -DEFAULT_BATCH_SIZE = 1 DEFAULT_MODELS = 'gemma_1.1_2b_IT:gemma_1.1_instruct_2b_en' _LlmHTTPEndpoints = lit_gcp_constants.LlmHTTPEndpoints @@ -59,11 +52,13 @@ def _handler(app: wsgi_app.App, request, unused_environ): f'Only 1 model configuration can be provided, got {num_configs}' ) - dl_framework = os.getenv('DL_FRAMEWORK', DEFAULT_DL_FRAMEWORK) - dl_runtime = os.getenv('DL_RUNTIME', DEFAULT_DL_RUNTIME) - precision = os.getenv('PRECISION', DEFAULT_PRECISION) - batch_size = int(os.getenv('BATCH_SIZE', DEFAULT_BATCH_SIZE)) - sequence_length = int(os.getenv('SEQUENCE_LENGTH', DEFAULT_SEQUENCE_LENGTH)) + dl_framework = os.getenv('DL_FRAMEWORK', pd_models.DEFAULT_DL_FRAMEWORK) + dl_runtime = os.getenv('DL_RUNTIME', pd_models.DEFAULT_DL_RUNTIME) + precision = os.getenv('PRECISION', pd_models.DEFAULT_PRECISION) + batch_size = int(os.getenv('BATCH_SIZE', pd_models.DEFAULT_BATCH_SIZE)) + sequence_length = int( + os.getenv('SEQUENCE_LENGTH', pd_models.DEFAULT_SEQUENCE_LENGTH) + ) models = pd_models.get_models( models_config=model_config, @@ -92,7 +87,8 @@ def _handler(app: wsgi_app.App, request, unused_environ): wrapped_handlers, project_root='gcp', index_file='index.html' ) -def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: + +def main(argv: Sequence[str]) -> wsgi_app.App: if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') diff --git a/lit_nlp/examples/gcp/model_server_gunicorn_config.py b/lit_nlp/examples/gcp/model_server_gunicorn_config.py index 78dbb00a..fb06905a 100644 --- a/lit_nlp/examples/gcp/model_server_gunicorn_config.py +++ b/lit_nlp/examples/gcp/model_server_gunicorn_config.py @@ -22,4 +22,4 @@ timeout = 3600 threads = 8 worker_class = 'gthread' -wsgi_app = f'lit_nlp.examples.gcp.model_server:get_wsgi_app()' +wsgi_app = 'lit_nlp.examples.gcp.model_server:get_wsgi_app()' diff --git a/lit_nlp/examples/gcp/server_gunicorn_config.py b/lit_nlp/examples/gcp/server_gunicorn_config.py index a5f08c3b..73bf8a2e 100644 --- a/lit_nlp/examples/gcp/server_gunicorn_config.py +++ b/lit_nlp/examples/gcp/server_gunicorn_config.py @@ -22,4 +22,4 @@ timeout = 3600 threads = 8 worker_class = 'gthread' -wsgi_app = f'lit_nlp.examples.gcp.server:get_wsgi_app()' +wsgi_app = 'lit_nlp.examples.gcp.server:get_wsgi_app()' diff --git a/lit_nlp/examples/gcp/vertexai_models.py b/lit_nlp/examples/gcp/vertexai_models.py index 04f3ee81..51c30b69 100644 --- a/lit_nlp/examples/gcp/vertexai_models.py +++ b/lit_nlp/examples/gcp/vertexai_models.py @@ -25,15 +25,11 @@ from lit_nlp.api import types as lit_types _MAX_NUM_RETRIES = 5 - _DEFAULT_CANDIDATE_COUNT = 1 - _DEFAULT_MAX_OUTPUT_TOKENS = 256 - _DEFAULT_TEMPERATURE = 0.7 -# TODO(fanyecourage): Rename this class. class GeminiFoundationalModel(lit_model.BatchedRemoteModel): """GeminiFoundationalModel is a wrapper for foundatoinal Gemini models in Vertex AI Model Garden model. From 702ed27cdccbf7f9b9bd8a2de8984d8c2e35fef8 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 15 Oct 2024 19:52:24 +0000 Subject: [PATCH 78/86] Addressing linter fixes --- lit_nlp/examples/gcp/constants.py | 5 ++- lit_nlp/examples/gcp/model.py | 22 ++++++----- lit_nlp/examples/gcp/model_server.py | 6 +-- lit_nlp/examples/gcp/model_server_test.py | 41 ++++++++++---------- lit_nlp/examples/gcp/server.py | 15 ++++--- lit_nlp/examples/gcp/vertexai_models_test.py | 4 +- lit_nlp/examples/glue/models.py | 2 +- lit_nlp/examples/penguin/model.py | 2 +- lit_nlp/examples/tools/glue_trainer.py | 1 - 9 files changed, 56 insertions(+), 42 deletions(-) diff --git a/lit_nlp/examples/gcp/constants.py b/lit_nlp/examples/gcp/constants.py index 0bf90c58..23d0e0a4 100644 --- a/lit_nlp/examples/gcp/constants.py +++ b/lit_nlp/examples/gcp/constants.py @@ -15,7 +15,10 @@ import enum + class LlmHTTPEndpoints(enum.Enum): + """Names of HTTP endpoints provided by the Model Server conainer.""" + GENERATE = 'predict' SALIENCE = 'salience' - TOKENIZE = 'tokenize' \ No newline at end of file + TOKENIZE = 'tokenize' diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index b20b4a43..ce794834 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -31,21 +31,22 @@ # `/create_model` API will validate the config with a `new_name` in it. 'new_name': lit_types.String(required=False), 'base_url': lit_types.String(), - 'identity_token': lit_types.String(default=""), + 'identity_token': lit_types.String(default=''), 'max_concurrent_requests': lit_types.Integer(default=1), 'max_qps': lit_types.Integer(default=25, required=False), } class LlmOverHTTP(lit_model.BatchedRemoteModel): + """Model wrapper LLMs hosted in a Model Server container.""" def __init__( - self, - base_url: str, - identity_token: str, - endpoint: str | _LlmHTTPEndpoints, - max_concurrent_requests: int = 4, - max_qps: int | float = 25 + self, + base_url: str, + identity_token: str, + endpoint: str | _LlmHTTPEndpoints, + max_concurrent_requests: int = 4, + max_qps: int | float = 25, ): super().__init__(max_concurrent_requests, max_qps) self.endpoint = _LlmHTTPEndpoints(endpoint) @@ -83,6 +84,9 @@ def predict_minibatch( Returns: list of outputs, following model.output_spec() + + Raises: + RuntimeError for non-200 HTTP Status Codes in the response. """ inputs = {'inputs': inputs} headers = { @@ -90,7 +94,7 @@ def predict_minibatch( 'Content-Type': 'application/json' } response = requests.post( - self.url, headers=headers,data=serialize.to_json(inputs, simple=True) + self.url, headers=headers, data=serialize.to_json(inputs, simple=True) ) if not (200 <= response.status_code < 300): @@ -129,4 +133,4 @@ def get_model_loaders() -> lit_app.ModelLoadersMap: initialize_model_group_for_salience, LLM_ON_GCP_INIT_SPEC ) - } \ No newline at end of file + } diff --git a/lit_nlp/examples/gcp/model_server.py b/lit_nlp/examples/gcp/model_server.py index 6a3d25a3..4be896ae 100644 --- a/lit_nlp/examples/gcp/model_server.py +++ b/lit_nlp/examples/gcp/model_server.py @@ -36,12 +36,12 @@ def get_wsgi_app() -> wsgi_app.App: def wrap_handler(predict_fn): @functools.wraps(predict_fn) - def _handler(app: wsgi_app.App, request, unused_environ): + def _handler(wsgiapp: wsgi_app.App, request, unused_environ): data = serialize.from_json(request.data) if len(request.data) else None inputs = data['inputs'] outputs = predict_fn(inputs) - response_body = serialize.to_json(list(outputs), simple=True) - return app.respond(request, response_body, 'application/json', 200) + response_body = serialize.to_json(list(outputs), simple=True) + return wsgiapp.respond(request, response_body, 'application/json', 200) return _handler diff --git a/lit_nlp/examples/gcp/model_server_test.py b/lit_nlp/examples/gcp/model_server_test.py index 11d940fe..f0e1291b 100644 --- a/lit_nlp/examples/gcp/model_server_test.py +++ b/lit_nlp/examples/gcp/model_server_test.py @@ -28,6 +28,8 @@ class TestWSGIApp(parameterized.TestCase): @classmethod def setUpClass(cls): + super().setUpClass() + test_model_name = 'lit_on_gcp_test_model' sal_name, tok_name = pd_utils.generate_model_group_names(test_model_name) test_model_config = f'{test_model_name}:test_model_path' @@ -54,27 +56,26 @@ def setUpClass(cls): tok_name: tokenize_model, } - @parameterized.named_parameters( - dict( - testcase_name=lit_gcp_constants.LlmHTTPEndpoints.GENERATE.value, - endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.GENERATE.value}', - expected=[{'response': 'test output text'}], - ), - dict( - testcase_name=lit_gcp_constants.LlmHTTPEndpoints.SALIENCE.value, - endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.SALIENCE.value}', - expected=[{ - 'tokens': ['test', 'output', 'text'], - 'grad_l2': [0.1234, 0.3456, 0.5678], - 'grad_dot_input': [0.1234, -0.3456, 0.5678], - }], - ), - dict( - testcase_name=lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE.value, - endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE.value}', - expected=[{'tokens': ['test', 'output', 'text']}], - ), + dict( + testcase_name=lit_gcp_constants.LlmHTTPEndpoints.GENERATE.value, + endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.GENERATE.value}', + expected=[{'response': 'test output text'}], + ), + dict( + testcase_name=lit_gcp_constants.LlmHTTPEndpoints.SALIENCE.value, + endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.SALIENCE.value}', + expected=[{ + 'tokens': ['test', 'output', 'text'], + 'grad_l2': [0.1234, 0.3456, 0.5678], + 'grad_dot_input': [0.1234, -0.3456, 0.5678], + }], + ), + dict( + testcase_name=lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE.value, + endpoint=f'/{lit_gcp_constants.LlmHTTPEndpoints.TOKENIZE.value}', + expected=[{'tokens': ['test', 'output', 'text']}], + ), ) @mock.patch('lit_nlp.examples.prompt_debugging.models.get_models') def test_endpoint(self, mock_get_models, endpoint, expected): diff --git a/lit_nlp/examples/gcp/server.py b/lit_nlp/examples/gcp/server.py index 1955022e..671b297a 100644 --- a/lit_nlp/examples/gcp/server.py +++ b/lit_nlp/examples/gcp/server.py @@ -24,7 +24,8 @@ from absl import logging from lit_nlp import dev_server from lit_nlp import server_flags -from lit_nlp.components import word_replacer, scrambler +from lit_nlp.components import scrambler +from lit_nlp.components import word_replacer from lit_nlp.examples.gcp import model as lit_gcp_model from lit_nlp.examples.gcp import vertexai_models from lit_nlp.examples.prompt_debugging import datasets as pd_datasets @@ -64,16 +65,20 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]: raise app.UsageError("Too many command-line arguments.") datasets = pd_datasets.get_datasets( - datasets_config=pd_datasets.DEFAULT_DATASETS, max_examples=pd_datasets.DEFAULT_MAX_EXAMPLES + datasets_config=pd_datasets.DEFAULT_DATASETS, + max_examples=pd_datasets.DEFAULT_MAX_EXAMPLES ) - model_loaders=lit_gcp_model.get_model_loaders() - model_loaders['gemini'] = ( + model_loaders = lit_gcp_model.get_model_loaders() + model_loaders["gemini"] = ( vertexai_models.GeminiFoundationalModel, vertexai_models.GeminiFoundationalModel.init_spec(), ) - generators = {'word_replacer': word_replacer.WordReplacer(), 'scrambler':scrambler.Scrambler()} + generators = { + "word_replacer": word_replacer.WordReplacer(), + "scrambler": scrambler.Scrambler(), + } lit_demo = dev_server.Server( models={}, diff --git a/lit_nlp/examples/gcp/vertexai_models_test.py b/lit_nlp/examples/gcp/vertexai_models_test.py index e5f0fa0b..d15cf084 100644 --- a/lit_nlp/examples/gcp/vertexai_models_test.py +++ b/lit_nlp/examples/gcp/vertexai_models_test.py @@ -91,7 +91,9 @@ def test_query_self_hosted_generative_model( ) mock_generate_content.side_effect = [response1, response2] - model = vertexai_models.SelfHostedGenerativeModel(aip_endpoint_name="endpoint_name") + model = vertexai_models.SelfHostedGenerativeModel( + aip_endpoint_name="endpoint_name" + ) model._endpoint = mock.MagicMock() model._endpoint.predict.side_effect = [response1, response2] diff --git a/lit_nlp/examples/glue/models.py b/lit_nlp/examples/glue/models.py index 7ea576a7..d5f35df8 100644 --- a/lit_nlp/examples/glue/models.py +++ b/lit_nlp/examples/glue/models.py @@ -3,8 +3,8 @@ # TODO(b/261736863): Update to PEP 585 typings, consider using f-strings, and # make common substrings into module CONSTANTS. -import os from collections.abc import Iterable, Sequence +import os import re import threading from typing import Any, Optional diff --git a/lit_nlp/examples/penguin/model.py b/lit_nlp/examples/penguin/model.py index 625d2d9c..510e4910 100644 --- a/lit_nlp/examples/penguin/model.py +++ b/lit_nlp/examples/penguin/model.py @@ -9,7 +9,7 @@ import numpy as np import tf_keras as keras -os.environ["TF_USE_LEGACY_KERAS"] = "1" +os.environ['TF_USE_LEGACY_KERAS'] = '1' _VOCABS = penguin_data.VOCABS diff --git a/lit_nlp/examples/tools/glue_trainer.py b/lit_nlp/examples/tools/glue_trainer.py index 60c848dc..ed0f021a 100644 --- a/lit_nlp/examples/tools/glue_trainer.py +++ b/lit_nlp/examples/tools/glue_trainer.py @@ -28,7 +28,6 @@ from lit_nlp.examples.glue import data as glue_data from lit_nlp.examples.glue import models as glue_models from lit_nlp.lib import serialize -import tensorflow as tf import tf_keras as keras os.environ["TF_USE_LEGACY_KERAS"] = "1" From 0d4faea878e01727f2e027aa63d972e91998bc19 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 15 Oct 2024 20:13:04 +0000 Subject: [PATCH 79/86] More linter fixes --- lit_nlp/examples/gcp/constants.py | 2 ++ lit_nlp/examples/gcp/model.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/lit_nlp/examples/gcp/constants.py b/lit_nlp/examples/gcp/constants.py index 23d0e0a4..d970998b 100644 --- a/lit_nlp/examples/gcp/constants.py +++ b/lit_nlp/examples/gcp/constants.py @@ -13,6 +13,8 @@ # limitations under the License. # ============================================================================== +"""Constants used across the Model Server and LIT Server code surfaces.""" + import enum diff --git a/lit_nlp/examples/gcp/model.py b/lit_nlp/examples/gcp/model.py index ce794834..e55865c1 100644 --- a/lit_nlp/examples/gcp/model.py +++ b/lit_nlp/examples/gcp/model.py @@ -86,7 +86,7 @@ def predict_minibatch( list of outputs, following model.output_spec() Raises: - RuntimeError for non-200 HTTP Status Codes in the response. + RuntimeError: Received non-200 HTTP Status Codes in the response. """ inputs = {'inputs': inputs} headers = { From 4d047993017c42f1e9558ea85e32fb3f0af1d45a Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 15 Oct 2024 21:24:13 +0000 Subject: [PATCH 80/86] Removing unnecessary docstring --- lit_nlp/examples/gcp/vertexai_models_test.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/lit_nlp/examples/gcp/vertexai_models_test.py b/lit_nlp/examples/gcp/vertexai_models_test.py index d15cf084..1510e528 100644 --- a/lit_nlp/examples/gcp/vertexai_models_test.py +++ b/lit_nlp/examples/gcp/vertexai_models_test.py @@ -13,8 +13,6 @@ # limitations under the License. # ============================================================================== -"""Tests for lit_nlp.examples.vertexai.models.""" - from unittest import mock from absl.testing import absltest from google.cloud import aiplatform From 0c6849634fd9ab2f14913debd6a747e300788891 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Fri, 18 Oct 2024 10:48:09 -0700 Subject: [PATCH 81/86] Adding pandas optional deps for TFDS dataset compatibility PiperOrigin-RevId: 687355649 --- pyproject.toml | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6a3f5687..14116c3e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,7 @@ dependencies = [ "matplotlib>=3.7.1", "ml-collections>=0.1.1", "numpy>=1.24.1,<2.0.0", - "pandas>=2.0.3", + "pandas[output-formatting]>=2.0.3", "Pillow>=10.0.0", "portpicker>=1.5.2", "requests>=2.31.0", diff --git a/requirements.txt b/requirements.txt index 8bcd7847..6e740ce9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,7 +24,7 @@ Levenshtein>=0.21.1 matplotlib>=3.6.0,<3.9.0 ml-collections>=0.1.1 numpy>=1.24.1,<2.0.0 -pandas>=2.0.3 +pandas[output-formatting]>=2.0.3 Pillow>=10.0.0 portpicker>=1.5.2 requests>=2.31.0 From a898390c18b42898a0fa2291ba0ae848bc2f454f Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 22 Oct 2024 06:06:23 -0700 Subject: [PATCH 82/86] README updates to accommodate LIT on GCP changes. PiperOrigin-RevId: 688520131 --- README.md | 110 +++++++++--------- pyproject.toml | 14 ++- ...equirements_examples_discriminative_ai.txt | 0 ...=> requirements_examples_generative_ai.txt | 0 requirements_test.txt | 4 +- 5 files changed, 65 insertions(+), 63 deletions(-) rename requirements_examples_descai.txt => requirements_examples_discriminative_ai.txt (100%) rename requirements_examples_genai.txt => requirements_examples_generative_ai.txt (100%) diff --git a/README.md b/README.md index a28e0aa4..13e74503 100644 --- a/README.md +++ b/README.md @@ -51,91 +51,81 @@ For a broader overview, check out [our paper](https://arxiv.org/abs/2008.05122) ## Download and Installation -LIT can be run via container image, installed via `pip` or built from source. -Building from source is necessary if you update any of the front-end or core -back-end code. +LIT can be installed via `pip` or built from source. Building from source is +necessary if you want to make code changes. -### Build container image +### Install from PyPI with pip -Build the image using `docker` or `podman`: ```sh -git clone https://github.com/PAIR-code/lit.git && cd lit -docker build --file Dockerfile --tag lit-nlp . +pip install lit-nlp ``` -See the [advanced guide](https://pair-code.github.io/lit/documentation/docker) for detailed instructions on using the -default LIT Docker image, running LIT as a containerized web app in different -scenarios, and how to creating your own LIT images. - -### pip installation +The default `pip` installation will install all required packages to use the LIT +Python API, built-in interpretability components, and web application. To +install dependencies for the provided demos or test suite, install LIT with the +appropriate optional dependencies. ```sh -pip install lit-nlp -``` +# To install dependencies for the discriminative AI examples (GLUE, Penguin) +pip install 'lit-nlp[examples-discriminative-ai]' -The `pip` installation will install all necessary prerequisite packages for use -of the core LIT package. +# To install dependencies for the generative AI examples (Prompt Debugging) +pip install 'lit-nlp[examples-generative-ai]' -It **does not** install the prerequisites for the provided demos, so you need to -install those yourself. See -[requirements_examples.txt](./requirements_examples.txt) for the list of -packages required to run the demos. +# To install dependencies for all examples plus the test suite +pip install 'lit-nlp[test]' +``` ### Install from source Clone the repo: ```sh -git clone https://github.com/PAIR-code/lit.git && cd lit +git clone https://github.com/PAIR-code/lit.git +cd lit ``` - Note: be sure you are running Python 3.9+. If you have a different version on your system, use the `conda` instructions below to set up a Python 3.9 environment. -Set up a Python environment with `venv`: +Set up a Python environment with `venv` (or your preferred environment manager). +Note that these instructions assume you will be making code changes to LIT and +includes the full requirements for all examples and the test suite. See the +other optional dependency possibilities in the install with pip section. ```sh python -m venv .venv source .venv/bin/activate +python -m pip install -e '.[test]' ``` -Or set up a Python environment using `conda`: +The LIT repo does not include a distributable version of the LIT app. You must +build it from source. ```sh -conda create --name lit-nlp -conda activate lit-nlp -conda install python=3.9 -conda install pip -``` - -Once you have the environment, install LIT's dependencies: -```sh -python -m pip install -r requirements.txt -python -m pip install cudnn cupti # optional, for GPU support -python -m pip install torch # optional, for PyTorch - -# Build the frontend (cd lit_nlp; yarn && yarn build) ``` -Note: Use the `-r requirements.txt` option to install every dependency required -for the LIT library, its test suite, and the built-in examples. You can also -install subsets of these using the `-r requirements_core.txt` (core library), -`-r requirements_test.txt` (test suite), `-r requirements_examples.txt` -(examples), and/or any combination thereof. - Note: if you see [an error](https://github.com/yarnpkg/yarn/issues/2821) running `yarn` on Ubuntu/Debian, be sure you have the [correct version installed](https://yarnpkg.com/en/docs/install#linux-tab). - ## Running LIT Explore a collection of hosted demos on the [demos page](https://pair-code.github.io/lit/demos). +### Using container images + +See the [containerization guide](https://pair-code.github.io/lit/documentation/docker) for instructions on using LIT +locally in Docker, Podman, etc. + +LIT also provides pre-built images that can take advantage of accelerators, +making Generative AI and LLM use cases easier to work with. Check out the +[LIT on GCP docs](https://codelabs.developers.google.com/codelabs/responsible-ai/lit-on-gcp) +for more. + ### Quick-start: classification and regression To explore classification and regression models tasks from the popular @@ -154,7 +144,6 @@ but you can switch to [STS-B](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) or [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) using the toolbar or the gear icon in the upper right. -``` And navigate to http://localhost:5432 for the UI. @@ -220,18 +209,19 @@ Google's [Python](https://google.github.io/styleguide/pyguide.html) and ```sh # Run Pylint on your code using the following command from the root of this repo -pushd lit_nlp & pylint & popd +(cd lit_nlp; pylint) # Run ESLint on your code using the following command from the root of this repo -pushd lit_nlp & yarn lint & popd +(cd lit_nlp; yarn lint) ``` ## Citing LIT -If you use LIT as part of your work, please cite -[our EMNLP paper](https://arxiv.org/abs/2008.05122): +If you use LIT as part of your work, please cite the +[EMNLP paper](https://arxiv.org/abs/2008.05122) or the +[Sequence Salience paper](https://arxiv.org/abs/2404.07498) -``` +```BibTeX @misc{tenney2020language, title={The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for {NLP} Models}, author={Ian Tenney and James Wexler and Jasmijn Bastings and Tolga Bolukbasi and Andy Coenen and Sebastian Gehrmann and Ellen Jiang and Mahima Pushkarna and Carey Radebaugh and Emily Reif and Ann Yuan}, @@ -243,12 +233,22 @@ If you use LIT as part of your work, please cite } ``` +```BibTeX +@article{tenney2024interactive, + title={Interactive prompt debugging with sequence salience}, + author={Tenney, Ian and Mullins, Ryan and Du, Bin and Pandya, Shree and Kahng, Minsuk and Dixon, Lucas}, + journal={arXiv preprint arXiv:2404.07498}, + year={2024} +} +``` + ## Disclaimer This is not an official Google product. -LIT is a research project and under active development by a small team. There -will be some bugs and rough edges, but we're releasing at an early stage because -we think it's pretty useful already. We want LIT to be an open platform, not a -walled garden, and we would love your suggestions and feedback - drop us a line -in the [issues](https://github.com/pair-code/lit/issues). +LIT is a research project and under active development by a small team. We want +LIT to be an open platform, not a walled garden, and would love your suggestions +and feedback – please +[report any bugs](https://github.com/pair-code/lit/issues) and reach out on the +[Discussions page](https://github.com/PAIR-code/lit/discussions/landing). + diff --git a/pyproject.toml b/pyproject.toml index 14116c3e..d85e7907 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,9 @@ build-backend = "setuptools.build_meta" [project] name = "lit-nlp" +# LINT.IfChange version = "1.2" +# LINT.ThenChange(./lit_nlp/package.json) authors = [ { name="Google, LLC", email="lit-dev@google.com" } ] @@ -85,14 +87,14 @@ examples-common = [ ] # LINT.ThenChange(./requirements_examples_common.txt) # LINT.IfChange -examples-descai = [ +examples-discriminative-ai = [ "lit-nlp[examples-common]", "tensorflow-datasets>=4.9.0", "tf-keras>=2.16", ] -# LINT.ThenChange(./requirements_examples_descai.txt) +# LINT.ThenChange(./requirements_examples_discriminative_ai.txt) # LINT.IfChange -examples-genai = [ +examples-generative-ai = [ "lit-nlp[examples-common]", "google-cloud-aiplatform>=1.60.0", "keras>=3.0.0", @@ -102,11 +104,11 @@ examples-genai = [ "torch>=2.0.0", "vertexai>=1.49.0", ] -# LINT.ThenChange(./requirements_examples_genai.txt) +# LINT.ThenChange(./requirements_examples_generative_ai.txt) # LINT.IfChange test = [ - "lit-nlp[examples-descai]", - "lit-nlp[examples-genai]", + "lit-nlp[examples-discriminative-ai]", + "lit-nlp[examples-generative-ai]", "lime==0.2.0.1", "pytest>=7.4.0,<8.0.0", "webtest>=2.0", diff --git a/requirements_examples_descai.txt b/requirements_examples_discriminative_ai.txt similarity index 100% rename from requirements_examples_descai.txt rename to requirements_examples_discriminative_ai.txt diff --git a/requirements_examples_genai.txt b/requirements_examples_generative_ai.txt similarity index 100% rename from requirements_examples_genai.txt rename to requirements_examples_generative_ai.txt diff --git a/requirements_test.txt b/requirements_test.txt index 5ab86459..367a238f 100644 --- a/requirements_test.txt +++ b/requirements_test.txt @@ -13,8 +13,8 @@ # limitations under the License. # ============================================================================== # LINT.IfChange --r requirements_examples_descai.txt --r requirements_examples_genai.txt +-r requirements_examples_discriminative_ai.txt +-r requirements_examples_generative_ai.txt lime==0.2.0.1 pytest>=7.4.0,<8.0.0 From 363d0e790913b2d781c11384f279b5b5ae447949 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 22 Oct 2024 08:49:00 -0700 Subject: [PATCH 83/86] LIT 1.3 release notes. PiperOrigin-RevId: 688566572 --- Dockerfile | 4 ++-- RELEASE.md | 42 ++++++++++++++++++++++++++++++++- lit_nlp/examples/gcp/Dockerfile | 8 +++---- lit_nlp/package.json | 2 +- pyproject.toml | 2 +- 5 files changed, 49 insertions(+), 9 deletions(-) diff --git a/Dockerfile b/Dockerfile index e21d2342..9b3a6da6 100644 --- a/Dockerfile +++ b/Dockerfile @@ -56,9 +56,9 @@ RUN apt update && apt -y install yarn # This step is slow as it installs many packages. COPY requirements.txt \ requirements_examples_common.txt \ - requirements_examples_descai.txt \ + requirements_examples_discriminative_ai.txt \ ./ -RUN python -m pip install -r requirements_examples_descai.txt +RUN python -m pip install -r requirements_examples_discriminative_ai.txt # Copy the rest of the lit_nlp package COPY . ./ diff --git a/RELEASE.md b/RELEASE.md index d6949d7c..248b6461 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,5 +1,45 @@ # Learning Interpretability Tool Release Notes +## Release 1.3 + +This release updates how the Learning Interpretability Tool (LIT) can be +deployed on Google Cloud. You can now use LIT to interpret foundation +models—including +[Gemini](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference), +[Gemma](https://ai.google.dev/gemma), [Llama](https://www.llama.com/), and +[Mistral](https://mistral.ai/technology/#models)—using LIT's prompt +debugging workflows. LIT now provides public container images to make it easier +to deploy on your hosting platform of choice, with an updated +[tutorial](https://codelabs.developers.google.com/codelabs/responsible-ai/lit-on-gcp) +for deploying LIT with [Cloud Run](https://cloud.google.com/run). + +### New Stuff +* LIT on GCP - +[1075325](https://github.com/PAIR-code/lit/commit/1075325c6a08d8fdef3bcf66f193b8d5aef673fb), +[1acc868](https://github.com/PAIR-code/lit/commit/1acc868d4a5fa0fd2a135f132f56bb4cb8ba3990), +[55bfc99](https://github.com/PAIR-code/lit/commit/55bfc993cc27fd25ae5089d58ae822bfeca296a3), +[180f68a](https://github.com/PAIR-code/lit/commit/180f68ad3774f8b276e262c0dcb7307ad87e42a3), +[64114d5](https://github.com/PAIR-code/lit/commit/64114d553ffd2c0ffd7bc674fb32a36e564ea0f4), +[2488aa7](https://github.com/PAIR-code/lit/commit/2488aa7cf8f8a112607ca0c8b40870efde73ec24), +[9baac29](https://github.com/PAIR-code/lit/commit/9baac29b96970ef7fa64f2f36ce2c79ff73707b7), +[60bdc7c](https://github.com/PAIR-code/lit/commit/60bdc7cf382bd0c5ead2576c119277230a6080c9), +[7681476](https://github.com/PAIR-code/lit/commit/7681476d5056d927905f24333b890501a36df040), +[4c81182](https://github.com/PAIR-code/lit/commit/4c81182a7db1fda7f8ba071a9542876f462a13fa), +[4e5e8e2](https://github.com/PAIR-code/lit/commit/4e5e8e25c2abb658dc141f0d9c6059dd41e14535), +[b9a0b82](https://github.com/PAIR-code/lit/commit/b9a0b8210263da9ee6d741e4e0f0444849e3a141), +[424adce](https://github.com/PAIR-code/lit/commit/424adce9cf8c9cbabdf5d89d485cdc5f3fd098ed), +[1d019c7](https://github.com/PAIR-code/lit/commit/1d019c7a1bf5f135ea42104889167b79c3f795cd), +[f4436a2](https://github.com/PAIR-code/lit/commit/f4436a26ed79f481e16e2c53c0551703e7ba8c4f), + +### Non-breaking Changes, Bug Fixes, and Enhancements +* Upgrade LIT to MobX v6. - [c1f5055](https://github.com/PAIR-code/lit/commit/c1f5055eb7ee8b3671484c863a0967c05fa58338) +* Fix indexing issue in Sequence Salience module. - [58b1d2](https://github.com/PAIR-code/lit/commit/58b1d2b6d0d27c6dca086520cef45bf75466a101) +* Load multiple model wrappers with shared model. - [ba4d975](https://github.com/PAIR-code/lit/commit/ba4d975a90612b0c41a02b3dcb4dbb548261fdd7) +* Add the custom model and dataset loaders to prompt debugging notebook. - [338c6b](https://github.com/PAIR-code/lit/commit/338c6b12de98b61287a25650ad2c6ad7f7bb80cd) +* Convert hosted demos images to multi-stage builds. - [4bf1f8](https://github.com/PAIR-code/lit/commit/4bf1f81666fe546357f00c86a2315d2852346ebe) +* Adding testing instructions to README. - [f24b841](https://github.com/PAIR-code/lit/commit/f24b841959f0402498a056a5164a86ecae6dbb94) +* More LIT documentation updates. - [2e9d267](https://github.com/PAIR-code/lit/commit/2e9d26738d9344cde0eebd66d49dfc14cd800e74) + ## Release 1.2 This release covers clean-ups on various obsolete demos, as well as improved @@ -270,7 +310,7 @@ A full list of contributors to this repo can be found at https://github.com/PAIR [a95ed67](https://github.com/PAIR-code/lit/commit/a95ed67100f24163624edb4bb659ccfa871dc9bf) * Add output embeddings and attention options to GlueConfig - [6e0df41](https://github.com/PAIR-code/lit/commit/6e0df41636405b4ee5556cbf797fcce5887c6070) -* Allow downloading/copying data from the slice editor - +* Allow downloading/copying data from the slice editor - [57fac3a](https://github.com/PAIR-code/lit/commit/57fac3aeb98fa49c508b20837eded3f4ec80e8f9) * Use new custom tooltip elemement in various places - [d409900](https://github.com/PAIR-code/lit/commit/d409900984336d4f8ac73735b1fff57c92623ca4), diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index 854c9dd2..707ed6a4 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -119,9 +119,9 @@ RUN apt update && apt -y install yarn # This step is slow as it installs many packages. COPY requirements.txt \ requirements_examples_common.txt \ - requirements_examples_genai.txt \ + requirements_examples_generative_ai.txt \ ./ -RUN python -m pip install -r requirements_examples_genai.txt +RUN python -m pip install -r requirements_examples_generative_ai.txt # Copy the rest of the lit_nlp package COPY . ./ @@ -157,9 +157,9 @@ RUN apt update && apt -y install yarn COPY requirements.txt \ requirements_examples_common.txt \ - requirements_examples_genai.txt \ + requirements_examples_generative_ai.txt \ ./ -RUN python -m pip install -r requirements_examples_genai.txt +RUN python -m pip install -r requirements_examples_generative_ai.txt # Copy the rest of the lit_nlp package COPY . ./ diff --git a/lit_nlp/package.json b/lit_nlp/package.json index 748ab3cc..b914daf4 100644 --- a/lit_nlp/package.json +++ b/lit_nlp/package.json @@ -1,6 +1,6 @@ { "name": "lit-client", - "version": "1.2", + "version": "1.3", "description": "Client app for LIT, the Learning Interpretability Tool", "engines": { "node": ">=18.0.0" diff --git a/pyproject.toml b/pyproject.toml index d85e7907..831d06e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta" [project] name = "lit-nlp" # LINT.IfChange -version = "1.2" +version = "1.3" # LINT.ThenChange(./lit_nlp/package.json) authors = [ { name="Google, LLC", email="lit-dev@google.com" } From 63f1eccbfdefdc8d451d867a9b15774b933af79d Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 22 Oct 2024 19:22:46 +0000 Subject: [PATCH 84/86] Updating website for v1.3 launch --- docs/demos/coref.html | 5 - docs/demos/images.html | 5 - docs/demos/index.html | 55 - docs/demos/is_eval.html | 5 - docs/demos/lm.html | 5 - docs/demos/t5.html | 5 - docs/documentation/_images/attention.png | Bin 188417 -> 0 bytes docs/documentation/_images/lit-attention.png | Bin 91620 -> 0 bytes .../_images/lit-colab-server-address.png | Bin 0 -> 28883 bytes .../_images/lit-datapoint-compare.png | Bin 459728 -> 368329 bytes .../documentation/_images/lit-s2s-journey.png | Bin 147695 -> 0 bytes .../_images/lit-ui-error-in-colab.png | Bin 0 -> 124899 bytes .../_images/lit-winogender-metrics.png | Bin 160995 -> 0 bytes docs/documentation/_images/lit-winogender.png | Bin 254066 -> 0 bytes docs/documentation/_images/pair-selection.png | Bin 34353 -> 159071 bytes docs/documentation/_sources/api.md.txt | 60 +- docs/documentation/_sources/components.md.txt | 89 +- docs/documentation/_sources/demos.md.txt | 99 +- docs/documentation/_sources/docker.md.txt | 34 +- docs/documentation/_sources/faq.md.txt | 75 +- .../_sources/frontend_development.md.txt | 16 +- .../_sources/getting_started.md.txt | 5 +- docs/documentation/_sources/index.md.txt | 2 +- docs/documentation/_sources/ui_guide.md.txt | 114 +- docs/documentation/_static/basic.css | 15 +- docs/documentation/_static/doctools.js | 7 - docs/documentation/_static/language_data.js | 9 +- docs/documentation/_static/pygments.css | 12 +- docs/documentation/_static/scripts/furo.js | 2 +- .../documentation/_static/scripts/furo.js.map | 2 +- docs/documentation/_static/searchtools.js | 200 +- .../_static/styles/furo-extensions.css | 2 +- .../_static/styles/furo-extensions.css.map | 2 +- docs/documentation/_static/styles/furo.css | 2 +- .../documentation/_static/styles/furo.css.map | 2 +- docs/documentation/api.html | 216 +- docs/documentation/components.html | 257 +- docs/documentation/demos.html | 226 +- docs/documentation/docker.html | 134 +- docs/documentation/faq.html | 193 +- docs/documentation/frontend_development.html | 136 +- docs/documentation/genindex.html | 85 +- docs/documentation/getting_started.html | 113 +- docs/documentation/glossary.html | 94 +- .../includes/highlight_demos.html | 92 +- docs/documentation/index.html | 103 +- docs/documentation/search.html | 90 +- docs/documentation/searchindex.js | 2 +- docs/documentation/ui_guide.html | 255 +- docs/index.html | 3 +- docs/tutorials/coref/index.html | 187 - docs/tutorials/generation/index.html | 149 - docs/tutorials/index.html | 8 - docs/tutorials/sentiment/index.html | 2 +- docs/tutorials/sequence-salience/index.html | 17 +- docs/tutorials/tab-feat-attr/index.html | 2 +- docs/tutorials/text-salience/index.html | 2 +- package-lock.json | 6 + website/package-lock.json | 8662 ----------------- 59 files changed, 1588 insertions(+), 10275 deletions(-) delete mode 100644 docs/demos/coref.html delete mode 100644 docs/demos/images.html delete mode 100644 docs/demos/is_eval.html delete mode 100644 docs/demos/lm.html delete mode 100644 docs/demos/t5.html delete mode 100644 docs/documentation/_images/attention.png delete mode 100644 docs/documentation/_images/lit-attention.png create mode 100644 docs/documentation/_images/lit-colab-server-address.png delete mode 100644 docs/documentation/_images/lit-s2s-journey.png create mode 100644 docs/documentation/_images/lit-ui-error-in-colab.png delete mode 100644 docs/documentation/_images/lit-winogender-metrics.png delete mode 100644 docs/documentation/_images/lit-winogender.png delete mode 100644 docs/tutorials/coref/index.html delete mode 100644 docs/tutorials/generation/index.html create mode 100644 package-lock.json delete mode 100644 website/package-lock.json diff --git a/docs/demos/coref.html b/docs/demos/coref.html deleted file mode 100644 index 1de185ef..00000000 --- a/docs/demos/coref.html +++ /dev/null @@ -1,5 +0,0 @@ - - - \ No newline at end of file diff --git a/docs/demos/images.html b/docs/demos/images.html deleted file mode 100644 index ce72cef5..00000000 --- a/docs/demos/images.html +++ /dev/null @@ -1,5 +0,0 @@ - - - \ No newline at end of file diff --git a/docs/demos/index.html b/docs/demos/index.html index 546221f4..3a33dc86 100644 --- a/docs/demos/index.html +++ b/docs/demos/index.html @@ -98,17 +98,6 @@

    Analyze a tabular data model with LIT, including exploring partial dependence plots and automatically finding counterfactuals.
    -
    -
    Image classification
    -
    images multiclass classification -
    -
    DATA SOURCES
    -
    - Imagenette -
    -
    Analyze an image classification model with LIT, including multiple image salience techniques.
    -
    -
    Classification and regression models
    BERT binary classification multi-class classification regression @@ -130,50 +119,6 @@
    Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.
    -
    -
    -
    Gender bias in coreference systems
    -
    BERT coreference fairness Winogender -
    -
    DATA SOURCES
    -
    - Winogender schemas -
    -
    Use LIT to explore gendered associations in a coreference system, which matches pronouns to their antecedents. This demo highlights how LIT can work with structured prediction models (edge classification), and its capability for disaggregated analysis.
    -
    -
    -
    -
    Fill in the blanks
    -
    BERT masked language model -
    -
    DATA SOURCES
    -
    - Stanford Sentiment Treebank, Movie Reviews -
    -
    Explore a BERT-based masked-language model. See what tokens the model predicts should fill in the blank when any token from an example sentence is masked out.
    -
    -
    -
    -
    Text generation
    -
    T5 generation -
    -
    DATA SOURCES
    -
    - CNN / Daily Mail -
    -
    Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.
    -
    -
    -
    -
    Evaluating input salience methods
    -
    BERT salience evaluation -
    -
    DATA SOURCES
    -
    - Stanford Sentiment Treebank, Toxicity -
    -
    Explore the faithfulness of input salience methods on a BERT-base model across different datasets and artificial shortcuts.
    -
    diff --git a/docs/demos/is_eval.html b/docs/demos/is_eval.html deleted file mode 100644 index 8a934f5f..00000000 --- a/docs/demos/is_eval.html +++ /dev/null @@ -1,5 +0,0 @@ - - - \ No newline at end of file diff --git a/docs/demos/lm.html b/docs/demos/lm.html deleted file mode 100644 index ff17ae2c..00000000 --- a/docs/demos/lm.html +++ /dev/null @@ -1,5 +0,0 @@ - - - \ No newline at end of file diff --git a/docs/demos/t5.html b/docs/demos/t5.html deleted file mode 100644 index f18bbe65..00000000 --- a/docs/demos/t5.html +++ /dev/null @@ -1,5 +0,0 @@ - - - \ No newline at end of file diff --git a/docs/documentation/_images/attention.png b/docs/documentation/_images/attention.png deleted file mode 100644 index c917ee70ce26c84a5d090094b62300e7144bac53..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 188417 zcmeFZbySpJ_cu(;&<%o!q;v|1bhos0mx6S64y{Oul%#}ocMl*9BHdlmLpMAZdf)4J z`@P@)-hZBFt>0SXsGON|owN5o`|Q0xpUrz^MQKd5CuneRaG0_(60hOl5Z!@494JV@ 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The Integrated Gradients saliency method additionally requires a `TokenEmbeddings` input and corresponding output, as well as a label field `Target` to pin the gradient target to the same class as an input and corresponding output. See the -[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py) +[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) for an example of these spec requirements. The core API involves implementing the `run()` method: @@ -675,7 +675,7 @@ Each `LitType` subclass encapsulates its own semantics (see * A field that appears in _both_ the model's input and output specs is assumed to represent the same value. This pattern is used for model-based input manipulation. For example, a - [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py) + [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) might output `'tokens': lit_types.Tokens(...)`, and accept as (optional) input `'tokens': lit_types.Tokens(required=False, ...)`. An interpretability component could take output from the former, swap one or more tokens (e.g. @@ -712,11 +712,9 @@ this can cause jitter (UI modules appearing, disappearing, reordering, resizing, etc.) when switching between models or datasets with heterogeneous `Spec`s. When implementing your own LIT components and modules, you can use -[`utils.find_spec_keys()`][utils-lib] -(Python) and -[`findSpecKeys()`][utils-lib] -(TypeScript) to identify fields of interest in a `Spec`. These methods recognize -and respect subclasses. For example, +[`utils.find_spec_keys()`][utils-lib-py] (Python) and +[`findSpecKeys()`][utils-lib] (TypeScript) to identify fields of interest in a +`Spec`. These methods recognize and respect subclasses. For example, `utils.find_spec_keys(spec, Scalar)` will also match any `RegressionScore` fields, but `utils.find_spec_keys(spec, RegressionScore)` will not return all `Scalar` fields in the `Spec`. @@ -807,8 +805,13 @@ _See the [examples](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples) ### Available types -The full set of `LitType`s is defined in [types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types such as `Integer` and `Scalar` have predefined ranges that can be overridden using corresponding `min_val` and `max_val` attributes as seen [here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438). The different types available in LIT are summarized -in the table below. +The full set of `LitType`s is defined in +[types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types +such as `Integer` and `Scalar` have predefined ranges that can be overridden +using corresponding `min_val` and `max_val` attributes as seen in +[penguin data](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py) +`INPUT_SPEC`. The different types available in LIT are summarized in the table +below. Note: Bracket syntax, such as `[num_tokens]`, refers to the shapes of NumPy arrays where each element inside the brackets is an integer. @@ -859,7 +862,7 @@ naming collisions with protected TypeScript keywords.* Some properties of the LIT frontend can be configured from Python as **arguments to `dev_server.Server()`**. These include: -* `page_title`: set a custom page title, such as "Coreference Demo". +* `page_title`: set a custom page title. * `canonical_url`: set a "canonical" URL (such as a shortlink) that will be used as the base when copying links from the LIT UI. * `default_layout`: set the default UI layout, by name. See `layout.ts` and @@ -886,22 +889,16 @@ You can specify custom web app layouts from Python via the `layouts=` attribute. The value should be a `Mapping[str, LitCanonicalLayout]`, such as: ```python -LM_LAYOUT = layout.LitCanonicalLayout( +PENGUIN_LAYOUT = layout.LitCanonicalLayout( upper={ - "Main": [ - modules.EmbeddingsModule, + 'Main': [ + modules.DiveModule, modules.DataTableModule, modules.DatapointEditorModule, ] }, - lower={ - "Predictions": [ - modules.LanguageModelPredictionModule, - modules.ConfusionMatrixModule, - ], - "Counterfactuals": [modules.GeneratorModule], - }, - description="Custom layout for language models.", + lower=layout.STANDARD_LAYOUT.lower, + description='Custom layout for the Palmer Penguins demo.', ) ``` @@ -912,14 +909,12 @@ lit_demo = dev_server.Server( models, datasets, # other args... - layouts={"lm": LM_LAYOUT}, + layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT}, + default_layout='penguins', **server_flags.get_flags()) return lit_demo.serve() ``` -For a full example, see -[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). - You can see the pre-configured layouts provided by LIT, as well as the list of modules that can be included in your custom layout in [`layout.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py). A @@ -989,15 +984,15 @@ needing to reload the server or click the UI. For example, to view examples in a dataset: ```python -from lit_nlp.examples.datasets import glue -dataset = glue.SST2Data('validation') +from lit_nlp.examples.glue import data as glue_data +dataset = glue_data.SST2Data('validation') print(dataset.examples) # list of records {"sentence": ..., "label": ...} ``` And to run inference on a few of them: ```python -from lit_nlp.examples.models import glue_models +from lit_nlp.examples.glue import models as glue_models model = glue_models.SST2Model("/path/to/model/files") preds = list(model.predict(dataset.examples[:5])) @@ -1021,16 +1016,19 @@ For a full working example in Colab, see [LIT_components_example.ipynb](https:// [build-metadata]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/app.py -[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py +[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py [curves-interp]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/curves.py [dataset-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py [grad-maps]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/gradient_maps.py [json]: https://www.json.org [mnli-dataset]: https://cims.nyu.edu/~sbowman/multinli/ + [mnli-demo]: https://pair-code.github.io/lit/demos/glue.html -[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py + +[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py [should_display_module]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts [types_py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py [types_ts]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts [utils-lib]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts +[utils-lib-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py [word-replacer]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py diff --git a/docs/documentation/_sources/components.md.txt b/docs/documentation/_sources/components.md.txt index f2366d7c..90b99dbd 100644 --- a/docs/documentation/_sources/components.md.txt +++ b/docs/documentation/_sources/components.md.txt @@ -1,6 +1,6 @@ # Components and Features - + @@ -44,10 +44,9 @@ every invocation.) Generally, you'll need to: directly. Alternatively, you can export to a `SavedModel` and load this in an eager mode -runtime. This leads to much simpler code (see -[this example](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&rcl=378713625)), -but may require changes to your `SavedModel` exporter in order to access model -internals like embeddings, gradients, or attention. +runtime. This leads to much simpler code, but may require changes to your +`SavedModel` exporter in order to access model internals like embeddings, +gradients, or attention. ### Remote or hosted models @@ -105,8 +104,6 @@ LIT supports many features for classification tasks, including common metrics, confusion matrices, and custom thresholding via the UI. Classification is implemented with the `MulticlassPreds` and `CategoryLabel` types. -* For a basic example on a binary sentiment task, see - [examples/simple_tf2_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py). * Models should define a `MulticlassPreds` field in their output spec with the `vocab=` attribute as the set of class labels, and for each example should return a vector of probabilities for each class. @@ -116,9 +113,7 @@ implemented with the `MulticlassPreds` and `CategoryLabel` types. * A negative class can be designated using the `null_idx` attribute of `MulticlassPreds` (most commonly, `null_idx=0`), and metrics such as precision, recall, F1 will be computed for the remaining classes. AUC and - AUCPR will be computed for binary classification tasks. For an example, see - the - [comment toxicity model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&rcl=386779180). + AUCPR will be computed for binary classification tasks. * If `null_idx` is set and there is only one other class, the other class (often, class `1`) is treated as a positive class, and the LIT UI can be used to change the classification threshold. If `null_idx` is set and there @@ -139,7 +134,7 @@ faceting, and scatterplots of scalar output. Regression is implemented with the which contains numerical targets, and the model's `RegressionScore` field should set `parent=` to the name of this field. * For an example, see the STS-B textual similarity task in - [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py). + [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). ### Multi-label classification @@ -147,8 +142,6 @@ LIT supports multi-label tasks, when a model can label a single example with more than one label. Multi-label classification is implemented with the `SparseMultilabelPreds` and `SparseMultilabel` types. -* For a basic example on an image labeling task, see - [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py). * Models should define a `SparseMultilabelPreds` field in their output spec with the`vocab=` attribute as the set of class labels, and for each example should return a list of class score tuples. Each tuple contains two @@ -179,8 +172,6 @@ or decoder. the model can also output a `ReferenceScores` field (with values as `list[float]`) with `parent=` set to reference a `TextSegment` or `ReferenceTexts` field from the input. -* For modeling examples, see - [examples/models/t5.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py) ![Generated Text Module](images/components/generation-results.png){w=600px align=center} @@ -215,7 +206,7 @@ and otherwise to different parts of the input. [Dataset class documentation](./api.md#datasets) and the corresponding [Model](./api.md#models). * For a more involved code example including per-token gradients, see - [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py). + [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). ### Tabular data @@ -233,7 +224,7 @@ these cases, LIT validates model compatibility by checking that: (`SparseMultilabel`) field. For a demo using a penguin stats dataset/binary classification task, see -[examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py). +[examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py). ### Images @@ -246,12 +237,6 @@ NOTE: We may transition images away from encoded strings, moving to individual pixel color values. We will ensure we don't break existing checked-in code with such a change. -* See [examples/datasets/open_images.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py) for a - dataset containing images, including converting images to base64 encoded - strings. -* For a demo of an image classifier, see - [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py). - ## Token-based Salience LIT supports several methods for token-based input salience, including @@ -301,8 +286,8 @@ $$S(i) \propto x_i \cdot \nabla_{x_i} \hat{y}$$ Compared to grad-norm, this gives directional scores: a positive score is can be interpreted as that token having a positive influence on the prediction -$\hat{y}$, while a negative score suggests that the prediction would be stronger -if that token was removed. +$\hat{y}$, while a negative score suggests that the prediction would be +stronger if that token was removed. To enable this method, your model should, as part of the [output spec and `predict()` implementation](./api.md#models): @@ -366,7 +351,7 @@ An example spec would look like: For a more concrete example that also supports multiple segments with separate gradients, see our -[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py), +[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py), or contact the LIT team for assistance. ### LIME @@ -448,7 +433,7 @@ left-to-right ("causal") language models: The UI supports multiple options for analysis, including: -* Select from pre-defined target sequences, or explain generations from the +* Select from predefined target sequences, or explain generations from the model. * Different salience methods, including [Gradient Norm](#gradient-norm) and [Gradient-dot-Input](#gradient-dot-input). @@ -456,7 +441,7 @@ The UI supports multiple options for analysis, including: to words, sentences, lines, or paragraphs. Quickly switch between different views to refine your analysis to different parts of a prompt. * Display density options to enable working with longer sequences, such as - document text, few-shot eaxmples, or chain-of-thought prompts. + document text, few-shot examples, or chain-of-thought prompts. For a walkthrough of how to use sequence salience to debug LLMs, check out the Responsible Generative AI Toolkit at @@ -482,13 +467,11 @@ using either KerasNLP or Transformers. * LIT-for-Gemma Colab: [`lit_gemma.ipynb`](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb) * Demo binary: - [`lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py) + https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py * KerasNLP model wrappers: - [`instrumented_keras_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py) + https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py * Transformers model wrappers: - [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py) - - + https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py ## Salience Clustering @@ -540,9 +523,7 @@ your model should, as part of the input image. The model should be able to accept input images as numpy arrays in addition - to accepting base64 URL encoded format. See - [mobilenet.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py) - for an example. + to accepting base64 URL encoded format. A variety of image saliency techniques are implemented for models that return image gradients, through use of the @@ -553,36 +534,6 @@ and XRAI. Each of these techniques returns a saliency map image as a base64-encoded string through the `ImageSalience` type. -## Attention - -LIT can display a visualization of attention heads from transformers and other -models: - -![Attention Visualization](./images/components/attention.png){w=400px align=center} - -To enable this, your model should return one or more fields of the type -`AttentionHeads`, with values as arrays of shape `[num_heads, num_tokens, -num_tokens]`. Each field represents a set of heads at a single layer of the -model, so models will often have more than one: - -```python - def output_spec(self) -> types.Spec: - return { - # ... - "tokens": lit_types.Tokens(parent="input_text"), - "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), - "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), - "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), - # ... - } -``` - -The `align_in` and `align_out` attributes are the names of `Tokens` fields for -the source and target tokens for that layer. For self-attention (such as in -BERT) these would name the same field, but for encoder-decoder attention they -could reference different token sets (such as "input_tokens" and -"target_tokens"). - ## Embedding Projector LIT includes a version of the @@ -614,7 +565,7 @@ example, we could facet by class label: ![Metrics Table](./images/components/metrics-table.png) -To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab. +To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab. To enable metrics, your model should set the `parent` attribute on one or more output fields, pointing to the name of the input field that it should be @@ -653,7 +604,7 @@ analysis. ![Confusion Matrix](./images/components/confusion-matrix.png){w=600px align=center} -To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab. +To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab. The confusion matrix is supported for classification models, or if the input data includes any categorical features (`CategoryLabel`). @@ -739,6 +690,8 @@ datapoints, giving a global view of feature effects. ![Partial Dependence Plots Module](./images/components/lit-pdps.png){w=400px align=center} +To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the "Predictions" tab. + ### Dive Dive is a visualization module, inspired by our prior work on diff --git a/docs/documentation/_sources/demos.md.txt b/docs/documentation/_sources/demos.md.txt index 306d3139..448c0d23 100644 --- a/docs/documentation/_sources/demos.md.txt +++ b/docs/documentation/_sources/demos.md.txt @@ -1,6 +1,6 @@ # Demos - + @@ -17,7 +17,7 @@ https://pair-code.github.io/lit/demos/. ### Sentiment and NLI **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html \ -**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) +**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) * Multi-task demo: * Sentiment analysis as a binary classification task @@ -36,26 +36,12 @@ https://pair-code.github.io/lit/demos/. the margin. * Embedding projector to find clusters in representation space. * Integrated Gradients, LIME, and other salience methods. - * Attention visualization. * Counterfactual generators, including HotFlip for targeted adversarial perturbations. Tip: check out a case study for this demo on the public LIT website: https://pair-code.github.io/lit/tutorials/sentiment -### Multilingual (XNLI) - -**Code:** [examples/xnli_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) - -* [XNLI](https://cims.nyu.edu/~sbowman/xnli/) dataset translates a subset of - MultiNLI into 14 different languages. -* Specify `--languages=en,jp,hi,...` flag to select which languages to load. -* NLI as a three-way classification task with two-segment input (premise, - hypothesis). -* Fine-tuned multilingual BERT model. -* Salience methods work with non-whitespace-delimited text, by using the - model's wordpiece tokenization. - -------------------------------------------------------------------------------- ## Regression / Scoring @@ -63,7 +49,7 @@ https://pair-code.github.io/lit/tutorials/sentiment ### Textual Similarity (STS-B) **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html?models=stsb&dataset=stsb_dev \ -**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) +**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) * STS-B textual similarity task, predicting scores on a range from 0 (unrelated) to 5 (very similar). @@ -74,7 +60,6 @@ https://pair-code.github.io/lit/tutorials/sentiment quick filtering. * Embedding projector to find clusters in representation space. * Integrated Gradients, LIME, and other salience methods. - * Attention visualization. -------------------------------------------------------------------------------- @@ -82,76 +67,20 @@ https://pair-code.github.io/lit/tutorials/sentiment ### Gemma -**Code:** [examples/lm_salience_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py) +**Code:** +[examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py) -* Supports Gemma 2B and 7B models using KerasNLP and TensorFlow. +* Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch) + and Transformers (with PyTorch). * Interactively debug LLM prompts using [sequence salience](./components.md#sequence-salience). * Multiple salience methods (grad-l2 and grad-dot-input), at multiple - granularities: token-, word-, sentence-, and paragraph-level. + granularities: token-, word-, line-, sentence-, and paragraph-level. Tip: check out the in-depth walkthrough at https://ai.google.dev/responsible/model_behavior, part of the Responsible Generative AI Toolkit. -### T5 - -**Hosted instance:** https://pair-code.github.io/lit/demos/t5.html \ -**Code:** [examples/t5_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) - -* Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel - formats. -* Visualize beam candidates and highlight diffs against references. -* Visualize per-token decoder hypotheses to see where the model veers away - from desired output. -* Filter examples by ROUGE score against reference. -* Embeddings from last layer of model, visualized with UMAP or PCA. -* Task wrappers to handle pre- and post-processing for summarization and - machine translation tasks. -* Pre-loaded eval sets for CNNDM and WMT. - -Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/generation - --------------------------------------------------------------------------------- - -## Language Modeling - -### BERT and GPT-2 - -**Hosted instance:** https://pair-code.github.io/lit/demos/lm.html \ -**Code:** [examples/lm_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py) - -* Compare multiple BERT and GPT-2 models side-by-side on a variety of - plain-text corpora. -* LM visualization supports different modes: - * BERT masked language model: click-to-mask, and query model at that - position. - * GPT-2 shows left-to-right hypotheses for each target token. -* Embedding projector to show latent space of the model. - --------------------------------------------------------------------------------- - -## Structured Prediction - -### Gender Bias in Coreference - -**Hosted instance:** https://pair-code.github.io/lit/demos/coref.html \ -**Code:** [examples/coref/coref_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py) - -* Gold-mention coreference model, trained on - [OntoNotes](https://catalog.ldc.upenn.edu/LDC2013T19). -* Evaluate on the Winogender schemas - ([Rudinger et al. 2018](https://arxiv.org/abs/1804.09301)) which test for - gendered associations with profession names. -* Visualizations of coreference edges, as well as binary classification - between two candidate referents. -* Stratified metrics for quantifying model bias as a function of pronoun - gender or Bureau of Labor Statistics profession data. - -Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/coref - -------------------------------------------------------------------------------- ## Multimodal @@ -159,7 +88,7 @@ https://pair-code.github.io/lit/tutorials/coref ### Tabular Data: Penguin Classification **Hosted instance:** https://pair-code.github.io/lit/demos/penguins.html \ -**Code:** [examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) +**Code:** [examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) * Binary classification on [penguin dataset](https://www.tensorflow.org/datasets/catalog/penguins). @@ -169,13 +98,3 @@ https://pair-code.github.io/lit/tutorials/coref * Use binary classifier threshold setters to find best thresholds for slices of examples to achieve specific fairness constraints, such as demographic parity. - -### Image Classification with MobileNet - -**Hosted instance:** https://pair-code.github.io/lit/demos/images.html \ -**Code:** [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) - -* Classification on ImageNet labels using a MobileNet model. -* Showing using of LIT on image data. -* Explore results of multiple gradient-based image saliency techniques in the - Salience Maps module. diff --git a/docs/documentation/_sources/docker.md.txt b/docs/documentation/_sources/docker.md.txt index c9cad085..ebf7eb7c 100644 --- a/docs/documentation/_sources/docker.md.txt +++ b/docs/documentation/_sources/docker.md.txt @@ -1,6 +1,6 @@ # Running LIT in a Docker container - + Users might want to deploy LIT onto servers for public-facing, long-running instances. This is how we host the LIT demos found on @@ -13,22 +13,21 @@ LIT can be run as a containerized app using [Docker](https://www.docker.com/) or your preferred engine. This is how we run our [hosted demos](https://pair-code.github.io/lit/demos/). -We provide a basic -[`Dockerfile`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile) that you can -use to build and run any of the demos in the `lit_nlp/examples` directory. The -`Dockerfile` installs all necessary dependencies for LIT and builds the +We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the `lit_nlp/examples` directory. +The `Dockerfile` installs all necessary dependencies for LIT and builds the front-end code from source. Then it runs [gunicorn](https://gunicorn.org/) as the HTTP server, invoking the `get_wsgi_app()` method from our demo file to get the WSGI app to serve. The options provided to gunicorn for our use-case can be found in [`gunicorn_config.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py). You can find a reference implementation in -[`glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) or -[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). +[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). -Use the following shell commands to build the default Docker image for LIT from -the provided `Dockerfile`, and then run a container from that image. Comments -are provided in-line to help explain what each step does. +Use the following shell +https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the +default Docker image for LIT from the provided `Dockerfile`, and then run a +container from that image. Comments are provided in-line to help explain what +each step does. ```shell # Build the docker image using the -t argument to name the image. Remember to @@ -48,14 +47,11 @@ below. ```shell # DEMO_NAME is used to complete the Python module path # -# "lit_nlp.examples.$DEMO_NAME" +# "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()" # # Therefore, valid values for DEMO_NAME are Python module paths in the -# lit_nlp/examples directory, such as -# -# * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc. -# * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc. -docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp +# lit_nlp/examples directory, such as glue, penguin, tydi, etc. +docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp # Use the DEMO_PORT environment variable as to change the port that LIT uses in # the container. Be sure to also change the -p option to map the container's @@ -66,15 +62,15 @@ docker run --rm -p 2345:2345 -e DEMO_PORT=2345 lit-nlp # containers on your machine using the combination of the DEMO_NAME and # DEMO_PORT arguments, and docker run with the -d flag to run the container in # the background. -docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp -docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp +docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp +docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp ``` ## Integrating Custom LIT Instances with the Default Docker Image Many LIT users create their own custom LIT server script to demo or serve, which involves creating an executable Python module with a `main()` method, as -described in the [Python API docs](https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data). +described in the [Python API docs](api.md#adding-models-and-data). These custom server scripts can be easily integrated with LIT's default image as long as your server meets two requirements: diff --git a/docs/documentation/_sources/faq.md.txt b/docs/documentation/_sources/faq.md.txt index de6a5b57..8e334b24 100644 --- a/docs/documentation/_sources/faq.md.txt +++ b/docs/documentation/_sources/faq.md.txt @@ -1,6 +1,6 @@ # Frequently Asked Questions - + @@ -17,9 +17,7 @@ works with any modern ML framework. For more information, see In addition to text, LIT has good support for different modalities, including images and tabular data. For examples, see: -* [Image demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) - - image classification, using a Mobilenet model. -* [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) - +* [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) - multi-class classification on tabular (numeric and categorical string) data, using the [Palmer Penguins](https://www.tensorflow.org/datasets/catalog/penguins) @@ -32,13 +30,7 @@ For more details, see All strings in LIT are unicode and most components use model-provided tokenization if available, so in most cases non-English languages and non-Latin -scripts should work without any modifications. For examples, see: - -* [XNLI demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) - - cross-lingual NLI, with up to 15 languages supported via a multilingual BERT - model. -* [T5 demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) - - includes WMT data for machine translation +scripts should work without any modifications. ## Scale @@ -99,8 +91,8 @@ manually entered in the web UI) are stored in server memory, and if `--data_dir` is specified, may be cached to disk. LIT has the ability to create or edit datapoints in the UI and then save them to -disk. If you do not want the tool to be able to write edited datapoints to -disk, then pass the `--demo_mode` runtime flag to the LIT server. +disk. If you do not want the tool to be able to write edited datapoints to disk, +then pass the `--demo_mode` runtime flag to the LIT server. ### I have proprietary data. Is LIT secure for my team to use? @@ -177,7 +169,58 @@ official training APIs. However, to facilitate code reuse you can easily add training methods to your model class. In fact, several of our demos do exactly this, using LIT's `Dataset` objects to manage training data along with standard training APIs (such as Keras' `model.fit()`). See -[`quickstart_sst_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py) -and/or -[`glue_models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py) +[`glue/models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) for examples. + +### Debug LIT UI in Colab + +The LIT instance launched from CLI typically has helpful error messages in the +UI. However, this is not the case for the LIT UI in Colab and the error message +does not report any stacktrace, which makes debugging very difficult. + +![LIT UI error in colab](./images/lit-ui-error-in-colab.png "LIT UI error in colab") + +While in +[Chrome developer tools](https://support.google.com/campaignmanager/answer/2828688?hl=en), +you will be able to debug issues solely related to the frontend, but not so for +issues related to the backend or on the HTTP request path. + +Thus, to show the full stacktrace, you would need to find the HTTP request sent +from the frontend to the backend, compose the same request in colab and send it +to the server. + +1. When rendering the UI, display it in a separate tab to make things a bit + easier to work with, e.g. `lit_widget.render(open_in_new_tab=True)`. +2. Open + [Chrome developer tools](https://support.google.com/campaignmanager/answer/2828688?hl=en), + go to "Sources" tab and find the file + [client/services/api_service.ts](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/services/api_service.ts) and set a + breakpoint right after where the HTTP request is set up in the `queryServer` + method, e.g. after this line `const res = await fetch(url, {method: 'POST', + body});`. + * Note it is possible that the whole frontend source code is compiled into + a `main.js` file, and the code is not exactly the same as that in LIT + frontend source code. You might have to do a bit digging to find the + right line. +3. Go to the UI and trigger the behavior that causes the error. Now in Chrome + developer tools you will be able to see the variables and their values in + the `queryServer` method. Copy the values of the `url` and `body` variables + in the method. +4. Go back to Colab, compose your HTTP request method. Look for the main server + address printed out from `lit_widget.render(open_in_new_tab=True)`. + +![LIT colab server address](./images/lit-colab-server-address.png "LIT colab server address") + +Let's say the server address is "https://localhost:32943/?" as shown above, the +`body` variable obtained earlier has value "request_body_text" and the `url` +variable has value "./get_preds?param1=value1". Then your HTTP request will be +like this: + +```sh +! curl -H "Content-Type: application/json" \ + -d "request_body_text" \ + -X POST "http://localhost:32943/get_preds?param1=value1" +``` + +Run this in Colab and you should be able to retrieve the full stacktrace of the +error. diff --git a/docs/documentation/_sources/frontend_development.md.txt b/docs/documentation/_sources/frontend_development.md.txt index a51aed81..fe8a82bb 100644 --- a/docs/documentation/_sources/frontend_development.md.txt +++ b/docs/documentation/_sources/frontend_development.md.txt @@ -1,6 +1,6 @@ # Frontend Developer Guide - + @@ -46,9 +46,9 @@ and rendering the various `LitModule` components, a process about which we'll go into greater detail later. The JS bundle entry point is -[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts), which -first imports the loaded, the `` web component is declared, and -attaches itself to the DOM, waiting for the app to be initialized. +[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts), which first +imports the loaded, the `` web component is declared, and attaches +itself to the DOM, waiting for the app to be initialized. The second step is kicking off app initialization. The [`LitApp`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts) singleton @@ -67,7 +67,7 @@ pre-configured layouts in * `default`: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and -* `experimental`: A three-panel layout that puts exploratory data +* `three_panel`: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -76,15 +76,15 @@ pre-configured layouts in You can also add [custom layouts](./api.md#customizing-the-layout) to your LIT instance by defining one or more `LitCanonicalLayout` instances and passing them -to the server. For an example, see `CUSTOM_LAYOUTS` in -[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). +to the server. For an example, see +[`prompt_debugging/layouts.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py). Note: The pre-configured layouts are added to every `LitApp` instance using [dictionary updates](https://docs.python.org/3/library/stdtypes.html#dict) where the Mapping passed to the `LitApp` constructor overrides the pre-configured layouts `Mapping`. Thus, you can remove or change these pre-configured layouts as you like by passing a `Mapping` where the values of `simple`, `default`, -and/or `experimental` is `None` (to remove) or a `LitCanonicalLayout` instance +and/or `three_panel` is `None` (to remove) or a `LitCanonicalLayout` instance (to override) as you desire. The actual layout of components in the LIT UI, see diff --git a/docs/documentation/_sources/getting_started.md.txt b/docs/documentation/_sources/getting_started.md.txt index 80e4eae9..0ca605bf 100644 --- a/docs/documentation/_sources/getting_started.md.txt +++ b/docs/documentation/_sources/getting_started.md.txt @@ -1,6 +1,6 @@ # Getting Started with LIT - + @@ -72,8 +72,7 @@ variety of model types. The code for these is under [examples](https://github.co ; each is a small script that loads one or more models and starts a LIT server. -Most demos can be run with a single blaze command. To run the default one, you -can do: +Most demos can be run with a single command. To run the default one, you can do: ```sh python -m lit_nlp.examples.glue.demo \ diff --git a/docs/documentation/_sources/index.md.txt b/docs/documentation/_sources/index.md.txt index 211c7321..64921be0 100644 --- a/docs/documentation/_sources/index.md.txt +++ b/docs/documentation/_sources/index.md.txt @@ -1,6 +1,6 @@ # Learning Interpretability Tool (LIT) - + diff --git a/docs/documentation/_sources/ui_guide.md.txt b/docs/documentation/_sources/ui_guide.md.txt index 4a38a88c..bb5028ae 100644 --- a/docs/documentation/_sources/ui_guide.md.txt +++ b/docs/documentation/_sources/ui_guide.md.txt @@ -1,6 +1,6 @@ # UI Guide - + This is a user guide for the Learning Interpretability Tool (LIT) UI. @@ -42,7 +42,7 @@ LIT provides three pre-configured layouts: * `default`: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and -* `experimental`: A three-panel layout that puts exploratory data +* `three_panel`: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -130,19 +130,26 @@ controls and information. The left side of the toolbar contains a set of menus for quickly controlling datapoint selection and coloring. This includes the following controls: -* The **"Select related"** option adds any datapoints "related" to the current - selection. In LIT, "related" is defined as datapoints created from some - source datapoint (through manual editing or a datapoint generator), or a - source datapoint that a selected datapoint was created from. -* The **"Clear selection"** button deselects all selected datapoints. The - dropdown also contains a color legend for the current color setting. -* The **Slices** option allows quick selection of an already-created slice of - datapoints. -* The **"Datapoint color"** menu enables setting of the color of each - datapoint in the modules that visualize all datapoints (such as the - *Embeddings* and *Scalars* modules) by any number of datapoint features or - model outputs on those datapoints (such as coloring by some categorical - input feature, or by prediction error for a regression task). +* The **"Select datapoint"** menu provides a drop-down of several options: + * the **"Random"** option selects a random datapoint, + * the **"All related"** option adds any datapoints "related" to the + current selection. In LIT, "related" is defined as datapoints created + from some source datapoint (through manual editing or a datapoint + generator), or a source datapoint that a selected datapoint was created + from, + * the **"Parents"** option adds the source datapoints that the selected + datapoints were created from, + * the **"Children"** option adds the datapoints created from the selected + datapoints (through manual editing or a datapoint generator), + * the **Slices** option allows quick selection of an already-created slice + of datapoints, + * the **"Clear selection"** button deselects all selected datapoints. +* The **"Color by"** menu enables setting of the color of each datapoint in + the modules that visualize all datapoints (such as the *Embeddings* and + *Scalars* modules) by any number of datapoint features or model outputs on + those datapoints (such as coloring by some categorical input feature, or by + prediction error for a regression task). +* The **Slices** menu allows adding/selecting/removing slices of datapoints. Next to the menus is a button for pinning/unpinning a datapoint. Pinning a datapoint puts LIT into datapoint comparison mode, where two datapoints can be @@ -322,9 +329,9 @@ such as model predictions, enable them from the "Columns" dropdown. The datapoint editor shows the details of the primary selected datapoint, if one is selected. Any field can be edited, and a new datapoint created with those -edits through the **"Make new datapoint"** button. Any edit to an existing -datapoint must be saved as a new datapoint to be explored, to keep datapoints -immutable for simplicity of use. +edits through the **"Add"** button. Any edit to an existing datapoint must be +saved as a new datapoint to be explored, to keep datapoints immutable for +simplicity of use. When no datapoint is selected, the editor shows a blank datapoint that can be filled out by hand to create a completely new datapoint. @@ -473,24 +480,15 @@ background of each text piece is colored by the salience of that piece on the prediction, and hovering on any piece will display the exact value calculated for that piece. -There is an **"autorun"** button by each methodology. If it is checked, then -that calculation is made when a new primary datapoint is selected. If it is +There is an **"autorun"** button by each methodology on the right side of the +bar (the methodoloy name is on the left side). If it is checked, then that +calculation is made when a new primary datapoint is selected. If it is unchecked, the calculation isn't made until it is checked. This can be valuable so that expensive, long-running saliency calculations (such as LIME) aren't performed on every datapoint selection, but only when explicitly asked for. ![LIT saliency maps](./images/lit-salience.png "LIT saliency maps") -### Attention - -For models that return attention head information along with predictions, the -attention module shows which tokens are attended to between layers of a model. -Dropdowns allow you to select which layer and attention head is being -visualized. Line opacity is controlled by the magnitude of the attention between -those tokens. - -![LIT attention](./images/lit-attention.png "LIT attention"){w=500px align=center} - ## User Journeys In this section, we explore some example user journeys and how LIT enables them. @@ -515,51 +513,11 @@ model. ![Sentiment analysis](./images/lit-sentiment-analysis.png "Sentiment analysis") -### Gender Bias in Coreference - -Does a system encode gendered associations, which might lead to incorrect -predictions? We load a coreference model trained on OntoNotes, and load the -Winogender dataset into LIT for evaluation. Each Winogender example has a -pronoun and two candidate referents, one a occupation term like (“technician”) -and one is an “other participant” (like “customer”). Our model predicts -coreference probabilities for each candidate. We can explore the model’s -sensitivity to pronouns by comparing two examples side-by-side (see structured -predictions in screenshot). We can see how commonly the model makes similar -errors by paging through the dataset, or by selecting specific slices of -interest. - -![Winogender analysis](./images/lit-winogender.png "Winogender analysis") - -In the *Metrics* table, we can slice this selection by pronoun type and by the -true referent. On the set of male-dominated occupations (< 25% female by BLS), -we see the model performs well when the ground-truth agrees with the -stereotype - e.g. when the answer is the occupation term, male pronouns are -correctly resolved 83% of the time, compared to female pronouns only 37.5% of -the time (screenshot section (c)). - -![Winogender analysis](./images/lit-winogender-metrics.png "Winogender analysis") - -### Debugging Text Generation - - - -Does the training data explain a particular error in text generation? We analyze -an older T5 model on the CNN-DM summarization task. LIT’s *Scalars* module -allows us to look at per-example ROUGE scores, and quickly select an example -with middling performance (screenshot section (a)). We find the generated text -(screenshot section (b)) contains an erroneous constituent: “alastair cook was -replaced as captain by former captain ...”. We can dig deeper, using LIT’s -language modeling module (screenshot section (c)) to see that the token “by” is -predicted with high probability (28.7%). - -To find out how T5 arrived at this prediction, we utilize the “similarity -searcher” component through the datapoint generator (screenshot section (d)). -This performs a fast approximate nearest-neighbor lookup from a pre-built index -over the training corpus, using embeddings from the T5 decoder. With one click, -we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection. -We see that the words “captain” and “former” appear 34 and 16 times in these -examples–along with 3 occurrences of “replaced by” (screenshot section (e)), -suggesting a strong prior toward our erroneous phrase. - -![LIT sequence-to-sequence analysis](./images/lit-s2s-journey.png "LIT sequence-to-sequence analysis"){w=500px align=center} +### Sequence salience + +Sequence salience generalizes token-based salience to text-to-text models, +allowing you to explain the impact of the prompt tokens on parts of the model +output. + +Check out [here](components.md#sequence-salience) for more details on how to +navigate the Sequence Salience UI module. diff --git a/docs/documentation/_static/basic.css b/docs/documentation/_static/basic.css index 30fee9d0..7ebbd6d0 100644 --- a/docs/documentation/_static/basic.css +++ b/docs/documentation/_static/basic.css @@ -1,12 +1,5 @@ /* - * basic.css - * ~~~~~~~~~ - * * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ /* -- main layout ----------------------------------------------------------- */ @@ -115,15 +108,11 @@ img { /* -- search page ----------------------------------------------------------- */ ul.search { - margin: 10px 0 0 20px; - padding: 0; + margin-top: 10px; } ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; + padding: 5px 0; } ul.search li a { diff --git a/docs/documentation/_static/doctools.js b/docs/documentation/_static/doctools.js index d06a71d7..0398ebb9 100644 --- a/docs/documentation/_static/doctools.js +++ b/docs/documentation/_static/doctools.js @@ -1,12 +1,5 @@ /* - * doctools.js - * ~~~~~~~~~~~ - * * Base JavaScript utilities for all Sphinx HTML documentation. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ "use strict"; diff --git a/docs/documentation/_static/language_data.js b/docs/documentation/_static/language_data.js index 250f5665..c7fe6c6f 100644 --- a/docs/documentation/_static/language_data.js +++ b/docs/documentation/_static/language_data.js @@ -1,19 +1,12 @@ /* - * language_data.js - * ~~~~~~~~~~~~~~~~ - * * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; -/* Non-minified version is copied as a separate JS file, is available */ +/* Non-minified version is copied as a separate JS file, if available */ /** * Porter Stemmer diff --git a/docs/documentation/_static/pygments.css b/docs/documentation/_static/pygments.css index c2e07c71..02b4b128 100644 --- a/docs/documentation/_static/pygments.css +++ b/docs/documentation/_static/pygments.css @@ -106,17 +106,17 @@ body[data-theme="dark"] .highlight .cp { color: #ff3a3a; font-weight: bold } /* body[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */ +body[data-theme="dark"] .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ body[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body[data-theme="dark"] .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ -body[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */ +body[data-theme="dark"] .highlight .gr { color: #ff3a3a } /* Generic.Error */ body[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */ body[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */ body[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */ body[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */ body[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */ body[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */ -body[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */ +body[data-theme="dark"] .highlight .gt { color: #ff3a3a } /* Generic.Traceback */ body[data-theme="dark"] .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */ body[data-theme="dark"] .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */ body[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */ @@ -192,17 +192,17 @@ body:not([data-theme="light"]) .highlight .cp { color: #ff3a3a; font-weight: bol body:not([data-theme="light"]) .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body:not([data-theme="light"]) .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body:not([data-theme="light"]) .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body:not([data-theme="light"]) .highlight .gd { color: #d22323 } /* Generic.Deleted */ +body:not([data-theme="light"]) .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ body:not([data-theme="light"]) .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body:not([data-theme="light"]) .highlight .ges { color: #d0d0d0; 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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the 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settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n var bounds = elem.getBoundingClientRect();\n var offset = getOffset(settings);\n if (bottom) {\n return (\n parseInt(bounds.bottom, 10) <\n (window.innerHeight || document.documentElement.clientHeight)\n );\n }\n return parseInt(bounds.top, 10) <= offset;\n };\n\n /**\n * Check if at the bottom of the viewport\n * @return {Boolean} If true, page is at the bottom of the viewport\n */\n var isAtBottom = function () {\n if (\n Math.ceil(window.innerHeight + window.pageYOffset) >=\n getDocumentHeight()\n )\n return true;\n return false;\n };\n\n /**\n * Check if the last item should be used (even if not at the top of the page)\n * @param {Object} item The last item\n * @param {Object} settings The settings for this instantiation\n * @return {Boolean} If true, use the last item\n */\n var useLastItem = function (item, settings) {\n if (isAtBottom() && isInView(item.content, settings, true)) return true;\n return false;\n };\n\n /**\n * Get the active content\n * @param {Array} contents The content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n contents.push({\n nav: item,\n content: content,\n });\n });\n\n // Sort contents by the order they appear in the DOM\n sortContents(contents);\n };\n\n /**\n * Detect which content is currently active\n */\n publicAPIs.detect = function () {\n // Get the active content\n var active = getActive(contents, settings);\n\n // if there's no active content, deactivate and bail\n if (!active) {\n if (current) {\n deactivate(current, settings);\n current = null;\n }\n return;\n }\n\n // If the active content is the one currently active, do nothing\n if (current && active.content === current.content) return;\n\n // Deactivate the current content and activate the new content\n deactivate(current, settings);\n activate(active, settings);\n\n // Update the currently active content\n current = active;\n };\n\n /**\n * Detect the active content on scroll\n * Debounced for performance\n */\n var scrollHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the current instantiation\n */\n var init = function () {\n // Merge user options into defaults\n settings = extend(defaults, options || {});\n\n // Setup variables based on the current DOM\n publicAPIs.setup();\n\n // Find the currently active content\n publicAPIs.detect();\n\n // Setup event listeners\n window.addEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.addEventListener(\"resize\", resizeHandler, false);\n }\n };\n\n //\n // Initialize and return the public APIs\n //\n\n init();\n return publicAPIs;\n };\n\n //\n // Return the Constructor\n //\n\n return Constructor;\n },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader(positionY) {\n const headerTop = Math.floor(header.getBoundingClientRect().top);\n\n console.log(`headerTop: ${headerTop}`);\n if (headerTop == 0 && positionY != headerTop) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n } else {\n if (positionY < lastScrollTop) {\n document.documentElement.classList.add(\"show-back-to-top\");\n } else if (positionY > lastScrollTop) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n }\n }\n lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n if (tocScroll === null) {\n return;\n }\n\n // top of page.\n if (positionY == 0) {\n tocScroll.scrollTo(0, 0);\n } else if (\n // bottom of page.\n Math.ceil(positionY) >=\n Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n ) {\n tocScroll.scrollTo(0, tocScroll.scrollHeight);\n } else {\n // somewhere in the middle.\n const current = document.querySelector(\".scroll-current\");\n if (current == null) {\n return;\n }\n\n // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n // // scroll the currently \"active\" heading in toc, into view.\n // const rect = current.getBoundingClientRect();\n // if (0 > rect.top) {\n // current.scrollIntoView(true); // the argument is \"alignTop\"\n // } else if (rect.bottom > window.innerHeight) {\n // current.scrollIntoView(false);\n // }\n }\n}\n\nfunction scrollHandler(positionY) {\n scrollHandlerForHeader(positionY);\n scrollHandlerForBackToTop(positionY);\n scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n mode = \"auto\";\n }\n\n document.body.dataset.theme = mode;\n localStorage.setItem(\"theme\", mode);\n console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n if (prefersDark) {\n // Auto (dark) -> Light -> Dark\n if (currentTheme === \"auto\") {\n setTheme(\"light\");\n } else if (currentTheme == \"light\") {\n setTheme(\"dark\");\n } else {\n setTheme(\"auto\");\n }\n } else {\n // Auto (light) -> Dark -> Light\n if (currentTheme === \"auto\") {\n setTheme(\"dark\");\n } else if (currentTheme == \"dark\") {\n setTheme(\"light\");\n } else {\n setTheme(\"auto\");\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n let last_known_scroll_position = 0;\n let ticking = false;\n\n window.addEventListener(\"scroll\", function (e) {\n last_known_scroll_position = window.scrollY;\n\n if (!ticking) {\n window.requestAnimationFrame(function () {\n scrollHandler(last_known_scroll_position);\n ticking = false;\n });\n\n ticking = true;\n }\n });\n window.scroll();\n}\n\nfunction setupScrollSpy() {\n if (tocScroll === null) {\n return;\n }\n\n // Scrollspy -- highlight table on contents, based on scroll\n new Gumshoe(\".toc-tree a\", {\n reflow: true,\n recursive: true,\n navClass: \"scroll-current\",\n offset: () => {\n let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n return header.getBoundingClientRect().height + 2.5 * rem + 1;\n },\n });\n}\n\nfunction setupTheme() {\n // Attach event handlers for toggling themes\n const buttons = document.getElementsByClassName(\"theme-toggle\");\n Array.from(buttons).forEach((btn) => {\n btn.addEventListener(\"click\", cycleThemeOnce);\n });\n}\n\nfunction setup() {\n setupTheme();\n setupScrollHandler();\n setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n document.body.parentNode.classList.remove(\"no-js\");\n\n header = document.querySelector(\"header\");\n tocScroll = document.querySelector(\".toc-scroll\");\n\n setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","headerTop","floor","scrollHandlerForHeader","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""} \ No newline at end of file diff --git a/docs/documentation/_static/searchtools.js b/docs/documentation/_static/searchtools.js index 7918c3fa..2c774d17 100644 --- a/docs/documentation/_static/searchtools.js +++ b/docs/documentation/_static/searchtools.js @@ -1,12 +1,5 @@ /* - * searchtools.js - * ~~~~~~~~~~~~~~~~ - * * Sphinx JavaScript utilities for the full-text search. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ "use strict"; @@ -20,7 +13,7 @@ if (typeof Scorer === "undefined") { // and returns the new score. /* score: result => { - const [docname, title, anchor, descr, score, filename] = result + const [docname, title, anchor, descr, score, filename, kind] = result return score }, */ @@ -47,6 +40,14 @@ if (typeof Scorer === "undefined") { }; } +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + const _removeChildren = (element) => { while (element && element.lastChild) element.removeChild(element.lastChild); }; @@ -64,9 +65,13 @@ const _displayItem = (item, searchTerms, highlightTerms) => { const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; const contentRoot = document.documentElement.dataset.content_root; - const [docName, title, anchor, descr, score, _filename] = item; + const [docName, title, anchor, descr, score, _filename, kind] = item; let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); let requestUrl; let linkUrl; if (docBuilder === "dirhtml") { @@ -99,7 +104,7 @@ const _displayItem = (item, searchTerms, highlightTerms) => { .then((data) => { if (data) listItem.appendChild( - Search.makeSearchSummary(data, searchTerms) + Search.makeSearchSummary(data, searchTerms, anchor) ); // highlight search terms in the summary if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js @@ -115,9 +120,11 @@ const _finishSearch = (resultCount) => { "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." ); else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); }; const _displayNextItem = ( results, @@ -137,6 +144,22 @@ const _displayNextItem = ( // search finished, update title and status message else _finishSearch(resultCount); }; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; /** * Default splitQuery function. Can be overridden in ``sphinx.search`` with a @@ -160,13 +183,26 @@ const Search = { _queued_query: null, _pulse_status: -1, - htmlToText: (htmlString) => { + htmlToText: (htmlString, anchor) => { const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; + if (docContent) return docContent.textContent; + console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." ); return ""; }, @@ -219,6 +255,7 @@ const Search = { searchSummary.classList.add("search-summary"); searchSummary.innerText = ""; const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); searchList.classList.add("search"); const out = document.getElementById("search-results"); @@ -239,16 +276,7 @@ const Search = { else Search.deferQuery(query); }, - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - + _parseQuery: (query) => { // stem the search terms and add them to the correct list const stemmer = new Stemmer(); const searchTerms = new Set(); @@ -284,22 +312,40 @@ const Search = { // console.info("required: ", [...searchTerms]); // console.info("excluded: ", [...excludedTerms]); - // array of [docname, title, anchor, descr, score, filename] - let results = []; + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + _removeChildren(document.getElementById("search-progress")); - const queryLower = query.toLowerCase(); + const queryLower = query.toLowerCase().trim(); for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ docNames[file], titles[file] !== title ? `${titles[file]} > ${title}` : title, id !== null ? "#" + id : "", null, - score, + score + boost, filenames[file], + SearchResultKind.title, ]); } } @@ -308,46 +354,48 @@ const Search = { // search for explicit entries in index directives for (const [entry, foundEntries] of Object.entries(indexEntries)) { if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ docNames[file], titles[file], id ? "#" + id : "", null, score, filenames[file], - ]); + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } } } } // lookup as object objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) + normalResults.push(...Search.performObjectSearch(term, objectTerms)) ); // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; // remove duplicate search results // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept @@ -361,7 +409,12 @@ const Search = { return acc; }, []); - results = results.reverse(); + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); // for debugging //Search.lastresults = results.slice(); // a copy @@ -432,6 +485,7 @@ const Search = { descr, score, filenames[match[0]], + SearchResultKind.object, ]); }; Object.keys(objects).forEach((prefix) => @@ -466,14 +520,18 @@ const Search = { // add support for partial matches if (word.length > 2) { const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } } // no match but word was a required one @@ -496,9 +554,8 @@ const Search = { // create the mapping files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); }); }); @@ -539,6 +596,7 @@ const Search = { null, score, filenames[file], + SearchResultKind.text, ]); } return results; @@ -549,8 +607,8 @@ const Search = { * search summary for a given text. keywords is a list * of stemmed words. */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); if (text === "") return null; const textLower = text.toLowerCase(); diff --git a/docs/documentation/_static/styles/furo-extensions.css b/docs/documentation/_static/styles/furo-extensions.css index bc447f22..82295876 100644 --- a/docs/documentation/_static/styles/furo-extensions.css +++ b/docs/documentation/_static/styles/furo-extensions.css @@ -1,2 +1,2 @@ -#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #646776; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2962ff;\n --color-brand-content: #2a5adf;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #ffffffcc; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2b8cee;\n --color-brand-content: #368ce2;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: var(--header-height)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 3.5rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\n.versionmodified\n font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(0.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the

    + +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    + + + + +
    @@ -200,13 +250,13 @@
    -
    +
    -

    LIT Python API#

    - +

    LIT Python API

    +
    -

    Design Overview#

    +

    Design Overview

    LIT is a modular system, comprising a collection of backend components (written in Python) and frontend modules (written in TypeScript). Most users will develop against the Python API, which is documented below and allows LIT to be extended @@ -223,7 +273,7 @@

    Design OverviewLit[1] for modularity and MobX for +Lit[1] for modularity and MobX for state management. It consists of a core UI framework, a set of shared “services” which manage persistent state, and a set of independent modules which render visualizations and support user interaction. For more details, see the @@ -231,7 +281,7 @@

    Design Overviewfrontend developer guide.

    -

    Adding Models and Data#

    +

    Adding Models and Data

    To run LIT with your own models and data, you can create a custom demo.py script that passes these to the LIT server. For example:

    def main(_):
@@ -261,7 +311,7 @@ 
    Adding Models and DataFor pre-built demo.py examples, check out
 https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples
    
 
    
-
    Validating Models and Data#
    
+
    Validating Models and Data
    
 
    Datasets and models can optionally be validated by LIT to ensure that dataset
 examples match their spec and that model output values match their spec.
 This can be very helpful during development of new model and dataset wrappers
@@ -279,7 +329,7 @@ 
    Validating Models and Data
-
    Datasets#
    
+
    Datasets
    
 
    Datasets (Dataset) are
 just a list of examples, with associated type information following LIT’s
 type system.
    
@@ -341,7 +391,7 @@ 
    Datasets
 these features for slicing, binning, etc., and they will also be available to
 interpretation components such as custom metrics.
    
 
    
-
    Transformations#
    
+
    Transformations
    
 
    The Dataset class also supports a limited set of transformations, similar to
 TensorFlow’s
 tf.data.Dataset
@@ -356,11 +406,11 @@ 
    Transformations"document" field can be used with a model expecting
-a "text" input via Dataset.remap({"document": "text"}).[2]
    
+a "text" input via Dataset.remap({"document": "text"}).[2]
    
 
    
 
    
 
    
-
    Models#
    
+
    Models
    
 
    Models (Model) are
 functions which take inputs and produce outputs, with associated type
 information following LIT’s type system. The core API consists
@@ -422,7 +472,7 @@ 
    Models#If your model is on a remote server, consider using the BatchedRemoteModel
 base class, which implements parallel batched requests using a thread pool.
    
 
    
-
    Adding more outputs#
    
+
    Adding more outputs
    
 
    The above example defined a black-box model, with predictions but no access to
 internals. If we want a richer view into the model’s behavior, we can add
 additional return fields corresponding to hidden-state activations, gradients,
@@ -471,7 +521,7 @@ 
    Adding more outputs
-
    Optional inputs#
    
+
    Optional inputs
    
 
    By default, LIT treats input_spec fields as required. However, this can be set
 to false if you wish to define optional model inputs. For example, a model that
 can accept pre-tokenized inputs might have the following spec:
    
@@ -495,21 +545,21 @@ 
    Optional inputs
-
    Interpretation Components#
    
+
    Interpretation Components
    
 
    Backend interpretation components include metrics, salience maps, visualization
 aids like UMAP, and
 counterfactual generator plug-ins.
    
 
    Most such components implement the
 Interpreter API.
 Conceptually, this is any function that takes a set of datapoints and a model,
-and produces some output.[3] For example,
+and produces some output.[3] For example,
 local gradient-based salience (GradientNorm)
 processes the TokenGradients and Tokens returned by a model and produces a
 list of scores for each token. The Integrated Gradients saliency method
 additionally requires a TokenEmbeddings input and corresponding output, as
 well as a label field Target to pin the gradient target to the same class as
 an input and corresponding output. See the
-GLUE models class
+GLUE models class
 for an example of these spec requirements.
    
 
    The core API involves implementing the run() method:
    
 
      def run(self,
@@ -568,7 +618,7 @@ 
    Interpretation Components
-
    Metrics#
    
+
    Metrics
    
 
    For metrics, the
 SimpleMetrics
 class implements the spec-matching and input-unpacking logic to satisfy the
@@ -602,7 +652,7 @@ 
    Metrics#<
 on the unpacked values.
    
 
    
 
    
-
    Generators#
    
+
    Generators
    
 
    Conceptually, a generator is just an interpreter that returns new input
 examples. These may depend on the input only, as for techniques such as back-
 translation, or can involve feedback from the model, such as for adversarial
@@ -638,7 +688,7 @@ 
    Generatorsconfig, such as the list of substitutions for the
 word replacer.
    
 
    
-
    Backtranslator Generator#
    
+
    Backtranslator Generator
    
 
    The backtranslator
 generator translates text segment inputs into foreign languages and back to the
 source language in order to create paraphrases.
@@ -652,7 +702,7 @@ 
    Backtranslator Generator
-
    Configuration UI#
    
+
    Configuration UI
    
 
    Interpreter components support an optional config option to specify run-time
 options, such as the number of samples for LIME or the pivot languages for
 back-translation. LIT provides a simple DSL to define these options, which will
@@ -708,7 +758,7 @@ 
    Configuration UI
-
    Type System#
    
+
    Type System
    
 
    LIT passes data around (e.g., between the server and the web app) as flat
 records with string keys. In Python types these are Mapping[str, ...] and in
 TypeScript types these are {[key: string]: unknown}. LIT serializes these
@@ -731,7 +781,7 @@ 
    Type Systemtypes.py, with
 parallel implementations in lit_types.ts.
    
 
    
-
    Conventions#
    
+
    Conventions
    
 
    LIT supports several different “kinds” of Specs (input vs output vs meta,
 etc.), and their use in context has specific implications, described
 per base class below.
    
@@ -747,7 +797,7 @@ 
    Conventionslit_nlp.api.model.Model
    
+
  • lit_nlp.api.model.Model
    
 
      
 
    • .input_spec() -> Spec describes the shape required of all records
 passed into the Model.predict() function via the inputs argument. LIT
@@ -762,7 +812,7 @@ 
      Conventionslit_nlp.api.components.[Interpreter | Generator]
      
+
    • lit_nlp.api.components.[Interpreter | Generator]
      
 
        
 
      • .config_spec() -> Spec describes the user-configurable parameters
 for running this component. Returning an empty Spec means that this
@@ -778,7 +828,7 @@ 
        Conventionslit_nlp.api.components.Metrics
        
+
      • lit_nlp.api.components.Metrics
        
 
          
 
        • .config_spec() -> Spec describes the user-configurable parameters
 for running this component. Returning an empty Spec means that this
@@ -818,7 +868,7 @@ 
          Conventionslanguage model
+language model
 might output 'tokens': lit_types.Tokens(...), and accept as (optional)
 input 'tokens': lit_types.Tokens(required=False, ...). An interpretability
 component could take output from the former, swap one or more tokens (e.g.
@@ -827,17 +877,17 @@ 
          Conventions
-
          Compatibility Checks#
          
+
          Compatibility Checks
          
 
          LIT’s type system plays a critical role in ensuring reliability of and
 interoperability between the Model, Dataset, Interpreter, Generator, and
 Metrics classes:
          
 
            
 
          • The Model-Dataset compatibility check ensures that the
 Model.input_spec() is a subset of the Dataset.spec(). The base
-Model class provides a robust and universal implementation of
+Model class provides a robust and universal implementation of
 this check in the is_compatible_with_dataset() API, but you can override
 this method in your Model subclass if you so choose.
            • 
-
          • All lit_nlp.api.components classes provide an
+
          • All lit_nlp.api.components classes provide an
 is_compatible API to check their compatibility against Models and
 Datasets, as appropriate. For example, the
 WordReplacer generator only checks against the Dataset
@@ -855,11 +905,9 @@ 
            Compatibility ChecksSpecs.
            
 
            When implementing your own LIT components and modules, you can use
-utils.find_spec_keys()
-(Python) and
-findSpecKeys()
-(TypeScript) to identify fields of interest in a Spec. These methods recognize
-and respect subclasses. For example,
+utils.find_spec_keys() (Python) and
+findSpecKeys() (TypeScript) to identify fields of interest in a
+Spec. These methods recognize and respect subclasses. For example,
 utils.find_spec_keys(spec, Scalar) will also match any RegressionScore
 fields, but utils.find_spec_keys(spec, RegressionScore) will not return all
 Scalar fields in the Spec.
            
@@ -872,7 +920,7 @@ 
            Compatibility Checks
 
    • 
 
    
-
    An In-Depth Example#
    
+
    An In-Depth Example
    
 
    Consider the following example from the MNLI demo. The
 MultiNLI dataset might define the following Spec.
    
 
    # Dataset.spec()
@@ -938,9 +986,14 @@ 
    An In-Depth Exampleexamples for more.
    
 
    
 
    
-
    Available types#
    
-
    The full set of LitTypes is defined in types.py. Numeric types such as Integer and Scalar have predefined ranges that can be overridden using corresponding min_val and max_val attributes as seen here. The different types available in LIT are summarized
-in the table below.
    
+
    Available types
    
+
    The full set of LitTypes is defined in
+types.py. Numeric types
+such as Integer and Scalar have predefined ranges that can be overridden
+using corresponding min_val and max_val attributes as seen in
+penguin data
+INPUT_SPEC. The different types available in LIT are summarized in the table
+below.
    
 
    Note: Bracket syntax, such as <float>[num_tokens], refers to the shapes of
 NumPy arrays where each element inside the brackets is an integer.
    
 
    
@@ -1084,11 +1137,11 @@ 
    Available types
-
    Server Configuration#
    
+
    Server Configuration
    
 
    Some properties of the LIT frontend can be configured from Python as
 arguments to dev_server.Server(). These include:
    
 
      
-
    • page_title: set a custom page title, such as “Coreference Demo”.
      • 
+
    • page_title: set a custom page title.
      • 
 
    • canonical_url: set a “canonical” URL (such as a shortlink) that will be
 used as the base when copying links from the LIT UI.
      • 
 
    • default_layout: set the default UI layout, by name. See layout.ts and
@@ -1108,25 +1161,19 @@ 
      Server Configurationdescription() method which can be used to specify a human-readable
 description or help text that will appear in the UI.
      
 
      
-
      Customizing the Layout#
      
+
      Customizing the Layout
      
 
      You can specify custom web app layouts from Python via the layouts= attribute.
 The value should be a Mapping[str, LitCanonicalLayout], such as:
      
-
      LM_LAYOUT = layout.LitCanonicalLayout(
+
      PENGUIN_LAYOUT = layout.LitCanonicalLayout(
     upper={
-        "Main": [
-            modules.EmbeddingsModule,
+        'Main': [
+            modules.DiveModule,
             modules.DataTableModule,
             modules.DatapointEditorModule,
         ]
     },
-    lower={
-        "Predictions": [
-            modules.LanguageModelPredictionModule,
-            modules.ConfusionMatrixModule,
-        ],
-        "Counterfactuals": [modules.GeneratorModule],
-    },
-    description="Custom layout for language models.",
+    lower=layout.STANDARD_LAYOUT.lower,
+    description='Custom layout for the Palmer Penguins demo.',
 )
 
      
 
      
@@ -1135,13 +1182,12 @@ 
      Customizing the Layoutmodels,
     datasets,
     # other args...
-    layouts={"lm": LM_LAYOUT},
+    layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT},
+    default_layout='penguins',
     **server_flags.get_flags())
 return lit_demo.serve()
 
      
 
      
-
      For a full example, see
-lm_demo.py.
      
 
      You can see the pre-configured layouts provided by LIT, as well as the list of
 modules that can be included in your custom layout in
 layout.py. A
@@ -1171,7 +1217,7 @@ 
      Customizing the Layout
 
      
 
      
-
      Accessing the LIT UI in Notebooks#
      
+
      Accessing the LIT UI in Notebooks
      
 
      As an alternative to running a LIT server and connecting to it through a web
 browser, LIT can be used directly inside of python notebook environments, such
 as Colab and
@@ -1197,20 +1243,20 @@ 
      Accessing the LIT UI in Notebooksexample notebook.
      
 
      
 
      
-
      Using LIT components outside of LIT#
      
+
      Using LIT components outside of LIT
      
 
      All LIT Python components (models, datasets, interpreters, metrics, generators,
 etc.) are standalone classes that do not depend on the serving framework. You
 can easily use them from Colab, in scripts, or in your libraries. This can
 also be handy for development, as you can test new models or components without
 needing to reload the server or click the UI.
      
 
      For example, to view examples in a dataset:
      
-
      from lit_nlp.examples.datasets import glue
-dataset = glue.SST2Data('validation')
+
      from lit_nlp.examples.glue import data as glue_data
+dataset = glue_data.SST2Data('validation')
 print(dataset.examples)  # list of records {"sentence": ..., "label": ...}
 
      
 
      
 
      And to run inference on a few of them:
      
-
      from lit_nlp.examples.models import glue_models
+
      from lit_nlp.examples.glue import models as glue_models
 
 model = glue_models.SST2Model("/path/to/model/files")
 preds = list(model.predict(dataset.examples[:5]))
@@ -1228,15 +1274,17 @@ 
      Using LIT components outside of LITLIT_components_example.ipynb.
      
 
+
      
+
    
 
    
 
    
-
    @@ -1357,8 +1403,8 @@

    Using LIT components outside of LIT - + - + \ No newline at end of file diff --git a/docs/documentation/components.html b/docs/documentation/components.html index bfbf62a6..bf6def94 100644 --- a/docs/documentation/components.html +++ b/docs/documentation/components.html @@ -5,11 +5,11 @@ - + Components and Features - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@ + +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    + + + + +
    @@ -200,13 +250,13 @@
    -
    +
    -

    Components and Features#

    - +

    Components and Features

    +
    -

    Framework and Model Support#

    +

    Framework and Model Support

    LIT is framework-agnostic and is compatible with any model that can be wrapped in a Python class for inference. In particular, we’ve tested with TF1.x, TF2, JAX, and PyTorch, as well as models that use custom C++ inference code (wrapped @@ -219,7 +269,7 @@

    Framework and Model Supportmodel and dataset classes, but we also have out-of-the-box support for a few modeling frameworks, described below.

    -

    HuggingFace Transformers#

    +

    HuggingFace Transformers

    Many of the open-source LIT examples use HuggingFace Transformers via their TF2/Keras model classes. These give easy @@ -230,7 +280,7 @@

    HuggingFace Transformers -

    TF1.x Estimator#

    +

    TF1.x Estimator

    LIT supports Estimator and other TF1.x models, but the model wrappers can be more involved due to the need to explicitly manage the graph and sessions. (In particular: Estimator.predict() cannot be used because it reloads the model on @@ -242,13 +292,12 @@

    TF1.x EstimatorSavedModel and load this in an eager mode -runtime. This leads to much simpler code (see -this example), -but may require changes to your SavedModel exporter in order to access model -internals like embeddings, gradients, or attention.

    +runtime. This leads to much simpler code, but may require changes to your +SavedModel exporter in order to access model internals like embeddings, +gradients, or attention.

    -

    Remote or hosted models#

    +

    Remote or hosted models

    LIT can easily interact with models hosted via an RPC or HTTP endpoint, including Servomatic. In this usage, the model weights and computation remain on the server, while the LIT Model implementation simply manages the RPC stub and @@ -259,7 +308,7 @@

    Remote or hosted models

    -

    Static predictions#

    +

    Static predictions

    LIT works best when the model can be queried interactively, but this isn’t always possible for all situations. The StaticPredictions @@ -270,7 +319,7 @@

    Static predictions -

    Data loading#

    +

    Data loading

    LIT can load data from almost any format, including TFRecord, Capacitor, SSTable, or even SQL queries, via a custom Python class that implements the Dataset API. Many of our demos use TFDS, and the LIT loader @@ -283,7 +332,7 @@

    Data loading -

    Input and Output Types#

    +

    Input and Output Types

    LIT uses an extensible system of semantic types to describe data and models. This allows for flexible support of a number of different input and output modalities common to NLP and other domains. For a full reference, see the @@ -297,13 +346,11 @@

    Input and Output Types
    -

    Classification#

    +

    Classification

    LIT supports many features for classification tasks, including common metrics, confusion matrices, and custom thresholding via the UI. Classification is implemented with the MulticlassPreds and CategoryLabel types.

    -

    Regression / Scoring#

    +

    Regression / Scoring

    Regression or scoring models also are well-supported with metrics, bucketed faceting, and scatterplots of scalar output. Regression is implemented with the Scalar (input) and RegressionScore (output) types.

    @@ -336,17 +381,15 @@

    Regression / ScoringRegressionScore field should set parent= to the name of this field.

  • For an example, see the STS-B textual similarity task in -examples/glue_demo.py.

    • +examples/glue/demo.py.

    -

    Multi-label classification#

    +

    Multi-label classification

    LIT supports multi-label tasks, when a model can label a single example with more than one label. Multi-label classification is implemented with the SparseMultilabelPreds and SparseMultilabel types.

    Generated Text Module

    -

    Span Labeling and Structured Prediction#

    +

    Span Labeling and Structured Prediction

    LIT can support a variety of structured prediction types, and provides rich, interactive visualizations.

      @@ -404,7 +445,7 @@

      Span Labeling and Structured PredictionStructured Predictions Module

    -

    Multiple input segments#

    +

    Multiple input segments

    LIT can easily handle multiple text fields, or a mix of text, categorical, scalar, and other input features. LIT does not explicitly “privilege” one input field, and metadata in the model spec can be used to align gradients, attention, @@ -414,11 +455,11 @@

    Multiple input segmentsDataset class documentation and the corresponding Model.

  • For a more involved code example including per-token gradients, see -examples/glue_demo.py.

    • +examples/glue/demo.py.

    -

    Tabular data#

    +

    Tabular data

    LIT can be used as a replacement for the What-If Tool but with more extensibility, when working with predictions over tabular data.

    Some interpreters, such as Kernel SHAP, require models that use tabular data. In @@ -432,10 +473,10 @@

    Tabular dataSparseMultilabel) field.

    For a demo using a penguin stats dataset/binary classification task, see -examples/penguin_demo.py.

    +examples/penguin/demo.py.

    -

    Images#

    +

    Images

    LIT also contains support for models with images as input features or generated images as model output. The LIT type ImageBytes can be used as a feature in datasets and as part of an input spec or output spec for a model. That feature’s @@ -443,17 +484,10 @@

    Images#NOTE: We may transition images away from encoded strings, moving to individual pixel color values. We will ensure we don’t break existing checked-in code with such a change.

    -

    -

    Token-based Salience#

    +

    Token-based Salience

    LIT supports several methods for token-based input salience, including gradient-based methods as well as black-box techniques like LIME that don’t require any access to model internals. Output is rendered in the Salience Maps @@ -465,7 +499,7 @@

    Token-based SalienceRegressionScore) and generation (GeneratedText or GeneratedTextCandidates) outputs.

    -

    Gradient Norm#

    +

    Gradient Norm

    This is a simple method, in which salience scores are proportional to the L2 norm of the gradient, i.e. the score for token \(i\) is:

    @@ -490,7 +524,7 @@

    Gradient Norm -

    Gradient-dot-Input#

    +

    Gradient-dot-Input

    In this method, salience scores are proportional to the dot product of the input embeddings and their gradients, i.e. for token \(i\) we compute:

    @@ -499,8 +533,8 @@

    Gradient-dot-Input\(\hat{y}\), while a negative score suggests that the prediction would be stronger -if that token was removed.

    +\(\hat{y}\), while a negative score suggests that the prediction would be +stronger if that token was removed.

    To enable this method, your model should, as part of the output spec and predict() implementation:

    -

    LIME#

    +

    LIME

    LIME is a black-box salience method that does not require access to any model internals. It works by generating a set of perturbed inputs - generally, by dropping out or masking tokens - and training a @@ -578,7 +612,7 @@

    LIME#RegressionScore) model.

    -

    Target Selection on Classification Output#

    +

    Target Selection on Classification Output

    For all salience methods, we require that the class to explain is given as a label field in the input. For example, if the input example is:

    {"text": "this movie was terrible!", "label": "0"}
@@ -615,7 +649,7 @@ 
    Target Selection on Classification Output
-
    Sequence Salience#
    
+
    Sequence Salience
    
 
    Sequence salience generalizes token-based salience to text-to-text models,
 allowing you to explain the impact of the prompt tokens on parts of the model
 output.
    
@@ -625,7 +659,7 @@ 
    Sequence SalienceSequence salience - visualization
    
 
    The UI supports multiple options for analysis, including:
    -

    Salience Clustering#

    +

    Salience Clustering

    LIT includes a basic implementation of the salience clustering method from Ebert et al. 2022, which uses k-means on a salience-weighted bag-of-words representation to find patterns in model @@ -676,7 +710,7 @@

    Salience Clusteringthe paper.

    -

    Tabular Feature Attribution#

    +

    Tabular Feature Attribution

    Tabular feature attribution seeks to understand the importance of a column of data on a model’s predictions. LIT’s tabular feature attribution module supports this analysis using the SHAP interpreter. @@ -687,7 +721,7 @@

    Tabular Feature AttributionTabular feature attribution module module

    -

    Pixel-based Salience#

    +

    Pixel-based Salience

    LIT also supports pixel-based salience methods, for models that take images as inputs. Output is rendered in the Salience Maps module in the LIT UI, which allows for comparison of multiple methods at once.

    @@ -707,9 +741,7 @@

    Pixel-based Salience

    The model should be able to accept input images as numpy arrays in addition -to accepting base64 URL encoded format. See -mobilenet.py -for an example.

    +to accepting base64 URL encoded format.

    A variety of image saliency techniques are implemented for models that return @@ -720,33 +752,8 @@

    Pixel-based SalienceEach of these techniques returns a saliency map image as a base64-encoded string through the ImageSalience type.

    -
    -

    Attention#

    -

    LIT can display a visualization of attention heads from transformers and other -models:

    -

    Attention Visualization

    -

    To enable this, your model should return one or more fields of the type -AttentionHeads, with values as arrays of shape <float>[num_heads, num_tokens, num_tokens]. Each field represents a set of heads at a single layer of the -model, so models will often have more than one:

    -
       def output_spec(self) -> types.Spec:
-     return {
-         # ...
-         "tokens": lit_types.Tokens(parent="input_text"),
-         "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
-         # ...
-     }
-
    -
    -

    The align_in and align_out attributes are the names of Tokens fields for -the source and target tokens for that layer. For self-attention (such as in -BERT) these would name the same field, but for encoder-decoder attention they -could reference different token sets (such as “input_tokens” and -“target_tokens”).

    -
    -

    Embedding Projector#

    +

    Embedding Projector

    LIT includes a version of the embedding projector which can be used to visualize the latent space of your model, in order to find clusters or patterns @@ -762,16 +769,16 @@

    Embedding Projector<float>[emb_dim] for each example.

    -

    Aggregate Analysis#

    +

    Aggregate Analysis

    -

    Metrics#

    +

    Metrics

    LIT includes common metrics for classification, regression, and seq2seq (BLEU) by default, which will appear in the table when the appropriate types are present in the model output and input data. Metrics can be computed on the whole dataset, a selected subset, or on facets defined by particular features. For example, we could facet by class label:

    Metrics Table

    -

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Performance” tab.

    +

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Metrics” tab.

    To enable metrics, your model should set the parent attribute on one or more output fields, pointing to the name of the input field that it should be evaluated against. For example, for classification, the data spec might have:

    @@ -796,19 +803,19 @@

    Metrics#< API documentation for more.

    -

    Confusion Matrix#

    +

    Confusion Matrix

    LIT includes a powerful and flexible confusion matrix, which can be used to compare predictions to gold labels as well as to compare between two models or between different categorical features. You can click cells or row/column headers to select a subset of examples, which is useful for intersectional analysis.

    Confusion Matrix

    -

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Performance” tab.

    +

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Metrics” tab.

    The confusion matrix is supported for classification models, or if the input data includes any categorical features (CategoryLabel).

    -

    Scalar Plots#

    +

    Scalar Plots

    LIT includes scatterplots for scalar features, including plain scalars (Scalar or RegressionScore) as well as per-class probabilities from classification output (MulticlassPreds).

    @@ -820,7 +827,7 @@

    Scalar Plotshttps://pair-code.github.io/lit/demos/glue.html and navigate to the “Predictions” tab.

    -

    Binary Classification Thresholds#

    +

    Binary Classification Thresholds

    For binary classification models, LIT contains a module for setting classification thresholds, which determine at what score for the positive class the model determines that an example should be classified as belonging to the @@ -856,7 +863,7 @@

    Binary Classification Thresholds

    -

    Partial Dependence Plots#

    +

    Partial Dependence Plots

    For classification or regression models with CategoryLabel or Scalar input features, the Partial Dependence Plots module shows plots indicating the effect that changing those individual features has on model output.

    @@ -874,9 +881,10 @@

    Partial Dependence PlotsPartial Dependence Plots Module

    +

    To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the “Predictions” tab.

    -

    Dive#

    +

    Dive

    Dive is a visualization module, inspired by our prior work on Facets Dive and its use in the What-If Tool, that enables @@ -900,7 +908,7 @@

    Dive#

    -

    TCAV#

    +

    TCAV

    Many interpretability methods provide importance values per input feature (e.g, token). By contrast, TCAV shows the importance of high-level concepts (e.g., color, gender, race) for a prediction @@ -918,7 +926,7 @@

    TCAV#
    -

    Example#

    +

    Example

    1.) To use TCAV, begin by creating one or more ‘concept’ slices.

    Every dataset/model is different, but for images, as low as 15 data points are shown to be sufficient. Start by adding at least 3 data points and add more as @@ -944,7 +952,7 @@

    Example#<

    TCAV2

    -

    Statistical Significance#

    +

    Statistical Significance

    One of the pitfalls with the TCAV method is the potential generating meaningless CAVs, since any randomly chosen set of images will still produce a CAV (even if it is not meaningful).

    @@ -968,13 +976,13 @@

    Statistical Significance -

    Sorting by Cosine Similarity#

    +

    Sorting by Cosine Similarity

    The option to sort examples by cosine similarity to a CAV will be available in an upcoming release.

    -

    Counterfactual Analysis#

    +

    Counterfactual Analysis

    While aggregate metrics can give a picture of overall behavior, and salience maps can give quick insight into a model’s local behavior, many questions about model behavior are best answered in a counterfactual setting: “How does my model @@ -985,7 +993,7 @@

    Counterfactual Analysis
    -

    Manual Editing#

    +

    Manual Editing

    Examples can be edited manually in the Datapoint Editor module:

    Manual Edit in the Datapoint Editor

    The “Add and Compare” button can be used to enter comparison mode, which will @@ -999,7 +1007,7 @@

    Manual EditingPair Selection Controls

    -

    Generators#

    +

    Generators

    The Generator Module supports automatic generation of counterfactuals through a variety of plug-in components:

    Generator Module

    @@ -1122,7 +1130,6 @@

    GeneratorsSalience Clustering
  • Tabular Feature Attribution
  • Pixel-based Salience
    • -
  • Attention
  • Embedding Projector
  • Aggregate Analysis @@ -288,91 +322,31 @@

    Textual Similarity (STS-B)
    -

    Sequence-to-Sequence #

    +

    Sequence-to-Sequence

    -

    Gemma #

    -

    Code: examples/lm_salience_demo.py

    +

    Gemma

    +

    Code: +examples/prompt_debugging/server.py

      -

    • Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.

      • +

    • Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch) +and Transformers (with PyTorch).

    • Interactively debug LLM prompts using sequence salience.

    • Multiple salience methods (grad-l2 and grad-dot-input), at multiple -granularities: token-, word-, sentence-, and paragraph-level.

      • +granularities: token-, word-, line-, sentence-, and paragraph-level.

    Tip: check out the in-depth walkthrough at https://ai.google.dev/responsible/model_behavior, part of the Responsible Generative AI Toolkit.

    -
    -

    T5 #

    -

    Hosted instance: https://pair-code.github.io/lit/demos/t5.html
    -Code: examples/t5_demo.py

    -
      -

    • Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel -formats.

      • -

    • Visualize beam candidates and highlight diffs against references.

      • -

    • Visualize per-token decoder hypotheses to see where the model veers away -from desired output.

      • -

    • Filter examples by ROUGE score against reference.

      • -

    • Embeddings from last layer of model, visualized with UMAP or PCA.

      • -

    • Task wrappers to handle pre- and post-processing for summarization and -machine translation tasks.

      • -

    • Pre-loaded eval sets for CNNDM and WMT.

      • -
    -

    Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/generation

    -
    -
    -
    -
    -

    Language Modeling #

    -
    -

    BERT and GPT-2 #

    -

    Hosted instance: https://pair-code.github.io/lit/demos/lm.html
    -Code: examples/lm_demo.py

    -
      -

    • Compare multiple BERT and GPT-2 models side-by-side on a variety of -plain-text corpora.

      • -

    • LM visualization supports different modes:

      -
        -

      • BERT masked language model: click-to-mask, and query model at that -position.

        • -

      • GPT-2 shows left-to-right hypotheses for each target token.

        • -
      -
      • -

    • Embedding projector to show latent space of the model.

      • -
    -
    -
    -
    -
    -

    Structured Prediction #

    -
    -

    Gender Bias in Coreference #

    -

    Hosted instance: https://pair-code.github.io/lit/demos/coref.html
    -Code: examples/coref/coref_demo.py

    -
      -

    • Gold-mention coreference model, trained on -OntoNotes.

      • -

    • Evaluate on the Winogender schemas -(Rudinger et al. 2018) which test for -gendered associations with profession names.

      • -

    • Visualizations of coreference edges, as well as binary classification -between two candidate referents.

      • -

    • Stratified metrics for quantifying model bias as a function of pronoun -gender or Bureau of Labor Statistics profession data.

      • -
    -

    Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/coref

    -
    -

    Multimodal #

    +

    Multimodal

    -

    Tabular Data: Penguin Classification #

    +

    Tabular Data: Penguin Classification

    Hosted instance: https://pair-code.github.io/lit/demos/penguins.html
    -Code: examples/penguin_demo.py

    +Code: examples/penguin/demo.py

    • @@ -456,7 +419,6 @@

    Image Classification with MobileNet
  • Regression / Scoring @@ -493,8 +445,8 @@

    Image Classification with MobileNet + Running LIT in a Docker container - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@ + +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    + + + + +
    @@ -200,34 +250,33 @@
    -
    +
    -

    Running LIT in a Docker container#

    - +

    Running LIT in a Docker container

    +

    Users might want to deploy LIT onto servers for public-facing, long-running instances. This is how we host the LIT demos found on https://pair-code.github.io/lit/demos/. This doc describes the basic usage of LIT’s built-in demos, how to integrate your custom demo into this

    -

    Basic Usage#

    +

    Basic Usage

    LIT can be run as a containerized app using Docker or your preferred engine. This is how we run our hosted demos.

    -

    We provide a basic -Dockerfile that you can -use to build and run any of the demos in the lit_nlp/examples directory. The -Dockerfile installs all necessary dependencies for LIT and builds the +

    We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the lit_nlp/examples directory. +The Dockerfile installs all necessary dependencies for LIT and builds the front-end code from source. Then it runs gunicorn as the HTTP server, invoking the get_wsgi_app() method from our demo file to get the WSGI app to serve. The options provided to gunicorn for our use-case can be found in gunicorn_config.py. You can find a reference implementation in -glue_demo.py or -lm_demo.py.

    -

    Use the following shell commands to build the default Docker image for LIT from -the provided Dockerfile, and then run a container from that image. Comments -are provided in-line to help explain what each step does.

    +glue/demo.py.

    +

    Use the following shell +https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the +default Docker image for LIT from the provided Dockerfile, and then run a +container from that image. Comments are provided in-line to help explain what +each step does.

    # Build the docker image using the -t argument to name the image. Remember to
 # include the trailing . so Docker knows where to look for the Dockerfile.
 docker build --file Dockerfile --tag lit-nlp .
@@ -243,14 +292,11 @@ 
    Basic Usage
    # DEMO_NAME is used to complete the Python module path
 #
-#     "lit_nlp.examples.$DEMO_NAME"
+#     "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()"
 #
 # Therefore, valid values for DEMO_NAME are Python module paths in the
-# lit_nlp/examples directory, such as
-#
-#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.
-#   * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc.
-docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp
+# lit_nlp/examples directory, such as glue, penguin, tydi, etc.
+docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
 
 # Use the DEMO_PORT environment variable as to change the port that LIT uses in
 # the container. Be sure to also change the -p option to map the container's
@@ -261,16 +307,16 @@ 
    Basic Usage# containers on your machine using the combination of the DEMO_NAME and
 # DEMO_PORT arguments, and docker run with the -d flag to run the container in
 # the background.
-docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp
-docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp
+docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
+docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp
    -

    Integrating Custom LIT Instances with the Default Docker Image#

    +

    Integrating Custom LIT Instances with the Default Docker Image

    Many LIT users create their own custom LIT server script to demo or serve, which involves creating an executable Python module with a main() method, as -described in the Python API docs.

    +described in the Python API docs.

    These custom server scripts can be easily integrated with LIT’s default image as long as your server meets two requirements:

      @@ -333,7 +379,7 @@

      Integrating Custom LIT Instances with the Default Docker Image -

      Building Your Own Image#

      +

      Building Your Own Image

      Coming soon.

    @@ -409,8 +455,8 @@

    Building Your Own Image

    - + - + \ No newline at end of file diff --git a/docs/documentation/faq.html b/docs/documentation/faq.html index 18038110..ebfd57fe 100644 --- a/docs/documentation/faq.html +++ b/docs/documentation/faq.html @@ -5,11 +5,11 @@ - + Frequently Asked Questions - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@ + +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    + + + + +
    @@ -200,25 +250,23 @@
    -
    +
    -

    Frequently Asked Questions#

    - +

    Frequently Asked Questions

    +

    Looking for help? Submit bugs, ask questions, suggest content, and request features on our Github issues list.

    -

    Model and Data Types#

    +

    Model and Data Types

    LIT can handle a variety of models with different input and output types, and works with any modern ML framework. For more information, see Framework & Model Support.

    In addition to text, LIT has good support for different modalities, including images and tabular data. For examples, see:

    -

    Languages#

    +

    Languages

    All strings in LIT are unicode and most components use model-provided tokenization if available, so in most cases non-English languages and non-Latin -scripts should work without any modifications. For examples, see:

    -
      -

    • XNLI demo - -cross-lingual NLI, with up to 15 languages supported via a multilingual BERT -model.

      • -

    • T5 demo - -includes WMT data for machine translation

      • -
    +scripts should work without any modifications.

    -

    Scale#

    +

    Scale

    -

    Dataset Size#

    +

    Dataset Size

    LIT can comfortably handle 10k-100k datapoints, depending on the speed of the server (for hosting the model) and your local machine (for viewing the UI). When working with large datasets, there are a couple caveats:

    @@ -269,7 +310,7 @@

    Dataset Size -

    Large Models#

    +

    Large Models

    LIT can work with large or slow models, as long as you can wrap them into the model API. If you have more than a few preloaded datapoints, however, you’ll probably want to use warm_start=1 (or pass --warm_start=1 as a flag) to @@ -288,17 +329,17 @@

    Large Models -

    Privacy and Security#

    +

    Privacy and Security

    LIT allows users to query the model, as well as to view the loaded evaluation data. The LIT UI state is ephemeral and exists only in the browser window; however, model predictions and any newly-generated examples (including as manually entered in the web UI) are stored in server memory, and if --data_dir is specified, may be cached to disk.

    LIT has the ability to create or edit datapoints in the UI and then save them to -disk. If you do not want the tool to be able to write edited datapoints to -disk, then pass the --demo_mode runtime flag to the LIT server.

    +disk. If you do not want the tool to be able to write edited datapoints to disk, +then pass the --demo_mode runtime flag to the LIT server.

    -

    I have proprietary data. Is LIT secure for my team to use?#

    +

    I have proprietary data. Is LIT secure for my team to use?

    We don’t store, collect or share datasets, models or any other information loaded into LIT. When you run a LIT server, anyone with access to the web address of the server will be able to see data from the loaded datasets and @@ -312,9 +353,9 @@

    I have proprietary data. Is LIT secure for my team to use? -

    Workflow and Integrations#

    +

    Workflow and Integrations

    -

    Sending examples from another tool#

    +

    Sending examples from another tool

    LIT can read input fields directly from the URL; they should be encoded as data_<fieldname>=<value>, and field names should match those in the (default) dataset.

    @@ -323,7 +364,7 @@

    Sending examples from another tooldata0, data1, data2, e.g. data0_<fieldname>=<value>.

    -

    Downloading or exporting data#

    +

    Downloading or exporting data

    Currently, there are three ways to export data from the LIT UI:

    • In the Data Table, you can copy or download the current view in CSV format - @@ -337,7 +378,7 @@

      Downloading or exporting data -

      Loading data from the UI#

      +

      Loading data from the UI

      There is limited support for this via the settings (⚙️) menu. Select a dataset, and enter a path to load from:

      Load data from the UI

      @@ -346,7 +387,7 @@

      Loading data from the UI -

      Using components outside the LIT UI#

      +

      Using components outside the LIT UI

      Python components such as models, datasets, and generators are designed to support standalone use. These don’t depend on the LIT serving framework, and you can treat them as any other Python class and use from Colab, regular scripts, @@ -362,17 +403,64 @@

      Using components outside the LIT UI -

      Training models with LIT#

      +

      Training models with LIT

      LIT is primarily an evaluation/inference-time tool, so we don’t provide any official training APIs. However, to facilitate code reuse you can easily add training methods to your model class. In fact, several of our demos do exactly this, using LIT’s Dataset objects to manage training data along with standard training APIs (such as Keras’ model.fit()). See -quickstart_sst_demo.py -and/or -glue_models.py +glue/models.py for examples.

    +
    +

    Debug LIT UI in Colab

    +

    The LIT instance launched from CLI typically has helpful error messages in the +UI. However, this is not the case for the LIT UI in Colab and the error message +does not report any stacktrace, which makes debugging very difficult.

    +

    LIT UI error in colab

    +

    While in +Chrome developer tools, +you will be able to debug issues solely related to the frontend, but not so for +issues related to the backend or on the HTTP request path.

    +

    Thus, to show the full stacktrace, you would need to find the HTTP request sent +from the frontend to the backend, compose the same request in colab and send it +to the server.

    +
      +

    1. When rendering the UI, display it in a separate tab to make things a bit +easier to work with, e.g. lit_widget.render(open_in_new_tab=True).

      2. +

    2. Open +Chrome developer tools, +go to “Sources” tab and find the file +client/services/api_service.ts and set a +breakpoint right after where the HTTP request is set up in the queryServer +method, e.g. after this line const res = await fetch(url, {method: 'POST', body});.

      +
        +

      • Note it is possible that the whole frontend source code is compiled into +a main.js file, and the code is not exactly the same as that in LIT +frontend source code. You might have to do a bit digging to find the +right line.

        • +
      +
      3. +

    3. Go to the UI and trigger the behavior that causes the error. Now in Chrome +developer tools you will be able to see the variables and their values in +the queryServer method. Copy the values of the url and body variables +in the method.

      4. +

    4. Go back to Colab, compose your HTTP request method. Look for the main server +address printed out from lit_widget.render(open_in_new_tab=True).

      5. +
    +

    LIT colab server address

    +

    Let’s say the server address is “https://localhost:32943/?” as shown above, the +body variable obtained earlier has value “request_body_text” and the url +variable has value “./get_preds?param1=value1”. Then your HTTP request will be +like this:

    +
    ! curl -H "Content-Type: application/json" \
+       -d "request_body_text" \
+       -X POST "http://localhost:32943/get_preds?param1=value1"
+
    +
    +

    Run this in Colab and you should be able to retrieve the full stacktrace of the +error.

    +
    @@ -441,6 +529,7 @@

    Training models with LITLoading data from the UI

  • Using components outside the LIT UI
  • Training models with LIT
    • +
  • Debug LIT UI in Colab
    • @@ -455,8 +544,8 @@

    Training models with LIT - + - + \ No newline at end of file diff --git a/docs/documentation/frontend_development.html b/docs/documentation/frontend_development.html index f3b9bbea..d484b264 100644 --- a/docs/documentation/frontend_development.html +++ b/docs/documentation/frontend_development.html @@ -5,11 +5,11 @@ - + Frontend Developer Guide - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@ + +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    + + + + +
    @@ -200,15 +250,15 @@
    -
    +
    -

    Frontend Developer Guide#

    - +

    Frontend Developer Guide

    +

    This document aims to describe the current LIT frontend system, including conventions, best practices, and gotchas.

    -

    High Level Overview#

    +

    High Level Overview

    LIT is powered by two central pieces of tech - lit-element for components and HTML rendering, and mobx for observable-oriented @@ -223,13 +273,13 @@

    High Level Overview -

    Application Architecture#

    +

    Application Architecture

    The LIT client frontend is roughly divided into three conceptual groups - Modules (which render visualizations), Services (which manage data), and the App itself (which coordinates initialization of services and determines which modules to render).

    -

    Bootstrapping#

    +

    Bootstrapping

    The LIT app bootstrapping takes place in two steps: First, the served index.html page contains a single web component for the @@ -241,16 +291,16 @@

    BootstrappingLitModule components, a process about which we’ll go into greater detail later.

    The JS bundle entry point is -main.ts, which -first imports the loaded, the <lit-app> web component is declared, and -attaches itself to the DOM, waiting for the app to be initialized.

    +main.ts, which first +imports the loaded, the <lit-app> web component is declared, and attaches +itself to the DOM, waiting for the app to be initialized.

    The second step is kicking off app initialization. The LitApp singleton class is provided with a layout declaring which LitModule components to use, then builds the app services and kicks off app initialization and loading data.

    -

    Layout#

    +

    Layout

    A layout defines the arraignment of LitModule classes in the UI. Layouts are specified in Python as LitCanonicalLayout instances, and LIT includes three pre-configured layouts in @@ -261,7 +311,7 @@

    Layout#

    default: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and

    -

  • experimental: A three-panel layout that puts exploratory data +

  • three_panel: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -270,14 +320,14 @@

    Layout#

    You can also add custom layouts to your LIT instance by defining one or more LitCanonicalLayout instances and passing them -to the server. For an example, see CUSTOM_LAYOUTS in -lm_demo.py.

    +to the server. For an example, see +prompt_debugging/layouts.py.

    Note: The pre-configured layouts are added to every LitApp instance using dictionary updates where the Mapping passed to the LitApp constructor overrides the pre-configured layouts Mapping. Thus, you can remove or change these pre-configured layouts as you like by passing a Mapping where the values of simple, default, -and/or experimental is None (to remove) or a LitCanonicalLayout instance +and/or three_panel is None (to remove) or a LitCanonicalLayout instance (to override) as you desire.

    The actual layout of components in the LIT UI, see <lit-modules>, @@ -289,7 +339,7 @@

    Layout#modules_service.

    • -

    Initialization#

    +

    Initialization

    Finally, the LIT App initializes by building the various service classes and starting the initial load of data from the server. This process consists of:

      @@ -301,7 +351,7 @@

      Initialization -

      Modules (LitModule)#

      +

      Modules (LitModule)

      The LitModule is the base class from which all module components derive. It provides a number @@ -373,7 +423,7 @@

      Modules (LitModule) -

      Setup#

      +

      Setup

      First, a LitModule must declare a static title string (1) and template function (2). The template function determines how the modules layout renders the component template and passes in module properties, such as the name of the @@ -406,7 +456,7 @@

      Setup#< other TypeScript files in different build units.

    -

    Functionality#

    +

    Functionality

    The above module has a very simple task - When the user selects input data, it makes a request to an API service to fetch and display a pig latin translation of the data. Since we’re using mobx observables to store and compute our state, @@ -445,7 +495,7 @@

    Functionality -

    Escape Hatches#

    +

    Escape Hatches

    Finally, it’s worth noting that the declarative template-based rendering setup, while effective for handling most component render logic, is sometimes inadequate for more advanced visualizations. In particular, the template @@ -475,7 +525,7 @@

    Escape Hatches -

    Stateful Child Elements#

    +

    Stateful Child Elements

    Some modules may contain stateful child elements, where the element has some internal state that can have an effect on the module that contains it. Examples of this include any modules that contain the @@ -514,7 +564,7 @@

    Stateful Child Elements

    -

    Style Guide#

    +

    Style Guide

    @@ -262,7 +310,6 @@

    Research
  • Salience Clustering
  • Tabular Feature Attribution
  • Pixel-based Salience
    • -
  • Attention
  • Embedding Projector
  • Aggregate Analysis
  • TCAV
    • @@ -370,8 +417,10 @@

    Research

    - + - + + + \ No newline at end of file diff --git a/docs/documentation/search.html b/docs/documentation/search.html index 1e5289dc..9af65ae7 100644 --- a/docs/documentation/search.html +++ b/docs/documentation/search.html @@ -1,12 +1,15 @@ - + + - Search - 🔥LIT 1.0 documentation - - + + +Search - 🔥LIT 1.0 documentation + + @@ -66,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -81,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -110,6 +154,8 @@ + +
    @@ -127,7 +173,8 @@
    @@ -186,7 +233,8 @@
    @@ -196,7 +244,7 @@
    -
    +
    - + - + diff --git a/docs/documentation/searchindex.js b/docs/documentation/searchindex.js index 75ece15f..a95d7a50 100644 --- a/docs/documentation/searchindex.js +++ b/docs/documentation/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "components", "demos", "docker", "faq", "frontend_development", "getting_started", "glossary", "includes/highlight_demos", "index", "ui_guide"], "filenames": ["api.md", "components.md", "demos.md", "docker.md", "faq.md", "frontend_development.md", "getting_started.md", "glossary.md", "includes/highlight_demos.md", "index.md", "ui_guide.md"], "titles": ["LIT Python API", "Components and Features", "Demos", "Running LIT in a Docker container", "Frequently Asked Questions", "Frontend Developer Guide", "Getting Started with LIT", "Glossary", "<no title>", "Learning Interpretability Tool (LIT)", "UI Guide"], "terms": {"i": [0, 1, 3, 5, 6, 7, 10], "modular": 0, "compris": [0, 10], "collect": [0, 4, 5, 10], "backend": [0, 4, 5, 7, 10], 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a/docs/documentation/ui_guide.html b/docs/documentation/ui_guide.html index 0945b12d..e58dea66 100644 --- a/docs/documentation/ui_guide.html +++ b/docs/documentation/ui_guide.html @@ -5,11 +5,11 @@ - + UI Guide - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@ + +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    + + + + +
    @@ -200,16 +250,16 @@
    -
    +
    -

    UI Guide#

    - +

    UI Guide

    +

    This is a user guide for the Learning Interpretability Tool (LIT) UI.

    For a quick video tour of LIT, check out this video.

    -

    General Layout#

    +

    General Layout

    LIT lives inside a single page web application, comprised of multiple toolbars and a main section consisting of individual modules. Modules will automatically display if they are applicable to the current model and dataset; for example, @@ -227,7 +277,7 @@

    General Layout -

    Layout Options#

    +

    Layout Options

    @@ -238,7 +288,7 @@

    Layout Optionsdefault: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and

    -

  • experimental: A three-panel layout that puts exploratory data +

  • three_panel: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -248,7 +298,7 @@

    Layout Options -

    Datapoint Selections#

    +

    Datapoint Selections

    LIT displays a loaded dataset and its model results across the set of selected models. Users can dive into detailed results by selecting datapoints from the dataset.

    @@ -278,16 +328,16 @@

    Datapoint SelectionsMetrics Module section.

    • -

    Toolbars#

    +

    Toolbars

    There are three toolbars provided in LIT. The top bar includes the selected model(s) and dataset, a settings button, and URL sharing functionality. Below that is the main toolbar with the menus and controls for navigation and selection. At the bottom of the page is a status bar.

    LIT toolbars

    -

    Top Bar#

    +

    Top Bar

    -

    Global Settings#

    +

    Global Settings

    The global settings dialog is accessible through the “Configure” button in the top bar.

    LIT can be launched with a set of models and datasets. The settings screen @@ -302,7 +352,7 @@

    Global Settings

    -

    URL Sharing#

    +

    URL Sharing

    Much of the LIT app’s state — the loaded models and datasets, selected datapoints, minimized and/or full-screen modules — is stored in URL parameters. The “Copy Link” button in the top bar allows a user to share @@ -313,25 +363,35 @@

    URL Sharing -

    Main Toolbar#

    +

    Main Toolbar

    The main toolbar is right below the top bar and contains a number of different controls and information. The left side of the toolbar contains a set of menus for quickly controlling datapoint selection and coloring. This includes the following controls:

      -

    • The “Select related” option adds any datapoints “related” to the current -selection. In LIT, “related” is defined as datapoints created from some -source datapoint (through manual editing or a datapoint generator), or a -source datapoint that a selected datapoint was created from.

      • -

    • The “Clear selection” button deselects all selected datapoints. The -dropdown also contains a color legend for the current color setting.

      • -

    • The Slices option allows quick selection of an already-created slice of -datapoints.

      • -

    • The “Datapoint color” menu enables setting of the color of each -datapoint in the modules that visualize all datapoints (such as the -Embeddings and Scalars modules) by any number of datapoint features or -model outputs on those datapoints (such as coloring by some categorical -input feature, or by prediction error for a regression task).

      • +

    • The “Select datapoint” menu provides a drop-down of several options:

      +
        +

      • the “Random” option selects a random datapoint,

        • +

      • the “All related” option adds any datapoints “related” to the +current selection. In LIT, “related” is defined as datapoints created +from some source datapoint (through manual editing or a datapoint +generator), or a source datapoint that a selected datapoint was created +from,

        • +

      • the “Parents” option adds the source datapoints that the selected +datapoints were created from,

        • +

      • the “Children” option adds the datapoints created from the selected +datapoints (through manual editing or a datapoint generator),

        • +

      • the Slices option allows quick selection of an already-created slice +of datapoints,

        • +

      • the “Clear selection” button deselects all selected datapoints.

        • +
      +
      • +

    • The “Color by” menu enables setting of the color of each datapoint in +the modules that visualize all datapoints (such as the Embeddings and +Scalars modules) by any number of datapoint features or model outputs on +those datapoints (such as coloring by some categorical input feature, or by +prediction error for a regression task).

      • +

    • The Slices menu allows adding/selecting/removing slices of datapoints.

    Next to the menus is a button for pinning/unpinning a datapoint. Pinning a datapoint puts LIT into datapoint comparison mode, where two datapoints can be @@ -349,7 +409,7 @@

    Main Toolbar -

    Status Bar#

    +

    Status Bar

    The status bar at the bottom of the tool contains a text area on the left side. If the tool is currently waiting on the results of a call to the backend (such as for running predictions or getting embeddings), this information will be @@ -362,7 +422,7 @@

    Status Bar -

    Comparing Models#

    +

    Comparing Models

    By loading more than one model in the global settings controls, LIT can compare multiple models. A subset of modules that show per-model information are then duplicated to allow easy comparison across two models. Other modules, such the @@ -371,7 +431,7 @@

    Comparing Models

    -

    Comparing Datapoints#

    +

    Comparing Datapoints

    Pinning a datapoint, through either the toolbar button or controls in modules (e.g., the pin icons in Data Table rows), puts LIT into datapoint comparison mode. In this mode, the pinned datapoint is used as a reference to compare the @@ -386,7 +446,7 @@

    Comparing DatapointsLIT datapoint comparison

    -

    Slices#

    +

    Slices

    The Slice Editor allow users to create, edit, select, and delete slices. The current selection can be saved as a slice by giving it a name and clicking “Create slice”. The slice list allows you to select any of the previously-saved @@ -405,14 +465,14 @@

    Slices#LIT slice controls

    -

    Module Details#

    +

    Module Details

    This section contains details on using and interacting with individual modules that are built into LIT. Note that this list may not be complete and additional modules can be created and used in LIT by clients.

    All modules can be toggled to be shown full-screen through use of the full-screen button in the top-right of each module.

    -

    Embedding Projector#

    +

    Embedding Projector

    When using LIT with a model that returns embeddings (or activations) in addition to predictions, the embedding projector will show all datapoints by their embeddings projected down to 3 dimensions. This is useful for exploring and @@ -430,7 +490,7 @@

    Embedding Projector -

    Data Table#

    +

    Data Table

    The data table shows all datapoints in a simple table. Datapoints can be selected or unselected through a click. Shift+click allows selecting a set of consecutive datapoints, and control+click allows selecting a set of individual @@ -483,12 +543,12 @@

    Data Table -

    Datapoint Editor#

    +

    Datapoint Editor

    The datapoint editor shows the details of the primary selected datapoint, if one is selected. Any field can be edited, and a new datapoint created with those -edits through the “Make new datapoint” button. Any edit to an existing -datapoint must be saved as a new datapoint to be explored, to keep datapoints -immutable for simplicity of use.

    +edits through the “Add” button. Any edit to an existing datapoint must be +saved as a new datapoint to be explored, to keep datapoints immutable for +simplicity of use.

    When no datapoint is selected, the editor shows a blank datapoint that can be filled out by hand to create a completely new datapoint.

    Features shown with a “(*)” next to their name are required as model input and @@ -496,7 +556,7 @@

    Datapoint EditorLIT datapoint editor

    -

    Datapoint Generator#

    +

    Datapoint Generator

    The datapoint generator module allows creation of new datapoints from all currently-selected datapoints (or the entire dataset if no datapoints are selected) through a set of counterfactual datapoint generators. These generators @@ -531,7 +591,7 @@

    Datapoint Generator

    -

    Metrics Table#

    +

    Metrics Table

    The metrics table shows model metrics for each model in a table format. The exact metric types are determined by the python metrics component that calculates metrics given the model types being evaluated. These can include @@ -553,7 +613,7 @@

    Metrics Table

    -

    Confusion Matrix#

    +

    Confusion Matrix

    The confusion matrix buckets all datapoints from the dataset (or the current selection, if one is made) into buckets in a 2D matrix. This is normally used to compare classification predictions on a model versus the ground truth classes of @@ -571,7 +631,7 @@

    Confusion Matrix

    -

    Scalars#

    +

    Scalars

    The scalars module shows a set of scatter or jitter plots, one for each scalar output of a loaded model (such as a regression score, or a classification score for a specific class). Each of them contains all datapoints in the dataset, laid @@ -595,7 +655,7 @@

    Scalars#<

    LIT prediction scores

    -

    Model Output#

    +

    Model Output

    Model output modules show the result of a model on the primary selected datapoint. The visuals of these modules depend on the model task being performed. For a simple classification task, it will show the class scores from @@ -608,7 +668,7 @@

    Model OutputLIT structured prediction

    -

    Salience Maps#

    +

    Salience Maps

    Salience maps show the influence of different parts of inputs features on a model’s prediction on the primary selection. This module can contain multiple methodologies for calculating this salience, depending on the capabilities of @@ -618,28 +678,20 @@

    Salience Maps

    -
    -

    Attention#

    -

    For models that return attention head information along with predictions, the -attention module shows which tokens are attended to between layers of a model. -Dropdowns allow you to select which layer and attention head is being -visualized. Line opacity is controlled by the magnitude of the attention between -those tokens.

    -

    LIT attention

    -
    -

    User Journeys#

    +

    User Journeys

    In this section, we explore some example user journeys and how LIT enables them.

    -

    Sentiment Analysis#

    +

    Sentiment Analysis

    How well does a sentiment classifier handle negation? We load the development set of the Stanford Sentiment Treebank, and use the search function in LIT’s data table to find the 56 datapoints containing the word “not”. Looking at the @@ -656,48 +708,13 @@

    Sentiment Analysis

    -
    -

    Gender Bias in Coreference#

    -

    Does a system encode gendered associations, which might lead to incorrect -predictions? We load a coreference model trained on OntoNotes, and load the -Winogender dataset into LIT for evaluation. Each Winogender example has a -pronoun and two candidate referents, one a occupation term like (“technician”) -and one is an “other participant” (like “customer”). Our model predicts -coreference probabilities for each candidate. We can explore the model’s -sensitivity to pronouns by comparing two examples side-by-side (see structured -predictions in screenshot). We can see how commonly the model makes similar -errors by paging through the dataset, or by selecting specific slices of -interest.

    -

    Winogender analysis

    -

    In the Metrics table, we can slice this selection by pronoun type and by the -true referent. On the set of male-dominated occupations (< 25% female by BLS), -we see the model performs well when the ground-truth agrees with the -stereotype - e.g. when the answer is the occupation term, male pronouns are -correctly resolved 83% of the time, compared to female pronouns only 37.5% of -the time (screenshot section (c)).

    -

    Winogender analysis

    -
    -
    -

    Debugging Text Generation#

    - -

    Does the training data explain a particular error in text generation? We analyze -an older T5 model on the CNN-DM summarization task. LIT’s Scalars module -allows us to look at per-example ROUGE scores, and quickly select an example -with middling performance (screenshot section (a)). We find the generated text -(screenshot section (b)) contains an erroneous constituent: “alastair cook was -replaced as captain by former captain …”. We can dig deeper, using LIT’s -language modeling module (screenshot section (c)) to see that the token “by” is -predicted with high probability (28.7%).

    -

    To find out how T5 arrived at this prediction, we utilize the “similarity -searcher” component through the datapoint generator (screenshot section (d)). -This performs a fast approximate nearest-neighbor lookup from a pre-built index -over the training corpus, using embeddings from the T5 decoder. With one click, -we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection. -We see that the words “captain” and “former” appear 34 and 16 times in these -examples–along with 3 occurrences of “replaced by” (screenshot section (e)), -suggesting a strong prior toward our erroneous phrase.

    -

    LIT sequence-to-sequence analysis

    +
    +

    Sequence salience

    +

    Sequence salience generalizes token-based salience to text-to-text models, +allowing you to explain the impact of the prompt tokens on parts of the model +output.

    +

    Check out here for more details on how to +navigate the Sequence Salience UI module.

    @@ -786,13 +803,11 @@

    Debugging Text GenerationScalars
  • Model Output
  • Salience Maps
    • -
  • Attention
  • User Journeys
    • @@ -807,8 +822,8 @@

    Debugging Text Generation - + - + \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index d6b44ac4..65c7ceaa 100644 --- a/docs/index.html +++ b/docs/index.html @@ -110,7 +110,6 @@

    Built-in capabilities

    Salience maps

    -

    Attention visualization

    Metrics calculations

    Counterfactual generation

    Model and datapoint comparison

    @@ -151,7 +150,7 @@

    What's the latest

    UPDATES
    -

    Version 1.1

    +

    Version 1.2

    Input salience for text-to-text LLMs, with wrappers for HuggingFace Transformers and KerasNLP models.
    See release notes
    diff --git a/docs/tutorials/coref/index.html b/docs/tutorials/coref/index.html deleted file mode 100644 index ea57ac9d..00000000 --- a/docs/tutorials/coref/index.html +++ /dev/null @@ -1,187 +0,0 @@ - - - - - - - - - - - Gender Bias in Coreference - - - - - - - - - - - - - - - -
    - -
    -
    - - - - - Learning Interpretability Tool - - - -
    - - -
    -
    -
    - Learning Interpretability Tool - -
    - - -
    -
    -
    -
    - Tutorials > Analysis > Coreference -
    -

    Gender Bias in Coreference

    -
    Explore this demo yourself.
    -

    Or, run your own with examples/coref/coref_demo.py

    -

    Does a system encode gendered associations, which might lead to incorrect predictions? We explore this for coreference, the task of identifying whether two mentions refer to the same (real-world) entity. For example, in the sentence "The technician told the customer that they could pay with cash.", we understand from the context that "they" refers to "the customer", the one paying.

    -

    The Winogender dataset introduced by Rudinger et al. 2018 presents a set of challenging coreference examples designed to explore gender bias. It consists of 120 templates, each with semantic context that makes it easy for humans to tell the answer. Each template is instantiated with different pronouns, in order to give a minimal pair:

    -
      -
    • "The technician told the customer that he could pay with cash."
      • -
    • "The technician told the customer that she could pay with cash."
      • -
    -

    In both cases, the pronoun should refer to the customer - but does our model agree? Or does it fall back on stereotypes about who can be a technician, or a customer? We can use LIT to explore this interactively, making use of the side-by-side functionality, structured prediction visualization, and powerful features for aggregate analysis to validate our findings.

    -

    We load our coreference model into LIT, along with a copy of the Winogender dataset. Our model predicts probabilities for each mention pair - in this case the (occupation, pronoun) and (participant, pronoun) pairs - and LIT renders this as a pair of edges:

    -
    - -
    - -
    -
    Above: A coreference prediction.
    -
    -

    We can select an example by clicking the row in the data table in the top left of the UI; the predictions will display automatically in the "Predictions" tab below. To look at two predictions side-by-side, we can enable "Compare datapoints" mode in the toolbar, which will pin our first selection as a "reference" and allow us to select another point to compare:

    -
    - -
    - -
    -
    Above: Selecting two datapoints to compare.
    -
    -

    We see that LIT automatically replicates the predictions view, allowing us to see how our model handles "he" and "she" differently on these two sentences:

    -
    - -
    - -
    -
    Above: Comparing coreference predictions of two datapoints.
    -
    -

    To see why this might be, we can make use of some additional information from the U.S. Bureau of Labor Statistics (BLS), which tabulates the gender percentages in different occupations. Our example loads this along with the dataset, and LIT shows this as a column in the data table:

    -
    - -
    - -
    -
    Above: Datapoints with extra informational feature columns.
    -
    -

    We see that "technician" is only 40% female, suggesting that our model might be picking up on social biases with its eagerness to identify "he" as the technician in the example above.

    -

    Is this a pattern? In addition to individual instances, we can use LIT to see if this holds on larger slices of the data. Turning to the "Performance" tab, we see that our model gets around 63% accuracy overall.

    -
    - -
    - -
    -
    Above: Overall model accuracy in the metrics table.
    -
    -

    Let's see how this breaks down. On the right, the Scalars module lets us select data based on scalar values, such as the percent female of each profession according to BLS. Let's select the points on the left, with professions that are stereotypically male (< 25% female). Additionally, we'll stratify our metrics based on the pronoun group, and whether the answer should be the occupation term or the other, neutral, participant:

    -
    - -
    - -
    -
    Above: Metrics faceted into sub-groups and scalar results plots.
    -
    -

    We can see that on this slice, our model performs very well when the ground truth agrees with the stereotype - i.e. when the answer is the occupation term, our model resolves male pronouns correctly 91% of the time, while only matching female pronouns 37% of the time in exactly the same contexts.

    - -
    -
    -
    -
    time to read
    -
    10 minutes
    -
    takeaways
    -
    Learn about how to explore fairness using datapoint comparison and metrics comparisons.
    -
    -
    -
    - The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our Github. -
    -
    - -
    -
    -
    - Google Research - Language -
    -
    Github
    -
    - - - -
    -
    - -
    - -
    - - - - - - - diff --git a/docs/tutorials/generation/index.html b/docs/tutorials/generation/index.html deleted file mode 100644 index 6647a8b1..00000000 --- a/docs/tutorials/generation/index.html +++ /dev/null @@ -1,149 +0,0 @@ - - - - - - - - - - - Debugging a Text Generator - - - - - - - - - - - - - - - -
    - -
    -
    - - - - - Learning Interpretability Tool - - - -
    - - -
    -
    -
    - Learning Interpretability Tool - -
    - - -
    -
    -
    -
    - Tutorials > Analysis > Generator -
    -

    Debugging a Text Generator

    -
    Explore this demo yourself.
    -

    Or, run your own with examples/t5_demo.py

    -

    Does the training data explain a particular error in text generation? We can use LIT to analyze a T5 model on the CNN-DM summarization task. LIT’s scalar plot module allows us to look at per-example ROUGE scores, and quickly select an example with middling performance.

    -
    - -
    - -
    -
    Above: The datapoint of interest, with its inaccurate summarization.
    -
    -

    We find the generated text contains an erroneous subject: "[A]lastair [C]ook was replaced as captain by former captain ...". In reading the input text, we can see that he was replaced as captain by Eoin Morgan, and not by a former captain. Another former captain does have a quote in the input text, and the model seemed to confuse these two subjects when generating its summary.

    -
    - -
    - -
    -
    Above: Finding the most similar datapoints from the training set.
    -
    -

    To investigate how T5 arrived at this prediction, we utilize the “similarity searcher” component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings -from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our datapoint of interest from the training set and add them to the LIT UI for inspection. We can see through the search capability in the data table that the words “captain” and “former” appear 34 and 16 times in these examples–along with 3 occurrences of “replaced by” and two occurrences of “by former”. This suggests a strong prior toward our erroneous phrase from the training data most related to our datapoint of interest.

    -
    - -
    - -
    -
    Above: An example of the existence of "by former" in a similar datapoint from the training set.
    -
    - -
    -
    -
    -
    time to read
    -
    3 minutes
    -
    takeaways
    -
    Learn about how to debug a prediction on a datapoint by searching for similar datapoints from the training set.
    -
    -
    -
    - The Learning Interpretability Tool is being actively developed and documentation is likely to change as we improve the tool. We want to hear from you! Leave us a note, feedback, or suggestion on our Github. -
    -
    - -
    -
    -
    - Google Research - Language -
    -
    Github
    -
    - - - -
    -
    - -
    - -
    - - - - - - - diff --git a/docs/tutorials/index.html b/docs/tutorials/index.html index d1d43d44..c415c6ce 100644 --- a/docs/tutorials/index.html +++ b/docs/tutorials/index.html @@ -115,14 +115,6 @@

    Global Model Analy

    Exploring a Sentiment Classifier

    Learn about how we used LIT to analyze a sentiment classifier.

    -
    -

    Debugging a Text Generator

    -
    Learn about how we used LIT to debug summarization by a text generation model.
    -
    -
    -

    Gender Bias in Coreference

    -
    Learn how we used LIT to explore gendered associations in a pronoun resolution model.
    -

    diff --git a/docs/tutorials/sentiment/index.html b/docs/tutorials/sentiment/index.html index 017f1694..1ee96c27 100644 --- a/docs/tutorials/sentiment/index.html +++ b/docs/tutorials/sentiment/index.html @@ -77,7 +77,7 @@

    Exploring a Sentiment Classifier

    Explore this demo yourself.
    -

    Or, run your own with examples/glue_demo.py

    +

    Or, run your own with examples/glue/demo.py

    How well does a sentiment classifier handle negation? We can use LIT to interactively ask this question and get answers. We loaded up LIT the development set of the Stanford Sentiment Treebank (SST), which contains sentences from movie reviews that have been human-labeled as having a negative sentiment (0), or a positive sentiment (1). For a model, we are using a BERT-based binary classifier that has been trained to classify sentiment.

    Using the search function in LIT’s data table, we find the 67 datapoints containing the word “not”. By selecting these datapoints and looking at the Metrics Table, we find that our BERT model gets 91% of these correct, which is slightly higher than the accuracy across the entire dataset.

    diff --git a/docs/tutorials/sequence-salience/index.html b/docs/tutorials/sequence-salience/index.html index 9e76d614..e499ef9a 100644 --- a/docs/tutorials/sequence-salience/index.html +++ b/docs/tutorials/sequence-salience/index.html @@ -14,7 +14,7 @@ gtag('config', 'G-Q74F5RJLXB'); - Prompt Engineering with Sequence Salience + Prompt Debugging with Sequence Salience @@ -73,11 +73,11 @@
    - Tutorials > Analysis > Prompt Engineering with Sequence Salience + Tutorials > Analysis > Prompt Debugging with Sequence Salience
    -

    Prompt Engineering with Sequence Salience

    +

    Prompt Debugging with Sequence Salience

    Follow along in Google Colab.
    -

    Or, run this locally with examples/lm_salience_demo.py

    +

    Or, run this locally with examples/prompt_debugging/server.py

    Large language models (LLMs), such as Gemini and GPT-4, have become ubiquitous. Recent releases of "open weights" models, including Llama 2, Mistral, and Gemma, have made it easier @@ -294,8 +294,7 @@

    Case Study 2: Assessing Constitutional Principles in Prompts

    them in prompt engineering use cases.

    Case Study 3: Side-by-Side Behavior Comparisons

    LIT support a side-by-side (SxS) mode that can be used to compare two -models, or here, compare model behavior on two related examples. (Check out the -gender bias tutorial for another case study in SxS analysis.) +models, or here, compare model behavior on two related examples. Let's see how we can use this to understand differences in prompt designs with Sequence Salience.

    GSM8K is a benchmark dataset of grade school math problems commonly @@ -441,8 +440,8 @@

    Conclusion

    research area. The LIT team has provided reference implementations for computing gradient-based salience— Grad L2 Norm and Grad · Input—for LLMs in two -popular frameworks: KerasNLP and -HuggingFace Transformers.

    +popular frameworks: KerasNLP and +HuggingFace Transformers.

    There is considerable opportunity to research how the model analysis foundations described in this tutorial can support richer workflows, particularly as they relate to aggregate analysis of salience results over many examples, and the @@ -469,7 +468,7 @@

    Further Reading

    time to read
    20 minutes
    takeaways
    -
    Learn to use LIT's Sequence Salience module for prompt engineering.
    +
    Learn to use LIT's Sequence Salience module for prompt debugging.
    diff --git a/docs/tutorials/tab-feat-attr/index.html b/docs/tutorials/tab-feat-attr/index.html index 5a6d8e6a..777b5603 100644 --- a/docs/tutorials/tab-feat-attr/index.html +++ b/docs/tutorials/tab-feat-attr/index.html @@ -78,7 +78,7 @@

    Tabular Feature Attribution

    Explore this demo yourself.

    Or, run your own with -examples/penguin_demo.py

    +examples/penguin/demo.py

    LIT supports many techniques like salience maps and counterfactual generators for text data. But what if you have a tabular dataset? You might want to find out which features (columns) are most relevant to the model’s predictions. LIT's diff --git a/docs/tutorials/text-salience/index.html b/docs/tutorials/text-salience/index.html index aa5cabf7..58e807a8 100644 --- a/docs/tutorials/text-salience/index.html +++ b/docs/tutorials/text-salience/index.html @@ -77,7 +77,7 @@

    Salience Maps for Text

    Explore this demo yourself.
    -

    Or, run your own with examples/glue_demo.py

    +

    Or, run your own with examples/glue/demo.py

    LIT enables users to analyze individual predictions for text input using salience maps, for which gradient-based and/or blackbox methods are available. In this tutorial, we will explore how to use salience maps to analyze a text diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 00000000..faafe41b --- /dev/null +++ b/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "lit", + "lockfileVersion": 2, + "requires": true, + "packages": {} +} diff --git a/website/package-lock.json b/website/package-lock.json deleted file mode 100644 index c1aca5cc..00000000 --- a/website/package-lock.json +++ /dev/null @@ -1,8662 +0,0 @@ -{ - "name": "lit-website", - "version": "1.0.0", - "lockfileVersion": 2, - "requires": true, - "packages": { - "": { - "name": "lit-website", - "version": "1.0.0", - "license": "Apache-2.0", - "devDependencies": { - "@11ty/eleventy-plugin-syntaxhighlight": "^3.0.4", - "markdown-it": "^11.0.0" - } - }, - "node_modules/@11ty/dependency-tree": { - "version": "2.0.1", - "resolved": "https://registry.npmjs.org/@11ty/dependency-tree/-/dependency-tree-2.0.1.tgz", - "integrity": "sha512-5R+DsT9LJ9tXiSQ4y+KLFppCkQyXhzAm1AIuBWE/sbU0hSXY5pkhoqQYEcPJQFg/nglL+wD55iv2j+7O96UAvg==", - 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"name": "lit", - "lockfileVersion": 2, - "requires": true, - "packages": {} -} From 7c3000c78c831e418fbc9a475dea7b5401242f49 Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 22 Oct 2024 19:53:24 +0000 Subject: [PATCH 86/86] Fixing prod pip installs in Dockerfiles --- Dockerfile | 2 +- lit_nlp/examples/gcp/Dockerfile | 4 ++-- lit_nlp/yarn.lock | 8 ++++---- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/Dockerfile b/Dockerfile index 9b3a6da6..5ca68273 100644 --- a/Dockerfile +++ b/Dockerfile @@ -35,7 +35,7 @@ COPY ./lit_nlp/examples/gunicorn_config.py ./ FROM lit-nlp-base AS lit-nlp-prod -RUN python -m pip install 'lit-nlp[examples-descai]' +RUN python -m pip install 'lit-nlp[examples-discriminative-ai]' WORKDIR $APP_HOME ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] diff --git a/lit_nlp/examples/gcp/Dockerfile b/lit_nlp/examples/gcp/Dockerfile index 707ed6a4..86217e1d 100644 --- a/lit_nlp/examples/gcp/Dockerfile +++ b/lit_nlp/examples/gcp/Dockerfile @@ -93,14 +93,14 @@ COPY ./lit_nlp/examples/gcp/model_server_gunicorn_config.py ./ FROM lit-gcp-app-server-base AS lit-gcp-app-server -RUN python -m pip install 'lit-nlp[examples-genai]' +RUN python -m pip install 'lit-nlp[examples-generative-ai]' ENTRYPOINT ["gunicorn", "--config=gunicorn_config.py"] FROM lit-gcp-model-server-base AS lit-gcp-model-server -RUN python -m pip install 'lit-nlp[examples-genai]' +RUN python -m pip install 'lit-nlp[examples-generative-ai]' ENTRYPOINT ["gunicorn", "--config=model_server_gunicorn_config.py"] diff --git a/lit_nlp/yarn.lock b/lit_nlp/yarn.lock index a8c81a07..5c259033 100644 --- a/lit_nlp/yarn.lock +++ b/lit_nlp/yarn.lock @@ -3613,10 +3613,10 @@ minimatch@^5.1.0: dependencies: brace-expansion "^2.0.1" -mobx@5.15.4: - version "5.15.4" - resolved "https://registry.yarnpkg.com/mobx/-/mobx-5.15.4.tgz#9da1a84e97ba624622f4e55a0bf3300fb931c2ab" - integrity sha512-xRFJxSU2Im3nrGCdjSuOTFmxVDGeqOHL+TyADCGbT0k4HHqGmx5u2yaHNryvoORpI4DfbzjJ5jPmuv+d7sioFw== +mobx@6.1.8: + version "6.1.8" + resolved "https://registry.yarnpkg.com/mobx/-/mobx-6.1.8.tgz#5d03cb76d8f7694dd82bfb2578d886945b66450d" + integrity sha512-U4yCvUeh6yKXRwFxm2lyJjXPVekOEar/R8ZKWAXem/3fthJqYflViawfjDAUh7lZEvbKqljC3NT/pSaUKpE+gg== ms@2.0.0: version "2.0.0"

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