From bea702b135bf4f7db46379ac3ad55d1ff674ae8f Mon Sep 17 00:00:00 2001 From: vinid Date: Thu, 11 Jul 2024 18:17:56 -0400 Subject: [PATCH 1/6] documentation fixes --- docs/Makefile | 20 ++ docs/make.bat | 35 ++++ docs/requirements.txt | 12 ++ docs/source/assets/analogy.png | Bin 0 -> 384554 bytes docs/source/assets/logo_full.png | Bin 0 -> 21095 bytes docs/source/conf.py | 72 ++++++++ docs/source/index.rst | 306 +++++++++++++++++++++++++++++++ docs/source/quickstart.rst | 46 +++++ docs/source/textgrad.rst | 14 ++ docs/source/usage.rst | 13 ++ readthedocs.yaml | 35 ++++ 11 files changed, 553 insertions(+) create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/requirements.txt create mode 100644 docs/source/assets/analogy.png create mode 100644 docs/source/assets/logo_full.png create mode 100644 docs/source/conf.py create mode 100644 docs/source/index.rst create mode 100644 docs/source/quickstart.rst create mode 100644 docs/source/textgrad.rst create mode 100644 docs/source/usage.rst create mode 100644 readthedocs.yaml diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..dc1312a --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..517d8ac --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,12 @@ +sphinx_autodoc_typehints +toml +sphinx-panels +sphinx-autobuild +sphinx_book_theme +sphinx-toolbox +sphinx-copybutton +sphinx_design +jinja2 +sphinx +setuptools +textgrad \ No newline at end of file diff --git a/docs/source/assets/analogy.png b/docs/source/assets/analogy.png new file mode 100644 index 0000000000000000000000000000000000000000..a94b3bd1077fbbd9f41a26e1eae70f3b22c797dc GIT binary patch literal 384554 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documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) + + +# -- Project information ----------------------------------------------------- + +project = 'TextGrad' +copyright = '2024, TextGrad authors' +author = 'TextGrad authors' + +# The full version, including alpha/beta/rc tags +release = '0.1.3' + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = ['sphinx.ext.duration', + 'sphinx.ext.doctest', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.napoleon', + 'sphinx_book_theme' + ] + +autodoc_default_options = { + 'members': True, + 'special-members': ',__call__', # Add other special methods as needed + 'undoc-members': True, + 'show-inheritance': True +} + + +autodoc_typehints = 'description' +autosummary_generate = True # Enable autosummary +autoclass_content = 'both' + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_book_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..1d7613f --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,306 @@ +.. TextGrad documentation master file, created by + sphinx-quickstart on Sat May 4 17:35:54 2024. + You can adapt this file completely to your liking, but it should at least + contain the root toctree directive. +Welcome to TextGrad's documentation! +==================================== + +TextGrad is a Python package that provides a simple interface to implement LLM-"gradients" pipelines for text optimization! + +Check out the :doc:`usage` section for further information, including how to +:doc:`install ` the project. Want to directly jump to the optimization process? +Check out the :doc:`quickstart` guide! + + +.. image:: assets/logo_full.png + :align: center + +An autograd engine -- for textual gradients! + +TextGrad is a powerful framework building automatic ``differentiation'' via text. +TextGrad implements backpropagation through text feedback provided by LLMs, strongly building on the gradient metaphor. + +We provide a simple and intuitive API that allows you to define your own loss functions and optimize them using text feedback. +This API is similar to the Pytorch API, making it simple to adapt to your use cases. + +.. image:: assets/analogy.png + :align: center + +QuickStart +========== + +If you know PyTorch, you know 80% of TextGrad. +Let's walk through the key components with a simple example. Say we want to use GPT-4o to generate a punchline for TextGrad. + +.. code-block:: python + + import textgrad as tg + # Step 1: Get an initial response from an LLM + model = tg.BlackboxLLM("gpt-4o") + punchline = model(tg.Variable("write a punchline for my github package about optimizing compound AI systems", role_description="prompt", requires_grad=False)) + punchline.set_role_description("a concise punchline that must hook everyone") + +Initial `punchline` from the model: +> Supercharge your AI synergy with our optimization toolkit – where compound intelligence meets peak performance! + +Not bad, but we (gpt-4o, I guess) can do better! Let's optimize the punchline using TextGrad. + +.. code-block:: python + + # Step 2: Define the loss function and the optimizer, just like in PyTorch! + loss_fn = tg.TextLoss("We want to have a super smart and funny punchline. Is the current one concise and addictive? Is the punch fun, makes sense, and subtle enough?") + optimizer = tg.TGD(parameters=[punchline]) + +.. code-block:: python + + # Step 3: Do the loss computation, backward pass, and update the punchline + loss = loss_fn(punchline) + loss.backward() + optimizer.step() + +Optimized punchline: +> Boost your AI with our toolkit – because even robots need a tune-up! + +Okay this model isn’t really ready for a comedy show yet (and maybe a bit cringy) but it is clearly trying. But who gets to maxima in one step? + +We have many more examples around how TextGrad can optimize all kinds of variables -- code, solutions to problems, molecules, prompts, and all that! + +Tutorials +--------- + +We have prepared a couple of tutorials to get you started with TextGrad. +You can run them directly in Google Colab by clicking on the links below. + +.. |primiti| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/github/zou-group/TextGrad/blob/main/examples/notebooks/Tutorial-Primitives.ipynb + :alt: Open In Colab + +.. |code| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/github/zou-group/textgrad/blob/main/examples/notebooks/Tutorial-Test-Time-Loss-for-Code.ipynb + :alt: Open In Colab + +.. |promptopt| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/github/zou-group/TextGrad/blob/main/examples/notebooks/Tutorial-Prompt-Optimization.ipynb + :alt: Open In Colab + +.. |solut| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/github/zou-group/TextGrad/blob/main/examples/notebooks/Tutorial-Solution-Optimization.ipynb + :alt: Open In Colab + + + ++--------------------------------------------------------------------------------+------------------+ +| Name | Link | ++================================================================================+==================+ +| Introduction to TextGrad primitives | |primiti| | ++--------------------------------------------------------------------------------+------------------+ +| Code Optimization and New Loss Implementation | |code| | ++--------------------------------------------------------------------------------+------------------+ +| Prompt Optimization | |promptopt| | ++--------------------------------------------------------------------------------+------------------+ +| Solution Optimization | |solut| | ++--------------------------------------------------------------------------------+------------------+ + +Installation +============ + +You can install TextGrad via pip: + +.. code-block:: bash + + pip install textgrad + +Examples +======== + +Minimal Instance Optimization Example +------------------------------------- + +TextGrad can optimize unstructured variables, such as text. Let us have an initial solution to a math problem that we want to improve. Here's how to do it with TextGrad, using GPT-4o: + +.. code-block:: python + + import textgrad as tg + tg.set_backward_engine(tg.get_engine("gpt-4o")) + + initial_solution = """To solve the equation 3x^2 - 7x + 2 = 0, we use the quadratic formula: + x = (-b ± √(b^2 - 4ac)) / 2a + a = 3, b = -7, c = 2 + x = (7 ± √((-7)^2 - 4 * 3(2))) / 6 + x = (7 ± √(7^3) / 6 + The solutions are: + x1 = (7 + √73) + x2 = (7 - √73)""" + + # Define the variable to optimize, let requires_grad=True to enable gradient computation + solution = tg.Variable(initial_solution, + requires_grad=True, + role_description="solution to the math question") + + # Define the loss function, via a system prompt to an LLM + loss_system_prompt = tg.Variable("""You will evaluate a solution to a math question. Do not attempt to solve it yourself, do not give a solution, only identify errors. Be super concise.""", + requires_grad=False, + role_description="system prompt") + + loss_fn = tg.TextLoss(loss_system_prompt) + + # Define the optimizer, let the optimizer know which variables to optimize + optimizer = tg.TGD(parameters=[solution]) + + loss = loss_fn(solution) + +Output: + + Variable(value=Errors: + 1. Incorrect sign in the discriminant calculation: it should be b^2 - 4ac, not b^2 + 4ac. + 2. Incorrect simplification of the quadratic formula: the denominator should be 2a, not 6. + 3. Final solutions are missing the division by 2a., role=response from the language model, grads=) + +.. code-block:: python + + loss.backward() + optimizer.step() + print(solution.value) + +Output: + + To solve the equation 3x^2 - 7x + 2 = 0, we use the quadratic formula: + x = (-b ± √(b^2 - 4ac)) / 2a + + Given: + a = 3, b = -7, c = 2 + + Substitute the values into the formula: + x = (7 ± √((-7)^2 - 4(3)(2))) / 6 + x = (7 ± √(49 - 24)) / 6 + x = (7 ± √25) / 6 + x = (7 ± 5) / 6 + + The solutions are: + x1 = (7 + 5) / 6 = 12 / 6 = 2 + x2 = (7 - 5) / 6 = 2 / 6 = 1/3 + +Minimal Prompt Optimization Example +----------------------------------- + +TextGrad can also optimize prompts in PyTorch style! Here's how to do it with TextGrad, using GPT-4o for feedback, and optimizing a prompt for gpt-3.5-turbo: + +.. code-block:: python + + import textgrad as tg + llm_engine = tg.get_engine("gpt-3.5-turbo") + tg.set_backward_engine(tg.get_engine("gpt-4o")) + + _, val_set, _, eval_fn = load_task("BBH_object_counting", llm_engine) + question_str, answer_str = val_set[0] + question = tg.Variable(question_str, role_description="question to the LLM", requires_grad=False) + answer = tg.Variable(answer_str, role_description="answer to the question", requires_grad=False) + +Question: + + I have two stalks of celery, two garlics, a potato, three heads of broccoli, a carrot, and a yam. How many vegetables do I have? + +Ground Truth Answer: + + 10 + +.. code-block:: python + + system_prompt = tg.Variable("You are a concise LLM. Think step by step.", + requires_grad=True, + role_description="system prompt to guide the LLM's reasoning strategy for accurate responses") + + model = tg.BlackboxLLM(llm_engine, system_prompt=system_prompt) + optimizer = tg.TGD(parameters=list(model.parameters())) + + prediction = model(question) + +Prediction: + + You have a total of seven vegetables: two stalks of celery, two garlics, one potato, three heads of broccoli, one carrot, and one yam. + +.. code-block:: python + + loss = eval_fn(inputs=dict(prediction=prediction, ground_truth_answer=answer)) + +Loss denoting accuracy: + + Variable(value=0, grads=) + +.. code-block:: python + + loss.backward() + +System prompt gradients: + + ... 2. **Encourage Explicit Summation**: - The prompt should encourage the model to explicitly state the summation process. This can help in verifying the accuracy of the count. For example, "Explain your calculations clearly and verify the total.".... + +.. code-block:: python + + optimizer.step() + +New system prompt value: + + You are a concise LLM. Think step by step. Prioritize accuracy in your calculations. Identify and count each item individually. Explain your calculations clearly and verify the total. After calculating, review your steps to ensure the total is correct. If you notice a discrepancy in your count, re-evaluate the list and correct any mistakes. + +.. code-block:: python + + prediction = model(question) + +New prediction: + + Let's count the number of each vegetable: + 1. Celery stalks: 2 + 2. Garlics: 2 + 3. Potato: 1 + 4. Broccoli heads: 3 + 5. Carrot: 1 + 6. Yam: 1 + + Now, let's add up the total number of vegetables: + 2 + 2 + 1 + 3 + 1 + 1 = 10 + + You have a total of 10 vegetables. + +Resources +========= + +Inspiration +----------- + +Many existing works greatly inspired this project! Here is a non-exhaustive list: + +- 📚 `PyTorch `_ The one and only. We owe a ton to PyTorch, hard to do justice here. +- 📚 `DSPy `_ is a pioneer in writing LM-based programs in many different ways! Has been a huge inspiration for us. +- 📚 `Micrograd `_: A tiny autograd engine greatly inspired our simple design! +- 📚 `ProTeGi `_: We owe the term "Textual Gradients" to ProTeGi! +- 📚 `Reflexion `_: A self-reflection that showed us the power of text-based reflection! + +Citation +======== + +.. code-block:: bibtex + + @article{yuksekgonul2024textgrad, + title={{TextGrad: Automatic ``Differentiation'' with Text}}, + author={Mert Yuksekgonul and Federico Bianchi and Joseph Boen and Sheng Liu and Zhi Huang and Carlos Guestrin and James Zou}, + year={2024}, + } + +Contents +-------- + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + usage + textgrad + quickstart + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` \ No newline at end of file diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst new file mode 100644 index 0000000..84544f2 --- /dev/null +++ b/docs/source/quickstart.rst @@ -0,0 +1,46 @@ +QuickStart +========== + +What can TextGrad do? TextGrad can optimize your prompts from a language model in an automatic way. + + +.. code-block:: python + + import textgrad + + # Set the backward engine as an External LLM API object. + See textgrad.config for more details. + textgrad.set_backward_engine(llm_api) + basic_system_prompt = "You are a language model that summarizes \ + a given document" + + system_prompt = textgrad.Variable(basic_system_prompt, requires_grad=True) + + api_model = textgrad.model.BlackboxLLM(llm_api) + + # this tells the model to use the following system prompt + api_model = api_model + system_prompt + + big_document = "This is a big document that we want to summarize." + + # Since we will not need the criticisms for the document, + # we will explicitly set requires_grad=False + doc = textgrad.Variable(data, requires_grad=False) + # Get the summary + summary = api_model(big_document) + + # Compute a loss + evaluation_prompt = "Evaluate if this is a good summary \ + based on completeness and fluency." + + loss_fn = textgrad.ResponseEvaluation(engine=llm_api, + evaluation_instruction=Variable(evaluation_prompt, + requires_grad=False)) + + loss = loss_fn(summary) + loss.backward() # This populates gradients + + optimizer = textgrad.TextualGradientDescent(engine=llm_api, + parameters=[system_prompt]) + optimizer.step() + print(system_prompt) \ No newline at end of file diff --git a/docs/source/textgrad.rst b/docs/source/textgrad.rst new file mode 100644 index 0000000..c4d578e --- /dev/null +++ b/docs/source/textgrad.rst @@ -0,0 +1,14 @@ +Documentation +============= + +.. autosummary:: + :toctree: modules + :recursive: + + textgrad.variable + textgrad.loss + textgrad.optimizer + textgrad.engine + textgrad.model + textgrad.autograd + \ No newline at end of file diff --git a/docs/source/usage.rst b/docs/source/usage.rst new file mode 100644 index 0000000..63bfee4 --- /dev/null +++ b/docs/source/usage.rst @@ -0,0 +1,13 @@ +Usage +===== + +.. _installation: + +Installation +------------ + +To use TextGrad, first install it using pip: + +.. code-block:: console + + $ pip install textgrad diff --git a/readthedocs.yaml b/readthedocs.yaml new file mode 100644 index 0000000..ac3d7ab --- /dev/null +++ b/readthedocs.yaml @@ -0,0 +1,35 @@ +# Read the Docs configuration file for Sphinx projects +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.12" + # You can also specify other tool versions: + # nodejs: "20" + # rust: "1.70" + # golang: "1.20" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/source/conf.py + # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs + # builder: "dirhtml" + # Fail on all warnings to avoid broken references + # fail_on_warning: true + +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - requirements: docs/requirements.txt \ No newline at end of file From 20b70c1031e16692cd008af93107fe8a9feb9076 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 11 Jul 2024 22:18:14 +0000 Subject: [PATCH 2/6] docs(contributor): contrib-readme-action has updated readme --- README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3a32cb0..afd5293 100644 --- a/README.md +++ b/README.md @@ -348,6 +348,15 @@ We are grateful for all the help we got from our contributors! David Ruan + + + nihalnayak +
+ Nihal Nayak +
+ + + sanowl @@ -355,8 +364,6 @@ We are grateful for all the help we got from our contributors! San - - huangzhii From 25d6a9a92e54f39486ff1bbc8c5f8b2a3aa7736c Mon Sep 17 00:00:00 2001 From: vinid Date: Thu, 11 Jul 2024 18:26:59 -0400 Subject: [PATCH 3/6] fixing --- docs/source/index.rst | 75 ++++++++++++++++++++++++++++---------- docs/source/quickstart.rst | 58 +++++++++++++++-------------- 2 files changed, 86 insertions(+), 47 deletions(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index 1d7613f..8f0289b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -31,39 +31,74 @@ QuickStart If you know PyTorch, you know 80% of TextGrad. Let's walk through the key components with a simple example. Say we want to use GPT-4o to generate a punchline for TextGrad. +## QuickStart +If you know PyTorch, you know 80% of TextGrad. +Let's walk through the key components with a simple example. Say we want to use GPT-4o to solve a simple +reasoning problem. + +The question is *If it takes 1 hour to dry 25 shirts under the sun, how long will it take to dry 30 shirts under the sun? Reason step by step.* (Thanks, [Reddit User](https://www.reddit.com/r/OpenAI/comments/18q479x/comment/kf444es/)) .. code-block:: python - import textgrad as tg - # Step 1: Get an initial response from an LLM - model = tg.BlackboxLLM("gpt-4o") - punchline = model(tg.Variable("write a punchline for my github package about optimizing compound AI systems", role_description="prompt", requires_grad=False)) - punchline.set_role_description("a concise punchline that must hook everyone") + import textgrad as tg -Initial `punchline` from the model: -> Supercharge your AI synergy with our optimization toolkit – where compound intelligence meets peak performance! + tg.set_backward_engine("gpt-4o", override=True) -Not bad, but we (gpt-4o, I guess) can do better! Let's optimize the punchline using TextGrad. + # Step 1: Get an initial response from an LLM. + model = tg.BlackboxLLM("gpt-4o") + question_string = ("If it takes 1 hour to dry 25 shirts under the sun, " + "how long will it take to dry 30 shirts under the sun? " + "Reason step by step") + + question = tg.Variable(question_string, + role_description="question to the LLM", + requires_grad=False) + + answer = model(question) + + +> :warning: **answer: To determine how long it will take to dry 30 shirts under the sun,** +> **we can use a proportional relationship based on the given information.** +> **Here’s the step-by-step reasoning: [.....]** +> **So, it will take 1.2 hours (or 1 hour and 12 minutes) to dry 30 shirts under the sun.** -.. code-block:: python - # Step 2: Define the loss function and the optimizer, just like in PyTorch! - loss_fn = tg.TextLoss("We want to have a super smart and funny punchline. Is the current one concise and addictive? Is the punch fun, makes sense, and subtle enough?") - optimizer = tg.TGD(parameters=[punchline]) +As you can see, **the model's answer is incorrect.** We can optimize the answer using TextGrad to get the correct answer. .. code-block:: python - # Step 3: Do the loss computation, backward pass, and update the punchline - loss = loss_fn(punchline) - loss.backward() - optimizer.step() + answer.set_role_description("concise and accurate answer to the question") + + # Step 2: Define the loss function and the optimizer, just like in PyTorch! + # Here, we don't have SGD, but we have TGD (Textual Gradient Descent) + # that works with "textual gradients". + optimizer = tg.TGD(parameters=[answer]) + evaluation_instruction = (f"Here's a question: {question_string}. " + "Evaluate any given answer to this question, " + "be smart, logical, and very critical. " + "Just provide concise feedback.") + + + # TextLoss is a natural-language specified loss function that describes + # how we want to evaluate the reasoning. + loss_fn = tg.TextLoss(evaluation_instruction) + +> :brain: **loss: [...] Your step-by-step reasoning is clear and logical,** +> **but it contains a critical flaw in the assumption that drying time is** +> **directly proportional to the number of shirts. [...]** + +.. code-block:: python -Optimized punchline: -> Boost your AI with our toolkit – because even robots need a tune-up! + # Step 3: Do the loss computation, backward pass, and update the punchline. + # Exact same syntax as PyTorch! + loss = loss_fn(answer) + loss.backward() + optimizer.step() + answer -Okay this model isn’t really ready for a comedy show yet (and maybe a bit cringy) but it is clearly trying. But who gets to maxima in one step? -We have many more examples around how TextGrad can optimize all kinds of variables -- code, solutions to problems, molecules, prompts, and all that! +> :white_check_mark: **answer: It will still take 1 hour to dry 30 shirts under the sun,** +> **assuming they are all laid out properly to receive equal sunlight.** Tutorials --------- diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 84544f2..f549b77 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -6,41 +6,45 @@ What can TextGrad do? TextGrad can optimize your prompts from a language model i .. code-block:: python - import textgrad + import textgrad as tg - # Set the backward engine as an External LLM API object. - See textgrad.config for more details. - textgrad.set_backward_engine(llm_api) - basic_system_prompt = "You are a language model that summarizes \ - a given document" + tg.set_backward_engine("gpt-4o", override=True) - system_prompt = textgrad.Variable(basic_system_prompt, requires_grad=True) + # Step 1: Get an initial response from an LLM. + model = tg.BlackboxLLM("gpt-4o") + question_string = ("If it takes 1 hour to dry 25 shirts under the sun, " + "how long will it take to dry 30 shirts under the sun? " + "Reason step by step") - api_model = textgrad.model.BlackboxLLM(llm_api) + question = tg.Variable(question_string, + role_description="question to the LLM", + requires_grad=False) - # this tells the model to use the following system prompt - api_model = api_model + system_prompt + answer = model(question) + +.. code-block:: python - big_document = "This is a big document that we want to summarize." - # Since we will not need the criticisms for the document, - # we will explicitly set requires_grad=False - doc = textgrad.Variable(data, requires_grad=False) - # Get the summary - summary = api_model(big_document) + answer.set_role_description("concise and accurate answer to the question") - # Compute a loss - evaluation_prompt = "Evaluate if this is a good summary \ - based on completeness and fluency." + # Step 2: Define the loss function and the optimizer, just like in PyTorch! + # Here, we don't have SGD, but we have TGD (Textual Gradient Descent) + # that works with "textual gradients". + optimizer = tg.TGD(parameters=[answer]) + evaluation_instruction = (f"Here's a question: {question_string}. " + "Evaluate any given answer to this question, " + "be smart, logical, and very critical. " + "Just provide concise feedback.") - loss_fn = textgrad.ResponseEvaluation(engine=llm_api, - evaluation_instruction=Variable(evaluation_prompt, - requires_grad=False)) - loss = loss_fn(summary) - loss.backward() # This populates gradients + # TextLoss is a natural-language specified loss function that describes + # how we want to evaluate the reasoning. + loss_fn = tg.TextLoss(evaluation_instruction) - optimizer = textgrad.TextualGradientDescent(engine=llm_api, - parameters=[system_prompt]) +.. code-block:: python + # Step 3: Do the loss computation, backward pass, and update the punchline. + # Exact same syntax as PyTorch! + loss = loss_fn(answer) + loss.backward() optimizer.step() - print(system_prompt) \ No newline at end of file + answer From cd0394ed0a9950c14564af9029fa9cf9a5dc4d2e Mon Sep 17 00:00:00 2001 From: Federico Bianchi Date: Fri, 12 Jul 2024 21:45:54 -0400 Subject: [PATCH 4/6] main into redocs (#66) * Ignore vLLM test (#57) * add: vllm support * fix: change model in test * formatted with black * add: ignore tests for vllm on github * ignore pytest for vllm * docs(contributor): contrib-readme-action has updated readme * pip3->pip; nit * Add multimodal tasks * docs(contributor): contrib-readme-action has updated readme * Update mathvista.py Added prompts for answer extraction. * Update mathvista.py Added the error handling when importing packages. * raise exception if cannot parse answer, do not return an arbitrary answer * remove unused comments * feat: Added Ollama engine via OpenAI api (#51) * feat: Added Ollama engine via OpenAI api * fix: Added PR remarks and test --------- Co-authored-by: Nihal Nayak Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Mert Yuksekgonul Co-authored-by: Pan Lu Co-authored-by: Atakan Tekparmak <59488384+AtakanTekparmak@users.noreply.github.com> --- .github/workflows/build.yml | 3 +- README.md | 25 +- tests/test_engines.py | 15 ++ textgrad/engine/__init__.py | 8 + textgrad/engine/openai.py | 29 ++- textgrad/tasks/multimodal/__init__.py | 16 ++ textgrad/tasks/multimodal/mathvista.py | 342 +++++++++++++++++++++++++ textgrad/tasks/multimodal/scienceqa.py | 227 ++++++++++++++++ 8 files changed, 657 insertions(+), 8 deletions(-) create mode 100644 tests/test_engines.py create mode 100644 textgrad/tasks/multimodal/__init__.py create mode 100644 textgrad/tasks/multimodal/mathvista.py create mode 100644 textgrad/tasks/multimodal/scienceqa.py diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 23efe9b..2f88adc 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -4,7 +4,6 @@ on: push: paths-ignore: - '**.rst' - - 'tests/test_vllm.py' # requires vllm to be installed jobs: build: @@ -26,7 +25,7 @@ jobs: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - name: Test with pytest run: | - pytest + pytest --ignore=tests/test_vllm.py contrib-readme-job: runs-on: ubuntu-latest diff --git a/README.md b/README.md index afd5293..5a3fb98 100644 --- a/README.md +++ b/README.md @@ -146,7 +146,7 @@ pip install git+https://github.com/zou-group/textgrad.git **Installing textgrad with vllm**: ```sh -pip3 install textgrad[vllm] +pip install textgrad[vllm] ``` See [here](https://pip.pypa.io/en/stable/cli/pip_install/) for more details on various methods of pip installation. @@ -327,6 +327,13 @@ We are grateful for all the help we got from our contributors! Mert Yuksekgonul + + + nihalnayak +
+ Nihal Nayak +
+ sugatoray @@ -348,6 +355,8 @@ We are grateful for all the help we got from our contributors! David Ruan + + nihalnayak @@ -384,6 +393,20 @@ We are grateful for all the help we got from our contributors!
tboen1
+ + + + nihalnayak +
+ Nihal Nayak +
+ + + + AtakanTekparmak +
+ Atakan Tekparmak +
diff --git a/tests/test_engines.py b/tests/test_engines.py new file mode 100644 index 0000000..ddd34d1 --- /dev/null +++ b/tests/test_engines.py @@ -0,0 +1,15 @@ +import pytest + +from textgrad.engine import get_engine + +def test_ollama_engine(): + # Declare test constants + OLLAMA_BASE_URL = 'http://localhost:11434/v1' + MODEL_STRING = "test-model-string" + + # Initialise the engine + engine = get_engine("ollama-" + MODEL_STRING) + + assert engine + assert engine.model_string == MODEL_STRING + assert engine.base_url == OLLAMA_BASE_URL \ No newline at end of file diff --git a/textgrad/engine/__init__.py b/textgrad/engine/__init__.py index bd3ab20..d710c50 100644 --- a/textgrad/engine/__init__.py +++ b/textgrad/engine/__init__.py @@ -55,6 +55,14 @@ def get_engine(engine_name: str, **kwargs) -> EngineLM: elif engine_name in ["command-r-plus", "command-r", "command", "command-light"]: from .cohere import ChatCohere return ChatCohere(model_string=engine_name, **kwargs) + elif engine_name.startswith("ollama"): + from .openai import ChatOpenAI, OLLAMA_BASE_URL + model_string = engine_name.replace("ollama-", "") + return ChatOpenAI( + model_string=model_string, + base_url=OLLAMA_BASE_URL, + **kwargs + ) elif "vllm" in engine_name: from .vllm import ChatVLLM engine_name = engine_name.replace("vllm-", "") diff --git a/textgrad/engine/openai.py b/textgrad/engine/openai.py index 0a922b4..723f04a 100644 --- a/textgrad/engine/openai.py +++ b/textgrad/engine/openai.py @@ -17,6 +17,12 @@ from .base import EngineLM, CachedEngine from .engine_utils import get_image_type_from_bytes +# Default base URL for OLLAMA +OLLAMA_BASE_URL = 'http://localhost:11434/v1' + +# Check if the user set the OLLAMA_BASE_URL environment variable +if os.getenv("OLLAMA_BASE_URL"): + OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL") class ChatOpenAI(EngineLM, CachedEngine): DEFAULT_SYSTEM_PROMPT = "You are a helpful, creative, and smart assistant." @@ -26,10 +32,12 @@ def __init__( model_string: str="gpt-3.5-turbo-0613", system_prompt: str=DEFAULT_SYSTEM_PROMPT, is_multimodal: bool=False, + base_url: str=None, **kwargs): """ :param model_string: :param system_prompt: + :param base_url: Used to support Ollama """ root = platformdirs.user_cache_dir("textgrad") cache_path = os.path.join(root, f"cache_openai_{model_string}.db") @@ -37,12 +45,23 @@ def __init__( super().__init__(cache_path=cache_path) self.system_prompt = system_prompt - if os.getenv("OPENAI_API_KEY") is None: - raise ValueError("Please set the OPENAI_API_KEY environment variable if you'd like to use OpenAI models.") + self.base_url = base_url - self.client = OpenAI( - api_key=os.getenv("OPENAI_API_KEY"), - ) + if not base_url: + if os.getenv("OPENAI_API_KEY") is None: + raise ValueError("Please set the OPENAI_API_KEY environment variable if you'd like to use OpenAI models.") + + self.client = OpenAI( + api_key=os.getenv("OPENAI_API_KEY") + ) + elif base_url and base_url == OLLAMA_BASE_URL: + self.client = OpenAI( + base_url=base_url, + api_key="ollama" + ) + else: + raise ValueError("Invalid base URL provided. Please use the default OLLAMA base URL or None.") + self.model_string = model_string self.is_multimodal = is_multimodal diff --git a/textgrad/tasks/multimodal/__init__.py b/textgrad/tasks/multimodal/__init__.py new file mode 100644 index 0000000..267779f --- /dev/null +++ b/textgrad/tasks/multimodal/__init__.py @@ -0,0 +1,16 @@ +from textgrad.engine import EngineLM + + +def load_multimodal_instance_task(task_name: str, evaluation_api: EngineLM, *args, **kwargs): + if task_name == "mathvista": + from textgrad.tasks.multimodal.mathvista import MathVistaDataset + test_set = MathVistaDataset(evaluation_api=evaluation_api, split="testmini", *args, **kwargs) + return test_set + + elif task_name == "scienceqa": + from textgrad.tasks.multimodal.scienceqa import ScienceQADataset + test_set = ScienceQADataset(evaluation_api=evaluation_api, split="test", *args, **kwargs) + return test_set + + else: + raise ValueError(f"Instance task {task_name} not found.") diff --git a/textgrad/tasks/multimodal/mathvista.py b/textgrad/tasks/multimodal/mathvista.py new file mode 100644 index 0000000..5f2c44b --- /dev/null +++ b/textgrad/tasks/multimodal/mathvista.py @@ -0,0 +1,342 @@ +import re +import io +import platformdirs +from PIL import Image + +from textgrad.tasks.base import Dataset +from textgrad.loss import ImageQALoss +from textgrad.variable import Variable +try: + from Levenshtein import distance +except ImportError: + raise ImportError("Please install the Levenshtein package using 'pip install python-Levenshtein' to use mathvista.") + + +def compress_image(decoded_image, max_size_bytes=3.6*1024*1024): + # Convert image to RGB if it's in a mode that JPEG does not support + if decoded_image.mode not in ['RGB', 'L']: + decoded_image = decoded_image.convert('RGB') + + buffer = io.BytesIO() + decoded_image.save(buffer, format='JPEG') + size = buffer.tell() + + if size <= max_size_bytes: + buffer.seek(0) + return buffer.getvalue() + + width, height = decoded_image.size + while size > max_size_bytes: + width = int(width * 0.9) + height = int(height * 0.9) + resized_image = decoded_image.resize((width, height), Image.LANCZOS) + + buffer = io.BytesIO() + resized_image.save(buffer, format='JPEG') + size = buffer.tell() + + if width <= 1 or height <= 1: + raise ValueError("Unable to compress image to the desired size without excessive loss of resolution") + + buffer.seek(0) + return buffer.getvalue() + + +# Demos (pids = 852, 104, 824, 506, 540) from MathVista +demo_prompt = """ +Please read the following example. Then extract the answer from the model response and type it at the end of the prompt. + +Hint: Please answer the question requiring an integer answer and provide the final value, e.g., 1, 2, 3, at the end. +Question: Which number is missing? + +Model response: The number missing in the sequence is 14. + +Extracted answer: 14 + +Hint: Please answer the question requiring a floating-point number with one decimal place and provide the final value, e.g., 1.2, 1.3, 1.4, at the end. +Question: What is the fraction of females facing the camera? + +Model response: The fraction of females facing the camera is 0.6, which means that six out of ten females in the group are facing the camera. + +Extracted answer: 0.6 + +Hint: Please answer the question requiring a floating-point number with two decimal places and provide the final value, e.g., 1.23, 1.34, 1.45, at the end. +Question: How much money does Luca need to buy a sour apple candy and a butterscotch candy? (Unit: $) + +Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy. + +Extracted answer: 1.45 + +Hint: Please answer the question requiring a Python list as an answer and provide the final list, e.g., [1, 2, 3], [1.2, 1.3, 1.4], at the end. +Question: Between which two years does the line graph saw its maximum peak? + +Model response: The line graph saw its maximum peak between 2007 and 2008. + +Extracted answer: [2007, 2008] + +Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end. +Question: What fraction of the shape is blue?\nChoices:\n(A) 3/11\n(B) 8/11\n(C) 6/11\n(D) 3/5 + +Model response: The correct answer is (B) 8/11. + +Extracted answer: B +""" + + +def verify_extraction(extraction): + extraction = extraction.strip() + if extraction == "" or extraction == None: + return False + return True + + +def create_test_prompt(demo_prompt, query, response): + demo_prompt = demo_prompt.strip() + test_prompt = f"{query}\n\n{response}" + full_prompt = f"{demo_prompt}\n\n{test_prompt}\n\nExtracted answer: " + return full_prompt + + +def extract_answer(response, problem, quick_extract=False): + question_type = problem['question_type'] + answer_type = problem['answer_type'] + choices = problem['choices'] + query = problem['query'] + pid = problem['pid'] + + if response == "": + return "" + + if question_type == 'multi_choice' and response in choices: + return response + + if answer_type == "integer": + try: + extraction = int(response) + return str(extraction) + except: + pass + + if answer_type == "float": + try: + extraction = str(float(response)) + return extraction + except: + pass + + if quick_extract: + try: + result = re.search(r'The answer is "(.*)"\.', response) + if result: + extraction = result.group(1) + return extraction + except Exception as e: + raise Exception(f"Error in extracting answer for {pid}: {e}. Remove this line responsibly.") + + try: + from textgrad.engine.openai import ChatOpenAI + local_llm_engine = ChatOpenAI(model_string="gpt-3.5-turbo", is_multimodal=False) + + full_prompt = create_test_prompt(demo_prompt, query, response) + extraction = local_llm_engine(full_prompt) + return extraction + except Exception as e: + raise Exception(f"Error in extracting answer for {pid}: {e}") + + +def get_most_similar(prediction, choices): + """ + Use the Levenshtein distance (or edit distance) to determine which of the choices is most similar to the given prediction + """ + distances = [distance(prediction, choice) for choice in choices] + ind = distances.index(min(distances)) + return choices[ind] + + +def normalize_extracted_answer(extraction, question_data): + """ + Normalize the extracted answer to match the answer type + """ + choices = question_data["choices"] + question_type = question_data["question_type"] + answer_type = question_data["answer_type"] + precision = question_data["precision"] + + if question_type == 'multi_choice': + # make sure the extraction is a string + if isinstance(extraction, str): + extraction = extraction.strip() + else: + try: + extraction = str(extraction) + except: + extraction = "" + + # extract "A" from "(A) text" + letter = re.findall(r'\(([a-zA-Z])\)', extraction) + if len(letter) > 0: + extraction = letter[0].upper() + + options = [chr(ord('A') + i) for i in range(len(choices))] + + if extraction in options: + # convert option letter to text, e.g. "A" -> "text" + ind = options.index(extraction) + extraction = choices[ind] + else: + # select the most similar option + extraction = get_most_similar(extraction, choices) + assert extraction in choices + + elif answer_type == 'integer': + try: + extraction = str(int(float(extraction))) + except: + extraction = None + + elif answer_type == 'float': + try: + extraction = str(round(float(extraction), int(precision))) + except: + extraction = None + + elif answer_type == 'list': + try: + extraction = str(extraction) + except: + extraction = None + + return extraction + + +def safe_equal(prediction, answer): + """ + Check if the prediction is equal to the answer, even if they are of different types + """ + try: + if prediction == answer: + return True + return False + except Exception as e: + print(e) + return False + + +class MathVistaDataset(Dataset): + def __init__(self, evaluation_api:str, root: str=None, split: str="testmini", task_instruction: str=None, evaluation_instruction: str=None, *args, **kwargs): + """MathVista dataset from HF.""" + from datasets import load_dataset + if root is None: + root = platformdirs.user_cache_dir("textgrad") + self.root = root + assert split in ["testmini", "test"] + self.data = load_dataset("AI4Math/MathVista", cache_dir=root, split=split) + self.split = split + self.evaluation_api = evaluation_api + self.anwer_extraction_openai_engine = "gpt-3.5-turbo" # robust enough for answer extraction + self.task_instruction = self.get_default_task_instruction(task_instruction) # NOTE: check the task instruction + self.evaluation_instruction = self.get_default_evaluation_instruction(evaluation_instruction) # NOTE: check the evaluation instruction + + def __getitem__(self, index): + row = self.data[index] + pid = row["pid"] + decoded_image = row["decoded_image"] + choices = row["choices"] + unit = row["unit"] + precision = row["precision"] + answer = row["answer"] + question_type = row["question_type"] + answer_type = row["answer_type"] + metadata = row["metadata"] + query = row["query"] + query = f"{self.task_instruction}\n{query}" # NOTE: Add the task description + + # NOTE: convert image to bytes + if "claude" in self.evaluation_api.model_string: + image_bytes = compress_image(decoded_image) + else: + buffer = io.BytesIO() + decoded_image.save(buffer, format='png') + image_bytes = buffer.getvalue() + buffer.close() + + # NOTE: ques_data stores other fields that might be useful later + ques_data = { + "pid": pid, + "query": query, + "choices": choices, + "unit": unit, + "precision": precision, + "answer": answer, + "question_type": question_type, + "answer_type": answer_type, + "metadata": metadata + } + test_time_objective = self._get_instance_test_time_objective(query, image_bytes) + instance_eval_fn = self._get_instance_eval_fn(query, answer, ques_data) + return image_bytes, query, answer, ques_data, test_time_objective, instance_eval_fn # NOTE: check the sample format + + def __len__(self): + return len(self.data) + + def get_default_task_instruction(self, instruction): + if instruction is not None: + print("Using user-defined task instruction:\n", instruction, "\n") + task_instruction = instruction + else: + task_instruction = "You will answer a mathematical reasoning question based on an image. Please ensure you accurately interpret the image and think step by step." + return task_instruction + + def get_default_evaluation_instruction(self, instruction): + if instruction is not None: + print("Using user-defined evaluation instruction:\n", instruction, "\n") + evaluation_instruction = instruction + else: + evaluation_instruction = "Please evaluate the existing answer to the visual math problem without solving it yourself. Verify that the answer provides accurate reasoning logic to address the question." + return evaluation_instruction + + def create_test_prompt(demo_prompt, query, response): + demo_prompt = demo_prompt.strip() + test_prompt = f"{query}\n\n{response}" + full_prompt = f"{demo_prompt}\n\n{test_prompt}\n\nExtracted answer: " + return full_prompt + + def _get_instance_test_time_objective(self, question: str, image: bytes): + """Define the loss function for the test time optimization.""" + eval_fn = ImageQALoss(evaluation_instruction=self.evaluation_instruction, engine=self.evaluation_api) + + def test_time_objective(instance: Variable): + var_image = Variable(image, role_description="image input", requires_grad=False) + var_question = Variable(question, role_description="question input", requires_grad=False) + return eval_fn(question=var_question, image=var_image, response=instance) + + return test_time_objective + + def eval_extraction_and_matching(self, response_text, correct_answer, question_data): + # Extract the precited answer text from the response + extracted_answer = extract_answer(response_text, question_data) + + # Normalize the extracted answer to match the answer type + normalized_answer = normalize_extracted_answer(extracted_answer, question_data) + + # Verify the prediction is true or false + true_false = safe_equal(normalized_answer, correct_answer) + + # Calculate the score and store the result data + # NOTE: check the result data format + score = 1 if true_false else 0 + result_data = { + "extracted_answer": extracted_answer, + "normalized_answer": normalized_answer, + "true_false": true_false + } + return score, result_data + + def _get_instance_eval_fn(self, question_prompt: str, answer: str, ques_data: dict): + """ + Define the evaluation function for scoring the response. + Extraxct the short answer from the response and compare it with the ground truth. + """ + # NOTE: check the evaluation function format + eval_extraction_based_fn = lambda response: self.eval_extraction_and_matching(response.value, answer, ques_data) + return eval_extraction_based_fn diff --git a/textgrad/tasks/multimodal/scienceqa.py b/textgrad/tasks/multimodal/scienceqa.py new file mode 100644 index 0000000..18a4633 --- /dev/null +++ b/textgrad/tasks/multimodal/scienceqa.py @@ -0,0 +1,227 @@ +import platformdirs +import re +import os +import io +from collections import Counter +from PIL import Image + +from textgrad.tasks.base import Dataset +from textgrad.loss import ImageQALoss +from textgrad.variable import Variable + + +def compress_image(decoded_image, max_size_bytes=3.6*1024*1024): + # First, try saving as PNG without any compression + buffer = io.BytesIO() + decoded_image.save(buffer, format='PNG') + size = buffer.tell() + + # If the original PNG is already small enough, return it + if size <= max_size_bytes: + buffer.seek(0) + return buffer.getvalue() + + # If PNG is too large, resize the image + width, height = decoded_image.size + while size > max_size_bytes: + print(f"Compressing image to {width}x{height}...") + width = int(width * 0.9) + height = int(height * 0.9) + resized_image = decoded_image.resize((width, height), Image.LANCZOS) + + buffer = io.BytesIO() + resized_image.save(buffer, format='PNG') + size = buffer.tell() + + if width <= 1 or height <= 1: + raise ValueError("Unable to compress image to the desired size without excessive loss of resolution") + + buffer.seek(0) + return buffer.getvalue() + +def preprocess_text(text): + # Remove punctuation and convert to lowercase + text = re.sub(r'[^\w\s]', '', text).lower() + return text + +def find_most_similar_choice(text, choices): + # Preprocess the given text + text = preprocess_text(text) + text_words = Counter(text.split()) + scores = [] + for choice in choices: + choice_text = preprocess_text(choice) + choice_words = Counter(choice_text.split()) + common_words = sum((text_words & choice_words).values()) # the number of common words + scores.append(common_words) + most_similar_index = scores.index(max(scores)) + return most_similar_index + +def extract_answer(response_text): + """ + Extract the answer from the response. + For example, "xxxxx. Answer: (A) a gas." -> "A" + If extraction fails, return the entire string after "Answer: ". + """ + # Attempt to match the format "Answer: (A)" or "Answer: (a)" in case-sensitive manner + match = re.search(r"Answer: \(([A-Z])\)", response_text) + if match: + return match.group(1).upper() # Return as uppercase + else: + # Fallback: match the format "Answer: " followed by any characters until the next period or end of line + fallback_match = re.search(r"Answer: ([^\.]+)", response_text) + if fallback_match: + return fallback_match.group(1).strip() + return response_text + +def normalize_extracted_answer(extracted_answer, question_data, options): + # Normalize the extracted answer + choices = question_data["choices"] + options = options[:len(choices)] + + # 'A' -> one of the choices + if extracted_answer in options: + normalized_answer = options.index(extracted_answer) + return normalized_answer + + # '(a) a gas.' + for choice in choices: + if choice.lower() in extracted_answer.lower(): + normalized_answer = choices.index(choice) + return normalized_answer + + # find the most similar choice + normalized_answer = find_most_similar_choice(extracted_answer, choices) + return normalized_answer + +def safe_equal(a, b): + # Check if two intergers are equal + return a == b + +class ScienceQADataset(Dataset): + def __init__(self, evaluation_api:str, root: str=None, split: str="test", task_instruction: str=None, evaluation_instruction: str=None, *args, **kwargs): + """ScienceQA dataset from HF.""" + if root is None: + root = platformdirs.user_cache_dir("textgrad") + self.root = root + self.split = split + assert split in ["test"] + self.data = self.load_scienceqa_data() + self.options = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"] + self.evaluation_api = evaluation_api + self.task_instruction = self.get_default_task_instruction(task_instruction) # NOTE: check the task instruction + self.evaluation_instruction = self.get_default_evaluation_instruction(evaluation_instruction) # NOTE: check the evaluation instruction + + def __getitem__(self, index): + row = self.data[index] + pid = row["pid"] + image = row["image"] + question = row["question"] + choices = row["choices"] + answer = row["answer"] + hint = row["hint"] + + query = f"{self.task_instruction}" # NOTE: Add the task description + if hint is not None and len(hint) > 0: + query += f"\nContext: {hint}" + query += f"\nQuestion: {question}" + if choices: + choice_list = [] + for i, c in enumerate(choices): + choice_list.append("({}) {}".format(self.options[i], c)) + choice_txt = " ".join(choice_list) + query += f"\nChoices: {choice_txt}" + + # NOTE: convert image to bytes + if "claude" in self.evaluation_api.model_string: + image_bytes = compress_image(image) + # print("Image size:", len(image_bytes)) + else: + buffer = io.BytesIO() + image.save(buffer, format='png') + image_bytes = buffer.getvalue() + buffer.close() + + # NOTE: ques_data stores other fields that might be useful later + ques_data = { + "pid": pid, + "question": question, + "choices": choices, + "hint": hint + } + test_time_objective = self._get_instance_test_time_objective(query, image_bytes) + instance_eval_fn = self._get_instance_eval_fn(query, answer, ques_data) + return image_bytes, query, answer, ques_data, test_time_objective, instance_eval_fn # NOTE: check the sample format + + def __len__(self): + return len(self.data) + + def get_default_task_instruction(self, instruction): + if instruction is not None: + print("Using user-defined task instruction:\n", instruction, "\n") + task_instruction = instruction + else: + task_instruction = "You will answer a scientific question based on an image. Please ensure you accurately interpret the image and think step by step. The last line of your answer should be formatted as follows: 'Answer: (X) Your Option.'" + return task_instruction + + def load_scienceqa_data(self): + scienceqa_dir = os.path.join(self.root, "scienceqa") + try: + from datasets import Dataset + data = Dataset.load_from_disk(scienceqa_dir) + print("Loaded ScienceQA dataset from cache.") + return data + except FileNotFoundError: + from datasets import load_dataset + data = load_dataset("derek-thomas/ScienceQA", split=self.split) + data_img = data.filter(lambda x: x['image'] is not None) # select examples with a non-empty question image + data_img = data_img.map(lambda x, i: {'pid': str(i), **x}, with_indices=True) # add index ID (string) for each example + data_img.save_to_disk(scienceqa_dir) + print("Loaded ScienceQA dataset from HF.") + return data_img + + def get_default_evaluation_instruction(self, instruction): + if instruction is not None: + print("Using user-defined evaluation instruction:\n", instruction, "\n") + evaluation_instruction = instruction + else: + evaluation_instruction = "Please evaluate the existing answer to the visual scientific problem without solving it yourself. Verify that the answer accurately understands the image, provides appropriate knowledge and reasoning logic to address the question." + return evaluation_instruction + + def _get_instance_test_time_objective(self, question: str, image: bytes): + """Define the loss function for the test time optimization.""" + eval_fn = ImageQALoss(evaluation_instruction=self.evaluation_instruction, engine=self.evaluation_api) + def test_time_objective(instance: Variable): + var_image = Variable(image, role_description="image input", requires_grad=False) + var_question = Variable(question, role_description="question input", requires_grad=False) + return eval_fn(question=var_question, image=var_image, response=instance) + return test_time_objective + + def eval_extraction_and_matching(self, response_text, correct_answer, question_data): + # Extract the precited answer text from the response + extracted_answer = extract_answer(response_text) + + # Normalize the extracted answer to match the answer type + normalized_answer = normalize_extracted_answer(extracted_answer, question_data, self.options) + + # Verify the prediction is true or false + true_false = safe_equal(normalized_answer, correct_answer) + + # Calculate the score and store the result data + # NOTE: check the result data format + score = 1 if true_false else 0 + result_data = { + "extracted_answer": extracted_answer, + "normalized_answer": normalized_answer, + "true_false": true_false + } + return score, result_data + + def _get_instance_eval_fn(self, question_prompt: str, answer: str, ques_data: dict): + """ + Define the evaluation function for scoring the response. + Extraxct the short answer from the response and compare it with the ground truth. + """ + # NOTE: check the evaluation function format + eval_extraction_based_fn = lambda response: self.eval_extraction_and_matching(response.value, answer, ques_data) + return eval_extraction_based_fn From a1b3c49cbc92ded5e9704d34bfd324a73d3daca1 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Sat, 13 Jul 2024 01:46:08 +0000 Subject: [PATCH 5/6] docs(contributor): contrib-readme-action has updated readme --- README.md | 46 +++++++++++++++------------------------------- 1 file changed, 15 insertions(+), 31 deletions(-) diff --git a/README.md b/README.md index 5a3fb98..f9f516d 100644 --- a/README.md +++ b/README.md @@ -313,6 +313,13 @@ We are grateful for all the help we got from our contributors! + + + - - - - - - - From 131fb0a8af498a6aa585d3780a12c2410ca08396 Mon Sep 17 00:00:00 2001 From: mertyg Date: Mon, 15 Jul 2024 05:57:10 -0700 Subject: [PATCH 6/6] fix version, add installation from github --- docs/source/conf.py | 2 +- docs/source/index.rst | 6 ++++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 997da48..a3e164a 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,7 @@ author = 'TextGrad authors' # The full version, including alpha/beta/rc tags -release = '0.1.3' +release = '0.1.4' # -- General configuration --------------------------------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index 8f0289b..f4068aa 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -145,6 +145,12 @@ You can install TextGrad via pip: pip install textgrad +For the latest and greatest, you can install the development version from GitHub: + +.. code-block:: bash + + pip install git+https://github.com/zou-group/textgrad.git + Examples ========
+ + mertyg +
+ Mert Yuksekgonul +
+
vinid @@ -321,10 +328,10 @@ We are grateful for all the help we got from our contributors! - - mertyg + + lupantech
- Mert Yuksekgonul + Pan Lu
@@ -342,12 +349,14 @@ We are grateful for all the help we got from our contributors! - - lupantech + + AtakanTekparmak
- Pan Lu + Atakan Tekparmak
ruanwz @@ -355,17 +364,6 @@ We are grateful for all the help we got from our contributors! David Ruan
- - nihalnayak -
- Nihal Nayak -
-
sanowl @@ -393,20 +391,6 @@ We are grateful for all the help we got from our contributors!
tboen1
-
- - nihalnayak -
- Nihal Nayak -
-
- - AtakanTekparmak -
- Atakan Tekparmak -