diff --git a/.envrc b/.envrc deleted file mode 100644 index 6e72843..0000000 --- a/.envrc +++ /dev/null @@ -1 +0,0 @@ -export PYTHONPATH=$PYTHONPATH:`pwd` diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index 636012e..1b96a0e 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -1,6 +1,9 @@ name: MyPy Type Checking -on: [pull_request] +on: + push: + branches: [main] + pull_request: jobs: type-check: @@ -17,6 +20,8 @@ jobs: - name: Set up Python run: uv sync + - name: install mypy + run: uv pip install mypy + - name: Type Check Source Code run: uv run mypy src - \ No newline at end of file diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index 023055e..8efd923 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -1,6 +1,9 @@ name: Pylint Linting -on: [pull_request] +on: + push: + branches: [main] + pull_request: jobs: linting: @@ -16,6 +19,9 @@ jobs: - name: Set up Python run: uv sync + + - name: Install Pylint + run: uv pip install pylint - name: Lint Source Code - run: uv run pylint src \ No newline at end of file + run: uv run pylint src diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml new file mode 100644 index 0000000..c2143ec --- /dev/null +++ b/.github/workflows/ruff.yml @@ -0,0 +1,27 @@ +name: Ruff Linting + +on: + push: + branches: [main] + pull_request: + +jobs: + linting: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 1 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python + run: uv sync + + - name: install ruff + run: uv tool install ruff@latest + + - name: Lint Source Code + run: uvx ruff check diff --git a/.github/workflows/unitttest.yml b/.github/workflows/unitttest.yml index f23191e..c2f19e5 100644 --- a/.github/workflows/unitttest.yml +++ b/.github/workflows/unitttest.yml @@ -1,6 +1,9 @@ name: Unit Testing -on: [pull_request] +on: + push: + branches: [main] + pull_request: jobs: unittest: @@ -18,4 +21,4 @@ jobs: run: uv sync - name: Run Unit Tests - run: uv run pytest + run: uv run pytest -n auto diff --git a/.pylintrc b/.pylintrc deleted file mode 100644 index 81676c9..0000000 --- a/.pylintrc +++ /dev/null @@ -1,643 +0,0 @@ -[MAIN] - -# Analyse import fallback blocks. This can be used to support both Python 2 and -# 3 compatible code, which means that the block might have code that exists -# only in one or another interpreter, leading to false positives when analysed. -analyse-fallback-blocks=no - -# Clear in-memory caches upon conclusion of linting. Useful if running pylint -# in a server-like mode. -clear-cache-post-run=no - -# Load and enable all available extensions. Use --list-extensions to see a list -# all available extensions. -#enable-all-extensions= - -# In error mode, messages with a category besides ERROR or FATAL are -# suppressed, and no reports are done by default. Error mode is compatible with -# disabling specific errors. -#errors-only= - -# Always return a 0 (non-error) status code, even if lint errors are found. -# This is primarily useful in continuous integration scripts. -#exit-zero= - -# A comma-separated list of package or module names from where C extensions may -# be loaded. Extensions are loading into the active Python interpreter and may -# run arbitrary code. -extension-pkg-allow-list= - -# A comma-separated list of package or module names from where C extensions may -# be loaded. Extensions are loading into the active Python interpreter and may -# run arbitrary code. (This is an alternative name to extension-pkg-allow-list -# for backward compatibility.) -extension-pkg-whitelist= - -# Return non-zero exit code if any of these messages/categories are detected, -# even if score is above --fail-under value. Syntax same as enable. Messages -# specified are enabled, while categories only check already-enabled messages. -fail-on= - -# Specify a score threshold under which the program will exit with error. -fail-under=9 - -# Interpret the stdin as a python script, whose filename needs to be passed as -# the module_or_package argument. -#from-stdin= - -# Files or directories to be skipped. They should be base names, not paths. -ignore=CVS - -# Add files or directories matching the regular expressions patterns to the -# ignore-list. The regex matches against paths and can be in Posix or Windows -# format. Because '\\' represents the directory delimiter on Windows systems, -# it can't be used as an escape character. -ignore-paths= - -# Files or directories matching the regular expression patterns are skipped. -# The regex matches against base names, not paths. The default value ignores -# Emacs file locks -ignore-patterns=^\.# - -# List of module names for which member attributes should not be checked -# (useful for modules/projects where namespaces are manipulated during runtime -# and thus existing member attributes cannot be deduced by static analysis). It -# supports qualified module names, as well as Unix pattern matching. -ignored-modules= - tree_sitter.binding, - jedi, - sansio_lsp_client - -# Python code to execute, usually for sys.path manipulation such as -# pygtk.require(). -#init-hook= - -# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the -# number of processors available to use, and will cap the count on Windows to -# avoid hangs. -jobs=1 - -# Control the amount of potential inferred values when inferring a single -# object. This can help the performance when dealing with large functions or -# complex, nested conditions. -limit-inference-results=100 - -# List of plugins (as comma separated values of python module names) to load, -# usually to register additional checkers. -load-plugins= - -# Pickle collected data for later comparisons. -persistent=yes - -# Minimum Python version to use for version dependent checks. Will default to -# the version used to run pylint. -py-version=3.10 - -# Discover python modules and packages in the file system subtree. -recursive=no - -# Add paths to the list of the source roots. Supports globbing patterns. The -# source root is an absolute path or a path relative to the current working -# directory used to determine a package namespace for modules located under the -# source root. -source-roots= - -# When enabled, pylint would attempt to guess common misconfiguration and emit -# user-friendly hints instead of false-positive error messages. -suggestion-mode=yes - -# Allow loading of arbitrary C extensions. Extensions are imported into the -# active Python interpreter and may run arbitrary code. -unsafe-load-any-extension=no - -# In verbose mode, extra non-checker-related info will be displayed. -#verbose= - - -[BASIC] - -# Naming style matching correct argument names. -argument-naming-style=snake_case - -# Regular expression matching correct argument names. Overrides argument- -# naming-style. If left empty, argument names will be checked with the set -# naming style. -#argument-rgx= - -# Naming style matching correct attribute names. -attr-naming-style=snake_case - -# Regular expression matching correct attribute names. Overrides attr-naming- -# style. If left empty, attribute names will be checked with the set naming -# style. -#attr-rgx= - -# Bad variable names which should always be refused, separated by a comma. -bad-names=foo, - bar, - baz, - toto, - tutu, - tata - -# Bad variable names regexes, separated by a comma. If names match any regex, -# they will always be refused -bad-names-rgxs= - -# Naming style matching correct class attribute names. -class-attribute-naming-style=any - -# Regular expression matching correct class attribute names. Overrides class- -# attribute-naming-style. If left empty, class attribute names will be checked -# with the set naming style. -#class-attribute-rgx= - -# Naming style matching correct class constant names. -class-const-naming-style=UPPER_CASE - -# Regular expression matching correct class constant names. Overrides class- -# const-naming-style. If left empty, class constant names will be checked with -# the set naming style. -#class-const-rgx= - -# Naming style matching correct class names. -class-naming-style=PascalCase - -# Regular expression matching correct class names. Overrides class-naming- -# style. If left empty, class names will be checked with the set naming style. -#class-rgx= - -# Naming style matching correct constant names. -const-naming-style=UPPER_CASE - -# Regular expression matching correct constant names. Overrides const-naming- -# style. If left empty, constant names will be checked with the set naming -# style. -#const-rgx= - -# Minimum line length for functions/classes that require docstrings, shorter -# ones are exempt. -docstring-min-length=-1 - -# Naming style matching correct function names. -function-naming-style=snake_case - -# Regular expression matching correct function names. Overrides function- -# naming-style. If left empty, function names will be checked with the set -# naming style. -#function-rgx= - -# Good variable names which should always be accepted, separated by a comma. -good-names=i, - j, - k, - ex, - Run, - _ - -# Good variable names regexes, separated by a comma. If names match any regex, -# they will always be accepted -good-names-rgxs= - -# Include a hint for the correct naming format with invalid-name. -include-naming-hint=no - -# Naming style matching correct inline iteration names. -inlinevar-naming-style=any - -# Regular expression matching correct inline iteration names. Overrides -# inlinevar-naming-style. If left empty, inline iteration names will be checked -# with the set naming style. -#inlinevar-rgx= - -# Naming style matching correct method names. -method-naming-style=snake_case - -# Regular expression matching correct method names. Overrides method-naming- -# style. If left empty, method names will be checked with the set naming style. -#method-rgx= - -# Naming style matching correct module names. -module-naming-style=snake_case - -# Regular expression matching correct module names. Overrides module-naming- -# style. If left empty, module names will be checked with the set naming style. -#module-rgx= - -# Colon-delimited sets of names that determine each other's naming style when -# the name regexes allow several styles. -name-group= - -# Regular expression which should only match function or class names that do -# not require a docstring. -no-docstring-rgx=^_ - -# List of decorators that produce properties, such as abc.abstractproperty. Add -# to this list to register other decorators that produce valid properties. -# These decorators are taken in consideration only for invalid-name. -property-classes=abc.abstractproperty - -# Regular expression matching correct type alias names. If left empty, type -# alias names will be checked with the set naming style. -#typealias-rgx= - -# Regular expression matching correct type variable names. If left empty, type -# variable names will be checked with the set naming style. -#typevar-rgx= - -# Naming style matching correct variable names. -variable-naming-style=snake_case - -# Regular expression matching correct variable names. Overrides variable- -# naming-style. If left empty, variable names will be checked with the set -# naming style. -#variable-rgx= - - -[CLASSES] - -# Warn about protected attribute access inside special methods -check-protected-access-in-special-methods=no - -# List of method names used to declare (i.e. assign) instance attributes. -defining-attr-methods=__init__, - __new__, - setUp, - asyncSetUp, - __post_init__ - -# List of member names, which should be excluded from the protected access -# warning. -exclude-protected=_asdict,_fields,_replace,_source,_make,os._exit - -# List of valid names for the first argument in a class method. -valid-classmethod-first-arg=cls - -# List of valid names for the first argument in a metaclass class method. -valid-metaclass-classmethod-first-arg=mcs - - -[DESIGN] - -# List of regular expressions of class ancestor names to ignore when counting -# public methods (see R0903) -exclude-too-few-public-methods= - -# List of qualified class names to ignore when counting class parents (see -# R0901) -ignored-parents= - -# Maximum number of arguments for function / method. -max-args=10 - -# Maximum number of attributes for a class (see R0902). -max-attributes=10 - -# Maximum number of boolean expressions in an if statement (see R0916). -max-bool-expr=5 - -# Maximum number of branch for function / method body. -max-branches=12 - -# Maximum number of locals for function / method body. -max-locals=15 - -# Maximum number of parents for a class (see R0901). -max-parents=7 - -# Maximum number of public methods for a class (see R0904). -max-public-methods=20 - -# Maximum number of return / yield for function / method body. -max-returns=6 - -# Maximum number of statements in function / method body. -max-statements=50 - -# Minimum number of public methods for a class (see R0903). -min-public-methods=2 - - -[EXCEPTIONS] - -# Exceptions that will emit a warning when caught. -overgeneral-exceptions=builtins.BaseException,builtins.Exception - - -[FORMAT] - -# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. -expected-line-ending-format= - -# Regexp for a line that is allowed to be longer than the limit. -ignore-long-lines=^\s*(# )??$ - -# Number of spaces of indent required inside a hanging or continued line. -indent-after-paren=4 - -# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 -# tab). -indent-string=' ' - -# Maximum number of characters on a single line. -max-line-length=100 - -# Maximum number of lines in a module. -max-module-lines=1000 - -# Allow the body of a class to be on the same line as the declaration if body -# contains single statement. -single-line-class-stmt=no - -# Allow the body of an if to be on the same line as the test if there is no -# else. -single-line-if-stmt=no - - -[IMPORTS] - -# List of modules that can be imported at any level, not just the top level -# one. -allow-any-import-level= - -# Allow explicit reexports by alias from a package __init__. -allow-reexport-from-package=no - -# Allow wildcard imports from modules that define __all__. -allow-wildcard-with-all=no - -# Deprecated modules which should not be used, separated by a comma. -deprecated-modules= - -# Output a graph (.gv or any supported image format) of external dependencies -# to the given file (report RP0402 must not be disabled). -ext-import-graph= - -# Output a graph (.gv or any supported image format) of all (i.e. internal and -# external) dependencies to the given file (report RP0402 must not be -# disabled). -import-graph= - -# Output a graph (.gv or any supported image format) of internal dependencies -# to the given file (report RP0402 must not be disabled). -int-import-graph= - -# Force import order to recognize a module as part of the standard -# compatibility libraries. -known-standard-library= - -# Force import order to recognize a module as part of a third party library. -known-third-party=enchant - -# Couples of modules and preferred modules, separated by a comma. -preferred-modules= - - -[LOGGING] - -# The type of string formatting that logging methods do. `old` means using % -# formatting, `new` is for `{}` formatting. -logging-format-style=old - -# Logging modules to check that the string format arguments are in logging -# function parameter format. -logging-modules=logging - -[MESSAGES CONTROL] - -# Only show warnings with the listed confidence levels. Leave empty to show -# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE, -# UNDEFINED. -confidence=HIGH, - CONTROL_FLOW, - INFERENCE, - INFERENCE_FAILURE, - UNDEFINED - -# Disable the message, report, category or checker with the given id(s). You -# can either give multiple identifiers separated by comma (,) or put this -# option multiple times (only on the command line, not in the configuration -# file where it should appear only once). You can also use "--disable=all" to -# disable everything first and then re-enable specific checks. For example, if -# you want to run only the similarities checker, you can use "--disable=all -# --enable=similarities". If you want to run only the classes checker, but have -# no Warning level messages displayed, use "--disable=all --enable=classes -# --disable=W". -disable=raw-checker-failed, - bad-inline-option, - locally-disabled, - file-ignored, - suppressed-message, - useless-suppression, - deprecated-pragma, - use-symbolic-message-instead, - logging-fstring-interpolation, - unspecified-encoding, - unused-import, - wrong-import-order, - ungrouped-imports, - subprocess-run-check, - too-many-instance-attributes, - missing-module-docstring, - missing-function-docstring, - missing-class-docstring, - -# Enable the message, report, category or checker with the given id(s). You can -# either give multiple identifier separated by comma (,) or put this option -# multiple time (only on the command line, not in the configuration file where -# it should appear only once). See also the "--disable" option for examples. -enable=c-extension-no-member - - -[METHOD_ARGS] - -# List of qualified names (i.e., library.method) which require a timeout -# parameter e.g. 'requests.api.get,requests.api.post' -timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request - - -[MISCELLANEOUS] - -# List of note tags to take in consideration, separated by a comma. -notes=FIXME, - XXX, - TODO - -# Regular expression of note tags to take in consideration. -notes-rgx= - - -[REFACTORING] - -# Maximum number of nested blocks for function / method body -max-nested-blocks=5 - -# Complete name of functions that never returns. When checking for -# inconsistent-return-statements if a never returning function is called then -# it will be considered as an explicit return statement and no message will be -# printed. -never-returning-functions=sys.exit,argparse.parse_error - - -[REPORTS] - -# Python expression which should return a score less than or equal to 10. You -# have access to the variables 'fatal', 'error', 'warning', 'refactor', -# 'convention', and 'info' which contain the number of messages in each -# category, as well as 'statement' which is the total number of statements -# analyzed. This score is used by the global evaluation report (RP0004). -evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)) - -# Template used to display messages. This is a python new-style format string -# used to format the message information. See doc for all details. -msg-template= - -# Set the output format. Available formats are text, parseable, colorized, json -# and msvs (visual studio). You can also give a reporter class, e.g. -# mypackage.mymodule.MyReporterClass. -#output-format= - -# Tells whether to display a full report or only the messages. -reports=no - -# Activate the evaluation score. -score=yes - - -[SIMILARITIES] - -# Comments are removed from the similarity computation -ignore-comments=yes - -# Docstrings are removed from the similarity computation -ignore-docstrings=yes - -# Imports are removed from the similarity computation -ignore-imports=yes - -# Signatures are removed from the similarity computation -ignore-signatures=yes - -# Minimum lines number of a similarity. -min-similarity-lines=4 - - -[SPELLING] - -# Limits count of emitted suggestions for spelling mistakes. -max-spelling-suggestions=4 - -# Spelling dictionary name. No available dictionaries : You need to install -# both the python package and the system dependency for enchant to work.. -spelling-dict= - -# List of comma separated words that should be considered directives if they -# appear at the beginning of a comment and should not be checked. -spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy: - -# List of comma separated words that should not be checked. -spelling-ignore-words= - -# A path to a file that contains the private dictionary; one word per line. -spelling-private-dict-file= - -# Tells whether to store unknown words to the private dictionary (see the -# --spelling-private-dict-file option) instead of raising a message. -spelling-store-unknown-words=no - - -[STRING] - -# This flag controls whether inconsistent-quotes generates a warning when the -# character used as a quote delimiter is used inconsistently within a module. -check-quote-consistency=no - -# This flag controls whether the implicit-str-concat should generate a warning -# on implicit string concatenation in sequences defined over several lines. -check-str-concat-over-line-jumps=no - - -[TYPECHECK] - -# List of decorators that produce context managers, such as -# contextlib.contextmanager. Add to this list to register other decorators that -# produce valid context managers. -contextmanager-decorators=contextlib.contextmanager - -# List of members which are set dynamically and missed by pylint inference -# system, and so shouldn't trigger E1101 when accessed. Python regular -# expressions are accepted. -generated-members=numpy.*, torch.* - -# Tells whether to warn about missing members when the owner of the attribute -# is inferred to be None. -ignore-none=yes - -# This flag controls whether pylint should warn about no-member and similar -# checks whenever an opaque object is returned when inferring. The inference -# can return multiple potential results while evaluating a Python object, but -# some branches might not be evaluated, which results in partial inference. In -# that case, it might be useful to still emit no-member and other checks for -# the rest of the inferred objects. -ignore-on-opaque-inference=yes - -# List of symbolic message names to ignore for Mixin members. -ignored-checks-for-mixins=no-member, - not-async-context-manager, - not-context-manager, - attribute-defined-outside-init - -# List of class names for which member attributes should not be checked (useful -# for classes with dynamically set attributes). This supports the use of -# qualified names. -ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace - -# Show a hint with possible names when a member name was not found. The aspect -# of finding the hint is based on edit distance. -missing-member-hint=yes - -# The minimum edit distance a name should have in order to be considered a -# similar match for a missing member name. -missing-member-hint-distance=1 - -# The total number of similar names that should be taken in consideration when -# showing a hint for a missing member. -missing-member-max-choices=1 - -# Regex pattern to define which classes are considered mixins. -mixin-class-rgx=.*[Mm]ixin - -# List of decorators that change the signature of a decorated function. -signature-mutators= - - -[VARIABLES] - -# List of additional names supposed to be defined in builtins. Remember that -# you should avoid defining new builtins when possible. -additional-builtins= - -# Tells whether unused global variables should be treated as a violation. -allow-global-unused-variables=yes - -# List of names allowed to shadow builtins -allowed-redefined-builtins= - -# List of strings which can identify a callback function by name. A callback -# name must start or end with one of those strings. -callbacks=cb_, - _cb - -# A regular expression matching the name of dummy variables (i.e. expected to -# not be used). -dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ - -# Argument names that match this expression will be ignored. -ignored-argument-names=_.*|^ignored_|^unused_ - -# Tells whether we should check for unused import in __init__ files. -init-import=no - -# List of qualified module names which can have objects that can redefine -# builtins. -redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io diff --git a/.python-version b/.python-version index 2c07333..e4fba21 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.11 +3.12 diff --git a/README.md b/README.md index db69a79..4d8c51a 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,24 @@ # TF-Bench -Towards Sound Evaluation of Program Logic Reasoning with Type Inference under System F +[![python](https://img.shields.io/badge/Python-3.12-3776AB.svg?style=flat&logo=python&logoColor=white)](https://www.python.org) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) + +Evaluating Program Semantics Reasoning with Type Inference in System _F_ + +![evaluation workflow](./imgs/tfb.png) + +If you find this work useful, please cite us as: +```bibtex +@inproceedings{he2025tfbench, + author = {He, Yifeng and Yang, Luning and Gonzalo, Christopher and Chen, Hao}, + title = {Evaluating Program Semantics Reasoning with Type Inference in System F}, + booktitle = {Neural Information Processing Systems (NeurIPS)}, + date = {2025-11-30/2025-12-07}, + address = {San Diego, CA, USA}, +} +``` -## Setup +## Development ### Python @@ -14,17 +30,32 @@ cd TF-Bench uv sync # create a virtual environment, and install dependencies ``` -## Building TF-Bench From Scratch (Optional) +### Haskell + +To run evaluation, you need GHC (the Glasgow Haskell Compiler) installed. +We recommend using [ghcup](https://www.haskell.org/ghcup/) to install. + +```sh +curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh +``` + +Due to the GHC dependency, the evaluation module currently only supports Linux and macOS. +Our evaluation requires Haskell language extensions [type operators](https://ghc.gitlab.haskell.org/ghc/doc/users_guide/exts/type_operators.html) +and [impredicative polymorphism](https://ghc.gitlab.haskell.org/ghc/doc/users_guide/exts/impredicative_types.html), +so we require GHC version >= 9.2.1. +Our evaluation used GHC-9.6.7. + +## Building TF-Bench from scratch (optional) -### TF-Bench +### TF-Bench (base) This script will build the benchmark (Prelude with NL) from the raw data. ```sh -uv run scripts/preprocess_benchmark.py +uv run scripts/preprocess_benchmark.py -o tfb.json ``` -### TF-Bench_pure +### TF-Bench (pure) ```sh git clone https://github.com/SecurityLab-UCD/alpharewrite.git @@ -36,47 +67,148 @@ stack exec alpharewrite-exe 1 ../tfb.json > ../tfb.pure.json cd .. ``` -For details, please take a look at the README of [alpharewrite](https://github.com/SecurityLab-UCD/alpharewrite). +For details, please check out the README of [alpharewrite](https://github.com/SecurityLab-UCD/alpharewrite). -## Download Pre-built Benchmark +## Download pre-built benchmark -You can also download our pre-built benchmark from [Zenodo](https://doi.org/10.5281/zenodo.14751813). +You can also use TF-Bench on HuggingFace datasets. -DOI +```python +from datasets import load_dataset -## Benchmarking! +split = "pure" # or "base" +dataset = load_dataset("SecLabUCD/TF-Bench", split=split) +``` + +Or through our provided package. + +```python +from tfbench import load_tfb_from_hf -### GPT Models +dataset = load_tfb_from_hf(split) +``` -To run single model: +## Using as an application ```sh -export OPENAI_API_KEY= # make sure your API key is in the environment -uv run main.py -i TF-Bench.json -m gpt-3.5-turbo +git clone https://github.com/SecurityLab-UCD/TF-Bench.git +cd TF-Bench +uv sync ``` -To run all GPT models: +Please have your API key ready in `.env`. + +### Proprietary models + +We use each provider's official SDK to access their models. +You can check our pre-supported models in `tfbench.lm` module. + +```python +from tfbench.lm import supported_models +print(supported_models) +``` + +To run single model, which runs both `base` and `pure` splits: ```sh -uv run run_all.py --option gpt +uv run main.py -m gpt-5-2025-08-07 ``` -### Open Source Models +### Open-weights models with Ollama -We use [Ollama](https://ollama.com/) to manage and run the OSS models. +We use [Ollama](https://ollama.com/) to manage and run the OSS models reported in the Appendix. +We switched to vLLM for better performance and SDK design. +Although the Ollama option is still available, +it is no longer maintained. +We recommend using vLLM instead. ```sh curl -fsSL https://ollama.com/install.sh | sh # install ollama, you need sudo for this ollama serve # start your own instance instead of a system service -uv run --project . scripts/ollama_pull.sh # install required models ``` +NOTE: we required the ollama version at least 0.9.0 to enable thinking parsers. +We use 0.11.7 for our experiments. + +```sh +> ollama --version +ollama version is 0.11.7 +``` + +Run the benchmark. + ```sh -uv run main.py -i Benchmark-F.json -m llama3:70b +uv run src/main.py -m llama3:8b ``` -To run all Ollama models: +### Running any model on HuggingFace Hub + +We also support running any model that is on HuggingFace Hub out-of-the-box. +We provide an example using Qwen3. ```sh -uv run run_all.py --option ollama +uv run src/main.py Qwen/Qwen3-4B-Instruct-2507 # or other models +``` + +Note that our `main.py` uses a pre-defined model router, +which routes all un-recognized model names to HuggingFace. +We use the `` token to parse thinking process, +if the model do it differently, please see the next section. + +### Running your own model + +To support your customized model, +you can input the path to your HuggingFace compatible checkpoint to our `main.py`. + +```sh +uv run src/main.py +``` + +## Using as a package + +Our package is also available on PyPi. + +```sh +uv add tfbench +``` + +Or directly using pip, you know the way + +```sh +pip install tfbench +``` + +### Proprietary model checkpoints that are not currently supported + +Our supported model list is used to route the model name to the correct SDK. +Even a newly released model is not in our supported models list, +you can still use it by specifying the SDK client directly. +We take OpenAI GPT-4.1 as and example here. + +```python +from tfbench.lm import OpenAIResponse +from tfbench import run_one_model + +model = "gpt-4.1" +split = "pure" +client = OpenAIResponses(model_name=model, pure=split == "pure", effort=None) +eval_result = run_one_model(client, pure=split == "pure", effort=None) +``` + +### Support other customized models + +You may implement an `LM` instance. + +```python +from tfbench.lm._types import LM, LMAnswer + +class YourLM(LM): + def __init__(self, model_name: str, pure: bool = False): + """initialize your model""" + super().__init__(model_name=model_name, pure=pure) + ... + + def _gen(self, prompt: str) -> LMAnswer: + """your generation logic here""" + return LMAnswer(answer=content, reasoning_steps=thinking_content) ``` diff --git a/benchmark/task_19.hs.md b/benchmark/task_19.hs.md index 29c01f0..46d4d6a 100644 --- a/benchmark/task_19.hs.md +++ b/benchmark/task_19.hs.md @@ -7,7 +7,7 @@ Ad-hoc # signature ```haskell -quot :: Integral => a -> a -> a +quot :: Integral a => a -> a -> a ``` # code diff --git a/benchmark/task_59.hs.md b/benchmark/task_59.hs.md index d2c3ba5..6c0e3a9 100644 --- a/benchmark/task_59.hs.md +++ b/benchmark/task_59.hs.md @@ -7,7 +7,7 @@ Parametric # signature ```haskell -foldl :: (b -> a -> b) -> b -> t a -> b +foldl :: Foldable t => (b -> a -> b) -> b -> t a -> b ``` # code diff --git a/imgs/tfb.png b/imgs/tfb.png new file mode 100644 index 0000000..c95537f Binary files /dev/null and b/imgs/tfb.png differ diff --git a/main.py b/main.py deleted file mode 100755 index 7bdc99c..0000000 --- a/main.py +++ /dev/null @@ -1,161 +0,0 @@ -import os -from typing import Callable -import json -from funcy_chain import Chain -from funcy import lmap -from tqdm import tqdm -from dacite import from_dict - - -from openai import OpenAI -from ollama import Client as OllamaClient -from anthropic import Anthropic -from google import genai - -import fire -from src.common import ( - BenchmarkTask, - get_prompt, -) - -from src.experiment import ( - OAI_MODELS, - OAI_TTC_MODELS, - CLAUDE_MODELS, - CLAUDE_TTC_MODELS, - DEEPSEEK_MODELS, - GEMINI_MODELS, - GEMINI_TTC_MODELS, - get_ant_model, - get_ant_ttc_model, - get_oai_model, - get_oai_ttc_model, - get_gemini_model, - get_gemini_ttc_model, -) -from src.experiment_ollama import OLLAMA_MODELS, get_ollama_model -from src.postprocessing import postprocess, RESPONSE_STRATEGIES -from src.evaluation import evaluate - - -def main( - model: str, - port: int = 11434, - pure: bool = False, - thinking_budget: int = 1024, - output_file: str | None = None, - log_file: str = "evaluation_log.jsonl", -): - """ - Run an experiment using various AI models to generate and evaluate type signatures. - - Parameters: - model (str): Name of the model to use for generating type signatures. Must be one of: - - GPT_MODELS: ["gpt-3.5-turbo-0125", "gpt-4-turbo-2024-04-09", ...] - - OLLAMA_MODELS, CLAUDE_MODELS, or O1_MODELS. - Default is "gpt-3.5-turbo". - - port (int): Port number for connecting to the Ollama server (if using Ollama models). - Ignored for other models. Default is 11434. - - pure (bool): If True, uses the original variable naming in type inference. - If False, uses rewritten variable naming (e.g., `v1`, `v2`, ...). Default is False. - """ - assert ( - model - in OAI_MODELS - + OAI_TTC_MODELS - + OLLAMA_MODELS - + DEEPSEEK_MODELS - + CLAUDE_MODELS - + CLAUDE_TTC_MODELS - + GEMINI_MODELS - + GEMINI_TTC_MODELS - ), f"{model} is not supported." - - # hard-coding benchmark file path for experiment - input_file = "tfb.pure.json" if pure else "tfb.json" - input_file = os.path.abspath(input_file) - assert os.path.exists( - input_file - ), f"{input_file} does not exist! Please download or build it first." - - if output_file is None: - os.makedirs("result", exist_ok=True) - output_file = f"result/{model}.txt" - - client: OpenAI | Anthropic | OllamaClient | genai.Client - generate: Callable[[str], str | None] - - if model in OAI_MODELS: - assert "OPENAI_API_KEY" in os.environ, "Please set OPEN_API_KEY in environment!" - client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) - generate = get_oai_model(client, model, pure) - - elif model in OAI_TTC_MODELS: - assert "OPENAI_API_KEY" in os.environ, "Please set OPEN_API_KEY in environment!" - client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) - generate = get_oai_ttc_model(client, model, pure) - elif model in CLAUDE_MODELS: - assert ( - "ANTHROPIC_API_KEY" in os.environ - ), "Please set ANTHROPIC_API_KEY in environment!" - client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"]) - generate = get_ant_model(client, model, pure) - elif model in CLAUDE_TTC_MODELS: - client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"]) - generate = get_ant_ttc_model(client, model, pure, thinking_budget) - - elif model in DEEPSEEK_MODELS: - assert ( - "DEEPSEEK_API_KEY" in os.environ - ), "Please set DEEPSEEK_API_KEY in environment!" - client = OpenAI( - api_key=os.environ["DEEPSEEK_API_KEY"], base_url="https://api.deepseek.com" - ) - generate = get_oai_model(client, model, pure) - - elif model in GEMINI_MODELS: - assert ( - "GOOGLE_API_KEY" in os.environ - ), "Please set GOOGLE_API_KEY in environment!" - client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) - generate = get_gemini_model(client, model, pure) - elif model in GEMINI_TTC_MODELS: - assert ( - "GOOGLE_API_KEY" in os.environ - ), "Please set GOOGLE_API_KEY in environment!" - client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) - generate = get_gemini_ttc_model(client, model, pure, thinking_budget) - - else: - client = OllamaClient(host=f"http://localhost:{port}") - generate = get_ollama_model(client, model, pure) - - with open(input_file, "r") as fp: - tasks = [from_dict(data_class=BenchmarkTask, data=d) for d in json.load(fp)] - - prompts = lmap(get_prompt, tasks) - responses = lmap(generate, tqdm(prompts, desc=model)) - gen_results = ( - Chain(responses) - .map(lambda x: x if x is not None else "") # convert None to empty string - .map(lambda s: postprocess(s, RESPONSE_STRATEGIES)) - .map(str.strip) - .value - ) - - with open(output_file, "w", errors="ignore") as file: - file.write("\n".join(gen_results)) - - eval_acc = evaluate(tasks, gen_results) - print(eval_acc) - - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with open(log_file, "a") as fp: - logging_result = {"model_name": model, **eval_acc, "pure": pure} - fp.write(f"{json.dumps(logging_result)}\n") - - -if __name__ == "__main__": - fire.Fire(main) diff --git a/mypy.ini b/mypy.ini deleted file mode 100644 index 15e3ee0..0000000 --- a/mypy.ini +++ /dev/null @@ -1,19 +0,0 @@ -[mypy] -python_version = 3.10 -warn_return_any = True -warn_unused_configs = True -ignore_missing_imports = True -check_untyped_defs = True -allow_redefinition = false -ignore_errors = false -implicit_reexport = false -local_partial_types = true -no_implicit_optional = true -strict_equality = true -strict_optional = true -warn_no_return = true -warn_redundant_casts = true -; warn_unreachable = true -warn_unused_ignores = false -plugins = - returns.contrib.mypy.returns_plugin \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index a1242f4..4235de8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,39 +1,111 @@ [project] -name = "tf-bench" +name = "tfbench" version = "0.1.0" description = "Add your description here" readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.12" dependencies = [ - "anthropic>=0.49.0", + "anthropic==0.49.0", "dacite>=1.8.1", + "datasets>=4.0.0", + "deprecated>=1.2.18", + "dotenv>=0.9.9", "fire==0.5.0", "funcy==2.0", "funcy-chain==0.2.0", - "google-genai>=1.11.0", - "groq==0.8.0", + "google-genai==1.31.0", "hypothesis>=6.98.6", "markdown-to-json==2.1.2", "matplotlib>=3.8.3", "numpy>=1.26.4", - "ollama>=0.2.1", - "openai==1.75.0", - "pathos>=0.3.3", - "pylint>=3.3.6", + "ollama==0.5.3", + "openai==1.99.9", + "orjson>=3.11.3", + "orjsonl>=1.0.0", + "pokepalette>=0.0.6", + "pyarrow>=21.0.0", + "pydantic>=2.11.7", "pytest>=8.0.0", "python-dotenv==1.0.1", "requests==2.32.3", - "returns[compatible-mypy]==0.22.0", + "returns[compatible-mypy]>=0.26.0", "seaborn==0.13.2", "tabulate>=0.9.0", + "tenacity>=9.1.2", "tiktoken==0.7.0", "tqdm>=4.66.2", + "transformers[torch]>=4.55.4", "tree-sitter==0.22.3", "tree-sitter-haskell==0.21.0", + "types-deprecated>=1.2.15.20250304", "types-requests>=2.31.0", ] -[tool.pytest.ini_options] -pythonpath = ["."] -[tool.uv] -package = true +[build-system] +requires = ["uv_build>=0.8.10,<0.9.0"] +build-backend = "uv_build" + +[tool.pylint.messages_control] +disable = [ + "raw-checker-failed", + "bad-inline-option", + "locally-disabled", + "file-ignored", + "suppressed-message", + "useless-suppression", + "deprecated-pragma", + "use-symbolic-message-instead", + "logging-fstring-interpolation", + "unused-import", + "ungrouped-imports", + "subprocess-run-check", + "too-many-instance-attributes", + "missing-module-docstring", + "unnecessary-ellipsis", # pylint cannot work with typed Protocol, + "too-many-arguments", + "too-many-locals", + "too-many-positional-arguments", + "too-many-statements", + "unspecified-encoding", + "missing-class-docstring", + "too-few-public-methods", # LM only have 1 public method +] +fail-under = 9 +max-line-length = 120 + +[tool.pylint] +extension-pkg-whitelist = [ + "orjson", # https://github.com/pylint-dev/pylint/issues/9762 +] + + +[tool.pylint.typecheck] +generated-members = ["cv2.*", "skimage.metrics.*"] + +[tool.mypy] +python_version = "3.11" +warn_return_any = true +warn_unused_configs = true +ignore_missing_imports = true +check_untyped_defs = true +allow_redefinition = false +ignore_errors = false +implicit_reexport = false +local_partial_types = true +no_implicit_optional = true +strict_equality = true +strict_optional = true +warn_no_return = true +warn_redundant_casts = true +warn_unreachable = true +warn_unused_ignores = false +plugins = ["returns.contrib.mypy.returns_plugin"] + +[tool.ruff] +exclude = ["tests/", "plots/"] + +[dependency-groups] +dev = [ + "pytest-cov>=6.2.1", + "pytest-xdist>=3.8.0", +] diff --git a/scripts/analysis_saved.py b/scripts/analysis_saved.py new file mode 100644 index 0000000..3945ea6 --- /dev/null +++ b/scripts/analysis_saved.py @@ -0,0 +1,58 @@ +from os.path import abspath, dirname, basename, join as pjoin +import os +import fire +from orjsonl import orjsonl +from tfbench import ( + analysis_multi_runs, + load_tfb_from_hf, + load_gen_results_jsonl, + prover_evaluate, +) + + +def eval_one_split(result_dir: str, split: str, log_file: str | None = None): + """ + Arguments: + result_dir (str): assumed in format `/some/path/...///`. + For example: results/gpt-5-nano-2025-08-07/base, + where is `gpt-5-nano-2025-08-07` and is `base`. + WARNING: we parse the and in this way. + log_file (str | None): path to the log file. If None, this script only prints to stdout. + """ + + result_dir = abspath(pjoin(result_dir, split)) + model = basename(dirname(result_dir)) + + tasks = load_tfb_from_hf(split) + # load all jsonl files from `result_dir` + jsonl_files = [ + pjoin(result_dir, f) for f in os.listdir(result_dir) if f.endswith(".jsonl") + ] + runs = [load_gen_results_jsonl(f) for f in jsonl_files] + accs = [prover_evaluate(tasks, run, split == "pure") for run in runs] + mean, std = analysis_multi_runs(accs) + + print(f"Model: {model}") + print(f"Split: {split}") + print(f"Accuracy: {mean:.4f} ± {std:.4f}") + + if log_file is not None: + log_obj = { + "model": model, + "split": split, + "effort": None, + "n_repeats": len(runs), + "mean": mean, + "std": std, + } + orjsonl.append(log_file, log_obj) + + +def main(result_dir: str, log_file: str | None = None): + """run evaluation on all jsonl files in the result directory""" + eval_one_split(result_dir, "base", log_file) + eval_one_split(result_dir, "pure", log_file) + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/scripts/error_analysis.py b/scripts/error_analysis.py new file mode 100644 index 0000000..12068fe --- /dev/null +++ b/scripts/error_analysis.py @@ -0,0 +1,67 @@ +from os.path import abspath, basename, join as pjoin +import os +from typing import Literal + +from openai import OpenAI +import fire +from tqdm import tqdm +from orjsonl import orjsonl + +from tfbench import ( + load_tfb_from_hf, + load_gen_results_jsonl, +) +from tfbench.evaluation import get_incorrect +from tfbench.error_analysis import error_analysis, ErrorAnalysisResult + + +def analysis(result_file_dir: str, split: Literal["base", "pure"], output_file: str): + """script to run error analysis fo incorrect TF-Bench tasks""" + client = OpenAI() + tasks = load_tfb_from_hf(split) + model = basename(abspath(result_file_dir)) + + split_result_dir = pjoin(result_file_dir, split) + + incorrect = [] + print(f"Collecting incorrect results from {split_result_dir} on split {split}") + for file in os.listdir(split_result_dir): + if not file.endswith(".jsonl"): + continue + result_file_path = pjoin(split_result_dir, file) + run_result = load_gen_results_jsonl(result_file_path) + + incorrect_of_run = get_incorrect(tasks, run_result, split == "pure") + incorrect.extend(incorrect_of_run) + + print(f"Running error classification on {len(incorrect)} incorrect results") + for task, answer, msg in tqdm(incorrect): + error = error_analysis(client, task, answer, error_msg=msg) + log_obj: ErrorAnalysisResult = { + "model": model, + "split": split, + "task_id": task.task_id, + "ground_truth": task.signature, + "predicted": answer.answer if answer else None, + "error_category": error.category, + } + orjsonl.append(output_file, log_obj) + + +def main(result_file_dir: str, output_file: str | None = None): + """run result analysis on both base and pure splits""" + + model = basename(abspath(result_file_dir)) + print(f"Running error analysis for model {model}") + if output_file is None: + dir_name = "err_analysis" + os.makedirs(dir_name, exist_ok=True) + output_file = f"{dir_name}/{model}.jsonl" + output_file = abspath(output_file) + + analysis(result_file_dir, "base", output_file) + analysis(result_file_dir, "pure", output_file) + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/scripts/error_plot.py b/scripts/error_plot.py new file mode 100644 index 0000000..5eba719 --- /dev/null +++ b/scripts/error_plot.py @@ -0,0 +1,181 @@ +from typing import get_args +import os + +import fire +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import pokepalette + +from tfbench.error_analysis import ErrorCategories + +plt.rcParams["pdf.fonttype"] = 42 +plt.rcParams["ps.fonttype"] = 42 +FONT_SIZE = 24 + +# CMAP = pokepalette.get_colormap("lapras") +CMAP = pokepalette.get_colormap("gengar") + + +def plot_error_categories_pie_charts(df: pd.DataFrame): + """ + Plot pie charts showing the proportion of error categories by split and model. + + Args: + df: pandas DataFrame containing ErrorAnalysisResult data + """ + # Get unique models and splits + models = sorted(df["model"].unique()) + splits = ["base", "pure"] # Ensure consistent ordering + + # Set up the subplot grid + n_models = len(models) + fig, axes = plt.subplots(2, n_models, figsize=(5 * n_models, 10)) + + # Handle case where there's only one model (axes won't be 2D) + if n_models == 1: + axes = axes.reshape(-1, 1) + + # Define colors for consistency across plots + error_categories = list(get_args(ErrorCategories)) + colors = CMAP(np.linspace(0, 1, len(error_categories))) + color_map = dict(zip(error_categories, colors)) + + # Create pie charts for each split-model combination + for split_idx, split in enumerate(splits): + for model_idx, model in enumerate(models): + # Filter data for current split and model + subset = df[(df["split"] == split) & (df["model"] == model)] + + if len(subset) == 0: + # Handle empty subset + axes[split_idx, model_idx].text( + 0.5, + 0.5, + "No Data", + ha="center", + va="center", + transform=axes[split_idx, model_idx].transAxes, + ) + axes[split_idx, model_idx].set_title(f"{model} ({split})") + continue + + # Count error categories and ensure consistent ordering + error_counts = subset["error_category"].value_counts() + + # Prepare data for pie chart with consistent ordering + labels = [] + sizes = [] + plot_colors = [] + + for category in error_categories: + if category in error_counts: + labels.append(category) + sizes.append(error_counts[category]) + plot_colors.append(color_map[category]) + + # Create pie chart (without labels and autopct since we'll add custom percentages) + wedges = axes[split_idx, model_idx].pie( + sizes, + colors=plot_colors, + startangle=90, + )[0] + + # Calculate percentages + total = sum(sizes) + percentages = [(size / total) * 100 for size in sizes] + + # Add percentage labels only for slices >= 5% + for i, (wedge, pct) in enumerate(zip(wedges, percentages)): + if pct >= 5: # Only show percentage if >= 5% + # Get wedge center angle + angle = (wedge.theta2 + wedge.theta1) / 2 + + # Place inside the pie slice + radius = 0.7 + + # Calculate text position + x = radius * np.cos(np.radians(angle)) + y = radius * np.sin(np.radians(angle)) + + # Add percentage text + axes[split_idx, model_idx].text( + x, + y, + f"{pct:.1f}%", + ha="center", + va="center", + fontweight="bold", + fontsize=10, + color="black", + ) + + # Add model names as column headers (only on top row) + for model_idx, model in enumerate(models): + axes[0, model_idx].set_title(model, fontsize=FONT_SIZE) + + # Add split names as row labels (only on leftmost column) + for split_idx, split in enumerate(splits): + axes[split_idx, 0].text( + -0.03, + 0.5, + split.upper(), + transform=axes[split_idx, 0].transAxes, + fontsize=FONT_SIZE, + ha="center", + va="center", + rotation=90, + ) + + # Adjust layout + plt.tight_layout() + plt.subplots_adjust( + top=0.93, + wspace=-0.15, # controls the space between columns + hspace=-0.1, # controls the space between rows + ) + + # Add legend with all error categories that appear anywhere in the data + all_categories_in_data = set(df["error_category"].unique()) + + # Create legend for all categories that appear in the data, in consistent order + legend_elements = [ + plt.Rectangle((0, 0), 1, 1, fc=color_map[cat]) + for cat in error_categories + if cat in all_categories_in_data + ] + legend_labels_filtered = [ + cat for cat in error_categories if cat in all_categories_in_data + ] + + fig.legend( + legend_elements, + legend_labels_filtered, + loc="center", + bbox_to_anchor=(0.5, 0.0), + ncol=min(4, len(legend_labels_filtered)), + fontsize=FONT_SIZE, + ) + + return fig + + +def main( + error_analysis_file_dir: str, + output_file: str = "error_analysis_pie_charts.png", +): + # Example usage: + # Assuming your DataFrame is called 'df' + files = [ + os.path.join(error_analysis_file_dir, f) + for f in os.listdir(error_analysis_file_dir) + if f.endswith(".jsonl") + ] + df = pd.concat([pd.read_json(f, lines=True) for f in files], ignore_index=True) + fig = plot_error_categories_pie_charts(df) + + fig.savefig(output_file, dpi=500, bbox_inches="tight") + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/scripts/experiment_ollama.py b/scripts/experiment_ollama.py new file mode 100644 index 0000000..433f167 --- /dev/null +++ b/scripts/experiment_ollama.py @@ -0,0 +1,176 @@ +""" +(deprecated) Experiment script for OSS models using Ollama +This script reproduce legacy results for OSS models using Ollama in our paper's Appendix. +New models should use our vLLM option instead. +""" + +from typing import Union +import os +import json + +from ollama import Client as OllamaClient, ResponseError +import fire +from dacite import from_dict +from tqdm import tqdm +from funcy_chain import Chain + +from tfbench.lm import get_sys_prompt +from tfbench.common import BenchmarkTask, get_prompt +from tfbench.postprocessing import postprocess, RESPONSE_STRATEGIES +from tfbench.evaluation import evaluate + +OLLAMA_OSS = [ + "phi3:3.8b", + "phi3:14b", + "mistral", + "mixtral:8x7b", + "mixtral:8x22b", + "llama3:8b", + "llama3:70b", + "llama3.1:8b", + "llama3.1:70b", + "llama3.1:405b", + "llama3.2:1b", + "llama3.2:3b", + "llama3.3:70b", + "gemma:2b", + "gemma:7b", + "gemma2:9b", + "gemma2:27b", + "qwen2:1.5b", + "qwen2:7b", + "qwen2:72b", + "qwen2.5:1.5b", + "qwen2.5:7b", + "qwen2.5:72b", + "deepseek-v2:16b", + "deepseek-v2:236b", + "deepseek-v2.5:236b", +] + + +OLLAMA_CODE = [ + "qwen2.5-coder:1.5b", + "qwen2.5-coder:7b", + "granite-code:3b", + "granite-code:8b", + "granite-code:20b", + "granite-code:34b", + "deepseek-coder-v2:16b", + "deepseek-coder-v2:236b", +] + +OLLAMA_MODELS = OLLAMA_OSS + OLLAMA_CODE + + +def get_ollama_model( + client: OllamaClient, + model: str = "llama3:8b", + pure: bool = False, +): + """ + Configure and return a function to generate type signatures using an Ollama model. + + Parameters: + client (OllamaClient): The Ollama client instance used for sending requests to the model. + + model (str): Name of the model to use for generating type signatures. + Must be one of the predefined models in OLLAMA_MODELS. + Default is "llama3:8b". + + pure (bool): If True, uses the original variable naming in type inference. + If False, uses rewritten variable naming (e.g., `v1`, `v2`, ...). Default is False. + + Returns: + Callable[[str], Union[str, None]]: + A function that takes a prompt string as input and returns the generated type + signature as a string, or None if the generation fails. + """ + + def generate_type_signature(prompt: str) -> Union[str, None]: + try: + response = client.chat( + messages=[ + { + "role": "system", + "content": get_sys_prompt(pure), + }, + {"role": "user", "content": prompt}, + ], + model=model, + ) + except ResponseError as e: + print(e) + return None + + message = response.message + if message.content: + return str(message.content) + + return None + + return generate_type_signature + + +def main( + model: str = "llama3:8b", + pure: bool = False, + port: int = 11434, + output_file: str | None = None, + log_file: str = "evaluation_log.jsonl", +): + """ + Run an experiment using various AI models to generate and evaluate type signatures. + + Parameters: + model (str): Name of the model to use for generating type signatures. Must be one of OLLAMA_MODELS + + port (int): Port number for connecting to the Ollama server. + Ignored for other models. Default is 11434. + + pure (bool): If True, uses the original variable naming in type inference. + If False, uses rewritten variable naming (e.g., `v1`, `v2`, ...). Default is False. + """ + assert model in OLLAMA_MODELS, f"{model} is not supported." + + # hard-coding benchmark file path for experiment + input_file = "tfb.pure.json" if pure else "tfb.json" + input_file = os.path.abspath(input_file) + assert os.path.exists( + input_file + ), f"{input_file} does not exist! Please download or build it first." + + if output_file is None: + os.makedirs("result", exist_ok=True) + output_file = f"result/{model}.txt" + + client = OllamaClient(host=f"http://localhost:{port}") + generate = get_ollama_model(client, model, pure) + + with open(input_file, "r") as fp: + tasks = [from_dict(data_class=BenchmarkTask, data=d) for d in json.load(fp)] + + prompts = map(get_prompt, tasks) + responses = map(generate, tqdm(prompts, desc=model)) + gen_results = ( + Chain(responses) + .map(lambda x: x if x is not None else "") # convert None to empty string + .map(lambda s: postprocess(s, RESPONSE_STRATEGIES)) + .map(str.strip) + .value + ) + + with open(output_file, "w", errors="ignore") as file: + file.write("\n".join(gen_results)) + + eval_acc = evaluate(tasks, gen_results) + print(eval_acc) + + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with open(log_file, "a") as fp: + logging_result = {"model_name": model, **eval_acc, "pure": pure} + fp.write(f"{json.dumps(logging_result)}\n") + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/scripts/huggingface_upload.py b/scripts/huggingface_upload.py new file mode 100644 index 0000000..f224c0f --- /dev/null +++ b/scripts/huggingface_upload.py @@ -0,0 +1,31 @@ +import os +import fire +from datasets.load import load_dataset + + +def main(base_path: str = "tfb.json", pure_path: str = "tfb.pure.json"): + """load TF-Bench from local JSON and upload to HuggingFace Hub + To run this script, you need: + 1. Build the base version of TF-Bench + 2. Build the pure version of TF-Bench + 3. login in to HuggingFace Hub with your HF_TOKEN + """ + base_path = os.path.abspath(base_path) + pure_path = os.path.abspath(pure_path) + dataset = load_dataset( + "json", + data_files={"base": base_path, "pure": pure_path}, + ) + print(dataset) + + base = dataset["base"] + pure = dataset["pure"] + + assert len(base) == 188 # type: ignore + assert len(base) == len(pure), "Base and pure datasets must have the same length" # type: ignore + + dataset.push_to_hub("SecLabUCD/TF-Bench") + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/scripts/ollama_pull.py b/scripts/ollama_pull.py index e32f07c..495bdd2 100644 --- a/scripts/ollama_pull.py +++ b/scripts/ollama_pull.py @@ -1,5 +1,5 @@ # script to pull all ollama models -from src.experiment_ollama import OLLAMA_MODELS +from tfbench.experiment_ollama import OLLAMA_MODELS import subprocess from tqdm import tqdm diff --git a/scripts/preprocess_benchmark.py b/scripts/preprocess_benchmark.py index 7c8d67f..cd4ada8 100644 --- a/scripts/preprocess_benchmark.py +++ b/scripts/preprocess_benchmark.py @@ -2,17 +2,21 @@ import json import os -import markdown_to_json + import fire -from src.common import md2task, BenchmarkTask from funcy import lmap +from tfbench.common import md2task, BenchmarkTask + def main(input_raw_benchmark_path: str = "benchmark", output_path: str = "tfb.json"): + """Process pre-extracted tasks from Markdown to JSON""" # read in all files ending with .md in the input_raw_benchmark_path tasks: list[BenchmarkTask] = [] - for file in os.listdir(input_raw_benchmark_path): + files = os.listdir(input_raw_benchmark_path) + files_w_order = sorted(files) + for file in files_w_order: if not file.endswith(".hs.md"): continue with open(os.path.join(input_raw_benchmark_path, file), "r") as f: diff --git a/scripts/result_token_stat.py b/scripts/result_token_stat.py index 7963cc9..e370a4f 100644 --- a/scripts/result_token_stat.py +++ b/scripts/result_token_stat.py @@ -1,11 +1,11 @@ +import json + import tiktoken import fire from dacite import from_dict -import json -from funcy_chain import Chain import pandas -from src.common import BenchmarkTask, get_prompt +from tfbench.common import BenchmarkTask def main(input_file="tfb.json"): @@ -20,5 +20,6 @@ def main(input_file="tfb.json"): print(f"min: {df.token_count.min()}") print(f"avg: {df.token_count.mean()}") + if __name__ == "__main__": fire.Fire(main) diff --git a/run_all.py b/scripts/run_all.py similarity index 87% rename from run_all.py rename to scripts/run_all.py index 904e728..3dca517 100644 --- a/run_all.py +++ b/scripts/run_all.py @@ -2,9 +2,9 @@ Script to run all experiments """ -from src.experiment import GPT_MODELS, CLAUDE_MODELS, O1_MODELS -from src.experiment_ollama import OLLAMA_MODELS, OLLAMA_OSS, OLLAMA_CODE -from src.common import SEED, TEMPERATURE +from tfbench.experiment import GPT_MODELS, CLAUDE_MODELS, O1_MODELS +from tfbench.experiment_ollama import OLLAMA_MODELS, OLLAMA_OSS, OLLAMA_CODE +from tfbench.common import SEED, TEMPERATURE from main import main as run_experiment import fire import sys diff --git a/src/__init__.py b/src/__init__.py deleted file mode 100644 index 55aa49a..0000000 --- a/src/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -import logging - -logging.getLogger("openai").setLevel(logging.ERROR) -logging.getLogger("httpx").setLevel(logging.ERROR) diff --git a/src/evaluation.py b/src/evaluation.py deleted file mode 100644 index 6f36860..0000000 --- a/src/evaluation.py +++ /dev/null @@ -1,108 +0,0 @@ -import fire -import json -import logging -from src.common import BenchmarkTask -from src.postprocessing import postprocess, TASK_STRATEGIES, RESPONSE_STRATEGIES -from funcy_chain import Chain -from dacite import from_dict -from itertools import starmap -import re - - -def tokenize_type_signature(sig: str) -> list[str]: - """ - A minimal tokenizer splitting on spaces, parentheses, commas, '->', [ and ], etc. - This won't handle all Haskell syntax, but might suffice for simpler cases. - - Example: "(a -> b) -> [a] -> [b]" - -> ["(", "a", "->", "b", ")", "->", "[", "a", "]", "->", "[", "b", "]"] - """ - # Notice we've added \[ and \] to the group of delimiters - pattern = r"(\s+|\(|\)|,|\[|\]|->)" - tokens = re.split(pattern, sig) - # Remove empty/whitespace-only tokens - tokens = [t for t in tokens if t.strip()] - return tokens - - -def normalize_type_vars(tokens: list[str]) -> list[str]: - """ - Replace each unique lowercase token that looks like a type variable - with a canonical 'v0', 'v1', etc., in the order encountered. - """ - var_map = {} - next_id = 0 - result = [] - - for tok in tokens: - # Very crude check: type variable starts with lowercase - # (We exclude known symbols like '->', parentheses, brackets, commas, etc.) - if re.fullmatch(r"[a-z]\w*", tok): - if tok not in var_map: - var_map[tok] = f"v{next_id}" - next_id += 1 - result.append(var_map[tok]) - else: - # Non-variable tokens remain as-is - result.append(tok) - - return result - - -def alpha_equiv(s1: str, s2: str) -> bool: - """ - Check if two type signatures are 'alpha-equivalent' under - a naive textual approach. - """ - t1 = tokenize_type_signature(s1) - t2 = tokenize_type_signature(s2) - - n1 = normalize_type_vars(t1) - n2 = normalize_type_vars(t2) - - return n1 == n2 - - -def evaluate_one_task(task: BenchmarkTask, result: str) -> bool: - ground_truth = postprocess(task.signature, TASK_STRATEGIES) - result = postprocess(result, RESPONSE_STRATEGIES) - return alpha_equiv(ground_truth, result) - - -def evaluate( - benchmark_f: list[BenchmarkTask], results: list[str] -) -> dict[str, int | float]: - - assert len(benchmark_f) == len(results) - eval_results = starmap(evaluate_one_task, zip(benchmark_f, results)) - n_correct = sum(eval_results) - acc = n_correct / len(benchmark_f) - - return { - "total": len(benchmark_f), - "n_correct": n_correct, - "accuracy": acc, - } - - -def main( - benchmark_file: str = "Benchmark-F.jsonl", - results_file: str = "data/experiment/gpt_generated_responses.jsonl", -): - with open(benchmark_file, "r") as file: - benchmark_f: list[BenchmarkTask] = ( - Chain(file.readlines()) - .map(json.loads) - .map(lambda d: from_dict(data_class=BenchmarkTask, data=d)) - .value - ) - with open(results_file, "r") as file: - results: list[str] = Chain(file.readlines()).map(json.loads).value - - eval_acc = evaluate(benchmark_f, results) - logging.info(json.dumps(eval_acc, indent=2)) - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - fire.Fire(main) diff --git a/src/experiment.py b/src/experiment.py deleted file mode 100644 index 7322873..0000000 --- a/src/experiment.py +++ /dev/null @@ -1,202 +0,0 @@ -""" -Experiment script for OpenAI models -""" - -from openai import OpenAI -from anthropic import Anthropic, InternalServerError -from typing import Callable - -from google import genai -from google.genai.types import GenerateContentConfig, ThinkingConfig - -from src.common import get_sys_prompt, MAX_TOKENS - -OAI_MODELS = [ - "gpt-3.5-turbo-0125", - "gpt-4-turbo-2024-04-09", - "gpt-4o-2024-11-20", - "gpt-4o-mini-2024-07-18", -] - -OAI_TTC_MODELS = [ - "o1-mini-2024-09-12", - "o1-preview-2024-09-12", - "o1-2024-12-17", - "o3-mini-2025-01-31", - "o3-2025-04-16", - "o4-mini-2025-04-16", -] - -CLAUDE_MODELS = [ - "claude-3-opus-20240229", - "claude-3-sonnet-20240229", - "claude-3-haiku-20240307", - "claude-3-5-sonnet-20240620", - "claude-3-5-sonnet-20241022", - "claude-3-5-haiku-20241022", -] - -CLAUDE_TTC_MODELS = [ - "claude-3-7-sonnet-20250219", -] - -DEEPSEEK_MODELS = [ - "deepseek-reasoner", - "deepseek-chat", -] - -GEMINI_MODELS = [ - "gemini-2.0-flash", - "gemini-2.0-flash-lite", - "gemini-1.5-flash", - "gemini-1.5-flash-8b", - "gemini-1.5-pro", -] - -GEMINI_TTC_MODELS = [ - "gemini-2.5-flash-preview-04-17", - "gemini-2.5-pro-preview-03-25", -] - - -def get_oai_ttc_model( - client: OpenAI, - model: str = "o1-preview-2024-09-12", - pure: bool = False, -) -> Callable[[str], str | None]: - def generate_type_signature(prompt: str) -> str | None: - completion = client.chat.completions.create( - messages=[ - {"role": "user", "content": get_sys_prompt(pure) + "\n\n" + prompt}, - ], - model=model, - ) - - content = completion.choices[0].message.content - return content if isinstance(content, str) else None - - return generate_type_signature - - -def get_oai_model( - client: OpenAI, - model: str = "gpt-3.5-turbo", - pure: bool = False, -) -> Callable[[str], str | None]: - def generate_type_signature(prompt: str) -> str | None: - completion = client.chat.completions.create( - messages=[ - { - "role": "system", - "content": get_sys_prompt(pure), - }, - {"role": "user", "content": prompt}, - ], - model=model, - ) - - content = completion.choices[0].message.content - return content if isinstance(content, str) else None - - return generate_type_signature - - -def get_ant_ttc_model( - client: Anthropic, - model: str = "claude-3-7-sonnet-20250219", - pure: bool = False, - thinking_budget: int = 1024, -) -> Callable[[str], str | None]: - def generate_type_signature(prompt: str) -> str | None: - try: - message = client.beta.messages.create( - model=model, - thinking={"type": "enabled", "budget_tokens": thinking_budget}, - system=get_sys_prompt(pure), - messages=[ - {"role": "user", "content": prompt}, - ], - max_tokens=thinking_budget + MAX_TOKENS, - betas=["output-128k-2025-02-19"], - ) - except InternalServerError as e: - print(e) - return None - - try: - thinking, answer = message.content - text = answer.text # type: ignore - return text if isinstance(text, str) else None - except: - return None - - return generate_type_signature - - -def get_ant_model( - client: Anthropic, - model: str = "claude-3-5-sonnet-20240620", - pure: bool = False, -) -> Callable[[str], str | None]: - def generate_type_signature(prompt: str) -> str | None: - try: - message = client.messages.create( - system=get_sys_prompt(pure), - messages=[ - {"role": "user", "content": prompt}, - ], - model=model, - max_tokens=MAX_TOKENS, - ) - except InternalServerError as e: - print(e) - return None - contents = message.content - if len(contents) > 0: - text = contents[0].text # type: ignore - return text if isinstance(text, str) else None - else: - return None - - return generate_type_signature - - -def get_gemini_model( - client: genai.Client, - model: str = "gemini-2.0-flash-lite", - pure: bool = False, -) -> Callable[[str], str | None]: - def generate_type_signature(prompt: str) -> str | None: - response = client.models.generate_content( - model=model, - contents=[prompt], - config=GenerateContentConfig( - system_instruction=[get_sys_prompt(pure)], - ), - ) - return response.text if isinstance(response.text, str) else None - - return generate_type_signature - - -def get_gemini_ttc_model( - client: genai.Client, - model: str = "gemini-2.5-flash-preview-04-17", - pure: bool = False, - thinking_budget: int = 1024, -) -> Callable[[str], str | None]: - def generate_type_signature(prompt: str) -> str | None: - response = client.models.generate_content( - model=model, - contents=[prompt], - config=GenerateContentConfig( - system_instruction=[get_sys_prompt(pure)], - thinking_config=ThinkingConfig( - thinking_budget=thinking_budget, - include_thoughts=False, - ), - ), - ) - return response.text if isinstance(response.text, str) else None - - return generate_type_signature diff --git a/src/experiment_ollama.py b/src/experiment_ollama.py deleted file mode 100644 index a11858d..0000000 --- a/src/experiment_ollama.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -Experiment script for OSS models using Ollama -""" - -from ollama import Client as OllamaClient, ResponseError -from typing import Union -from src.common import get_sys_prompt - -OLLAMA_OSS = [ - "phi3:3.8b", - "phi3:14b", - "mistral", - "mixtral:8x7b", - "mixtral:8x22b", - "llama3:8b", - "llama3:70b", - "llama3.1:8b", - "llama3.1:70b", - "llama3.1:405b", - "llama3.2:1b", - "llama3.2:3b", - "llama3.3:70b", - "gemma:2b", - "gemma:7b", - "gemma2:9b", - "gemma2:27b", - "qwen2:1.5b", - "qwen2:7b", - "qwen2:72b", - "qwen2.5:1.5b", - "qwen2.5:7b", - "qwen2.5:72b", - "deepseek-v2:16b", - "deepseek-v2:236b", - "deepseek-v2.5:236b", -] - - -OLLAMA_CODE = [ - "qwen2.5-coder:1.5b", - "qwen2.5-coder:7b", - "granite-code:3b", - "granite-code:8b", - "granite-code:20b", - "granite-code:34b", - "deepseek-coder-v2:16b", - "deepseek-coder-v2:236b", -] - -OLLAMA_MODELS = OLLAMA_OSS + OLLAMA_CODE - - -def get_ollama_model( - client: OllamaClient, - model: str = "llama3:8b", - pure: bool = False, -): - """ - Configure and return a function to generate type signatures using an Ollama model. - - Parameters: - client (OllamaClient): The Ollama client instance used for sending requests to the model. - - model (str): Name of the model to use for generating type signatures. Must be one of the predefined models in OLLAMA_MODELS. - Default is "llama3:8b". - - pure (bool): If True, uses the original variable naming in type inference. - If False, uses rewritten variable naming (e.g., `v1`, `v2`, ...). Default is False. - - Returns: - Callable[[str], Union[str, None]]: A function that takes a prompt string as input and returns the generated type - signature as a string, or None if the generation fails. - """ - - def generate_type_signature(prompt: str) -> Union[str, None]: - try: - response = client.chat( - messages=[ - { - "role": "system", - "content": get_sys_prompt(pure), - }, - {"role": "user", "content": prompt}, - ], - model=model, - ) - except ResponseError as e: - print(e) - return None - - if isinstance(response, dict) and "message" in response: - message = response["message"] - if isinstance(message, dict) and "content" in message: - content = message["content"] - if isinstance(content, str): - return content - - return None - - return generate_type_signature diff --git a/src/hs_parser/__init__.py b/src/hs_parser/__init__.py deleted file mode 100644 index 79b1982..0000000 --- a/src/hs_parser/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from tree_sitter import Language -import tree_sitter_haskell as ts_haskell - - -HASKELL_LANGUAGE = Language(ts_haskell.language()) diff --git a/src/main.py b/src/main.py new file mode 100755 index 0000000..997bdbc --- /dev/null +++ b/src/main.py @@ -0,0 +1,65 @@ +from os.path import join as pjoin, abspath +import os + +import fire +from orjsonl import orjsonl + +from tfbench import run_one_model, analysis_multi_runs, EvalResult +from tfbench.lm import router + + +def main( + model: str, + effort: str | None = None, + n_repeats: int = 3, + log_file: str = "evaluation_log.jsonl", +): + """Ready-to use evaluation script for a single model. + + Args: + model (str): The model's name, please refer to `tfbench.lm.supported_models` for supported models. + effort (str | None, optional): The effort level to use for evaluation. Defaults to None. + n_repeats (int, optional): The number of times to repeat the evaluation. Defaults to 3. + log_file (str, optional): The file to log results to. Defaults to "evaluation_log.jsonl". + """ + + def _run(pure: bool): + client = router(model, pure, effort) + results: list[EvalResult] = [] + split = "pure" if pure else "base" + result_dir = abspath(pjoin("results", model, split)) + for i in range(n_repeats): + os.makedirs(result_dir, exist_ok=True) + result_file = pjoin(result_dir, f"run-{i}.jsonl") + r = run_one_model( + client, + pure=pure, + output_file=result_file, + ) + results.append(r) + return analysis_multi_runs(results) + + def _eval(pure: bool): + split = "pure" if pure else "base" + print(f"Running {model} on TF-Bench ({split}):") + mean, std = _run(pure=pure) + print(f"Accuracy: {mean:.4f} ± {std:.4f}") + print("====================================") + orjsonl.append( + log_file, + { + "model": model, + "split": split, + "effort": effort, + "n_repeats": n_repeats, + "mean": mean, + "std": std, + }, + ) + + _eval(pure=False) + _eval(pure=True) + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/src/tfbench/__init__.py b/src/tfbench/__init__.py new file mode 100644 index 0000000..87f2ee6 --- /dev/null +++ b/src/tfbench/__init__.py @@ -0,0 +1,22 @@ +from dotenv import load_dotenv + +from .experiment import run_one_model +from .evaluation import ( + EvalResult, + analysis_multi_runs, + prover_evaluate, +) +from .load import load_tfb_from_hf, load_gen_results_jsonl +from .lm import LMAnswer + +load_dotenv(override=True) + +__all__ = [ + "run_one_model", + "EvalResult", + "analysis_multi_runs", + "prover_evaluate", + "load_tfb_from_hf", + "load_gen_results_jsonl", + "LMAnswer", +] diff --git a/src/add_dependency.py b/src/tfbench/add_dependency.py similarity index 84% rename from src/add_dependency.py rename to src/tfbench/add_dependency.py index fd377e0..86e8c61 100644 --- a/src/add_dependency.py +++ b/src/tfbench/add_dependency.py @@ -1,17 +1,17 @@ +import json +from typing import Iterable + import fire -import funcy from funcy_chain import Chain -import logging -import json from dacite import from_dict -from src.common import extract_function_name -from src.hs_parser import HASKELL_LANGUAGE -from src.hs_parser.ast_util import AST -from src.common import BenchmarkTask -from typing import Iterable + +from tfbench.common import extract_function_name +from tfbench.hs_parser import AST +from tfbench.common import BenchmarkTask def build_dependency_dict(tasks: list[BenchmarkTask]) -> dict[str, str]: + """dependency mapping from function names to their signatures""" return { fn_name: t.signature for t in tasks @@ -24,7 +24,7 @@ def get_func_calls(task: BenchmarkTask) -> set[str]: fn_name = extract_function_name(task) assert fn_name is not None - ast = AST(task.code, HASKELL_LANGUAGE) + ast = AST(task.code) root = ast.root calls: list[str] = ( @@ -48,10 +48,11 @@ def get_func_calls(task: BenchmarkTask) -> set[str]: def _is_input(code: str, call: str) -> bool: + """check if a function call is an input to the task""" inputs: list[list[str]] = ( Chain(code.splitlines()) - .filter(lambda l: "=" in l) - .map(lambda l: l.split("=")[0]) + .filter(lambda line: "=" in line) + .map(lambda line: line.split("=")[0]) .map(str.strip) .map(str.split) .value @@ -60,6 +61,8 @@ def _is_input(code: str, call: str) -> bool: def add_dependencies(dependency_dict: dict[str, str]): + """add dependencies to benchmark tasks""" + def add_for_task(task: BenchmarkTask) -> BenchmarkTask: calls: Iterable[str] = get_func_calls(task) calls = filter(lambda c: not _is_input(task.code, c), calls) @@ -73,6 +76,7 @@ def main( input_file: str = "data/source/base-4.20.0.0.jsonl", output_file: str = "out.jsonl", ): + """add dependency to a task json file""" with open(input_file, "r") as fp: tasks: list[BenchmarkTask] = ( Chain(fp.readlines()) diff --git a/src/common.py b/src/tfbench/common.py similarity index 79% rename from src/common.py rename to src/tfbench/common.py index e6338bb..1df74d0 100644 --- a/src/common.py +++ b/src/tfbench/common.py @@ -1,34 +1,12 @@ from dataclasses import dataclass, field import re import copy -from funcy import lmap import sys import io +from funcy import lmap import markdown_to_json -# Default hyper-parameters -MAX_TOKENS = 1024 - -SYSTEM_PROMPT = """ -Act as a static analysis tool for type inference. -ONLY output the type signature. -Do Not Provide any additional commentaries or explanations.""" - -INSTRUCT_PROMPT = """ -Remember that in Haskell: -1. The list type `[a]` is a polymorphic type, defined as `data [] a = [] | (:) a [a]`, -so `(:)` is a constructor for list type. -2. The String type is a list of characters, defined as `type String = [Char]`.""" - -CORE_PROMPT = """ -For polymorphic types variables, you can use type variables like `a`, `b`, `c`, etc. -You should start with `a` and increment the alphabet as needed.""" - -PURE_PROMPT = """ -For polymorphic types variables, you can use type variables like `t1`, `t2`, `t3`, etc. -You should start with `t1` and increment the number as needed.""" - @dataclass class BenchmarkTask: @@ -40,10 +18,16 @@ class BenchmarkTask: def extract_function_name(task: BenchmarkTask) -> str | None: + """get function name from a task + For example, given a task with signature 'foo :: Int -> Int', + it will return 'foo'. + """ return task.signature.split("::")[0].strip() def clean_tab_spaces(task: BenchmarkTask) -> BenchmarkTask: + """remove tab spaces from the code""" + def clean(s: str) -> str: return re.sub(r"[ \t]+", " ", s) @@ -56,6 +40,7 @@ def clean(s: str) -> str: def remove_comments(code: str) -> str: + """remove Haskell comments""" # multi-line # code = re.sub(r"\{\-[\s\S]*?\-\}", "", code) code = re.sub(r"\{\-.*?\-\}", "", code, flags=re.DOTALL) @@ -64,11 +49,6 @@ def remove_comments(code: str) -> str: return code -def get_sys_prompt(pure: bool) -> str: - sys_prompt = SYSTEM_PROMPT + INSTRUCT_PROMPT - sys_prompt += PURE_PROMPT if pure else CORE_PROMPT - return sys_prompt - def remove_return_type(sig: str) -> str: """ @@ -99,9 +79,9 @@ def remove_return_type(sig: str) -> str: # Keep '->' and discard what follows new_type = type_part[: last_arrow_index + 2] return func_part + " " + new_type.rstrip() - else: - # No top-level arrow found; return as-is - return sig + + # No top-level arrow found; return as-is + return sig def get_prompt(task: BenchmarkTask) -> str: diff --git a/src/dataset.py b/src/tfbench/dataset.py similarity index 87% rename from src/dataset.py rename to src/tfbench/dataset.py index 4d2a063..66789f1 100644 --- a/src/dataset.py +++ b/src/tfbench/dataset.py @@ -1,25 +1,24 @@ """main script to build Haskell dataset""" +from enum import IntEnum +import os +import logging +import json + import fire from returns.result import Success, Failure from returns.io import IOResult, IOSuccess, IOFailure from funcy_chain import Chain -import os -import logging -from enum import IntEnum -import json from tqdm import tqdm from funcy import lmap -from src.hs_parser import HASKELL_LANGUAGE -from src.hs_parser.ast_util import AST, HaskellFunction -from src.hs_parser.polymorphism import get_polymorphic_type, PolymorphicType -from src.common import remove_comments + +from tfbench.hs_parser import AST, HaskellFunction +from tfbench.hs_parser.type_util import get_polymorphic_type +from tfbench.common import remove_comments def wrap_repo(s: str) -> str: - # NOTE: this is a placeholder function - # the implementation depends on how the repo is downloaded - # please refer to HMTypes4Py repo + """NOTE: this is a placeholder function""" return s @@ -44,10 +43,11 @@ def collect_hs_files(root: str): def collect_from_file(file_path: str) -> list[dict[str, str]]: + """extract benchmark tasks from a Haskell file""" with open(file_path, "r", errors="replace") as fp: code = fp.read() - ast = AST(code, HASKELL_LANGUAGE) + ast = AST(code) def _to_json(func: HaskellFunction) -> dict[str, str]: func_id = f"{file_path}--{ast.get_fn_name(func.type_signature).value_or(None)}" @@ -66,6 +66,7 @@ def _to_json(func: HaskellFunction) -> dict[str, str]: def collect_from_repo( repo_id: str, repo_root: str, source_root: str ) -> IOResult[int, CollectionErrorCode]: + """Collect all tasks from a Haskell repository""" repo_path = os.path.join(repo_root, wrap_repo(repo_id)) if not os.path.exists(repo_path) or not os.path.isdir(repo_path): return IOFailure(CollectionErrorCode.REPO_NOT_FOUND) @@ -98,8 +99,9 @@ def main( repo_root: str = "data/repos", oroot: str = "data/source", ): + """script to extract benchmark tasks from Haskell repositories""" with open(input_repo_list_path) as fp: - repo_id_list = [l.strip() for l in fp.readlines()] + repo_id_list = [line.strip() for line in fp.readlines()] logging.info(f"Loaded {len(repo_id_list)} repos to be processed") num_func = 0 diff --git a/src/tfbench/env.py b/src/tfbench/env.py new file mode 100644 index 0000000..f0fa17c --- /dev/null +++ b/src/tfbench/env.py @@ -0,0 +1,3 @@ +from dotenv import load_dotenv + +load_dotenv(override=True) # override existing env vars with those in .env diff --git a/src/tfbench/error_analysis.py b/src/tfbench/error_analysis.py new file mode 100644 index 0000000..e5fde3a --- /dev/null +++ b/src/tfbench/error_analysis.py @@ -0,0 +1,121 @@ +from typing import TypedDict, Literal + +from pydantic import BaseModel +from openai import OpenAI + +from .common import get_prompt as get_task_prompt, BenchmarkTask +from .lm import LMAnswer + +PROMPT_TEMPLATE = """ +The Haskell type inference task is as follows: +{task} + +The ground-truth correct answer is: +{correct_answer} + +My incorrect answer is: +{wrong_answer} + +My reasoning behind my answer is: +{reasoning} + +The error message from GHC's type checker is: +{ghc_error} + +What mistake did I make? +""" + +INSTRUCTION = """ +You are a programming language and logic expert. +You will be shown a Haskell type inference task, +an incorrect answer, and the reasoning behind it. +The type signatures should be alpha-equivalent to the ground-truth answer. +Your job is to identify the mistake in the answer, +and classify the mistake in the following category. +The error categories and their definitions are: + +- OverGeneralization: Choose a type that is too general—used broader polymorphism +(e.g., different input/output type variables) where the most general valid type actually requires them to be the same. + +- UnderGeneralization: Added an unnecessary/stronger type-class constraint that is not provided by the implementation, +making the signature more specific than the most general valid type. + +- ArgOrderMismatch: Right type variables but in the wrong parameter order; +the type's argument sequence doesn't match the implementation (a permutation error, not a generality/constraint issue). + +- ArityMismatch: Provided a type with the wrong number of arguments (too many or too few) compared to the implementation. + +- ConstraintError: Used incorrect type-class constraints that don't align with the implementation's requirements. +The wrong type-class constraints were applied to the type variables. + +- SyntaxError: Provided an answer that is not a valid Haskell type signature. + +- InstructionFollowing: Failed to follow the instructions given in the prompt. + +- ResponseError: No answer was provided, or the answer is completely unrelated to the task. + +The prompt asked to only output the type signature, +but the answer contains additional text or explanation. +Choose one category from the above. +Only output the one-word classification. +""" + +ErrorCategories = Literal[ + "OverGeneralization", + "UnderGeneralization", + "ArgOrderMismatch", + "ArityMismatch", + "ConstraintError", + "SyntaxError", + "InstructionFollowing", + "ResponseError", +] + + +class ErrorAnalysisResponse(BaseModel): + category: ErrorCategories + + +def get_error_analysis_prompt( + task: BenchmarkTask, answer: LMAnswer, error_msg: str +) -> str: + """construct classification prompt for one task and answer pair""" + prompt = PROMPT_TEMPLATE.format( + task=get_task_prompt(task), + correct_answer=task.signature, + wrong_answer=answer.answer, + reasoning=answer.reasoning_steps, + ghc_error=error_msg, + ) + return prompt + + +def error_analysis( + client: OpenAI, + task: BenchmarkTask, + answer: LMAnswer | None, + error_msg: str, + model: str = "gpt-5-mini", +) -> ErrorAnalysisResponse: + """classify errors for all incorrect answers in the run_result""" + if answer is None: + return ErrorAnalysisResponse(category="ResponseError") + + response = client.responses.parse( + model=model, + instructions=INSTRUCTION, + input=get_error_analysis_prompt(task, answer, error_msg=error_msg), + reasoning={"effort": "medium"}, + text_format=ErrorAnalysisResponse, + ) + assert response.output_parsed is not None + return response.output_parsed + + +class ErrorAnalysisResult(TypedDict): + model: str + split: Literal["base", "pure"] + task_id: str + ground_truth: str + predicted: str | None + error_category: ErrorCategories diff --git a/src/tfbench/evaluation.py b/src/tfbench/evaluation.py new file mode 100644 index 0000000..82d727c --- /dev/null +++ b/src/tfbench/evaluation.py @@ -0,0 +1,189 @@ +from itertools import starmap +import re +from typing import TypedDict +from multiprocessing import Pool, cpu_count + +import numpy as np +from deprecated import deprecated +from returns.result import Success, Failure, Result + +from .common import BenchmarkTask +from .postprocessing import postprocess, TASK_STRATEGIES, RESPONSE_STRATEGIES +from .lm import LMAnswer +from .ghc import get_prover, ghc_prove_equiv +from .type_def import get_type_defs + + +def tokenize_type_signature(sig: str) -> list[str]: + """ + A minimal tokenizer splitting on spaces, parentheses, commas, '->', [ and ], etc. + This won't handle all Haskell syntax, but might suffice for simpler cases. + + Example: "(a -> b) -> [a] -> [b]" + -> ["(", "a", "->", "b", ")", "->", "[", "a", "]", "->", "[", "b", "]"] + """ + # Notice we've added \[ and \] to the group of delimiters + pattern = r"(\s+|\(|\)|,|\[|\]|->)" + tokens = re.split(pattern, sig) + # Remove empty/whitespace-only tokens + tokens = [t for t in tokens if t.strip()] + return tokens + + +def normalize_type_vars(tokens: list[str]) -> list[str]: + """ + Replace each unique lowercase token that looks like a type variable + with a canonical 'v0', 'v1', etc., in the order encountered. + """ + var_map = {} + next_id = 0 + result = [] + + for tok in tokens: + # Very crude check: type variable starts with lowercase + # (We exclude known symbols like '->', parentheses, brackets, commas, etc.) + if re.fullmatch(r"[a-z]\w*", tok): + if tok not in var_map: + var_map[tok] = f"v{next_id}" + next_id += 1 + result.append(var_map[tok]) + else: + # Non-variable tokens remain as-is + result.append(tok) + + return result + + +@deprecated(reason="Use `ghc_prove_equiv` instead", version="0.1.0") +def alpha_equiv(s1: str, s2: str) -> bool: + """ + Check if two type signatures are 'alpha-equivalent' under + a naive textual approach. + """ + t1 = tokenize_type_signature(s1) + t2 = tokenize_type_signature(s2) + + n1 = normalize_type_vars(t1) + n2 = normalize_type_vars(t2) + + return n1 == n2 + + +@deprecated(reason="Use `prove_one_task` instead", version="0.1.0") +def evaluate_one_task(task: BenchmarkTask, result: LMAnswer | None) -> bool: + """evaluate a single task against its result by alpha equivalence""" + if result is None: + return False + + ground_truth = postprocess(task.signature, TASK_STRATEGIES).strip() + predicted = postprocess(result.answer, RESPONSE_STRATEGIES).strip() + return alpha_equiv(ground_truth, predicted) + + +class EvalResult(TypedDict): + total: int + n_correct: int + accuracy: float + + +@deprecated(reason="Use `prover_evaluate` instead", version="0.1.0") +def evaluate(tasks: list[BenchmarkTask], results: list[LMAnswer | None]) -> EvalResult: + """evaluate all generation results""" + + assert len(tasks) == len(results) + eval_results = starmap(evaluate_one_task, zip(tasks, results)) + n_correct = sum(eval_results) + acc = n_correct / len(tasks) + + return { + "total": len(tasks), + "n_correct": n_correct, + "accuracy": acc, + } + + +def prove_one_task( + task: BenchmarkTask, result: LMAnswer | None, pure: bool = False +) -> Result[None, str]: + """prove two type signatures are equivalent using GHC""" + if result is None: + return Failure("Generation Failed") + + predicted_body = postprocess(result.answer, RESPONSE_STRATEGIES).strip() + predicted = f"f :: {predicted_body}" + defs = get_type_defs(task) if pure else [] + # only failing case from get_prover is syntax error of generated type signature + equiv = ( + get_prover(task.signature, predicted, defs) + .alt(lambda _: "Syntax Error: Tree-Sitter Parsing Failed") + .bind(ghc_prove_equiv) + ) + return equiv + + +def prover_evaluate( + tasks: list[BenchmarkTask], + results: list[LMAnswer | None], + pure: bool = False, + nproc: int = cpu_count(), +) -> EvalResult: + """evaluate all generation results using GHC to prove equivalence + + NOTE: currently only support the `base` split + + Args: + tasks (list[BenchmarkTask]): list of benchmark tasks + results (list[LMAnswer | None]): list of generation results + pure (bool, optional): whether to evaluate on the `pure` split or not. + Since we use TypeOperators to *prove type equivalence, + we need to define all custom types in the `pure` split. + Defaults to False. + nproc (int, optional): number of processes to use. + Defaults to cpu_count() to use all available CPUs. + """ + assert len(tasks) == len(results) + + with Pool(processes=nproc) as pool: + eval_results = pool.starmap( + prove_one_task, zip(tasks, results, [pure] * len(tasks)) + ) + + n_correct = sum(1 for r in eval_results if isinstance(r, Success)) + acc = n_correct / len(tasks) + + return { + "total": len(tasks), + "n_correct": n_correct, + "accuracy": acc, + } + + +def analysis_multi_runs(results: list[EvalResult]) -> tuple[float, float]: + """calculate mean and std of accuracy of multiple runs""" + accs = list(map(lambda r: r["accuracy"], results)) + return np.mean(accs).item(), np.std(accs).item() + + +def get_incorrect( + tasks: list[BenchmarkTask], + results: list[LMAnswer | None], + pure: bool = False, + nproc: int = cpu_count(), +) -> list[tuple[BenchmarkTask, LMAnswer | None, str]]: + """Get a list of tasks that were incorrectly answered.""" + + assert len(tasks) == len(results) + + with Pool(processes=nproc) as pool: + eval_results = pool.starmap( + prove_one_task, zip(tasks, results, [pure] * len(tasks)) + ) + + incorrect = [] + for task, result, eval_result in zip(tasks, results, eval_results): + match eval_result: + case Success(_): + continue + case Failure(message): + incorrect.append((task, result, message)) + return incorrect diff --git a/src/tfbench/experiment.py b/src/tfbench/experiment.py new file mode 100644 index 0000000..b4fc7e8 --- /dev/null +++ b/src/tfbench/experiment.py @@ -0,0 +1,45 @@ +""" +Experiment script +""" + +from tqdm import tqdm +from orjsonl import orjsonl + +from .common import get_prompt +from .evaluation import prover_evaluate, EvalResult +from .lm import LMAnswer, LM +from .load import load_tfb_from_hf + + +def run_one_model( + client: LM, + pure: bool = False, + output_file: str | None = None, +) -> EvalResult: + """Running the generation & evaluation pipeline for one pre-supported model + + Args: + client (LM): some LM client wrapper to use `generate` + pure (bool, optional): To evaluate on the `pure` split or not. Defaults to False. + output_file (str | None, optional): The file to save generation result. Defaults to None. + Warning: If None, generation results will not be saved to disk. + effort (str | None, optional): Reasoning effort. Defaults to None. + Warning: Different model handles None(default) effort differently. + + Returns: + EvalResult: evaluation result including accuracy + """ + + tasks = load_tfb_from_hf("pure" if pure else "base") + gen_results: list[LMAnswer | None] = [] + for task in tqdm(tasks, desc=client.model_name): + prompt = get_prompt(task) + + response = client.generate(prompt) + r: LMAnswer | None = response.value_or(None) + gen_results.append(r) + if output_file: + orjsonl.append(output_file, r if r else {"error": str(response.failure())}) + + eval_acc = prover_evaluate(tasks, gen_results, pure=pure) + return eval_acc diff --git a/src/tfbench/ghc.py b/src/tfbench/ghc.py new file mode 100644 index 0000000..7e2210a --- /dev/null +++ b/src/tfbench/ghc.py @@ -0,0 +1,107 @@ +"""Util functions for using GHC""" + +import os +import tempfile +import subprocess +from string import Template # NOTE: use t-string after py3.14 +from returns.result import Result, Success, Failure, safe + +from .hs_parser import get_type_vars, get_type_constraints + + +def ghc_prove_equiv(code: str) -> Result[None, str]: + """let GHC typecheck the given code snippet by compiling it + + Args: + code: Haskell code snippet to typecheck + Returns: + Success(None) if typechecks, Failure(error_message) otherwise + """ + file_name = "Check.hs" + with tempfile.TemporaryDirectory() as tmpdir: + path = os.path.join(tmpdir, file_name) + with open(path, "w") as f: + f.write(code) + # -fno-code = typecheck only; -v0 = quiet + process = subprocess.run( + ["ghc", "-fno-code", "-v0", file_name], + capture_output=True, + text=True, + cwd=tmpdir, + ) + + if process.returncode == 0: + return Success(None) + + return Failure(process.stderr) + + +PROVER = Template( + """ +{-# LANGUAGE TypeOperators #-} +{-# LANGUAGE ImpredicativeTypes #-} +module Check where + +import Data.Type.Equality + +-- Some predefined types synonyms to avoid name clashes +type Int_ = Int +type Bool_ = Bool +type Char_ = Char +type Float_ = Float +type Double_ = Double +data Natural = Natural + +$new_types + +type TRUTH $truth_vars = $truth_signature +type ANSWER $answer_vars = $answer_signature + +proof :: TRUTH $truth_vars :~: ANSWER $truth_vars +proof = Refl +""" +) + + +def _get_var_str(source_code: str) -> str: + ty_vars = get_type_vars(source_code) + return " ".join(ty_vars) + + +def _get_body_str(source_code: str) -> str: + assert "::" in source_code, "invalid type signature" + return source_code.split("::", 1)[1].strip() + + +def reorder_constraints(type_signature: str) -> str: + """Reorder type classes constrains in a type signature to a canonical order""" + # No type classes to reorder + if "=>" not in type_signature: + return type_signature + + assert "::" in type_signature, "invalid type signature" + prefix, body = type_signature.split("::", 1) + + _, rest = body.split("=>", 1) + constrains = get_type_constraints(type_signature) + constrains.sort() # Sort alphabetically + return f"{prefix}:: ({', '.join(constrains)}) => {rest}" + + +@safe +def get_prover( + ground_truth: str, answer: str, types_defs: list[str] | None = None +) -> str: + """Construct Prover program based on Haskell TypeOperators and Type.Equality, + If this prover compiles, then the answer is equivalent to the ground truth. + """ + types_defs = types_defs or [] + ground_truth = reorder_constraints(ground_truth) + answer = reorder_constraints(answer) + return PROVER.substitute( + new_types="\n".join(types_defs), + truth_vars=_get_var_str(ground_truth), + truth_signature=_get_body_str(ground_truth), + answer_vars=_get_var_str(answer), + answer_signature=_get_body_str(answer), + ) diff --git a/src/hoogle.py b/src/tfbench/hoogle.py similarity index 91% rename from src/hoogle.py rename to src/tfbench/hoogle.py index f5ed3fb..0cc578c 100644 --- a/src/hoogle.py +++ b/src/tfbench/hoogle.py @@ -1,23 +1,25 @@ # importing the requests library from io import TextIOWrapper -from src.hs_parser.ast_util import AST import json +from functools import lru_cache +from urllib.parse import quote + +import requests from dacite import from_dict import fire -from funcy_chain import Chain -import requests -from urllib.parse import quote -from src.common import BenchmarkTask, extract_function_name -from src.hs_parser import HASKELL_LANGUAGE -from functools import lru_cache from tree_sitter import Node from funcy import lmap -from src.manual import MANUAL_TASKS +from funcy_chain import Chain + +from tfbench.common import BenchmarkTask, extract_function_name +from tfbench.hs_parser import AST +from tfbench.manual import MANUAL_TASKS -def get_all_first_child(ast: AST, type: str) -> list[Node]: +def get_all_first_child(ast: AST, node_type: str) -> list[Node]: + """Extract the first nodes of a specific type""" children: list[Node] = lmap( - lambda node: node.child(0), ast.get_all_nodes_of_type(ast.root, type) + lambda node: node.child(0), ast.get_all_nodes_of_type(ast.root, node_type) ) return children @@ -26,7 +28,7 @@ def generate_variable_banlist(code: str): """ Generates list of variables that are already defined in the code """ - ast = AST(code, HASKELL_LANGUAGE) + ast = AST(code) root = ast.root # Remove variables that are already defined in the code @@ -58,7 +60,7 @@ def get_where_blacklist(task: BenchmarkTask) -> set[str]: where_index = task.code.index("where") where_code = task.code[(where_index + 5) :].strip() - ast = AST(where_code, HASKELL_LANGUAGE) + ast = AST(where_code) root = ast.root ban_list: list[str] = generate_variable_banlist(where_code) @@ -84,7 +86,7 @@ def get_func_calls(task: BenchmarkTask) -> set[str]: assert fn_name is not None print(f"Function: {fn_name}") - ast = AST(task.code, HASKELL_LANGUAGE) + ast = AST(task.code) root = ast.root variables: list[str] = ( @@ -141,7 +143,7 @@ def get_func_calls(task: BenchmarkTask) -> set[str]: filtered_final_list = ( Chain(final_list) .filter(lambda d: not (len(d.strip("'")) == 1 and d.strip("'").isalnum())) - .filter(lambda d: not (len(d) == 0)) + .filter(lambda d: len(d) != 0) .filter( lambda d: d not in ["(:)", "otherwise", "[]", "_", "xs", "ys", "return"] ) @@ -171,7 +173,7 @@ def add_dependencies(task: BenchmarkTask, banned_fp: TextIOWrapper) -> Benchmark str_sig = str(sig) if ( depedencies[i] == fn_name - or sig == None + or sig is None or "::" not in str_sig or "data " in str_sig ): @@ -188,7 +190,7 @@ def add_dependencies(task: BenchmarkTask, banned_fp: TextIOWrapper) -> Benchmark # Set the type signature type_signature[i] = str_sig task.dependencies = list(set(type_signature)) - print(f"Status: Valid\n") + print("Status: Valid\n") return task @@ -201,10 +203,10 @@ def get_type_signature(name: str) -> str | None: # Format using quote and strip url_string = quote(name.strip("()")) # api-endpoint - URL = f"https://hoogle.haskell.org?mode=json&format=text&hoogle={url_string}+is%3Aexact&start=1&count=1" + url = f"https://hoogle.haskell.org?mode=json&format=text&hoogle={url_string}+is%3Aexact&start=1&count=1" # sending get request to get hoogle result - r = requests.get(url=URL) + r = requests.get(url=url, timeout=60) # extracting data in json format data = r.json() @@ -222,7 +224,7 @@ def main( output_file: str = "out.json", banned_file: str = "banned.txt", ): - banned_fp = open(banned_file, "w") + """mean script for dependency-solving using Hoogle""" # For reading json files (Benchmark-F.json) with open(input_file, "r") as fp: tasks: Chain = Chain(json.load(fp)).map( @@ -238,7 +240,8 @@ def main( # ) # Generate dependencies - tasks_w_dep = tasks.map(lambda d: add_dependencies(d, banned_fp)) + with open(banned_file, "w") as banned_fp: + tasks_w_dep = tasks.map(lambda d: add_dependencies(d, banned_fp)) # Remove any tasks with dependencies that could not be found # Also transform them back into dictionaries for json format diff --git a/src/tfbench/hs_parser/__init__.py b/src/tfbench/hs_parser/__init__.py new file mode 100644 index 0000000..77ea621 --- /dev/null +++ b/src/tfbench/hs_parser/__init__.py @@ -0,0 +1,21 @@ +from .ast_util import AST, HASKELL_LANGUAGE, HaskellFunction +from .type_util import ( + PolymorphicType, + get_polymorphic_type, + to_type_node, + get_type_vars, + get_type_constraints, +) +from .extractor import TypeExtractor + +__all__ = [ + "AST", + "HASKELL_LANGUAGE", + "HaskellFunction", + "PolymorphicType", + "get_polymorphic_type", + "to_type_node", + "get_type_vars", + "get_type_constraints", + "TypeExtractor", +] diff --git a/src/hs_parser/ast_util.py b/src/tfbench/hs_parser/ast_util.py similarity index 92% rename from src/hs_parser/ast_util.py rename to src/tfbench/hs_parser/ast_util.py index 2f74252..f4ce11c 100644 --- a/src/hs_parser/ast_util.py +++ b/src/tfbench/hs_parser/ast_util.py @@ -1,9 +1,13 @@ +from typing import Optional +from dataclasses import dataclass + from tree_sitter import Language, Parser, Tree, Node +import tree_sitter_haskell as ts_haskell from returns.maybe import Maybe, Nothing, Some -from dataclasses import dataclass from funcy_chain import Chain from funcy import lmap -from typing import Optional + +HASKELL_LANGUAGE = Language(ts_haskell.language()) @dataclass @@ -34,7 +38,7 @@ def from_pair(p: tuple[Node, list[Node]]): class AST: """Helper class to build, read, and manipulate ASTs using tree-sitter.""" - def __init__(self, source_code: str, lang: Language) -> None: + def __init__(self, source_code: str) -> None: """ Initializes an AST object with the given source code and language. @@ -44,7 +48,7 @@ def __init__(self, source_code: str, lang: Language) -> None: """ self.src = source_code self.parser = Parser() - self.parser.language = lang # Directly assign the language + self.parser.language = HASKELL_LANGUAGE self.tree: Tree = self.parser.parse(bytes(self.src, "utf8")) @property @@ -95,19 +99,6 @@ def get_fn_name(self, node: Node) -> Maybe[str]: return Nothing return Some(fn_name.strip()) - def get_fn_docstring(self, node: Node) -> Maybe[str]: - """ - Retrieves the docstring associated with a function node. - - Args: - node (Node): The AST node representing a function. - - Returns: - Maybe[str]: A Maybe containing the docstring if found, or Nothing otherwise. - """ - # todo: implement docstring finder - raise NotImplementedError - def func2src(self, func: HaskellFunction) -> tuple[str, str]: """ Converts a `HaskellFunction` object into its corresponding type signature and code source. @@ -202,10 +193,10 @@ def get_all_nodes_of_type( nodes: list[Node] = [] if max_level == 0: return nodes + if node_type is None or root.type == node_type: + nodes.append(root) for child in root.children: - if node_type is None or child.type == node_type: - nodes.append(child) nodes += AST.get_all_nodes_of_type( child, node_type, max_level=max_level - 1 ) @@ -240,6 +231,7 @@ def get_all_nodes_of_name( @staticmethod def get_nodes_start_bytes(nodes: list[Node]) -> dict[str, int]: + """Get the start byte positions of nodes by their names.""" start_bytes: dict[str, int] = {} for node in nodes: diff --git a/src/tfbench/hs_parser/extractor.py b/src/tfbench/hs_parser/extractor.py new file mode 100644 index 0000000..e92122b --- /dev/null +++ b/src/tfbench/hs_parser/extractor.py @@ -0,0 +1,124 @@ +from collections import defaultdict, Counter +from dataclasses import dataclass +from tree_sitter import Node +from .ast_util import AST + + +class TypeExtractor(AST): + """Static analyzer for Haskell type signatures. + NOTE: this analyzer works on the body of a type signature only, + i.e. the part after the `=>` symbol if it has constraints, + or otherwise after the `::` symbol. + The constraints (if any) are handled in other modules. + """ + + def __init__(self, code: str): + super().__init__(code) + self.constructors: dict[str, Counter] = defaultdict(Counter) + self.names: set[str] = set() + + self._analysis_types() + + @property + def type_constructors(self) -> dict[str, int]: + """Get a mapping of type constructor names to their maximum observed arity (i.e. number of parameters).""" + return {k: max(v.keys()) for k, v in self.constructors.items()} + + def _analysis_types(self): + """analysis types in the function signature to fill out self.constructors and self.names""" + sigs = self.get_all_nodes_of_type(self.root, "signature") + functions = self.get_all_nodes_of_type(sigs[0], "function") + if len(functions) > 0: + self._visit(functions[0]) + + def _collect_from_tuple(self, node: Node): + # record tuple arity if you care: arity = count of element children + # then continue walking children + for ch in node.named_children: + self._visit(ch) + + def _visit(self, n: Node): + t = n.type + + if t == "apply": + # Count this application chain once, at the top-most 'apply' only. + parent = n.parent + if not ( + parent + and parent.type == "apply" + and parent.child_by_field_name("constructor") is n + ): + apply_chain = _peel_apply_chain(n) + ctor_name = self.get_src_from_node(apply_chain.constructor) + self.constructors[ctor_name][apply_chain.arity] += 1 + # Recurse into children so we also catch nested names/applications. + for ch in n.named_children: + self._visit(ch) + return + + if t == "constructor": + # Zero-arity constructor occurrence (e.g., `Int`) not part of an apply + parent = n.parent + if not ( + parent + and parent.type == "apply" + and parent.child_by_field_name("constructor") is n + ): + name_node = n.child_by_field_name("name") or ( + n.named_children[0] if n.named_children else None + ) + if name_node: + constructor_name = self.get_src_from_node(name_node) + self.constructors[constructor_name][0] += 1 + # still walk inside + for ch in n.named_children: + self._visit(ch) + return + + if t == "tuple": + self._collect_from_tuple(n) + return + + if t == "name": + # Treat as a plain type variable/name when not under a constructor role. + p = n.parent + # If its parent is 'constructor', it's part of a constructor; skip here. + if p is None or p.type != "constructor": + self.names.add(self.get_src_from_node(n)) + return + + # default: recurse + for ch in n.named_children: + self._visit(ch) + + +@dataclass +class TypeApplyChain: + constructor: Node + arity: int + arguments: list[Node] + + +def _peel_apply_chain(node: Node) -> TypeApplyChain: + """ + Given an (apply ...) subtree, walk left through nested apply nodes to + find the root constructor name and count how many arguments were applied. + # Returns (arity, arg_nodes_list, constructor_node). + """ + args = [] + arity = 0 + cur = node + while cur.type == "apply": + arity += 1 + arg = cur.child_by_field_name("argument") + if arg is not None: + args.append(arg) + # could be 'constructor' or another 'apply' + next_level = cur.child_by_field_name("constructor") + if not next_level: + break + cur = next_level + + # now cur is either a 'constructor' node or a 'name' (rare) + ctor_node = cur + return TypeApplyChain(constructor=ctor_node, arity=arity, arguments=args) diff --git a/src/hs_parser/polymorphism.py b/src/tfbench/hs_parser/type_util.py similarity index 51% rename from src/hs_parser/polymorphism.py rename to src/tfbench/hs_parser/type_util.py index 4ef2073..1830e2e 100644 --- a/src/hs_parser/polymorphism.py +++ b/src/tfbench/hs_parser/type_util.py @@ -1,5 +1,6 @@ from enum import Enum from tree_sitter import Node +from funcy_chain import Chain from .ast_util import AST @@ -20,6 +21,7 @@ class PolymorphicType(str, Enum): def to_type_node(type_signature: Node) -> Node: + """Get the type node from a type signature.""" type_nodes = type_signature.children_by_field_name("type") assert len(type_nodes) == 1, "each type signature should has only 1 `type` child" return type_nodes[0] @@ -47,3 +49,47 @@ def get_polymorphic_type(type_signature: Node) -> PolymorphicType: return PolymorphicType.PARAMETRIC return PolymorphicType.MONO + + +def get_type_vars(source_code: str) -> list[str]: + """extract type variables from a type signature source code. + + NOTE: since GHC proves the `forall` quantification of type variables, + the order of type variables does not really matter + as long as they are **consistent**. + + Args: + source_code (str): the source code of the type signature + + Returns: + list[str]: type variables + """ + ast = AST(source_code=source_code) + sig = ast.get_all_nodes_of_type(ast.root, "signature")[0] + type_node = to_type_node(sig) + + ty_vars = [ + ast.get_src_from_node(n) + for n in ast.get_all_nodes_of_type(type_node, "variable") + ] + return list(dict.fromkeys(ty_vars)) # remove duplicates while preserving order + + +def get_type_constraints(source_code: str) -> list[str]: + """extract type class constraints from a type signature source code""" + assert "=>" in source_code, "no type class constraints found" + + ast = AST(source_code) + signature = ast.get_all_nodes_of_type(ast.root, "signature")[0] + + # context node is the body of type signature + context = ast.get_all_nodes_of_type(signature, "context")[0] + + type_constrains: list[str] = ( + Chain(ast.get_all_nodes_of_type(context.children[0], "apply")) + .map(ast.get_src_from_node) + .map(str.strip) + .filter(lambda c: c[0].isupper()) + .value + ) + return type_constrains diff --git a/src/tfbench/lm/__init__.py b/src/tfbench/lm/__init__.py new file mode 100644 index 0000000..70bea06 --- /dev/null +++ b/src/tfbench/lm/__init__.py @@ -0,0 +1,49 @@ +import logging + +from .prompts import get_sys_prompt +from .settings import MAX_TOKENS +from ._openai import ( + OAI_MODELS, + OAI_TTC_MODELS, + OAI_O5, + OpenAIChatCompletion, + OpenAIResponses, +) +from ._google import GEMINI_MODELS, GEMINI_TTC_MODELS, GeminiChat, GeminiReasoning +from ._anthropic import CLAUDE_MODELS, CLAUDE_TTC_MODELS, ClaudeChat, ClaudeReasoning +from ._ollama import OLLAMA_TTC_MODELS, OllamaChat +from ._hf import HFChat +from ._types import LM, LMAnswer +from .utils import router, extract_response + +logging.getLogger("openai").setLevel(logging.ERROR) +logging.getLogger("httpx").setLevel(logging.ERROR) + +supported_models = ( + OAI_MODELS + + OAI_TTC_MODELS + + OAI_O5 + + GEMINI_MODELS + + GEMINI_TTC_MODELS + + CLAUDE_MODELS + + CLAUDE_TTC_MODELS + + OLLAMA_TTC_MODELS +) + +__all__ = [ + "get_sys_prompt", + "MAX_TOKENS", + "LMAnswer", + "LM", + "OpenAIChatCompletion", + "OpenAIResponses", + "GeminiChat", + "GeminiReasoning", + "ClaudeChat", + "ClaudeReasoning", + "OllamaChat", + "HFChat", + "router", + "extract_response", + "supported_models", +] diff --git a/src/tfbench/lm/_anthropic.py b/src/tfbench/lm/_anthropic.py new file mode 100644 index 0000000..f799c86 --- /dev/null +++ b/src/tfbench/lm/_anthropic.py @@ -0,0 +1,103 @@ +import os +from anthropic import Anthropic +from ._types import LM, LMAnswer, ReasoningEffort, EFFORT_TOKEN_MAP, NoneResponseError +from .settings import MAX_TOKENS + +CLAUDE_MODELS = [ + "claude-3-opus-20240229", + "claude-3-sonnet-20240229", + "claude-3-haiku-20240307", + "claude-3-5-sonnet-20240620", + "claude-3-5-sonnet-20241022", + "claude-3-5-haiku-20241022", +] + +CLAUDE_TTC_MODELS = [ + "claude-3-7-sonnet-20250219", + "claude-opus-4-1-20250805", + "claude-opus-4-20250514", + "claude-sonnet-4-20250514", +] + + +class ClaudeChat(LM): + """API client for Claude models. + + SDK wrapper for legacy Claude models with no reasoning. + """ + + def __init__(self, model_name: str, pure: bool = False): + """Initialize the Claude model client.""" + super().__init__(model_name=model_name, pure=pure) + + api_key = os.getenv("ANTHROPIC_API_KEY") + assert api_key, "Please set ANTHROPIC_API_KEY in environment!" + self.client = Anthropic(api_key=api_key) + + def _gen(self, prompt: str) -> LMAnswer: + """Generate using Anthropic's Chat Completions API.""" + message = self.client.messages.create( + system=self.instruction, + messages=[ + {"role": "user", "content": prompt}, + ], + model=self.model_name, + max_tokens=MAX_TOKENS, + ) + + text = None + for c in message.content: + if c.type == "text": + text = c.text + break + + if text is None: + raise NoneResponseError(self.model_name) + + return LMAnswer(answer=text) + + +class ClaudeReasoning(LM): + """API client for Claude models.""" + + def __init__( + self, model_name: str, pure: bool = False, effort: ReasoningEffort | None = None + ): + """Initialize the Claude model client.""" + super().__init__(model_name=model_name, pure=pure) + + api_key = os.getenv("ANTHROPIC_API_KEY") + assert api_key, "Please set ANTHROPIC_API_KEY in environment!" + self.client = Anthropic(api_key=api_key) + + self.effort: ReasoningEffort = effort or "medium" + + def _gen(self, prompt: str) -> LMAnswer: + """Generate using Anthropic's Chat Completions API.""" + rsn_tokens = EFFORT_TOKEN_MAP[self.effort] + message = self.client.messages.create( + system=self.instruction, + messages=[ + {"role": "user", "content": prompt}, + ], + thinking={ + "type": "enabled", + "budget_tokens": rsn_tokens, + }, + model=self.model_name, + max_tokens=MAX_TOKENS + rsn_tokens, + ) + text = None + thinking = None + for block in message.content: + if block.type == "text": + text = block.text + if block.type == "thinking": + thinking = block.thinking + if text and thinking: + break + + if text is None: + raise NoneResponseError(self.model_name) + + return LMAnswer(answer=text, reasoning_steps=thinking) diff --git a/src/tfbench/lm/_google.py b/src/tfbench/lm/_google.py new file mode 100644 index 0000000..2142d4a --- /dev/null +++ b/src/tfbench/lm/_google.py @@ -0,0 +1,123 @@ +"""Inference helper for Google Gemini""" + +from typing import Literal +import os + +from google import genai +from google.genai.types import GenerateContentConfig, ThinkingConfig + +from ._types import LM, LMAnswer, ReasoningEffort, EFFORT_TOKEN_MAP, NoneResponseError + +GEMINI_MODELS = [ + "gemini-2.0-flash", + "gemini-2.0-flash-lite", + # ! gemini-1.5 models are deprecated + # "gemini-1.5-flash", + # "gemini-1.5-flash-8b", + # "gemini-1.5-pro", +] + +GEMINI_TTC_MODELS = [ + "gemini-2.5-pro", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", + # ! gemini-2.5-preview models are deprecated + # "gemini-2.5-flash-preview-04-17", + # "gemini-2.5-pro-preview-03-25", +] + +GeminiReasoningEffort = ReasoningEffort | Literal["dynamic"] +# Upcast to a Mapping with the *wider* key type +GEMINI_MAP: dict[str, int] = { + **EFFORT_TOKEN_MAP, + "dynamic": -1, +} + + +class GeminiChat(LM): + """Wrapper class for `google-genai` SDK for chat models + NOTE: this class is only used for legacy Gemini models (2.0-flash) that cannot "think". + + For reasoning models with "thinking" disabled, please use `GeminiReasoning` + with `reasoning_effort` set to "off". + We keep this implementation since there are default thinking config for each model, + and `thinking_config=None` has different behavior with `thinking_budget=0`. + """ + + def __init__(self, model_name: str, pure: bool = False): + super().__init__(model_name=model_name, pure=pure) + + api_key = os.getenv("GEMINI_API_KEY") + assert api_key, "Please set GEMINI_API_KEY in environment!" + self.client = genai.Client(api_key=api_key) + + def _gen(self, prompt: str) -> LMAnswer: + """generation function for legacy Gemini models (2.0)""" + + response = self.client.models.generate_content( + model=self.model_name, + contents=prompt, + config=GenerateContentConfig( + system_instruction=[self.instruction], + ), + ) + content = response.text + if content is None: + raise NoneResponseError(self.model_name) + return LMAnswer(answer=content) + + +class GeminiReasoning(LM): + """Wrapper class for `google-genai` SDK for reasoning models""" + + def __init__( + self, + model_name: str, + pure: bool = False, + effort: GeminiReasoningEffort | None = None, + ): + """Initialize the GeminiReasoning model. + effort (ReasoningEffort, optional): Defaults to None, which is `dynamic`. + """ + + super().__init__(model_name=model_name, pure=pure) + + api_key = os.getenv("GEMINI_API_KEY") + assert api_key, "Please set GEMINI_API_KEY in environment!" + self.client = genai.Client(api_key=api_key) + + # default to dynamic if effort is None + self.effort = effort or "dynamic" + + def _gen(self, prompt: str) -> LMAnswer: + + response = self.client.models.generate_content( + model=self.model_name, + contents=prompt, + config=GenerateContentConfig( + system_instruction=[self.instruction], + thinking_config=ThinkingConfig( + thinking_budget=GEMINI_MAP[self.effort], + include_thoughts=True, + ), + ), + ) + + if not response.candidates: + raise ValueError("No candidates returned from Gemini model.") + + candidate = response.candidates[0] + if not candidate.content or not candidate.content.parts: + raise NoneResponseError(self.model_name) + + answer = "" + thinking = "" + for part in candidate.content.parts: + if not part.text: + continue + if part.thought: + thinking += part.text + else: + answer += part.text + + return LMAnswer(answer=answer, reasoning_steps=thinking) diff --git a/src/tfbench/lm/_hf.py b/src/tfbench/lm/_hf.py new file mode 100644 index 0000000..711aeab --- /dev/null +++ b/src/tfbench/lm/_hf.py @@ -0,0 +1,53 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer + +from ._types import LM, LMAnswer + + +def extract_thinking_content(output: str) -> tuple[str, str | None]: + """Extract the thinking content and the final answer from the model output. + based on and tags. + + Args: + output (str): The model output. + Returns: + tuple[str, str | None]: The thinking content and the final answer. + """ + if "" in output and "" in output: + thinking_content = output.split("")[1].split("")[0].strip() + content = output.split("")[-1].strip() + return content, thinking_content + + return output, None + + +class HFChat(LM): + + def __init__(self, model_name: str, pure: bool = False): + super().__init__(model_name=model_name, pure=pure) + + # load the tokenizer and the model + self.tokenizer = AutoTokenizer.from_pretrained(model_name) + self.model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype="auto", device_map="auto" + ) + + def _gen(self, prompt: str) -> LMAnswer: + messages = [ + {"role": "system", "content": self.instruction}, + {"role": "user", "content": prompt}, + ] + text = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + + model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device) + + # conduct text completion + generated_ids = self.model.generate(**model_inputs, max_new_tokens=32768) + output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist() + output = self.tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n") + + content, thinking_content = extract_thinking_content(output) + return LMAnswer(answer=content, reasoning_steps=thinking_content) diff --git a/src/tfbench/lm/_ollama.py b/src/tfbench/lm/_ollama.py new file mode 100644 index 0000000..9e848e6 --- /dev/null +++ b/src/tfbench/lm/_ollama.py @@ -0,0 +1,41 @@ +from ollama import chat, ChatResponse + +from ._types import LM, LMAnswer + +OLLAMA_TTC_MODELS = [ + "qwen3:30b", + "qwen3:32b", + "qwen3:235b", + "gpt-oss:20b", + "gpt-oss:120b", + "deepseek-r1:32b", + "deepseek-r1:70b", + "magistral:24b", +] + + +class OllamaChat(LM): + """Generate using Ollama's chat API,""" + + def __init__(self, model_name: str, pure: bool = False): + super().__init__(model_name=model_name, pure=pure) + + def _gen(self, prompt: str) -> LMAnswer: + response: ChatResponse = chat( + model=self.model_name, + messages=[ + { + "role": "system", + "content": self.instruction, + }, + { + "role": "user", + "content": prompt, + }, + ], + think=True, + ) + return LMAnswer( + answer=response.message.content, # type: ignore + reasoning_steps=response.message.thinking, # type: ignore + ) diff --git a/src/tfbench/lm/_openai.py b/src/tfbench/lm/_openai.py new file mode 100644 index 0000000..e36cb9f --- /dev/null +++ b/src/tfbench/lm/_openai.py @@ -0,0 +1,125 @@ +import os + +from openai import OpenAI, NOT_GIVEN +from openai.types.shared.reasoning_effort import ReasoningEffort +from openai.types.responses.response import Response + +from ._types import LM, LMAnswer, NoneResponseError + +OAI_MODELS = [ + "gpt-3.5-turbo-0125", + "gpt-4-turbo-2024-04-09", + "gpt-4o-2024-11-20", + "gpt-4o-mini-2024-07-18", +] + +OAI_TTC_MODELS = [ + "o1-mini-2024-09-12", + "o1-preview-2024-09-12", + "o1-2024-12-17", + "o3-mini-2025-01-31", + "o3-2025-04-16", + "o4-mini-2025-04-16", +] + +OAI_O5 = [ + "gpt-5-2025-08-07", + "gpt-5-mini-2025-08-07", + "gpt-5-nano-2025-08-07", + "gpt-5-chat-latest", +] + + +class OpenAIChatCompletion(LM): + """ + Generate using OpenAI SDK's Chat Completions API. + This is the previous standard (supported indefinitely). + This client should be only used for legacy non-reasoning models, + i.e., (gpt-3.5-turbo, gpt-4-turbo, gpt-4o). + """ + + def __init__(self, model_name: str, pure: bool = False): + super().__init__(model_name=model_name, pure=pure) + + api_key = os.getenv("OPENAI_API_KEY") + assert api_key, "Please set OPENAI_API_KEY in environment!" + self.client = OpenAI(api_key=api_key) + + def _gen(self, prompt: str) -> LMAnswer: + """Generate using OpenAI SDK's Chat Completions API.""" + completion = self.client.chat.completions.create( + messages=[ + { + "role": "developer", + "content": self.instruction, + }, + {"role": "user", "content": prompt}, + ], + model=self.model_name, + ) + + content = completion.choices[0].message.content + if content is None: + raise NoneResponseError(self.model_name) + + return LMAnswer( + answer=content, + ) + + +class OpenAIResponses(LM): + """Client for New OpenAI Responses API. + This should be used for newer *reasoning* models, + especially GPT-5. + """ + + def __init__( + self, + model_name: str, + pure: bool = False, + effort: ReasoningEffort = None, + ): + """OpenAI Responses SDK Client + If `effort` is None (not provided), we disable reasoning mode. + """ + super().__init__(model_name=model_name, pure=pure) + + api_key = os.getenv("OPENAI_API_KEY") + assert api_key, "Please set OPENAI_API_KEY in environment!" + self.client = OpenAI(api_key=api_key) + + self.effort: ReasoningEffort = effort + + def _gen(self, prompt: str) -> LMAnswer: + """Generate using OpenAI SDK's Responses API.""" + + response = self.client.responses.create( + model=self.model_name, + instructions=self.instruction, + input=prompt, + reasoning=( + {"effort": self.effort, "summary": "detailed"} + if self.effort + else NOT_GIVEN + ), + ) + return LMAnswer( + answer=response.output_text, + reasoning_steps=_reasoning_summary(response), + ) + + +def _reasoning_summary(response: Response) -> str: + """helper function to extract response from OpenAI Response API, + implementation follows `openai/types/responses/response.py#L275` + for format, please see + https://platform.openai.com/docs/guides/reasoning?api-mode=responses#reasoning-summaries + """ + texts: list[str] = [] + for output in response.output: + if output.type == "reasoning" and output.summary is not None: + for summary in output.summary: + if summary.type == "summary_text": + texts.append(summary.text) + + return "".join(texts) diff --git a/src/tfbench/lm/_types.py b/src/tfbench/lm/_types.py new file mode 100644 index 0000000..6d739d9 --- /dev/null +++ b/src/tfbench/lm/_types.py @@ -0,0 +1,53 @@ +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Literal + +from returns.result import safe +from tenacity import ( + retry, + stop_after_attempt, + wait_random_exponential, +) # for exponential backoff + +from .prompts import get_sys_prompt + + +@dataclass +class LMAnswer: + answer: str + reasoning_steps: str | None = None + + +class LM(ABC): + def __init__(self, model_name: str, pure: bool = False): + self.model_name = model_name + self.pure = pure + self.instruction = get_sys_prompt(self.pure) + + @safe + @retry(wait=wait_random_exponential(min=1, max=10), stop=stop_after_attempt(3)) + def generate(self, prompt: str) -> LMAnswer: + """Generate a response for the given prompt. + We use factory mode to for @safe and @retry, + see https://www.oodesign.com/factory-method-pattern + """ + return self._gen(prompt) + + @abstractmethod + def _gen(self, prompt) -> LMAnswer: + """The actual generation method for different clients""" + + +ReasoningEffort = Literal["low", "medium", "high", "off"] + +EFFORT_TOKEN_MAP: dict[str, int] = { + "low": 1024, + "medium": 4096, + "high": 8192, + "off": 0, +} + + +class NoneResponseError(Exception): + def __init__(self, model: str): + super().__init__(f"None response from model: {model}") diff --git a/src/tfbench/lm/prompts.py b/src/tfbench/lm/prompts.py new file mode 100644 index 0000000..51e8b0b --- /dev/null +++ b/src/tfbench/lm/prompts.py @@ -0,0 +1,27 @@ +"""This file contains the generic default prompts for the LLMs used in the project.""" + +SYSTEM_PROMPT = """ +Act as a static analysis tool for type inference. +ONLY output the type signature. +Do Not Provide any additional commentaries or explanations.""" + +INSTRUCT_PROMPT = """ +Remember that in Haskell: +1. The list type `[a]` is a polymorphic type, defined as `data [] a = [] | (:) a [a]`, +so `(:)` is a constructor for list type. +2. The String type is a list of characters, defined as `type String = [Char]`.""" + +CORE_PROMPT = """ +For polymorphic types variables, you can use type variables like `a`, `b`, `c`, etc. +You should start with `a` and increment the alphabet as needed.""" + +PURE_PROMPT = """ +For polymorphic types variables, you can use type variables like `t1`, `t2`, `t3`, etc. +You should start with `t1` and increment the number as needed.""" + + +def get_sys_prompt(pure: bool) -> str: + """helper function to get system prompt for pure version of TF-Bench""" + sys_prompt = SYSTEM_PROMPT + INSTRUCT_PROMPT + sys_prompt += PURE_PROMPT if pure else CORE_PROMPT + return sys_prompt diff --git a/src/tfbench/lm/settings.py b/src/tfbench/lm/settings.py new file mode 100644 index 0000000..21fceac --- /dev/null +++ b/src/tfbench/lm/settings.py @@ -0,0 +1,2 @@ +# Default hyper-parameters +MAX_TOKENS = 1024 diff --git a/src/tfbench/lm/utils.py b/src/tfbench/lm/utils.py new file mode 100644 index 0000000..2864a23 --- /dev/null +++ b/src/tfbench/lm/utils.py @@ -0,0 +1,104 @@ +import logging +from typing import Literal, get_args, cast, Any, Union, get_origin +from types import UnionType + +from returns.result import ResultE, Success, Failure +from openai.types.shared.reasoning_effort import ReasoningEffort as OAIReasoningEffort + +from ._types import LM, LMAnswer +from ._openai import ( + OpenAIChatCompletion, + OpenAIResponses, + OAI_MODELS, + OAI_TTC_MODELS, + OAI_O5, +) +from ._google import GeminiChat, GeminiReasoning, GEMINI_MODELS, GEMINI_TTC_MODELS +from ._anthropic import ClaudeChat, ClaudeReasoning, CLAUDE_MODELS, CLAUDE_TTC_MODELS +from ._ollama import OllamaChat, OLLAMA_TTC_MODELS +from ._hf import HFChat + +from ._google import GeminiReasoningEffort +from ._types import ReasoningEffort + + +def _literal_options(tp: Any) -> set[str]: + """Collect string values from Literal[...] inside tp (handles Unions).""" + origin = get_origin(tp) + if origin is Literal: + # Direct Literal["a", "b", ...] + return set(get_args(tp)) + if origin is Union or (UnionType is not None and origin is UnionType): + vals: set[str] = set() + for arg in get_args(tp): + vals |= _literal_options(arg) + return vals + return set() # not a Literal or Union of Literals + + +def _assert_valid_effort(model: str, effort: str | None, effort_cls: Any) -> None: + """Check if the given effort is valid for the model.""" + if effort is None: + return + allowed = _literal_options(effort_cls) + assert effort in allowed, f"`{effort}` is not a valid reasoning effort for {model}." + + +def router( + model_name: str, + pure: bool, + effort: str | None = None, +) -> LM: + """Route the model name to the appropriate LM class.""" + if model_name in OAI_MODELS: + return OpenAIChatCompletion(model_name=model_name, pure=pure) + + if model_name in OAI_TTC_MODELS: + return OpenAIResponses(model_name=model_name, pure=pure) + + if model_name in OAI_O5: + _assert_valid_effort(model_name, effort, OAIReasoningEffort) + # cast is safe here after assertion + return OpenAIResponses( + model_name=model_name, + pure=pure, + effort=cast(OAIReasoningEffort, effort), + ) + + if model_name in GEMINI_MODELS: + return GeminiChat(model_name=model_name, pure=pure) + + if model_name in GEMINI_TTC_MODELS: + _assert_valid_effort(model_name, effort, GeminiReasoningEffort) + return GeminiReasoning( + model_name=model_name, + pure=pure, + effort=cast(GeminiReasoningEffort, effort), + ) + + if model_name in CLAUDE_MODELS: + return ClaudeChat(model_name=model_name, pure=pure) + + if model_name in CLAUDE_TTC_MODELS: + _assert_valid_effort(model_name, effort, ReasoningEffort) + return ClaudeReasoning( + model_name=model_name, + pure=pure, + effort=cast(ReasoningEffort, effort), + ) + + if model_name in OLLAMA_TTC_MODELS: + return OllamaChat(model_name=model_name, pure=pure) + + return HFChat(model_name=model_name, pure=pure) + + +def extract_response(response: ResultE[LMAnswer]) -> str: + """Extract the answer from the LMAnswer or return an empty string if None.""" + match response: + case Success(r): + return r.answer or "" + case Failure(e): + logging.error(f"Error generating response: {e}") + return "" + return "" diff --git a/src/tfbench/load.py b/src/tfbench/load.py new file mode 100644 index 0000000..73714c1 --- /dev/null +++ b/src/tfbench/load.py @@ -0,0 +1,39 @@ +from typing import Any, Mapping, cast +from datasets import load_dataset + +from dacite import from_dict +from orjsonl import orjsonl + +from .common import BenchmarkTask +from .lm import LMAnswer + + +def _cast(data_entry) -> BenchmarkTask: + r = cast(Mapping[str, Any], data_entry) + return BenchmarkTask( + task_id=r["task_id"], + poly_type=r["poly_type"], + signature=r["signature"], + code=r["code"], + dependencies=r["dependencies"], + ) + + +def load_tfb_from_hf(split: str = "base") -> list[BenchmarkTask]: + """Load TF-Bench dataset from HuggingFace Hub. + + Args: + split (str): The dataset split to load. Options are "base" or "pure". Default is "base". + + Returns: + list[BenchmarkTask]: A list of BenchmarkTask instances. + """ + + dataset = load_dataset("SecLabUCD/TF-Bench", split=split) + return [_cast(d) for d in dataset] + + +def load_gen_results_jsonl(result_file: str) -> list[LMAnswer | None]: + """load generation results from a jsonl file""" + objs: list[dict[str, str]] = orjsonl.load(result_file) # type: ignore + return [from_dict(LMAnswer, obj) if "answer" in obj else None for obj in objs] diff --git a/src/manual.py b/src/tfbench/manual.py similarity index 97% rename from src/manual.py rename to src/tfbench/manual.py index 8cf1377..4f5200b 100644 --- a/src/manual.py +++ b/src/tfbench/manual.py @@ -1,7 +1,8 @@ """ -Tasks whose dependencies cannot be directly added using `hoogle.py` +Tasks whose dependencies cannot be directly added using `hoogle.py` """ +# pylint: disable=line-too-long MANUAL_TASKS = [ { "task_id": "data/repos/ghc-internal-9.1001.0/src/GHC/Internal/Data/OldList.hs--lines", diff --git a/src/postprocessing.py b/src/tfbench/postprocessing.py similarity index 92% rename from src/postprocessing.py rename to src/tfbench/postprocessing.py index 439e6e4..5dc0ee6 100644 --- a/src/postprocessing.py +++ b/src/tfbench/postprocessing.py @@ -2,6 +2,9 @@ from functools import reduce from typing import Callable +# pylint: disable=missing-function-docstring +# function docstring for postprocessing is disable since too simple + def char_list_to_str(text: str) -> str: return text.replace("[Char]", "String") diff --git a/src/prelude.py b/src/tfbench/prelude.py similarity index 88% rename from src/prelude.py rename to src/tfbench/prelude.py index 694d28d..ec52c83 100644 --- a/src/prelude.py +++ b/src/tfbench/prelude.py @@ -1,15 +1,14 @@ import json -import fire -import os from os.path import join as pjoin, abspath, exists + +import fire from funcy import lmap from funcy_chain import Chain from dacite import from_dict -from src.hs_parser import HASKELL_LANGUAGE -from src.hs_parser.ast_util import AST -from src.add_dependency import add_dependencies -from src.common import clean_tab_spaces, BenchmarkTask, task2md +from tfbench.hs_parser import AST +from tfbench.add_dependency import add_dependencies +from tfbench.common import clean_tab_spaces, BenchmarkTask, task2md def main( @@ -17,6 +16,7 @@ def main( ghc_internal: str = "data/source/ghc-internal-9.1001.0.jsonl", output_dir: str = "benchmark", ): + """extract tasks from Haskell prelude""" ghc_internal = abspath(ghc_internal) prelude = abspath(prelude) assert exists(ghc_internal) and exists(prelude) @@ -24,7 +24,7 @@ def main( with open(prelude, "r") as fp: prelude_code = fp.read() - ast = AST(prelude_code, HASKELL_LANGUAGE) + ast = AST(prelude_code) root = ast.root prelude_vars = lmap( ast.get_src_from_node, ast.get_all_nodes_of_type(root, "variable") diff --git a/src/tfbench/type_def.py b/src/tfbench/type_def.py new file mode 100644 index 0000000..6ea920a --- /dev/null +++ b/src/tfbench/type_def.py @@ -0,0 +1,74 @@ +from funcy import lfilter + +from .common import BenchmarkTask +from .hs_parser import AST, get_type_constraints +from .hs_parser.extractor import TypeExtractor + + +def _is_type(code: str, type_name: str) -> bool: + ast = AST(code) + decl = ast.get_all_nodes_of_type(ast.root, "declarations")[0] + decl_fst_child = decl.child(0) + return decl_fst_child is not None and decl_fst_child.type == type_name + + +def is_data_type(code: str) -> bool: + """check if the given line of code is a data type definition""" + return _is_type(code, "data_type") + + +def is_class(code: str) -> bool: + """check if the given line of code is a type class definition""" + return _is_type(code, "class") + + +def def_new_type(type_name: str) -> str: + """construct a new, empty yet unique type definition for a given Monomorphic type name""" + return f"data {type_name} = {type_name}" + + +def def_new_type_class(class_name: str, type_vars: list[str]) -> str: + """construct a new, empty yet unique type class definition for a given Ad-hoc type class name""" + return f"class {class_name} {' '.join(type_vars)}" + + +def def_new_type_constructor(constructor_name: str, type_vars: list[str]) -> str: + """construct a new, empty yet unique type constructor definition for a given type constructor name""" + return f"data {constructor_name} {' '.join(type_vars)}" + + +def is_type_def(code: str) -> bool: + """check if the given line of code is a type definition (data type or type class)""" + return is_data_type(code) or is_class(code) + + +def is_type_defined(type_name: str, type_defs: list[str]) -> bool: + """check if a type name is defined in the given list of type definitions""" + return any(type_name in td for td in type_defs) + + +def get_type_defs(task: BenchmarkTask) -> list[str]: + """Get Haskell type definitions from a BenchmarkTask""" + existing_defs = lfilter(is_type_def, task.dependencies) + + if "=>" in task.signature: + constrains = get_type_constraints(task.signature) + for c in constrains: + [ty_class, *ty_vars] = c.split(" ") + if is_type_defined(ty_class, existing_defs): + continue + existing_defs.append(def_new_type_class(ty_class, ty_vars)) + + extractor = TypeExtractor(task.signature) + for ctor_name, arity in extractor.type_constructors.items(): + if is_type_defined(ctor_name, existing_defs): + continue + type_vars = [f"t{i}" for i in range(arity)] + existing_defs.append(def_new_type_constructor(ctor_name, type_vars)) + + for type_name in extractor.names: + if is_type_defined(type_name, existing_defs): + continue + existing_defs.append(def_new_type(type_name)) + + return list(existing_defs) diff --git a/tests/ast_node_test.py b/tests/ast_node_test.py index 9f1267c..26c3f55 100644 --- a/tests/ast_node_test.py +++ b/tests/ast_node_test.py @@ -1,17 +1,7 @@ # importing the requests library -from src.hs_parser.ast_util import AST -import json -from dacite import from_dict -import fire from funcy_chain import Chain -import requests -from urllib.parse import quote -from src.common import BenchmarkTask -from src.common import extract_function_name -from src.hs_parser import HASKELL_LANGUAGE -from functools import lru_cache from pprint import pprint -from tree_sitter import Node +from tfbench.hs_parser import AST """ This is a test file for seeing all the Nodes in the AST of certain pieces of code @@ -21,7 +11,7 @@ def main(): assert True code = "lines \"\" = []\nlines s = cons (case break (== '\\n') s of\n (l, s') -> (l, case s' of\n [] -> []\n _:s'' -> lines s''))\n where\n cons ~(h, t) = h : t" - ast = AST(code, HASKELL_LANGUAGE) + ast = AST(code) root = ast.root # Get both types and type classes diff --git a/tests/test_alpha_equiv.py b/tests/test_alpha_equiv.py index 2ee9a06..fe2f2c3 100644 --- a/tests/test_alpha_equiv.py +++ b/tests/test_alpha_equiv.py @@ -1,4 +1,4 @@ -from src.evaluation import alpha_equiv +from tfbench.evaluation import alpha_equiv def test_simple(): diff --git a/tests/test_ast.py b/tests/test_ast.py index 1ca41cf..c1873f3 100644 --- a/tests/test_ast.py +++ b/tests/test_ast.py @@ -1,10 +1,9 @@ -import pytest -from src.hs_parser import HASKELL_LANGUAGE -from src.hs_parser.ast_util import AST, ASTLoc from hypothesis import given import hypothesis.strategies as st from funcy_chain import Chain -from operator import add + +from tfbench.hs_parser import HASKELL_LANGUAGE +from tfbench.hs_parser import AST def test_function_extract(): @@ -36,13 +35,8 @@ def test_function_extract(): @given(st.permutations(hs_lines)) def haskell_lines_in_any_order(lines): code = "\n".join(lines) - ast = AST(code, HASKELL_LANGUAGE) - fs = ( - Chain(ast.get_functions()) - .map(ast.func2src) - .map(lambda xs: "\n".join(xs)) - .value - ) + ast = AST(code) + fs = Chain(ast.get_functions()).map(ast.func2src).map("\n".join).value fs.sort() assert fs[0] == fn_add.strip("\n") diff --git a/tests/test_dependency.py b/tests/test_dependency.py index 7c6e71a..ca2a33c 100644 --- a/tests/test_dependency.py +++ b/tests/test_dependency.py @@ -1,7 +1,7 @@ -from src.common import BenchmarkTask -from src.add_dependency import get_func_calls from dacite import from_dict import traceback +from tfbench.common import BenchmarkTask +from tfbench.add_dependency import get_func_calls def test_len(): diff --git a/tests/test_eval_diff.py b/tests/test_eval_diff.py new file mode 100644 index 0000000..38bf147 --- /dev/null +++ b/tests/test_eval_diff.py @@ -0,0 +1,71 @@ +from os.path import abspath, dirname, basename, join as pjoin +import os +from itertools import starmap +from multiprocessing import Pool + +import pytest +import fire +from orjsonl import orjsonl +from tqdm import tqdm +from tfbench import ( + analysis_multi_runs, + load_tfb_from_hf, + load_gen_results_jsonl, + prover_evaluate, +) +from tfbench.ghc import get_prover +from tfbench.evaluation import evaluate_one_task, prove_one_task +from tfbench.common import task2md +from tfbench.type_def import get_type_defs +from tfbench.postprocessing import postprocess, TASK_STRATEGIES, RESPONSE_STRATEGIES + + +def diff_one_file(file_path: str, split: str): + tasks = load_tfb_from_hf(split) + answers = load_gen_results_jsonl(abspath(file_path)) + + old_eval = starmap(evaluate_one_task, zip(tasks, answers)) + with Pool() as pool: + new_eval = pool.starmap( + prove_one_task, zip(tasks, answers, [split == "pure"] * len(tasks)) + ) + + for t, a, o, n in zip(tasks, answers, old_eval, new_eval): + if a is None: + continue + # if o: + # assert n, "both evaluations should return a result" + if o and not n: + print(task2md(t)) + defs = get_type_defs(t) + + predicted_body = postprocess(a.answer, RESPONSE_STRATEGIES).strip() + predicted = f"f :: {predicted_body}" + print(get_prover(t.signature, predicted, defs).unwrap()) + assert False + + +def test_diff_recorded(): + """different test evaluation function with recorded results + Since the new prover evaluation fixes the false negative issue, + we assume if an answer is determined as correct by the old evaluation, + it should also be correct by the new evaluation. + """ + + result_path = abspath("results") + # skip the test if there are not recorded results + if not os.path.exists(result_path): + pytest.skip("No recorded results found, skip the test.") + + # walk the result directory to find all jsonl files + for b, _, f in os.walk(result_path): + for file in f: + if file.endswith(".jsonl"): + file_path = pjoin(b, file) + split = basename(b) + print(f"Diffing {file_path} ...") + diff_one_file(file_path, split) + + +if __name__ == "__main__": + fire.Fire(test_diff_recorded) diff --git a/tests/test_extractor.py b/tests/test_extractor.py new file mode 100644 index 0000000..aa19f97 --- /dev/null +++ b/tests/test_extractor.py @@ -0,0 +1,49 @@ +from tfbench.hs_parser import TypeExtractor + + +def test_real_cases(): + code = "f:: T1 t1 => t1" + et = TypeExtractor(code) + assert not et.type_constructors + + code = "f:: T1 t1 => T2 -> t1" + et = TypeExtractor(code) + assert not et.type_constructors + assert et.names == {"T2"} + + code = "f:: T1 t1 => T2 T3 -> t1" + et = TypeExtractor(code) + assert et.type_constructors == {"T2": 1} + assert et.names == {"T2", "T3"} + + code = "f:: T1 -> T2 T3 -> Either T1 T3 -> (T1, T3, T2 T3)" + et = TypeExtractor(code) + assert et.type_constructors == {"T2": 1, "Either": 2} + assert et.names == {"T1", "T2", "T3", "Either"} + + code = "g:: Ord a => Int -> Either String a -> T3 T1 T2 T4" + et = TypeExtractor(code) + assert et.type_constructors == {"Either": 2, "T3": 3} + assert et.names == {"Int", "String", "T1", "T2", "T3", "T4", "Either"} + + +def test_list_tuple(): + code = "f :: [Int] -> T1 [Int]" + et = TypeExtractor(code) + assert et.type_constructors == {"T1": 1} + assert et.names == {"Int", "T1"} + + code = "f :: (Int, T1) -> T2 (Int, T1)" + et = TypeExtractor(code) + assert et.type_constructors == {"T2": 1} + assert et.names == {"Int", "T1", "T2"} + + code = "f :: Maybe a -> a" + et = TypeExtractor(code) + assert et.type_constructors == {"Maybe": 1} + assert et.names == {"Maybe"} + + code = "f :: [Int] -> Int" + et = TypeExtractor(code) + assert not et.type_constructors + assert et.names == {"Int"} diff --git a/tests/test_ghc.py b/tests/test_ghc.py new file mode 100644 index 0000000..ca1a54e --- /dev/null +++ b/tests/test_ghc.py @@ -0,0 +1,335 @@ +from returns.result import Result, Success, Failure +from tfbench.ghc import ghc_prove_equiv, get_prover, reorder_constraints + + +def _equiv( + truth: str, + answer: str, + new_types: list[str] | None = None, + should_pass: bool = True, +): + equiv = get_prover(truth, answer, new_types).alt(str).bind(ghc_prove_equiv) + match equiv: + case Success(_): + assert should_pass + case Failure(err): + assert not should_pass, err + + +def _not_equiv( + truth: str, + answer: str, + new_types: list[str] | None = None, +): + _equiv(truth, answer, new_types, should_pass=False) + + +def test_monomorphic(): + """test GHC type equivalence prover for monomorphic types""" + + _equiv("f::Int -> Int", "g ::Int -> Int") + _equiv("f::(Int, Bool) -> Int", "g ::(Int, Bool) -> Int") + _equiv("f::Int -> Bool -> Int", "g ::Int -> Bool -> Int") + _equiv( + "f::(Int -> Bool) -> [Int] -> [Bool]", + "g ::(Int -> Bool) -> [Int] -> [Bool]", + ) + _equiv( + "f::(Int -> Bool) -> [Int] -> Maybe Bool", + "g ::(Int -> Bool) -> [Int] -> Maybe Bool", + ) + + # negative cases + _not_equiv("f::Int -> Int", "g ::Bool -> Bool") + _not_equiv("f::(Int, Bool) -> Int", "g ::(Bool, Int) -> Int") + _not_equiv( + "f::(Int -> Bool) -> [Int] -> [Bool]", + "g ::(Bool -> Int) -> [Int] -> [Bool]", + ) + + +def test_parametric(): + """test GHC type equivalence prover for parametric types""" + + _equiv("f :: a -> a", "g :: b -> b") + _equiv("f :: a -> b -> a", "g :: x -> y -> x") + _equiv("f :: (a, b) -> (b, a)", "g :: (x, y) -> (y, x)") + _equiv("f :: (a -> b) -> [a] -> [b]", "g :: (x -> y) -> [x] -> [y]") + _equiv( + "f :: (a -> b) -> [a] -> Maybe b", + "g :: (x -> y) -> [x] -> Maybe y", + ) + _equiv("g :: t1 -> t2 -> (t1, t2)", "h :: u1 -> u2 -> (u1, u2)") + _equiv( + "h :: (m -> n) -> (n -> o) -> m -> o", + "k :: (x -> y) -> (y -> z) -> x -> z", + ) + _equiv( + "k :: (a -> b) -> (b -> c) -> (c -> d) -> a -> d", + "m :: (x -> y) -> (y -> z) -> (z -> w) -> x -> w", + ) + _equiv( + "m :: (x -> y) -> [x] -> Maybe y", + "n :: (a -> b) -> [a] -> Maybe b", + ) + _equiv( + "n :: (f a -> f b) -> [a] -> [b]", + "p :: (g x -> g y) -> [x] -> [y]", + ) + + # although it is very rare to have these kinds of function types, + # the following should still hold, since a, b, c,d are arbitrary type variables + # it is not really possible to write a function that return any type variable + _equiv("f :: a -> b", "g :: b -> a") + _equiv("f :: a -> b", "g :: c -> d") + + # however, the following should not hold + # although a, b are arbitrary type variables + # `g` requires both input and output to be the same type variable, + # whereas `f` does not. + _not_equiv("f :: a -> b", "g :: a -> a") + + # negative cases + _not_equiv("f :: a -> a", "g :: a -> b") + _not_equiv("f :: a -> b", "g :: a -> a") + + _not_equiv("f :: a -> b", "g :: Int-> Int") + _not_equiv( + "f :: (a, b) -> c", + "g :: (Int, Int) -> Int", + ) + + +def test_mono_para_mixed(): + """test GHC type equivalence prover for mixed monomorphic and parametric types""" + + _equiv("f :: a -> Int", "g :: b -> Int") + _equiv("f :: Int -> a", "g :: Int -> b") + _equiv("f :: (a, Int) -> (Int, a)", "g :: (b, Int) -> (Int, b)") + _equiv( + "f :: (a -> Int) -> [Int] -> [a]", + "g :: (b -> Int) -> [Int] -> [b]", + ) + _equiv( + "f :: (a -> Int) -> [Int] -> Maybe a", + "g :: (b -> Int) -> [Int] -> Maybe b", + ) + _equiv( + "g :: t1 -> Int -> (t1, Int)", + "h :: u1 -> Int -> (u1, Int)", + ) + _equiv( + "h :: (Int -> n) -> (n -> Int) -> Int -> Int", + "k :: (Int -> y) -> (y -> Int) -> Int -> Int", + ) + _equiv( + "k :: (a -> Int) -> (Int -> c) -> (c -> d) -> a -> d", + "m :: (x -> Int) -> (Int -> y) -> (y -> z) -> x -> z", + ) + _equiv( + "m :: (x -> y) -> [Int] -> Maybe y", + "n :: (a -> b) -> [Int] -> Maybe b", + ) + _equiv( + "n :: (f a -> f b) -> [Int] -> [b]", + "p :: (g x -> g y) -> [Int] -> [y]", + ) + + # negative cases + _not_equiv("f :: a -> Int", "g :: a-> a") + _not_equiv("f :: a-> a", "g :: a-> Int") + + _not_equiv("f :: a-> Int", "g :: Int-> Int") + _not_equiv( + "f :: (a, Int) -> Int", + "g :: (Int, Int) -> Int", + ) + + +def test_adhoc(): + """test GHC type equivalence prover for ad-hoc polymorphic types, + i.e., with type class constraints + """ + + _equiv("f :: Eq a => a -> a", "g :: Eq b => b -> b") + _equiv("f :: (Eq a) => a -> a", "g :: (Eq b) => b -> b") + # test with parenthesis around constraints + _equiv("f :: (Eq a) => a -> a", "g :: Eq b => b -> b") + _equiv("f :: (Eq a, Show b) => a -> b -> a", "g :: (Eq x, Show y) => x -> y -> x") + _equiv( + "f :: (Ord a, Show b) => (a, b) -> (b, a)", + "g :: (Ord x, Show y) => (x, y) -> (y, x)", + ) + _equiv( + "f :: (Eq a, Show b) => (a -> b) -> [a] -> [b]", + "g :: (Eq x, Show y) => (x -> y) -> [x] -> [y]", + ) + _equiv( + "f :: (Eq a, Show b) => (a -> b) -> [a] -> Maybe b", + "g :: (Eq x, Show y) => (x -> y) -> [x] -> Maybe y", + ) + _equiv( + "m :: (Eq x, Show y) => (x -> y) -> [x] -> Maybe y", + "n :: (Eq a, Show b) => (a -> b) -> [a] -> Maybe b", + ) + _equiv( + "n :: (Eq (f a), Show (f b)) => (f a -> f b) -> [a] -> [b]", + "p :: (Eq (g x), Show (g y)) => (g x -> g y) -> [x] -> [y]", + ) + + # negative cases + _not_equiv("f :: Eq a => a -> a", "g :: a-> a") + _not_equiv("f :: a-> a", "g :: Eq a => a-> a") + + _not_equiv("f :: Eq a => a-> Int", "g :: Int-> Int") + _not_equiv( + "f :: (Eq a, Show b) => (a, Int) -> Int", + "g :: (Int, Int) -> Int", + ) + + +def test_para_adhoc_mix(): + """test GHC type equivalence prover for mixed parametric and ad-hoc polymorphic types""" + + _equiv("f :: Eq a => a -> Int", "g :: Eq b => b -> Int") + _equiv("f :: Int -> a", "g :: Int -> b") + _equiv("f :: (Eq a) => (a, Int) -> (Int, a)", "g :: (Eq b) => (b, Int) -> (Int, b)") + _equiv( + "f :: (Ord a, Show b) => (a -> Int) -> [Int] -> [a]", + "g :: (Ord x, Show y) => (x -> Int) -> [Int] -> [x]", + ) + _equiv( + "f :: (Eq a, Show b) => (a -> Int) -> [Int] -> Maybe a", + "g :: (Eq x, Show y) => (x -> Int) -> [Int] -> Maybe x", + ) + _equiv( + "g :: Eq t1 => t1 -> Int -> (t1, Int)", + "h :: Eq u1 => u1 -> Int -> (u1, Int)", + ) + _equiv( + "h :: (Int -> n) -> (n -> Int) -> Int -> Int", + "k :: (Int -> y) -> (y -> Int) -> Int -> Int", + ) + _equiv( + "k :: (Eq a, Show c) => (a -> Int) -> (Int -> c) -> (c -> d) -> a -> d", + "m :: (Eq x, Show y) => (x -> Int) -> (Int -> y) -> (y -> z) -> x -> z", + ) + _equiv( + "m :: (Eq x, Show y) => (x -> y) -> [Int] -> Maybe y", + "n :: (Eq a, Show b) => (a -> b) -> [Int] -> Maybe b", + ) + _equiv( + "n :: (Eq (f a), Show (f b)) => (f a -> f b) -> [a] -> [b]", + "p :: (Eq (g x), Show (g y)) => (g x -> g y) -> [x] -> [y]", + ) + _not_equiv( + "n :: (Eq (f a), Show (f b)) => (f a -> f b) -> [Int] -> [b]", + "p :: (Eq (g x), Show (g y)) => (g x -> g y) -> [x] -> [y]", + ) + + +def test_constructors(): + _equiv( + "f :: Maybe a -> Maybe a", + "g :: Maybe b -> Maybe b", + ) + + _equiv( + "f :: Either a b -> Either a b", + "g :: Either x y -> Either x y", + ) + + _equiv( + "f :: (a, b) -> (b, a)", + "g :: (x, y) -> (y, x)", + ) + + _equiv( + "f :: [a] -> [a]", + "g :: [b] -> [b]", + ) + + _equiv( + "f :: (Maybe a, [a]) -> (Maybe a, [a])", + "g :: (Maybe b, [b]) -> (Maybe b, [b])", + ) + + _equiv( + "f :: [Int] -> [Int]", + "g :: [Int] -> [Int]", + ) + + _equiv( + "f::[Char] -> [Char]", + "g :: String -> String", + ) + + _not_equiv( + "f :: Maybe a -> Maybe a", + "g :: a -> Maybe a", + ) + + _not_equiv( + "f:: [Int] -> [Int]", + "g :: [Bool] -> [Bool]", + ) + _not_equiv( + "f:: [Int] -> [Int]", + "g ::[Int] -> [Bool]", + ) + + +def test_tfb_real(): + """test cases from TF-Bench real tasks, + where the deprecated evaluate failed + """ + + # type -> is right-associative + _equiv( + "uncurry :: (a -> b -> c) -> ((a, b) -> c)", + "g::(a -> b -> c) -> (a, b) -> c", + ) + _equiv( + "(.) :: (b -> c) -> (a -> b) -> a -> c", + "(.) :: (b -> c) -> (a -> b) -> (a -> c)", + ) + _equiv("($) :: (a -> b) -> a -> b", "($) :: (a -> b) -> (a -> b)") + + # type class constraints are commutative + _equiv( + "elem :: (Foldable t, Eq a) => a -> t a -> Bool", + "g :: (Eq a, Foldable t) => a -> t a -> Bool", + ) + + # type alias not expanded + _equiv( + "showList :: Show a => [a] -> ShowS", + "g :: Show a => [a] -> String -> String", + ) + + +def test_reorder(): + """test reorder_type_classes function""" + s1 = "f :: (Eq a, Show a) => a -> String" + s2 = "f :: (Show a, Eq a) => a -> String" + + rs1 = reorder_constraints(s1) + rs2 = reorder_constraints(s2) + + _equiv(rs1, rs2) + _equiv(rs2, rs2) + _equiv(rs1, rs1) + + _equiv(s1, rs2) + _equiv(s2, rs1) + _equiv(s1, s2) + + # single constraint + s3 = "f :: Eq a => a -> String" + rs3 = reorder_constraints(s3) + _equiv(s3, rs3) + + # constrain in body + s4 = "f :: a -> Maybe a" + rs4 = reorder_constraints(s4) + _equiv(s4, rs4) diff --git a/tests/test_ghc_pure.py b/tests/test_ghc_pure.py new file mode 100644 index 0000000..caee0c7 --- /dev/null +++ b/tests/test_ghc_pure.py @@ -0,0 +1,111 @@ +from returns.result import Result, Success, Failure +from funcy import lmap +from tfbench.ghc import ghc_prove_equiv, get_prover, reorder_constraints +from tfbench.type_def import def_new_type, def_new_type_constructor + + +def _equiv( + truth: str, + answer: str, + new_types: list[str] | None = None, + should_pass: bool = True, +): + equiv = get_prover(truth, answer, new_types or []).alt(str).bind(ghc_prove_equiv) + match equiv: + case Success(_): + assert should_pass + case Failure(err): + assert not should_pass, err + + +def _not_equiv( + truth: str, + answer: str, + new_types: list[str] | None = None, +): + _equiv(truth, answer, new_types, should_pass=False) + + +def test_mono(): + """test GHC type equivalence prover for monomorphic types after rewriting""" + # check with type after rewriting, + # i.e. T1, ... T_n + _equiv("f:: T1-> T1", "g ::T1 -> T1", new_types=[def_new_type("T1")]) + _not_equiv( + "f:: T1-> T1", + "g ::T2 -> T2", + new_types=lmap(def_new_type, ["T1", "T2"]), + ) + + _equiv( + "f:: (T1, T2) -> T1", + "g ::(T1, T2) -> T1", + new_types=lmap(def_new_type, ["T1", "T2"]), + ) + _equiv( + "f:: (Int, T2) -> Int", + "g ::(Int, T2) -> Int", + new_types=lmap(def_new_type, ["T2"]), + ) + _not_equiv( + "f:: (Int, T2) -> Int", + "g ::(Int, T2) -> T2", + new_types=lmap(def_new_type, ["T2"]), + ) + + +def test_typeclass_in_body(): + f = "f :: T1 -> T2 T3" + _equiv( + f, + f, + new_types=[ + def_new_type("T1"), + def_new_type("T3"), + def_new_type_constructor("T2", ["a"]), + ], + ) + + +def test_constructor(): + _not_equiv( + "f :: Maybe T1 -> T1", + "g :: Maybe T2 -> T2", + new_types=[ + def_new_type("T1"), + def_new_type("T2"), + ], + ) + + _equiv( + "f :: T3 T1 -> T1", + "g :: T3 T1 -> T1", + new_types=[ + def_new_type("T1"), + def_new_type_constructor("T3", ["a"]), + ], + ) + + _equiv( + "f::[a] -> T1 a", + "g::[b] -> T1 b", + new_types=[ + def_new_type_constructor("T1", ["a"]), + ], + ) + _equiv( + "f::[T1] -> [a]", + "g::[T1] -> [b]", + new_types=[ + def_new_type("T1"), + ], + ) + + _not_equiv( + "f::[T1] -> [a]", + "g::[T2] -> [b]", + new_types=[ + def_new_type("T1"), + def_new_type("T2"), + ], + ) diff --git a/tests/test_polymorphism.py b/tests/test_polymorphism.py index 0243cd8..e1de5ad 100644 --- a/tests/test_polymorphism.py +++ b/tests/test_polymorphism.py @@ -1,9 +1,4 @@ -from src.hs_parser import HASKELL_LANGUAGE -from src.hs_parser.ast_util import AST, ASTLoc, HaskellFunction -from hypothesis import given -import hypothesis.strategies as st -from funcy_chain import Chain -from src.hs_parser.polymorphism import get_polymorphic_type, PolymorphicType +from tfbench.hs_parser import get_polymorphic_type, PolymorphicType, AST def test_polymorphism(): @@ -15,7 +10,7 @@ def test_polymorphism(): f1 :: forall a b. a -> b -> a """ - ast = AST(types, HASKELL_LANGUAGE) + ast = AST(types) fn_addInt, fn_map, fn_id, fn_elem, fn_f1 = ast.get_functions() assert get_polymorphic_type(fn_addInt.type_signature) == PolymorphicType.MONO diff --git a/tests/test_remove_comments.py b/tests/test_remove_comments.py index 087ebb7..ac030df 100644 --- a/tests/test_remove_comments.py +++ b/tests/test_remove_comments.py @@ -1,5 +1,5 @@ import unittest -from src.common import remove_comments +from tfbench.common import remove_comments class TestRemoveHaskellComments(unittest.TestCase): diff --git a/tests/test_task2md.py b/tests/test_task2md.py index ce12ed3..44197a3 100644 --- a/tests/test_task2md.py +++ b/tests/test_task2md.py @@ -1,6 +1,6 @@ import pytest from hypothesis import given, strategies as st -from src.common import BenchmarkTask, md2task, task2md +from tfbench.common import BenchmarkTask, md2task, task2md # Strategy for generating BenchmarkTask instances diff --git a/tests/test_type_def.py b/tests/test_type_def.py new file mode 100644 index 0000000..fb1029e --- /dev/null +++ b/tests/test_type_def.py @@ -0,0 +1,20 @@ +from tfbench.hs_parser import AST +from tfbench.type_def import is_class, is_data_type + + +def test_def_type_checker(): + type1 = "data T1 = T1" + class1 = "class T1 a" + f = "f :: Int -> Int" + assert is_data_type(type1) + assert not is_data_type(class1) + assert is_class(class1) + assert not is_class(type1) + assert not is_class(f) + assert not is_data_type(f) + + +def test_checker_after_rewrite(): + t1 = "data T1 a = Nothing | Just a" + assert is_data_type(t1) + assert not is_class(t1) diff --git a/tests/test_type_vars.py b/tests/test_type_vars.py new file mode 100644 index 0000000..5d3bd7e --- /dev/null +++ b/tests/test_type_vars.py @@ -0,0 +1,125 @@ +from tfbench.hs_parser import get_type_vars + + +def test_monomorphic(): + """ + test type var extraction for monomorphic types, + which should return empty set no matter how + """ + candidates = [ + "f :: Int -> Int", + "f::Int->Int", + "f :: Int -> Int ", + "f:: Int -> Int ", + "f::Int-> Int", + "f ::Int->Int", + "g :: Int -> Int -- with comment", + "g::Int->Int--with comment", + "g:: Int", + "f1 :: Char -> Char\n", + "f2 :: T1 -> T2", + "f3 :: (Int, Char) -> (Char, Int)", + ] + + ty_vars = map(get_type_vars, candidates) + assert all(len(vs) == 0 for vs in ty_vars) + + +def _assert_equal(sig: str, expected: list[str]): + ty_vars = get_type_vars(sig) + assert ty_vars == expected, f"for {sig}, expected {expected}, got {ty_vars}" + + +def test_parametric(): + """test type var extraction for parametric types""" + + _assert_equal("f :: a -> a", ["a"]) + _assert_equal("f :: a -> b -> a", ["a", "b"]) + _assert_equal("f :: (a, b) -> (b, a)", ["a", "b"]) + _assert_equal("f :: (a -> b) -> [a] -> [b]", ["a", "b"]) + _assert_equal("f :: (a -> b) -> [a] -> Maybe b", ["a", "b"]) + _assert_equal("g :: t1 -> t2 -> (t1, t2)", ["t1", "t2"]) + _assert_equal("h :: (m -> n) -> (n -> o) -> m -> o", ["m", "n", "o"]) + _assert_equal( + "k :: (a -> b) -> (b -> c) -> (c -> d) -> a -> d", + ["a", "b", "c", "d"], + ) + _assert_equal("m :: (x -> y) -> [x] -> Maybe y", ["x", "y"]) + _assert_equal("n :: (f a -> f b) -> [a] -> [b]", ["f", "a", "b"]) + + # arbitrary order + _assert_equal("p :: b -> a -> b", ["b", "a"]) + _assert_equal("q :: (b, a) -> (a, b)", ["b", "a"]) + _assert_equal("r :: (f b -> f a) -> [b] -> [a]", ["f", "b", "a"]) + + +def test_mono_para_mixed(): + """test type var extraction for mixed monomorphic and parametric types""" + + _assert_equal("f :: a -> Int", ["a"]) + _assert_equal("f :: Int -> a", ["a"]) + _assert_equal("f :: (a, Int) -> (Int, a)", ["a"]) + _assert_equal("f :: (Int -> a) -> [Int] -> [a]", ["a"]) + _assert_equal("f :: (Int -> a) -> [Int] -> Maybe a", ["a"]) + _assert_equal("g :: t1 -> Int -> (t1, Int)", ["t1"]) + _assert_equal("h :: (Int -> n) -> (n -> Int) -> Int -> Int", ["n"]) + _assert_equal( + "k :: (a -> Int) -> (Int -> c) -> (c -> d) -> a -> d", + ["a", "c", "d"], + ) + _assert_equal("m :: (x -> y) -> [Int] -> Maybe y", ["x", "y"]) + _assert_equal("n :: (f a -> f b) -> [Int] -> [b]", ["f", "a", "b"]) + + # arbitrary order + _assert_equal("p :: b -> Int -> b", ["b"]) + _assert_equal("q :: (b, Int) -> (Int, b)", ["b"]) + _assert_equal("r :: (f b -> f a) -> [Int] -> [a]", ["f", "b", "a"]) + + +def test_adhoc(): + """test type var extraction for ad-hoc polymorphic types, + i.e., with type class constraints + """ + + _assert_equal("f :: Eq a => a -> a", ["a"]) + _assert_equal("f :: (Eq a) => a -> a", ["a"]) + _assert_equal("f :: (Eq a, Show b) => a -> b -> a", ["a", "b"]) + _assert_equal("f :: (Ord a, Show b) => (a, b) -> (b, a)", ["a", "b"]) + _assert_equal( + "f :: (Eq a, Show b) => (a -> b) -> [a] -> [b]", + ["a", "b"], + ) + _assert_equal( + "f :: (Eq a, Show b) => (a -> b) -> [a] -> Maybe b", + ["a", "b"], + ) + _assert_equal( + "m :: (Eq x, Show y) => (x -> y) -> [x] -> Maybe y", + ["x", "y"], + ) + _assert_equal( + "n :: (Eq (f a), Show (f b)) => (f a -> f b) -> [a] -> [b]", + ["f", "a", "b"], + ) + + +def test_para_adhoc_mix(): + """test type var extraction for mixed parametric and ad-hoc polymorphic types""" + _assert_equal("g :: Eq t1 => t1 -> t2 -> (t1, t2)", ["t1", "t2"]) + _assert_equal( + "h :: (Ord m, Eq n) => (m -> n) -> (n -> o) -> m -> o", + ["m", "n", "o"], + ) + # type class constraints order affects the order of type vars + _assert_equal( + "k :: (Eq a, Show c) => (a -> b) -> (b -> c) -> (c -> d) -> a -> d", + ["a", "c", "b", "d"], + ) + + # arbitrary order + _assert_equal("p :: Show b => b -> a -> b", ["b", "a"]) + _assert_equal("q :: Ord b => (b, a) -> (a, b)", ["b", "a"]) + _assert_equal( + "r :: Eq (f b) => (f b -> f a) -> [b] -> [a]", + ["f", "b", "a"], + ) diff --git a/uv.lock b/uv.lock index 72d397a..c246bfa 100644 --- a/uv.lock +++ b/uv.lock @@ -1,9 +1,96 @@ version = 1 revision = 1 -requires-python = ">=3.11" -resolution-markers = [ - "python_full_version >= '3.12'", - "python_full_version < '3.12'", +requires-python = ">=3.12" + 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