2024

.pre-commit-config.yaml

@@ -28,35 +28,17 @@ repos:
   - id: requirements-txt-fixer
   - id: trailing-whitespace
 
-- repo: https://github.com/timothycrosley/isort
-  rev: 5.10.1
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.6.1
   hooks:
-  - id: isort
+    # Run the linter.
+    - id: ruff
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format
 
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.971
+- repo: https://github.com/RobertCraigie/pyright-python
+  rev: v1.1.376
   hooks:
-  - id: mypy
-    exclude: ^(docs/)|(project/)|(assignments/)
-
-
-# Black, the code formatter, natively supports pre-commit
-- repo: https://github.com/psf/black
-  rev: 22.6.0
-  hooks:
-  - id: black
-
-# Flake8 also supports pre-commit natively (same author)
-- repo: https://github.com/PyCQA/flake8
-  rev: 5.0.4
-  hooks:
-  - id: flake8
-    additional_dependencies:
-    - pep8-naming
-    exclude: ^(docs/)|(assignments/)
-
-# Doc linters
-- repo: https://github.com/terrencepreilly/darglint
-  rev: v1.8.1
-  hooks:
-  - id: darglint
+    - id: pyright

README.md

@@ -14,4 +14,4 @@ python sync_previous_module.py previous-module-dir current-module-dir
 
 The files that will be synced are:
 
-        minitorch/operators.py minitorch/module.py tests/test_module.py tests/test_operators.py project/run_manual.py
+        minitorch/operators.py minitorch/module.py tests/test_module.py tests/test_operators.py project/run_manual.py

minitorch/__init__.py

@@ -1,8 +1,8 @@
+from .testing import MathTest, MathTestVariable  # type: ignore # noqa: F401,F403
 from .autodiff import *  # noqa: F401,F403
-from .datasets import *  # noqa: F401,F403
-from .module import *  # noqa: F401,F403
-from .optim import *  # noqa: F401,F403
 from .scalar import *  # noqa: F401,F403
 from .scalar_functions import *  # noqa: F401,F403
+from .optim import *  # noqa: F401,F403
+from .datasets import *  # noqa: F401,F403
 from .testing import *  # noqa: F401,F403
-from .testing import MathTest, MathTestVariable  # type: ignore # noqa: F401,F403
+from .module import *  # noqa: F401,F403

minitorch/autodiff.py

@@ -1,26 +1,29 @@
+from __future__ import annotations
+
 from dataclasses import dataclass
-from typing import Any, Iterable, List, Tuple
+from typing import Any, Iterable, List, Tuple, Protocol
 
-from typing_extensions import Protocol
 
 # ## Task 1.1
 # Central Difference calculation
 
 
 def central_difference(f: Any, *vals: Any, arg: int = 0, epsilon: float = 1e-6) -> Any:
-    r"""
-    Computes an approximation to the derivative of `f` with respect to one arg.
+    r"""Computes an approximation to the derivative of `f` with respect to one arg.
 
     See :doc:`derivative` or https://en.wikipedia.org/wiki/Finite_difference for more details.
 
     Args:
+    ----
         f : arbitrary function from n-scalar args to one value
         *vals : n-float values $x_0 \ldots x_{n-1}$
         arg : the number $i$ of the arg to compute the derivative
         epsilon : a small constant
 
     Returns:
+    -------
         An approximation of $f'_i(x_0, \ldots, x_{n-1})$
+
     """
     # TODO: Implement for Task 1.1.
     raise NotImplementedError("Need to implement for Task 1.1")
@@ -30,67 +33,62 @@ def central_difference(f: Any, *vals: Any, arg: int = 0, epsilon: float = 1e-6)
 
 
 class Variable(Protocol):
-    def accumulate_derivative(self, x: Any) -> None:
-        pass
+    def accumulate_derivative(self, x: Any) -> None: ...
 
     @property
-    def unique_id(self) -> int:
-        pass
+    def unique_id(self) -> int: ...
 
-    def is_leaf(self) -> bool:
-        pass
+    def is_leaf(self) -> bool: ...
 
-    def is_constant(self) -> bool:
-        pass
+    def is_constant(self) -> bool: ...
 
     @property
-    def parents(self) -> Iterable["Variable"]:
-        pass
+    def parents(self) -> Iterable["Variable"]: ...
 
-    def chain_rule(self, d_output: Any) -> Iterable[Tuple["Variable", Any]]:
-        pass
+    def chain_rule(self, d_output: Any) -> Iterable[Tuple[Variable, Any]]: ...
 
 
 def topological_sort(variable: Variable) -> Iterable[Variable]:
-    """
-    Computes the topological order of the computation graph.
+    """Computes the topological order of the computation graph.
 
     Args:
+    ----
         variable: The right-most variable
 
     Returns:
+    -------
         Non-constant Variables in topological order starting from the right.
+
     """
     # TODO: Implement for Task 1.4.
     raise NotImplementedError("Need to implement for Task 1.4")
 
 
 def backpropagate(variable: Variable, deriv: Any) -> None:
-    """
-    Runs backpropagation on the computation graph in order to
+    """Runs backpropagation on the computation graph in order to
     compute derivatives for the leave nodes.
 
     Args:
+    ----
         variable: The right-most variable
         deriv  : Its derivative that we want to propagate backward to the leaves.
 
     No return. Should write to its results to the derivative values of each leaf through `accumulate_derivative`.
+
     """
     # TODO: Implement for Task 1.4.
     raise NotImplementedError("Need to implement for Task 1.4")
 
 
 @dataclass
 class Context:
-    """
-    Context class is used by `Function` to store information during the forward pass.
-    """
+    """Context class is used by `Function` to store information during the forward pass."""
 
     no_grad: bool = False
     saved_values: Tuple[Any, ...] = ()
 
     def save_for_backward(self, *values: Any) -> None:
-        "Store the given `values` if they need to be used during backpropagation."
+        """Store the given `values` if they need to be used during backpropagation."""
         if self.no_grad:
             return
         self.saved_values = values

minitorch/module.py

@@ -4,11 +4,11 @@
 
 
 class Module:
-    """
-    Modules form a tree that store parameters and other
+    """Modules form a tree that store parameters and other
     submodules. They make up the basis of neural network stacks.
 
-    Attributes:
+    Attributes
+    ----------
         _modules : Storage of the child modules
         _parameters : Storage of the module's parameters
         training : Whether the module is in training mode or evaluation mode
@@ -25,42 +25,44 @@ def __init__(self) -> None:
         self.training = True
 
     def modules(self) -> Sequence[Module]:
-        "Return the direct child modules of this module."
+        """Return the direct child modules of this module."""
         m: Dict[str, Module] = self.__dict__["_modules"]
         return list(m.values())
 
     def train(self) -> None:
-        "Set the mode of this module and all descendent modules to `train`."
+        """Set the mode of this module and all descendent modules to `train`."""
         raise NotImplementedError("Need to include this file from past assignment.")
 
     def eval(self) -> None:
-        "Set the mode of this module and all descendent modules to `eval`."
+        """Set the mode of this module and all descendent modules to `eval`."""
         raise NotImplementedError("Need to include this file from past assignment.")
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
-        """
-        Collect all the parameters of this module and its descendents.
+        """Collect all the parameters of this module and its descendents.
 
-
-        Returns:
+        Returns
+        -------
             The name and `Parameter` of each ancestor parameter.
+
         """
         raise NotImplementedError("Need to include this file from past assignment.")
 
     def parameters(self) -> Sequence[Parameter]:
-        "Enumerate over all the parameters of this module and its descendents."
+        """Enumerate over all the parameters of this module and its descendents."""
         raise NotImplementedError("Need to include this file from past assignment.")
 
     def add_parameter(self, k: str, v: Any) -> Parameter:
-        """
-        Manually add a parameter. Useful helper for scalar parameters.
+        """Manually add a parameter. Useful helper for scalar parameters.
 
         Args:
+        ----
             k: Local name of the parameter.
             v: Value for the parameter.
 
         Returns:
+        -------
             Newly created parameter.
+
         """
         val = Parameter(v, k)
         self.__dict__["_parameters"][k] = val
@@ -114,8 +116,7 @@ def _addindent(s_: str, numSpaces: int) -> str:
 
 
 class Parameter:
-    """
-    A Parameter is a special container stored in a `Module`.
+    """A Parameter is a special container stored in a `Module`.
 
     It is designed to hold a `Variable`, but we allow it to hold
     any value for testing.
@@ -130,7 +131,7 @@ def __init__(self, x: Any, name: Optional[str] = None) -> None:
                 self.value.name = self.name
 
     def update(self, x: Any) -> None:
-        "Update the parameter value."
+        """Update the parameter value."""
         self.value = x
         if hasattr(x, "requires_grad_"):
             self.value.requires_grad_(True)