Agents

AGENTS.md

@@ -0,0 +1,15 @@
+## Project knowledge
+- **Tech Stack:** Python3 
+- **File Structure:**
+  - `src/common` – Application source code (autograd engine in here)
+  - `src/tests/` – All tests in here 
+
+## Commands you can use
+Run tests: `pytest src/tests/differential_test.py` 
+
+## Documentation practices
+Don't make PR's to big, be smart and prioritize readability over efficiency.
+Write so that a new developer to this codebase can understand your writing, don’t assume your audience are experts in the topic/area you are writing about.
+
+## Boundaries
+- ✅ **Always do:** Make sure you are actually using the `autograd.py` and `Module.py` files in the implementation of any models.

pyproject.toml

@@ -49,4 +49,9 @@ disallow_untyped_defs = true
 explicit_package_bases = true
 namespace_packages = true
 disallow_untyped_calls = false
-disable_error_code = ["import-untyped"]
+disable_error_code = ["import-untyped"]
+mypy_path = ["src"]
+
+[[tool.mypy.overrides]]
+module = ["cupy"]
+ignore_missing_imports = true

src/common/MLP.py

@@ -5,7 +5,7 @@
 from common.Module import Module
 
 
-class Neuron(Module):  # type: ignore[misc]
+class Neuron(Module):
     def __init__(self, nin: int, nonlin: bool = True) -> None:
         super().__init__()
         self.w = [Value(random.uniform(-1, 1)) for _ in range(nin)]
@@ -23,7 +23,7 @@ def __repr__(self) -> str:
         return f"{'ReLU' if self.nonlin else 'Linear'}Neuron({len(self.w)})"
 
 
-class Layer(Module):  # type: ignore[misc]
+class Layer(Module):
     def __init__(self, nin: int, nout: int, **kwargs: Any) -> None:
         super().__init__()
         self.neurons = [Neuron(nin, **kwargs) for _ in range(nout)]
@@ -39,7 +39,7 @@ def __repr__(self) -> str:
         return f"Layer of [{', '.join(str(n) for n in self.neurons)}]"
 
 
-class MLP(Module):  # type: ignore[misc]
+class MLP(Module):
     def __init__(self, nin: int, nouts: list[int]) -> None:
         super().__init__()
         sz = [nin] + nouts

src/common/Module.py

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
-from typing import Any, Generator
+from collections.abc import Iterable
+from typing import Any
 
 from common.autograd import Value
 
@@ -34,17 +35,17 @@ def forward(self, x):
         must be made before assignment on the child."""
 
     def __init__(self) -> None:
-        self.params: dict[
-            str, Value | "Module"
-        ] = {}  # real param store: name -> Value or Module
+        self.params: dict[str, Value | "Module"] = (
+            {}
+        )  # real param store: name -> Value or Module
 
     @abstractmethod
-    def forward(self, *args: Any, **kwargs: Any) -> Value:
+    def forward(self, *args: Any, **kwargs: Any) -> Any:
         """Compute the forward pass for this module."""
 
         raise NotImplementedError
 
-    def __call__(self, *args: Any, **kwargs: Any) -> Value:
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
         # will call self.forward!
         return self.forward(*args, **kwargs)
 
@@ -67,8 +68,10 @@ def __setattr__(self, key: str, value: Any) -> None:
         # registry in sync with the current state of the object.
         if isinstance(value, (Value, Module)):
             self.params[key] = value
+        elif key in self.params:
+            del self.params[key]
 
-    def parameters(self) -> Generator[Value, None, None]:
+    def parameters(self) -> Iterable[Value]:
         # recursively collects parameters and yields them (use a Generator)
         for key, param in self.params.items():
             if isinstance(param, Module):

src/common/autograd.py

@@ -1,18 +1,20 @@
-from typing import Union
+from __future__ import annotations
 
 try:
     import cupy as np
 except ImportError:
     print("CUDA not available, defaulting to numpy")
     import numpy as np
 
+Scalar = float | int
+
 
 class Value:
     """stores value's and its gradient's"""
 
     def __init__(
         self,
-        data: float | np.ndarray,
+        data: Scalar | np.ndarray,
         _children: tuple["Value", ...] = (),
         _op: str = "",
     ) -> None:
@@ -65,7 +67,7 @@ def sum_to_shape(
                 result = result.sum(axis=i, keepdims=True)
         return result
 
-    def __add__(self, other: "Value") -> "Value":
+    def __add__(self, other: Value | Scalar) -> Value:
         other = other if isinstance(other, Value) else Value(other)
         out = Value(self.data + other.data, (self, other), "+")
 
@@ -86,11 +88,7 @@ def _backward() -> None:
 
         return out
 
-    def __mul__(
-        self, other: Union["Value", int]
-    ) -> (
-        "Value"
-    ):  # some weird python black thing, so we made it use the union from typing instead(cuz it thinks its a string)
+    def __mul__(self, other: Value | Scalar) -> Value:
         other = other if isinstance(other, Value) else Value(other)
         out = Value(self.data * other.data, (self, other), "*")
 
@@ -281,22 +279,22 @@ def build_topo(v: "Value") -> None:
     def __neg__(self) -> "Value":  # -self
         return self * -1
 
-    def __radd__(self, other: "Value") -> "Value":  # other + self
+    def __radd__(self, other: Value | Scalar) -> Value:  # other + self
         return self + other
 
-    def __sub__(self, other: "Value") -> "Value":  # self - other
+    def __sub__(self, other: Value | Scalar) -> Value:  # self - other
         return self + (-other)
 
-    def __rsub__(self, other: "Value") -> "Value":  # other - self
+    def __rsub__(self, other: Value | Scalar) -> Value:  # other - self
         return other + (-self)
 
-    def __rmul__(self, other: "Value") -> "Value":  # other * self
+    def __rmul__(self, other: Value | Scalar) -> Value:  # other * self
         return self * other
 
-    def __truediv__(self, other: "Value") -> "Value":  # self / other
+    def __truediv__(self, other: Value | Scalar) -> Value:  # self / other
         return self * other**-1
 
-    def __rtruediv__(self, other: "Value") -> "Value":  # other / self
+    def __rtruediv__(self, other: Value | Scalar) -> Value:  # other / self
         return other * self**-1
 
     def __repr__(self) -> "str":