Small improvements/deletions

edugrad/data.py

@@ -1,7 +1,10 @@
 """Defines the TensorData class, a container for tensor data (tensor.Tensor.data), represented as numpy arrays.
 
 It facilitates direct manipulation of tensor data through a range of basic operation ("low-level ops"). These operations
-are building blocks for defining forward and backward passes of differentiable function.Functions. ("mid-level ops")
+are building blocks for defining forward and backward passes of differentiable function.Functions. ("mid-level ops").
+
+For simplicity and to ensure that compatible dtypes operate with each other, we enforce two of the three supported
+dtypes (bool and float32) with a typecast in each elementwise operation.
 
 The ops are executed immediately on the CPU using numpy. This approach contrasts with deferred computation models that
 analyze subsequent delayed operations in order to find an optimized equivalent final optimization at the point where

edugrad/dtypes.py

@@ -52,8 +52,8 @@ def from_py(x) -> DType:
 
     # Definition of various data types
     bool: Final[DType] = DType(0, 1, "bool", np.bool_)
-    float32: Final[DType] = DType(10, 4, "float", np.float32)
-    int32: Final[DType] = DType(5, 4, "int", np.int32)
+    float32: Final[DType] = DType(2, 4, "float", np.float32)
+    int32: Final[DType] = DType(1, 4, "int", np.int32)
 
     only_float: ClassVar[DType] = float32
     only_int: ClassVar[DType] = int32

edugrad/tensor.py

@@ -11,7 +11,7 @@
 from __future__ import annotations
 import time
 import math
-from typing import ClassVar, Sequence, Any
+from typing import ClassVar, Sequence, Any, Type
 
 import numpy as np
 
@@ -74,7 +74,7 @@ def __init__(
 
         # --------------------------------------------------------------------------------------------------------------
         # Handles Tensor(x) for x with different data types.
-        # We cast x = list(y) up to float32 for every case
+        # We cast x = list(y) up to float32 (default_type) for every type that y can have
 
         if isinstance(data, TensorData):
             assert dtype is None or dtype == data.dtype, "dtype doesn't match, and casting isn't supported"
@@ -87,9 +87,6 @@ def __init__(
 
         elif data is None:
             data = TensorData.loadop(LoadOps.EMPTY, (0,), dtype or dtypes.only_float)
-
-        elif isinstance(data, bytes):
-            data = TensorData(np.frombuffer(data, np.uint8))
 
         elif isinstance(data, np.ndarray):
             assert dtype is None or dtype.np is not None, f"{dtype} doesn't have a numpy dtype"