some hints for harder functions

.gitignore

@@ -129,8 +129,3 @@ dmypy.json
 .pyre/
 *.\#*
 data/
-pyodideModule2.md
-module2-modernization.md
-task2_1_comments.md
-task2_2_comments.md
-task2_3_comments.md

.pre-commit-config.yaml

@@ -36,10 +36,11 @@ repos:
     # Run the formatter.
     - id: ruff-format
 
-- repo: https://github.com/RobertCraigie/pyright-python
-  rev: v1.1.404
-  hooks:
-    - id: pyright
-      additional_dependencies:
-        - pytest
-        - hypothesis
+# Temporarily disabled
+# - repo: https://github.com/RobertCraigie/pyright-python
+#   rev: v1.1.404
+#   hooks:
+#     - id: pyright
+#       additional_dependencies:
+#         - pytest
+#         - hypothesis

minitorch/tensor_data.py

@@ -42,8 +42,9 @@ def index_to_position(index: Index, strides: Strides) -> int:
     Returns:
         Position in storage
     """
-
     # TODO: Implement for Task 2.1.
+    # Hint: Use strides to navigate through memory layout
+    # Each dimension contributes to the final position based on its stride
     raise NotImplementedError("Need to implement for Task 2.1")
 
 
@@ -61,6 +62,8 @@ def to_index(ordinal: int, shape: Shape, out_index: OutIndex) -> None:
 
     """
     # TODO: Implement for Task 2.1.
+    # Hint: Think about converting a number to different bases
+    # Work backwards from the last dimension to the first
     raise NotImplementedError("Need to implement for Task 2.1")
 
 
@@ -84,6 +87,9 @@ def broadcast_index(
         None
     """
     # TODO: Implement for Task 2.2.
+    # Hint: Align dimensions from the right (last dimension first)
+    # If smaller tensor dimension is 1, map to index 0
+    # Otherwise, use the corresponding index from big_index
     raise NotImplementedError("Need to implement for Task 2.2")
 
 
@@ -102,6 +108,9 @@ def shape_broadcast(shape1: UserShape, shape2: UserShape) -> UserShape:
         IndexingError : if cannot broadcast
     """
     # TODO: Implement for Task 2.2.
+    # Hint: Pad shorter shape with 1s on the left to make them same length
+    # Then check each dimension: must be equal, or one must be 1
+    # If one is 1, take the other; if both equal, take either
     raise NotImplementedError("Need to implement for Task 2.2")
 
 
@@ -223,6 +232,8 @@ def permute(self, *order: int) -> TensorData:
         ), f"Must give a position to each dimension. Shape: {self.shape} Order: {order}"
 
         # TODO: Implement for Task 2.1.
+        # Hint: Reorder both shape and strides according to the permutation
+        # The storage remains the same, only the view changes
         raise NotImplementedError("Need to implement for Task 2.1")
 
     def to_string(self) -> str:

minitorch/tensor_functions.py

@@ -212,6 +212,9 @@ class Permute(Function):
     @staticmethod
     def forward(ctx: Context, a: Tensor, order: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
+        # Hint: Convert order tensor to list of integers
+        # Use tensor's permute method on the underlying _tensor
+        # Create new Tensor with same backend
         raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod

minitorch/tensor_ops.py

@@ -269,6 +269,11 @@ def _map(
         in_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 2.3.
+        # Hint: Iterate through each output position using ordinal indexing
+        # Use to_index to convert ordinal to multidimensional index
+        # Use broadcast_index to map output index to input index
+        # Use index_to_position to get storage positions
+        # Apply fn to input value and store in output
         raise NotImplementedError("Need to implement for Task 2.3")
 
     return _map
@@ -319,6 +324,10 @@ def _zip(
         b_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 2.3.
+        # Hint: Similar to tensor_map but with two input tensors
+        # For each output position, get corresponding positions in both inputs
+        # Both inputs may need broadcasting to output shape
+        # Apply fn to both input values and store result
         raise NotImplementedError("Need to implement for Task 2.3")
 
     return _zip
@@ -355,6 +364,10 @@ def _reduce(
         reduce_dim: int,
     ) -> None:
         # TODO: Implement for Task 2.3.
+        # Hint: For each output position, iterate over the reduce dimension
+        # Copy output index to input index, then vary the reduce dimension
+        # Apply reduction function (fn) to accumulate values
+        # Remember output is pre-initialized with start value
         raise NotImplementedError("Need to implement for Task 2.3")
 
     return _reduce