Fix comments

Author: oliverflood

demos/tinystories/CMakeLists.txt

@@ -11,4 +11,4 @@ chai_add_executable(TinyStoriesTestConv2d
 chai_add_executable(OliverHowTo
     ${CMAKE_CURRENT_SOURCE_DIR}/oh2.chpl
     ${PROJECT_ROOT_DIR}/lib
-)
+)   

demos/tinystories/testArgmaxDecode.chpl

@@ -1,33 +1,22 @@
-// testArgmaxDecode.chpl
-use ChAI;      // Likely needed for core types or general setup
-use NDArray;   // Where your ndarray type and argmaxDecode method are defined
+use ChAI;
+use NDArray;
 
 proc main() {
-  // 1. Create a Chapel array with some values
-  //    Use real(32) to match typical float precision in LibTorch/ChAI
   var chapArray: [0..2] real(32) = [1.0, 7.0, 3.0]; // Expected argmax index: 1
 
-  // 2. Construct an ndarray from the Chapel array.
-  //    This will use your existing `proc init(const Arr: [])` in NDArray.chpl
   var myNdArray = new ndarray(chapArray);
 
-  // 3. Call the argmaxDecode method on the ndarray instance
-  //    The `this._tensorHandle` will be created/accessed via `toBridgeTensor`
-  //    or the implicit cast `operator :` before calling Bridge.argmaxdecode.
   var argmaxIndex = myNdArray.argmaxDecode();
 
-  // 4. Print the result
   writeln("Chapel array: ", chapArray);
   writeln("Argmax index: ", argmaxIndex); // Should print 1 for the example above
 
-  // Test with another example
   var chapArray2: [0..3] real(32) = [10.0, 5.0, 20.0, 15.0]; // Expected argmax index: 2
   var myNdArray2 = new ndarray(chapArray2);
   var argmaxIndex2 = myNdArray2.argmaxDecode();
   writeln("Chapel array 2: ", chapArray2);
   writeln("Argmax index 2: ", argmaxIndex2); // Should print 2
 
-  // You can keep the accelerator checks for good measure
   if Bridge.acceleratorAvailable() then
     writeln("Accelerator (CUDA/MPS) is available!");
   else
@@ -38,8 +27,4 @@ proc main() {
   Bridge.debugCpuOnlyMode(false);
   writeln("Debug CPU only mode is OFF. Accelerator available: ", Bridge.acceleratorAvailable());
 
-  // IMPORTANT: Ensure tensors are freed!
-  // If `myNdArray`'s deinit() correctly calls Bridge.freeBridgeTensorHandle,
-  // then memory will be managed. If not, you might need explicit calls or a
-  // more robust memory management strategy.
 }

demos/tinystories/testConv2d.chpl

@@ -2,7 +2,6 @@ use ChAI;
 use NDArray;
 
 proc main() {
-  // Construct input tensor: shape (1, 1, 5, 5) for batch=1, channels=1, height=5, width=5
   var inputData: [0..0, 0..0, 0..4, 0..4] real = 
     [[[ [1.0, 2.0, 3.0, 4.0, 5.0],
         [6.0, 7.0, 8.0, 9.0, 10.0],
@@ -12,19 +11,16 @@ proc main() {
 
   var input = new ndarray(real, inputData.domain, inputData);
 
-  // Kernel tensor: shape (1, 1, 3, 3)
   var kernelData: [0..0, 0..0, 0..2, 0..2] real = 
     [[[ [1.0, 0.0, -1.0],
         [1.0, 0.0, -1.0],
         [1.0, 0.0, -1.0] ]]];
 
   var kernel = new ndarray(real, kernelData.domain, kernelData);
 
-  // Bias tensor: shape (1)
   var biasData: [0..0] real = [0.0];
   var bias = new ndarray(real, biasData.domain, biasData);
 
-  // Call conv2d with stride=1, padding=1 (same padding)
   var output = input.conv2d(kernel, bias, 1:int(32), 1:int(32));
 
   writeln("Output shape: ", output.domain);