Fix comment typos in training scripts

scripts/base_eval.py

@@ -64,7 +64,7 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
             data = [json.loads(line.strip()) for line in f]
 
         # shuffle the data because in many cases it appears ordered but we want
-        # the abillity to only run a subset of the data for debugging purposes etc.
+        # the ability to only run a subset of the data for debugging purposes etc.
         shuffle_rng = random.Random(1337)
         shuffle_rng.shuffle(data)
         if max_per_task > 0:

scripts/base_train.py

@@ -261,7 +261,7 @@ for step in range(num_iterations + 1):
         loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
         loss.backward()
         x, y = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
-    # gradient clipping (TODO possibly expertiment with)
+    # gradient clipping (TODO possibly experiment with)
     if grad_clip > 0.0:
         torch.nn.utils.clip_grad_norm_(orig_model.parameters(), grad_clip)
     # step the optimizers

scripts/chat_eval.py

@@ -115,7 +115,7 @@ def run_categorical_eval(task_object, tokenizer, model, batch_size, max_problems
             logits = model(prompt_ids) # (B, T, V)
 
         # Focus on the available answer on just the letters corresponding to choices
-        # Note that this helps the evaluation a lot because it specifically narrows the focus to only the avilable letters
+        # Note that this helps the evaluation a lot because it specifically narrows the focus to only the available letters
         # The much harder alternative would be to just generate from the Assistant and check if it responded with the correct
         # letter (e.g. A, B, C, D), but evaluations typically make the task easier in this way.
         for idx, conversation in enumerate(conversations):

scripts/chat_rl.py

@@ -206,7 +206,7 @@ def get_lr_multiplier(it):
     lrm = 1.0 - it / num_steps
     return lrm
 
-# Calculate the number of examples each rank handles to achive the desired examples_per_step
+# Calculate the number of examples each rank handles to achieve the desired examples_per_step
 print0(f"Total sequences per step: {examples_per_step * num_samples}") # total batch size in sequences/step
 assert examples_per_step % ddp_world_size == 0, "Desired examples per step must be divisible by the number of ranks"
 examples_per_rank = examples_per_step // ddp_world_size # per GPU