fix: rename samples to seq_lens and fix typos

- Rename variable 'samples' to 'seq_lens' for clarity since it holds sequence lengths
- Remove unused assignment (dead code)
- Fix typo 'mirobatches' -> 'microbatches' in comments

Author: JackXu0

slime/backends/megatron_utils/data.py

@@ -308,14 +308,14 @@ def _generate_data_iterator(rollout_data, micro_batch_size, micro_batch_indices=
         data_iterator = _generate_data_iterator(rollout_data, args.micro_batch_size)
     else:
         assert args.max_tokens_per_gpu is not None
-        # calculate the number of mirobatches for each step
-        samples = rollout_data["total_lengths"]
-        assert len(samples) == num_local_samples
+        # calculate the number of microbatches for each step
+        seq_lens = rollout_data["total_lengths"]
+        assert len(seq_lens) == num_local_samples
         num_microbatches = []
         for i in range(num_steps_per_rollout):
             start, end = i * num_local_gbs, (i + 1) * num_local_gbs
             num_microbatches.append(
-                get_minimum_num_micro_batch_size(samples[start:end], args.max_tokens_per_gpu * cp_size)
+                get_minimum_num_micro_batch_size(seq_lens[start:end], args.max_tokens_per_gpu * cp_size)
             )
 
         num_microbatches = torch.tensor(num_microbatches, dtype=torch.int, device=torch.cuda.current_device())
@@ -330,14 +330,12 @@ def _generate_data_iterator(rollout_data, micro_batch_size, micro_batch_indices=
 
         num_microbatches = num_microbatches.tolist()
 
-        # balance the each micro batch
-        samples = rollout_data["total_lengths"]
-        # balance the number of mirobatches across steps
+        # balance the number of microbatches across steps
         micro_batch_indices = []
         for i, num_mbs in enumerate(num_microbatches):
             start, end = i * num_local_gbs, (i + 1) * num_local_gbs
-            samples = rollout_data["total_lengths"][start:end]
-            partitions = get_seqlen_balanced_partitions(samples, num_mbs, equal_size=False)
+            seq_lens = rollout_data["total_lengths"][start:end]
+            partitions = get_seqlen_balanced_partitions(seq_lens, num_mbs, equal_size=False)
             for j in range(num_mbs):
                 for k in range(len(partitions[j])):
                     partitions[j][k] += start