[Distributed] Fix new token's shape

dist_run.py

@@ -203,7 +203,7 @@ def _batch_decode_next_tokens(
     # Take the next token logits for each prompt
     next_token_logits = output[:, pos, :]
     # Argmax (deterministic) TODO: add temperature
-    next_token = torch.argmax(next_token_logits, dim=-1)
+    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
     # Token ids in int tensor form
     return next_token
 
@@ -418,9 +418,7 @@ def get_example_ins_outs(seqlen: int) -> Tuple[torch.Tensor, torch.Tensor]:
 
     # Decode token id into string and print it
     def decode_in_flight(token):
-        # Make a 2D tensor with ids on row dimension
-        unsqueezed = torch.unsqueeze(token, 1)
-        token_str = tokenizer.decode(unsqueezed.tolist())
+        token_str = tokenizer.decode(token.tolist())
         if tp_rank == 0:
             logger.info(
                 f"{color.green} responses ====>>>> "
@@ -498,8 +496,10 @@ def decode_in_flight(token):
 
     # output formatted response via last pp group and tp rank 0
     if pp_rank == last_pp_rank and tp_rank == 0:
-        # `res` is a list of tensors, each being a batch of generated token ids
-        res = torch.stack(res, dim=1)
+        # `res` is a list of tensors, each being a batch of generated token ids.
+        # We need to concatenate them to get the full sequence of generated
+        # token ids. Thus cat'ing along dim 1.
+        res = torch.cat(res, dim=1)
         res_list = res.tolist()
         response = tokenizer.decode(res_list)
         for i in range(len(response)):