[Distributed] Fix new token's shape (#1254)

dist_run.py

@@ -209,6 +209,7 @@ def _batch_decode_next_tokens(
     batch_size, seq_len, vocab_size = output.shape
 
     if step != -1:
+        # `pos` is not provided, so we can use the first token
         next_token_logits = output[:, 0, :]
     else:
         # get the logits for each prompt at the specified positions
@@ -228,9 +229,9 @@ def _batch_decode_next_tokens(
         ).squeeze(-1)
     else:
         # Argmax (deterministic)
-        next_tokens = torch.argmax(next_token_logits, dim=-1)
+        next_tokens = torch.argmax(next_token_logits, dim=-1, keepdim=True)
 
-    logger.info(f"{color.yellow}Next tokens: {color.blue}{next_tokens}{color.reset}")
+    # Token ids in int tensor form
     return next_tokens
 
 
@@ -247,6 +248,11 @@ def _update_padded_sequence(
 # Decode token id into string and print it
 def _decode_in_flight(token, tokenizer, tp_rank):
     """decode token ids for all prompts in the batch and log them"""
+    # `token` is a tensor of shape (batch_size, 1).
+    # For TiktokenTokenizer, we need to squeeze it to 1D.
+    # For SentencePieceProcessor, we don't.
+    if isinstance(tokenizer, TiktokenTokenizer):
+        token = torch.squeeze(token, dim=1)
     token_str = tokenizer.decode(token.tolist())
     # print the token string on tp rank 0
     if tp_rank == 0:
@@ -530,14 +536,12 @@ def get_example_ins_outs(seqlen: int) -> Tuple[torch.Tensor, torch.Tensor]:
 
     # 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_stacked = torch.stack(res, dim=1)
-        res_list = res_stacked.tolist()
-
-        # Decode the output as comprehension instead of loop
-        responses = [tokenizer.decode(sequence) for sequence in res_list]
-
+        # `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()
+        responses = tokenizer.decode(res_list)
         # Show prompts and responses
         for prompt_text, response_text in zip(prompt, responses):
             logger.info(f"Prompt: {color.green}{prompt_text} {color.reset}")