Set add_special_tokens=False to not add EOS unexpectedly (#287)

Do not `add_special_tokens` when preprocessing prompts.  This changes performance (reward) for the better for non-GPT models (which don't add EOS by default) due to not ending the prompt with `<endoftext>`. 

Although adding `<endofcontext>` (EOC, from HH) could be something to add in trhe future.

examples/summarize_daily_cnn/t5_summarize_daily_cnn.py

@@ -51,14 +51,14 @@ def reward_fn(samples: List[str], prompts: List[str], outputs: List[str]):
 
     for i in tqdm(range(len(prompts))):
         key = tokenizer.decode(
-            tokenizer(prompts[i], truncation=True, max_length=max_length)["input_ids"],
+            tokenizer(prompts[i], truncation=True, max_length=max_length, add_special_tokens=False)["input_ids"],
             skip_special_tokens=True,
         )  # get prompt like trlx's prompt
         prompt_label[key.strip()] = summaries[i]
 
     for i in tqdm(range(len(val_prompts))):
         key = tokenizer.decode(
-            tokenizer(val_prompts[i], truncation=True, max_length=max_length)["input_ids"],
+            tokenizer(val_prompts[i], truncation=True, max_length=max_length, add_special_tokens=False)["input_ids"],
             skip_special_tokens=True,
         )  # get prompt like trlx's prompt
         prompt_label[key.strip()] = val_summaries[i]

examples/summarize_rlhf/trlx_gptj_text_summarization.py

@@ -67,12 +67,13 @@ def get_prompt_dataset(prompts, max_length):
                     prompts[i].split("TL;DR:")[0],
                     truncation=True,
                     max_length=max_length - 5,  # to make sure "TL;DR" dont get truncated
+                    add_special_tokens=False,
                 )["input_ids"],
                 skip_special_tokens=True,
             ).strip()
             tmp = tmp + "\nTL;DR:"
             tmp = tokenizer.decode(
-                tokenizer(tmp, truncation=True, max_length=max_length)["input_ids"],
+                tokenizer(tmp, truncation=True, max_length=max_length, add_special_tokens=False)["input_ids"],
                 skip_special_tokens=True,
             ).strip()
             formatted_prompts.append(tmp)

trlx/pipeline/offline_pipeline.py

@@ -3,7 +3,7 @@
 import torch
 from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import DataLoader
-from transformers import DataCollatorWithPadding
+from transformers import DataCollatorWithPadding, PreTrainedTokenizer
 
 from trlx.data.ilql_types import ILQLBatch, ILQLElement
 from trlx.pipeline import BasePipeline, BaseRolloutStore, register_datapipeline
@@ -23,7 +23,8 @@ def tokenize_dialogue(dialogue: Union[str, List[str]], tokenizer, max_length=204
     ctx_length = max_length
     if tokenizer.truncation_side == "left":
         for phrase in reversed(dialogue):
-            tokens = tokenizer(phrase).input_ids[-ctx_length:]
+            # Manually added BOS and EOS above so we don't want to add special tokens here
+            tokens = tokenizer(phrase, add_special_tokens=False).input_ids[-ctx_length:]
             ctx_length -= len(tokens)
             out.insert(0, tokens)
             if ctx_length == 0:
@@ -38,7 +39,8 @@ def tokenize_dialogue(dialogue: Union[str, List[str]], tokenizer, max_length=204
 
     elif tokenizer.truncation_side == "right":
         for phrase in dialogue:
-            tokens = tokenizer(phrase).input_ids[:ctx_length]
+            # Manually added BOS and EOS above so we don't want to add special tokens here
+            tokens = tokenizer(phrase, add_special_tokens=False).input_ids[:ctx_length]
             ctx_length -= len(tokens)
             out.append(tokens)
             if ctx_length == 0:
@@ -52,13 +54,20 @@ class PromptPipeline(BasePipeline):
     Tokenizes prompts, unless they are already tokenized, and truncates them to `max_prompt_length` from the right
     """
 
-    def __init__(self, prompts: List[str], max_prompt_length: int, tokenizer=None):
+    def __init__(self, prompts: List[str], max_prompt_length: int, tokenizer: PreTrainedTokenizer):
         super().__init__()
-        model_inputs = tokenizer(prompts, truncation=True, padding=False, max_length=max_prompt_length)
-        prompts = model_inputs["input_ids"]
+
+        model_inputs = tokenizer(
+            prompts, truncation=True, padding=False, max_length=max_prompt_length, add_special_tokens=False
+        )
+
+        prompts_tokens = model_inputs["input_ids"]
         attention_mask = model_inputs["attention_mask"]
+
         self.tokenizer = tokenizer
-        self.prompts = [{"input_ids": prompt, "attention_mask": mask} for prompt, mask in zip(prompts, attention_mask)]
+        self.prompts = [
+            {"input_ids": tokens, "attention_mask": mask} for tokens, mask in zip(prompts_tokens, attention_mask)
+        ]
 
     def __getitem__(self, ix: int):
         return self.prompts[ix]

trlx/trainer/accelerate_base_trainer.py

@@ -3,7 +3,7 @@
 import sys
 from abc import abstractmethod
 from time import time
-from typing import Dict, List, Optional, Sequence, Tuple, Union
+from typing import Dict, List, Optional, Tuple
 
 import ray
 import torch
@@ -175,25 +175,6 @@ def setup_scheduler(self):
         scheduler = scheduler_class(self.opt, **self.config.scheduler.kwargs)
         return scheduler
 
-    def tokenize(self, text: Union[Sequence[str], Sequence[torch.LongTensor]]):
-        """
-        Tokenize a batch of text after adding bos token to each of the samples
-        """
-        if isinstance(text[0], torch.LongTensor):
-            return text
-
-        text = [self.tokenizer.bos_token + txt for txt in text]
-        return self.tokenizer(
-            text,
-            truncation=True,
-            max_length=self.config.seq_length,
-            return_tensors="pt",
-            # NOTE: We manually add special tokens (bos) above so we set this False
-            # to avoid models that automatically add special tokens (e.g. OPT)
-            # adding them twice more.
-            add_special_tokens=False,
-        )
-
     def decode(
         self,
         prompts: List[torch.LongTensor],

trlx/trainer/accelerate_ilql_trainer.py

@@ -1,5 +1,5 @@
 import os
-from typing import Optional, Sequence, Union, cast
+from typing import Optional, cast
 
 import numpy as np
 import torch
@@ -43,22 +43,6 @@ def get_arch(self, config):
             num_layers_unfrozen=config.model.num_layers_unfrozen,
         )
 
-    def tokenize(self, texts: Union[Sequence[str], Sequence[torch.LongTensor]]):
-        if isinstance(texts[0], torch.LongTensor):
-            return texts
-
-        tokenized = self.tokenizer(
-            [self.tokenizer.bos_token + x + self.tokenizer.eos_token for x in texts],
-            max_length=self.max_length,
-            truncation=True,
-            # NOTE: We manually add special tokens (bos) above so we set this False
-            # to avoid models that automatically add special tokens (e.g. OPT)
-            # adding them twice more.
-            add_special_tokens=False,
-        )
-        input_ids = list(map(torch.as_tensor, tokenized.input_ids))
-        return input_ids
-
     def post_backward_callback(self):
         if self.iter_count % self.config.method.steps_for_target_q_sync == 0:
             self.accelerator.unwrap_model(self.model).sync_target_q_heads()

trlx/trainer/nemo_ilql_trainer.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Iterable, Sequence, Union, cast
+from typing import Iterable, Sequence, cast
 
 import numpy as np
 import torch
@@ -156,22 +156,6 @@ def __init__(
         if stop_sequences is not None and len(stop_sequences) > 0:
             logging.warning(f"Ignoring stop_sequences {stop_sequences=}")
 
-    def tokenize(self, texts: Union[Sequence[str], Sequence[torch.LongTensor]]):
-        if isinstance(texts[0], torch.LongTensor):
-            return texts
-
-        tokenized = self.tokenizer(
-            [self.tokenizer.bos_token + x + self.tokenizer.eos_token for x in texts],
-            max_length=self.max_length,
-            truncation=True,
-            # NOTE: We manually add special tokens (bos) above so we set this False
-            # to avoid models that automatically add special tokens (e.g. OPT)
-            # adding them twice more.
-            add_special_tokens=False,
-        )
-        input_ids = list(map(torch.as_tensor, tokenized.input_ids))
-        return input_ids
-
     def learn(self):
         def collate_fn(elems: Iterable[ILQLElement]):
             batch = ilql_collate_fn(elems)