Enable conversational pipeline for GPTSw3Tokenizer (#24648)

* feat: Add `_build_conversation_input_ids` to GPT-SW3 tokenizer, adjust line length

* feat: Merge in PR #24504.

This allows the GPT-SW3 models (and other GPT-2 based models) to be 4-bit quantised
using `load_in_4bit` with `bitsandbytes`.

* fix: F-string

* fix: F-string

* fix: Remove EOS token from all responses

* fix: Remove redundant newlines

* feat: Add `load_in_4bit` to `Pipeline`

* fix: Separate turns with `\n<s>\n` rather than `<s>`

* fix: Add missing newline in prompt

* tests: Add unit tests for the new `_build_conversation_input_ids` method

* style: Automatic style correction

* tests: Compare encodings rather than decodings

* fix: Remove `load_in_4bit` from pipeline arguments

* docs: Add description and references of the GPT-SW3 chat format

* style: Line breaks

* Apply suggestions from code review

Fix Conversation type hints

Co-authored-by: Arthur <[email protected]>

* fix: Import TYPE_CHECKING

* style: Run automatic fixes

* tests: Remove `_build_conversation_input_ids` unit tests

* tests: Remove import of `Conversation` in GPT-SW3 unit test

* style: Revert formatting

* style: Move TYPE_CHECKING line after all imports

* style: Imports order

* fix: Change prompt to ensure that `sp_model.encode` and `encode` yields same result

* docs: Add TODO comment related to the addition of whitespace during decoding

* style: Automatic style checks

* fix: Remove final whitespace in prompt, as prefix whitespace is used by sentencepiece

---------

Co-authored-by: Arthur <[email protected]>

src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py

@@ -1,20 +1,23 @@
+"""The tokenizer used by the GPT-SW3 models."""
+
 import os
 import re
 import unicodedata
+from shutil import copyfile
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
-from ... import is_torch_available
+import sentencepiece as spm
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import is_torch_available, logging
 
 
 if is_torch_available():
     import torch
 
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
 
-import sentencepiece as spm
-
-from ...tokenization_utils import PreTrainedTokenizer
-from ...utils import logging
+if TYPE_CHECKING:
+    from transformers.pipelines.conversational import Conversation
 
 
 logger = logging.get_logger(__name__)
@@ -230,8 +233,10 @@ def convert_tokens_to_string(self, tokens: List[str]) -> str:
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
+                # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                 if not prev_is_special:
                     out_string += " "
+
                 out_string += self.sp_model.decode(current_sub_tokens) + token
                 prev_is_special = True
                 current_sub_tokens = []
@@ -312,3 +317,32 @@ def decode_fast(self, token_ids: Union[int, List[int]]) -> str:
         """
 
         return self.sp_model.decode(token_ids)
+
+    def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
+        """Builds the input ids for a conversation.
+
+        This is the format used in the original GPT-SW3 paper [1] and which is also mentioned in the model card [2].
+        The format is inspired by the ChatML format [3]. Concretely, the chat format is set up as follows:
+
+        ```
+        <eos><bos>User: Jag tycker träd är fina<bos>Bot: Kul att du tycker det!<bos>...
+        ```
+
+        Args:
+            conversation (`Conversation`):
+                Conversation to build input ids for.
+
+        Returns:
+            `List[int]`:
+                Input ids for the conversation.
+
+        References:
+            - [1] https://doi.org/10.48550/arXiv.2305.12987
+            - [2] https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m-instruct
+            - [3] https://github.com/openai/openai-python/blob/main/chatml.md
+        """
+        all_responses = [f"User: {text}" if is_user else f"Bot: {text}" for is_user, text in conversation.iter_texts()]
+        prompt = (
+            f"{self.eos_token}{self.bos_token}" + f"{self.bos_token}".join(all_responses) + f"{self.bos_token}Bot:"
+        )
+        return self.encode(text=prompt)