gemma_explicit_tokenizer.py

"""
Custom Gemma Tokenizer for explicit Format

This tokenizer implements the explicit format for message processing:
Format: Uses the standard chat template with proper role labels (user/assistant)

The explicit format uses the model's built-in chat template and includes proper 
loss computation flags for training.

To save:
uv run tokenizers/gemma_explicit_tokenizer.py 
which will save the tokenizer to the repos/explicit-gemma-tokenizer directory.
mkdir repos/explicit12b
# copy model over
cp models_v8/base_modified-google-gemma-3-12b-pt-/models/_explicit/checkpoint-8/* repos/explicit12b/
# copy tokenizer over
cp repos/explicit-gemma-tokenizer/* repos/explicit12b/
# upload to hf

uv run upload_to_hf.py \
  --folder repos/explicit12b \
  --repo-id tsor13/explicit12b
"""

from typing import List, Dict, Any, Optional, Union
from transformers import AutoTokenizer
from transformers.models.gemma.tokenization_gemma_fast import GemmaTokenizerFast
from transformers.models.gemma.tokenization_gemma import GemmaTokenizer
import warnings
import difflib
import json
import os
import sys

# Add parent directory to path to import chat_utils
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from chat_utils import chat_messages_to_text_loss, chat_messages_to_raw_text


class GemmaExplicitTokenizer(GemmaTokenizerFast):
    """
    Custom tokenizer for Gemma models that implements explicit format message processing.
    
    This tokenizer formats messages using the explicit format where:
    - Messages use the standard chat template with proper role labels
    - Uses the model's built-in chat formatting
    - Loss is computed on the assistant/output sections
    
    Attributes:
        start_string (str): The starting string used for output generation (depends on tokenizer)
        end_string (str): The ending string used for output generation (depends on tokenizer)
    """
    
    def __init__(self, *args, **kwargs):
        """
        Initialize the custom tokenizer.
        
        Accepts the same arguments as GemmaTokenizerFast.
        """
        super().__init__(*args, **kwargs)
        
        # For explicit format, we use the tokenizer's own chat template
        # The start/end strings will be determined by the chat template
        self.start_string = None  # Will be set dynamically
        self.end_string = None    # Will be set dynamically
        
        # Add custom attributes to the tokenizer config for saving/loading
        if not hasattr(self, 'init_kwargs'):
            self.init_kwargs = {}
        self.init_kwargs['start_string'] = self.start_string
        self.init_kwargs['end_string'] = self.end_string
    
    @classmethod
    def from_gemma_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
        """
        Load a tokenizer from a pretrained model or path.
        
        This method ensures our custom class is used instead of the base GemmaTokenizerFast.
        """
        # Load the base tokenizer first to get all configuration
        base_tokenizer = GemmaTokenizerFast.from_pretrained(
            pretrained_model_name_or_path, *args, **kwargs
        )
        
        # Create new instance of our custom class by copying the base tokenizer
        custom_tokenizer = cls.__new__(cls)
        
        # Copy all attributes from base tokenizer
        for attr, value in base_tokenizer.__dict__.items():
            setattr(custom_tokenizer, attr, value)
        
        # Initialize our custom attributes for explicit format
        custom_tokenizer.start_string = None  # Will be determined dynamically
        custom_tokenizer.end_string = None    # Will be determined dynamically
        
        # Update init_kwargs to include our custom attributes
        if not hasattr(custom_tokenizer, 'init_kwargs'):
            custom_tokenizer.init_kwargs = {}
        custom_tokenizer.init_kwargs['start_string'] = custom_tokenizer.start_string
        custom_tokenizer.init_kwargs['end_string'] = custom_tokenizer.end_string
        
        return custom_tokenizer
    
    def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):
        """
        Save the tokenizer to a directory, including custom configuration.
        """
        # Call parent save method
        super().save_pretrained(save_directory, **kwargs)
        
        # Save custom configuration
        config_file = os.path.join(save_directory, "tokenizer_config.json")
        if os.path.exists(config_file):
            with open(config_file, 'r') as f:
                config = json.load(f)
        else:
            config = {}
        
        # Add our custom class info
        config["tokenizer_class"] = "GemmaExplicitTokenizer"
        config["start_string"] = self.start_string
        config["end_string"] = self.end_string
        # Point to our custom class in the uploaded file
        config["auto_map"] = {
            "AutoTokenizer": ["gemma_explicit_tokenizer.GemmaExplicitTokenizer", "gemma_explicit_tokenizer.GemmaExplicitTokenizer"]
        }
        
        with open(config_file, 'w') as f:
            json.dump(config, f, indent=2)
    
    def messages_to_loss_texts(
            self,
            messages: List[Dict[str, Any]], 
            loss_on_start_token: bool = False, 
        ) -> List[Dict[str, Any]]:
        """
        From messages (description / input / output) to texts (text / compute_loss) with whether or not loss should be calculated on the text for training.
        Uses the explicit format from chat_utils.
        """
        return chat_messages_to_text_loss(messages, self, loss_on_start_token, start_gen_as="output")
    
    def messages_to_text(
            self, 
            messages: List[Dict[str, Any]], 
            start_generation: bool = False, 
        ) -> str:
        """
        Messages (description / input / output) to raw text (text).
        Uses the explicit format from chat_utils.
        """
        return chat_messages_to_raw_text(messages, self, start_generation=start_generation, start_gen_as="output")
    

    def tokenize_messages(
            self, 
            messages: List[Dict[str, Any]] | List[List[Dict[str, Any]]],
            start_generation: bool = False, 
            **kwargs,
        ):
        """
        For tokenizing from messages to texts. Supports batching. Good for generation
        """
        if isinstance(messages, list) and isinstance(messages[0], list):
            # Handle list of lists of messages
            all_texts = []
            for message_list in messages:
                texts = self.messages_to_text(message_list, start_generation)
                all_texts.append(texts)
        else:
            # Handle single list of messages
            texts = self.messages_to_text(messages, start_generation)
            all_texts = [texts]
        
        # Tokenize all texts
        processed = self(text=all_texts, **kwargs)
        return processed
    
    
    def tokenize_loss_texts(
            self, 
            texts: List[Dict[str, Any]], 
            loss_on_start_token: bool = False, 
            loss_on_eos: bool = False, 
            include_eos: bool = True,
        ):
        """
        Tokenize texts (text / compute_loss) to tokenized texts (input_ids / attention_mask / labels).

        Needs more complex logic to handle the back and forth labeling.
        """
        if loss_on_eos:
            raise ValueError("Loss on EOS is not currently supported.")
        
        # Handle single string input
        if isinstance(texts, str):
            processed = self(text=texts)
            # Add EOS token if needed
            if (self.eos_token_id is not None and 
                processed["input_ids"][-1] != self.eos_token_id):
                processed["input_ids"] = processed["input_ids"] + [self.eos_token_id]
                processed["attention_mask"] = processed["attention_mask"] + [1]
            return processed
        
        # Handle list of text dictionaries
        all_processed = []
        all_texts = ''
        example_inds = []
        dataset_inds = []
        
        for i, item in enumerate(texts):
            processed = self(text=item["text"])
            
            # Remove BOS token from all but first item
            if i != 0 and self.bos_token_id == processed["input_ids"][0]:
                processed["input_ids"] = processed["input_ids"][1:]
                processed["attention_mask"] = processed["attention_mask"][1:]
            
            # Remove EOS token if present at the end
            if processed["input_ids"][-1] == self.eos_token_id:
                processed["input_ids"] = processed["input_ids"][:-1]
                processed["attention_mask"] = processed["attention_mask"][:-1]
            
            # Check for EOS token in the middle (with special handling for <|im_end|>)
            if self.eos_token_id in processed["input_ids"]:
                if not self.decode([self.eos_token_id]) == "<|im_end|>":
                    raise ValueError(f"EOS token is present in input_ids: {processed['input_ids']}. Not currently supported.")
            
            # Set labels based on compute_loss flag
            if item["compute_loss"]:
                processed["labels"] = processed["input_ids"].copy()
            else:
                processed["labels"] = [-100] * len(processed["input_ids"])
            
            # Remove duplicate BOS tokens
            if all_processed:
                if processed["input_ids"][0] == self.bos_token_id:
                    processed["input_ids"] = processed["input_ids"][1:]
                    processed["attention_mask"] = processed["attention_mask"][1:]
                    processed["labels"] = processed["labels"][1:]
            
            all_processed.append(processed)
            all_texts += item["text"]
            
            # Handle example indices
            this_num = -1
            if 'example_ind' in item.keys():
                if item["example_ind"] is not None:
                    this_num = item["example_ind"]
            example_inds.extend([this_num] * len(processed["input_ids"]))
            
            # Handle dataset indices
            dataset_ind = -1
            if "data_id" in item.keys():
                if item["data_id"] is not None:
                    dataset_ind = item["data_id"]
            dataset_inds.extend([dataset_ind] * len(processed["input_ids"]))
        
        # Combine all processed results
        processed = all_processed[0].copy()
        processed["input_ids"] = [item for sublist in [p["input_ids"] for p in all_processed] for item in sublist]
        processed["attention_mask"] = [item for sublist in [p["attention_mask"] for p in all_processed] for item in sublist]
        processed["labels"] = [item for sublist in [p["labels"] for p in all_processed] for item in sublist]
        processed["example_inds"] = example_inds
        processed["data_ids"] = dataset_inds
        
        # Validate by tokenizing all_texts at once and comparing
        processed_all = self(text=all_texts)
        if len(processed_all["input_ids"]) != len(processed["input_ids"]):
            warnings.warn(f"All texts are not the same length as the first text. Please check your dataset. {len(processed_all['input_ids'])} != {len(processed['input_ids'])}")
            
            # Generate diff for debugging
            all_text = self.decode(processed_all["input_ids"], skip_special_tokens=False)
            processed_text = self.decode(processed["input_ids"], skip_special_tokens=False)
            
            diff = difflib.unified_diff(all_text.splitlines(), processed_text.splitlines())
            diff_str = "\n".join(diff)
            print("Diff between texts:")
            print(diff_str)
            
            # Token diff
            all_tokens_str = '\n'.join([str(s) for s in processed_all["input_ids"]])
            processed_tokens_str = '\n'.join([str(s) for s in processed["input_ids"]])
            token_diff = difflib.unified_diff(all_tokens_str.splitlines(), processed_tokens_str.splitlines())
            token_diff_str = "\n".join(token_diff)
            print("Diff between tokenized texts:")
            print(token_diff_str)
        
        # Add EOS token if needed
        if (self.eos_token_id is not None and 
            processed["input_ids"][-1] != self.eos_token_id):
            processed["input_ids"] = processed["input_ids"] + [self.eos_token_id]
            processed["example_inds"] = processed["example_inds"] + [-1]
            processed["attention_mask"] = processed["attention_mask"] + [1]
            if processed["labels"] is not None:
                if loss_on_eos:
                    processed["labels"] = processed["labels"] + [self.eos_token_id]
                else:
                    processed["labels"] = processed["labels"] + [-100]
            if "data_ids" in processed:
                processed["data_ids"] = processed["data_ids"] + [-1]
            
        if not include_eos:
            # check if EOS token is present
            if processed["input_ids"][-1] == self.eos_token_id:
                # remove EOS token
                processed["input_ids"] = processed["input_ids"][:-1]
                processed["attention_mask"] = processed["attention_mask"][:-1]
                processed["labels"] = processed["labels"][:-1]
                processed["example_inds"] = processed["example_inds"][:-1]
                processed["data_ids"] = processed["data_ids"][:-1]
        
        return processed
    
    def tokenize_messages(
            self, 
            messages: List[Dict[str, Any]], 
            loss_on_start_token: bool = False, 
            loss_on_eos: bool = False,
            include_eos: bool = True,
        ) -> Dict[str, Any]:
        """
        Intended for tokenize from messages to tokenized texts with the loss applied.
        """
        # First convert messages to text with loss computation flags
        texts = self.messages_to_loss_texts(messages, loss_on_start_token)
        
        # Then tokenize the texts
        return self.tokenize_loss_texts(texts, loss_on_eos, include_eos = include_eos)
    



# Register tokenizer classes for AutoTokenizer
AutoTokenizer.register("GemmaExplicitTokenizer", slow_tokenizer_class=None, fast_tokenizer_class=GemmaExplicitTokenizer)


if __name__ == "__main__":
    # Example usage
    # for first load
    custom_tokenizer = GemmaExplicitTokenizer.from_gemma_pretrained("google/gemma-3-1b-it")

    # for subsequent loads
    # custom_tokenizer = GemmaExplicitTokenizer.from_pretrained("tsor13/explicit-gemma-12b-pt")
    # custom_tokenizer = GemmaExplicitTokenizer.from_pretrained("repos/explicit-gemma-12b-pt")
    
    # Test messages in role/content format
    messages = [
        {"role": "description", "content": "This is a test task"},
        {"role": "input", "content": "What is 2+2?"},
        {"role": "output", "content": "4"},
        {"role": "input", "content": "What is 3+3?"},
        # {"role": "output", "content": "6"}
    ]

    # get messages to text_loss
    texts = custom_tokenizer.messages_to_loss_texts(messages)
    print("Texts with loss flags:")
    for i, text in enumerate(texts):
        print(f"  {i}: {text}")

    text = custom_tokenizer.messages_to_text(messages, start_generation=True)
    print(f"\nFull text with generation prompt:")
    print(text)
    
    print("\nTesting save/load cycle:")
    # Test saving and loading
    tokenizer_path = "repos/explicit-gemma-tokenizer"
    custom_tokenizer.save_pretrained(tokenizer_path)
    print("Tokenizer saved successfully!")

    # also save this file in the tokenizer_path
    import shutil
    shutil.copy(__file__, os.path.join(tokenizer_path, "gemma_explicit_tokenizer.py"))
    print("GemmaExplicitTokenizer.py saved successfully!")