Upload tokenizer

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
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+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
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+[More Information Needed]
+
+**APA:**
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+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

gptx_tokenizer.py

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+from __future__ import annotations
+
+import json
+import os
+import warnings
+from pathlib import Path
+from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
+
+import sentencepiece as spm
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download, list_repo_files, try_to_load_from_cache
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
+
+
+REPO_ID = "lamarr-llm-development/elbedding-v2-AutoGPTQ-4bit"
+
+
+class HFGPTXTokenizer(PreTrainedTokenizer):
+    """
+    A custom tokenizer class that extends Hugging Face's PreTrainedTokenizer.
+    It is specifically designed to work with SentencePiece models and integrates
+    with Hugging Face's tokenizer utilities.
+    """
+    
+    model_file_glob = "*tokenizer.json"
+    vocab_files_names = {"tokenizer_file": "tokenizer.json"}
+    decode_kwargs: List[str] = []
+
+    def _encode(self, text: str, return_tokens: bool = False, is_continuation: bool = False):
+        """
+        Encode a given text using the tokenizer.
+        
+        Args:
+            text (str): The text to encode.
+            return_tokens (bool): If True, returns token strings instead of token IDs.
+            is_continuation (bool): If True, uses a continuation tokenizer (if available).
+        Returns:
+            List[int] or List[str]: Encoded text as a list of token IDs or token strings.
+        """
+        assert self.tok is not None, "No tokenizer is currently loaded"
+
+        # Variant with additional sp processor:
+        tokenizer = self.continuation_tokenizer if is_continuation else self.tok
+
+        if return_tokens:
+            return tokenizer.encode_as_pieces(text)
+        else:
+            return tokenizer.encode(text)
+    
+    def create_list_of_special_tokens(self) -> List[str]:
+        """
+        Create a list of special tokens, including the BOS, EOS, PAD, EOD tokens,
+        and 256 additional placeholder tokens.
+        Returns:
+            List[str]: List of special tokens.
+        """
+        return [self.bos_token, self.eos_token, self.pad_token, self.eod_token] + [
+            f"<placeholder_tok_{i}>" for i in range(256)
+        ]
+    
+    def find_tokenizer_config(self, config_path: Path, repo_id: str = None) -> Optional[Path]:
+        if not os.path.isfile(config_path):
+            config_path = try_to_load_from_cache(repo_id=repo_id, filename=Path(config_path).name)
+            if not config_path:
+                config_path = self._download_config_from_hub(repo_id=repo_id)
+
+        return config_path
+
+    
+    def instantiate_from_file_or_name(self, model_file_or_name: str, repo_id: str = None):
+        """
+        Load the tokenizer model from a file or download it from a repository.
+
+        Args:
+            model_file_or_name (str): Path to the model file or the model name.
+            repo_id (str, optional): Repository ID from which to download the model file.
+
+        Returns:
+            spm.SentencePieceProcessor: Loaded SentencePieceProcessor instance.
+
+        Raises:
+            ValueError: If repo_id is not provided when model_file_or_name is not a file.
+            OSError: If the model file cannot be loaded or downloaded.
+        """
+        if not os.path.isfile(model_file_or_name):
+            model_file_or_name = try_to_load_from_cache(repo_id=repo_id, filename=Path(model_file_or_name).name)
+            if not model_file_or_name:
+                model_file_or_name = self._download_model_from_hub(repo_id=repo_id)
+
+        try:
+            return spm.SentencePieceProcessor(model_file=model_file_or_name)
+        except Exception as e:
+            raise OSError(f"Failed to load tokenizer model: {str(e)}")
+
+    def _download_model_from_hub(self, repo_id: str) -> Optional[str]:
+        try:
+            # List all files in the repo
+            repo_files = list_repo_files(repo_id)
+
+            # Find the tokenizer model file
+            tokenizer_files = [f for f in repo_files if f.endswith('.model')]
+            if not tokenizer_files:
+                raise FileNotFoundError(f"No .model file found in repository {repo_id}")
+
+            # Use the first .model file found
+            model_file = tokenizer_files[0]
+            print(f"Found tokenizer model file: {model_file}")
+
+            # Download the file
+            model_file_or_name = hf_hub_download(repo_id=repo_id, filename=model_file)
+            print(f"Downloaded tokenizer model to: {model_file_or_name}")
+        except Exception as e:
+            raise OSError(f"Failed to download tokenizer model: {str(e)}")
+
+        return model_file_or_name
+
+    def _download_config_from_hub(self, repo_id: str):
+        if repo_id is None:
+            raise ValueError("repo_id must be provided if config_path is not a local file")
+
+        try:
+            # List all files in the repo
+            repo_files = list_repo_files(repo_id)
+
+            # Find the tokenizer config file
+            tokenizer_files = [f for f in repo_files if f.endswith('tokenizer_config.json')]
+            if not tokenizer_files:
+                raise FileNotFoundError(f"No tokenizer_config.json file found in repository {repo_id}")
+
+            # Use the first tokenizer_config.json file found
+            tokenizer_config_file = tokenizer_files[0]
+            print(f"Found tokenizer config file: {tokenizer_config_file}")
+
+            # Download the file
+            tokenizer_config_file_or_name = hf_hub_download(repo_id=repo_id, filename=tokenizer_config_file)
+            print(f"Downloaded tokenizer config file to: {tokenizer_config_file_or_name}")
+            return tokenizer_config_file_or_name
+        except Exception as e:
+            raise OSError(f"Failed to download tokenizer model: {str(e)}")    
+    def __init__(
+        self,
+        model_path: Optional[str] = None,
+        config_path: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        """
+        Initialize the tokenizer.
+        Args:
+            model_path (Optional[str]): Path to the tokenizer model file.
+            config_path (Optional[str]): Path to the tokenizer configuration file.
+            **kwargs: Additional keyword arguments passed to the superclass.
+        This method also ensures backward compatibility by setting
+        `clean_up_tokenization_spaces` to False by default.
+        """
+        # Prevent cleanup of tokenization spaces to maintain backward compatibility
+        self.clean_up_tokenization_spaces = kwargs.setdefault("clean_up_tokenization_spaces", False)
+        self.vocab = None
+        cp_path = kwargs.get("name_or_path", ".")
+        if model_path is None:
+            model_path = str(Path(cp_path) / self.vocab_files_names["tokenizer_file"])
+        self.tok = self.instantiate_from_file_or_name(model_path, repo_id=REPO_ID)
+
+        super().__init__(**kwargs)
+
+        # Specify special tokens which we know the value of.
+        # EOD from `tok` is used as what is called EOS in HuggingFace.
+        # Since there is no corresponding mapping for EOS from `tok` in
+        # HuggingFace, it is treated as an additional special token.
+        # Same for all other special tokens.
+        
+        
+        self.unk_token = "<unk>"
+        self.eos_token = "</s>"
+        self.bos_token = "<s>"
+        self.pad_token = "<pad>"
+        self.eod_token = "<eod>"
+        
+        self.additional_special_tokens = self.create_list_of_special_tokens()
+    
+        if config_path is None:
+            config_path = str(Path(cp_path) / TOKENIZER_CONFIG_FILE)
+
+        if os.path.isfile(config_path):
+            self.tokenizer_config = self.load_json(Path(config_path))
+        else: # Load from repo
+            self.tokenizer_config = self.load_json(Path(self.find_tokenizer_config(Path(config_path), repo_id=REPO_ID)))
+
+    @property
+    def vocab_size(self) -> int:
+        """
+        Get the size of the tokenizer vocabulary.
+        Returns:
+            int: The size of the vocabulary.
+        """
+        return self.tok.GetPieceSize()
+
+    def get_vocab(self) -> Dict[str, int]:
+        """
+        Get the vocabulary as a dictionary mapping token strings to their IDs.
+        Returns:
+            Dict[str, int]: Vocabulary mapping.
+        """
+        if self.vocab is None:
+            self.vocab = {self.tok.IdToPiece(i): i for i in range(self.vocab_size)}
+        return self.vocab
+
+    def _tokenize(self, text: str, **kwargs) -> List[int]:
+        """
+        Tokenize the input text.
+        Args:
+            text (str): Text to tokenize.
+            **kwargs: Additional keyword arguments.
+        Returns:
+            List[int]: List of token IDs.
+        """
+        return_tokens = kwargs.pop("return_tokens", True)
+        return self._encode(text, return_tokens=return_tokens, **kwargs)
+
+    def _convert_token_to_id(self, token: str) -> int:
+        """
+        Convert a token string to its corresponding ID.
+        Args:
+            token (str): The token to convert.
+        Returns:
+            int: The token's ID.
+        Raises:
+            ValueError: If the token is unknown and cannot be encoded to a single ID.
+        """
+        return self.tok.PieceToId(token)
+
+
+    def decode(
+        self,
+        token_ids: Union[List[int], List[List[int]]],
+        num_threads: Optional[int] = None,
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = False,
+    ) -> str:
+        """
+        Decode a list of token IDs into a string.
+        Args:
+            token_ids (Union[List[int], List[List[int]]]): List of token IDs or lists of token IDs.
+            num_threads (Optional[int]): Number of threads to use for decoding.
+        Returns:
+            str: Decoded string.
+        """
+        if isinstance(token_ids, torch.Tensor):  # For PyTorch tensors
+            token_ids = token_ids.tolist()
+        elif isinstance(token_ids, np.ndarray):  # For NumPy arrays
+            token_ids = token_ids.tolist()
+        
+        output = self.tok.decode(input=token_ids, num_threads=num_threads)
+        if skip_special_tokens:
+            for substring in self.additional_special_tokens:
+                output = output.replace(substring, "")
+        
+        if clean_up_tokenization_spaces:
+            warnings.warn(
+                "when cleaning up tokenization spaces, this will not behave "
+                "like the original `GPTXTokenizer`., Please supply "
+                "`clean_up_tokenization_spaces=False` for decoding."
+            )
+            output = self.clean_up_tokenization(output)
+        
+        return output
+
+    
+    def _convert_id_to_token(self, index: int) -> str:
+        """
+        Convert a token ID to its corresponding token string.
+        Args:
+            index (int): Token ID.
+        Returns:
+            str: Corresponding token string.
+        """
+        return self.tok.IdToPiece(index)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """
+        Convert a list of tokens into a single string.
+        Args:
+            tokens (List[str]): List of token strings.
+        Returns:
+            str: Concatenated string of tokens.
+        """
+        return self.tok.DecodePieces(tokens)
+
+    def _tok_decode(self, token_ids: List[int], **kwargs: Any) -> str:
+        """
+        Internal method to decode token IDs with additional arguments.
+        Args:
+            token_ids (List[int]): List of token IDs.
+            **kwargs: Additional arguments to pass to the decode method.
+        Returns:
+            str: Decoded string.
+        This method also issues a warning if unsupported arguments are provided.
+        """
+        passed_kwargs = {key: value for (key, value) in kwargs.items() if key in self.decode_kwargs}
+        if len(passed_kwargs) != len(kwargs):
+            warnings.warn("silently ignoring some arguments to `decode` due to missing " "support from the tokenizer.")
+        text = self.decode(token_ids, **passed_kwargs)
+        return text
+    
+    def save_tokenizer(self, save_dir: str) -> None:
+        if not os.path.isdir(save_dir):
+            print(f"Vocabulary path ({save_dir}) should be a directory")
+            return
+        out_vocab_file = os.path.join(save_dir, "tokenizer.model")
+
+        # if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+        #     copyfile(self.vocab_file, out_vocab_file)
+        # elif not os.path.isfile(self.vocab_file):
+        with open(out_vocab_file, "wb") as f:
+            content_spiece_model = self.tok.serialized_model_proto()
+            f.write(content_spiece_model)
+
+        return (out_vocab_file,)
+        
+    def _decode(
+        self,
+        token_ids: List[int],
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = None,
+        spaces_between_special_tokens: bool = True,
+        **kwargs: Any,
+    ) -> str:
+        text = self._tok_decode(
+            token_ids,
+            skip_special_tokens=skip_special_tokens,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            **kwargs,
+        )
+
+        clean_up_tokenization_spaces = (
+            clean_up_tokenization_spaces
+            if clean_up_tokenization_spaces is not None
+            else self.clean_up_tokenization_spaces
+        )
+        if clean_up_tokenization_spaces:
+            warnings.warn(
+                "when cleaning up tokenization spaces, this will not behave "
+                "like the original `GPTXTokenizer`., Please supply "
+                "`clean_up_tokenization_spaces=False` for decoding."
+            )
+            clean_text = self.clean_up_tokenization(text)
+            return clean_text
+        else:
+            return text
+        
+    def save_vocabulary(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None,
+    ) -> Tuple[str]:
+        filename_prefix = filename_prefix + "-" if filename_prefix else ""
+        save_directory = Path(save_directory)
+
+        self._save_tokenizer_config(save_directory, filename_prefix)
+        tokenizer_file_path = self._save_tokenizer(save_directory, filename_prefix)
+
+        return (tokenizer_file_path,)
+
+    def _save_tokenizer_config(
+        self,
+        save_directory: Path,
+        filename_prefix: str,
+    ) -> str:
+        self.save_tokenizer_config(save_directory)
+        old_tokenizer_config_path = save_directory / TOKENIZER_CONFIG_FILE
+        assert old_tokenizer_config_path.is_file(), "tokenizer config path changed"
+        new_tokenizer_config_path = save_directory / (filename_prefix + old_tokenizer_config_path.name)
+        old_tokenizer_config_path.replace(new_tokenizer_config_path)
+        return str(new_tokenizer_config_path)
+
+    def _find_tokenizer_files(self, save_directory: Path) -> List[Path]:
+        files = list(Path(save_directory).glob(self.model_file_glob))
+        return files
+
+    def _get_tokenizer_file(self, files: List[Path]):
+        assert files, "no saved tokenizer file found"
+        assert len(files) <= 1, "cannot handle multiple saved tokenizer files"
+        return files[0]
+    
+    def _save_tokenizer(
+        self,
+        save_directory: Path,
+        filename_prefix: str,
+    ) -> str:
+        self.save_tokenizer(str(save_directory))
+        tokenizer_files = self._find_tokenizer_files(save_directory)
+        old_tokenizer_file_path = self._get_tokenizer_file(tokenizer_files)
+        assert old_tokenizer_file_path.is_file(), "could not access saved tokenizer file"
+        new_tokenizer_file_path = save_directory / (filename_prefix + self.vocab_files_names["tokenizer_file"])
+        old_tokenizer_file_path.replace(new_tokenizer_file_path)
+        return str(new_tokenizer_file_path)
+    
+    def save_tokenizer_config(self, save_dir: Path) -> None:
+        # convert Path to str
+        for k in self.tokenizer_config:
+            if isinstance(self.tokenizer_config[k], Path):
+                self.tokenizer_config[k] = str(self.tokenizer_config[k])
+
+        info_file = save_dir / "tokenizer_config.json"
+        with info_file.open("w") as f:
+            json.dump(self.tokenizer_config, f, indent=4)
+            
+    def load_json(self, path: Path) -> dict:
+        with path.open("r") as f:
+            return json.load(f)
+        
+class SPTokenizer(HFGPTXTokenizer):
+    model_file_glob = "*tokenizer.model"
+    vocab_files_names = {"tokenizer_file": "tokenizer.model"}
+    decode_kwargs = ["num_threads"]
+    # `is_continuation` does not work without this, but it doesn't
+    # implement all APIs of `PreTrainedTokenizer`.
+    def encode(self, text: str, **kwargs) -> List[int]:
+        return_tokens = kwargs.pop('return_tokens', False)
+        is_continuation = kwargs.pop('is_continuation', False)
+        return self._encode(
+            text,
+            return_tokens=return_tokens,
+            is_continuation=is_continuation,
+        )
+        
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.eos_token = "</s>"
+        self.eos_token_id = 2

special_tokens_map.json

@@ -0,0 +1,268 @@
+{
+  "additional_special_tokens": [
+    "<s>",
+    "</s>",
+    "<pad>",
+    "<eod>",
+    "<placeholder_tok_0>",
+    "<placeholder_tok_1>",
+    "<placeholder_tok_2>",
+    "<placeholder_tok_3>",
+    "<placeholder_tok_4>",
+    "<placeholder_tok_5>",
+    "<placeholder_tok_6>",
+    "<placeholder_tok_7>",
+    "<placeholder_tok_8>",
+    "<placeholder_tok_9>",
+    "<placeholder_tok_10>",
+    "<placeholder_tok_11>",
+    "<placeholder_tok_12>",
+    "<placeholder_tok_13>",
+    "<placeholder_tok_14>",
+    "<placeholder_tok_15>",
+    "<placeholder_tok_16>",
+    "<placeholder_tok_17>",
+    "<placeholder_tok_18>",
+    "<placeholder_tok_19>",
+    "<placeholder_tok_20>",
+    "<placeholder_tok_21>",
+    "<placeholder_tok_22>",
+    "<placeholder_tok_23>",
+    "<placeholder_tok_24>",
+    "<placeholder_tok_25>",
+    "<placeholder_tok_26>",
+    "<placeholder_tok_27>",
+    "<placeholder_tok_28>",
+    "<placeholder_tok_29>",
+    "<placeholder_tok_30>",
+    "<placeholder_tok_31>",
+    "<placeholder_tok_32>",
+    "<placeholder_tok_33>",
+    "<placeholder_tok_34>",
+    "<placeholder_tok_35>",
+    "<placeholder_tok_36>",
+    "<placeholder_tok_37>",
+    "<placeholder_tok_38>",
+    "<placeholder_tok_39>",
+    "<placeholder_tok_40>",
+    "<placeholder_tok_41>",
+    "<placeholder_tok_42>",
+    "<placeholder_tok_43>",
+    "<placeholder_tok_44>",
+    "<placeholder_tok_45>",
+    "<placeholder_tok_46>",
+    "<placeholder_tok_47>",
+    "<placeholder_tok_48>",
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