.

README.md

@@ -1,3 +1,47 @@
 ---
 license: apache-2.0
 ---
+
+# XGen
+
+Official research release for the family of **XGen** models (`7B`) by Salesforce AI Research:
+
+*Title*: [Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length](https://blog.salesforceairesearch.com/xgen/)
+
+## Models
+
+* [XGen-7B-4K-Base](https://huggingface.co/Salesforce/xgen-7b-4k-base): Pretrained XGen-7B model trained under 4K sequence length.
+  * License: Apache-2.0
+* [XGen-7B-8K-Base](https://huggingface.co/Salesforce/xgen-7b-8k-base): Pretrained XGen-7B model trained under 8K sequence length.
+  * License: Apache-2.0
+
+The training data for the models are tokenized with OpenAI Tiktoken library.
+To use this model, install tiktoken library via `pip`:
+
+```sh
+pip install tiktoken
+```
+
+The models can be used as auto-regressive samplers as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-4k-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-4k-base", torch_dtype=torch.bfloat16)
+inputs = tokenizer("The world is", return_tensors="pt")
+sample = model.generate(**inputs, max_length=128)
+print(tokenizer.decode(sample[0]))
+```
+
+## Citation
+
+```bibtex
+@misc{XGen,
+  title={Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length},
+  author={Salesforce AI Research},
+  howpublished={Salesforce AI Research Blog},
+  year={2023},
+  url={https://blog.salesforceairesearch.com/xgen-7b/}
+}
+```

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.2",
+  "use_cache": true,
+  "vocab_size": 51200
+}

generation_config.json

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+{
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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.29.2"
+}

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+  }
+}

tokenization_xgen.py

@@ -0,0 +1,211 @@
+"""Tokenization classes for xgen."""
+
+from typing import List, Optional
+
+import tiktoken
+
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+MAX_MODEL_INPUT_SIZES = {
+    "Salesforce/xgen-7b-4k-base": 4096,
+    "Salesforce/xgen-7b-8k-base": 8192,
+    "Salesforce/xgen-7b-4k-inst": 4096,
+    "Salesforce/xgen-7b-8k-inst": 8192
+}
+
+
+def tiktoken_tokenizer(base="gpt2", add_special=True):
+    if not add_special:
+        return tiktoken.get_encoding(base)
+
+    def include_whitespace(n_min=2, n_max=20):
+        whitespaces = [" " * n for n in reversed(range(n_min, n_max))]
+        return whitespaces
+
+    def include_tabs(n_min=2, n_max=20):
+        tabs = ["\t" * n for n in reversed(range(n_min, n_max))]
+        return tabs
+
+    def include_fim_tokens():
+        fim_tokens = [
+            "<fim_prefix>",
+            "<fim_middle>",
+            "<fim_suffix>",
+            "<fim_pad>",
+            "<filename>",
+            "<gh_stars>",
+            "<issue_start>",
+            "<issue_comment>",
+            "<issue_closed>",
+            "<jupyter_start>",
+            "<jupyter_text>",
+            "<jupyter_code>",
+            "<jupyter_output>",
+            "<empty_output>",
+            "<commit_before>",
+            "<commit_msg>",
+            "<commit_after>",
+            "<reponame>"
+        ]
+        return fim_tokens
+
+    add_whitespaces = include_whitespace(n_min=2, n_max=32)
+    add_tabs = include_tabs(n_min=2, n_max=10)
+    fim_tokens = include_fim_tokens()
+
+    tokenizer = tiktoken.get_encoding(base)
+
+    idx = tokenizer.n_vocab
+
+    bpe_ranks = tokenizer._mergeable_ranks
+
+    for wsp in add_whitespaces:
+        bpe_ranks[bytes(wsp, 'ascii')] = idx
+        idx += 1
+    for t in add_tabs:
+        bpe_ranks[bytes(t, 'ascii')] = idx
+        idx += 1
+
+    special_tokens = dict()
+
+    for sp in fim_tokens:
+        special_tokens[sp] = idx
+        idx += 1
+
+    # In production, load the arguments directly instead of accessing private attributes
+    # See openai_public.py for examples of arguments for specific encodings
+    enc = tiktoken.Encoding(
+        # If you're changing the set of special tokens, make sure to use a different name
+        # It should be clear from the name what behaviour to expect.
+        name=base.replace("base", "im"),
+        pat_str=tokenizer._pat_str,
+        mergeable_ranks=bpe_ranks,
+        special_tokens={
+            **tokenizer._special_tokens,
+            **special_tokens
+        }
+    )
+    return enc
+
+
+class XgenTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Xgen tokenizer. Based on byte-level Byte-Pair-Encoding.
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+    max_model_input_sizes = MAX_MODEL_INPUT_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+            self,
+            pad_token=None,
+            add_eos_token=False,
+            add_special_tokens=True,
+            **kwargs,
+    ):
+        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+        super().__init__(
+            pad_token=pad_token,
+            add_eos_token=add_eos_token,
+            add_special_tokens=add_special_tokens,
+            **kwargs,
+        )
+        self.add_eos_token = add_eos_token
+        self.encoder = tiktoken_tokenizer(base="gpt2", add_special=add_special_tokens)
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.encoder.n_vocab
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        return vocab
+
+    def _tokenize(self, text, **kwargs):
+        """Returns a tokenized string."""
+        return self.encoder.encode(text, allowed_special="all")
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return token
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.encoder.decode_single_token_bytes(index)
+
+    def _decode(self, token_ids: List[int], skip_special_tokens: bool = False, **kwargs):
+        return self.encoder.decode(token_ids)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
+        """Build model inputs from a sequence by appending eos_token_id."""
+        eos_token_id = [50256] if self.add_eos_token else []
+
+        output = token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1 + eos_token_id
+
+        return output
+
+    def get_special_tokens_mask(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None,
+            already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether the token list is already formatted with special tokens for the model.
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return ([0] * len(token_ids_0)) + eos_token_id
+        return ([0] * len(token_ids_0)) + eos_token_id + ([0] * len(token_ids_1)) + eos_token_id
+
+    def create_token_type_ids_from_sequences(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        eos_token_id = [50256] if self.add_eos_token else []
+
+        output = [0] * len(token_ids_0 + eos_token_id)
+
+        if token_ids_1 is not None:
+            output += [1] * len(token_ids_1 + eos_token_id)
+
+        return output

tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "add_eos_token": false,
+  "add_special_tokens": true,
+  "clean_up_tokenization_spaces": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "tokenizer_class": "XgenTokenizer",
+  "auto_map": {
+    "AutoTokenizer": ["tokenization_xgen.XgenTokenizer", null]
+  }
+}