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---
license: apache-2.0
datasets:
- bigcode/starcoderdata
language:
- code
pipeline_tag: text-generation
---
# This repo is a fork for flatline_lsp
See [flatline_lsp](https://github.com/okdshin/flatline_lsp).
This repository is a fork of [Salesforce/codegen25-7b-multi](https://huggingface.co/Salesforce/codegen25-7b-multi).
This repository contains gguf files but its tokenizer is dummy.
---
# CodeGen2.5-7B-multi
Title: [**CodeGen2.5: Small, but mighty**](https://blog.salesforceairesearch.com/codegen25)
Authors: [Erik Nijkamp](https://eriknijkamp.com)\*, [Hiroaki Hayashi](https://hiroakih.me)\*, Yingbo Zhou, Caiming Xiong
(\* equal contribution)
## Model description
[CodeGen2.5](https://github.com/salesforce/CodeGen) is a family of autoregressive language models for **program synthesis**.
Building upon [CodeGen2](https://arxiv.org/abs/2305.02309), the model is trained on [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) for 1.4T tokens, achieving competitive results compared to StarCoderBase-15.5B with less than half the size.
Like CodeGen2, this model is capable of infilling, and supports multiple programming languages.
We then further train on Python, then on instruction data. We release all the models as follows:
* **CodeGen2.5-7B-multi** (this repo): Trained on StarCoderData. Licensed under Apache-2.0.
* **CodeGen2.5-7B-mono**: Further trained on additional Python tokens. Licensed under Apache-2.0.
* **CodeGen2.5-7B-instruct**: Further trained from CodeGen2.5-7B-mono on instruction data. *Research purposes only*.
## How to use
This model can be easily loaded using the `AutoModelForCausalLM` functionality.
### Pre-requisite
Please install OpenAI `tiktoken` for the tokenizer.
```bash
pip install tiktoken==0.4.0
```
### Causal sampling (code autocompletion)
For regular causal sampling, simply generate completions given the context:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-multi", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-multi")
text = "def hello_world():"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
### Infill sampling
For **infill** sampling, we follow the CodeGen2 format:
* `<mask_N>`: N-th span to be masked. In practice, use `<mask_1>` to where you want to sample infill.
* `<sep>`: Separator token between the suffix and the infilled sample. See below.
* `<eom>`: "End-Of-Mask" token that model will output at the end of infilling. You may use this token to truncate the output.
For example, if we want to generate infill for the following cursor position of a function:
```python
def hello_world():
|
return name
```
we construct an input to the model by
1. Inserting `<mask_1>` token in place of cursor position
2. Append `<sep>` token to indicate the boundary
3. Insert another `<mask_1>` to indicate which mask we want to infill.
The final snippet looks as follows:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-multi", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-multi")
def format(prefix, suffix):
return prefix + "<mask_1>" + suffix + "<|endoftext|>" + "<sep>" + "<mask_1>"
prefix = "def hello_world():\n "
suffix = " return name"
text = format(prefix, suffix)
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=False)[len(text):])
```
You might want to truncate the model output with `<eom>`.
## Evaluation results
We evaluate our models on HumanEval and HumanEval-Infill.
Please refer to the [blog](https://blog.salesforceairesearch.com/codegen25) for more details.
## Intended use and limitations
As an autoregressive language model, CodeGen2.5 is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
## Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses only.
Nevertheless, the model can generate source code verbatim from the dataset.
The code's license might require attribution and/or other specific requirements that must be respected.
The data provider BigCode provides a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that lets you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
## BibTeX entry and citation info
Please cite CodeGen2 paper:
```bibtex
@article{Nijkamp2023codegen2,
title={CodeGen2: Lessons for Training LLMs on Programming and Natural Languages},
author={Nijkamp, Erik and Hayashi, Hiroaki and Xiong, Caiming and Savarese, Silvio and Zhou, Yingbo},
journal={arXiv preprint},
year={2023}
}
```

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}

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# Copyright (c) 2023, salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: Apache-2.0
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/Apache-2.0
"""Tokenization classes for CodeGen2.5."""
from typing import List, Optional, Union
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
from transformers.utils import logging
try:
import tiktoken
except ModuleNotFoundError as e:
raise ModuleNotFoundError("CodeGen2.5 requires the installation of tiktoken. Please install it via `pip install tiktoken`.") from e
logger = logging.get_logger(__name__)
MAX_MODEL_INPUT_SIZES = {
"Salesforce/codegen25-7b-multi": 2048,
"Salesforce/codegen25-7b-mono": 2048,
"Salesforce/codegen25-7b-instruct": 2048,
}
def tiktoken_tokenizer(base="gpt2", pad_token=None, 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
def include_codegen2_tokens():
tokens = []
tokens += [f"<dummy_{i}>" for i in range(4)]
tokens.append("<sep>") # 50317
tokens.append("<eom>") # 50318
tokens += [f"<mask_{i}>" for i in reversed(range(1, 51199-50318+1))]
return 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()
codegen2_tokens = include_codegen2_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
for sp in codegen2_tokens:
special_tokens[sp] = idx
idx += 1
if pad_token and pad_token not in tokenizer._special_tokens and pad_token not in special_tokens:
special_tokens[pad_token] = 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 CodeGen25Tokenizer(PreTrainedTokenizer):
"""
Construct a CodeGen2.5 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,
eos_token="<|endoftext|>",
add_eos_token=False,
add_special_tokens=True,
**kwargs,
):
pad_token_added = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
eos_token_added = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
super().__init__(
pad_token=pad_token_added,
eos_token=eos_token_added,
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", pad_token=pad_token, 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."""
if isinstance(token, str):
return self.encoder.encode_single_token(token)
else:
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).decode("utf-8")
def _decode(self, token_ids: Union[int, List[int]], skip_special_tokens: bool = False, **kwargs):
if isinstance(token_ids, int):
token_ids = [token_ids]
if skip_special_tokens:
token_ids = [t for t in token_ids if t not in self.all_special_ids]
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 = [self.eos_token_id] 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 = [self.eos_token_id] 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
# has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
return ()

12
tokenizer_config.json Normal file
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@@ -0,0 +1,12 @@
{
"add_eos_token": false,
"add_special_tokens": true,
"clean_up_tokenization_spaces": true,
"eos_token": "<|endoftext|>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": null,
"tokenizer_class": "CodeGen25Tokenizer",
"auto_map": {
"AutoTokenizer": ["tokenization_codegen25.CodeGen25Tokenizer", null]
}
}