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  ---
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  license: apache-2.0
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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  ---
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+
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+ # CodeGen2.5-7B-mono
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+
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+ Title: [**CodeGen2.5: Small, but mighty**](https://blog.salesforceairesearch.com/codegen25)
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+
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+ Authors: [Erik Nijkamp](https://eriknijkamp.com)\*, [Hiroaki Hayashi](https://hiroakih.me)\*, Yingbo Zhou, Caiming Xiong
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+
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+ (\* equal contribution)
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+
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+ ## Model description
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+
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+ [CodeGen2.5](https://github.com/salesforce/CodeGen) is a family of autoregressive language models for **program synthesis**.
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+
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+ 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.
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+
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+ Like CodeGen2, this model is capable of infilling, and supports multiple programming languages.
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+
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+ We then further train on Python, then on instruction data. We release all the models as follows:
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+
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+ * **CodeGen2.5-7B-multi**: Trained on StarCoderData. Licensed under Apache-2.0.
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+ * **CodeGen2.5-7B-mono** (this repo): Further trained on additional Python tokens. Licensed under Apache-2.0.
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+ * **CodeGen2.5-7B-instruct**: Further trained from CodeGen2.5-7B-mono on instruction data. *Research purposes only*.
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+
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+ ## How to use
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+
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+ This model can be easily loaded using the `AutoModelForCausalLM` functionality.
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+
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+ ### Pre-requisite
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+
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+ Please install OpenAI `tiktoken` for the tokenizer.
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+
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+ ```bash
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+ pip install tiktoken==0.4.0
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+ ```
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+
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+ ### Causal sampling (code autocompletion)
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+
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+ For regular causal sampling, simply generate completions given the context:
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-mono", trust_remote_code=True)
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+ model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-mono")
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+
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+ text = "def hello_world():"
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+ generated_ids = model.generate(input_ids, max_length=128)
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+ print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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+ ```
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+
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+ ### Infill sampling
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+
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+ For **infill** sampling, we follow the CodeGen2 format:
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+
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+ * `<mask_N>`: N-th span to be masked. In practice, use `<mask_1>` to where you want to sample infill.
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+ * `<sep>`: Separator token between the suffix and the infilled sample. See below.
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+ * `<eom>`: "End-Of-Mask" token that model will output at the end of infilling. You may use this token to truncate the output.
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+
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+ For example, if we want to generate infill for the following cursor position of a function:
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+ ```python
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+ def hello_world():
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+ |
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+ return name
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+ ```
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+ we construct an input to the model by
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+
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+ 1. Inserting `<mask_1>` token in place of cursor position
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+ 2. Append `<sep>` token to indicate the boundary
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+ 3. Insert another `<mask_1>` to indicate which mask we want to infill.
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+
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+ The final snippet looks as follows:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-mono")
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+ model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2k-7b-mono")
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+
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+
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+ def format(prefix, suffix):
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+ return prefix + "<mask_1>" + suffix + "<|endoftext|>" + "<sep>" + "<mask_1>"
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+
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+
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+ prefix = "def hello_world():\n "
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+ suffix = " return name"
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+ text = format(prefix, suffix)
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+ generated_ids = model.generate(input_ids, max_length=128)
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+ print(tokenizer.decode(generated_ids[0], skip_special_tokens=False)[len(text):])
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+ ```
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+
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+ You might want to truncate the model output with `<eom>`.
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+
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+ ## Evaluation results
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+
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+ We evaluate our models on HumanEval and HumanEval-Infill.
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+ Please refer to the [blog](https://blog.salesforceairesearch.com/codegen25) for more details.
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+
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+ ## Intended use and limitations
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+
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+ 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.
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+ 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.
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+
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+ ## BibTeX entry and citation info
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+
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+ Please cite CodeGen2 paper:
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+
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+ ```bibtex
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+ @article{Nijkamp2023codegen2,
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+ title={CodeGen2: Lessons for Training LLMs on Programming and Natural Languages},
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+ author={Nijkamp, Erik and Hayashi, Hiroaki and Xiong, Caiming and Savarese, Silvio and Zhou, Yingbo},
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+ journal={arXiv preprint},
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+ year={2023}
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+ }
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+ ```
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+ "model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00003.bin",
324
+ "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
325
+ "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00003.bin",
326
+ "model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00003.bin",
327
+ "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00003.bin",
328
+ "model.norm.weight": "pytorch_model-00003-of-00003.bin"
329
+ }
330
+ }
tokenization_codegen25.py ADDED
@@ -0,0 +1,245 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) 2023, salesforce.com, inc.
2
+ # All rights reserved.
3
+ # SPDX-License-Identifier: Apache-2.0
4
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/Apache-2.0
5
+ """Tokenization classes for CodeGen2.5."""
6
+
7
+ from typing import List, Optional
8
+
9
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
10
+ from transformers.utils import logging
11
+
12
+ try:
13
+ import tiktoken
14
+ except ModuleNotFoundError as e:
15
+ raise ModuleNotFoundError("CodeGen2.5 requires the installation of tiktoken. Please install it via `pip install tiktoken`.") from e
16
+
17
+
18
+ logger = logging.get_logger(__name__)
19
+
20
+ MAX_MODEL_INPUT_SIZES = {
21
+ "Salesforce/codegen25-7b-multi": 2048,
22
+ "Salesforce/codegen25-7b-mono": 2048,
23
+ "Salesforce/codegen25-7b-instruct": 2048,
24
+ }
25
+
26
+
27
+ def tiktoken_tokenizer(base="gpt2", pad_token=None, add_special=True):
28
+ if not add_special:
29
+ return tiktoken.get_encoding(base)
30
+
31
+ def include_whitespace(n_min=2, n_max=20):
32
+ whitespaces = [" " * n for n in reversed(range(n_min, n_max))]
33
+ return whitespaces
34
+
35
+ def include_tabs(n_min=2, n_max=20):
36
+ tabs = ["\t" * n for n in reversed(range(n_min, n_max))]
37
+ return tabs
38
+
39
+ def include_fim_tokens():
40
+ fim_tokens = [
41
+ "<fim_prefix>",
42
+ "<fim_middle>",
43
+ "<fim_suffix>",
44
+ "<fim_pad>",
45
+ "<filename>",
46
+ "<gh_stars>",
47
+ "<issue_start>",
48
+ "<issue_comment>",
49
+ "<issue_closed>",
50
+ "<jupyter_start>",
51
+ "<jupyter_text>",
52
+ "<jupyter_code>",
53
+ "<jupyter_output>",
54
+ "<empty_output>",
55
+ "<commit_before>",
56
+ "<commit_msg>",
57
+ "<commit_after>",
58
+ "<reponame>"
59
+ ]
60
+ return fim_tokens
61
+
62
+ def include_codegen2_tokens():
63
+ tokens = []
64
+ tokens += [f"<dummy_{i}>" for i in range(4)]
65
+ tokens.append("<sep>") # 50317
66
+ tokens.append("<eom>") # 50318
67
+ tokens += [f"<mask_{i}>" for i in reversed(range(1, 51199-50318+1))]
68
+ return tokens
69
+
70
+ add_whitespaces = include_whitespace(n_min=2, n_max=32)
71
+ add_tabs = include_tabs(n_min=2, n_max=10)
72
+ fim_tokens = include_fim_tokens()
73
+ codegen2_tokens = include_codegen2_tokens()
74
+
75
+ tokenizer = tiktoken.get_encoding(base)
76
+
77
+ idx = tokenizer.n_vocab
78
+
79
+ bpe_ranks = tokenizer._mergeable_ranks
80
+
81
+ for wsp in add_whitespaces:
82
+ bpe_ranks[bytes(wsp, 'ascii')] = idx
83
+ idx += 1
84
+ for t in add_tabs:
85
+ bpe_ranks[bytes(t, 'ascii')] = idx
86
+ idx += 1
87
+
88
+ special_tokens = dict()
89
+
90
+ for sp in fim_tokens:
91
+ special_tokens[sp] = idx
92
+ idx += 1
93
+ for sp in codegen2_tokens:
94
+ special_tokens[sp] = idx
95
+ idx += 1
96
+
97
+ if pad_token and pad_token not in tokenizer._special_tokens and pad_token not in special_tokens:
98
+ special_tokens[pad_token] = idx
99
+ idx += 1
100
+ # In production, load the arguments directly instead of accessing private attributes
101
+ # See openai_public.py for examples of arguments for specific encodings
102
+ enc = tiktoken.Encoding(
103
+ # If you're changing the set of special tokens, make sure to use a different name
104
+ # It should be clear from the name what behaviour to expect.
105
+ name=base.replace("base", "im"),
106
+ pat_str=tokenizer._pat_str,
107
+ mergeable_ranks=bpe_ranks,
108
+ special_tokens={
109
+ **tokenizer._special_tokens,
110
+ **special_tokens
111
+ }
112
+ )
113
+ return enc
114
+
115
+
116
+ class CodeGen25Tokenizer(PreTrainedTokenizer):
117
+ """
118
+ Construct a CodeGen2.5 tokenizer. Based on byte-level Byte-Pair-Encoding.
119
+ Args:
120
+ vocab_file (`str`):
121
+ Path to the vocabulary file.
122
+ """
123
+ max_model_input_sizes = MAX_MODEL_INPUT_SIZES
124
+ model_input_names = ["input_ids", "attention_mask"]
125
+
126
+ def __init__(
127
+ self,
128
+ pad_token=None,
129
+ eos_token="<|endoftext|>",
130
+ add_eos_token=False,
131
+ add_special_tokens=True,
132
+ **kwargs,
133
+ ):
134
+ pad_token_added = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
135
+ eos_token_added = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
136
+ super().__init__(
137
+ pad_token=pad_token_added,
138
+ eos_token=eos_token_added,
139
+ add_eos_token=add_eos_token,
140
+ add_special_tokens=add_special_tokens,
141
+ **kwargs,
142
+ )
143
+ self.add_eos_token = add_eos_token
144
+ self.encoder = tiktoken_tokenizer(base="gpt2", pad_token=pad_token, add_special=add_special_tokens)
145
+
146
+ @property
147
+ def vocab_size(self):
148
+ """Returns vocab size"""
149
+ return self.encoder.n_vocab
150
+
151
+ def get_vocab(self):
152
+ """Returns vocab as a dict"""
153
+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
154
+ return vocab
155
+
156
+ def _tokenize(self, text, **kwargs):
157
+ """Returns a tokenized string."""
158
+ return self.encoder.encode(text, allowed_special="all")
159
+
160
+ def _convert_token_to_id(self, token):
161
+ """Converts a token (str) in an id using the vocab."""
162
+ if isinstance(token, str):
163
+ return self.encoder.encode_single_token(token)
164
+ else:
165
+ return token
166
+
167
+ def _convert_id_to_token(self, index):
168
+ """Converts an index (integer) in a token (str) using the vocab."""
169
+ return self.encoder.decode_single_token_bytes(index).decode("utf-8")
170
+
171
+ def _decode(self, token_ids: List[int], skip_special_tokens: bool = False, **kwargs):
172
+ if skip_special_tokens:
173
+ token_ids = [t for t in token_ids if t not in self.all_special_ids]
174
+ return self.encoder.decode(token_ids)
175
+
176
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
177
+ """Build model inputs from a sequence by appending eos_token_id."""
178
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
179
+
180
+ output = token_ids_0 + eos_token_id
181
+
182
+ if token_ids_1 is not None:
183
+ output = output + token_ids_1 + eos_token_id
184
+
185
+ return output
186
+
187
+ def get_special_tokens_mask(
188
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None,
189
+ already_has_special_tokens: bool = False
190
+ ) -> List[int]:
191
+ """
192
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
193
+ special tokens using the tokenizer `prepare_for_model` method.
194
+ Args:
195
+ token_ids_0 (`List[int]`):
196
+ List of IDs.
197
+ token_ids_1 (`List[int]`, *optional*):
198
+ Optional second list of IDs for sequence pairs.
199
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
200
+ Whether the token list is already formatted with special tokens for the model.
201
+ Returns:
202
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
203
+ """
204
+ if already_has_special_tokens:
205
+ return super().get_special_tokens_mask(
206
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
207
+ )
208
+
209
+ eos_token_id = [1] if self.add_eos_token else []
210
+
211
+ if token_ids_1 is None:
212
+ return ([0] * len(token_ids_0)) + eos_token_id
213
+ return ([0] * len(token_ids_0)) + eos_token_id + ([0] * len(token_ids_1)) + eos_token_id
214
+
215
+ def create_token_type_ids_from_sequences(
216
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
217
+ ) -> List[int]:
218
+ """
219
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
220
+ sequence pair mask has the following format:
221
+ ```
222
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
223
+ | first sequence | second sequence |
224
+ ```
225
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
226
+ Args:
227
+ token_ids_0 (`List[int]`):
228
+ List of ids.
229
+ token_ids_1 (`List[int]`, *optional*):
230
+ Optional second list of IDs for sequence pairs.
231
+ Returns:
232
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
233
+ """
234
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
235
+
236
+ output = [0] * len(token_ids_0 + eos_token_id)
237
+
238
+ if token_ids_1 is not None:
239
+ output += [1] * len(token_ids_1 + eos_token_id)
240
+
241
+ return output
242
+
243
+ # has no vocab file
244
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
245
+ return ()
tokenizer_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_eos_token": false,
3
+ "add_special_tokens": true,
4
+ "clean_up_tokenization_spaces": true,
5
+ "eos_token": "<|endoftext|>",
6
+ "model_max_length": 1000000000000000019884624838656,
7
+ "pad_token": null,
8
+ "tokenizer_class": "CodeGen25Tokenizer",
9
+ "auto_map": {
10
+ "AutoTokenizer": ["tokenization_codegen25.CodeGen25Tokenizer", null]
11
+ }
12
+ }