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README.md CHANGED
@@ -1,5 +1,56 @@
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- ---
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- license: other
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- license_name: license
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- license_link: LICENSE
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: other
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+ license_name: license
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+ license_link: LICENSE
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+ ---
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+ <div align="center">
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+ <h1>
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+ Index-1.9B-Chat
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+ </h1>
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+ </div>
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+
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+ ## Model Introduction
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+
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+ We are excited to announce the release of a lightweight version from the Index series models: the Index-1.9B series.
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+ The open-source Index-1.9B series includes the following models:
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+ - Index-1.9B base: The base model, with 1.9 billion non-embedding parameters, pre-trained on a 2.8T corpus mainly in Chinese and English. It leads in multiple evaluation benchmarks compared to models of the same level.
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+ - Index-1.9B pure : A control version of the base model with the same parameters and training strategy, but strictly filtered out all instruction-related data from the corpus to verify the impact of instructions on benchmarks.
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+ - **Index-1.9B chat (this repository's model)** : A dialogue model aligned with SFT and DPO based on the Index-1.9B base. We found that due to the introduction of a lot of internet community corpus in our pre-training, the model has significantly more interesting chatting capabilities.
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+ - Index-1.9B character : Introduces RAG on top of SFT and DPO to achieve few-shots role-playing customization.
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+
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+ Adapted to llamacpp and Ollama, see [Index-1.9B-Chat-GGUF](https://huggingface.co/IndexTeam/Index-1.9B-Chat-GGUF)
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+
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+ For more details, see our [GitHub](https://github.com/bilibili/Index-1.9B) and [Index-1.9B Technical Report](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf)
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+
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+ ### Loading with Transformers
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+
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+ You can load the Index-1.9B-Chat model for dialogue using the following code:
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+
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+ ```python
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+ import argparse
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+ from transformers import AutoTokenizer, pipeline
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+
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+ # Attention! The directory must not contain "." and can be replaced with "_".
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+ parser = argparse.ArgumentParser()
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+ parser.add_argument('--model_path', default="IndexTeam/Index-1.9B-Chat", type=str, help="")
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+ parser.add_argument('--device', default="cpu", type=str, help="") # also could be "cuda" or "mps" for Apple silicon
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+ args = parser.parse_args()
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+
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+ tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
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+ generator = pipeline("text-generation",
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+ model=args.model_path,
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+ tokenizer=tokenizer, trust_remote_code=True,
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+ device=args.device)
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+
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+
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+ system_message = "你是由哔哩哔哩自主研发的大语言模型,名为“Index”。你能够根据用户传入的信息,帮助用户完成指定的任务,并生成恰当的、符合要求的回复。"
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+ query = "续写 天不生我金坷垃"
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+ model_input = []
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+ model_input.append({"role": "system", "content": system_message})
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+ model_input.append({"role": "user", "content": query})
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+
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+ model_output = generator(model_input, max_new_tokens=300, top_k=5, top_p=0.8, temperature=0.3, repetition_penalty=1.1, do_sample=True)
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+
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+ print('User:', query)
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+ print('Model:', model_output)
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+ ```
README_zh.md ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ license_name: license
4
+ license_link: LICENSE
5
+ ---
6
+ <div align="center">
7
+ <h1>
8
+ Index-1.9B-Chat
9
+ </h1>
10
+ </div>
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+
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+ ## 模型介绍
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+
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+ 我们很高兴首次发布Index系列模型中的轻量版本:Index-1.9B系列
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+ 本次开源的Index-1.9B 系列包含以下模型:
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+ - Index-1.9B base : 基座模型,具有 19亿 非词嵌入参数量,在2.8T 中英文为主的语料上预训练,多个评测基准上与同级别模型比处于领先。
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+ - Index-1.9B pure : 基座模型的对照组,与base具有相同的参数和训练策略,不同之处在于我们严格过滤了该版本语料中所有指令相关的数据,以此来验证指令对benchmark的影响。
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+ - **Index-1.9B chat(本仓库模型)** : 基于index-1.9B base通过SFT和DPO对齐后的对话模型,我们发现由于预训练中引入了较多定向清洗的对话类语料,聊天的趣味性明显更强。
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+ - Index-1.9B character : 在SFT和DPO的基础上引入了RAG来实现fewshots角色扮演定制。
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+
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+ 已适配llamacpp和Ollama,详见[Index-1.9B-Chat-GGUF](https://huggingface.co/IndexTeam/Index-1.9B-Chat-GGUF)
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+
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+ 更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf)
24
+
25
+ ### Transformers 加载方式
26
+
27
+ 可通过以下代码加载 Index-1.9B-Chat 模型来进行对话:
28
+
29
+ ```python
30
+ import argparse
31
+ from transformers import AutoTokenizer, pipeline
32
+
33
+ # 注意!目录不能含有".",可以替换成"_"
34
+ parser = argparse.ArgumentParser()
35
+ parser.add_argument('--model_path', default="IndexTeam/Index-1.9B-Chat", type=str, help="")
36
+ parser.add_argument('--device', default="cpu", type=str, help="") # also could be "cuda" or "mps" for Apple silicon
37
+ args = parser.parse_args()
38
+
39
+ tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
40
+ generator = pipeline("text-generation",
41
+ model=args.model_path,
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+ tokenizer=tokenizer, trust_remote_code=True,
43
+ device=args.device)
44
+
45
+
46
+ system_message = "你是由哔哩哔哩自主研发的大语言模型,名为“Index”。你能够根据用户传入的信息,帮助用户完成指定的任务,并生成恰当的、符合要求的回复。"
47
+ query = "续写 天不生我金坷垃"
48
+ model_input = []
49
+ model_input.append({"role": "system", "content": system_message})
50
+ model_input.append({"role": "user", "content": query})
51
+
52
+ model_output = generator(model_input, max_new_tokens=300, top_k=5, top_p=0.8, temperature=0.3, repetition_penalty=1.1, do_sample=True)
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+
54
+ print('User:', query)
55
+ print('Model:', model_output)
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+ ```
config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_name_or_path": "Index_1_9B",
3
+ "architectures": [
4
+ "IndexForCausalLM"
5
+ ],
6
+ "auto_map": {
7
+ "AutoConfig": "configuration_index.IndexConfig",
8
+ "AutoModelForCausalLM": "modeling_index.IndexForCausalLM"
9
+ },
10
+ "bos_token_id": 1,
11
+ "eos_token_id": 2,
12
+ "pad_token_id": 0,
13
+ "hidden_act": "silu",
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+ "hidden_size": 2048,
15
+ "initializer_range": 0.01,
16
+ "intermediate_size": 5888,
17
+ "max_length": 4096,
18
+ "max_position_embeddings": 4096,
19
+ "model_type": "index",
20
+ "num_attention_heads": 16,
21
+ "num_key_value_heads": 16,
22
+ "num_hidden_layers": 36,
23
+ "rms_norm_eps": 1e-06,
24
+ "rope_scaling": null,
25
+ "tie_word_embeddings": false,
26
+ "norm_head":1,
27
+ "torch_dtype": "bfloat16",
28
+ "transformers_version": "4.39.2",
29
+ "use_cache": true,
30
+ "vocab_size": 65029
31
+ }
configuration_index.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ Index model configuration"""
21
+
22
+ from transformers.configuration_utils import PretrainedConfig
23
+ from transformers.utils import logging
24
+
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+ INDEX_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
29
+
30
+
31
+ class IndexConfig(PretrainedConfig):
32
+ r"""
33
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
34
+ documentation from [`PretrainedConfig`] for more information.
35
+
36
+
37
+ Args:
38
+ vocab_size (`int`, *optional*, defaults to 65029):
39
+ Vocabulary size of the Index model. Defines the number of different tokens that can be represented by the
40
+ `inputs_ids` passed when calling [`IndexModel`]
41
+ hidden_size (`int`, *optional*, defaults to 4096):
42
+ Dimension of the hidden representations.
43
+ intermediate_size (`int`, *optional*, defaults to 11008):
44
+ Dimension of the MLP representations.
45
+ num_hidden_layers (`int`, *optional*, defaults to 32):
46
+ Number of hidden layers in the Transformer decoder.
47
+ num_attention_heads (`int`, *optional*, defaults to 32):
48
+ Number of attention heads for each attention layer in the Transformer decoder.
49
+ num_key_value_heads (`int`, *optional*):
50
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
51
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
52
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
53
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
54
+ by meanpooling all the original heads within that group. For more details checkout [this
55
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
56
+ `num_attention_heads`.
57
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
58
+ The non-linear activation function (function or string) in the decoder.
59
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
60
+ The maximum sequence length that this model might ever be used with. Index 1 supports up to 2048 tokens,
61
+ Index 2 up to 4096, CodeIndex up to 16384.
62
+ initializer_range (`float`, *optional*, defaults to 0.02):
63
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
64
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
65
+ The epsilon used by the rms normalization layers.
66
+ use_cache (`bool`, *optional*, defaults to `True`):
67
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
68
+ relevant if `config.is_decoder=True`.
69
+ pad_token_id (`int`, *optional*):
70
+ Padding token id.
71
+ bos_token_id (`int`, *optional*, defaults to 1):
72
+ Beginning of stream token id.
73
+ eos_token_id (`int`, *optional*, defaults to 2):
74
+ End of stream token id.
75
+ pretraining_tp (`int`, *optional*, defaults to 1):
76
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
77
+ document](https://hf-mirror.com/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
78
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
79
+ issue](https://github.com/pytorch/pytorch/issues/76232).
80
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
81
+ Whether to tie weight embeddings
82
+ rope_theta (`float`, *optional*, defaults to 10000.0):
83
+ The base period of the RoPE embeddings.
84
+ rope_scaling (`Dict`, *optional*):
85
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
86
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
87
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
88
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
89
+ these scaling strategies behave
90
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
91
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
92
+ attention_dropout (`float`, *optional*, defaults to 0.0):
93
+ The dropout ratio for the attention probabilities.
94
+
95
+ ```python
96
+ >>> from transformers import IndexModel, IndexConfig
97
+
98
+ >>> configuration = IndexConfig()
99
+ >>> model = IndexModel(configuration)
100
+ >>> configuration = model.config
101
+ ```"""
102
+
103
+ model_type = "index"
104
+ keys_to_ignore_at_inference = ["past_key_values"]
105
+
106
+ def __init__(
107
+ self,
108
+ vocab_size=65029,
109
+ hidden_size=4096,
110
+ intermediate_size=11008,
111
+ num_hidden_layers=32,
112
+ num_attention_heads=32,
113
+ num_key_value_heads=None,
114
+ hidden_act="silu",
115
+ max_position_embeddings=2048,
116
+ initializer_range=0.02,
117
+ rms_norm_eps=1e-6,
118
+ use_cache=True,
119
+ pad_token_id=None,
120
+ bos_token_id=1,
121
+ eos_token_id=2,
122
+ pretraining_tp=1,
123
+ tie_word_embeddings=False,
124
+ norm_head=False,
125
+ rope_theta=10000.0,
126
+ rope_scaling=None,
127
+ attention_bias=False,
128
+ attention_dropout=0.0,
129
+ **kwargs,
130
+ ):
131
+ self.vocab_size = vocab_size
132
+ self.max_position_embeddings = max_position_embeddings
133
+ self.hidden_size = hidden_size
134
+ self.intermediate_size = intermediate_size
135
+ self.num_hidden_layers = num_hidden_layers
136
+ self.num_attention_heads = num_attention_heads
137
+
138
+ # for backward compatibility
139
+ if num_key_value_heads is None:
140
+ num_key_value_heads = num_attention_heads
141
+
142
+ self.num_key_value_heads = num_key_value_heads
143
+ self.hidden_act = hidden_act
144
+ self.initializer_range = initializer_range
145
+ self.rms_norm_eps = rms_norm_eps
146
+ self.pretraining_tp = pretraining_tp
147
+ self.use_cache = use_cache
148
+ self.rope_theta = rope_theta
149
+ self.rope_scaling = rope_scaling
150
+ self._rope_scaling_validation()
151
+ self.attention_bias = attention_bias
152
+ self.attention_dropout = attention_dropout
153
+
154
+ self.norm_head = norm_head
155
+
156
+ super().__init__(
157
+ pad_token_id=pad_token_id,
158
+ bos_token_id=bos_token_id,
159
+ eos_token_id=eos_token_id,
160
+ tie_word_embeddings=tie_word_embeddings,
161
+ **kwargs,
162
+ )
163
+
164
+ def _rope_scaling_validation(self):
165
+ """
166
+ Validate the `rope_scaling` configuration.
167
+ """
168
+ if self.rope_scaling is None:
169
+ return
170
+
171
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
172
+ raise ValueError(
173
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
174
+ f"got {self.rope_scaling}"
175
+ )
176
+ rope_scaling_type = self.rope_scaling.get("type", None)
177
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
178
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
179
+ raise ValueError(
180
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
181
+ )
182
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
183
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
generation_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.39.2",
7
+ "top_k": 5,
8
+ "top_p": 0.8,
9
+ "temperature": 0.3,
10
+ "repetition_penalty":1.1,
11
+ "do_sample": true
12
+ }
huggingface-metadata.txt ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ url: https://huggingface.co/IndexTeam/Index-1.9B-Chat
2
+ branch: main
3
+ download date: 2024-06-26 21:50:45
4
+ sha256sum:
5
+ 2d5cf8e663977e64946246ae5baadf930951006a024ac025a8b7fe33667e8728 pytorch_model.bin
6
+ 3a11626b3c80cbfdd5f4b5585fce8938d178f3a0df739c3dd1c708ce944878af tokenizer.model
modeling_index.py ADDED
@@ -0,0 +1,1048 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ PyTorch Index model."""
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+
24
+ import torch
25
+ import torch.nn.functional as F
26
+ import torch.utils.checkpoint
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+
30
+ from transformers.activations import ACT2FN
31
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
32
+ from transformers.modeling_utils import PreTrainedModel
33
+ from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
34
+ from .configuration_index import IndexConfig
35
+
36
+
37
+ logger = logging.get_logger(__name__)
38
+
39
+ _CONFIG_FOR_DOC = "IndexConfig"
40
+
41
+
42
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
43
+ def _make_causal_mask(
44
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
45
+ ):
46
+ """
47
+ Make causal mask used for bi-directional self-attention.
48
+ """
49
+ bsz, tgt_len = input_ids_shape
50
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
51
+ mask_cond = torch.arange(mask.size(-1), device=device)
52
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
53
+ mask = mask.to(dtype)
54
+
55
+ if past_key_values_length > 0:
56
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
57
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
58
+
59
+
60
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
61
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
62
+ """
63
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
64
+ """
65
+ bsz, src_len = mask.size()
66
+ tgt_len = tgt_len if tgt_len is not None else src_len
67
+
68
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
69
+
70
+ inverted_mask = 1.0 - expanded_mask
71
+
72
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
73
+
74
+
75
+ class IndexRMSNorm(nn.Module):
76
+ def __init__(self, hidden_size, eps=1e-6):
77
+ """
78
+ IndexRMSNorm is equivalent to T5LayerNorm
79
+ """
80
+ super().__init__()
81
+ self.weight = nn.Parameter(torch.ones(hidden_size))
82
+ self.variance_epsilon = eps
83
+
84
+ def forward(self, hidden_states):
85
+ input_dtype = hidden_states.dtype
86
+ hidden_states = hidden_states.to(torch.float32)
87
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
88
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
89
+ return self.weight * hidden_states.to(input_dtype)
90
+
91
+
92
+ class IndexRotaryEmbedding(torch.nn.Module):
93
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
94
+ super().__init__()
95
+
96
+ self.dim = dim
97
+ self.max_position_embeddings = max_position_embeddings
98
+ self.base = base
99
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
100
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
101
+
102
+ # Build here to make `torch.jit.trace` work.
103
+ self._set_cos_sin_cache(
104
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
105
+ )
106
+
107
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
108
+ self.max_seq_len_cached = seq_len
109
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
110
+
111
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
112
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
113
+ emb = torch.cat((freqs, freqs), dim=-1)
114
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
115
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
116
+
117
+ def forward(self, x, seq_len=None):
118
+ # x: [bs, num_attention_heads, seq_len, head_size]
119
+ if seq_len > self.max_seq_len_cached:
120
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
121
+
122
+ return (
123
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
124
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
125
+ )
126
+
127
+
128
+ class IndexLinearScalingRotaryEmbedding(IndexRotaryEmbedding):
129
+ """IndexRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
130
+
131
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
132
+ self.scaling_factor = scaling_factor
133
+ super().__init__(dim, max_position_embeddings, base, device)
134
+
135
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
136
+ self.max_seq_len_cached = seq_len
137
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
138
+ t = t / self.scaling_factor
139
+
140
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
141
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
142
+ emb = torch.cat((freqs, freqs), dim=-1)
143
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
144
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
145
+
146
+
147
+ class IndexDynamicNTKScalingRotaryEmbedding(IndexRotaryEmbedding):
148
+ """IndexRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
149
+
150
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
151
+ self.scaling_factor = scaling_factor
152
+ super().__init__(dim, max_position_embeddings, base, device)
153
+
154
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
155
+ self.max_seq_len_cached = seq_len
156
+
157
+ if seq_len > self.max_position_embeddings:
158
+ base = self.base * (
159
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
160
+ ) ** (self.dim / (self.dim - 2))
161
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
162
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
163
+
164
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
165
+
166
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
167
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
168
+ emb = torch.cat((freqs, freqs), dim=-1)
169
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
170
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
171
+
172
+
173
+ def rotate_half(x):
174
+ """Rotates half the hidden dims of the input."""
175
+ x1 = x[..., : x.shape[-1] // 2]
176
+ x2 = x[..., x.shape[-1] // 2 :]
177
+ return torch.cat((-x2, x1), dim=-1)
178
+
179
+
180
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
181
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
182
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
183
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
184
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
185
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
186
+ q_embed = (q * cos) + (rotate_half(q) * sin)
187
+ k_embed = (k * cos) + (rotate_half(k) * sin)
188
+ return q_embed, k_embed
189
+
190
+
191
+ class IndexMLP(nn.Module):
192
+ def __init__(self, config):
193
+ super().__init__()
194
+ self.config = config
195
+ self.hidden_size = config.hidden_size
196
+ self.intermediate_size = config.intermediate_size
197
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
198
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
199
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
200
+ self.act_fn = ACT2FN[config.hidden_act]
201
+
202
+ def forward(self, x):
203
+ if self.config.pretraining_tp > 1:
204
+ slice = self.intermediate_size // self.config.pretraining_tp
205
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
206
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
207
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
208
+
209
+ gate_proj = torch.cat(
210
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
211
+ )
212
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
213
+
214
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
215
+ down_proj = [
216
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
217
+ ]
218
+ down_proj = sum(down_proj)
219
+ else:
220
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
221
+
222
+ return down_proj
223
+
224
+
225
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
226
+ """
227
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
228
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
229
+ """
230
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
231
+ if n_rep == 1:
232
+ return hidden_states
233
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
234
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
235
+
236
+
237
+ class IndexAttention(nn.Module):
238
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
239
+
240
+ def __init__(self, config: IndexConfig):
241
+ super().__init__()
242
+ self.config = config
243
+ self.hidden_size = config.hidden_size
244
+ self.num_heads = config.num_attention_heads
245
+ self.head_dim = self.hidden_size // self.num_heads
246
+ self.num_key_value_heads = config.num_key_value_heads
247
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
248
+ self.max_position_embeddings = config.max_position_embeddings
249
+ self.rope_theta = config.rope_theta
250
+
251
+ if (self.head_dim * self.num_heads) != self.hidden_size:
252
+ raise ValueError(
253
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
254
+ f" and `num_heads`: {self.num_heads})."
255
+ )
256
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
257
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
258
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
259
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
260
+ self._init_rope()
261
+
262
+ def _init_rope(self):
263
+ if self.config.rope_scaling is None:
264
+ self.rotary_emb = IndexRotaryEmbedding(
265
+ self.head_dim,
266
+ max_position_embeddings=self.max_position_embeddings,
267
+ base=self.rope_theta,
268
+ )
269
+ else:
270
+ scaling_type = self.config.rope_scaling["type"]
271
+ scaling_factor = self.config.rope_scaling["factor"]
272
+ if scaling_type == "linear":
273
+ self.rotary_emb = IndexLinearScalingRotaryEmbedding(
274
+ self.head_dim,
275
+ max_position_embeddings=self.max_position_embeddings,
276
+ scaling_factor=scaling_factor,
277
+ base=self.rope_theta,
278
+ )
279
+ elif scaling_type == "dynamic":
280
+ self.rotary_emb = IndexDynamicNTKScalingRotaryEmbedding(
281
+ self.head_dim,
282
+ max_position_embeddings=self.max_position_embeddings,
283
+ scaling_factor=scaling_factor,
284
+ base=self.rope_theta,
285
+ )
286
+ else:
287
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
288
+
289
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
290
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
291
+
292
+ def forward(
293
+ self,
294
+ hidden_states: torch.Tensor,
295
+ attention_mask: Optional[torch.Tensor] = None,
296
+ position_ids: Optional[torch.LongTensor] = None,
297
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
298
+ output_attentions: bool = False,
299
+ use_cache: bool = False,
300
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
301
+ bsz, q_len, _ = hidden_states.size()
302
+
303
+ if self.config.pretraining_tp > 1:
304
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
305
+ query_slices = self.q_proj.weight.split(
306
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
307
+ )
308
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
309
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
310
+
311
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
312
+ query_states = torch.cat(query_states, dim=-1)
313
+
314
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
315
+ key_states = torch.cat(key_states, dim=-1)
316
+
317
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
318
+ value_states = torch.cat(value_states, dim=-1)
319
+
320
+ else:
321
+ query_states = self.q_proj(hidden_states)
322
+ key_states = self.k_proj(hidden_states)
323
+ value_states = self.v_proj(hidden_states)
324
+
325
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
326
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
327
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
328
+
329
+ kv_seq_len = key_states.shape[-2]
330
+ if past_key_value is not None:
331
+ kv_seq_len += past_key_value[0].shape[-2]
332
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
333
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
334
+
335
+ if past_key_value is not None:
336
+ # reuse k, v, self_attention
337
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
338
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
339
+
340
+ past_key_value = (key_states, value_states) if use_cache else None
341
+
342
+ # repeat k/v heads if n_kv_heads < n_heads
343
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
344
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
345
+
346
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
347
+
348
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
349
+ raise ValueError(
350
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
351
+ f" {attn_weights.size()}"
352
+ )
353
+
354
+ if attention_mask is not None:
355
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
356
+ raise ValueError(
357
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
358
+ )
359
+ attn_weights = attn_weights + attention_mask
360
+
361
+ # upcast attention to fp32
362
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
363
+ attn_output = torch.matmul(attn_weights, value_states)
364
+
365
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
366
+ raise ValueError(
367
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
368
+ f" {attn_output.size()}"
369
+ )
370
+
371
+ attn_output = attn_output.transpose(1, 2).contiguous()
372
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
373
+
374
+ if self.config.pretraining_tp > 1:
375
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
376
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
377
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
378
+ else:
379
+ attn_output = self.o_proj(attn_output)
380
+
381
+ if not output_attentions:
382
+ attn_weights = None
383
+
384
+ return attn_output, attn_weights, past_key_value
385
+
386
+
387
+ class IndexDecoderLayer(nn.Module):
388
+ def __init__(self, config: IndexConfig):
389
+ super().__init__()
390
+ self.hidden_size = config.hidden_size
391
+ self.self_attn = IndexAttention(config=config)
392
+ self.mlp = IndexMLP(config)
393
+ self.input_layernorm = IndexRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
394
+ self.post_attention_layernorm = IndexRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
395
+
396
+ def forward(
397
+ self,
398
+ hidden_states: torch.Tensor,
399
+ attention_mask: Optional[torch.Tensor] = None,
400
+ position_ids: Optional[torch.LongTensor] = None,
401
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
402
+ output_attentions: Optional[bool] = False,
403
+ use_cache: Optional[bool] = False,
404
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
405
+ """
406
+ Args:
407
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
408
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
409
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
410
+ output_attentions (`bool`, *optional*):
411
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
412
+ returned tensors for more detail.
413
+ use_cache (`bool`, *optional*):
414
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
415
+ (see `past_key_values`).
416
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
417
+ """
418
+
419
+ residual = hidden_states
420
+
421
+ hidden_states = self.input_layernorm(hidden_states)
422
+
423
+ # Self Attention
424
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
425
+ hidden_states=hidden_states,
426
+ attention_mask=attention_mask,
427
+ position_ids=position_ids,
428
+ past_key_value=past_key_value,
429
+ output_attentions=output_attentions,
430
+ use_cache=use_cache,
431
+ )
432
+ hidden_states = residual + hidden_states
433
+
434
+ # Fully Connected
435
+ residual = hidden_states
436
+ hidden_states = self.post_attention_layernorm(hidden_states)
437
+ hidden_states = self.mlp(hidden_states)
438
+ hidden_states = residual + hidden_states
439
+
440
+ outputs = (hidden_states,)
441
+
442
+ if output_attentions:
443
+ outputs += (self_attn_weights,)
444
+
445
+ if use_cache:
446
+ outputs += (present_key_value,)
447
+
448
+ return outputs
449
+
450
+
451
+ INDEX_START_DOCSTRING = r"""
452
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
453
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
454
+ etc.)
455
+
456
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
457
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
458
+ and behavior.
459
+
460
+ Parameters:
461
+ config ([`IndexConfig`]):
462
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
463
+ load the weights associated with the model, only the configuration. Check out the
464
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
465
+ """
466
+
467
+
468
+ @add_start_docstrings(
469
+ "The bare Index Model outputting raw hidden-states without any specific head on top.",
470
+ INDEX_START_DOCSTRING,
471
+ )
472
+ class IndexPreTrainedModel(PreTrainedModel):
473
+ config_class = IndexConfig
474
+ base_model_prefix = "model"
475
+ supports_gradient_checkpointing = True
476
+ _no_split_modules = ["IndexDecoderLayer"]
477
+ _skip_keys_device_placement = "past_key_values"
478
+
479
+ def _init_weights(self, module):
480
+ std = self.config.initializer_range
481
+ if isinstance(module, nn.Linear):
482
+ module.weight.data.normal_(mean=0.0, std=std)
483
+ if module.bias is not None:
484
+ module.bias.data.zero_()
485
+ elif isinstance(module, nn.Embedding):
486
+ module.weight.data.normal_(mean=0.0, std=std)
487
+ if module.padding_idx is not None:
488
+ module.weight.data[module.padding_idx].zero_()
489
+
490
+ def _set_gradient_checkpointing(self, module, value=False):
491
+ if isinstance(module, IndexModel):
492
+ module.gradient_checkpointing = value
493
+
494
+
495
+ INDEX_INPUTS_DOCSTRING = r"""
496
+ Args:
497
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
498
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
499
+ it.
500
+
501
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
502
+ [`PreTrainedTokenizer.__call__`] for details.
503
+
504
+ [What are input IDs?](../glossary#input-ids)
505
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
506
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
507
+
508
+ - 1 for tokens that are **not masked**,
509
+ - 0 for tokens that are **masked**.
510
+
511
+ [What are attention masks?](../glossary#attention-mask)
512
+
513
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
514
+ [`PreTrainedTokenizer.__call__`] for details.
515
+
516
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
517
+ `past_key_values`).
518
+
519
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
520
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
521
+ information on the default strategy.
522
+
523
+ - 1 indicates the head is **not masked**,
524
+ - 0 indicates the head is **masked**.
525
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
526
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
527
+ config.n_positions - 1]`.
528
+
529
+ [What are position IDs?](../glossary#position-ids)
530
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
531
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
532
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
533
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
534
+
535
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
536
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
537
+
538
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
539
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
540
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
541
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
542
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
543
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
544
+ model's internal embedding lookup matrix.
545
+ use_cache (`bool`, *optional*):
546
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
547
+ `past_key_values`).
548
+ output_attentions (`bool`, *optional*):
549
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
550
+ tensors for more detail.
551
+ output_hidden_states (`bool`, *optional*):
552
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
553
+ more detail.
554
+ return_dict (`bool`, *optional*):
555
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
556
+ """
557
+
558
+
559
+ @add_start_docstrings(
560
+ "The bare Index Model outputting raw hidden-states without any specific head on top.",
561
+ INDEX_START_DOCSTRING,
562
+ )
563
+ class IndexModel(IndexPreTrainedModel):
564
+ """
565
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`IndexDecoderLayer`]
566
+
567
+ Args:
568
+ config: IndexConfig
569
+ """
570
+
571
+ def __init__(self, config: IndexConfig):
572
+ super().__init__(config)
573
+ self.padding_idx = config.pad_token_id
574
+ self.vocab_size = config.vocab_size
575
+
576
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
577
+ self.layers = nn.ModuleList([IndexDecoderLayer(config) for _ in range(config.num_hidden_layers)])
578
+ self.norm = IndexRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
579
+
580
+ self.gradient_checkpointing = False
581
+ # Initialize weights and apply final processing
582
+ self.post_init()
583
+
584
+ def get_input_embeddings(self):
585
+ return self.embed_tokens
586
+
587
+ def set_input_embeddings(self, value):
588
+ self.embed_tokens = value
589
+
590
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
591
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
592
+ # create causal mask
593
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
594
+ combined_attention_mask = None
595
+ if input_shape[-1] > 1:
596
+ combined_attention_mask = _make_causal_mask(
597
+ input_shape,
598
+ inputs_embeds.dtype,
599
+ device=inputs_embeds.device,
600
+ past_key_values_length=past_key_values_length,
601
+ )
602
+
603
+ if attention_mask is not None:
604
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
605
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
606
+ inputs_embeds.device
607
+ )
608
+ combined_attention_mask = (
609
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
610
+ )
611
+
612
+ return combined_attention_mask
613
+
614
+ @add_start_docstrings_to_model_forward(INDEX_INPUTS_DOCSTRING)
615
+ def forward(
616
+ self,
617
+ input_ids: torch.LongTensor = None,
618
+ attention_mask: Optional[torch.Tensor] = None,
619
+ position_ids: Optional[torch.LongTensor] = None,
620
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
621
+ inputs_embeds: Optional[torch.FloatTensor] = None,
622
+ use_cache: Optional[bool] = None,
623
+ output_attentions: Optional[bool] = None,
624
+ output_hidden_states: Optional[bool] = None,
625
+ return_dict: Optional[bool] = None,
626
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
627
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
628
+ output_hidden_states = (
629
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
630
+ )
631
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
632
+
633
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
634
+
635
+ # retrieve input_ids and inputs_embeds
636
+ if input_ids is not None and inputs_embeds is not None:
637
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
638
+ elif input_ids is not None:
639
+ batch_size, seq_length = input_ids.shape
640
+ elif inputs_embeds is not None:
641
+ batch_size, seq_length, _ = inputs_embeds.shape
642
+ else:
643
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
644
+
645
+ seq_length_with_past = seq_length
646
+ past_key_values_length = 0
647
+
648
+ if past_key_values is not None:
649
+ past_key_values_length = past_key_values[0][0].shape[2]
650
+ seq_length_with_past = seq_length_with_past + past_key_values_length
651
+
652
+ if position_ids is None:
653
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
654
+ position_ids = torch.arange(
655
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
656
+ )
657
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
658
+ else:
659
+ position_ids = position_ids.view(-1, seq_length).long()
660
+
661
+ if inputs_embeds is None:
662
+ inputs_embeds = self.embed_tokens(input_ids)
663
+ # embed positions
664
+ if attention_mask is None:
665
+ attention_mask = torch.ones(
666
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
667
+ )
668
+ attention_mask = self._prepare_decoder_attention_mask(
669
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
670
+ )
671
+
672
+ hidden_states = inputs_embeds
673
+
674
+ if self.gradient_checkpointing and self.training:
675
+ if use_cache:
676
+ logger.warning_once(
677
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
678
+ )
679
+ use_cache = False
680
+
681
+ # decoder layers
682
+ all_hidden_states = () if output_hidden_states else None
683
+ all_self_attns = () if output_attentions else None
684
+ next_decoder_cache = () if use_cache else None
685
+
686
+ for idx, decoder_layer in enumerate(self.layers):
687
+ if output_hidden_states:
688
+ all_hidden_states += (hidden_states,)
689
+
690
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
691
+
692
+ if self.gradient_checkpointing and self.training:
693
+
694
+ def create_custom_forward(module):
695
+ def custom_forward(*inputs):
696
+ # None for past_key_value
697
+ return module(*inputs, past_key_value, output_attentions)
698
+
699
+ return custom_forward
700
+
701
+ layer_outputs = torch.utils.checkpoint.checkpoint(
702
+ create_custom_forward(decoder_layer),
703
+ hidden_states,
704
+ attention_mask,
705
+ position_ids,
706
+ )
707
+ else:
708
+ layer_outputs = decoder_layer(
709
+ hidden_states,
710
+ attention_mask=attention_mask,
711
+ position_ids=position_ids,
712
+ past_key_value=past_key_value,
713
+ output_attentions=output_attentions,
714
+ use_cache=use_cache,
715
+ )
716
+
717
+ hidden_states = layer_outputs[0]
718
+
719
+ if use_cache:
720
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
721
+
722
+ if output_attentions:
723
+ all_self_attns += (layer_outputs[1],)
724
+
725
+ hidden_states = self.norm(hidden_states)
726
+
727
+ # add hidden states from the last decoder layer
728
+ if output_hidden_states:
729
+ all_hidden_states += (hidden_states,)
730
+
731
+ next_cache = next_decoder_cache if use_cache else None
732
+ if not return_dict:
733
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
734
+ return BaseModelOutputWithPast(
735
+ last_hidden_state=hidden_states,
736
+ past_key_values=next_cache,
737
+ hidden_states=all_hidden_states,
738
+ attentions=all_self_attns,
739
+ )
740
+
741
+
742
+ class NormHead(nn.Module):
743
+ def __init__(self, hidden_size, vocab_size, bias=False):
744
+ super().__init__()
745
+ self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
746
+ nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
747
+ self.first_flag = True
748
+
749
+ def forward(self, hidden_states):
750
+ if self.training:
751
+ norm_weight = nn.functional.normalize(self.weight)
752
+ self.first_flag = True
753
+ elif self.first_flag:
754
+ self.first_flag = False
755
+ self.weight = nn.Parameter(nn.functional.normalize(self.weight))
756
+ norm_weight = self.weight
757
+ else:
758
+ norm_weight = self.weight
759
+ return nn.functional.linear(hidden_states, norm_weight)
760
+
761
+
762
+ class IndexForCausalLM(IndexPreTrainedModel):
763
+ _tied_weights_keys = ["lm_head.weight"]
764
+
765
+ def __init__(self, config):
766
+ super().__init__(config)
767
+ self.model = IndexModel(config)
768
+ self.vocab_size = config.vocab_size
769
+ if config.norm_head:
770
+ self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
771
+ else:
772
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
773
+
774
+ # Initialize weights and apply final processing
775
+ self.post_init()
776
+
777
+ def get_input_embeddings(self):
778
+ return self.model.embed_tokens
779
+
780
+ def set_input_embeddings(self, value):
781
+ self.model.embed_tokens = value
782
+
783
+ def get_output_embeddings(self):
784
+ return self.lm_head
785
+
786
+ def set_output_embeddings(self, new_embeddings):
787
+ self.lm_head = new_embeddings
788
+
789
+ def set_decoder(self, decoder):
790
+ self.model = decoder
791
+
792
+ def get_decoder(self):
793
+ return self.model
794
+
795
+ @add_start_docstrings_to_model_forward(INDEX_INPUTS_DOCSTRING)
796
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
797
+ def forward(
798
+ self,
799
+ input_ids: torch.LongTensor = None,
800
+ attention_mask: Optional[torch.Tensor] = None,
801
+ position_ids: Optional[torch.LongTensor] = None,
802
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
803
+ inputs_embeds: Optional[torch.FloatTensor] = None,
804
+ labels: Optional[torch.LongTensor] = None,
805
+ use_cache: Optional[bool] = None,
806
+ output_attentions: Optional[bool] = None,
807
+ output_hidden_states: Optional[bool] = None,
808
+ return_dict: Optional[bool] = None,
809
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
810
+ r"""
811
+ Args:
812
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
813
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
814
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
815
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
816
+
817
+ Returns:
818
+
819
+ Example:
820
+
821
+ ```python
822
+ >>> from transformers import AutoTokenizer, IndexForCausalLM
823
+
824
+ >>> model = IndexForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
825
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
826
+
827
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
828
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
829
+
830
+ >>> # Generate
831
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
832
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
833
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
834
+ ```"""
835
+
836
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
837
+ output_hidden_states = (
838
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
839
+ )
840
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
841
+
842
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
843
+ outputs = self.model(
844
+ input_ids=input_ids,
845
+ attention_mask=attention_mask,
846
+ position_ids=position_ids,
847
+ past_key_values=past_key_values,
848
+ inputs_embeds=inputs_embeds,
849
+ use_cache=use_cache,
850
+ output_attentions=output_attentions,
851
+ output_hidden_states=output_hidden_states,
852
+ return_dict=return_dict,
853
+ )
854
+
855
+ hidden_states = outputs[0]
856
+ if self.config.pretraining_tp > 1:
857
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
858
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
859
+ logits = torch.cat(logits, dim=-1)
860
+ else:
861
+ logits = self.lm_head(hidden_states)
862
+ logits = logits.float()
863
+
864
+ loss = None
865
+ if labels is not None:
866
+ # Shift so that tokens < n predict n
867
+ shift_logits = logits[..., :-1, :].contiguous()
868
+ shift_labels = labels[..., 1:].contiguous()
869
+ # Flatten the tokens
870
+ loss_fct = CrossEntropyLoss()
871
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
872
+ shift_labels = shift_labels.view(-1)
873
+ # Enable model parallelism
874
+ shift_labels = shift_labels.to(shift_logits.device)
875
+ loss = loss_fct(shift_logits, shift_labels)
876
+
877
+ if not return_dict:
878
+ output = (logits,) + outputs[1:]
879
+ return (loss,) + output if loss is not None else output
880
+
881
+ return CausalLMOutputWithPast(
882
+ loss=loss,
883
+ logits=logits,
884
+ past_key_values=outputs.past_key_values,
885
+ hidden_states=outputs.hidden_states,
886
+ attentions=outputs.attentions,
887
+ )
888
+
889
+ def prepare_inputs_for_generation(
890
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
891
+ ):
892
+ if past_key_values:
893
+ input_ids = input_ids[:, -1:]
894
+
895
+ position_ids = kwargs.get("position_ids", None)
896
+ if attention_mask is not None and position_ids is None:
897
+ # create position_ids on the fly for batch generation
898
+ position_ids = attention_mask.long().cumsum(-1) - 1
899
+ position_ids.masked_fill_(attention_mask == 0, 1)
900
+ if past_key_values:
901
+ position_ids = position_ids[:, -1].unsqueeze(-1)
902
+
903
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
904
+ if inputs_embeds is not None and past_key_values is None:
905
+ model_inputs = {"inputs_embeds": inputs_embeds}
906
+ else:
907
+ model_inputs = {"input_ids": input_ids}
908
+
909
+ model_inputs.update(
910
+ {
911
+ "position_ids": position_ids,
912
+ "past_key_values": past_key_values,
913
+ "use_cache": kwargs.get("use_cache"),
914
+ "attention_mask": attention_mask,
915
+ }
916
+ )
917
+ return model_inputs
918
+
919
+ @staticmethod
920
+ def _reorder_cache(past_key_values, beam_idx):
921
+ reordered_past = ()
922
+ for layer_past in past_key_values:
923
+ reordered_past += (
924
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
925
+ )
926
+ return reordered_past
927
+
928
+
929
+ @add_start_docstrings(
930
+ """
931
+ The Index Model transformer with a sequence classification head on top (linear layer).
932
+
933
+ [`IndexForSequenceClassification`] uses the last token in order to do the classification, as other causal models
934
+ (e.g. GPT-2) do.
935
+
936
+ Since it does classification on the last token, it requires to know the position of the last token. If a
937
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
938
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
939
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
940
+ each row of the batch).
941
+ """,
942
+ INDEX_START_DOCSTRING,
943
+ )
944
+ class IndexForSequenceClassification(IndexPreTrainedModel):
945
+ def __init__(self, config):
946
+ super().__init__(config)
947
+ self.num_labels = config.num_labels
948
+ self.model = IndexModel(config)
949
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
950
+
951
+ # Initialize weights and apply final processing
952
+ self.post_init()
953
+
954
+ def get_input_embeddings(self):
955
+ return self.model.embed_tokens
956
+
957
+ def set_input_embeddings(self, value):
958
+ self.model.embed_tokens = value
959
+
960
+ @add_start_docstrings_to_model_forward(INDEX_INPUTS_DOCSTRING)
961
+ def forward(
962
+ self,
963
+ input_ids: torch.LongTensor = None,
964
+ attention_mask: Optional[torch.Tensor] = None,
965
+ position_ids: Optional[torch.LongTensor] = None,
966
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
967
+ inputs_embeds: Optional[torch.FloatTensor] = None,
968
+ labels: Optional[torch.LongTensor] = None,
969
+ use_cache: Optional[bool] = None,
970
+ output_attentions: Optional[bool] = None,
971
+ output_hidden_states: Optional[bool] = None,
972
+ return_dict: Optional[bool] = None,
973
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
974
+ r"""
975
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
976
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
977
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
978
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
979
+ """
980
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
981
+
982
+ transformer_outputs = self.model(
983
+ input_ids,
984
+ attention_mask=attention_mask,
985
+ position_ids=position_ids,
986
+ past_key_values=past_key_values,
987
+ inputs_embeds=inputs_embeds,
988
+ use_cache=use_cache,
989
+ output_attentions=output_attentions,
990
+ output_hidden_states=output_hidden_states,
991
+ return_dict=return_dict,
992
+ )
993
+ hidden_states = transformer_outputs[0]
994
+ logits = self.score(hidden_states)
995
+
996
+ if input_ids is not None:
997
+ batch_size = input_ids.shape[0]
998
+ else:
999
+ batch_size = inputs_embeds.shape[0]
1000
+
1001
+ if self.config.pad_token_id is None and batch_size != 1:
1002
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1003
+ if self.config.pad_token_id is None:
1004
+ sequence_lengths = -1
1005
+ else:
1006
+ if input_ids is not None:
1007
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
1008
+ logits.device
1009
+ )
1010
+ else:
1011
+ sequence_lengths = -1
1012
+
1013
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1014
+
1015
+ loss = None
1016
+ if labels is not None:
1017
+ labels = labels.to(logits.device)
1018
+ if self.config.problem_type is None:
1019
+ if self.num_labels == 1:
1020
+ self.config.problem_type = "regression"
1021
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1022
+ self.config.problem_type = "single_label_classification"
1023
+ else:
1024
+ self.config.problem_type = "multi_label_classification"
1025
+
1026
+ if self.config.problem_type == "regression":
1027
+ loss_fct = MSELoss()
1028
+ if self.num_labels == 1:
1029
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1030
+ else:
1031
+ loss = loss_fct(pooled_logits, labels)
1032
+ elif self.config.problem_type == "single_label_classification":
1033
+ loss_fct = CrossEntropyLoss()
1034
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1035
+ elif self.config.problem_type == "multi_label_classification":
1036
+ loss_fct = BCEWithLogitsLoss()
1037
+ loss = loss_fct(pooled_logits, labels)
1038
+ if not return_dict:
1039
+ output = (pooled_logits,) + transformer_outputs[1:]
1040
+ return ((loss,) + output) if loss is not None else output
1041
+
1042
+ return SequenceClassifierOutputWithPast(
1043
+ loss=loss,
1044
+ logits=pooled_logits,
1045
+ past_key_values=transformer_outputs.past_key_values,
1046
+ hidden_states=transformer_outputs.hidden_states,
1047
+ attentions=transformer_outputs.attentions,
1048
+ )
special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "</s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "unk_token": {
17
+ "content": "<unk>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
tokenization_index.py ADDED
@@ -0,0 +1,266 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+
21
+ """Tokenization classes for Index, Modify from llama tokenzier."""
22
+ import os
23
+ from shutil import copyfile
24
+ from typing import Any, Dict, List, Optional, Tuple
25
+
26
+ import sentencepiece as spm
27
+
28
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
29
+ from transformers.utils import logging
30
+
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+ VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
35
+
36
+ PRETRAINED_VOCAB_FILES_MAP = {}
37
+
38
+ SPIECE_UNDERLINE = "▁"
39
+
40
+
41
+ class IndexTokenizer(PreTrainedTokenizer):
42
+ """
43
+ Construct a Index tokenizer. Based on Byte-Pair-Encoding.
44
+
45
+ Args:
46
+ vocab_file (`str`):
47
+ Path to the vocabulary file.
48
+ """
49
+
50
+ vocab_files_names = VOCAB_FILES_NAMES
51
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
52
+ model_input_names = ["input_ids", "attention_mask"]
53
+
54
+ def __init__(
55
+ self,
56
+ vocab_file,
57
+ unk_token="<unk>",
58
+ bos_token="<s>",
59
+ eos_token="</s>",
60
+ pad_token=None,
61
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
62
+ add_bos_token=False,
63
+ add_eos_token=False,
64
+ decode_with_prefix_space=False,
65
+ clean_up_tokenization_spaces=False,
66
+ legacy=False,
67
+ **kwargs,
68
+ ):
69
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
70
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
71
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
72
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
73
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
74
+ self.legacy = legacy
75
+
76
+ self.vocab_file = vocab_file
77
+ self.add_bos_token = add_bos_token
78
+ self.add_eos_token = add_eos_token
79
+ self.decode_with_prefix_space = decode_with_prefix_space
80
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
81
+ self.sp_model.Load(vocab_file)
82
+ self._no_prefix_space_tokens = None
83
+
84
+ super().__init__(
85
+ bos_token=bos_token,
86
+ eos_token=eos_token,
87
+ unk_token=unk_token,
88
+ pad_token=pad_token,
89
+ add_bos_token=add_bos_token,
90
+ add_eos_token=add_eos_token,
91
+ sp_model_kwargs=self.sp_model_kwargs,
92
+ decode_with_prefix_space=decode_with_prefix_space,
93
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
94
+ legacy=legacy,
95
+ **kwargs,
96
+ )
97
+
98
+ @property
99
+ def no_prefix_space_tokens(self):
100
+ if self._no_prefix_space_tokens is None:
101
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
102
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
103
+ return self._no_prefix_space_tokens
104
+
105
+ @property
106
+ def vocab_size(self):
107
+ """Returns vocab size"""
108
+ return self.sp_model.get_piece_size()
109
+
110
+ @property
111
+ def bos_token_id(self) -> Optional[int]:
112
+ return self.sp_model.bos_id()
113
+
114
+ @property
115
+ def eos_token_id(self) -> Optional[int]:
116
+ return self.sp_model.eos_id()
117
+
118
+ def get_vocab(self):
119
+ """Returns vocab as a dict"""
120
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
121
+ vocab.update(self.added_tokens_encoder)
122
+ return vocab
123
+
124
+ def _tokenize(self, text):
125
+ """Returns a tokenized string."""
126
+ return self.sp_model.encode(text, out_type=str)
127
+
128
+ def _convert_token_to_id(self, token):
129
+ """Converts a token (str) in an id using the vocab."""
130
+ return self.sp_model.piece_to_id(token)
131
+
132
+ def _convert_id_to_token(self, index):
133
+ """Converts an index (integer) in a token (str) using the vocab."""
134
+ token = self.sp_model.IdToPiece(index)
135
+ return token
136
+
137
+ def convert_tokens_to_string(self, tokens):
138
+ """Converts a sequence of tokens (string) in a single string."""
139
+ # since we manually add the prefix space, we have to remove it when decoding
140
+ if tokens[0].startswith(SPIECE_UNDERLINE):
141
+ tokens[0] = tokens[0][1:]
142
+
143
+ current_sub_tokens = []
144
+ out_string = ""
145
+ prev_is_special = False
146
+ for i, token in enumerate(tokens):
147
+ # make sure that special tokens are not decoded using sentencepiece model
148
+ if token in self.all_special_tokens:
149
+ if not prev_is_special and i != 0 and self.legacy:
150
+ out_string += " "
151
+ out_string += self.sp_model.decode(current_sub_tokens) + token
152
+ prev_is_special = True
153
+ current_sub_tokens = []
154
+ else:
155
+ current_sub_tokens.append(token)
156
+ prev_is_special = False
157
+ out_string += self.sp_model.decode(current_sub_tokens)
158
+ return out_string
159
+
160
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
161
+ """
162
+ Save the vocabulary and special tokens file to a directory.
163
+
164
+ Args:
165
+ save_directory (`str`):
166
+ The directory in which to save the vocabulary.
167
+
168
+ Returns:
169
+ `Tuple(str)`: Paths to the files saved.
170
+ """
171
+ if not os.path.isdir(save_directory):
172
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
173
+ return
174
+ out_vocab_file = os.path.join(
175
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
176
+ )
177
+
178
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
179
+ copyfile(self.vocab_file, out_vocab_file)
180
+ elif not os.path.isfile(self.vocab_file):
181
+ with open(out_vocab_file, "wb") as fi:
182
+ content_spiece_model = self.sp_model.serialized_model_proto()
183
+ fi.write(content_spiece_model)
184
+
185
+ return (out_vocab_file,)
186
+
187
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
188
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
189
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
190
+
191
+ output = bos_token_id + token_ids_0 + eos_token_id
192
+
193
+ if token_ids_1 is not None:
194
+ output = output + bos_token_id + token_ids_1 + eos_token_id
195
+
196
+ return output
197
+
198
+ def get_special_tokens_mask(
199
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
200
+ ) -> List[int]:
201
+ """
202
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
203
+ special tokens using the tokenizer `prepare_for_model` method.
204
+
205
+ Args:
206
+ token_ids_0 (`List[int]`):
207
+ List of IDs.
208
+ token_ids_1 (`List[int]`, *optional*):
209
+ Optional second list of IDs for sequence pairs.
210
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
211
+ Whether or not the token list is already formatted with special tokens for the model.
212
+
213
+ Returns:
214
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
215
+ """
216
+ if already_has_special_tokens:
217
+ return super().get_special_tokens_mask(
218
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
219
+ )
220
+
221
+ bos_token_id = [1] if self.add_bos_token else []
222
+ eos_token_id = [1] if self.add_eos_token else []
223
+
224
+ if token_ids_1 is None:
225
+ return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
226
+ return (
227
+ bos_token_id
228
+ + ([0] * len(token_ids_0))
229
+ + eos_token_id
230
+ + bos_token_id
231
+ + ([0] * len(token_ids_1))
232
+ + eos_token_id
233
+ )
234
+
235
+ def create_token_type_ids_from_sequences(
236
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
237
+ ) -> List[int]:
238
+ """
239
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
240
+ sequence pair mask has the following format:
241
+
242
+ ```
243
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
244
+ | first sequence | second sequence |
245
+ ```
246
+
247
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
248
+
249
+ Args:
250
+ token_ids_0 (`List[int]`):
251
+ List of ids.
252
+ token_ids_1 (`List[int]`, *optional*):
253
+ Optional second list of IDs for sequence pairs.
254
+
255
+ Returns:
256
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
257
+ """
258
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
259
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
260
+
261
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
262
+
263
+ if token_ids_1 is not None:
264
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
265
+
266
+ return output
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a11626b3c80cbfdd5f4b5585fce8938d178f3a0df739c3dd1c708ce944878af
3
+ size 1010047
tokenizer_config.json ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": false,
3
+ "add_eos_token": false,
4
+ "bos_token": {
5
+ "__type": "AddedToken",
6
+ "content": "<s>",
7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false
11
+ },
12
+ "eos_token": {
13
+ "__type": "AddedToken",
14
+ "content": "</s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ },
20
+ "unk_token": {
21
+ "__type": "AddedToken",
22
+ "content": "<unk>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false
27
+ },
28
+ "pad_token": {
29
+ "__type": "AddedToken",
30
+ "content": "<unk>",
31
+ "lstrip": false,
32
+ "normalized": false,
33
+ "rstrip": false,
34
+ "single_word": true
35
+ },
36
+ "added_tokens_decoder": {
37
+ "3": {
38
+ "content": "reserved_0",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false,
43
+ "special": true
44
+ },
45
+ "4": {
46
+ "content": "reserved_1",
47
+ "lstrip": false,
48
+ "normalized": false,
49
+ "rstrip": false,
50
+ "single_word": false,
51
+ "special": true
52
+ }
53
+ },
54
+ "clean_up_tokenization_spaces": false,
55
+ "spaces_between_special_tokens": false,
56
+ "add_prefix_space": false,
57
+ "legacy": false,
58
+ "model_max_length": 1e+30,
59
+ "sp_model_kwargs": {},
60
+ "tokenizer_class": "IndexTokenizer",
61
+ "auto_map": {
62
+ "AutoTokenizer": [
63
+ "tokenization_index.IndexTokenizer",
64
+ null
65
+ ]
66
+ },
67
+ "use_default_system_prompt": false,
68
+ "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ unk_token + system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'reserved_0' + content.strip() + 'reserved_1' }}{% elif message['role'] == 'assistant' %}{{ content.strip() }}{% endif %}{% endfor %}"
69
+ }