picocreator commited on
Commit
27839c7
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WIP reposetup (from rwkv-6-world-1b6)

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
NOTES.md ADDED
@@ -0,0 +1,171 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ #### GPU
3
+
4
+ ```python
5
+ import torch
6
+ from transformers import AutoModelForCausalLM, AutoTokenizer
7
+
8
+ def generate_prompt(instruction, input=""):
9
+ instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
10
+ input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
11
+ if input:
12
+ return f"""Instruction: {instruction}
13
+
14
+ Input: {input}
15
+
16
+ Response:"""
17
+ else:
18
+ return f"""User: hi
19
+
20
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
21
+
22
+ User: {instruction}
23
+
24
+ Assistant:"""
25
+
26
+
27
+ model = AutoModelForCausalLM.from_pretrained("RWKV/v6-Finch-14B-HF", trust_remote_code=True, torch_dtype=torch.float16).to('cuda')
28
+ tokenizer = AutoTokenizer.from_pretrained("RWKV/v6-Finch-14B-HF", trust_remote_code=True, padding_side='left', pad_token="<s>")
29
+
30
+ text = "介绍一下大熊猫"
31
+ prompt = generate_prompt(text)
32
+
33
+ inputs = tokenizer(prompt, return_tensors="pt").to(0)
34
+ output = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, )
35
+ print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
36
+ ```
37
+
38
+ output:
39
+
40
+ ```shell
41
+ User: hi
42
+
43
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
44
+
45
+ User: 介绍一下大熊猫
46
+
47
+ Assistant: 大熊猫是一种中国特有的哺乳动物,也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体,有着黑色的毛发和白色的耳朵。大熊猫的食物主要是竹子,它们会在竹林中寻找竹子,并且会将竹子放在竹笼中进行储存。大熊猫的寿命约为20至30年,但由于栖息地的丧失和人类活动的
48
+ ```
49
+
50
+ #### Batch Inference
51
+
52
+ ```python
53
+ import torch
54
+ from transformers import AutoModelForCausalLM, AutoTokenizer
55
+
56
+ def generate_prompt(instruction, input=""):
57
+ instruction = instruction.strip().replace('\r\n', '\n').replace('\n\n', '\n')
58
+ input = input.strip().replace('\r\n', '\n').replace('\n\n', '\n')
59
+ if input:
60
+ return f"""Instruction: {instruction}
61
+
62
+ Input: {input}
63
+
64
+ Response:"""
65
+ else:
66
+ return f"""User: hi
67
+
68
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
69
+
70
+ User: {instruction}
71
+
72
+ Assistant:"""
73
+
74
+ model = AutoModelForCausalLM.from_pretrained("RWKV/v6-Finch-14B-HF", trust_remote_code=True).to(torch.float32)
75
+ tokenizer = AutoTokenizer.from_pretrained("RWKV/v6-Finch-14B-HF", trust_remote_code=True, padding_side='left', pad_token="<s>")
76
+
77
+ texts = ["请介绍北京的旅游景点", "介绍一下大熊猫", "乌兰察布"]
78
+ prompts = [generate_prompt(text) for text in texts]
79
+
80
+ inputs = tokenizer(prompts, return_tensors="pt", padding=True)
81
+ outputs = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, )
82
+
83
+ for output in outputs:
84
+ print(tokenizer.decode(output.tolist(), skip_special_tokens=True))
85
+
86
+ ```
87
+
88
+ output:
89
+
90
+ ```shell
91
+ User: hi
92
+
93
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
94
+
95
+ User: 请介绍北京的旅游景点
96
+
97
+ Assistant: 北京是中国的首都,拥有丰富的旅游资源和历史文化遗产。以下是一些北京的旅游景点:
98
+ 1. 故宫:位于北京市中心,是明清两代的皇宫,是中国最大的古代宫殿建筑群之一。
99
+ 2. 天安门广场:位于北京市中心,是中国最著名的城市广场之一,也是中国最大的城市广场。
100
+ 3. 颐和
101
+ User: hi
102
+
103
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
104
+
105
+ User: 介绍一下大熊猫
106
+
107
+ Assistant: 大熊猫是一种生活在中国中部地区的哺乳动物,也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体,有着黑色的毛发和圆圆的眼睛。大熊猫是一种濒危物种,目前只有在野外的几个保护区才能看到它们的身影。大熊猫的食物主要是竹子,它们会在竹子上寻找食物,并且可以通
108
+ User: hi
109
+
110
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
111
+
112
+ User: 乌兰察布
113
+
114
+ Assistant: 乌兰察布是中国新疆维吾尔自治区的一个县级市,位于新疆维吾尔自治区中部,是新疆的第二大城市。乌兰察布市是新疆的第一大城市,也是新疆的重要城市之一。乌兰察布市是新疆的经济中心,也是新疆的重要交通枢纽之一。乌兰察布市的人口约为2.5万人,其中汉族占绝大多数。乌
115
+ ```
116
+
117
+ #### CPU
118
+
119
+ ```python
120
+ import torch
121
+ from transformers import AutoModelForCausalLM, AutoTokenizer
122
+
123
+ def generate_prompt(instruction, input=""):
124
+ instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
125
+ input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
126
+ if input:
127
+ return f"""Instruction: {instruction}
128
+
129
+ Input: {input}
130
+
131
+ Response:"""
132
+ else:
133
+ return f"""User: hi
134
+
135
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
136
+
137
+ User: {instruction}
138
+
139
+ Assistant:"""
140
+
141
+
142
+ model = AutoModelForCausalLM.from_pretrained("RWKV/v6-Finch-14B-HF", trust_remote_code=True).to(torch.float32)
143
+ tokenizer = AutoTokenizer.from_pretrained("RWKV/v6-Finch-14B-HF", trust_remote_code=True, padding_side='left', pad_token="<s>")
144
+
145
+ text = "请介绍北京的旅游景点"
146
+ prompt = generate_prompt(text)
147
+
148
+ inputs = tokenizer(prompt, return_tensors="pt")
149
+ output = model.generate(inputs["input_ids"], max_new_tokens=333, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, )
150
+ print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
151
+ ```
152
+
153
+ output:
154
+
155
+ ```shell
156
+ User: hi
157
+
158
+ Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
159
+
160
+ User: 请介绍北京的旅游景点
161
+
162
+ Assistant: 北京是中国的首都,拥有众多的旅游景点,以下是其中一些著名的景点:
163
+ 1. 故宫:位于北京市中心,是明清两代的皇宫,内有大量的文物和艺术品。
164
+ 2. 天安门广场:是中国最著名的广场之一,是中国人民政治协商会议的旧址,也是中国人民政治协商会议的中心。
165
+ 3. 颐和园:是中国古代皇家园林之一,有着悠久的历史和丰富的文化内涵。
166
+ 4. 长城:是中国古代的一道长城,全长约万里,是中国最著名的旅游景点之一。
167
+ 5. 北京大学:是中国著名的高等教育机构之一,有着悠久的历史和丰富的文化内涵。
168
+ 6. 北京动物园:是中国最大的动物园之一,有着丰富的动物资源和丰富的文化内涵。
169
+ 7. 故宫博物院:是中国最著名的博物馆之一,收藏了大量的文物和艺术品,是中国最重要的文化遗产之一。
170
+ 8. 天坛:是中国古代皇家
171
+ ```
README.md CHANGED
@@ -1,3 +1,10 @@
1
- ---
2
- license: apache-2.0
3
- ---
 
 
 
 
 
 
 
 
1
+ ### v6-Finch-14B-HF
2
+
3
+ > HF compatible model for Finch-14B.
4
+ > This is an early preview for benchmarking
5
+
6
+ ![Crimson Finch Bird](./imgs/crimson-finch-unsplash-david-clode.jpg)
7
+
8
+ > origin pth weight at https://huggingface.co/BlinkDL/rwkv-6-world/blob/main/ .
9
+
10
+ More details to be done.
added_tokens.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ {
2
+ "<s>": 0
3
+ }
config.json ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "Rwkv6ForCausalLM"
4
+ ],
5
+ "auto_map": {
6
+ "AutoConfig": "configuration_rwkv6.Rwkv6Config",
7
+ "AutoModelForCausalLM": "modeling_rwkv6.Rwkv6ForCausalLM"
8
+ },
9
+ "attention_hidden_size": 4096,
10
+ "bos_token_id": 0,
11
+ "eos_token_id": 0,
12
+ "head_size": 64,
13
+ "head_size_divisor": 8,
14
+ "hidden_size": 4096,
15
+ "intermediate_size": null,
16
+ "layer_norm_epsilon": 1e-05,
17
+ "model_type": "rwkv6",
18
+ "num_attention_heads": 64,
19
+ "num_hidden_layers": 61,
20
+ "rescale_every": 6,
21
+ "tie_word_embeddings": false,
22
+ "transformers_version": "4.34.0",
23
+ "use_cache": true,
24
+ "vocab_size": 65536
25
+ }
configuration_rwkv6.py ADDED
@@ -0,0 +1,118 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
3
+ # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """ RWKV configuration"""
17
+
18
+ from transformers.configuration_utils import PretrainedConfig
19
+ from transformers.utils import logging
20
+
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+ RWKV6_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
25
+
26
+
27
+ class Rwkv6Config(PretrainedConfig):
28
+ """
29
+ This is the configuration class to store the configuration of a [`Rwkv6Model`]. It is used to instantiate a RWKV6
30
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
31
+ defaults will yield a similar configuration to that of the RWVK-4
32
+ [RWKV/rwkv-5-world-1b5](https://huggingface.co/RWKV/rwkv-5-world-1b5) architecture.
33
+
34
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
35
+ documentation from [`PretrainedConfig`] for more information.
36
+
37
+
38
+ Args:
39
+ vocab_size (`int`, *optional*, defaults to 65536):
40
+ Vocabulary size of the RWKV6 model. Defines the number of different tokens that can be represented by the
41
+ `inputs_ids` passed when calling [`Rwkv6Model`].
42
+ hidden_size (`int`, *optional*, defaults to 768):
43
+ Dimensionality of the embeddings and hidden states.
44
+ num_hidden_layers (`int`, *optional*, defaults to 24):
45
+ Number of hidden layers in the model.
46
+ attention_hidden_size (`int`, *optional*):
47
+ Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
48
+ num_attention_heads (`int`, *optional*, defaults to 64):
49
+ The attention heads to use in rwkv6 self_attention module.
50
+ head_size (`int`, *optional*, defaults to 64): head_size of rwkv6 self_attention module.
51
+ intermediate_size (`int`, *optional*):
52
+ Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset.
53
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
54
+ The epsilon to use in the layer normalization layers.
55
+ bos_token_id (`int`, *optional*, defaults to 0):
56
+ The id of the beginning of sentence token in the vocabulary. Defaults to 0.
57
+ eos_token_id (`int`, *optional*, defaults to 0):
58
+ The id of the end of sentence token in the vocabulary. Defaults to 0.
59
+ rescale_every (`int`, *optional*, defaults to 6):
60
+ At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every
61
+ `rescale_every` layer. If set to 0 or a negative number, no rescale is done.
62
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
63
+ Whether or not to tie the word embeddings with the input token embeddings.
64
+ use_cache (`bool`, *optional*, defaults to `True`):
65
+ Whether or not the model should return the last state.
66
+
67
+
68
+ Example:
69
+
70
+ ```python
71
+ >>> from transformers import Rwkv6Config, Rwkv6Model
72
+
73
+ >>> # Initializing a Rwkv6 configuration
74
+ >>> configuration = Rwkv6Config()
75
+
76
+ >>> # Initializing a model (with random weights) from the configuration
77
+ >>> model = Rwkv6Model(configuration)
78
+
79
+ >>> # Accessing the model configuration
80
+ >>> configuration = model.config
81
+ ```"""
82
+
83
+ model_type = "rwkv6"
84
+
85
+ def __init__(
86
+ self,
87
+ vocab_size=65536,
88
+ hidden_size=768,
89
+ num_hidden_layers=24,
90
+ attention_hidden_size=None,
91
+ head_size=64,
92
+ head_size_divisor=8,
93
+ intermediate_size=None,
94
+ layer_norm_epsilon=1e-5,
95
+ bos_token_id=0,
96
+ eos_token_id=0,
97
+ rescale_every=6,
98
+ tie_word_embeddings=False,
99
+ use_cache=True,
100
+ **kwargs,
101
+ ):
102
+ self.vocab_size = vocab_size
103
+ self.hidden_size = hidden_size
104
+ self.num_hidden_layers = num_hidden_layers
105
+ self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
106
+ self.head_size = head_size
107
+ self.head_size_divisor = head_size_divisor
108
+ self.intermediate_size = None
109
+ self.layer_norm_epsilon = layer_norm_epsilon
110
+ self.rescale_every = rescale_every
111
+ self.use_cache = use_cache
112
+
113
+ self.bos_token_id = bos_token_id
114
+ self.eos_token_id = eos_token_id
115
+
116
+ super().__init__(
117
+ tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
118
+ )
generation_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "chat_format": "chatml",
3
+ "eos_token_id": 0,
4
+ "pad_token_id": 0,
5
+ "max_window_size": 2048,
6
+ "max_new_tokens": 2048,
7
+ "do_sample": true,
8
+ "top_k": 0,
9
+ "top_p": 0.1,
10
+ "repetition_penalty": 1.0,
11
+ "transformers_version": "4.31.1"
12
+ }
hf_rwkv_tokenizer.py ADDED
@@ -0,0 +1,279 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2024 The HuggingFace Inc. team.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """Tokenization classes for RWKV6."""
16
+
17
+ import os
18
+ import re
19
+ from typing import TYPE_CHECKING, List, Optional, Tuple
20
+
21
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
22
+ from transformers.utils import logging
23
+
24
+
25
+ if TYPE_CHECKING:
26
+ pass
27
+
28
+ logger = logging.get_logger(__name__)
29
+
30
+
31
+ VOCAB_FILES_NAMES = {
32
+ "vocab_file": "rwkv_vocab_v20230424.txt",
33
+ }
34
+
35
+ class TRIE:
36
+ __slots__ = tuple("ch,to,values,front".split(","))
37
+ to: list
38
+ values: set
39
+
40
+ def __init__(self, front=None, ch=None):
41
+ self.ch = ch
42
+ self.to = [None for ch in range(256)]
43
+ self.values = set()
44
+ self.front = front
45
+
46
+ def __repr__(self):
47
+ fr = self
48
+ ret = []
49
+ while fr != None:
50
+ if fr.ch != None:
51
+ ret.append(fr.ch)
52
+ fr = fr.front
53
+ return "<TRIE %s %s>" % (ret[::-1], self.values)
54
+
55
+ def add(self, key: bytes, idx: int = 0, val=None):
56
+ if idx == len(key):
57
+ if val is None:
58
+ val = key
59
+ self.values.add(val)
60
+ return self
61
+ ch = key[idx]
62
+ if self.to[ch] is None:
63
+ self.to[ch] = TRIE(front=self, ch=ch)
64
+ return self.to[ch].add(key, idx=idx + 1, val=val)
65
+
66
+ def find_longest(self, key: bytes, idx: int = 0):
67
+ u: TRIE = self
68
+ ch: int = key[idx]
69
+
70
+ while u.to[ch] is not None:
71
+ u = u.to[ch]
72
+ idx += 1
73
+ if u.values:
74
+ ret = idx, u, u.values
75
+ if idx == len(key):
76
+ break
77
+ ch = key[idx]
78
+ return ret
79
+
80
+
81
+ class RWKV_TOKENIZER:
82
+ def __init__(self, file_name):
83
+ self.idx2token = {}
84
+ sorted = [] # must be already sorted
85
+ with open(file_name, "r", encoding="utf-8") as f:
86
+ lines = f.readlines()
87
+ for l in lines:
88
+ idx = int(l[: l.index(" ")])
89
+ x = eval(l[l.index(" ") : l.rindex(" ")])
90
+ x = x.encode("utf-8") if isinstance(x, str) else x
91
+ assert isinstance(x, bytes)
92
+
93
+ assert len(x) == int(l[l.rindex(" ") :])
94
+ sorted += [x]
95
+ self.idx2token[idx] = x
96
+
97
+ self.token2idx = {}
98
+ for k, v in self.idx2token.items():
99
+ self.token2idx[v] = int(k)
100
+
101
+ self.root = TRIE()
102
+ for t, i in self.token2idx.items():
103
+ _ = self.root.add(t, val=(t, i))
104
+
105
+ def encodeBytes(self, src: bytes):
106
+ idx: int = 0
107
+ tokens = []
108
+ while idx < len(src):
109
+ _idx: int = idx
110
+ idx, _, values = self.root.find_longest(src, idx)
111
+ assert idx != _idx
112
+ _, token = next(iter(values))
113
+ tokens.append(token)
114
+ return tokens
115
+
116
+ def decodeBytes(self, tokens):
117
+ return b"".join(map(lambda i: self.idx2token[i], tokens))
118
+
119
+ def encode(self, src):
120
+ if isinstance(src, str):
121
+ return [self.encodeBytes(src.encode("utf-8"))]
122
+ elif isinstance(src, list):
123
+ return [self.encodeBytes(s.encode("utf-8")) for s in src]
124
+
125
+ def decode(self, tokens):
126
+ return [self.decodeBytes(batch).decode("utf-8") for batch in tokens]
127
+ # try:
128
+ # return self.decodeBytes(tokens).decode('utf-8')
129
+ # except:
130
+ # return '\ufffd' # bad utf-8
131
+
132
+ def printTokens(self, tokens):
133
+ for i in tokens:
134
+ s = self.idx2token[i]
135
+ try:
136
+ s = s.decode("utf-8")
137
+ except:
138
+ pass
139
+ print(f"{repr(s)}{i}", end=" ")
140
+ print()
141
+
142
+
143
+ class Rwkv6Tokenizer(PreTrainedTokenizer):
144
+ vocab_files_names = VOCAB_FILES_NAMES
145
+ model_input_names = ["input_ids", "attention_mask"]
146
+
147
+ def __init__(
148
+ self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", **kwargs
149
+ ):
150
+ if not os.path.isfile(vocab_file):
151
+ raise ValueError(
152
+ f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
153
+ " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
154
+ )
155
+
156
+ with open(vocab_file, "r", encoding="utf-8") as reader:
157
+ tokens = reader.readlines()
158
+
159
+ if "add_bos_token" in kwargs:
160
+ self.add_bos_token = kwargs["add_bos_token"]
161
+ else:
162
+ self.add_bos_token = False
163
+ self.trie_tokenizer = RWKV_TOKENIZER(vocab_file)
164
+ vocab = self.trie_tokenizer.token2idx
165
+ self.encoder = vocab
166
+ self.decoder = {v: k for k, v in vocab.items()}
167
+ self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
168
+ super().__init__(
169
+ bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs
170
+ )
171
+
172
+ @property
173
+ def vocab_size(self):
174
+ return len(self.encoder)
175
+
176
+ def get_vocab(self):
177
+ vocab = {str(self.convert_ids_to_tokens(i)): i for i in range(self.vocab_size)}
178
+ vocab.update(self.added_tokens_encoder)
179
+ return vocab
180
+
181
+ def _tokenize(self, text, split_special_tokens=False):
182
+ # return self.wordpiece_tokenizer.tokenize(text.encode("utf-8"))
183
+ return self.trie_tokenizer.encode(text)[0]
184
+
185
+ def _convert_token_to_id(self, token):
186
+ return token
187
+
188
+ def _convert_id_to_token(self, index):
189
+ """Converts an index (integer) in a token (byte) using the vocab."""
190
+ token = self.decoder.get(index, self.unk_token)
191
+ if isinstance(token, (bytes)):
192
+ token = token.decode("utf-8", errors="replace")
193
+ return token
194
+
195
+ def convert_tokens_to_string(self, tokens):
196
+ """Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes"""
197
+ out_string = b"".join(
198
+ [k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]
199
+ ).decode("utf-8")
200
+ return out_string
201
+
202
+ def save_vocabulary(
203
+ self, save_directory: str, filename_prefix: Optional[str] = None
204
+ ) -> Tuple[str]:
205
+ index = 0
206
+ if os.path.isdir(save_directory):
207
+ vocab_file = os.path.join(
208
+ save_directory,
209
+ (filename_prefix + "-" if filename_prefix else "") + "vocab.txt",
210
+ )
211
+ else:
212
+ vocab_file = (
213
+ filename_prefix + "-" if filename_prefix else ""
214
+ ) + save_directory
215
+ with open(vocab_file, "w", encoding="utf-8") as writer:
216
+ for token, token_index in sorted(
217
+ self.encoder.items(), key=lambda kv: kv[1]
218
+ ):
219
+ if index != token_index:
220
+ logger.warning(
221
+ f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
222
+ " Please check that the vocabulary is not corrupted!"
223
+ )
224
+ index = token_index
225
+ writer.write(str(token) + "\n")
226
+ index += 1
227
+ return (vocab_file,)
228
+
229
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
230
+ if self.add_bos_token:
231
+ bos_token_ids = [self.bos_token_id]
232
+ else:
233
+ bos_token_ids = []
234
+
235
+ output = bos_token_ids + token_ids_0
236
+
237
+ if token_ids_1 is None:
238
+ return output
239
+
240
+ return output + bos_token_ids + token_ids_1
241
+
242
+ def get_special_tokens_mask(
243
+ self,
244
+ token_ids_0: List[int],
245
+ token_ids_1: Optional[List[int]] = None,
246
+ already_has_special_tokens: bool = False,
247
+ ) -> List[int]:
248
+ """
249
+ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
250
+ special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
251
+
252
+ Args:
253
+ token_ids_0 (`List[int]`):
254
+ List of IDs.
255
+ token_ids_1 (`List[int]`, *optional*):
256
+ Optional second list of IDs for sequence pairs.
257
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
258
+ Whether or not the token list is already formatted with special tokens for the model.
259
+
260
+ Returns:
261
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
262
+ """
263
+ if already_has_special_tokens:
264
+ return super().get_special_tokens_mask(
265
+ token_ids_0=token_ids_0,
266
+ token_ids_1=token_ids_1,
267
+ already_has_special_tokens=True,
268
+ )
269
+
270
+ if not self.add_bos_token:
271
+ return super().get_special_tokens_mask(
272
+ token_ids_0=token_ids_0,
273
+ token_ids_1=token_ids_1,
274
+ already_has_special_tokens=False,
275
+ )
276
+
277
+ if token_ids_1 is None:
278
+ return [1] + ([0] * len(token_ids_0))
279
+ return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
imgs/crimson-finch-unsplash-david-clode.jpg ADDED
modeling_rwkv6.py ADDED
@@ -0,0 +1,746 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2024 The RWKV team and HuggingFace Inc. team.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """PyTorch RWKV6 World model."""
16
+
17
+ from dataclasses import dataclass
18
+ from typing import List, Optional, Tuple, Union
19
+
20
+ from pathlib import Path
21
+
22
+ import torch
23
+ import torch.nn.functional as F
24
+ import torch.utils.checkpoint
25
+ from torch import nn
26
+ from torch.nn import CrossEntropyLoss
27
+
28
+ from transformers.modeling_utils import PreTrainedModel
29
+ from transformers.utils import (
30
+ ModelOutput,
31
+ add_code_sample_docstrings,
32
+ add_start_docstrings,
33
+ add_start_docstrings_to_model_forward,
34
+ is_ninja_available,
35
+ is_torch_cuda_available,
36
+ logging,
37
+ )
38
+
39
+ from .configuration_rwkv6 import Rwkv6Config
40
+ try:
41
+ from fla.ops.rwkv6.recurrent_fuse import fused_recurrent_rwkv6
42
+ except ImportError:
43
+ print("Required module is not installed. Please install it using the following commands:")
44
+ print("pip install -U git+https://github.com/sustcsonglin/flash-linear-attention")
45
+ print("Additionally, ensure you have the correct version of Triton installed:")
46
+ print("pip install triton==2.2.0")
47
+
48
+
49
+ logger = logging.get_logger(__name__)
50
+
51
+ _CHECKPOINT_FOR_DOC = "RWKV/rwkv-6-world-1b6"
52
+ _CONFIG_FOR_DOC = "Rwkv6Config"
53
+
54
+ def rwkv6_linear_attention_cpu(receptance, key, value, time_decay, time_first, state):
55
+ # For CPU fallback. Will be slower and probably take more memory than the custom CUDA kernel if not executed
56
+ # within a torch.no_grad.
57
+ batch, seq_length, _ = receptance.shape
58
+ num_heads, head_size = time_first.shape
59
+ key = key.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2).transpose(-2, -1)
60
+ value = value.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2)
61
+ receptance = receptance.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2)
62
+ time_decay = torch.exp(-torch.exp(time_decay.float())).view(batch, seq_length, num_heads, head_size).permute(0, 2, 3, 1)
63
+ time_first = time_first.float().reshape(-1, 1, 1).reshape(num_heads, -1, 1)
64
+ out = torch.zeros_like(key).reshape(batch, seq_length, num_heads, head_size)
65
+
66
+ for current_index in range(seq_length):
67
+ current_receptance = receptance[:, :, current_index:current_index+1, :]
68
+ current_key = key[:, :, :, current_index:current_index+1]
69
+ current_value = value[:, :, current_index:current_index+1, :]
70
+ current_time_decay = time_decay[:, :, :, current_index:current_index+1]
71
+ attention_output = current_key @ current_value
72
+ out[:, current_index] = (current_receptance @ (time_first * attention_output + state)).squeeze(2)
73
+ with torch.no_grad():
74
+ state = attention_output + current_time_decay * state
75
+
76
+ return out, state
77
+
78
+ def rwkv6_linear_attention(
79
+ training,
80
+ receptance,
81
+ key,
82
+ value,
83
+ time_decay,
84
+ time_first,
85
+ state,
86
+ ):
87
+ no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, receptance, key, value])
88
+ # Launching the CUDA kernel for just one token will actually be slower (there is no for loop in the CPU version
89
+ # in this case).
90
+ one_token = key.size(1) == 1
91
+ if not training or no_cuda or one_token:
92
+ return rwkv6_linear_attention_cpu(
93
+ receptance, key, value, time_decay, time_first, state
94
+ )
95
+ else:
96
+ batch, seq_length, _ = receptance.shape
97
+ num_heads, head_size = time_first.shape
98
+ key = key.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2) # B, T, H, K -> B, H, T, K
99
+ value = value.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2) # B, T, H, K - > B, H, T, V
100
+ receptance = receptance.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2) # B, H, T, K
101
+ time_decay = -torch.exp(time_decay.float()).view(batch, seq_length, num_heads, head_size).permute(0, 2, 1, 3) # B, T, H, K -> B, H, T, K
102
+ time_first = time_first.float().reshape(num_heads, head_size) # H, K
103
+ out, state = fused_recurrent_rwkv6(receptance, key, value, time_decay, time_first, scale=1.0, initial_state=state, output_final_state=True)
104
+ return out.transpose(1, 2), state
105
+
106
+
107
+ class Rwkv6SelfAttention(nn.Module):
108
+ def __init__(self, config, layer_id=0):
109
+ super().__init__()
110
+ self.config = config
111
+ self.layer_id = layer_id
112
+ hidden_size = config.hidden_size
113
+ attention_hidden_size = config.attention_hidden_size
114
+ self.attention_hidden_size = attention_hidden_size
115
+ head_size = config.head_size
116
+ num_heads = attention_hidden_size // head_size
117
+
118
+ self.time_maa_x = nn.Parameter(torch.empty(1, 1, hidden_size))
119
+ self.time_maa_w = nn.Parameter(torch.empty(1, 1, hidden_size))
120
+ self.time_maa_k = nn.Parameter(torch.empty(1, 1, hidden_size))
121
+ self.time_maa_v = nn.Parameter(torch.empty(1, 1, hidden_size))
122
+ self.time_maa_r = nn.Parameter(torch.empty(1, 1, hidden_size))
123
+ self.time_maa_g = nn.Parameter(torch.empty(1, 1, hidden_size))
124
+
125
+ TIME_MIX_EXTRA_DIM = 32 # generate TIME_MIX for w,k,v,r,g
126
+ self.time_maa_w1 = nn.Parameter(torch.empty(hidden_size, TIME_MIX_EXTRA_DIM*5))
127
+ self.time_maa_w2 = nn.Parameter(torch.empty(5, TIME_MIX_EXTRA_DIM, hidden_size))
128
+
129
+ self.time_decay = nn.Parameter(torch.empty(1, 1, attention_hidden_size))
130
+
131
+ TIME_DECAY_EXTRA_DIM = 64
132
+ self.time_decay_w1 = nn.Parameter(torch.empty(hidden_size, TIME_DECAY_EXTRA_DIM))
133
+ self.time_decay_w2 = nn.Parameter(torch.empty(TIME_DECAY_EXTRA_DIM, attention_hidden_size))
134
+
135
+ self.time_faaaa = nn.Parameter(torch.empty(num_heads, config.head_size))
136
+
137
+
138
+ self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
139
+ self.receptance = nn.Linear(hidden_size, attention_hidden_size, bias=False)
140
+ self.key = nn.Linear(hidden_size, attention_hidden_size, bias=False)
141
+ self.value = nn.Linear(hidden_size, attention_hidden_size, bias=False)
142
+ self.gate = nn.Linear(hidden_size, attention_hidden_size, bias=False)
143
+ self.output = nn.Linear(attention_hidden_size, hidden_size, bias=False)
144
+ self.ln_x = nn.GroupNorm(num_heads, hidden_size, eps=(1e-5)*(config.head_size_divisor**2))
145
+
146
+ def extract_key_value(self, hidden, state=None):
147
+ # Mix hidden with the previous timestep to produce key, value, receptance
148
+ if hidden.size(1) == 1 and state is not None:
149
+ shifted = state[0][:, :, self.layer_id]
150
+ else:
151
+ shifted = self.time_shift(hidden)
152
+ if state is not None:
153
+ shifted[:, 0] = state[0][:, :, self.layer_id]
154
+ if len(shifted.size()) == 2:
155
+ shifted = shifted.unsqueeze(1)
156
+
157
+ x = hidden
158
+
159
+ B, T, C = hidden.shape
160
+
161
+ xx = shifted - x
162
+
163
+ xxx = x + xx * self.time_maa_x
164
+ xxx = torch.tanh(xxx @ self.time_maa_w1).view(B*T, 5, -1).transpose(0, 1)
165
+ xxx = torch.bmm(xxx, self.time_maa_w2).view(5, B, T, -1)
166
+ mw, mk, mv, mr, mg = xxx.unbind(dim=0)
167
+
168
+ time_decay = x + xx * (self.time_maa_w + mw)
169
+ key = x + xx * (self.time_maa_k + mk)
170
+ value = x + xx * (self.time_maa_v + mv)
171
+ receptance = x + xx * (self.time_maa_r + mr)
172
+ gate = x + xx * (self.time_maa_g + mg)
173
+
174
+ receptance = self.receptance(receptance)
175
+ key = self.key(key)
176
+ value = self.value(value)
177
+ gate = F.silu(self.gate(gate))
178
+
179
+ time_decay = torch.tanh(time_decay @ self.time_decay_w1) @ self.time_decay_w2
180
+ time_decay = self.time_decay + time_decay
181
+
182
+ if state is not None:
183
+ state[0][:, :, self.layer_id] = hidden[:, -1]
184
+
185
+ return receptance, key, value, gate, time_decay, state
186
+
187
+ def forward(self, hidden, state=None, use_cache=False, seq_mode=True):
188
+ receptance, key, value, gate, time_decay, state = self.extract_key_value(hidden, state=state)
189
+
190
+ B,T,C = receptance.shape
191
+ H, S = self.time_faaaa.shape
192
+
193
+ layer_state = state[1][:, :, :, :, self.layer_id] if state is not None else None
194
+ out, layer_state = rwkv6_linear_attention(
195
+ self.training, receptance, key, value, time_decay, self.time_faaaa, layer_state,
196
+ )
197
+
198
+ if layer_state is not None:
199
+ state[1][:, :, :, :, self.layer_id] = layer_state
200
+
201
+ out = out.reshape(B * T, H * S)
202
+ out = F.group_norm(out, num_groups=H, weight=self.ln_x.weight.to(out.dtype), bias=self.ln_x.bias.to(out.dtype), eps=self.ln_x.eps).reshape(B, T, H * S)
203
+ out = out.to(dtype=hidden.dtype) * gate
204
+ out = self.output(out)
205
+ return out, state
206
+
207
+
208
+ class Rwkv6FeedForward(nn.Module):
209
+ def __init__(self, config, layer_id=0):
210
+ super().__init__()
211
+ self.config = config
212
+ self.layer_id = layer_id
213
+ hidden_size = config.hidden_size
214
+ # https://github.com/BlinkDL/RWKV-LM/blob/3db37a72356b736966ddd377268f02b80963af3f/RWKV-v4neo/train.py#L168
215
+ intermediate_size = (
216
+ config.intermediate_size
217
+ if config.intermediate_size is not None
218
+ else int((config.hidden_size * 3.5) // 32 * 32)
219
+ )
220
+
221
+ self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
222
+ self.time_maa_k = nn.Parameter(torch.empty(1, 1, hidden_size))
223
+ self.time_maa_r = nn.Parameter(torch.empty(1, 1, hidden_size))
224
+
225
+ self.key = nn.Linear(hidden_size, intermediate_size, bias=False)
226
+ self.receptance = nn.Linear(hidden_size, hidden_size, bias=False)
227
+ self.value = nn.Linear(intermediate_size, hidden_size, bias=False)
228
+
229
+ def forward(self, hidden, state=None):
230
+ if hidden.size(1) == 1 and state is not None:
231
+ shifted = state[2][:, :, self.layer_id]
232
+ else:
233
+ shifted = self.time_shift(hidden)
234
+ if state is not None:
235
+ shifted[:, 0] = state[2][:, :, self.layer_id]
236
+ if len(shifted.size()) == 2:
237
+ shifted = shifted.unsqueeze(1)
238
+
239
+ delta_hidden_to_shifted = shifted - hidden
240
+ key = hidden + delta_hidden_to_shifted * self.time_maa_k
241
+ receptance = hidden + delta_hidden_to_shifted * self.time_maa_r
242
+
243
+ key = torch.square(torch.relu(self.key(key)))
244
+ value = self.value(key)
245
+ receptance = torch.sigmoid(self.receptance(receptance))
246
+
247
+ if state is not None:
248
+ state[2][:, :, self.layer_id] = hidden[:, -1]
249
+
250
+ return receptance * value, state
251
+
252
+
253
+ class Rwkv6Block(nn.Module):
254
+ def __init__(self, config, layer_id):
255
+ super().__init__()
256
+ self.config = config
257
+ self.layer_id = layer_id
258
+
259
+ if layer_id == 0:
260
+ self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
261
+
262
+ self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
263
+ self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
264
+
265
+ self.attention = Rwkv6SelfAttention(config, layer_id)
266
+ self.feed_forward = Rwkv6FeedForward(config, layer_id)
267
+
268
+ def forward(self, hidden, state=None, use_cache=False, output_attentions=False, seq_mode=True):
269
+ if self.layer_id == 0:
270
+ hidden = self.pre_ln(hidden)
271
+ attention, state = self.attention(self.ln1(hidden), state=state, use_cache=use_cache, seq_mode=seq_mode)
272
+ hidden = hidden + attention
273
+
274
+ feed_forward, state = self.feed_forward(self.ln2(hidden), state=state)
275
+ hidden = hidden + feed_forward
276
+
277
+ outputs = (hidden, state)
278
+ if output_attentions:
279
+ outputs += (attention,)
280
+ else:
281
+ outputs += (None,)
282
+
283
+ return outputs
284
+
285
+
286
+ class Rwkv6PreTrainedModel(PreTrainedModel):
287
+ """
288
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
289
+ models.
290
+ """
291
+
292
+ config_class = Rwkv6Config
293
+ base_model_prefix = "rwkv6"
294
+ _no_split_modules = ["Rwkv6Block"]
295
+ _keep_in_fp32_modules = ["time_decay", "time_first"]
296
+ supports_gradient_checkpointing = True
297
+
298
+ def _init_weights(self, module):
299
+ """Initialize the weights."""
300
+ if isinstance(module, Rwkv6SelfAttention):
301
+ layer_id = module.layer_id
302
+ num_hidden_layers = module.config.num_hidden_layers
303
+ hidden_size = module.config.hidden_size
304
+ attention_hidden_size = module.attention_hidden_size
305
+ head_size = module.config.head_size
306
+ num_heads = attention_hidden_size // head_size
307
+
308
+ ratio_0_to_1 = layer_id / (num_hidden_layers - 1) # 0 to 1
309
+ ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0
310
+
311
+ time_weight = torch.tensor(
312
+ [i / hidden_size for i in range(hidden_size)],
313
+ dtype=module.time_maa_k.dtype,
314
+ device=module.time_maa_k.device,
315
+ )
316
+ time_weight = time_weight[None, None, :]
317
+
318
+ decay_speed = [
319
+ -6.0 + 5.0 * (h / (attention_hidden_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
320
+ for h in range(attention_hidden_size)
321
+ ]
322
+ decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device)
323
+ tmp = torch.tensor(
324
+ [
325
+ (1.0 - (i / (attention_hidden_size - 1.0))) * ratio_0_to_1 + 0.1 * ((i + 1) % 3 - 1)
326
+ for i in range(attention_hidden_size)
327
+ ],
328
+ dtype=module.time_faaaa.dtype,
329
+ device=module.time_faaaa.device,
330
+ )
331
+
332
+ with torch.no_grad():
333
+ module.time_maa_x.data = 1.0 - torch.pow(time_weight, ratio_1_to_almost0)
334
+ module.time_maa_w.data = 1.0 - torch.pow(time_weight, ratio_1_to_almost0)
335
+ module.time_maa_k.data = 1.0 - torch.pow(time_weight, ratio_1_to_almost0)
336
+ module.time_maa_v.data = 1.0 - (torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1)
337
+ module.time_maa_r.data = 1.0 - torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
338
+ module.time_maa_g.data = 1.0 - torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
339
+
340
+ TIME_MIX_EXTRA_DIM = 32 # generate TIME_MIX for w,k,v,r,g
341
+ module.time_maa_w1.data = torch.zeros(hidden_size, TIME_MIX_EXTRA_DIM*5, dtype=module.time_maa_w1.dtype, device=module.time_maa_w1.device).uniform_(-1e-4, 1e-4)
342
+ module.time_maa_w2.data = torch.zeros(5, TIME_MIX_EXTRA_DIM, hidden_size, dtype=module.time_maa_w2.dtype, device=module.time_maa_w2.device).uniform_(-1e-4, 1e-4)
343
+
344
+ TIME_DECAY_EXTRA_DIM = 64
345
+ module.time_decay_w1.data = torch.zeros(hidden_size, TIME_DECAY_EXTRA_DIM, dtype=module.time_decay_w1.dtype, device=module.time_decay_w1.device).uniform_(-1e-4, 1e-4)
346
+ module.time_decay_w2.data = torch.zeros(TIME_DECAY_EXTRA_DIM, attention_hidden_size, dtype=module.time_decay_w2.dtype, device=module.time_decay_w2.device).uniform_(-1e-4, 1e-4)
347
+
348
+ module.time_decay.data = decay_speed.reshape(num_heads, head_size)
349
+ module.time_faaaa.data = tmp.reshape(num_heads, head_size)
350
+
351
+ elif isinstance(module, Rwkv6FeedForward):
352
+ layer_id = module.layer_id
353
+ num_hidden_layers = module.config.num_hidden_layers
354
+ hidden_size = module.config.hidden_size
355
+
356
+ ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0
357
+
358
+ time_weight = torch.tensor(
359
+ [i / hidden_size for i in range(hidden_size)],
360
+ dtype=module.time_maa_k.dtype,
361
+ device=module.time_maa_k.device,
362
+ )
363
+ time_weight = time_weight[None, None, :]
364
+
365
+ with torch.no_grad():
366
+ module.time_maa_k.data = 1.0 - torch.pow(time_weight, ratio_1_to_almost0)
367
+ module.time_maa_r.data = 1.0 - torch.pow(time_weight, ratio_1_to_almost0)
368
+
369
+
370
+ @dataclass
371
+ class Rwkv6Output(ModelOutput):
372
+ """
373
+ Class for the RWKV model outputs.
374
+
375
+ Args:
376
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
377
+ Sequence of hidden-states at the output of the last layer of the model.
378
+ state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
379
+ The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
380
+ avoid providing the old `input_ids`.
381
+ hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
382
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
383
+ one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
384
+ the model at the output of each layer plus the optional initial embedding outputs.
385
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
386
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
387
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
388
+ the self-attention heads.
389
+ """
390
+
391
+ last_hidden_state: torch.FloatTensor = None
392
+ state: Optional[List[torch.FloatTensor]] = None
393
+ hidden_states: Optional[Tuple[torch.FloatTensor]] = None
394
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
395
+
396
+
397
+ @dataclass
398
+ class Rwkv6CausalLMOutput(ModelOutput):
399
+ """
400
+ Base class for causal language model (or autoregressive) outputs.
401
+
402
+ Args:
403
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
404
+ Language modeling loss (for next-token prediction).
405
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
406
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
407
+ state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
408
+ The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
409
+ avoid providing the old `input_ids`.
410
+ hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
411
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
412
+ one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
413
+ the model at the output of each layer plus the optional initial embedding outputs.
414
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
415
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
416
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
417
+ the self-attention heads.
418
+ """
419
+
420
+ loss: Optional[torch.FloatTensor] = None
421
+ logits: torch.FloatTensor = None
422
+ state: Optional[List[torch.FloatTensor]] = None
423
+ hidden_states: Optional[Tuple[torch.FloatTensor]] = None
424
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
425
+
426
+
427
+ RWKV6_START_DOCSTRING = r"""
428
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
429
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
430
+ etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
431
+ subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
432
+ general usage and behavior.
433
+
434
+ Parameters:
435
+ config ([`Rwkv6Config`]): Model configuration class with all the parameters of the model.
436
+ Initializing with a config file does not load the weights associated with the model, only the
437
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
438
+ """
439
+
440
+ RWKV6_INPUTS_DOCSTRING = r"""
441
+ Args:
442
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
443
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
444
+ `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
445
+ sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their
446
+ past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See
447
+ [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
448
+ IDs?](../glossary#input-ids)
449
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
450
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
451
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
452
+ model's internal embedding lookup matrix.
453
+ state (tuple of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`, *optional*):
454
+ If passed along, the model uses the previous state in all the blocks (which will give the output for the
455
+ `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
456
+ use_cache (`bool`, *optional*):
457
+ If set to `True`, the last state is returned and can be used to quickly generate the next logits.
458
+ output_attentions (`bool`, *optional*):
459
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
460
+ tensors for more detail.
461
+ output_hidden_states (`bool`, *optional*):
462
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
463
+ more detail.
464
+ return_dict (`bool`, *optional*):
465
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
466
+ """
467
+
468
+
469
+ @add_start_docstrings(
470
+ "The bare RWKV6 Model transformer outputting raw hidden-states without any specific head on top.",
471
+ RWKV6_START_DOCSTRING,
472
+ )
473
+ class Rwkv6Model(Rwkv6PreTrainedModel):
474
+ def __init__(self, config):
475
+ super().__init__(config)
476
+
477
+ self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
478
+ self.blocks = nn.ModuleList([Rwkv6Block(config, layer_id=idx) for idx in range(config.num_hidden_layers)])
479
+ self.ln_out = nn.LayerNorm(config.hidden_size)
480
+
481
+ self.layers_are_rescaled = False
482
+ self.gradient_checkpointing = False
483
+
484
+ # Initialize weights and apply final processing
485
+ self.post_init()
486
+
487
+ def get_input_embeddings(self):
488
+ return self.embeddings
489
+
490
+ def set_input_embeddings(self, new_embeddings):
491
+ self.embeddings = new_embeddings
492
+
493
+ @add_start_docstrings_to_model_forward(RWKV6_INPUTS_DOCSTRING)
494
+ @add_code_sample_docstrings(
495
+ checkpoint=_CHECKPOINT_FOR_DOC,
496
+ output_type=Rwkv6Output,
497
+ config_class=_CONFIG_FOR_DOC,
498
+ )
499
+ def forward(
500
+ self,
501
+ input_ids: Optional[torch.LongTensor] = None,
502
+ attention_mask: Optional[torch.LongTensor] = None, # noqa
503
+ inputs_embeds: Optional[torch.FloatTensor] = None,
504
+ state: Optional[List[torch.FloatTensor]] = None,
505
+ use_cache: Optional[bool] = None,
506
+ output_attentions: Optional[bool] = None,
507
+ output_hidden_states: Optional[bool] = None,
508
+ return_dict: Optional[bool] = None,
509
+ ) -> Union[Tuple, Rwkv6Output]:
510
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
511
+ output_hidden_states = (
512
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
513
+ )
514
+ # FIXME - training is supportable with the CUDA code
515
+ # rwkv6 only support inference in huggingface.
516
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
517
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
518
+
519
+ if self.training == self.layers_are_rescaled and (
520
+ self.embeddings.weight.dtype == torch.float16 or self.embeddings.weight.dtype == torch.bfloat16
521
+ ):
522
+ self._rescale_layers()
523
+
524
+ if input_ids is not None and inputs_embeds is not None:
525
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
526
+ elif input_ids is None and inputs_embeds is None:
527
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
528
+
529
+ if inputs_embeds is None:
530
+ inputs_embeds = self.embeddings(input_ids)
531
+
532
+ if state is None:
533
+ state = []
534
+ head_size = self.config.head_size
535
+ num_heads = self.config.attention_hidden_size // head_size
536
+ state_attn_x = torch.zeros(
537
+ (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers),
538
+ dtype=inputs_embeds.dtype,
539
+ requires_grad=False,
540
+ device=inputs_embeds.device,
541
+ ).contiguous()
542
+ state_attn_kv = torch.zeros(
543
+ (
544
+ inputs_embeds.size(0),
545
+ num_heads,
546
+ head_size,
547
+ head_size,
548
+ self.config.num_hidden_layers,
549
+ ),
550
+ dtype=torch.float32,
551
+ requires_grad=False,
552
+ device=inputs_embeds.device,
553
+ ).contiguous()
554
+ state_ffn_x = torch.zeros(
555
+ (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers),
556
+ dtype=inputs_embeds.dtype,
557
+ requires_grad=False,
558
+ device=inputs_embeds.device,
559
+ ).contiguous()
560
+ state.append(state_attn_x)
561
+ state.append(state_attn_kv)
562
+ state.append(state_ffn_x)
563
+
564
+ seq_mode = inputs_embeds.shape[1] > 1
565
+ hidden_states = inputs_embeds
566
+
567
+ all_self_attentions = () if output_attentions else None
568
+ all_hidden_states = () if output_hidden_states else None
569
+ for idx, block in enumerate(self.blocks):
570
+ hidden_states, state, attentions = block(
571
+ hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions, seq_mode=seq_mode
572
+ )
573
+ if (
574
+ self.layers_are_rescaled
575
+ and self.config.rescale_every > 0
576
+ and (idx + 1) % self.config.rescale_every == 0
577
+ ):
578
+ hidden_states = hidden_states / 2
579
+
580
+ if output_hidden_states:
581
+ all_hidden_states = all_hidden_states + (hidden_states,)
582
+
583
+ if output_attentions:
584
+ all_self_attentions = all_self_attentions + (attentions,)
585
+
586
+ hidden_states = self.ln_out(hidden_states)
587
+
588
+ if output_hidden_states:
589
+ all_hidden_states = all_hidden_states + (hidden_states,)
590
+
591
+ if not return_dict:
592
+ return (hidden_states, state, all_hidden_states, all_self_attentions)
593
+
594
+ return Rwkv6Output(
595
+ last_hidden_state=hidden_states,
596
+ state=state,
597
+ hidden_states=all_hidden_states, # None
598
+ attentions=all_self_attentions, # None
599
+ )
600
+
601
+ def _rescale_layers(self):
602
+ # Layers should be rescaled for inference only.
603
+ if self.layers_are_rescaled == (not self.training):
604
+ return
605
+ if self.config.rescale_every > 0:
606
+ with torch.no_grad():
607
+ for block_id, block in enumerate(self.blocks):
608
+ if self.training:
609
+ block.attention.output.weight.mul_(2 ** int(block_id // self.config.rescale_every))
610
+ block.feed_forward.value.weight.mul_(2 ** int(block_id // self.config.rescale_every))
611
+ else:
612
+ # Deal with quantization statistics
613
+ if hasattr(block.attention.output.weight, "SCB"):
614
+ block.attention.output.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
615
+ block.feed_forward.value.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
616
+ elif hasattr(block.attention.output.weight, "quant_state"):
617
+ self._bnb_4bit_dequantize_and_rescale(block.attention.output, block_id)
618
+ self._bnb_4bit_dequantize_and_rescale(block.feed_forward.value, block_id)
619
+ else:
620
+ block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every))
621
+ block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every))
622
+
623
+ self.layers_are_rescaled = not self.training
624
+
625
+ def _bnb_4bit_dequantize_and_rescale(self, target_layer, block_id):
626
+ r"""
627
+ Perform the dequantization and rescaling of the weights of a given layer. After that operation the layer will
628
+ be quantized again.
629
+ """
630
+ if not is_bitsandbytes_available():
631
+ raise ImportError("Please install bitsandbytes to use this method.")
632
+ import bitsandbytes as bnb
633
+
634
+ dequant_weights = bnb.functional.dequantize_4bit(target_layer.weight.data, target_layer.weight.quant_state)
635
+
636
+ dequant_weights.div_(2 ** int(block_id // self.config.rescale_every))
637
+
638
+ # re-quantize the model:
639
+ # we need to put it first on CPU then back to the device
640
+ # this will create an overhead :/
641
+ # We set requires_grad=False as we cannot compute gradients on top of 4bit parameters anyway and to avoid
642
+ # bugs with bnb
643
+ quant_weight = bnb.nn.Params4bit(dequant_weights.to("cpu"), requires_grad=False).to(dequant_weights.device)
644
+ setattr(target_layer, "weight", quant_weight)
645
+
646
+
647
+ # copied from HuggingFace https://github.com/huggingface/transformers/blob/main/src/transformers/models/rwkv/modeling_rwkv.py
648
+ @add_start_docstrings(
649
+ """
650
+ The RWKV6 Model transformer with a language modeling head on top (linear layer with weights tied to the input
651
+ embeddings).
652
+ """,
653
+ RWKV6_START_DOCSTRING,
654
+ )
655
+ class Rwkv6ForCausalLM(Rwkv6PreTrainedModel):
656
+ _tied_weights_keys = ["head.weight"]
657
+
658
+ def __init__(self, config):
659
+ super().__init__(config)
660
+ self.rwkv = Rwkv6Model(config)
661
+ self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
662
+
663
+ # Initialize weights and apply final processing
664
+ self.post_init()
665
+
666
+ def get_output_embeddings(self):
667
+ return self.head
668
+
669
+ def set_output_embeddings(self, new_embeddings):
670
+ self.head = new_embeddings
671
+
672
+ def prepare_inputs_for_generation(self, input_ids, state=None, inputs_embeds=None, **kwargs):
673
+ # only last token for inputs_ids if the state is passed along.
674
+ if state is not None:
675
+ input_ids = input_ids[:, -1].unsqueeze(-1)
676
+
677
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
678
+ if inputs_embeds is not None and state is None:
679
+ model_inputs = {"inputs_embeds": inputs_embeds}
680
+ else:
681
+ model_inputs = {"input_ids": input_ids}
682
+
683
+ model_inputs["state"] = state
684
+ return model_inputs
685
+
686
+ @add_start_docstrings_to_model_forward(RWKV6_INPUTS_DOCSTRING)
687
+ @add_code_sample_docstrings(
688
+ checkpoint=_CHECKPOINT_FOR_DOC,
689
+ output_type=Rwkv6CausalLMOutput,
690
+ config_class=_CONFIG_FOR_DOC,
691
+ )
692
+ def forward(
693
+ self,
694
+ input_ids: Optional[torch.LongTensor] = None,
695
+ attention_mask: Optional[torch.LongTensor] = None,
696
+ inputs_embeds: Optional[torch.FloatTensor] = None,
697
+ state: Optional[List[torch.FloatTensor]] = None,
698
+ labels: Optional[torch.LongTensor] = None,
699
+ use_cache: Optional[bool] = None,
700
+ output_attentions: Optional[bool] = None,
701
+ output_hidden_states: Optional[bool] = None,
702
+ return_dict: Optional[bool] = None,
703
+ ) -> Union[Tuple, Rwkv6CausalLMOutput]:
704
+ r"""
705
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
706
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
707
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
708
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
709
+ """
710
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
711
+
712
+ outputs = self.rwkv(
713
+ input_ids,
714
+ inputs_embeds=inputs_embeds,
715
+ state=state,
716
+ use_cache=use_cache,
717
+ output_attentions=output_attentions,
718
+ output_hidden_states=output_hidden_states,
719
+ return_dict=return_dict,
720
+ )
721
+ hidden_states = outputs[0]
722
+
723
+ logits = self.head(hidden_states)
724
+
725
+ loss = None
726
+ if labels is not None:
727
+ # move labels to correct device to enable model parallelism
728
+ labels = labels.to(logits.device)
729
+ # Shift so that tokens < n predict n
730
+ shift_logits = logits[..., :-1, :].contiguous()
731
+ shift_labels = labels[..., 1:].contiguous()
732
+ # Flatten the tokens
733
+ loss_fct = CrossEntropyLoss()
734
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
735
+
736
+ if not return_dict:
737
+ output = (logits,) + outputs[1:]
738
+ return ((loss,) + output) if loss is not None else output
739
+
740
+ return Rwkv6CausalLMOutput(
741
+ loss=loss,
742
+ logits=logits,
743
+ state=outputs.state,
744
+ hidden_states=outputs.hidden_states,
745
+ attentions=outputs.attentions,
746
+ )
rwkv_vocab_v20230424.txt ADDED
The diff for this file is too large to render. See raw diff
 
special_tokens_map.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "<s>",
3
+ "eos_token": "<s>",
4
+ "pad_token": "<s>",
5
+ "unk_token": "<s>"
6
+ }
tokenizer_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "name_or_path": "rwkv-6-tokenizer",
3
+ "add_prefix_space": false,
4
+ "tokenizer_class": "Rwkv6Tokenizer",
5
+ "use_fast": false,
6
+ "auto_map": {
7
+ "AutoTokenizer": [
8
+ "hf_rwkv_tokenizer.Rwkv6Tokenizer",
9
+ null
10
+ ]
11
+ }
12
+ }