Upload folder using huggingface_hub
Browse files- all_punct.txt +113 -0
- byteVocab.txt +512 -0
- config.json +52 -0
- configuration_deepseek.py +206 -0
- generation_config.json +9 -0
- model-00001-of-000004.safetensors +3 -0
- model-00002-of-000004.safetensors +3 -0
- model-00003-of-000004.safetensors +3 -0
- model-00004-of-000004.safetensors +3 -0
- model.safetensors.index.json +0 -0
- modeling_deepseek.py +1936 -0
- tokenization_deepseek.py +328 -0
- tokenization_deepseek_fast.py +38 -0
- tokenizer.json +0 -0
- tokenizer_config.json +35 -0
all_punct.txt
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byteVocab.txt
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| 1 |
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0 数
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1 学
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2 是
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3 利
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4 用
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5 符
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6 号
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7 语
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8 言
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9 研
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10 究
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11 數
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12 量
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13 结
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14 构
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15 变
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16 化
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17 以
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18 及
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19 空
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20 间
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21 等
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22 概
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23 念
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24 的
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25 一
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26 門
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27 科
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28 从
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29 某
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30 种
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31 角
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32 度
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33 看
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34 屬
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35 於
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| 37 |
+
36 形
|
| 38 |
+
37 式
|
| 39 |
+
38 學
|
| 40 |
+
39 種
|
| 41 |
+
40 透
|
| 42 |
+
41 過
|
| 43 |
+
42 抽
|
| 44 |
+
43 象
|
| 45 |
+
44 和
|
| 46 |
+
45 邏
|
| 47 |
+
46 輯
|
| 48 |
+
47 推
|
| 49 |
+
48 理
|
| 50 |
+
49 使
|
| 51 |
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50 由
|
| 52 |
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51 計
|
| 53 |
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52 算
|
| 54 |
+
53 對
|
| 55 |
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54 物
|
| 56 |
+
55 體
|
| 57 |
+
56 狀
|
| 58 |
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57 運
|
| 59 |
+
58 動
|
| 60 |
+
59 觀
|
| 61 |
+
60 察
|
| 62 |
+
61 而
|
| 63 |
+
62 產
|
| 64 |
+
63 生
|
| 65 |
+
64 家
|
| 66 |
+
65 們
|
| 67 |
+
66 拓
|
| 68 |
+
67 展
|
| 69 |
+
68 這
|
| 70 |
+
69 些
|
| 71 |
+
70 為
|
| 72 |
+
71 了
|
| 73 |
+
72 公
|
| 74 |
+
73 新
|
| 75 |
+
74 猜
|
| 76 |
+
75 想
|
| 77 |
+
76 從
|
| 78 |
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77 選
|
| 79 |
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78 定
|
| 80 |
+
79 義
|
| 81 |
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80 中
|
| 82 |
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81 建
|
| 83 |
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82 立
|
| 84 |
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83 起
|
| 85 |
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84 嚴
|
| 86 |
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85 謹
|
| 87 |
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86 導
|
| 88 |
+
87 出
|
| 89 |
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88 基
|
| 90 |
+
89 礎
|
| 91 |
+
90 知
|
| 92 |
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91 識
|
| 93 |
+
92 與
|
| 94 |
+
93 總
|
| 95 |
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94 個
|
| 96 |
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95 人
|
| 97 |
+
96 團
|
| 98 |
+
97 活
|
| 99 |
+
98 不
|
| 100 |
+
99 可
|
| 101 |
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100 或
|
| 102 |
+
101 缺
|
| 103 |
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102 環
|
| 104 |
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103 本
|
| 105 |
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104 完
|
| 106 |
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105 善
|
| 107 |
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106 早
|
| 108 |
+
107 在
|
| 109 |
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108 古
|
| 110 |
+
109 埃
|
| 111 |
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110 美
|
| 112 |
+
111 索
|
| 113 |
+
112 達
|
| 114 |
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113 米
|
| 115 |
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114 亞
|
| 116 |
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115 印
|
| 117 |
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116 內
|
| 118 |
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117 代
|
| 119 |
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118 文
|
| 120 |
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119 便
|
| 121 |
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120 見
|
| 122 |
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121 希
|
| 123 |
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122 臘
|
| 124 |
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123 那
|
| 125 |
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124 裡
|
| 126 |
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125 有
|
| 127 |
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126 更
|
| 128 |
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127 處
|
| 129 |
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128 時
|
| 130 |
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129 開
|
| 131 |
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130 始
|
| 132 |
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131 發
|
| 133 |
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132 持
|
| 134 |
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133 續
|
| 135 |
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134 斷
|
| 136 |
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135 地
|
| 137 |
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136 小
|
| 138 |
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137 幅
|
| 139 |
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138 進
|
| 140 |
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139 至
|
| 141 |
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140 世
|
| 142 |
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141 紀
|
| 143 |
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142 藝
|
| 144 |
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143 復
|
| 145 |
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144 興
|
| 146 |
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145 期
|
| 147 |
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146 因
|
| 148 |
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147 为
|
| 149 |
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148 現
|
| 150 |
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149 革
|
| 151 |
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150 兩
|
| 152 |
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151 者
|
| 153 |
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152 交
|
| 154 |
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153 互
|
| 155 |
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154 致
|
| 156 |
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155 加
|
| 157 |
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156 速
|
| 158 |
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157 发
|
| 159 |
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158 直
|
| 160 |
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159 今
|
| 161 |
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160 日
|
| 162 |
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161 并
|
| 163 |
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162 成
|
| 164 |
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163 許
|
| 165 |
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164 多
|
| 166 |
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165 國
|
| 167 |
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166 區
|
| 168 |
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167 教
|
| 169 |
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168 育
|
| 170 |
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169 範
|
| 171 |
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170 疇
|
| 172 |
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171 部
|
| 173 |
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172 分
|
| 174 |
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173 同
|
| 175 |
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174 領
|
| 176 |
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175 域
|
| 177 |
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176 包
|
| 178 |
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177 括
|
| 179 |
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178 工
|
| 180 |
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179 程
|
| 181 |
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180 醫
|
| 182 |
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181 經
|
| 183 |
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182 濟
|
| 184 |
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183 金
|
| 185 |
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184 融
|
| 186 |
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185 應
|
| 187 |
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186 通
|
| 188 |
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187 常
|
| 189 |
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188 被
|
| 190 |
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189 稱
|
| 191 |
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190 亦
|
| 192 |
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191 會
|
| 193 |
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192 激
|
| 194 |
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193 並
|
| 195 |
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194 全
|
| 196 |
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195 例
|
| 197 |
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196 如
|
| 198 |
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197 实
|
| 199 |
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198 质
|
| 200 |
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199 性
|
| 201 |
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200 论
|
| 202 |
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201 对
|
| 203 |
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202 于
|
| 204 |
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203 问
|
| 205 |
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204 题
|
| 206 |
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205 思
|
| 207 |
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206 考
|
| 208 |
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207 也
|
| 209 |
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208 純
|
| 210 |
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209 就
|
| 211 |
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210 身
|
| 212 |
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211 容
|
| 213 |
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212 任
|
| 214 |
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213 何
|
| 215 |
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214 實
|
| 216 |
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215 際
|
| 217 |
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216 目
|
| 218 |
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217 標
|
| 219 |
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218 雖
|
| 220 |
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219 然
|
| 221 |
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220 但
|
| 222 |
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221 其
|
| 223 |
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222 过
|
| 224 |
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223 之
|
| 225 |
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224 处
|
| 226 |
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225 詞
|
| 227 |
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226 源
|
| 228 |
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227 西
|
| 229 |
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228 方
|
| 230 |
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229 自
|
| 231 |
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230 語
|
| 232 |
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231 習
|
| 233 |
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232 問
|
| 234 |
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233 另
|
| 235 |
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234 外
|
| 236 |
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235 還
|
| 237 |
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236 較
|
| 238 |
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237 狹
|
| 239 |
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238 且
|
| 240 |
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239 技
|
| 241 |
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240 術
|
| 242 |
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241 意
|
| 243 |
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242 即
|
| 244 |
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243 關
|
| 245 |
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244 功
|
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245 來
|
| 247 |
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246 指
|
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247 英
|
| 249 |
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248 表
|
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249 面
|
| 251 |
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250 上
|
| 252 |
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251 複
|
| 253 |
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252 法
|
| 254 |
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253 溯
|
| 255 |
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254 拉
|
| 256 |
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255 丁
|
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256 塞
|
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257 罗
|
| 259 |
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258 譯
|
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259 此
|
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260 亚
|
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261 里
|
| 263 |
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262 士
|
| 264 |
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263 德
|
| 265 |
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264 拿
|
| 266 |
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265 萬
|
| 267 |
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266 皆
|
| 268 |
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267 汉
|
| 269 |
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268 字
|
| 270 |
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269 示
|
| 271 |
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270 大
|
| 272 |
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271 約
|
| 273 |
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272 产
|
| 274 |
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273 国
|
| 275 |
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274 宋
|
| 276 |
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275 元
|
| 277 |
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276 含
|
| 278 |
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277 天
|
| 279 |
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278 秦
|
| 280 |
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279 九
|
| 281 |
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280 韶
|
| 282 |
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281 章
|
| 283 |
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282 永
|
| 284 |
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283 樂
|
| 285 |
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284 典
|
| 286 |
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285 記
|
| 287 |
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286 書
|
| 288 |
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287 周
|
| 289 |
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288 密
|
| 290 |
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289 所
|
| 291 |
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290 著
|
| 292 |
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291 癸
|
| 293 |
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292 辛
|
| 294 |
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293 雜
|
| 295 |
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294 爲
|
| 296 |
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295 略
|
| 297 |
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296 軌
|
| 298 |
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297 明
|
| 299 |
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298 柯
|
| 300 |
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299 尚
|
| 301 |
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300 遷
|
| 302 |
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301 钥
|
| 303 |
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302 清
|
| 304 |
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303 杜
|
| 305 |
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304 耕
|
| 306 |
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305 拾
|
| 307 |
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306 遺
|
| 308 |
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307 取
|
| 309 |
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308 忠
|
| 310 |
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309 撰
|
| 311 |
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310 到
|
| 312 |
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311 年
|
| 313 |
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312 名
|
| 314 |
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313 審
|
| 315 |
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314 查
|
| 316 |
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315 委
|
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316 員
|
| 318 |
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317 況
|
| 319 |
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318 後
|
| 320 |
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319 確
|
| 321 |
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320 認
|
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321 历
|
| 323 |
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322 史
|
| 324 |
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323 着
|
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324 久
|
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325 遠
|
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326 歷
|
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327 它
|
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328 類
|
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329 六
|
| 331 |
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330 艺
|
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331 腊
|
| 333 |
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332 础
|
| 334 |
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333 识
|
| 335 |
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334 前
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335 已
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336 嘗
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337 試
|
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338 則
|
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339 衡
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340 質
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341 少
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342 間
|
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343 長
|
| 345 |
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344 短
|
| 346 |
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345 係
|
| 347 |
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346 比
|
| 348 |
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347 时
|
| 349 |
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348 单
|
| 350 |
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349 位
|
| 351 |
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350 季
|
| 352 |
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351 節
|
| 353 |
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352 減
|
| 354 |
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353 乘
|
| 355 |
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354 除
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| 356 |
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355 石
|
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356 碑
|
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357 泥
|
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358 版
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359 證
|
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360 當
|
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361 幾
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362 步
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363 需
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| 365 |
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364 要
|
| 366 |
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365 寫
|
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366 作
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367 他
|
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368 錄
|
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369 系
|
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370 統
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371 木
|
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372 帝
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373 儲
|
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374 存
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375 據
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376 奇
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377 普
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378 曾
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379 最
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380 初
|
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381 候
|
| 383 |
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382 主
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383 原
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384 做
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385 稅
|
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386 務
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387 貿
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388 易
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389 相
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390 解
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391 測
|
| 393 |
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392 土
|
| 394 |
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393 預
|
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394 事
|
| 396 |
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395 件
|
| 397 |
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396 这
|
| 398 |
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397 简
|
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398 纪
|
| 400 |
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399 术
|
| 401 |
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400 三
|
| 402 |
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401 体
|
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402 备
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403 们
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| 405 |
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404 开
|
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405 与
|
| 407 |
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406 关
|
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407 图
|
| 409 |
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408 换
|
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409 微
|
| 411 |
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410 积
|
| 412 |
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411 随
|
| 413 |
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412 轉
|
| 414 |
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413 向
|
| 415 |
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414 集
|
| 416 |
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415 合
|
| 417 |
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416 逻
|
| 418 |
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417 辑
|
| 419 |
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418 重
|
| 420 |
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419 心
|
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420 求
|
| 422 |
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421 际
|
| 423 |
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422 转
|
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423 般
|
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424 延
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425 豐
|
| 427 |
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426 富
|
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427 都
|
| 429 |
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428 受
|
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429 惠
|
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430 彼
|
| 432 |
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431 月
|
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432 刊
|
| 434 |
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433 說
|
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434 放
|
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435 評
|
| 437 |
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436 論
|
| 438 |
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437 資
|
| 439 |
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438 料
|
| 440 |
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439 庫
|
| 441 |
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440 籍
|
| 442 |
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441 創
|
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442 份
|
| 444 |
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443 超
|
| 445 |
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444 百
|
| 446 |
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445 十
|
| 447 |
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446 每
|
| 448 |
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447 增
|
| 449 |
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448 七
|
| 450 |
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449 五
|
| 451 |
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450 千
|
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451 海
|
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452 絕
|
| 454 |
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453 涉
|
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+
454 結
|
| 456 |
+
455 構
|
| 457 |
+
456 變
|
| 458 |
+
457 困
|
| 459 |
+
458 難
|
| 460 |
+
459 題
|
| 461 |
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460 具
|
| 462 |
+
461 去
|
| 463 |
+
462 決
|
| 464 |
+
463 往
|
| 465 |
+
464 值
|
| 466 |
+
465 得
|
| 467 |
+
466 給
|
| 468 |
+
467 牛
|
| 469 |
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468 頓
|
| 470 |
+
469 莱
|
| 471 |
+
470 布
|
| 472 |
+
471 尼
|
| 473 |
+
472 兹
|
| 474 |
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473 積
|
| 475 |
+
474 費
|
| 476 |
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475 曼
|
| 477 |
+
476 路
|
| 478 |
+
477 徑
|
| 479 |
+
478 洞
|
| 480 |
+
479 二
|
| 481 |
+
480 弦
|
| 482 |
+
481 引
|
| 483 |
+
482 申
|
| 484 |
+
483 只
|
| 485 |
+
484 答
|
| 486 |
+
485 途
|
| 487 |
+
486 非
|
| 488 |
+
487 尋
|
| 489 |
+
488 諾
|
| 490 |
+
489 貝
|
| 491 |
+
490 爾
|
| 492 |
+
491 獎
|
| 493 |
+
492 維
|
| 494 |
+
493 格
|
| 495 |
+
494 納
|
| 496 |
+
495 像
|
| 497 |
+
496 效
|
| 498 |
+
497 爆
|
| 499 |
+
498 專
|
| 500 |
+
499 業
|
| 501 |
+
500 歧
|
| 502 |
+
501 又
|
| 503 |
+
502 電
|
| 504 |
+
503 腦
|
| 505 |
+
504 談
|
| 506 |
+
505 優
|
| 507 |
+
506 簡
|
| 508 |
+
507 單
|
| 509 |
+
508 巧
|
| 510 |
+
509 妙
|
| 511 |
+
510 歐
|
| 512 |
+
511 無
|
config.json
ADDED
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"DeepseekV2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"auto_map": {
|
| 8 |
+
"AutoConfig": "configuration_deepseek.DeepseekV2Config",
|
| 9 |
+
"AutoModel": "modeling_deepseek.DeepseekV2Model",
|
| 10 |
+
"AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
|
| 11 |
+
},
|
| 12 |
+
"aux_loss_alpha": 0.001,
|
| 13 |
+
"bos_token_id": 100000,
|
| 14 |
+
"eos_token_id": 100001,
|
| 15 |
+
"ep_size": 1,
|
| 16 |
+
"first_k_dense_replace": 1,
|
| 17 |
+
"hidden_act": "silu",
|
| 18 |
+
"hidden_size": 2048,
|
| 19 |
+
"initializer_range": 0.02,
|
| 20 |
+
"intermediate_size": 10944,
|
| 21 |
+
"kv_lora_rank": 512,
|
| 22 |
+
"max_position_embeddings": 4096,
|
| 23 |
+
"model_type": "deepseek_v2",
|
| 24 |
+
"moe_intermediate_size": 1408,
|
| 25 |
+
"moe_layer_freq": 1,
|
| 26 |
+
"n_group": 1,
|
| 27 |
+
"n_routed_experts": 64,
|
| 28 |
+
"n_shared_experts": 2,
|
| 29 |
+
"norm_topk_prob": false,
|
| 30 |
+
"num_attention_heads": 16,
|
| 31 |
+
"num_experts_per_tok": 6,
|
| 32 |
+
"num_hidden_layers": 27,
|
| 33 |
+
"num_key_value_heads": 16,
|
| 34 |
+
"pretraining_tp": 1,
|
| 35 |
+
"q_lora_rank": null,
|
| 36 |
+
"qk_nope_head_dim": 128,
|
| 37 |
+
"qk_rope_head_dim": 64,
|
| 38 |
+
"rms_norm_eps": 1e-06,
|
| 39 |
+
"rope_scaling": null,
|
| 40 |
+
"rope_theta": 10000,
|
| 41 |
+
"routed_scaling_factor": 1.0,
|
| 42 |
+
"scoring_func": "softmax",
|
| 43 |
+
"seq_aux": true,
|
| 44 |
+
"tie_word_embeddings": false,
|
| 45 |
+
"topk_group": 1,
|
| 46 |
+
"topk_method": "greedy",
|
| 47 |
+
"torch_dtype": "bfloat16",
|
| 48 |
+
"transformers_version": "4.33.1",
|
| 49 |
+
"use_cache": true,
|
| 50 |
+
"v_head_dim": 128,
|
| 51 |
+
"vocab_size": 102400
|
| 52 |
+
}
|
configuration_deepseek.py
ADDED
|
@@ -0,0 +1,206 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 2 |
+
from transformers.utils import logging
|
| 3 |
+
|
| 4 |
+
logger = logging.get_logger(__name__)
|
| 5 |
+
|
| 6 |
+
DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
| 7 |
+
class DeepseekV2Config(PretrainedConfig):
|
| 8 |
+
r"""
|
| 9 |
+
This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
|
| 10 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
| 11 |
+
defaults will yield a similar configuration to that of the DeepSeek-V2.
|
| 12 |
+
|
| 13 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 14 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
Args:
|
| 18 |
+
vocab_size (`int`, *optional*, defaults to 102400):
|
| 19 |
+
Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
|
| 20 |
+
`inputs_ids` passed when calling [`DeepseekV2Model`]
|
| 21 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
| 22 |
+
Dimension of the hidden representations.
|
| 23 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
| 24 |
+
Dimension of the MLP representations.
|
| 25 |
+
moe_intermediate_size (`int`, *optional*, defaults to 1407):
|
| 26 |
+
Dimension of the MoE representations.
|
| 27 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
| 28 |
+
Number of hidden layers in the Transformer decoder.
|
| 29 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 30 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
| 31 |
+
n_shared_experts (`int`, *optional*, defaults to None):
|
| 32 |
+
Number of shared experts, None means dense model.
|
| 33 |
+
n_routed_experts (`int`, *optional*, defaults to None):
|
| 34 |
+
Number of routed experts, None means dense model.
|
| 35 |
+
routed_scaling_factor (`float`, *optional*, defaults to 1.0):
|
| 36 |
+
Scaling factor or routed experts.
|
| 37 |
+
topk_method (`str`, *optional*, defaults to `gready`):
|
| 38 |
+
Topk method used in routed gate.
|
| 39 |
+
n_group (`int`, *optional*, defaults to None):
|
| 40 |
+
Number of groups for routed experts.
|
| 41 |
+
topk_group (`int`, *optional*, defaults to None):
|
| 42 |
+
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
|
| 43 |
+
num_experts_per_tok (`int`, *optional*, defaults to None):
|
| 44 |
+
Number of selected experts, None means dense model.
|
| 45 |
+
moe_layer_freq (`int`, *optional*, defaults to 1):
|
| 46 |
+
The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
|
| 47 |
+
first_k_dense_replace (`int`, *optional*, defaults to 0):
|
| 48 |
+
Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
|
| 49 |
+
\--k dense layers--/
|
| 50 |
+
norm_topk_prob (`bool`, *optional*, defaults to False):
|
| 51 |
+
Whether to normalize the weights of the routed experts.
|
| 52 |
+
scoring_func (`str`, *optional*, defaults to 'softmax'):
|
| 53 |
+
Method of computing expert weights.
|
| 54 |
+
aux_loss_alpha (`float`, *optional*, defaults to 0.001):
|
| 55 |
+
Auxiliary loss weight coefficient.
|
| 56 |
+
seq_aux = (`bool`, *optional*, defaults to True):
|
| 57 |
+
Whether to compute the auxiliary loss for each individual sample.
|
| 58 |
+
num_key_value_heads (`int`, *optional*):
|
| 59 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 60 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 61 |
+
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 62 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 63 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
| 64 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
| 65 |
+
`num_attention_heads`.
|
| 66 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| 67 |
+
The non-linear activation function (function or string) in the decoder.
|
| 68 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
| 69 |
+
The maximum sequence length that this model might ever be used with.
|
| 70 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 71 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 72 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
| 73 |
+
The epsilon used by the rms normalization layers.
|
| 74 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 75 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 76 |
+
relevant if `config.is_decoder=True`.
|
| 77 |
+
pad_token_id (`int`, *optional*):
|
| 78 |
+
Padding token id.
|
| 79 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
| 80 |
+
Beginning of stream token id.
|
| 81 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
| 82 |
+
End of stream token id.
|
| 83 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
| 84 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
| 85 |
+
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
| 86 |
+
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
| 87 |
+
issue](https://github.com/pytorch/pytorch/issues/76232).
|
| 88 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 89 |
+
Whether to tie weight embeddings
|
| 90 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
| 91 |
+
The base period of the RoPE embeddings.
|
| 92 |
+
rope_scaling (`Dict`, *optional*):
|
| 93 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
| 94 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
| 95 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| 96 |
+
`max_position_embeddings` to the expected new maximum.
|
| 97 |
+
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
| 98 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
| 99 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
| 100 |
+
The dropout ratio for the attention probabilities.
|
| 101 |
+
|
| 102 |
+
```python
|
| 103 |
+
>>> from transformers import DeepseekV2Model, DeepseekV2Config
|
| 104 |
+
|
| 105 |
+
>>> # Initializing a Deepseek-V2 style configuration
|
| 106 |
+
>>> configuration = DeepseekV2Config()
|
| 107 |
+
|
| 108 |
+
>>> # Accessing the model configuration
|
| 109 |
+
>>> configuration = model.config
|
| 110 |
+
```"""
|
| 111 |
+
|
| 112 |
+
model_type = "deepseek_v2"
|
| 113 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 114 |
+
|
| 115 |
+
def __init__(
|
| 116 |
+
self,
|
| 117 |
+
vocab_size=102400,
|
| 118 |
+
hidden_size=4096,
|
| 119 |
+
intermediate_size=11008,
|
| 120 |
+
moe_intermediate_size = 1407,
|
| 121 |
+
num_hidden_layers=30,
|
| 122 |
+
num_attention_heads=32,
|
| 123 |
+
num_key_value_heads=32,
|
| 124 |
+
n_shared_experts = None,
|
| 125 |
+
n_routed_experts = None,
|
| 126 |
+
ep_size = 1,
|
| 127 |
+
routed_scaling_factor = 1.0,
|
| 128 |
+
kv_lora_rank = 512,
|
| 129 |
+
q_lora_rank = 1536,
|
| 130 |
+
qk_rope_head_dim = 64,
|
| 131 |
+
v_head_dim = 128,
|
| 132 |
+
qk_nope_head_dim = 128,
|
| 133 |
+
topk_method = 'gready',
|
| 134 |
+
n_group = None,
|
| 135 |
+
topk_group = None,
|
| 136 |
+
num_experts_per_tok = None,
|
| 137 |
+
moe_layer_freq = 1,
|
| 138 |
+
first_k_dense_replace = 0,
|
| 139 |
+
norm_topk_prob = False,
|
| 140 |
+
scoring_func = 'softmax',
|
| 141 |
+
aux_loss_alpha = 0.001,
|
| 142 |
+
seq_aux = True,
|
| 143 |
+
hidden_act="silu",
|
| 144 |
+
max_position_embeddings=2048,
|
| 145 |
+
initializer_range=0.02,
|
| 146 |
+
rms_norm_eps=1e-6,
|
| 147 |
+
use_cache=True,
|
| 148 |
+
pad_token_id=None,
|
| 149 |
+
bos_token_id=100000,
|
| 150 |
+
eos_token_id=100001,
|
| 151 |
+
pretraining_tp=1,
|
| 152 |
+
tie_word_embeddings=False,
|
| 153 |
+
rope_theta=10000.0,
|
| 154 |
+
rope_scaling=None,
|
| 155 |
+
attention_bias=False,
|
| 156 |
+
attention_dropout=0.0,
|
| 157 |
+
**kwargs,
|
| 158 |
+
):
|
| 159 |
+
self.vocab_size = vocab_size
|
| 160 |
+
self.max_position_embeddings = max_position_embeddings
|
| 161 |
+
self.hidden_size = hidden_size
|
| 162 |
+
self.intermediate_size = intermediate_size
|
| 163 |
+
self.moe_intermediate_size = moe_intermediate_size
|
| 164 |
+
self.num_hidden_layers = num_hidden_layers
|
| 165 |
+
self.num_attention_heads = num_attention_heads
|
| 166 |
+
self.n_shared_experts = n_shared_experts
|
| 167 |
+
self.n_routed_experts = n_routed_experts
|
| 168 |
+
self.ep_size = ep_size
|
| 169 |
+
self.routed_scaling_factor = routed_scaling_factor
|
| 170 |
+
self.kv_lora_rank = kv_lora_rank
|
| 171 |
+
self.q_lora_rank = q_lora_rank
|
| 172 |
+
self.qk_rope_head_dim = qk_rope_head_dim
|
| 173 |
+
self.v_head_dim = v_head_dim
|
| 174 |
+
self.qk_nope_head_dim = qk_nope_head_dim
|
| 175 |
+
self.topk_method = topk_method
|
| 176 |
+
self.n_group = n_group
|
| 177 |
+
self.topk_group = topk_group
|
| 178 |
+
self.num_experts_per_tok = num_experts_per_tok
|
| 179 |
+
self.moe_layer_freq = moe_layer_freq
|
| 180 |
+
self.first_k_dense_replace = first_k_dense_replace
|
| 181 |
+
self.norm_topk_prob = norm_topk_prob
|
| 182 |
+
self.scoring_func = scoring_func
|
| 183 |
+
self.aux_loss_alpha = aux_loss_alpha
|
| 184 |
+
self.seq_aux = seq_aux
|
| 185 |
+
# for backward compatibility
|
| 186 |
+
if num_key_value_heads is None:
|
| 187 |
+
num_key_value_heads = num_attention_heads
|
| 188 |
+
|
| 189 |
+
self.num_key_value_heads = num_key_value_heads
|
| 190 |
+
self.hidden_act = hidden_act
|
| 191 |
+
self.initializer_range = initializer_range
|
| 192 |
+
self.rms_norm_eps = rms_norm_eps
|
| 193 |
+
self.pretraining_tp = pretraining_tp
|
| 194 |
+
self.use_cache = use_cache
|
| 195 |
+
self.rope_theta = rope_theta
|
| 196 |
+
self.rope_scaling = rope_scaling
|
| 197 |
+
self.attention_bias = attention_bias
|
| 198 |
+
self.attention_dropout = attention_dropout
|
| 199 |
+
|
| 200 |
+
super().__init__(
|
| 201 |
+
pad_token_id=pad_token_id,
|
| 202 |
+
bos_token_id=bos_token_id,
|
| 203 |
+
eos_token_id=eos_token_id,
|
| 204 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 205 |
+
**kwargs,
|
| 206 |
+
)
|
generation_config.json
ADDED
|
@@ -0,0 +1,9 @@
|
|
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|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 100000,
|
| 4 |
+
"eos_token_id": 100001,
|
| 5 |
+
"do_sample": true,
|
| 6 |
+
"temperature": 0.3,
|
| 7 |
+
"top_p": 0.95,
|
| 8 |
+
"transformers_version": "4.39.3"
|
| 9 |
+
}
|
model-00001-of-000004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
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|
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|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b9918de0d7b98d0313f6aa7ee52b5b4c1995237ac811b18e9eaaf9b248a75363
|
| 3 |
+
size 8594887408
|
model-00002-of-000004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4ffee204b4162ab7fbb732d5e6f156079d14410115b357527667caa4b68a6044
|
| 3 |
+
size 8591757448
|
model-00003-of-000004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b0a93987f8dcd969e03512ed99b1373028a35fb66f3217e2e63b2a47291db726
|
| 3 |
+
size 8590718520
|
model-00004-of-000004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e491207f17337f7c3da8c6bd60c0b2244d6f2e72388ee086f3db89acb521b730
|
| 3 |
+
size 5636263200
|
model.safetensors.index.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
modeling_deepseek.py
ADDED
|
@@ -0,0 +1,1936 @@
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|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2023 DeepSeek-AI 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 DeepSeek model."""
|
| 21 |
+
import math
|
| 22 |
+
import warnings
|
| 23 |
+
from typing import List, Optional, Tuple, Union
|
| 24 |
+
from collections import Counter
|
| 25 |
+
import os
|
| 26 |
+
|
| 27 |
+
import torch
|
| 28 |
+
import torch.nn.functional as F
|
| 29 |
+
import torch.utils.checkpoint
|
| 30 |
+
from torch import nn
|
| 31 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
| 32 |
+
|
| 33 |
+
from transformers.activations import ACT2FN
|
| 34 |
+
from transformers.cache_utils import Cache, DynamicCache
|
| 35 |
+
from transformers.modeling_attn_mask_utils import (
|
| 36 |
+
AttentionMaskConverter,
|
| 37 |
+
_prepare_4d_attention_mask,
|
| 38 |
+
_prepare_4d_causal_attention_mask,
|
| 39 |
+
)
|
| 40 |
+
from transformers.modeling_outputs import (
|
| 41 |
+
BaseModelOutputWithPast,
|
| 42 |
+
CausalLMOutputWithPast,
|
| 43 |
+
SequenceClassifierOutputWithPast,
|
| 44 |
+
)
|
| 45 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 46 |
+
from transformers.pytorch_utils import (
|
| 47 |
+
ALL_LAYERNORM_LAYERS,
|
| 48 |
+
is_torch_greater_or_equal_than_1_13,
|
| 49 |
+
)
|
| 50 |
+
from transformers.utils import (
|
| 51 |
+
add_start_docstrings,
|
| 52 |
+
add_start_docstrings_to_model_forward,
|
| 53 |
+
is_flash_attn_2_available,
|
| 54 |
+
is_flash_attn_greater_or_equal_2_10,
|
| 55 |
+
logging,
|
| 56 |
+
replace_return_docstrings,
|
| 57 |
+
)
|
| 58 |
+
from transformers.utils.import_utils import is_torch_fx_available
|
| 59 |
+
from .configuration_deepseek import DeepseekV2Config
|
| 60 |
+
import torch.distributed as dist
|
| 61 |
+
import numpy as np
|
| 62 |
+
|
| 63 |
+
if is_flash_attn_2_available():
|
| 64 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
| 65 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
|
| 69 |
+
# It means that the function will not be traced through and simply appear as a node in the graph.
|
| 70 |
+
if is_torch_fx_available():
|
| 71 |
+
if not is_torch_greater_or_equal_than_1_13:
|
| 72 |
+
import torch.fx
|
| 73 |
+
|
| 74 |
+
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
logger = logging.get_logger(__name__)
|
| 78 |
+
|
| 79 |
+
_CONFIG_FOR_DOC = "DeepseekV2Config"
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
def _get_unpad_data(attention_mask):
|
| 83 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
| 84 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
| 85 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
| 86 |
+
cu_seqlens = F.pad(
|
| 87 |
+
torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
|
| 88 |
+
)
|
| 89 |
+
return (
|
| 90 |
+
indices,
|
| 91 |
+
cu_seqlens,
|
| 92 |
+
max_seqlen_in_batch,
|
| 93 |
+
)
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
class DeepseekV2RMSNorm(nn.Module):
|
| 97 |
+
def __init__(self, hidden_size, eps=1e-6):
|
| 98 |
+
"""
|
| 99 |
+
DeepseekV2RMSNorm is equivalent to T5LayerNorm
|
| 100 |
+
"""
|
| 101 |
+
super().__init__()
|
| 102 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
| 103 |
+
self.variance_epsilon = eps
|
| 104 |
+
|
| 105 |
+
def forward(self, hidden_states):
|
| 106 |
+
input_dtype = hidden_states.dtype
|
| 107 |
+
hidden_states = hidden_states.to(torch.float32)
|
| 108 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
| 109 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
| 110 |
+
return self.weight * hidden_states.to(input_dtype)
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
class DeepseekV2RotaryEmbedding(nn.Module):
|
| 117 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
| 118 |
+
super().__init__()
|
| 119 |
+
|
| 120 |
+
self.dim = dim
|
| 121 |
+
self.max_position_embeddings = max_position_embeddings
|
| 122 |
+
self.base = base
|
| 123 |
+
inv_freq = 1.0 / (
|
| 124 |
+
self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
|
| 125 |
+
)
|
| 126 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 127 |
+
|
| 128 |
+
# Build here to make `torch.jit.trace` work.
|
| 129 |
+
self._set_cos_sin_cache(
|
| 130 |
+
seq_len=max_position_embeddings,
|
| 131 |
+
device=self.inv_freq.device,
|
| 132 |
+
dtype=torch.get_default_dtype(),
|
| 133 |
+
)
|
| 134 |
+
self.max_seq_len_cached = None
|
| 135 |
+
|
| 136 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 137 |
+
self.max_seq_len_cached = seq_len
|
| 138 |
+
t = torch.arange(
|
| 139 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
| 140 |
+
)
|
| 141 |
+
|
| 142 |
+
freqs = torch.outer(t, self.inv_freq.to(t.device))
|
| 143 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 144 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 145 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 146 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 147 |
+
|
| 148 |
+
def forward(self, x, seq_len=None):
|
| 149 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
| 150 |
+
if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
|
| 151 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
| 152 |
+
|
| 153 |
+
return (
|
| 154 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
| 155 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
| 156 |
+
)
|
| 157 |
+
|
| 158 |
+
|
| 159 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
|
| 160 |
+
class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
|
| 161 |
+
"""DeepseekV2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
| 162 |
+
|
| 163 |
+
def __init__(
|
| 164 |
+
self,
|
| 165 |
+
dim,
|
| 166 |
+
max_position_embeddings=2048,
|
| 167 |
+
base=10000,
|
| 168 |
+
device=None,
|
| 169 |
+
scaling_factor=1.0,
|
| 170 |
+
):
|
| 171 |
+
self.scaling_factor = scaling_factor
|
| 172 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 173 |
+
|
| 174 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 175 |
+
self.max_seq_len_cached = seq_len
|
| 176 |
+
t = torch.arange(
|
| 177 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
| 178 |
+
)
|
| 179 |
+
t = t / self.scaling_factor
|
| 180 |
+
|
| 181 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 182 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 183 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 184 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 185 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 186 |
+
|
| 187 |
+
|
| 188 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
|
| 189 |
+
class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
|
| 190 |
+
"""DeepseekV2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
| 191 |
+
|
| 192 |
+
def __init__(
|
| 193 |
+
self,
|
| 194 |
+
dim,
|
| 195 |
+
max_position_embeddings=2048,
|
| 196 |
+
base=10000,
|
| 197 |
+
device=None,
|
| 198 |
+
scaling_factor=1.0,
|
| 199 |
+
):
|
| 200 |
+
self.scaling_factor = scaling_factor
|
| 201 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 202 |
+
|
| 203 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 204 |
+
self.max_seq_len_cached = seq_len
|
| 205 |
+
|
| 206 |
+
if seq_len > self.max_position_embeddings:
|
| 207 |
+
base = self.base * (
|
| 208 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings)
|
| 209 |
+
- (self.scaling_factor - 1)
|
| 210 |
+
) ** (self.dim / (self.dim - 2))
|
| 211 |
+
inv_freq = 1.0 / (
|
| 212 |
+
base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
|
| 213 |
+
)
|
| 214 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 215 |
+
|
| 216 |
+
t = torch.arange(
|
| 217 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
| 218 |
+
)
|
| 219 |
+
|
| 220 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 221 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 222 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 223 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 224 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 225 |
+
|
| 226 |
+
|
| 227 |
+
# Inverse dim formula to find dim based on number of rotations
|
| 228 |
+
def yarn_find_correction_dim(
|
| 229 |
+
num_rotations, dim, base=10000, max_position_embeddings=2048
|
| 230 |
+
):
|
| 231 |
+
return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
|
| 232 |
+
2 * math.log(base)
|
| 233 |
+
)
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
# Find dim range bounds based on rotations
|
| 237 |
+
def yarn_find_correction_range(
|
| 238 |
+
low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
|
| 239 |
+
):
|
| 240 |
+
low = math.floor(
|
| 241 |
+
yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
|
| 242 |
+
)
|
| 243 |
+
high = math.ceil(
|
| 244 |
+
yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
|
| 245 |
+
)
|
| 246 |
+
return max(low, 0), min(high, dim - 1) # Clamp values just in case
|
| 247 |
+
|
| 248 |
+
|
| 249 |
+
def yarn_get_mscale(scale=1, mscale=1):
|
| 250 |
+
if scale <= 1:
|
| 251 |
+
return 1.0
|
| 252 |
+
return 0.1 * mscale * math.log(scale) + 1.0
|
| 253 |
+
|
| 254 |
+
|
| 255 |
+
def yarn_linear_ramp_mask(min, max, dim):
|
| 256 |
+
if min == max:
|
| 257 |
+
max += 0.001 # Prevent singularity
|
| 258 |
+
|
| 259 |
+
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
|
| 260 |
+
ramp_func = torch.clamp(linear_func, 0, 1)
|
| 261 |
+
return ramp_func
|
| 262 |
+
|
| 263 |
+
|
| 264 |
+
class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
|
| 265 |
+
|
| 266 |
+
def __init__(
|
| 267 |
+
self,
|
| 268 |
+
dim,
|
| 269 |
+
max_position_embeddings=2048,
|
| 270 |
+
base=10000,
|
| 271 |
+
device=None,
|
| 272 |
+
scaling_factor=1.0,
|
| 273 |
+
original_max_position_embeddings=4096,
|
| 274 |
+
beta_fast=32,
|
| 275 |
+
beta_slow=1,
|
| 276 |
+
mscale=1,
|
| 277 |
+
mscale_all_dim=0,
|
| 278 |
+
):
|
| 279 |
+
self.scaling_factor = scaling_factor
|
| 280 |
+
self.original_max_position_embeddings = original_max_position_embeddings
|
| 281 |
+
self.beta_fast = beta_fast
|
| 282 |
+
self.beta_slow = beta_slow
|
| 283 |
+
self.mscale = mscale
|
| 284 |
+
self.mscale_all_dim = mscale_all_dim
|
| 285 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 286 |
+
|
| 287 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 288 |
+
self.max_seq_len_cached = seq_len
|
| 289 |
+
dim = self.dim
|
| 290 |
+
|
| 291 |
+
freq_extra = 1.0 / (
|
| 292 |
+
self.base
|
| 293 |
+
** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
|
| 294 |
+
)
|
| 295 |
+
freq_inter = 1.0 / (
|
| 296 |
+
self.scaling_factor
|
| 297 |
+
* self.base
|
| 298 |
+
** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
|
| 299 |
+
)
|
| 300 |
+
|
| 301 |
+
low, high = yarn_find_correction_range(
|
| 302 |
+
self.beta_fast,
|
| 303 |
+
self.beta_slow,
|
| 304 |
+
dim,
|
| 305 |
+
self.base,
|
| 306 |
+
self.original_max_position_embeddings,
|
| 307 |
+
)
|
| 308 |
+
inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
|
| 309 |
+
device=device, dtype=torch.float32
|
| 310 |
+
)
|
| 311 |
+
inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
|
| 312 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 313 |
+
|
| 314 |
+
t = torch.arange(seq_len, device=device, dtype=torch.float32)
|
| 315 |
+
|
| 316 |
+
freqs = torch.outer(t, inv_freq)
|
| 317 |
+
|
| 318 |
+
_mscale = float(
|
| 319 |
+
yarn_get_mscale(self.scaling_factor, self.mscale)
|
| 320 |
+
/ yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
|
| 321 |
+
)
|
| 322 |
+
|
| 323 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 324 |
+
self.register_buffer(
|
| 325 |
+
"cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
|
| 326 |
+
)
|
| 327 |
+
self.register_buffer(
|
| 328 |
+
"sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
|
| 329 |
+
)
|
| 330 |
+
|
| 331 |
+
|
| 332 |
+
# Copied from transformers.models.llama.modeling_llama.rotate_half
|
| 333 |
+
def rotate_half(x):
|
| 334 |
+
"""Rotates half the hidden dims of the input."""
|
| 335 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 336 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 337 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 338 |
+
|
| 339 |
+
|
| 340 |
+
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
|
| 341 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
| 342 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
| 343 |
+
|
| 344 |
+
Args:
|
| 345 |
+
q (`torch.Tensor`): The query tensor.
|
| 346 |
+
k (`torch.Tensor`): The key tensor.
|
| 347 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
| 348 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
| 349 |
+
position_ids (`torch.Tensor`):
|
| 350 |
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
| 351 |
+
used to pass offsetted position ids when working with a KV-cache.
|
| 352 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
| 353 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
| 354 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
| 355 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
| 356 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
| 357 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
| 358 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
| 359 |
+
Returns:
|
| 360 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
| 361 |
+
"""
|
| 362 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
| 363 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
| 364 |
+
|
| 365 |
+
b, h, s, d = q.shape
|
| 366 |
+
q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
|
| 367 |
+
|
| 368 |
+
b, h, s, d = k.shape
|
| 369 |
+
k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
|
| 370 |
+
|
| 371 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
| 372 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
| 373 |
+
return q_embed, k_embed
|
| 374 |
+
|
| 375 |
+
|
| 376 |
+
class DeepseekV2MLP(nn.Module):
|
| 377 |
+
def __init__(self, config, hidden_size=None, intermediate_size=None):
|
| 378 |
+
super().__init__()
|
| 379 |
+
self.config = config
|
| 380 |
+
self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
|
| 381 |
+
self.intermediate_size = (
|
| 382 |
+
config.intermediate_size if intermediate_size is None else intermediate_size
|
| 383 |
+
)
|
| 384 |
+
|
| 385 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 386 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 387 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
| 388 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
| 389 |
+
|
| 390 |
+
def forward(self, x):
|
| 391 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
| 392 |
+
return down_proj
|
| 393 |
+
|
| 394 |
+
|
| 395 |
+
class MoEGate(nn.Module):
|
| 396 |
+
def __init__(self, config):
|
| 397 |
+
super().__init__()
|
| 398 |
+
self.config = config
|
| 399 |
+
self.top_k = config.num_experts_per_tok
|
| 400 |
+
self.n_routed_experts = config.n_routed_experts
|
| 401 |
+
self.routed_scaling_factor = config.routed_scaling_factor
|
| 402 |
+
self.scoring_func = config.scoring_func
|
| 403 |
+
self.alpha = config.aux_loss_alpha
|
| 404 |
+
self.seq_aux = config.seq_aux
|
| 405 |
+
self.topk_method = config.topk_method
|
| 406 |
+
self.n_group = config.n_group
|
| 407 |
+
self.topk_group = config.topk_group
|
| 408 |
+
|
| 409 |
+
# topk selection algorithm
|
| 410 |
+
self.norm_topk_prob = config.norm_topk_prob
|
| 411 |
+
self.gating_dim = config.hidden_size
|
| 412 |
+
self.weight = nn.Parameter(
|
| 413 |
+
torch.empty((self.n_routed_experts, self.gating_dim))
|
| 414 |
+
)
|
| 415 |
+
self.reset_parameters()
|
| 416 |
+
|
| 417 |
+
def reset_parameters(self) -> None:
|
| 418 |
+
import torch.nn.init as init
|
| 419 |
+
|
| 420 |
+
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
|
| 421 |
+
|
| 422 |
+
def forward(self, hidden_states):
|
| 423 |
+
bsz, seq_len, h = hidden_states.shape
|
| 424 |
+
### compute gating score
|
| 425 |
+
hidden_states = hidden_states.view(-1, h)
|
| 426 |
+
logits = F.linear(
|
| 427 |
+
hidden_states.type(torch.float32), self.weight.type(torch.float32), None
|
| 428 |
+
)
|
| 429 |
+
if self.scoring_func == "softmax":
|
| 430 |
+
scores = logits.softmax(dim=-1, dtype=torch.float32)
|
| 431 |
+
else:
|
| 432 |
+
raise NotImplementedError(
|
| 433 |
+
f"insupportable scoring function for MoE gating: {self.scoring_func}"
|
| 434 |
+
)
|
| 435 |
+
|
| 436 |
+
### select top-k experts
|
| 437 |
+
if self.topk_method == "greedy":
|
| 438 |
+
topk_weight, topk_idx = torch.topk(
|
| 439 |
+
scores, k=self.top_k, dim=-1, sorted=False
|
| 440 |
+
)
|
| 441 |
+
elif self.topk_method == "group_limited_greedy":
|
| 442 |
+
group_scores = (
|
| 443 |
+
scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
|
| 444 |
+
) # [n, n_group]
|
| 445 |
+
group_idx = torch.topk(
|
| 446 |
+
group_scores, k=self.topk_group, dim=-1, sorted=False
|
| 447 |
+
)[
|
| 448 |
+
1
|
| 449 |
+
] # [n, top_k_group]
|
| 450 |
+
group_mask = torch.zeros_like(group_scores) # [n, n_group]
|
| 451 |
+
group_mask.scatter_(1, group_idx, 1) # [n, n_group]
|
| 452 |
+
score_mask = (
|
| 453 |
+
group_mask.unsqueeze(-1)
|
| 454 |
+
.expand(
|
| 455 |
+
bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
|
| 456 |
+
)
|
| 457 |
+
.reshape(bsz * seq_len, -1)
|
| 458 |
+
) # [n, e]
|
| 459 |
+
tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e]
|
| 460 |
+
topk_weight, topk_idx = torch.topk(
|
| 461 |
+
tmp_scores, k=self.top_k, dim=-1, sorted=False
|
| 462 |
+
)
|
| 463 |
+
|
| 464 |
+
### norm gate to sum 1
|
| 465 |
+
if self.top_k > 1 and self.norm_topk_prob:
|
| 466 |
+
denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
|
| 467 |
+
topk_weight = topk_weight / denominator
|
| 468 |
+
else:
|
| 469 |
+
topk_weight = topk_weight * self.routed_scaling_factor
|
| 470 |
+
### expert-level computation auxiliary loss
|
| 471 |
+
if self.training and self.alpha > 0.0:
|
| 472 |
+
scores_for_aux = scores
|
| 473 |
+
aux_topk = self.top_k
|
| 474 |
+
# always compute aux loss based on the naive greedy topk method
|
| 475 |
+
topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
|
| 476 |
+
if self.seq_aux:
|
| 477 |
+
scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
|
| 478 |
+
ce = torch.zeros(
|
| 479 |
+
bsz, self.n_routed_experts, device=hidden_states.device
|
| 480 |
+
)
|
| 481 |
+
ce.scatter_add_(
|
| 482 |
+
1,
|
| 483 |
+
topk_idx_for_aux_loss,
|
| 484 |
+
torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
|
| 485 |
+
).div_(seq_len * aux_topk / self.n_routed_experts)
|
| 486 |
+
aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
|
| 487 |
+
dim=1
|
| 488 |
+
).mean() * self.alpha
|
| 489 |
+
else:
|
| 490 |
+
mask_ce = F.one_hot(
|
| 491 |
+
topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
|
| 492 |
+
)
|
| 493 |
+
ce = mask_ce.float().mean(0)
|
| 494 |
+
Pi = scores_for_aux.mean(0)
|
| 495 |
+
fi = ce * self.n_routed_experts
|
| 496 |
+
aux_loss = (Pi * fi).sum() * self.alpha
|
| 497 |
+
else:
|
| 498 |
+
aux_loss = None
|
| 499 |
+
return topk_idx, topk_weight, aux_loss
|
| 500 |
+
|
| 501 |
+
|
| 502 |
+
class AddAuxiliaryLoss(torch.autograd.Function):
|
| 503 |
+
"""
|
| 504 |
+
The trick function of adding auxiliary (aux) loss,
|
| 505 |
+
which includes the gradient of the aux loss during backpropagation.
|
| 506 |
+
"""
|
| 507 |
+
|
| 508 |
+
@staticmethod
|
| 509 |
+
def forward(ctx, x, loss):
|
| 510 |
+
assert loss.numel() == 1
|
| 511 |
+
ctx.dtype = loss.dtype
|
| 512 |
+
ctx.required_aux_loss = loss.requires_grad
|
| 513 |
+
return x
|
| 514 |
+
|
| 515 |
+
@staticmethod
|
| 516 |
+
def backward(ctx, grad_output):
|
| 517 |
+
grad_loss = None
|
| 518 |
+
if ctx.required_aux_loss:
|
| 519 |
+
grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
|
| 520 |
+
return grad_output, grad_loss
|
| 521 |
+
|
| 522 |
+
|
| 523 |
+
class DeepseekV2MoE(nn.Module):
|
| 524 |
+
"""
|
| 525 |
+
A mixed expert module containing shared experts.
|
| 526 |
+
"""
|
| 527 |
+
|
| 528 |
+
def __init__(self, config, layer_idx):
|
| 529 |
+
super().__init__()
|
| 530 |
+
self.config = config
|
| 531 |
+
self.layer_idx = layer_idx
|
| 532 |
+
self.num_experts_per_tok = config.num_experts_per_tok
|
| 533 |
+
|
| 534 |
+
if hasattr(config, "ep_size") and config.ep_size > 1:
|
| 535 |
+
assert config.ep_size == dist.get_world_size()
|
| 536 |
+
self.ep_size = config.ep_size
|
| 537 |
+
self.experts_per_rank = config.n_routed_experts // config.ep_size
|
| 538 |
+
self.ep_rank = dist.get_rank()
|
| 539 |
+
self.experts = nn.ModuleList(
|
| 540 |
+
[
|
| 541 |
+
(
|
| 542 |
+
DeepseekV2MLP(
|
| 543 |
+
config, intermediate_size=config.moe_intermediate_size
|
| 544 |
+
)
|
| 545 |
+
if i >= self.ep_rank * self.experts_per_rank
|
| 546 |
+
and i < (self.ep_rank + 1) * self.experts_per_rank
|
| 547 |
+
else None
|
| 548 |
+
)
|
| 549 |
+
for i in range(config.n_routed_experts)
|
| 550 |
+
]
|
| 551 |
+
)
|
| 552 |
+
else:
|
| 553 |
+
self.ep_size = 1
|
| 554 |
+
self.experts_per_rank = config.n_routed_experts
|
| 555 |
+
self.ep_rank = 0
|
| 556 |
+
self.experts = nn.ModuleList(
|
| 557 |
+
[
|
| 558 |
+
DeepseekV2MLP(
|
| 559 |
+
config, intermediate_size=config.moe_intermediate_size
|
| 560 |
+
)
|
| 561 |
+
for i in range(config.n_routed_experts)
|
| 562 |
+
]
|
| 563 |
+
)
|
| 564 |
+
self.gate = MoEGate(config)
|
| 565 |
+
if config.n_shared_experts is not None:
|
| 566 |
+
intermediate_size = config.moe_intermediate_size * config.n_shared_experts
|
| 567 |
+
self.shared_experts = DeepseekV2MLP(
|
| 568 |
+
config=config, intermediate_size=intermediate_size
|
| 569 |
+
)
|
| 570 |
+
|
| 571 |
+
def forward(self, hidden_states):
|
| 572 |
+
identity = hidden_states
|
| 573 |
+
orig_shape = hidden_states.shape
|
| 574 |
+
topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
|
| 575 |
+
if hasattr(self.config, 'log_expert_weights') and self.config.log_expert_weights:
|
| 576 |
+
assert hasattr(self.config, 'expert_log_dir'), "Please specify expert_log_dir in the config to log the expert weights"
|
| 577 |
+
log_dir = self.config.expert_log_dir
|
| 578 |
+
file_path = os.path.join(log_dir, f"expert_weights_{self.layer_idx}.txt")
|
| 579 |
+
with open(file_path, "a") as f:
|
| 580 |
+
tk_idx_list = topk_idx.view(-1).tolist()
|
| 581 |
+
tk_weight_list = topk_weight.view(-1).tolist()
|
| 582 |
+
f.write("\t".join([str(i) for i in tk_idx_list]) + "\t\t" + "\t".join([str(round(i, 4)) for i in tk_weight_list]) + "\n")
|
| 583 |
+
|
| 584 |
+
|
| 585 |
+
|
| 586 |
+
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
| 587 |
+
flat_topk_idx = topk_idx.view(-1)
|
| 588 |
+
if self.training:
|
| 589 |
+
hidden_states = hidden_states.repeat_interleave(
|
| 590 |
+
self.num_experts_per_tok, dim=0
|
| 591 |
+
)
|
| 592 |
+
y = torch.empty_like(hidden_states)
|
| 593 |
+
for i, expert in enumerate(self.experts):
|
| 594 |
+
y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
|
| 595 |
+
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
|
| 596 |
+
y = y.to(hidden_states.dtype).view(*orig_shape)
|
| 597 |
+
y = AddAuxiliaryLoss.apply(y, aux_loss)
|
| 598 |
+
else:
|
| 599 |
+
y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
|
| 600 |
+
if self.config.n_shared_experts is not None:
|
| 601 |
+
y = y + self.shared_experts(identity)
|
| 602 |
+
return y
|
| 603 |
+
|
| 604 |
+
@torch.no_grad()
|
| 605 |
+
def moe_infer(self, x, topk_ids, topk_weight):
|
| 606 |
+
cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
|
| 607 |
+
cnts.scatter_(1, topk_ids, 1)
|
| 608 |
+
tokens_per_expert = cnts.sum(dim=0)
|
| 609 |
+
idxs = topk_ids.view(-1).argsort()
|
| 610 |
+
sorted_tokens = x[idxs // topk_ids.shape[1]]
|
| 611 |
+
sorted_tokens_shape = sorted_tokens.shape
|
| 612 |
+
if self.ep_size > 1:
|
| 613 |
+
tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
|
| 614 |
+
tokens_per_expert_group = tokens_per_expert.new_empty(
|
| 615 |
+
tokens_per_expert.shape[0]
|
| 616 |
+
)
|
| 617 |
+
dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
|
| 618 |
+
output_splits = (
|
| 619 |
+
tokens_per_expert_group.view(self.ep_size, -1)
|
| 620 |
+
.sum(1)
|
| 621 |
+
.cpu()
|
| 622 |
+
.numpy()
|
| 623 |
+
.tolist()
|
| 624 |
+
)
|
| 625 |
+
gathered_tokens = sorted_tokens.new_empty(
|
| 626 |
+
tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
|
| 627 |
+
)
|
| 628 |
+
input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
|
| 629 |
+
dist.all_to_all(
|
| 630 |
+
list(gathered_tokens.split(output_splits)),
|
| 631 |
+
list(sorted_tokens.split(input_split_sizes)),
|
| 632 |
+
)
|
| 633 |
+
tokens_per_expert_post_gather = tokens_per_expert_group.view(
|
| 634 |
+
self.ep_size, self.experts_per_rank
|
| 635 |
+
).sum(dim=0)
|
| 636 |
+
gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
|
| 637 |
+
s = 0
|
| 638 |
+
for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
|
| 639 |
+
gatherd_idxs[s : s + k] = i % self.experts_per_rank
|
| 640 |
+
s += k
|
| 641 |
+
gatherd_idxs = gatherd_idxs.argsort()
|
| 642 |
+
sorted_tokens = gathered_tokens[gatherd_idxs]
|
| 643 |
+
tokens_per_expert = tokens_per_expert_post_gather
|
| 644 |
+
tokens_per_expert = tokens_per_expert.cpu().numpy()
|
| 645 |
+
|
| 646 |
+
outputs = []
|
| 647 |
+
start_idx = 0
|
| 648 |
+
for i, num_tokens in enumerate(tokens_per_expert):
|
| 649 |
+
end_idx = start_idx + num_tokens
|
| 650 |
+
if num_tokens == 0:
|
| 651 |
+
continue
|
| 652 |
+
expert = self.experts[i + self.ep_rank * self.experts_per_rank]
|
| 653 |
+
tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
|
| 654 |
+
expert_out = expert(tokens_for_this_expert)
|
| 655 |
+
outputs.append(expert_out)
|
| 656 |
+
start_idx = end_idx
|
| 657 |
+
|
| 658 |
+
outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
|
| 659 |
+
if self.ep_size > 1:
|
| 660 |
+
new_x = torch.empty_like(outs)
|
| 661 |
+
new_x[gatherd_idxs] = outs
|
| 662 |
+
gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
|
| 663 |
+
dist.all_to_all(
|
| 664 |
+
list(gathered_tokens.split(input_split_sizes)),
|
| 665 |
+
list(new_x.split(output_splits)),
|
| 666 |
+
)
|
| 667 |
+
outs = gathered_tokens
|
| 668 |
+
|
| 669 |
+
new_x = torch.empty_like(outs)
|
| 670 |
+
new_x[idxs] = outs
|
| 671 |
+
final_out = (
|
| 672 |
+
new_x.view(*topk_ids.shape, -1)
|
| 673 |
+
.type(topk_weight.dtype)
|
| 674 |
+
.mul_(topk_weight.unsqueeze(dim=-1))
|
| 675 |
+
.sum(dim=1)
|
| 676 |
+
.type(new_x.dtype)
|
| 677 |
+
)
|
| 678 |
+
return final_out
|
| 679 |
+
|
| 680 |
+
|
| 681 |
+
# Copied from transformers.models.llama.modeling_llama.repeat_kv
|
| 682 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 683 |
+
"""
|
| 684 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 685 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 686 |
+
"""
|
| 687 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 688 |
+
if n_rep == 1:
|
| 689 |
+
return hidden_states
|
| 690 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(
|
| 691 |
+
batch, num_key_value_heads, n_rep, slen, head_dim
|
| 692 |
+
)
|
| 693 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 694 |
+
|
| 695 |
+
|
| 696 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
|
| 697 |
+
class DeepseekV2Attention(nn.Module):
|
| 698 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 699 |
+
|
| 700 |
+
def __init__(self, config: DeepseekV2Config, layer_idx: Optional[int] = None):
|
| 701 |
+
super().__init__()
|
| 702 |
+
self.config = config
|
| 703 |
+
self.layer_idx = layer_idx
|
| 704 |
+
if layer_idx is None:
|
| 705 |
+
logger.warning_once(
|
| 706 |
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
| 707 |
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
| 708 |
+
"when creating this class."
|
| 709 |
+
)
|
| 710 |
+
|
| 711 |
+
self.attention_dropout = config.attention_dropout
|
| 712 |
+
self.hidden_size = config.hidden_size
|
| 713 |
+
self.num_heads = config.num_attention_heads
|
| 714 |
+
|
| 715 |
+
self.max_position_embeddings = config.max_position_embeddings
|
| 716 |
+
self.rope_theta = config.rope_theta
|
| 717 |
+
self.q_lora_rank = config.q_lora_rank
|
| 718 |
+
self.qk_rope_head_dim = config.qk_rope_head_dim
|
| 719 |
+
self.kv_lora_rank = config.kv_lora_rank
|
| 720 |
+
self.v_head_dim = config.v_head_dim
|
| 721 |
+
self.qk_nope_head_dim = config.qk_nope_head_dim
|
| 722 |
+
self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
|
| 723 |
+
|
| 724 |
+
self.is_causal = True
|
| 725 |
+
|
| 726 |
+
if self.q_lora_rank is None:
|
| 727 |
+
self.q_proj = nn.Linear(
|
| 728 |
+
self.hidden_size, self.num_heads * self.q_head_dim, bias=False
|
| 729 |
+
)
|
| 730 |
+
else:
|
| 731 |
+
self.q_a_proj = nn.Linear(
|
| 732 |
+
self.hidden_size, config.q_lora_rank, bias=config.attention_bias
|
| 733 |
+
)
|
| 734 |
+
self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
|
| 735 |
+
self.q_b_proj = nn.Linear(
|
| 736 |
+
config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
|
| 737 |
+
)
|
| 738 |
+
|
| 739 |
+
self.kv_a_proj_with_mqa = nn.Linear(
|
| 740 |
+
self.hidden_size,
|
| 741 |
+
config.kv_lora_rank + config.qk_rope_head_dim,
|
| 742 |
+
bias=config.attention_bias,
|
| 743 |
+
)
|
| 744 |
+
self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
|
| 745 |
+
self.kv_b_proj = nn.Linear(
|
| 746 |
+
config.kv_lora_rank,
|
| 747 |
+
self.num_heads
|
| 748 |
+
* (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
|
| 749 |
+
bias=False,
|
| 750 |
+
)
|
| 751 |
+
|
| 752 |
+
self.o_proj = nn.Linear(
|
| 753 |
+
self.num_heads * self.v_head_dim,
|
| 754 |
+
self.hidden_size,
|
| 755 |
+
bias=config.attention_bias,
|
| 756 |
+
)
|
| 757 |
+
self._init_rope()
|
| 758 |
+
|
| 759 |
+
self.softmax_scale = self.q_head_dim ** (-0.5)
|
| 760 |
+
if self.config.rope_scaling is not None:
|
| 761 |
+
mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
|
| 762 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
| 763 |
+
if mscale_all_dim:
|
| 764 |
+
mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
|
| 765 |
+
self.softmax_scale = self.softmax_scale * mscale * mscale
|
| 766 |
+
|
| 767 |
+
def _init_rope(self):
|
| 768 |
+
if self.config.rope_scaling is None:
|
| 769 |
+
self.rotary_emb = DeepseekV2RotaryEmbedding(
|
| 770 |
+
self.qk_rope_head_dim,
|
| 771 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 772 |
+
base=self.rope_theta,
|
| 773 |
+
)
|
| 774 |
+
else:
|
| 775 |
+
scaling_type = self.config.rope_scaling["type"]
|
| 776 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
| 777 |
+
if scaling_type == "linear":
|
| 778 |
+
self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
|
| 779 |
+
self.qk_rope_head_dim,
|
| 780 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 781 |
+
scaling_factor=scaling_factor,
|
| 782 |
+
base=self.rope_theta,
|
| 783 |
+
)
|
| 784 |
+
elif scaling_type == "dynamic":
|
| 785 |
+
self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
|
| 786 |
+
self.qk_rope_head_dim,
|
| 787 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 788 |
+
scaling_factor=scaling_factor,
|
| 789 |
+
base=self.rope_theta,
|
| 790 |
+
)
|
| 791 |
+
elif scaling_type == "yarn":
|
| 792 |
+
kwargs = {
|
| 793 |
+
key: self.config.rope_scaling[key]
|
| 794 |
+
for key in [
|
| 795 |
+
"original_max_position_embeddings",
|
| 796 |
+
"beta_fast",
|
| 797 |
+
"beta_slow",
|
| 798 |
+
"mscale",
|
| 799 |
+
"mscale_all_dim",
|
| 800 |
+
]
|
| 801 |
+
if key in self.config.rope_scaling
|
| 802 |
+
}
|
| 803 |
+
self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
|
| 804 |
+
self.qk_rope_head_dim,
|
| 805 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 806 |
+
scaling_factor=scaling_factor,
|
| 807 |
+
base=self.rope_theta,
|
| 808 |
+
**kwargs,
|
| 809 |
+
)
|
| 810 |
+
else:
|
| 811 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
| 812 |
+
|
| 813 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
| 814 |
+
return (
|
| 815 |
+
tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim)
|
| 816 |
+
.transpose(1, 2)
|
| 817 |
+
.contiguous()
|
| 818 |
+
)
|
| 819 |
+
|
| 820 |
+
def forward(
|
| 821 |
+
self,
|
| 822 |
+
hidden_states: torch.Tensor,
|
| 823 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 824 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 825 |
+
past_key_value: Optional[Cache] = None,
|
| 826 |
+
output_attentions: bool = False,
|
| 827 |
+
use_cache: bool = False,
|
| 828 |
+
**kwargs,
|
| 829 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 830 |
+
if "padding_mask" in kwargs:
|
| 831 |
+
warnings.warn(
|
| 832 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
| 833 |
+
)
|
| 834 |
+
bsz, q_len, _ = hidden_states.size()
|
| 835 |
+
|
| 836 |
+
if self.q_lora_rank is None:
|
| 837 |
+
q = self.q_proj(hidden_states)
|
| 838 |
+
else:
|
| 839 |
+
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
|
| 840 |
+
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
|
| 841 |
+
q_nope, q_pe = torch.split(
|
| 842 |
+
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
|
| 843 |
+
)
|
| 844 |
+
|
| 845 |
+
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
|
| 846 |
+
compressed_kv, k_pe = torch.split(
|
| 847 |
+
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
|
| 848 |
+
)
|
| 849 |
+
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
|
| 850 |
+
kv = (
|
| 851 |
+
self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
|
| 852 |
+
.view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
|
| 853 |
+
.transpose(1, 2)
|
| 854 |
+
)
|
| 855 |
+
|
| 856 |
+
k_nope, value_states = torch.split(
|
| 857 |
+
kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
|
| 858 |
+
)
|
| 859 |
+
kv_seq_len = value_states.shape[-2]
|
| 860 |
+
if past_key_value is not None:
|
| 861 |
+
if self.layer_idx is None:
|
| 862 |
+
raise ValueError(
|
| 863 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
| 864 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
| 865 |
+
"with a layer index."
|
| 866 |
+
)
|
| 867 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
| 868 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 869 |
+
|
| 870 |
+
q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
|
| 871 |
+
|
| 872 |
+
query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
|
| 873 |
+
query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
|
| 874 |
+
query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
|
| 875 |
+
|
| 876 |
+
key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
|
| 877 |
+
key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
|
| 878 |
+
key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
|
| 879 |
+
if past_key_value is not None:
|
| 880 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
| 881 |
+
key_states, value_states = past_key_value.update(
|
| 882 |
+
key_states, value_states, self.layer_idx, cache_kwargs
|
| 883 |
+
)
|
| 884 |
+
|
| 885 |
+
attn_weights = (
|
| 886 |
+
torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale
|
| 887 |
+
)
|
| 888 |
+
|
| 889 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
| 890 |
+
raise ValueError(
|
| 891 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
| 892 |
+
f" {attn_weights.size()}"
|
| 893 |
+
)
|
| 894 |
+
assert attention_mask is not None
|
| 895 |
+
if attention_mask is not None:
|
| 896 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
| 897 |
+
raise ValueError(
|
| 898 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
| 899 |
+
)
|
| 900 |
+
attn_weights = attn_weights + attention_mask
|
| 901 |
+
|
| 902 |
+
# upcast attention to fp32
|
| 903 |
+
attn_weights = nn.functional.softmax(
|
| 904 |
+
attn_weights, dim=-1, dtype=torch.float32
|
| 905 |
+
).to(query_states.dtype)
|
| 906 |
+
attn_weights = nn.functional.dropout(
|
| 907 |
+
attn_weights, p=self.attention_dropout, training=self.training
|
| 908 |
+
)
|
| 909 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 910 |
+
|
| 911 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
|
| 912 |
+
raise ValueError(
|
| 913 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is"
|
| 914 |
+
f" {attn_output.size()}"
|
| 915 |
+
)
|
| 916 |
+
|
| 917 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 918 |
+
|
| 919 |
+
attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
|
| 920 |
+
|
| 921 |
+
attn_output = self.o_proj(attn_output)
|
| 922 |
+
|
| 923 |
+
if not output_attentions:
|
| 924 |
+
attn_weights = None
|
| 925 |
+
|
| 926 |
+
return attn_output, attn_weights, past_key_value
|
| 927 |
+
|
| 928 |
+
|
| 929 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
|
| 930 |
+
class DeepseekV2FlashAttention2(DeepseekV2Attention):
|
| 931 |
+
"""
|
| 932 |
+
DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` as the weights of the module stays
|
| 933 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
| 934 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
| 935 |
+
"""
|
| 936 |
+
|
| 937 |
+
def __init__(self, *args, **kwargs):
|
| 938 |
+
super().__init__(*args, **kwargs)
|
| 939 |
+
|
| 940 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
| 941 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
| 942 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
| 943 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
| 944 |
+
|
| 945 |
+
def forward(
|
| 946 |
+
self,
|
| 947 |
+
hidden_states: torch.Tensor,
|
| 948 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
| 949 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 950 |
+
past_key_value: Optional[Cache] = None,
|
| 951 |
+
output_attentions: bool = False,
|
| 952 |
+
use_cache: bool = False,
|
| 953 |
+
**kwargs,
|
| 954 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 955 |
+
# DeepseekV2FlashAttention2 attention does not support output_attentions
|
| 956 |
+
if "padding_mask" in kwargs:
|
| 957 |
+
warnings.warn(
|
| 958 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
| 959 |
+
)
|
| 960 |
+
|
| 961 |
+
# overwrite attention_mask with padding_mask
|
| 962 |
+
attention_mask = kwargs.pop("padding_mask")
|
| 963 |
+
|
| 964 |
+
output_attentions = False
|
| 965 |
+
|
| 966 |
+
bsz, q_len, _ = hidden_states.size()
|
| 967 |
+
|
| 968 |
+
if self.q_lora_rank is None:
|
| 969 |
+
q = self.q_proj(hidden_states)
|
| 970 |
+
else:
|
| 971 |
+
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
|
| 972 |
+
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
|
| 973 |
+
q_nope, q_pe = torch.split(
|
| 974 |
+
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
|
| 975 |
+
)
|
| 976 |
+
|
| 977 |
+
# Flash attention requires the input to have the shape
|
| 978 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
| 979 |
+
# therefore we just need to keep the original shape
|
| 980 |
+
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
|
| 981 |
+
compressed_kv, k_pe = torch.split(
|
| 982 |
+
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
|
| 983 |
+
)
|
| 984 |
+
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
|
| 985 |
+
kv = (
|
| 986 |
+
self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
|
| 987 |
+
.view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
|
| 988 |
+
.transpose(1, 2)
|
| 989 |
+
)
|
| 990 |
+
|
| 991 |
+
k_nope, value_states = torch.split(
|
| 992 |
+
kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
|
| 993 |
+
)
|
| 994 |
+
kv_seq_len = value_states.shape[-2]
|
| 995 |
+
|
| 996 |
+
kv_seq_len = value_states.shape[-2]
|
| 997 |
+
if past_key_value is not None:
|
| 998 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
| 999 |
+
|
| 1000 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 1001 |
+
q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
|
| 1002 |
+
|
| 1003 |
+
query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
|
| 1004 |
+
query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
|
| 1005 |
+
query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
|
| 1006 |
+
|
| 1007 |
+
key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
|
| 1008 |
+
key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
|
| 1009 |
+
key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
|
| 1010 |
+
|
| 1011 |
+
if self.q_head_dim != self.v_head_dim:
|
| 1012 |
+
value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
|
| 1013 |
+
|
| 1014 |
+
if past_key_value is not None:
|
| 1015 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
| 1016 |
+
key_states, value_states = past_key_value.update(
|
| 1017 |
+
key_states, value_states, self.layer_idx, cache_kwargs
|
| 1018 |
+
)
|
| 1019 |
+
|
| 1020 |
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
| 1021 |
+
# to be able to avoid many of these transpose/reshape/view.
|
| 1022 |
+
query_states = query_states.transpose(1, 2)
|
| 1023 |
+
key_states = key_states.transpose(1, 2)
|
| 1024 |
+
value_states = value_states.transpose(1, 2)
|
| 1025 |
+
|
| 1026 |
+
dropout_rate = self.attention_dropout if self.training else 0.0
|
| 1027 |
+
|
| 1028 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
| 1029 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
| 1030 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
| 1031 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
| 1032 |
+
# in fp32. (DeepseekV2RMSNorm handles it correctly)
|
| 1033 |
+
|
| 1034 |
+
input_dtype = query_states.dtype
|
| 1035 |
+
if input_dtype == torch.float32:
|
| 1036 |
+
# Handle the case where the model is quantized
|
| 1037 |
+
if hasattr(self.config, "_pre_quantization_dtype"):
|
| 1038 |
+
target_dtype = self.config._pre_quantization_dtype
|
| 1039 |
+
elif torch.is_autocast_enabled():
|
| 1040 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
| 1041 |
+
else:
|
| 1042 |
+
target_dtype = (
|
| 1043 |
+
self.q_proj.weight.dtype
|
| 1044 |
+
if self.q_lora_rank is None
|
| 1045 |
+
else self.q_a_proj.weight.dtype
|
| 1046 |
+
)
|
| 1047 |
+
|
| 1048 |
+
logger.warning_once(
|
| 1049 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
| 1050 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
| 1051 |
+
f" {target_dtype}."
|
| 1052 |
+
)
|
| 1053 |
+
|
| 1054 |
+
query_states = query_states.to(target_dtype)
|
| 1055 |
+
key_states = key_states.to(target_dtype)
|
| 1056 |
+
value_states = value_states.to(target_dtype)
|
| 1057 |
+
|
| 1058 |
+
attn_output = self._flash_attention_forward(
|
| 1059 |
+
query_states,
|
| 1060 |
+
key_states,
|
| 1061 |
+
value_states,
|
| 1062 |
+
attention_mask,
|
| 1063 |
+
q_len,
|
| 1064 |
+
dropout=dropout_rate,
|
| 1065 |
+
softmax_scale=self.softmax_scale,
|
| 1066 |
+
)
|
| 1067 |
+
if self.q_head_dim != self.v_head_dim:
|
| 1068 |
+
attn_output = attn_output[:, :, :, : self.v_head_dim]
|
| 1069 |
+
|
| 1070 |
+
attn_output = attn_output.reshape(
|
| 1071 |
+
bsz, q_len, self.num_heads * self.v_head_dim
|
| 1072 |
+
).contiguous()
|
| 1073 |
+
attn_output = self.o_proj(attn_output)
|
| 1074 |
+
|
| 1075 |
+
if not output_attentions:
|
| 1076 |
+
attn_weights = None
|
| 1077 |
+
|
| 1078 |
+
return attn_output, attn_weights, past_key_value
|
| 1079 |
+
|
| 1080 |
+
def _flash_attention_forward(
|
| 1081 |
+
self,
|
| 1082 |
+
query_states,
|
| 1083 |
+
key_states,
|
| 1084 |
+
value_states,
|
| 1085 |
+
attention_mask,
|
| 1086 |
+
query_length,
|
| 1087 |
+
dropout=0.0,
|
| 1088 |
+
softmax_scale=None,
|
| 1089 |
+
):
|
| 1090 |
+
"""
|
| 1091 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
| 1092 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
| 1093 |
+
|
| 1094 |
+
Args:
|
| 1095 |
+
query_states (`torch.Tensor`):
|
| 1096 |
+
Input query states to be passed to Flash Attention API
|
| 1097 |
+
key_states (`torch.Tensor`):
|
| 1098 |
+
Input key states to be passed to Flash Attention API
|
| 1099 |
+
value_states (`torch.Tensor`):
|
| 1100 |
+
Input value states to be passed to Flash Attention API
|
| 1101 |
+
attention_mask (`torch.Tensor`):
|
| 1102 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
| 1103 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
| 1104 |
+
dropout (`int`, *optional*):
|
| 1105 |
+
Attention dropout
|
| 1106 |
+
softmax_scale (`float`, *optional*):
|
| 1107 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
| 1108 |
+
"""
|
| 1109 |
+
if not self._flash_attn_uses_top_left_mask:
|
| 1110 |
+
causal = self.is_causal
|
| 1111 |
+
else:
|
| 1112 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV2FlashAttention2 __init__.
|
| 1113 |
+
causal = self.is_causal and query_length != 1
|
| 1114 |
+
|
| 1115 |
+
# Contains at least one padding token in the sequence
|
| 1116 |
+
if attention_mask is not None:
|
| 1117 |
+
batch_size = query_states.shape[0]
|
| 1118 |
+
(
|
| 1119 |
+
query_states,
|
| 1120 |
+
key_states,
|
| 1121 |
+
value_states,
|
| 1122 |
+
indices_q,
|
| 1123 |
+
cu_seq_lens,
|
| 1124 |
+
max_seq_lens,
|
| 1125 |
+
) = self._upad_input(
|
| 1126 |
+
query_states, key_states, value_states, attention_mask, query_length
|
| 1127 |
+
)
|
| 1128 |
+
|
| 1129 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
| 1130 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
| 1131 |
+
|
| 1132 |
+
attn_output_unpad = flash_attn_varlen_func(
|
| 1133 |
+
query_states,
|
| 1134 |
+
key_states,
|
| 1135 |
+
value_states,
|
| 1136 |
+
cu_seqlens_q=cu_seqlens_q,
|
| 1137 |
+
cu_seqlens_k=cu_seqlens_k,
|
| 1138 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
| 1139 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
| 1140 |
+
dropout_p=dropout,
|
| 1141 |
+
softmax_scale=softmax_scale,
|
| 1142 |
+
causal=causal,
|
| 1143 |
+
)
|
| 1144 |
+
|
| 1145 |
+
attn_output = pad_input(
|
| 1146 |
+
attn_output_unpad, indices_q, batch_size, query_length
|
| 1147 |
+
)
|
| 1148 |
+
else:
|
| 1149 |
+
attn_output = flash_attn_func(
|
| 1150 |
+
query_states,
|
| 1151 |
+
key_states,
|
| 1152 |
+
value_states,
|
| 1153 |
+
dropout,
|
| 1154 |
+
softmax_scale=softmax_scale,
|
| 1155 |
+
causal=causal,
|
| 1156 |
+
)
|
| 1157 |
+
|
| 1158 |
+
return attn_output
|
| 1159 |
+
|
| 1160 |
+
def _upad_input(
|
| 1161 |
+
self, query_layer, key_layer, value_layer, attention_mask, query_length
|
| 1162 |
+
):
|
| 1163 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
| 1164 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
| 1165 |
+
|
| 1166 |
+
key_layer = index_first_axis(
|
| 1167 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
|
| 1168 |
+
indices_k,
|
| 1169 |
+
)
|
| 1170 |
+
value_layer = index_first_axis(
|
| 1171 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
|
| 1172 |
+
indices_k,
|
| 1173 |
+
)
|
| 1174 |
+
if query_length == kv_seq_len:
|
| 1175 |
+
query_layer = index_first_axis(
|
| 1176 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
|
| 1177 |
+
indices_k,
|
| 1178 |
+
)
|
| 1179 |
+
cu_seqlens_q = cu_seqlens_k
|
| 1180 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
| 1181 |
+
indices_q = indices_k
|
| 1182 |
+
elif query_length == 1:
|
| 1183 |
+
max_seqlen_in_batch_q = 1
|
| 1184 |
+
cu_seqlens_q = torch.arange(
|
| 1185 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
| 1186 |
+
) # There is a memcpy here, that is very bad.
|
| 1187 |
+
indices_q = cu_seqlens_q[:-1]
|
| 1188 |
+
query_layer = query_layer.squeeze(1)
|
| 1189 |
+
else:
|
| 1190 |
+
# The -q_len: slice assumes left padding.
|
| 1191 |
+
attention_mask = attention_mask[:, -query_length:]
|
| 1192 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
|
| 1193 |
+
query_layer, attention_mask
|
| 1194 |
+
)
|
| 1195 |
+
|
| 1196 |
+
return (
|
| 1197 |
+
query_layer,
|
| 1198 |
+
key_layer,
|
| 1199 |
+
value_layer,
|
| 1200 |
+
indices_q,
|
| 1201 |
+
(cu_seqlens_q, cu_seqlens_k),
|
| 1202 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
| 1203 |
+
)
|
| 1204 |
+
|
| 1205 |
+
|
| 1206 |
+
ATTENTION_CLASSES = {
|
| 1207 |
+
"eager": DeepseekV2Attention,
|
| 1208 |
+
"flash_attention_2": DeepseekV2FlashAttention2,
|
| 1209 |
+
}
|
| 1210 |
+
|
| 1211 |
+
|
| 1212 |
+
class DeepseekV2DecoderLayer(nn.Module):
|
| 1213 |
+
def __init__(self, config: DeepseekV2Config, layer_idx: int):
|
| 1214 |
+
super().__init__()
|
| 1215 |
+
self.hidden_size = config.hidden_size
|
| 1216 |
+
|
| 1217 |
+
self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
|
| 1218 |
+
config=config, layer_idx=layer_idx
|
| 1219 |
+
)
|
| 1220 |
+
|
| 1221 |
+
self.mlp = (
|
| 1222 |
+
DeepseekV2MoE(config, layer_idx)
|
| 1223 |
+
if (
|
| 1224 |
+
config.n_routed_experts is not None
|
| 1225 |
+
and layer_idx >= config.first_k_dense_replace
|
| 1226 |
+
and layer_idx % config.moe_layer_freq == 0
|
| 1227 |
+
)
|
| 1228 |
+
else DeepseekV2MLP(config)
|
| 1229 |
+
)
|
| 1230 |
+
self.input_layernorm = DeepseekV2RMSNorm(
|
| 1231 |
+
config.hidden_size, eps=config.rms_norm_eps
|
| 1232 |
+
)
|
| 1233 |
+
self.post_attention_layernorm = DeepseekV2RMSNorm(
|
| 1234 |
+
config.hidden_size, eps=config.rms_norm_eps
|
| 1235 |
+
)
|
| 1236 |
+
|
| 1237 |
+
def forward(
|
| 1238 |
+
self,
|
| 1239 |
+
hidden_states: torch.Tensor,
|
| 1240 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1241 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1242 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 1243 |
+
output_attentions: Optional[bool] = False,
|
| 1244 |
+
use_cache: Optional[bool] = False,
|
| 1245 |
+
**kwargs,
|
| 1246 |
+
) -> Tuple[
|
| 1247 |
+
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
|
| 1248 |
+
]:
|
| 1249 |
+
"""
|
| 1250 |
+
Args:
|
| 1251 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
| 1252 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
| 1253 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
| 1254 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
| 1255 |
+
output_attentions (`bool`, *optional*):
|
| 1256 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
| 1257 |
+
returned tensors for more detail.
|
| 1258 |
+
use_cache (`bool`, *optional*):
|
| 1259 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
| 1260 |
+
(see `past_key_values`).
|
| 1261 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
| 1262 |
+
"""
|
| 1263 |
+
if "padding_mask" in kwargs:
|
| 1264 |
+
warnings.warn(
|
| 1265 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
| 1266 |
+
)
|
| 1267 |
+
residual = hidden_states
|
| 1268 |
+
|
| 1269 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 1270 |
+
|
| 1271 |
+
# Self Attention
|
| 1272 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
| 1273 |
+
hidden_states=hidden_states,
|
| 1274 |
+
attention_mask=attention_mask,
|
| 1275 |
+
position_ids=position_ids,
|
| 1276 |
+
past_key_value=past_key_value,
|
| 1277 |
+
output_attentions=output_attentions,
|
| 1278 |
+
use_cache=use_cache,
|
| 1279 |
+
**kwargs,
|
| 1280 |
+
)
|
| 1281 |
+
hidden_states = residual + hidden_states
|
| 1282 |
+
|
| 1283 |
+
# Fully Connected
|
| 1284 |
+
residual = hidden_states
|
| 1285 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
| 1286 |
+
hidden_states = self.mlp(hidden_states)
|
| 1287 |
+
hidden_states = residual + hidden_states
|
| 1288 |
+
|
| 1289 |
+
outputs = (hidden_states,)
|
| 1290 |
+
|
| 1291 |
+
if output_attentions:
|
| 1292 |
+
outputs += (self_attn_weights,)
|
| 1293 |
+
|
| 1294 |
+
if use_cache:
|
| 1295 |
+
outputs += (present_key_value,)
|
| 1296 |
+
|
| 1297 |
+
return outputs
|
| 1298 |
+
|
| 1299 |
+
|
| 1300 |
+
DeepseekV2_START_DOCSTRING = r"""
|
| 1301 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
| 1302 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
| 1303 |
+
etc.)
|
| 1304 |
+
|
| 1305 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
| 1306 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
| 1307 |
+
and behavior.
|
| 1308 |
+
|
| 1309 |
+
Parameters:
|
| 1310 |
+
config ([`DeepseekV2Config`]):
|
| 1311 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
| 1312 |
+
load the weights associated with the model, only the configuration. Check out the
|
| 1313 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
| 1314 |
+
"""
|
| 1315 |
+
|
| 1316 |
+
|
| 1317 |
+
@add_start_docstrings(
|
| 1318 |
+
"The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
|
| 1319 |
+
DeepseekV2_START_DOCSTRING,
|
| 1320 |
+
)
|
| 1321 |
+
class DeepseekV2PreTrainedModel(PreTrainedModel):
|
| 1322 |
+
config_class = DeepseekV2Config
|
| 1323 |
+
base_model_prefix = "model"
|
| 1324 |
+
supports_gradient_checkpointing = True
|
| 1325 |
+
_no_split_modules = ["DeepseekV2DecoderLayer"]
|
| 1326 |
+
_skip_keys_device_placement = "past_key_values"
|
| 1327 |
+
_supports_flash_attn_2 = True
|
| 1328 |
+
_supports_cache_class = True
|
| 1329 |
+
|
| 1330 |
+
def _init_weights(self, module):
|
| 1331 |
+
std = self.config.initializer_range
|
| 1332 |
+
if isinstance(module, nn.Linear):
|
| 1333 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 1334 |
+
if module.bias is not None:
|
| 1335 |
+
module.bias.data.zero_()
|
| 1336 |
+
elif isinstance(module, nn.Embedding):
|
| 1337 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 1338 |
+
if module.padding_idx is not None:
|
| 1339 |
+
module.weight.data[module.padding_idx].zero_()
|
| 1340 |
+
|
| 1341 |
+
|
| 1342 |
+
DeepseekV2_INPUTS_DOCSTRING = r"""
|
| 1343 |
+
Args:
|
| 1344 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
| 1345 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
| 1346 |
+
it.
|
| 1347 |
+
|
| 1348 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 1349 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 1350 |
+
|
| 1351 |
+
[What are input IDs?](../glossary#input-ids)
|
| 1352 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 1353 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
| 1354 |
+
|
| 1355 |
+
- 1 for tokens that are **not masked**,
|
| 1356 |
+
- 0 for tokens that are **masked**.
|
| 1357 |
+
|
| 1358 |
+
[What are attention masks?](../glossary#attention-mask)
|
| 1359 |
+
|
| 1360 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 1361 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 1362 |
+
|
| 1363 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
| 1364 |
+
`past_key_values`).
|
| 1365 |
+
|
| 1366 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
| 1367 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
| 1368 |
+
information on the default strategy.
|
| 1369 |
+
|
| 1370 |
+
- 1 indicates the head is **not masked**,
|
| 1371 |
+
- 0 indicates the head is **masked**.
|
| 1372 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 1373 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
| 1374 |
+
config.n_positions - 1]`.
|
| 1375 |
+
|
| 1376 |
+
[What are position IDs?](../glossary#position-ids)
|
| 1377 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
| 1378 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
| 1379 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
| 1380 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
| 1381 |
+
|
| 1382 |
+
Two formats are allowed:
|
| 1383 |
+
- a [`~cache_utils.Cache`] instance;
|
| 1384 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
| 1385 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
| 1386 |
+
cache format.
|
| 1387 |
+
|
| 1388 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
| 1389 |
+
legacy cache format will be returned.
|
| 1390 |
+
|
| 1391 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
| 1392 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
| 1393 |
+
of shape `(batch_size, sequence_length)`.
|
| 1394 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
| 1395 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
| 1396 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
| 1397 |
+
model's internal embedding lookup matrix.
|
| 1398 |
+
use_cache (`bool`, *optional*):
|
| 1399 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
| 1400 |
+
`past_key_values`).
|
| 1401 |
+
output_attentions (`bool`, *optional*):
|
| 1402 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
| 1403 |
+
tensors for more detail.
|
| 1404 |
+
output_hidden_states (`bool`, *optional*):
|
| 1405 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
| 1406 |
+
more detail.
|
| 1407 |
+
return_dict (`bool`, *optional*):
|
| 1408 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
| 1409 |
+
"""
|
| 1410 |
+
|
| 1411 |
+
|
| 1412 |
+
@add_start_docstrings(
|
| 1413 |
+
"The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
|
| 1414 |
+
DeepseekV2_START_DOCSTRING,
|
| 1415 |
+
)
|
| 1416 |
+
class DeepseekV2Model(DeepseekV2PreTrainedModel):
|
| 1417 |
+
"""
|
| 1418 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
|
| 1419 |
+
|
| 1420 |
+
Args:
|
| 1421 |
+
config: DeepseekV2Config
|
| 1422 |
+
"""
|
| 1423 |
+
|
| 1424 |
+
def __init__(self, config: DeepseekV2Config):
|
| 1425 |
+
super().__init__(config)
|
| 1426 |
+
self.padding_idx = config.pad_token_id
|
| 1427 |
+
self.vocab_size = config.vocab_size
|
| 1428 |
+
|
| 1429 |
+
self.embed_tokens = nn.Embedding(
|
| 1430 |
+
config.vocab_size, config.hidden_size, self.padding_idx
|
| 1431 |
+
)
|
| 1432 |
+
self.layers = nn.ModuleList(
|
| 1433 |
+
[
|
| 1434 |
+
DeepseekV2DecoderLayer(config, layer_idx)
|
| 1435 |
+
for layer_idx in range(config.num_hidden_layers)
|
| 1436 |
+
]
|
| 1437 |
+
)
|
| 1438 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
| 1439 |
+
self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 1440 |
+
|
| 1441 |
+
self.gradient_checkpointing = False
|
| 1442 |
+
# Initialize weights and apply final processing
|
| 1443 |
+
self.post_init()
|
| 1444 |
+
|
| 1445 |
+
def get_input_embeddings(self):
|
| 1446 |
+
return self.embed_tokens
|
| 1447 |
+
|
| 1448 |
+
def set_input_embeddings(self, value):
|
| 1449 |
+
self.embed_tokens = value
|
| 1450 |
+
|
| 1451 |
+
@add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
|
| 1452 |
+
def forward(
|
| 1453 |
+
self,
|
| 1454 |
+
input_ids: torch.LongTensor = None,
|
| 1455 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1456 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1457 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1458 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1459 |
+
use_cache: Optional[bool] = None,
|
| 1460 |
+
output_attentions: Optional[bool] = None,
|
| 1461 |
+
output_hidden_states: Optional[bool] = None,
|
| 1462 |
+
return_dict: Optional[bool] = None,
|
| 1463 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
| 1464 |
+
output_attentions = (
|
| 1465 |
+
output_attentions
|
| 1466 |
+
if output_attentions is not None
|
| 1467 |
+
else self.config.output_attentions
|
| 1468 |
+
)
|
| 1469 |
+
output_hidden_states = (
|
| 1470 |
+
output_hidden_states
|
| 1471 |
+
if output_hidden_states is not None
|
| 1472 |
+
else self.config.output_hidden_states
|
| 1473 |
+
)
|
| 1474 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 1475 |
+
|
| 1476 |
+
return_dict = (
|
| 1477 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 1478 |
+
)
|
| 1479 |
+
|
| 1480 |
+
# retrieve input_ids and inputs_embeds
|
| 1481 |
+
if input_ids is not None and inputs_embeds is not None:
|
| 1482 |
+
raise ValueError(
|
| 1483 |
+
"You cannot specify both input_ids and inputs_embeds at the same time"
|
| 1484 |
+
)
|
| 1485 |
+
elif input_ids is not None:
|
| 1486 |
+
batch_size, seq_length = input_ids.shape[:2]
|
| 1487 |
+
elif inputs_embeds is not None:
|
| 1488 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
| 1489 |
+
else:
|
| 1490 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
| 1491 |
+
|
| 1492 |
+
if self.gradient_checkpointing and self.training:
|
| 1493 |
+
if use_cache:
|
| 1494 |
+
logger.warning_once(
|
| 1495 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
|
| 1496 |
+
)
|
| 1497 |
+
use_cache = False
|
| 1498 |
+
|
| 1499 |
+
past_key_values_length = 0
|
| 1500 |
+
if use_cache:
|
| 1501 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
| 1502 |
+
if use_legacy_cache:
|
| 1503 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
| 1504 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
| 1505 |
+
|
| 1506 |
+
if position_ids is None:
|
| 1507 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
| 1508 |
+
position_ids = torch.arange(
|
| 1509 |
+
past_key_values_length,
|
| 1510 |
+
seq_length + past_key_values_length,
|
| 1511 |
+
dtype=torch.long,
|
| 1512 |
+
device=device,
|
| 1513 |
+
)
|
| 1514 |
+
position_ids = position_ids.unsqueeze(0)
|
| 1515 |
+
|
| 1516 |
+
if inputs_embeds is None:
|
| 1517 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 1518 |
+
|
| 1519 |
+
if self._use_flash_attention_2:
|
| 1520 |
+
# 2d mask is passed through the layers
|
| 1521 |
+
attention_mask = (
|
| 1522 |
+
attention_mask
|
| 1523 |
+
if (attention_mask is not None and 0 in attention_mask)
|
| 1524 |
+
else None
|
| 1525 |
+
)
|
| 1526 |
+
else:
|
| 1527 |
+
# 4d mask is passed through the layers
|
| 1528 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
| 1529 |
+
attention_mask,
|
| 1530 |
+
(batch_size, seq_length),
|
| 1531 |
+
inputs_embeds,
|
| 1532 |
+
past_key_values_length,
|
| 1533 |
+
)
|
| 1534 |
+
|
| 1535 |
+
# embed positions
|
| 1536 |
+
hidden_states = inputs_embeds
|
| 1537 |
+
|
| 1538 |
+
# decoder layers
|
| 1539 |
+
all_hidden_states = () if output_hidden_states else None
|
| 1540 |
+
all_self_attns = () if output_attentions else None
|
| 1541 |
+
next_decoder_cache = None
|
| 1542 |
+
|
| 1543 |
+
for decoder_layer in self.layers:
|
| 1544 |
+
if output_hidden_states:
|
| 1545 |
+
all_hidden_states += (hidden_states,)
|
| 1546 |
+
|
| 1547 |
+
if self.gradient_checkpointing and self.training:
|
| 1548 |
+
layer_outputs = self._gradient_checkpointing_func(
|
| 1549 |
+
decoder_layer.__call__,
|
| 1550 |
+
hidden_states,
|
| 1551 |
+
attention_mask,
|
| 1552 |
+
position_ids,
|
| 1553 |
+
past_key_values,
|
| 1554 |
+
output_attentions,
|
| 1555 |
+
use_cache,
|
| 1556 |
+
)
|
| 1557 |
+
else:
|
| 1558 |
+
layer_outputs = decoder_layer(
|
| 1559 |
+
hidden_states,
|
| 1560 |
+
attention_mask=attention_mask,
|
| 1561 |
+
position_ids=position_ids,
|
| 1562 |
+
past_key_value=past_key_values,
|
| 1563 |
+
output_attentions=output_attentions,
|
| 1564 |
+
use_cache=use_cache,
|
| 1565 |
+
)
|
| 1566 |
+
|
| 1567 |
+
hidden_states = layer_outputs[0]
|
| 1568 |
+
|
| 1569 |
+
if use_cache:
|
| 1570 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
| 1571 |
+
|
| 1572 |
+
if output_attentions:
|
| 1573 |
+
all_self_attns += (layer_outputs[1],)
|
| 1574 |
+
|
| 1575 |
+
hidden_states = self.norm(hidden_states)
|
| 1576 |
+
|
| 1577 |
+
# add hidden states from the last decoder layer
|
| 1578 |
+
if output_hidden_states:
|
| 1579 |
+
all_hidden_states += (hidden_states,)
|
| 1580 |
+
|
| 1581 |
+
next_cache = None
|
| 1582 |
+
if use_cache:
|
| 1583 |
+
next_cache = (
|
| 1584 |
+
next_decoder_cache.to_legacy_cache()
|
| 1585 |
+
if use_legacy_cache
|
| 1586 |
+
else next_decoder_cache
|
| 1587 |
+
)
|
| 1588 |
+
if not return_dict:
|
| 1589 |
+
return tuple(
|
| 1590 |
+
v
|
| 1591 |
+
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
|
| 1592 |
+
if v is not None
|
| 1593 |
+
)
|
| 1594 |
+
return BaseModelOutputWithPast(
|
| 1595 |
+
last_hidden_state=hidden_states,
|
| 1596 |
+
past_key_values=next_cache,
|
| 1597 |
+
hidden_states=all_hidden_states,
|
| 1598 |
+
attentions=all_self_attns,
|
| 1599 |
+
)
|
| 1600 |
+
|
| 1601 |
+
|
| 1602 |
+
class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
|
| 1603 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 1604 |
+
|
| 1605 |
+
def __init__(self, config):
|
| 1606 |
+
super().__init__(config)
|
| 1607 |
+
self.model = DeepseekV2Model(config)
|
| 1608 |
+
self.vocab_size = config.vocab_size
|
| 1609 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 1610 |
+
|
| 1611 |
+
# Initialize weights and apply final processing
|
| 1612 |
+
self.post_init()
|
| 1613 |
+
|
| 1614 |
+
def get_input_embeddings(self):
|
| 1615 |
+
return self.model.embed_tokens
|
| 1616 |
+
|
| 1617 |
+
def set_input_embeddings(self, value):
|
| 1618 |
+
self.model.embed_tokens = value
|
| 1619 |
+
|
| 1620 |
+
def get_output_embeddings(self):
|
| 1621 |
+
return self.lm_head
|
| 1622 |
+
|
| 1623 |
+
def set_output_embeddings(self, new_embeddings):
|
| 1624 |
+
self.lm_head = new_embeddings
|
| 1625 |
+
|
| 1626 |
+
def set_decoder(self, decoder):
|
| 1627 |
+
self.model = decoder
|
| 1628 |
+
|
| 1629 |
+
def get_decoder(self):
|
| 1630 |
+
return self.model
|
| 1631 |
+
|
| 1632 |
+
@add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
|
| 1633 |
+
@replace_return_docstrings(
|
| 1634 |
+
output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
|
| 1635 |
+
)
|
| 1636 |
+
def forward(
|
| 1637 |
+
self,
|
| 1638 |
+
input_ids: torch.LongTensor = None,
|
| 1639 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1640 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1641 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1642 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1643 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1644 |
+
use_cache: Optional[bool] = None,
|
| 1645 |
+
output_attentions: Optional[bool] = None,
|
| 1646 |
+
output_hidden_states: Optional[bool] = None,
|
| 1647 |
+
return_dict: Optional[bool] = None,
|
| 1648 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 1649 |
+
r"""
|
| 1650 |
+
Args:
|
| 1651 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 1652 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
|
| 1653 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 1654 |
+
(masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
|
| 1655 |
+
|
| 1656 |
+
Returns:
|
| 1657 |
+
|
| 1658 |
+
Example:
|
| 1659 |
+
|
| 1660 |
+
```python
|
| 1661 |
+
>>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
|
| 1662 |
+
|
| 1663 |
+
>>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
| 1664 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
| 1665 |
+
|
| 1666 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
| 1667 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
| 1668 |
+
|
| 1669 |
+
>>> # Generate
|
| 1670 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
| 1671 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
| 1672 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
| 1673 |
+
```"""
|
| 1674 |
+
output_attentions = (
|
| 1675 |
+
output_attentions
|
| 1676 |
+
if output_attentions is not None
|
| 1677 |
+
else self.config.output_attentions
|
| 1678 |
+
)
|
| 1679 |
+
output_hidden_states = (
|
| 1680 |
+
output_hidden_states
|
| 1681 |
+
if output_hidden_states is not None
|
| 1682 |
+
else self.config.output_hidden_states
|
| 1683 |
+
)
|
| 1684 |
+
return_dict = (
|
| 1685 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 1686 |
+
)
|
| 1687 |
+
|
| 1688 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
| 1689 |
+
outputs = self.model(
|
| 1690 |
+
input_ids=input_ids,
|
| 1691 |
+
attention_mask=attention_mask,
|
| 1692 |
+
position_ids=position_ids,
|
| 1693 |
+
past_key_values=past_key_values,
|
| 1694 |
+
inputs_embeds=inputs_embeds,
|
| 1695 |
+
use_cache=use_cache,
|
| 1696 |
+
output_attentions=output_attentions,
|
| 1697 |
+
output_hidden_states=output_hidden_states,
|
| 1698 |
+
return_dict=return_dict,
|
| 1699 |
+
)
|
| 1700 |
+
|
| 1701 |
+
hidden_states = outputs[0]
|
| 1702 |
+
logits = self.lm_head(hidden_states)
|
| 1703 |
+
logits = logits.float()
|
| 1704 |
+
|
| 1705 |
+
loss = None
|
| 1706 |
+
if labels is not None:
|
| 1707 |
+
# Shift so that tokens < n predict n
|
| 1708 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
| 1709 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 1710 |
+
# Flatten the tokens
|
| 1711 |
+
loss_fct = CrossEntropyLoss()
|
| 1712 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
| 1713 |
+
shift_labels = shift_labels.view(-1)
|
| 1714 |
+
# Enable model parallelism
|
| 1715 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
| 1716 |
+
loss = loss_fct(shift_logits, shift_labels)
|
| 1717 |
+
|
| 1718 |
+
if not return_dict:
|
| 1719 |
+
output = (logits,) + outputs[1:]
|
| 1720 |
+
return (loss,) + output if loss is not None else output
|
| 1721 |
+
|
| 1722 |
+
return CausalLMOutputWithPast(
|
| 1723 |
+
loss=loss,
|
| 1724 |
+
logits=logits,
|
| 1725 |
+
past_key_values=outputs.past_key_values,
|
| 1726 |
+
hidden_states=outputs.hidden_states,
|
| 1727 |
+
attentions=outputs.attentions,
|
| 1728 |
+
)
|
| 1729 |
+
|
| 1730 |
+
def prepare_inputs_for_generation(
|
| 1731 |
+
self,
|
| 1732 |
+
input_ids,
|
| 1733 |
+
past_key_values=None,
|
| 1734 |
+
attention_mask=None,
|
| 1735 |
+
inputs_embeds=None,
|
| 1736 |
+
**kwargs,
|
| 1737 |
+
):
|
| 1738 |
+
if past_key_values is not None:
|
| 1739 |
+
if isinstance(past_key_values, Cache):
|
| 1740 |
+
cache_length = past_key_values.get_seq_length()
|
| 1741 |
+
past_length = past_key_values.seen_tokens
|
| 1742 |
+
max_cache_length = past_key_values.get_max_length()
|
| 1743 |
+
else:
|
| 1744 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
| 1745 |
+
max_cache_length = None
|
| 1746 |
+
|
| 1747 |
+
# Keep only the unprocessed tokens:
|
| 1748 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
| 1749 |
+
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
|
| 1750 |
+
# input)
|
| 1751 |
+
if (
|
| 1752 |
+
attention_mask is not None
|
| 1753 |
+
and attention_mask.shape[1] > input_ids.shape[1]
|
| 1754 |
+
):
|
| 1755 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
| 1756 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
| 1757 |
+
# input_ids based on the past_length.
|
| 1758 |
+
elif past_length < input_ids.shape[1]:
|
| 1759 |
+
input_ids = input_ids[:, past_length:]
|
| 1760 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
| 1761 |
+
|
| 1762 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
| 1763 |
+
if (
|
| 1764 |
+
max_cache_length is not None
|
| 1765 |
+
and attention_mask is not None
|
| 1766 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
| 1767 |
+
):
|
| 1768 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
| 1769 |
+
|
| 1770 |
+
position_ids = kwargs.get("position_ids", None)
|
| 1771 |
+
if attention_mask is not None and position_ids is None:
|
| 1772 |
+
# create position_ids on the fly for batch generation
|
| 1773 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 1774 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 1775 |
+
if past_key_values:
|
| 1776 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
| 1777 |
+
|
| 1778 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 1779 |
+
if inputs_embeds is not None and past_key_values is None:
|
| 1780 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
| 1781 |
+
else:
|
| 1782 |
+
model_inputs = {"input_ids": input_ids}
|
| 1783 |
+
|
| 1784 |
+
model_inputs.update(
|
| 1785 |
+
{
|
| 1786 |
+
"position_ids": position_ids,
|
| 1787 |
+
"past_key_values": past_key_values,
|
| 1788 |
+
"use_cache": kwargs.get("use_cache"),
|
| 1789 |
+
"attention_mask": attention_mask,
|
| 1790 |
+
}
|
| 1791 |
+
)
|
| 1792 |
+
return model_inputs
|
| 1793 |
+
|
| 1794 |
+
@staticmethod
|
| 1795 |
+
def _reorder_cache(past_key_values, beam_idx):
|
| 1796 |
+
reordered_past = ()
|
| 1797 |
+
for layer_past in past_key_values:
|
| 1798 |
+
reordered_past += (
|
| 1799 |
+
tuple(
|
| 1800 |
+
past_state.index_select(0, beam_idx.to(past_state.device))
|
| 1801 |
+
for past_state in layer_past
|
| 1802 |
+
),
|
| 1803 |
+
)
|
| 1804 |
+
return reordered_past
|
| 1805 |
+
|
| 1806 |
+
|
| 1807 |
+
@add_start_docstrings(
|
| 1808 |
+
"""
|
| 1809 |
+
The DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
|
| 1810 |
+
|
| 1811 |
+
[`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
| 1812 |
+
(e.g. GPT-2) do.
|
| 1813 |
+
|
| 1814 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
| 1815 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
| 1816 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
| 1817 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
| 1818 |
+
each row of the batch).
|
| 1819 |
+
""",
|
| 1820 |
+
DeepseekV2_START_DOCSTRING,
|
| 1821 |
+
)
|
| 1822 |
+
class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
|
| 1823 |
+
def __init__(self, config):
|
| 1824 |
+
super().__init__(config)
|
| 1825 |
+
self.num_labels = config.num_labels
|
| 1826 |
+
self.model = DeepseekV2Model(config)
|
| 1827 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
| 1828 |
+
|
| 1829 |
+
# Initialize weights and apply final processing
|
| 1830 |
+
self.post_init()
|
| 1831 |
+
|
| 1832 |
+
def get_input_embeddings(self):
|
| 1833 |
+
return self.model.embed_tokens
|
| 1834 |
+
|
| 1835 |
+
def set_input_embeddings(self, value):
|
| 1836 |
+
self.model.embed_tokens = value
|
| 1837 |
+
|
| 1838 |
+
@add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
|
| 1839 |
+
def forward(
|
| 1840 |
+
self,
|
| 1841 |
+
input_ids: torch.LongTensor = None,
|
| 1842 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1843 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1844 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1845 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1846 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1847 |
+
use_cache: Optional[bool] = None,
|
| 1848 |
+
output_attentions: Optional[bool] = None,
|
| 1849 |
+
output_hidden_states: Optional[bool] = None,
|
| 1850 |
+
return_dict: Optional[bool] = None,
|
| 1851 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
| 1852 |
+
r"""
|
| 1853 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1854 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
|
| 1855 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1856 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1857 |
+
"""
|
| 1858 |
+
return_dict = (
|
| 1859 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 1860 |
+
)
|
| 1861 |
+
|
| 1862 |
+
transformer_outputs = self.model(
|
| 1863 |
+
input_ids,
|
| 1864 |
+
attention_mask=attention_mask,
|
| 1865 |
+
position_ids=position_ids,
|
| 1866 |
+
past_key_values=past_key_values,
|
| 1867 |
+
inputs_embeds=inputs_embeds,
|
| 1868 |
+
use_cache=use_cache,
|
| 1869 |
+
output_attentions=output_attentions,
|
| 1870 |
+
output_hidden_states=output_hidden_states,
|
| 1871 |
+
return_dict=return_dict,
|
| 1872 |
+
)
|
| 1873 |
+
hidden_states = transformer_outputs[0]
|
| 1874 |
+
logits = self.score(hidden_states)
|
| 1875 |
+
|
| 1876 |
+
if input_ids is not None:
|
| 1877 |
+
batch_size = input_ids.shape[0]
|
| 1878 |
+
else:
|
| 1879 |
+
batch_size = inputs_embeds.shape[0]
|
| 1880 |
+
|
| 1881 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
| 1882 |
+
raise ValueError(
|
| 1883 |
+
"Cannot handle batch sizes > 1 if no padding token is defined."
|
| 1884 |
+
)
|
| 1885 |
+
if self.config.pad_token_id is None:
|
| 1886 |
+
sequence_lengths = -1
|
| 1887 |
+
else:
|
| 1888 |
+
if input_ids is not None:
|
| 1889 |
+
sequence_lengths = (
|
| 1890 |
+
torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
| 1891 |
+
).to(logits.device)
|
| 1892 |
+
else:
|
| 1893 |
+
sequence_lengths = -1
|
| 1894 |
+
|
| 1895 |
+
pooled_logits = logits[
|
| 1896 |
+
torch.arange(batch_size, device=logits.device), sequence_lengths
|
| 1897 |
+
]
|
| 1898 |
+
|
| 1899 |
+
loss = None
|
| 1900 |
+
if labels is not None:
|
| 1901 |
+
labels = labels.to(logits.device)
|
| 1902 |
+
if self.config.problem_type is None:
|
| 1903 |
+
if self.num_labels == 1:
|
| 1904 |
+
self.config.problem_type = "regression"
|
| 1905 |
+
elif self.num_labels > 1 and (
|
| 1906 |
+
labels.dtype == torch.long or labels.dtype == torch.int
|
| 1907 |
+
):
|
| 1908 |
+
self.config.problem_type = "single_label_classification"
|
| 1909 |
+
else:
|
| 1910 |
+
self.config.problem_type = "multi_label_classification"
|
| 1911 |
+
|
| 1912 |
+
if self.config.problem_type == "regression":
|
| 1913 |
+
loss_fct = MSELoss()
|
| 1914 |
+
if self.num_labels == 1:
|
| 1915 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
| 1916 |
+
else:
|
| 1917 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1918 |
+
elif self.config.problem_type == "single_label_classification":
|
| 1919 |
+
loss_fct = CrossEntropyLoss()
|
| 1920 |
+
loss = loss_fct(
|
| 1921 |
+
pooled_logits.view(-1, self.num_labels), labels.view(-1)
|
| 1922 |
+
)
|
| 1923 |
+
elif self.config.problem_type == "multi_label_classification":
|
| 1924 |
+
loss_fct = BCEWithLogitsLoss()
|
| 1925 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1926 |
+
if not return_dict:
|
| 1927 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
| 1928 |
+
return ((loss,) + output) if loss is not None else output
|
| 1929 |
+
|
| 1930 |
+
return SequenceClassifierOutputWithPast(
|
| 1931 |
+
loss=loss,
|
| 1932 |
+
logits=pooled_logits,
|
| 1933 |
+
past_key_values=transformer_outputs.past_key_values,
|
| 1934 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 1935 |
+
attentions=transformer_outputs.attentions,
|
| 1936 |
+
)
|
tokenization_deepseek.py
ADDED
|
@@ -0,0 +1,328 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
|
| 2 |
+
#
|
| 3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
+
# you may not use this file except in compliance with the License.
|
| 5 |
+
# You may obtain a copy of the License at
|
| 6 |
+
#
|
| 7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
+
#
|
| 9 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
+
# See the License for the specific language governing permissions and
|
| 13 |
+
# limitations under the License.
|
| 14 |
+
|
| 15 |
+
"""
|
| 16 |
+
Forked from the file src/transformers/models/bert_generation/tokenization_bert_generation.py from the HuggingFace Transformers library.
|
| 17 |
+
Permalink: https://github.com/huggingface/transformers/blob/04ab5605fbb4ef207b10bf2772d88c53fc242e83/src/transformers/models/bert_generation/tokenization_bert_generation.py
|
| 18 |
+
Tokenizer class for ReplitLM
|
| 19 |
+
Class is modified for compatibility with custom vocabulary and to achieve desired encode/decode behavior for Replit Code V1 3B model.
|
| 20 |
+
"""
|
| 21 |
+
import os
|
| 22 |
+
import sentencepiece as spm
|
| 23 |
+
from sentencepiece import SentencePieceProcessor
|
| 24 |
+
from shutil import copyfile
|
| 25 |
+
from transformers import PreTrainedTokenizer
|
| 26 |
+
from typing import Any, Dict, List, Optional, Tuple
|
| 27 |
+
import base64
|
| 28 |
+
|
| 29 |
+
VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'}
|
| 30 |
+
|
| 31 |
+
class Tokenizer:
|
| 32 |
+
def __init__(self, model_path="/weka-jd/prod/deepseek/permanent/shared/mingchuan/llama_data/tokenizer.model"):
|
| 33 |
+
# reload tokenizer
|
| 34 |
+
assert os.path.isfile(model_path), model_path
|
| 35 |
+
self.sp_model = SentencePieceProcessor(model_file=model_path)
|
| 36 |
+
|
| 37 |
+
# # ? print spm for debugging
|
| 38 |
+
# spm_proto = sp_pb2_model.ModelProto()
|
| 39 |
+
# spm_proto.ParseFromString(self.sp_model.serialized_model_proto())
|
| 40 |
+
# print(dir(spm_proto))
|
| 41 |
+
# attrs = ['denormalizer_spec', 'normalizer_spec', 'trainer_spec']
|
| 42 |
+
# print('=======' * 5)
|
| 43 |
+
# for attr in attrs:
|
| 44 |
+
# print('=======', attr, '=======')
|
| 45 |
+
# print(getattr(spm_proto, attr))
|
| 46 |
+
|
| 47 |
+
# BOS / EOS token IDs
|
| 48 |
+
self.n_words: int = self.sp_model.vocab_size()
|
| 49 |
+
self.bos_id: int = self.sp_model.bos_id()
|
| 50 |
+
self.eos_id: int = self.sp_model.eos_id()
|
| 51 |
+
self.pad_id: int = self.sp_model.pad_id()
|
| 52 |
+
assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
|
| 53 |
+
|
| 54 |
+
def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
|
| 55 |
+
assert type(s) is str
|
| 56 |
+
t = self.sp_model.encode(s)
|
| 57 |
+
if bos:
|
| 58 |
+
t = [self.bos_id] + t
|
| 59 |
+
if eos:
|
| 60 |
+
t = t + [self.eos_id]
|
| 61 |
+
return t
|
| 62 |
+
|
| 63 |
+
def decode(self, t: List[int]) -> str:
|
| 64 |
+
return self.sp_model.decode(t)
|
| 65 |
+
|
| 66 |
+
class LineBBPETokenizer(Tokenizer):
|
| 67 |
+
def __init__(self,
|
| 68 |
+
model_path="/3fs-jd/prod/deepseek/shared/daidamai/data/bbpe/spm_0717_final/100000/bbpe_full_bytes.model",
|
| 69 |
+
ignore_decode_err=False, attachfile_path=None):
|
| 70 |
+
super().__init__(model_path=model_path)
|
| 71 |
+
self.ignore_decode_err = ignore_decode_err
|
| 72 |
+
Bvocab_path = attachfile_path + "/byteVocab.txt"
|
| 73 |
+
#'/3fs-jd/prod/deepseek/shared/daidamai/data/bbpe/byteVocab.txt'
|
| 74 |
+
punct_path = attachfile_path + "/all_punct.txt"
|
| 75 |
+
#punct_path = '/3fs-jd/prod/deepseek/shared/daidamai/data/bbpe/all_punct.txt'
|
| 76 |
+
Bvocab = open(Bvocab_path, 'r', encoding = 'utf-8')
|
| 77 |
+
self.punct = []
|
| 78 |
+
with open(punct_path, 'r', encoding='utf-8') as f:
|
| 79 |
+
lines = f.readlines()
|
| 80 |
+
for line in lines:
|
| 81 |
+
line = line.strip()
|
| 82 |
+
if line:
|
| 83 |
+
self.punct.append(line)
|
| 84 |
+
|
| 85 |
+
self.numchars = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
|
| 86 |
+
self.white_space = [' ']
|
| 87 |
+
self.special_chars = set(self.numchars) | set(self.punct) | set(self.white_space)
|
| 88 |
+
|
| 89 |
+
# ! remove chars that will be encoded to 0 (unk_id)
|
| 90 |
+
unk_ch = set()
|
| 91 |
+
for ch in self.special_chars:
|
| 92 |
+
ids = self.sp_model.encode(ch)
|
| 93 |
+
if 0 in ids:
|
| 94 |
+
unk_ch.update(ch)
|
| 95 |
+
self.special_chars = self.special_chars - unk_ch
|
| 96 |
+
|
| 97 |
+
self.byte2ch = [-1] * 256
|
| 98 |
+
self.ch2byte = {}
|
| 99 |
+
for line in list(Bvocab.readlines())[:256]:
|
| 100 |
+
tokens = line.strip().split('\t')
|
| 101 |
+
self.byte2ch[int(tokens[0])] = tokens[1]
|
| 102 |
+
self.ch2byte[tokens[1]] = int(tokens[0])
|
| 103 |
+
self.b16_dec = {}
|
| 104 |
+
self.b16_enc = ['x'] * 16
|
| 105 |
+
for i in range(10):
|
| 106 |
+
self.b16_dec[str(i)] = i
|
| 107 |
+
self.b16_enc[i] = str(i)
|
| 108 |
+
self.b16_dec['A'] = 10
|
| 109 |
+
self.b16_dec['B'] = 11
|
| 110 |
+
self.b16_dec['C'] = 12
|
| 111 |
+
self.b16_dec['D'] = 13
|
| 112 |
+
self.b16_dec['E'] = 14
|
| 113 |
+
self.b16_dec['F'] = 15
|
| 114 |
+
self.b16_enc[10] = 'A'
|
| 115 |
+
self.b16_enc[11] = 'B'
|
| 116 |
+
self.b16_enc[12] = 'C'
|
| 117 |
+
self.b16_enc[13] = 'D'
|
| 118 |
+
self.b16_enc[14] = 'E'
|
| 119 |
+
self.b16_enc[15] = 'F'
|
| 120 |
+
|
| 121 |
+
self.new_line_id = self.sp_model.encode(self.mapping_raw_to_256ch('\n'))[-1]
|
| 122 |
+
|
| 123 |
+
def base16encode(self, n):
|
| 124 |
+
return self.b16_enc[n // 16] + self.b16_enc[n % 16]
|
| 125 |
+
|
| 126 |
+
def base16decode(self, s):
|
| 127 |
+
return self.b16_dec[s[0]] * 16 + self.b16_dec[s[1]]
|
| 128 |
+
|
| 129 |
+
def mapping_raw_to_256ch(self, s: str) -> str:
|
| 130 |
+
mapped_s = []
|
| 131 |
+
for token in s:
|
| 132 |
+
if token in self.special_chars:
|
| 133 |
+
mapped_s.append(token)
|
| 134 |
+
continue
|
| 135 |
+
tk = str(base64.b16encode(token.encode("utf-8")))[2:-1]
|
| 136 |
+
num = len(tk) // 2
|
| 137 |
+
for i in range(num):
|
| 138 |
+
mapped_s.append(self.byte2ch[(self.base16decode(tk[2*i:2*i+2]))])
|
| 139 |
+
return ''.join(mapped_s)
|
| 140 |
+
|
| 141 |
+
def mapping_256ch_to_raw(self, s: str) -> str:
|
| 142 |
+
mapped_s = ''
|
| 143 |
+
for token in s:
|
| 144 |
+
if token in self.ch2byte:
|
| 145 |
+
mapped_s += self.base16encode(self.ch2byte[token])
|
| 146 |
+
else:
|
| 147 |
+
mapped_s += str(base64.b16encode(token.encode("utf-8")))[2:-1]
|
| 148 |
+
# decode utf-8 string to text string
|
| 149 |
+
byte_s = bytes.fromhex(mapped_s)
|
| 150 |
+
if self.ignore_decode_err:
|
| 151 |
+
try:
|
| 152 |
+
mapped_s = byte_s.decode('utf-8')
|
| 153 |
+
except UnicodeDecodeError:
|
| 154 |
+
mapped_s = ''
|
| 155 |
+
else:
|
| 156 |
+
mapped_s = byte_s.decode('utf-8')
|
| 157 |
+
return mapped_s
|
| 158 |
+
|
| 159 |
+
def encode_line(self, s):
|
| 160 |
+
if s == '\n':
|
| 161 |
+
return [self.new_line_id]
|
| 162 |
+
ss = self.mapping_raw_to_256ch(s)
|
| 163 |
+
t = self.sp_model.encode(ss)
|
| 164 |
+
return t
|
| 165 |
+
|
| 166 |
+
def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
|
| 167 |
+
assert type(s) is str
|
| 168 |
+
t = []
|
| 169 |
+
lines = s.split('\n')
|
| 170 |
+
n_lines = len(lines)
|
| 171 |
+
for i in range(n_lines):
|
| 172 |
+
if i != n_lines - 1:
|
| 173 |
+
line = lines[i] + '\n'
|
| 174 |
+
else:
|
| 175 |
+
line = lines[i]
|
| 176 |
+
tt = self.encode_line(line)
|
| 177 |
+
t += tt
|
| 178 |
+
if bos:
|
| 179 |
+
t = [self.bos_id] + t
|
| 180 |
+
if eos:
|
| 181 |
+
t = t + [self.eos_id]
|
| 182 |
+
return t
|
| 183 |
+
|
| 184 |
+
def get_restored_white_space(self, t):
|
| 185 |
+
t = t[:3]
|
| 186 |
+
if t[0] == self.bos_id:
|
| 187 |
+
t = t[1:]
|
| 188 |
+
decoded = self.sp_model.decode(t)
|
| 189 |
+
encoded = self.sp_model.encode(decoded)
|
| 190 |
+
if len(encoded) < len(t):
|
| 191 |
+
return ' '
|
| 192 |
+
else:
|
| 193 |
+
return ''
|
| 194 |
+
|
| 195 |
+
def decode_line(self, t):
|
| 196 |
+
if len(t) == 1 and t[0] == self.new_line_id:
|
| 197 |
+
return '\n'
|
| 198 |
+
# ? special bug fixing for a single whitespace in the line beginning, sentencepiece will consume it, we restore it
|
| 199 |
+
restored_white_space = self.get_restored_white_space(t)
|
| 200 |
+
ss = self.sp_model.decode(t)
|
| 201 |
+
s = restored_white_space + self.mapping_256ch_to_raw(ss)
|
| 202 |
+
return s
|
| 203 |
+
|
| 204 |
+
def decode(self, t: List[int]) -> str:
|
| 205 |
+
s = ''
|
| 206 |
+
new_line_indices = [index for index, value in enumerate(t) if value == self.new_line_id]
|
| 207 |
+
last_idx = 0
|
| 208 |
+
for i in range(len(new_line_indices)):
|
| 209 |
+
line_id = t[last_idx:new_line_indices[i] + 1]
|
| 210 |
+
ss = self.decode_line(line_id)
|
| 211 |
+
s += ss
|
| 212 |
+
last_idx = new_line_indices[i] + 1
|
| 213 |
+
if last_idx < len(t):
|
| 214 |
+
line_id = t[last_idx:]
|
| 215 |
+
ss = self.decode_line(line_id)
|
| 216 |
+
s += ss
|
| 217 |
+
return s
|
| 218 |
+
|
| 219 |
+
def add_special(self, special_tokens):
|
| 220 |
+
'''
|
| 221 |
+
add special tokens to the tokenizer
|
| 222 |
+
'''
|
| 223 |
+
spm_proto = sp_pb2_model.ModelProto()
|
| 224 |
+
spm_proto.ParseFromString(self.sp_model.serialized_model_proto())
|
| 225 |
+
for special_token in special_tokens:
|
| 226 |
+
new_p = sp_pb2_model.ModelProto().SentencePiece()
|
| 227 |
+
new_p.piece = self.mapping_raw_to_256ch(special_token)
|
| 228 |
+
new_p.score = 0.0
|
| 229 |
+
new_p.type = 4
|
| 230 |
+
spm_proto.pieces.append(new_p)
|
| 231 |
+
print(f'special token added: {special_token}')
|
| 232 |
+
self.sp_model.LoadFromSerializedProto(spm_proto.SerializeToString())
|
| 233 |
+
|
| 234 |
+
class DeepSeekTokenizer(PreTrainedTokenizer):
|
| 235 |
+
"""
|
| 236 |
+
Construct a ReplitLMTokenizer tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
|
| 237 |
+
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods.
|
| 238 |
+
Args:
|
| 239 |
+
vocab_file (`str`):
|
| 240 |
+
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
|
| 241 |
+
contains the vocabulary necessary to instantiate a tokenizer.
|
| 242 |
+
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
|
| 243 |
+
The end of sequence token.
|
| 244 |
+
bos_token (`str`, *optional*, defaults to `None`):
|
| 245 |
+
The begin of sequence token.
|
| 246 |
+
unk_token (`str`, *optional*, defaults to `"<|unk|>"`):
|
| 247 |
+
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
| 248 |
+
token instead.
|
| 249 |
+
pad_token (`str`, *optional*, defaults to `"<|pad|>"`):
|
| 250 |
+
The token used for padding, for example when batching sequences of different lengths.
|
| 251 |
+
sp_model_kwargs (`dict`, *optional*):
|
| 252 |
+
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
|
| 253 |
+
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
|
| 254 |
+
to set:
|
| 255 |
+
- `enable_sampling`: Enable subword regularization.
|
| 256 |
+
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
|
| 257 |
+
- `nbest_size = {0,1}`: No sampling is performed.
|
| 258 |
+
- `nbest_size > 1`: samples from the nbest_size results.
|
| 259 |
+
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
|
| 260 |
+
using forward-filtering-and-backward-sampling algorithm.
|
| 261 |
+
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
|
| 262 |
+
BPE-dropout.
|
| 263 |
+
"""
|
| 264 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
| 265 |
+
prefix_tokens: List[int] = []
|
| 266 |
+
model_input_names = ['input_ids', 'attention_mask']
|
| 267 |
+
|
| 268 |
+
def __init__(self, vocab_file, bos_token="<s>", eos_token='</s>', unk_token=None, pad_token=None, sep_token='</s>', sp_model_kwargs: Optional[Dict[str, Any]]=None, name_or_path=None, **kwargs) -> None:
|
| 269 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
| 270 |
+
super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
|
| 271 |
+
#obtain the current directory of py
|
| 272 |
+
vocab_path = name_or_path
|
| 273 |
+
print("vocab_path: ", vocab_path)
|
| 274 |
+
self.vocab_path = vocab_path
|
| 275 |
+
self.vocab_file = vocab_path + '/tokenizer.model'
|
| 276 |
+
self.token = LineBBPETokenizer(model_path=self.vocab_file, attachfile_path=vocab_path, ignore_decode_err=True)
|
| 277 |
+
|
| 278 |
+
@property
|
| 279 |
+
def vocab_size(self):
|
| 280 |
+
return self.token.sp_model.get_piece_size()
|
| 281 |
+
|
| 282 |
+
def get_vocab(self):
|
| 283 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
| 284 |
+
vocab.update(self.added_tokens_encoder)
|
| 285 |
+
return vocab
|
| 286 |
+
|
| 287 |
+
def __getstate__(self):
|
| 288 |
+
state = self.__dict__.copy()
|
| 289 |
+
state['token'] = None
|
| 290 |
+
return state
|
| 291 |
+
|
| 292 |
+
def __setstate__(self, d):
|
| 293 |
+
self.__dict__ = d
|
| 294 |
+
if not hasattr(self, 'sp_model_kwargs'):
|
| 295 |
+
self.sp_model_kwargs = {}
|
| 296 |
+
self.token = LineBBPETokenizer(model_path=self.vocab_file, attachfile_path=self.vocab_path)
|
| 297 |
+
|
| 298 |
+
def _tokenize(self, text: str) -> List[str]:
|
| 299 |
+
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
|
| 300 |
+
token_ids = self.token.encode(text, bos=True, eos=False)
|
| 301 |
+
string_tokens = [self._convert_id_to_token(token_id) for token_id in token_ids]
|
| 302 |
+
return string_tokens
|
| 303 |
+
|
| 304 |
+
def _convert_token_to_id(self, token):
|
| 305 |
+
"""Converts a token (str) in an id using the vocab."""
|
| 306 |
+
return self.token.sp_model.piece_to_id(token)
|
| 307 |
+
|
| 308 |
+
def _convert_id_to_token(self, index):
|
| 309 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
| 310 |
+
token = self.token.sp_model.id_to_piece(index)
|
| 311 |
+
return token
|
| 312 |
+
|
| 313 |
+
def convert_tokens_to_string(self, tokens):
|
| 314 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
| 315 |
+
ids = [self._convert_token_to_id(token) for token in tokens]
|
| 316 |
+
return self.token.decode(ids)
|
| 317 |
+
|
| 318 |
+
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> Tuple[str]:
|
| 319 |
+
if not os.path.isdir(save_directory):
|
| 320 |
+
raise ValueError(f'Vocabulary path ({save_directory}) should be a directory')
|
| 321 |
+
out_vocab_file = os.path.join(save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
|
| 322 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
| 323 |
+
copyfile(self.vocab_file, out_vocab_file)
|
| 324 |
+
elif not os.path.isfile(self.vocab_file):
|
| 325 |
+
with open(out_vocab_file, 'wb') as fi:
|
| 326 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
| 327 |
+
fi.write(content_spiece_model)
|
| 328 |
+
return (out_vocab_file,)
|
tokenization_deepseek_fast.py
ADDED
|
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from typing import List, Optional, Union
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
from transformers.models.llama import LlamaTokenizerFast
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
class DeepseekTokenizerFast(LlamaTokenizerFast):
|
| 8 |
+
|
| 9 |
+
def convert_ids_to_tokens(
|
| 10 |
+
self, ids: Union[int, List[int]], skip_special_tokens: bool = False
|
| 11 |
+
) -> Union[str, List[str]]:
|
| 12 |
+
"""
|
| 13 |
+
Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
|
| 14 |
+
added tokens.
|
| 15 |
+
|
| 16 |
+
Args:
|
| 17 |
+
ids (`int` or `List[int]`):
|
| 18 |
+
The token id (or token ids) to convert to tokens.
|
| 19 |
+
skip_special_tokens (`bool`, *optional*, defaults to `False`):
|
| 20 |
+
Whether or not to remove special tokens in the decoding.
|
| 21 |
+
|
| 22 |
+
Returns:
|
| 23 |
+
`str` or `List[str]`: The decoded token(s).
|
| 24 |
+
"""
|
| 25 |
+
if isinstance(ids, int):
|
| 26 |
+
return self._convert_id_to_token(ids)
|
| 27 |
+
tokens = []
|
| 28 |
+
for index in ids:
|
| 29 |
+
index = int(index)
|
| 30 |
+
if skip_special_tokens and index in self.all_special_ids:
|
| 31 |
+
continue
|
| 32 |
+
token = self._tokenizer.id_to_token(index)
|
| 33 |
+
tokens.append(token if token is not None else "")
|
| 34 |
+
return tokens
|
| 35 |
+
|
| 36 |
+
def _convert_id_to_token(self, index: int) -> Optional[str]:
|
| 37 |
+
token = self._tokenizer.id_to_token(int(index))
|
| 38 |
+
return token if token is not None else ""
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"bos_token": {
|
| 5 |
+
"__type": "AddedToken",
|
| 6 |
+
"content": "<｜begin▁of▁sentence｜>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": true,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false
|
| 11 |
+
},
|
| 12 |
+
"clean_up_tokenization_spaces": false,
|
| 13 |
+
"eos_token": {
|
| 14 |
+
"__type": "AddedToken",
|
| 15 |
+
"content": "<｜end▁of▁sentence｜>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": true,
|
| 18 |
+
"rstrip": false,
|
| 19 |
+
"single_word": false
|
| 20 |
+
},
|
| 21 |
+
"legacy": true,
|
| 22 |
+
"model_max_length": 16384,
|
| 23 |
+
"pad_token": {
|
| 24 |
+
"__type": "AddedToken",
|
| 25 |
+
"content": "<｜end▁of▁sentence｜>",
|
| 26 |
+
"lstrip": false,
|
| 27 |
+
"normalized": true,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
},
|
| 31 |
+
"sp_model_kwargs": {},
|
| 32 |
+
"unk_token": null,
|
| 33 |
+
"tokenizer_class": "LlamaTokenizerFast",
|
| 34 |
+
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
|
| 35 |
+
}
|