File size: 8,622 Bytes
3e26f8a c5f11e3 3e26f8a c5f11e3 3e26f8a c5f11e3 3e26f8a c5f11e3 3e26f8a c5f11e3 3e26f8a c5f11e3 3e26f8a c5f11e3 3e26f8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 | {
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from datasets import load_dataset\n",
"from transformers import AutoTokenizer, AutoConfig, SwitchTransformersForConditionalGeneration\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building custom DataLoader"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"class CustomDataLoader(Dataset):\n",
" def __init__(self, dataframe, tokenizer, source_len, summ_len) -> None:\n",
" self.tokenizer = tokenizer\n",
" self.data = dataframe\n",
" self.source_len = source_len\n",
" self.summ_len = summ_len\n",
" self.context = self.data['document']\n",
" self.summaries = self.data['summaries']\n",
"\n",
" def __len__(self):\n",
" return len(self.context)\n",
"\n",
" def __getitem__(self, index) :\n",
" context = self.context[index]\n",
" summary = self.summaries[index]\n",
"\n",
" source = self.tokenizer.batch_encode_plus([context], max_length = self.source_len, pad_to_max_length=True, return_tensors='pt')\n",
" target = self.tokenizer.batch_encode_plus([summary], max_length = self.summ_len, pad_to_max_length=True, return_tensors='pt')\n",
"\n",
" source_ids = source['input_ids'].squeeze()\n",
" source_mask = source['attention_mask'].squeeze()\n",
" target_ids = target['input_ids'].squeeze()\n",
" target_mask = target['attention_mask'].squeeze()\n",
"\n",
" return {\n",
" \"source_ids\" : source_ids.to(dtype = torch.long),\n",
" \"source_mask\": source_mask.to(dtype=torch.long),\n",
" \"target_ids\": target_ids.to(dtype=torch.long),\n",
" \"target_mask\": target_mask.to(dtype=torch.long)\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training Loop"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def train(epoch, tokenizer, model, loader, optimizer):\n",
" model.train()\n",
" for _, data in enumerate(loader, 0):\n",
" labels = data['target_ids'].to(device, dtype=torch.long)\n",
" labels = model._shift_right(labels)\n",
"\n",
" labels = labels.masked_fill_(labels == 0, -100)\n",
" ids = data['source_ids'].to(device, dtype=torch.long)\n",
" mask = data['source_mask'].to(device, dtype=torch.long)\n",
" decoder_input_ids = torch.zeros_like(labels).long()\n",
"\n",
" outputs = model(input_ids = ids, attention_mask = mask, labels=labels, output_router_logits=True, return_dict = True)\n",
" loss = outputs[0]\n",
"\n",
" if _ % 10 == 0:\n",
" print(f\"Training Loss: {loss.item()}\")\n",
"\n",
" if (_+1) % 2000 == 0:\n",
" break\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from numpy import dtype\n",
"\n",
"\n",
"def validate(epoch, tokenizer, model, device, loader):\n",
" model.eval()\n",
" predictions = []\n",
" actuals = []\n",
" with torch.no_grad():\n",
" for _, data in enumerate(loader, 0):\n",
" y = data['target_ids'].to(device, dtype=torch.long)\n",
" ids = data['source_ids'].to(device, dtype=torch.long)\n",
" mask = data['source_mask'].to(device, dtype= torch.long)\n",
"\n",
" generated_ids = model.generate(\n",
" input_ids = ids,\n",
" attention_mask = mask,\n",
" max_length = 150,\n",
" num_beams = 2,\n",
" repition_penalty=2.5,\n",
" length_penalty=1.0,\n",
" early_stopping = True\n",
" )\n",
"\n",
" preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
" target = [tokenizer.decode(t, skip_special_tokens=True, claen_up_tokenization_spaces=True) for t in y]\n",
" if _ %100 == 0:\n",
" print(f\"Completed {_}\")\n",
" break\n",
"\n",
" predictions.extend(preds)\n",
" actuals.extend(target)\n",
"\n",
" return predictions , actuals"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def main():\n",
" train_batch_size = 16\n",
" val_batch_size = 16\n",
" epochs = 2\n",
" val_epochs = 1\n",
" learning_rate = 1e-4\n",
" max_len = 256\n",
" summary_len = 256\n",
"\n",
" tokenizer = AutoTokenizer.from_pretrained(\"google/switch-base-8\")\n",
"\n",
" dataset = load_dataset(\"xsum\")\n",
" def preprend(example):\n",
" return {\"document\": [\"summarization: \" + x for x in example['document']]}\n",
" encoded_dataset = dataset.map(preprend,batched=True)\n",
"\n",
" train_dataset = encoded_dataset['train'] # type: ignore\n",
" val_dataset = encoded_dataset['validation'] # type: ignore\n",
"\n",
" training_set = CustomDataLoader(train_dataset, tokenizer, max_len, summary_len)\n",
" val_set = CustomDataLoader(val_dataset, tokenizer, max_len, summary_len)\n",
"\n",
" train_params = {\n",
" 'batch_size' : train_batch_size,\n",
" 'shuffle' : True,\n",
" 'num_workers': 0\n",
" }\n",
"\n",
" val_params = {\n",
" \"batch_size\" : val_batch_size,\n",
" \"shuffle\": False,\n",
" \"num_workers\": 0\n",
" }\n",
"\n",
" train_loader = DataLoader(training_set,**train_params )\n",
" val_loader = DataLoader(val_set, **val_params)\n",
"\n",
" model = SwitchTransformersForConditionalGeneration.from_pretrained(\"google/switch-base-8\", torch_dtype= torch.bfloat16)\n",
" model = model.to(device) # type: ignore\n",
"\n",
" optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # type: ignore\n",
" print(\"initializing Fine-Tuning \")\n",
"\n",
" for epoch in range(epochs):\n",
" train(epoch, tokenizer, model, train_loader, optimizer)\n",
"\n",
" print(\"Validating on the fine-tuned model\")\n",
" for epoch in range(val_epochs):\n",
" predictions , actuals = validate(epoch, tokenizer, model, device, val_loader)\n",
" final_df = pd.DataFrame({\"Generated Text\" :predictions, \"Actual Text\":actuals})\n",
" final_df.to_csv(\"./output/predictions.csv\")\n",
" print(\"Output Files generated\")\n",
"\n",
" model.save_pretrained(\"switch-transformer\")\n",
" tokenizer.save_pretrained(\"switch-transformer-tokenizer\")\n",
" return model, tokenizer\n",
"\n",
"\n",
"\n",
"trained_model, tokenizer = main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = SwitchTransformersForConditionalGeneration.from_pretrained(\"switch-transformer\", torch_dtype=torch.bfloat16)\n",
"tokenizer = AutoTokenizer.from_pretrained(\"switch-transformer-tokenizer\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|