{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "71fbfca2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===================================BUG REPORT===================================\n",
"Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
"For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
"================================================================================\n",
"CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
"CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
"CUDA SETUP: Detected CUDA version 117\n",
"CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
]
}
],
"source": [
"from transformers import AutoModelForCausalLM\n",
"from peft import PeftModel, PeftConfig\n",
"import torch\n",
"from datasets import load_dataset\n",
"import os\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
"from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
"device = \"cuda\"\n",
"model_name_or_path = \"bigscience/bloomz-7b1\"\n",
"tokenizer_name_or_path = \"bigscience/bloomz-7b1\"\n",
"dataset_name = \"twitter_complaints\"\n",
"text_column = \"Tweet text\"\n",
"label_column = \"text_label\"\n",
"max_length = 64\n",
"lr = 1e-3\n",
"num_epochs = 50\n",
"batch_size = 8"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1a3648b",
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"ought/raft\", dataset_name)\n",
"\n",
"classes = [k.replace(\"_\", \" \") for k in dataset[\"train\"].features[\"Label\"].names]\n",
"print(classes)\n",
"dataset = dataset.map(\n",
" lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
")\n",
"print(dataset)\n",
"dataset[\"train\"][0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fe12d4d3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "10cabeec92ab428f9a660ebaecbaf865",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Running tokenizer on dataset: 0%| | 0/1 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a344e989ab34c71b230acee68b477e8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Running tokenizer on dataset: 0%| | 0/4 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# data preprocessing\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
"if tokenizer.pad_token_id is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
"print(target_max_length)\n",
"\n",
"\n",
"def preprocess_function(examples):\n",
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
" targets = [str(x) for x in examples[label_column]]\n",
" model_inputs = tokenizer(inputs)\n",
" labels = tokenizer(targets, add_special_tokens=False) # don't add bos token because we concatenate with inputs\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i] + [tokenizer.eos_token_id]\n",
" # print(i, sample_input_ids, label_input_ids)\n",
" model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids\n",
" labels[\"input_ids\"][i] = [-100] * len(sample_input_ids) + label_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n",
" # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i]\n",
" model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
" max_length - len(sample_input_ids)\n",
" ) + sample_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
" \"attention_mask\"\n",
" ][i]\n",
" labels[\"input_ids\"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
" labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
" return model_inputs\n",
"\n",
"\n",
"processed_datasets = dataset.map(\n",
" preprocess_function,\n",
" batched=True,\n",
" num_proc=1,\n",
" remove_columns=dataset[\"train\"].column_names,\n",
" load_from_cache_file=False,\n",
" desc=\"Running tokenizer on dataset\",\n",
")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"\n",
"\n",
"train_dataloader = DataLoader(\n",
" train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2795b9d0",
"metadata": {},
"outputs": [],
"source": [
"def test_preprocess_function(examples):\n",
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
" model_inputs = tokenizer(inputs)\n",
" # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
" max_length - len(sample_input_ids)\n",
" ) + sample_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
" \"attention_mask\"\n",
" ][i]\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
" return model_inputs\n",
"\n",
"\n",
"processed_datasets = dataset.map(\n",
" test_preprocess_function,\n",
" batched=True,\n",
" num_proc=1,\n",
" remove_columns=dataset[\"train\"].column_names,\n",
" load_from_cache_file=False,\n",
" desc=\"Running tokenizer on dataset\",\n",
")\n",
"\n",
"eval_dataset = processed_datasets[\"train\"]\n",
"test_dataset = processed_datasets[\"test\"]\n",
"\n",
"eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
"test_dataloader = DataLoader(test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
"print(next(iter(eval_dataloader)))\n",
"print(next(iter(test_dataloader)))"
]
},
{
"cell_type": "markdown",
"id": "42b14a11",
"metadata": {},
"source": [
"You can load model from hub or local\n",
"\n",
"- Load model from Hugging Face Hub, you can change to your own model id\n",
"```python\n",
"peft_model_id = \"username/twitter_complaints_bigscience_bloomz-7b1_LORA_CAUSAL_LM\"\n",
"```\n",
"- Or load model form local\n",
"```python\n",
"peft_model_id = \"twitter_complaints_bigscience_bloomz-7b1_LORA_CAUSAL_LM\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9caac014",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sourab/pet/src/peft/tuners/lora.py:143: UserWarning: fan_in_fan_out is set to True but the target module is not a Conv1D. Setting fan_in_fan_out to False.\n",
" warnings.warn(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc38030106a14173a1363eb1ee388eda",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/15.8M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from peft import PeftModel, PeftConfig\n",
"\n",
"max_memory = {0: \"1GIB\", 1: \"1GIB\", 2: \"2GIB\", 3: \"10GIB\", \"cpu\": \"30GB\"}\n",
"peft_model_id = \"smangrul/twitter_complaints_bigscience_bloomz-7b1_LORA_CAUSAL_LM\"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, device_map=\"auto\", max_memory=max_memory)\n",
"model = PeftModel.from_pretrained(model, peft_model_id, device_map=\"auto\", max_memory=max_memory)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "6fac10b5",
"metadata": {},
"outputs": [],
"source": [
"# model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2a08ee6d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'base_model.model.transformer.word_embeddings': 3,\n",
" 'base_model.model.lm_head': 3,\n",
" 'base_model.model.transformer.word_embeddings_layernorm': 3,\n",
" 'base_model.model.transformer.h.0': 3,\n",
" 'base_model.model.transformer.h.1': 3,\n",
" 'base_model.model.transformer.h.2': 3,\n",
" 'base_model.model.transformer.h.3': 3,\n",
" 'base_model.model.transformer.h.4': 3,\n",
" 'base_model.model.transformer.h.5': 3,\n",
" 'base_model.model.transformer.h.6': 3,\n",
" 'base_model.model.transformer.h.7': 3,\n",
" 'base_model.model.transformer.h.8': 'cpu',\n",
" 'base_model.model.transformer.h.9': 'cpu',\n",
" 'base_model.model.transformer.h.10': 'cpu',\n",
" 'base_model.model.transformer.h.11': 'cpu',\n",
" 'base_model.model.transformer.h.12': 'cpu',\n",
" 'base_model.model.transformer.h.13': 'cpu',\n",
" 'base_model.model.transformer.h.14': 'cpu',\n",
" 'base_model.model.transformer.h.15': 'cpu',\n",
" 'base_model.model.transformer.h.16': 'cpu',\n",
" 'base_model.model.transformer.h.17': 'cpu',\n",
" 'base_model.model.transformer.h.18': 'cpu',\n",
" 'base_model.model.transformer.h.19': 'cpu',\n",
" 'base_model.model.transformer.h.20': 'cpu',\n",
" 'base_model.model.transformer.h.21': 'cpu',\n",
" 'base_model.model.transformer.h.22': 'cpu',\n",
" 'base_model.model.transformer.h.23': 'cpu',\n",
" 'base_model.model.transformer.h.24': 'cpu',\n",
" 'base_model.model.transformer.h.25': 'cpu',\n",
" 'base_model.model.transformer.h.26': 'cpu',\n",
" 'base_model.model.transformer.h.27': 'cpu',\n",
" 'base_model.model.transformer.h.28': 'cpu',\n",
" 'base_model.model.transformer.h.29': 'cpu',\n",
" 'base_model.model.transformer.ln_f': 'cpu'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.hf_device_map"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "b33be5e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"@HondaCustSvc Your customer service has been horrible during the recall process. I will never purchase a Honda again.\n",
"{'input_ids': tensor([[227985, 5484, 915, 2566, 216744, 38, 1316, 54, 42705,\n",
" 32465, 52166, 9440, 1809, 3784, 88483, 9411, 368, 84342,\n",
" 4451, 17, 473, 2152, 11705, 82406, 267, 51591, 5734,\n",
" 17, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1]])}\n",
"tensor([[227985, 5484, 915, 2566, 216744, 38, 1316, 54, 42705,\n",
" 32465, 52166, 9440, 1809, 3784, 88483, 9411, 368, 84342,\n",
" 4451, 17, 473, 2152, 11705, 82406, 267, 51591, 5734,\n",
" 17, 77658, 915, 210, 16449, 5952, 3, 3, 3,\n",
" 3, 3, 3, 3, 3]])\n",
"['Tweet text : @HondaCustSvc Your customer service has been horrible during the recall process. I will never purchase a Honda again. Label : complaint']\n"
]
}
],
"source": [
"model.eval()\n",
"i = 89\n",
"inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n",
"print(dataset[\"test\"][i][\"Tweet text\"])\n",
"print(inputs)\n",
"\n",
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
" print(outputs)\n",
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b6d6cd5b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [01:42<00:00, 14.70s/it]\n"
]
}
],
"source": [
"model.eval()\n",
"eval_preds = []\n",
"for _, batch in enumerate(tqdm(eval_dataloader)):\n",
" batch = {k: v for k, v in batch.items() if k != \"labels\"}\n",
" with torch.no_grad():\n",
" outputs = model.generate(**batch, max_new_tokens=10)\n",
" preds = outputs[:, max_length:].detach().cpu().numpy()\n",
" eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "61264abe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy=100.0\n",
"eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']\n",
"dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']\n"
]
}
],
"source": [
"correct = 0\n",
"total = 0\n",
"for pred, true in zip(eval_preds, dataset[\"train\"][label_column]):\n",
" if pred.strip() == true.strip():\n",
" correct += 1\n",
" total += 1\n",
"accuracy = correct / total * 100\n",
"print(f\"{accuracy=}\")\n",
"print(f\"{eval_preds[:10]=}\")\n",
"print(f\"{dataset['train'][label_column][:10]=}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a70802a3",
"metadata": {},
"outputs": [],
"source": [
"model.eval()\n",
"test_preds = []\n",
"\n",
"for _, batch in enumerate(tqdm(test_dataloader)):\n",
" batch = {k: v for k, v in batch.items() if k != \"labels\"}\n",
" with torch.no_grad():\n",
" outputs = model.generate(**batch, max_new_tokens=10)\n",
" preds = outputs[:, max_length:].detach().cpu().numpy()\n",
" test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))\n",
" if len(test_preds) > 100:\n",
" break\n",
"test_preds"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1c4ad9c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.4"
},
"vscode": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}