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Phylliida/fineTune.ipynb

Created April 2, 2021 19:11
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fineTune.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 95,
"id": "distributed-failing",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hi\n"
]
}
],
"source": [
"from transformers import (\n",
" CTRLLMHeadModel,\n",
" CTRLTokenizer,\n",
" GPT2LMHeadModel,\n",
" GPT2Tokenizer,\n",
" OpenAIGPTLMHeadModel,\n",
" OpenAIGPTTokenizer,\n",
" TransfoXLLMHeadModel,\n",
" TransfoXLTokenizer,\n",
" XLMTokenizer,\n",
" XLMWithLMHeadModel,\n",
" XLNetLMHeadModel,\n",
" XLNetTokenizer,\n",
" Trainer,\n",
" TrainingArguments,\n",
" default_data_collator,\n",
" HfArgumentParser,\n",
" CONFIG_MAPPING,\n",
" MODEL_FOR_CAUSAL_LM_MAPPING,\n",
" AutoConfig,\n",
" AutoTokenizer,\n",
" AutoModelForCausalLM\n",
" \n",
")\n",
"from dataclasses import dataclass, field\n",
"\n",
"model_name = 'gpt2'\n",
"\n",
"MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())\n",
"MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)\n",
"config = AutoConfig.from_pretrained(model_name)\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"model = AutoModelForCausalLM.from_pretrained(model_name)\n",
"\n",
"import torch\n",
"preprocessing_num_workers = 16\n",
"tokens_per_sample = 30\n",
"model_class, tokenizer_class = MODEL_CLASSES[model_name]\n",
"model_class_or_name = model_name\n",
"device = 'cuda'\n",
"model = model.to(device)\n",
"model.resize_token_embeddings(len(tokenizer))\n",
"print(\"hi\")"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "american-lobby",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
}
],
"source": [
"def generateDataset(seed, tokenizer, model, args):\n",
" encoded_prompt = tokenizer.encode(seed, return_tensors=\"pt\").to(device)\n",
" if encoded_prompt.size()[-1] == 0:\n",
" input_ids = None\n",
" else:\n",
" input_ids = encoded_prompt\n",
"\n",
" output_sequences = model.generate(\n",
" input_ids=input_ids,\n",
" max_length=args.length,\n",
" temperature=args.temperature,\n",
" #top_k=args.k,\n",
" #top_p=args.p,\n",
" #num_beams=args.num_beams,\n",
" #no_repeat_ngram_size=3,\n",
" #repetition_penalty=args.repetition_penalty,\n",
" do_sample=True,\n",
" num_return_sequences=args.num_return_sequences\n",
" )\n",
" return output_sequences\n",
"\n",
"\n",
"class Config(object):\n",
" def __init__(self, **kwargs):\n",
" for k, v in kwargs.items():\n",
" setattr(self, k, v)\n",
"\n",
"tokenizer.encode('hi how are you doing cool thx', return_tensors='pt')\n",
"def generateTextDataset(tensorDataset):\n",
" return \"\".join([tokenizer.decode(x) for x in tensorDataset])\n",
"text = []\n",
"for i in range(1):\n",
" dat = generateDataset(\"<|endoftext|>\", tokenizer, model, Config(temperature=1.0, length=tokens_per_sample, num_return_sequences=200))\n",
" text.append(generateTextDataset(dat))\n",
"text = \"\".join(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "alone-speaking",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 97,
"id": "framed-quantum",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration default-fc930bdf745b69b1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset text/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/yams/.cache/huggingface/datasets/text/default-fc930bdf745b69b1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"0 tables [00:00, ? tables/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset text downloaded and prepared to /home/yams/.cache/huggingface/datasets/text/default-fc930bdf745b69b1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5. Subsequent calls will reuse this data.\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"f = open(\"data.txt\", \"w\")\n",
"f.write(text)\n",
"f.close()\n",
"data_files = {\n",
" 'train': 'data.txt'\n",
"}\n",
"\n",
"dataset = load_dataset('text', data_files=data_files)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "freelance-quality",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['text'],\n",
" num_rows: 237\n",
" })\n",
"})"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "gothic-reconstruction",
"metadata": {},
"outputs": [],
"source": [
"# from https://raw.githubusercontent.com/huggingface/transformers/master/examples/language-modeling/run_clm.py\n",
"def tokenize_function(examples):\n",
" return tokenizer(examples['text'])\n",
"\n",
"tokenized_dataset = dataset.map(tokenize_function,\n",
" batched=True,\n",
" num_proc=preprocessing_num_workers,\n",
" remove_columns=dataset['train'].column_names,\n",
" load_from_cache_file=False\n",
")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "demographic-pipeline",
"metadata": {},
"outputs": [],
"source": [
"# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.\n",
"def group_texts(examples):\n",
" # Concatenate all texts.\n",
" concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
" total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
" # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n",
" # customize this part to your needs.\n",
" total_length = (total_length // block_size) * block_size\n",
" # Split by chunks of max_len.\n",
" result = {\n",
" k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
" for k, t in concatenated_examples.items()\n",
" }\n",
" result[\"labels\"] = result[\"input_ids\"].copy()\n",
" return result\n",
"\n",
"lm_dataset = tokenized_dataset.map(\n",
" group_texts,\n",
" batched=True,\n",
" num_proc=preprocessing_num_workers,\n",
" load_from_cache_file=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 109,
"id": "expired-organizer",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from typing import Optional\n",
"@dataclass\n",
"class ModelArguments:\n",
" \"\"\"\n",
" Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.\n",
" \"\"\"\n",
"\n",
" model_name_or_path: Optional[str] = field(\n",
" default=None,\n",
" metadata={\n",
" \"help\": \"The model checkpoint for weights initialization.\"\n",
" \"Don't set if you want to train a model from scratch.\"\n",
" },\n",
" )\n",
" model_type: Optional[str] = field(\n",
" default=None,\n",
" metadata={\"help\": \"If training from scratch, pass a model type from the list: \" + \", \".join(MODEL_TYPES)},\n",
" )\n",
" config_name: Optional[str] = field(\n",
" default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n",
" )\n",
" tokenizer_name: Optional[str] = field(\n",
" default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n",
" )\n",
" cache_dir: Optional[str] = field(\n",
" default=None,\n",
" metadata={\"help\": \"Where do you want to store the pretrained models downloaded from huggingface.co\"},\n",
" )\n",
" use_fast_tokenizer: bool = field(\n",
" default=True,\n",
" metadata={\"help\": \"Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.\"},\n",
" )\n",
" model_revision: str = field(\n",
" default=\"main\",\n",
" metadata={\"help\": \"The specific model version to use (can be a branch name, tag name or commit id).\"},\n",
" )\n",
" use_auth_token: bool = field(\n",
" default=False,\n",
" metadata={\n",
" \"help\": \"Will use the token generated when running `transformers-cli login` (necessary to use this script \"\n",
" \"with private models).\"\n",
" },\n",
" )\n",
"\n",
"\n",
"@dataclass\n",
"class DataTrainingArguments:\n",
" \"\"\"\n",
" Arguments pertaining to what data we are going to input our model for training and eval.\n",
" \"\"\"\n",
"\n",
" block_size: Optional[int] = field(\n",
" default=None,\n",
" metadata={\n",
" \"help\": \"Optional input sequence length after tokenization.\"\n",
" \"The training dataset will be truncated in block of this size for training.\"\n",
" \"Default to the model max input length for single sentence inputs (take into account special tokens).\"\n",
" },\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "alleged-australia",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [
{
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]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lm_dataset['train']['input_ids']"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "associate-clarity",
"metadata": {},
"outputs": [],
"source": [
"parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n",
"model_args, data_args, training_args = parser.parse_dict({\n",
" 'output_dir': 'fine-tune-test'\n",
"})\n"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "afraid-conversion",
"metadata": {},
"outputs": [],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=lm_dataset['train'],\n",
" tokenizer=tokenizer,\n",
" data_collator=default_data_collator\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "restricted-driver",
"metadata": {},
"outputs": [
{
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