DataLoaderfrom transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmupfrom tqdm import tqdm, trangeimport torch.nn.functional as Fimport csv### Prepare datalyrics = pd.read_csv('lyrics-data.csv'
def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_scheduler, config): """Single training step.""" args = get_args() timers = get_timers() # Set grad to zero. for model_chunk in model: model_chunk.zero_grad_buffer() optimizer.zero_grad() # Forward pass....
result_dir='/content/drive/MyDrive/GPT2_Lab_DTS/results'data_file_path='/content/drive/MyDrive/GPT2_Lab_DTS/data/my_company_info.json'os.environ["HF_HOME"]="/content/huggingface"# Replacewithyour desired directoryprint("Please replace it with your hf access token:")os.environ["HF_HOME_...
python3 ./data/prepare.py TrainingYou can train the model by calling:python3 ./train.py Or with DDP (if you have multiple GPUs - highly suggested):# DDP on 4 gpus on 1 node (for example) torchrun --standalone --nproc_per_node=4 train.py Note that this, by default, loads the ...
2Tokenizerimportnumpy as npimportrandomimporttorchfromtorch.utils.dataimportDataset, DataLoaderfromtransformersimportGPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmupfromtqdmimporttqdm, trangeimporttorch.nn.functional as Fimportcsv### Prepare datalyrics = pd.read_csv('lyrics-data....
# prepare data batch = samples[step * batch_size: (step + 1) * batch_size] batch_inputs = [] for ids in batch: int_ids = [int(x) for x in ids] batch_inputs.append(int_ids) batch_inputs = torch.tensor(batch_inputs).long().to(device)...
python3 data/shakespeare_char/prepare.py 接下来我们训练一个初级的GPT模型 : View Code 从配置文件中可以看到,我们本质上是在训练一个上下文大小高达 256 个字符、384 个特征通道的 GPT,它是一个 6 层 Transformer,每层有 6 个头。如果是在A100 GPU 上,此训练运行大约需要 3 分钟,最佳loss为 1.4697,而在...
首先分析train_gpt2.c主函数的逻辑,主函数分两部分,第一部分for循环前面这部分是准备工作,for循环是训练。 第一部分准备阶段代码如下: // build theGPT-2model from a checkpointGPT2model;gpt2_build_from_checkpoint(&model,"gpt2_124M.bin");// build the DataLoaders from tokens files. for now use ...
# Step 5: Prepare DataLoader seq_length = 10 batch_size = 8 dataset = MultiStockDataset(df, seq_length=seq_length) train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) # Step 6: Set up Model and Optimizer device = torch.device("cuda:0" if torch.cuda.is_available(...
Setting ds_accelerator to cuda (auto detect) Generate Samples WARNING: No training data specified using world size: 1 and model-parallel size: 1 > using dynamic loss scaling > initializing model parallel with size 1 > initializing model parallel cuda seeds on global rank 0, model parallel rank...