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142 lines
5.4 KiB
Python
142 lines
5.4 KiB
Python
########################################################################################################
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# The RWKV v2-RNN Language Model - https://github.com/BlinkDL/RWKV-LM
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########################################################################################################
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import logging
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import datetime
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import json
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from src.model import GPT, GPTConfig
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from src.trainer import Trainer, TrainerConfig
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from torch.utils.data import Dataset
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import torch
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import numpy as np
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.allow_tf32 = True
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torch.backends.cuda.matmul.allow_tf32 = True
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### Step 1: set training data ##########################################################################
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datafile = "enwik8"
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datafile_encoding = 'utf-8'
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# datafile_encoding = 'utf-16le'
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### Step 2: set model size #############################################################################
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ctx_len = 1024 # ===> increase T_MAX in model.py if your ctx_len > 1024
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n_layer = 6
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n_embd = 512
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# 'RWKV' (better for char-level English) or 'RWKV-ffnPre' (better in some cases)
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model_type = 'RWKV'
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### Step 3: set batch size #############################################################################
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# ===> batch_size must be divisible by B_GROUP_FORWARD and B_GROUP_BACKWARD in model.py
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# For example, if your batch_size = 20, you can set B_GROUP_FORWARD = 4, B_GROUP_BACKWARD = 2
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# If you see "CUDA out of memory", reduce it. Use GPU-Z to find the highest value for your VRAM.
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batch_size = 12
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### Step 4: set learning rate, training 'epochs' #######################################################
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lr_init = 6e-4
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lr_final = 1e-5
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# the 'epoch' here is very short and of fixed length (ctx_len * epoch_length_fixed tokens)
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n_epoch = 500
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# 0 = never, 1 = every 'epoch', 2 = every two 'epoch', etc.
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epoch_save_frequency = 30
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epoch_save_path = 'trained-'
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epoch_length_fixed = 10000
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########################################################################################################
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# import src.utils
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# src.utils.set_seed(42) # remember to change seed if you load a model
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np.set_printoptions(precision=4, suppress=True, linewidth=200)
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logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO,)
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grad_norm_clip = 1.0
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warmup_tokens = 0
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betas = (0.9, 0.99)
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eps = 4e-9
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num_workers = 0
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########################################################################################################
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# Load data
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########################################################################################################
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print('loading data... ' + datafile)
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class Dataset(Dataset):
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def __init__(self, data, ctx_len):
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print('building token list...', end=' ')
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unique = sorted(list(set(data)))
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# print()
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# for u in unique:
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# print(u, end=' ')
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# print('\n\n')
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xx = 0
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xxObj = {}
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for u in unique:
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xxObj[xx] = u
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xx += 1
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with open('vocab.json', "w", encoding="utf-16") as vocab_file:
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vocab_file.write(json.dumps(xxObj, ensure_ascii=False))
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data_size, vocab_size = len(data), len(unique)
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print('data has %d tokens, %d unique.' % (data_size, vocab_size))
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self.stoi = {ch: i for i, ch in enumerate(unique)}
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self.itos = {i: ch for i, ch in enumerate(unique)}
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self.ctx_len = ctx_len
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self.vocab_size = vocab_size
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self.data = data
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def __len__(self):
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return epoch_length_fixed
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def __getitem__(self, idx):
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# cheat: pick a random spot in dataset
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i = np.random.randint(0, len(self.data) - (self.ctx_len + 1))
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chunk = self.data[i:i+self.ctx_len+1]
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dix = [self.stoi[s] for s in chunk]
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x = torch.tensor(dix[:-1], dtype=torch.long,
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device=torch.device('cuda'))
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y = torch.tensor(dix[1:], dtype=torch.long,
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device=torch.device('cuda'))
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return x, y
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train_dataset = Dataset(
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open(datafile, "r", encoding=datafile_encoding).read(), ctx_len)
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########################################################################################################
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# Train model
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########################################################################################################
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if __name__ == '__main__':
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model = GPT(GPTConfig(train_dataset.vocab_size, train_dataset.ctx_len, model_type=model_type,
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n_layer=n_layer, n_embd=n_embd)).cuda()
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# # # load a trained model. remember to change random seed
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# m2 = torch.load('trained-61.pth')
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# model.load_state_dict(m2)
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print('model', model_type, 'epoch', n_epoch, 'batchsz', batch_size, 'betas',
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betas, 'eps', eps, 'ctx', ctx_len, 'layer', n_layer, 'embd', n_embd, )
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tconf = TrainerConfig(model_type=model_type, max_epochs=n_epoch, batch_size=batch_size,
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learning_rate=lr_init, lr_decay=True, lr_final=lr_final, betas=betas, eps=eps, grad_norm_clip=grad_norm_clip,
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warmup_tokens=warmup_tokens, final_tokens=n_epoch*len(train_dataset)*ctx_len, num_workers=num_workers, epoch_save_frequency=epoch_save_frequency, epoch_save_path=epoch_save_path)
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trainer = Trainer(model, train_dataset, None, tconf)
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trainer.train()
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torch.save(model.state_dict(), 'trained-' + str(n_epoch) + '-' + trainer.get_run_name() +
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'-' + datetime.datetime.today().strftime('%Y-%m-%d-%H-%M-%S') + '.pth')
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