import os import math from tensorboardX import SummaryWriter from tqdm import trange, tqdm import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from pytorch_transformers import AdamW, WarmupLinearSchedule from utils import logger from dataset import Dataset class Distiller: def __init__(self, params: dict, dataloader: Dataset, token_probs: torch.tensor, student: nn.Module, teacher: nn.Module): logger.info('Initializing Distiller') self.params = params self.dump_path = params.dump_path self.multi_gpu = params.multi_gpu self.fp16 = params.fp16 self.student = student self.teacher = teacher self.dataloader = dataloader if self.params.n_gpu > 1: self.dataloader.split() self.get_iterator(seed=params.seed) self.temperature = params.temperature assert self.temperature > 0. self.alpha_ce = params.alpha_ce self.alpha_mlm = params.alpha_mlm self.alpha_mse = params.alpha_mse assert self.alpha_ce >= 0. assert self.alpha_mlm >= 0. assert self.alpha_mse >= 0. assert self.alpha_ce + self.alpha_mlm + self.alpha_mse > 0. self.mlm_mask_prop = params.mlm_mask_prop assert 0.0 <= self.mlm_mask_prop <= 1.0 assert params.word_mask + params.word_keep + params.word_rand == 1.0 self.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand]) self.pred_probs = self.pred_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else self.pred_probs self.token_probs = token_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else token_probs if self.fp16: self.pred_probs = self.pred_probs.half() self.token_probs = self.token_probs.half() self.epoch = 0 self.n_iter = 0 self.n_total_iter = 0 self.n_sequences_epoch = 0 self.total_loss_epoch = 0 self.last_loss = 0 self.last_loss_ce = 0 self.last_loss_mlm = 0 self.last_loss_mse = 0 self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean') self.mlm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1) self.mse_loss_fct = nn.MSELoss(reduction='sum') logger.info('--- Initializing model optimizer') assert params.gradient_accumulation_steps >= 1 self.num_steps_epoch = int(len(self.dataloader) / params.batch_size) + 1 num_train_optimization_steps = int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1 warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop) no_decay = ['bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in student.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': params.weight_decay}, {'params': [p for n, p in student.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': 0.0} ] logger.info("------ Number of trainable parameters (student): %i" % sum([p.numel() for p in self.student.parameters() if p.requires_grad])) logger.info("------ Number of parameters (student): %i" % sum([p.numel() for p in self.student.parameters()])) self.optimizer = AdamW(optimizer_grouped_parameters, lr=params.learning_rate, eps=params.adam_epsilon, betas=(0.9, 0.98)) self.scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=warmup_steps, t_total=num_train_optimization_steps) if self.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") logger.info(f"Using fp16 training: {self.params.fp16_opt_level} level") self.student, self.optimizer = amp.initialize(self.student, self.optimizer, opt_level=self.params.fp16_opt_level) self.teacher = self.teacher.half() if self.multi_gpu: if self.fp16: from apex.parallel import DistributedDataParallel logger.info("Using apex.parallel.DistributedDataParallel for distributed training.") self.student = DistributedDataParallel(self.student) else: from torch.nn.parallel import DistributedDataParallel logger.info("Using nn.parallel.DistributedDataParallel for distributed training.") self.student = DistributedDataParallel(self.student, device_ids=[params.local_rank], output_device=params.local_rank) self.is_master = params.is_master if self.is_master: logger.info('--- Initializing Tensorboard') self.tensorboard = SummaryWriter(log_dir=os.path.join(self.dump_path, 'log', 'train')) self.tensorboard.add_text(tag='config', text_string=str(self.params), global_step=0) def get_iterator(self, seed: int = None): """ Initialize the data iterator. Each process has its own data iterator (iterating on his own random portion of the dataset). Input: ------ seed: `int` - The random seed. """ logger.info('--- Initializing Data Iterator') self.data_iterator = self.dataloader.get_iterator(seed=seed) def get_batch(self): """ Call the data iterator to output a new batch. If the data iterator went through the whole dataset, create a new iterator. """ assert hasattr(self, 'data_iterator') try: x = next(self.data_iterator) except StopIteration: logger.warning('--- Went through the whole dataset. Creating new data iterator.') self.data_iterator = self.dataloader.get_iterator() x = next(self.data_iterator) return x def prepare_batch(self, batch): """ Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the masked label for MLM. Input: ------ batch: `Tuple` token_ids: `torch.tensor(bs, seq_length)` - The token ids for each of the sequence. It is padded. lengths: `torch.tensor(bs)` - The lengths of each of the sequences in the batch. Output: ------- token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM. attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention. mlm_labels: `torch.tensor(bs, seq_length)` - The masked languge modeling labels. There is a -1 where there is nothing to predict. """ token_ids, lengths = batch token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths) assert token_ids.size(0) == lengths.size(0) attn_mask = (torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None]) bs, max_seq_len = token_ids.size() mlm_labels = token_ids.new(token_ids.size()).copy_(token_ids) x_prob = self.token_probs[token_ids.flatten()] n_tgt = math.ceil(self.mlm_mask_prop * lengths.sum().item()) tgt_ids = torch.multinomial(x_prob / x_prob.sum(), n_tgt, replacement=False) pred_mask = torch.zeros(bs * max_seq_len, dtype=torch.uint8, device=token_ids.device) pred_mask[tgt_ids] = 1 pred_mask = pred_mask.view(bs, max_seq_len) pred_mask[token_ids == self.params.special_tok_ids['pad_token']] = 0 # mask a number of words == 0 [8] (faster with fp16) if self.fp16: n1 = pred_mask.sum().item() if n1 > 8: pred_mask = pred_mask.view(-1) n2 = max(n1 % 8, 8 * (n1 // 8)) if n2 != n1: pred_mask[torch.nonzero(pred_mask).view(-1)[:n1-n2]] = 0 pred_mask = pred_mask.view(bs, max_seq_len) assert pred_mask.sum().item() % 8 == 0, pred_mask.sum().item() _token_ids_real = token_ids[pred_mask] _token_ids_rand = _token_ids_real.clone().random_(self.params.vocab_size) _token_ids_mask = _token_ids_real.clone().fill_(self.params.special_tok_ids['mask_token']) probs = torch.multinomial(self.pred_probs, len(_token_ids_real), replacement=True) _token_ids = _token_ids_mask * (probs == 0).long() + _token_ids_real * (probs == 1).long() + _token_ids_rand * (probs == 2).long() token_ids = token_ids.masked_scatter(pred_mask, _token_ids) mlm_labels[1-pred_mask] = -1 return token_ids, attn_mask, mlm_labels def round_batch(self, x: torch.tensor, lengths: torch.tensor): """ For float16 only. Sub-sample sentences in a batch, and add padding, so that each dimension is a multiple of 8. Input: ------ x: `torch.tensor(bs, seq_length)` - The token ids. lengths: `torch.tensor(bs, seq_length)` - The lengths of each of the sequence in the batch. Output: ------- x: `torch.tensor(new_bs, new_seq_length)` - The updated token ids. lengths: `torch.tensor(new_bs, new_seq_length)` - The updated lengths. """ if not self.fp16 or len(lengths) < 8: return x, lengths # number of sentences == 0 [8] bs1 = len(lengths) bs2 = 8 * (bs1 // 8) assert bs2 > 0 and bs2 % 8 == 0 if bs1 != bs2: idx = torch.randperm(bs1)[:bs2] lengths = lengths[idx] slen = lengths.max().item() x = x[idx, :slen] else: idx = None # sequence length == 0 [8] ml1 = x.size(1) if ml1 % 8 != 0: pad = 8 - (ml1 % 8) ml2 = ml1 + pad pad_id = self.params.special_tok_ids['pad_token'] padding_tensor = torch.zeros(bs2, pad, dtype=torch.long, device=x.device).fill_(pad_id) x = torch.cat([x, padding_tensor], 1) assert x.size() == (bs2, ml2) assert x.size(0) % 8 == 0 assert x.size(1) % 8 == 0 return x, lengths def train(self): """ The real training loop. """ if self.is_master: logger.info('Starting training') self.student.train() self.teacher.eval() for _ in range(self.params.n_epoch): if self.is_master: logger.info(f'--- Starting epoch {self.epoch}/{self.params.n_epoch-1}') iter_bar = trange(self.num_steps_epoch, desc="-Iter", disable=self.params.local_rank not in [-1, 0]) for __ in range(self.num_steps_epoch): batch = self.get_batch() if self.params.n_gpu > 0: batch = tuple(t.to(f'cuda:{self.params.local_rank}') for t in batch) token_ids, attn_mask, mlm_labels = self.prepare_batch(batch=batch) self.step(input_ids=token_ids, attention_mask=attn_mask, mlm_labels=mlm_labels) iter_bar.update() iter_bar.set_postfix({'Last_loss': f'{self.last_loss:.2f}', 'Avg_cum_loss': f'{self.total_loss_epoch/self.n_iter:.2f}'}) iter_bar.close() if self.is_master: logger.info(f'--- Ending epoch {self.epoch}/{self.params.n_epoch-1}') self.end_epoch() if self.is_master: logger.info('Training is finished') def step(self, input_ids: torch.tensor, attention_mask: torch.tensor, mlm_labels: torch.tensor): """ One optimization step: forward of student AND teacher, backward on the loss (for gradient accumulation), and possibly a parameter update (depending on the gradient accumulation). Input: ------ input_ids: `torch.tensor(bs, seq_length)` - The token ids. attention_mask: `torch.tensor(bs, seq_length)` - The attention mask for self attention. mlm_labels: `torch.tensor(bs, seq_length)` - The masked language modeling labels. """ s_logits = self.student(input_ids=input_ids, attention_mask=attention_mask)[0] # (bs, seq_length, voc_size) with torch.no_grad(): t_logits = self.teacher(input_ids=input_ids, attention_mask=attention_mask)[0] # (bs, seq_length, voc_size) assert s_logits.size() == t_logits.size() #https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100 #https://github.com/peterliht/knowledge-distillation-pytorch/issues/2 if self.params.restrict_ce_to_mask: mask = (mlm_labels>-1).unsqueeze(-1).expand_as(s_logits) # (bs, seq_lenth, voc_size) else: mask = attention_mask.unsqueeze(-1).expand_as(s_logits) # (bs, seq_lenth, voc_size) s_logits_slct = torch.masked_select(s_logits, mask) # (bs * seq_length * voc_size) modulo the 1s in mask s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1)) # (bs * seq_length, voc_size) modulo the 1s in mask t_logits_slct = torch.masked_select(t_logits, mask) # (bs * seq_length * voc_size) modulo the 1s in mask t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1)) # (bs * seq_length, voc_size) modulo the 1s in mask assert t_logits_slct.size() == s_logits_slct.size() loss_ce = self.ce_loss_fct(F.log_softmax(s_logits_slct/self.temperature, dim=-1), F.softmax(t_logits_slct/self.temperature, dim=-1)) * (self.temperature)**2 loss = self.alpha_ce*loss_ce if self.alpha_mlm > 0.: loss_mlm = self.mlm_loss_fct(s_logits.view(-1, s_logits.size(-1)), mlm_labels.view(-1)) loss += self.alpha_mlm * loss_mlm if self.alpha_mse > 0.: loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct)/s_logits_slct.size(0) # Reproducing batchmean reduction loss += self.alpha_mse * loss_mse self.total_loss_epoch += loss.item() self.last_loss = loss.item() self.last_loss_ce = loss_ce.item() if self.alpha_mlm > 0.: self.last_loss_mlm = loss_mlm.item() if self.alpha_mse > 0.: self.last_loss_mse = loss_mse.item() self.optimize(loss) self.n_sequences_epoch += input_ids.size(0) def optimize(self, loss): """ Normalization on the loss (gradient accumulation or distributed training), followed by backward pass on the loss, possibly followed by a parameter update (depending on the gradient accumulation). Also update the metrics for tensorboard. """ # Check for NaN if (loss != loss).data.any(): logger.error('NaN detected') exit() if self.multi_gpu: loss = loss.mean() if self.params.gradient_accumulation_steps > 1: loss = loss / self.params.gradient_accumulation_steps if self.fp16: from apex import amp with amp.scale_loss(loss, self.optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() self.iter() if self.n_iter % self.params.gradient_accumulation_steps == 0: if self.fp16: torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.params.max_grad_norm) else: torch.nn.utils.clip_grad_norm_(self.student.parameters(), self.params.max_grad_norm) self.scheduler.step() self.optimizer.step() self.optimizer.zero_grad() def iter(self): """ Update global counts, write to tensorboard and save checkpoint. """ self.n_iter += 1 self.n_total_iter += 1 if self.n_total_iter % self.params.log_interval == 0: self.log_tensorboard() if self.n_total_iter % self.params.checkpoint_interval == 0: self.save_checkpoint() def log_tensorboard(self): """ Log into tensorboard. Only by the master process. """ if not self.is_master: return for param_name, param in self.student.named_parameters(): self.tensorboard.add_scalar(tag='parameter_mean/' + param_name, scalar_value=param.data.mean(), global_step=self.n_total_iter) self.tensorboard.add_scalar(tag='parameter_std/' + param_name, scalar_value=param.data.std(), global_step=self.n_total_iter) if param.grad is None: continue self.tensorboard.add_scalar(tag="grad_mean/" + param_name, scalar_value=param.grad.data.mean(),global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="grad_std/" + param_name, scalar_value=param.grad.data.std(), global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="losses/cum_avg_loss_epoch", scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="losses/loss", scalar_value=self.last_loss, global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="losses/loss_ce", scalar_value=self.last_loss_ce, global_step=self.n_total_iter) if self.alpha_mlm > 0.: self.tensorboard.add_scalar(tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter) if self.alpha_mse > 0.: self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter) self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter) def end_epoch(self): """ Finally arrived at the end of epoch (full pass on dataset). Do some tensorboard logging and checkpoint saving. """ logger.info(f'{self.n_sequences_epoch} sequences have been trained during this epoch.') if self.is_master: self.save_checkpoint(checkpoint_name=f'model_epoch_{self.epoch}.pth') self.tensorboard.add_scalar(tag='epoch/loss', scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.epoch) self.epoch += 1 self.n_sequences_epoch = 0 self.n_iter = 0 self.total_loss_epoch = 0 def save_checkpoint(self, checkpoint_name: str = 'checkpoint.pth'): """ Save the current state. Only by the master process. """ if not self.is_master: return mdl_to_save = self.student.module if hasattr(self.student, 'module') else self.student mdl_to_save.config.save_pretrained(self.dump_path) state_dict = mdl_to_save.state_dict() torch.save(state_dict, os.path.join(self.dump_path, checkpoint_name))