# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import logging import os import random import time import math from functools import partial import numpy as np import paddle import paddle.nn.functional as F from paddle.io import DataLoader from paddle.metric import Metric, Accuracy, Precision, Recall from paddlenlp.data import Stack, Tuple, Pad from paddlenlp.data.sampler import SamplerHelper from paddlenlp.transformers import BertModel, BertForSequenceClassification, BertTokenizer from paddlenlp.utils.log import logger from paddlenlp.metrics import AccuracyAndF1, Mcc, PearsonAndSpearman import paddlenlp.datasets as datasets from paddleslim.nas.ofa import OFA, RunConfig, DistillConfig, utils from paddleslim.nas.ofa.utils import nlp_utils from paddleslim.nas.ofa.convert_super import Convert, supernet TASK_CLASSES = { "cola": (datasets.GlueCoLA, Mcc), "sst-2": (datasets.GlueSST2, Accuracy), "mrpc": (datasets.GlueMRPC, AccuracyAndF1), "sts-b": (datasets.GlueSTSB, PearsonAndSpearman), "qqp": (datasets.GlueQQP, AccuracyAndF1), "mnli": (datasets.GlueMNLI, Accuracy), "qnli": (datasets.GlueQNLI, Accuracy), "rte": (datasets.GlueRTE, Accuracy), } MODEL_CLASSES = {"bert": (BertForSequenceClassification, BertTokenizer), } def parse_args(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(TASK_CLASSES.keys()), ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join( sum([ list(classes[-1].pretrained_init_configuration.keys()) for classes in MODEL_CLASSES.values() ], [])), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.", ) parser.add_argument( "--batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument( "--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument( "--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--lambda_logit", default=1.0, type=float, help="lambda for logit loss.") parser.add_argument( "--lambda_rep", default=0.1, type=float, help="lambda for hidden state distillation loss.") parser.add_argument( "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument( "--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument( "--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument( "--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") parser.add_argument( "--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--n_gpu", type=int, default=1, help="number of gpus to use, 0 for cpu.") parser.add_argument( '--width_mult_list', nargs='+', type=float, default=[1.0, 5 / 6, 2 / 3, 0.5], help="width mult in compress") parser.add_argument( '--depth_mult_list', nargs='+', type=float, default=[1.0, 0.75, 0.5], help="width mult in compress") args = parser.parse_args() return args def set_seed(args): random.seed(args.seed + paddle.distributed.get_rank()) np.random.seed(args.seed + paddle.distributed.get_rank()) paddle.seed(args.seed + paddle.distributed.get_rank()) def evaluate(model, criterion, metric, data_loader, width_mult=1.0, depth_mult=1.0): with paddle.no_grad(): model.eval() metric.reset() for batch in data_loader: input_ids, segment_ids, labels = batch logits = model(input_ids, segment_ids, attention_mask=[None, None]) if isinstance(logits, tuple): logits = logits[0] loss = criterion(logits, labels) correct = metric.compute(logits, labels) metric.update(correct) results = metric.accumulate() print( "depth_mult: %f, width_mult: %f, eval loss: %f, %s: %s\n" % (depth_mult, width_mult, loss.numpy(), metric.name(), results), end='') model.train() ### monkey patch for bert forward to accept [attention_mask, head_mask] as attention_mask def bert_forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=[None, None], depth_mult=1.0): wtype = self.pooler.dense.fn.weight.dtype if hasattr( self.pooler.dense, 'fn') else self.pooler.dense.weight.dtype if attention_mask[0] is None: attention_mask[0] = paddle.unsqueeze( (input_ids == self.pad_token_id).astype(wtype) * -1e9, axis=[1, 2]) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids) encoder_outputs = self.encoder( embedding_output, attention_mask, depth_mult=depth_mult) sequence_output = encoder_outputs pooled_output = self.pooler(sequence_output) return sequence_output, pooled_output BertModel.forward = bert_forward def transformer_encoder_forward(self, src, src_mask=None, depth_mult=1.): output = src depth = round(self.num_layers * depth_mult) kept_layers_index = [] for i in range(1, depth + 1): kept_layers_index.append(math.floor(i / depth_mult) - 1) for i in kept_layers_index: output = self.layers[i](output, src_mask=src_mask) if self.norm is not None: output = self.norm(output) return output paddle.nn.TransformerEncoder.forward = transformer_encoder_forward def sequence_forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=[None, None], depth=1.0): _, pooled_output = self.bert( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, depth_mult=depth) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) return logits BertForSequenceClassification.forward = sequence_forward def soft_cross_entropy(inp, target): inp_likelihood = F.log_softmax(inp, axis=-1) target_prob = F.softmax(target, axis=-1) return -1. * paddle.mean(paddle.sum(inp_likelihood * target_prob, axis=-1)) def convert_example(example, tokenizer, label_list, max_seq_length=512, is_test=False): """convert a glue example into necessary features""" def _truncate_seqs(seqs, max_seq_length): if len(seqs) == 1: # single sentence # Account for [CLS] and [SEP] with "- 2" seqs[0] = seqs[0][0:(max_seq_length - 2)] else: # sentence pair # Account for [CLS], [SEP], [SEP] with "- 3" tokens_a, tokens_b = seqs max_seq_length -= 3 while True: # truncate with longest_first strategy total_length = len(tokens_a) + len(tokens_b) if total_length <= max_seq_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() return seqs def _concat_seqs(seqs, separators, seq_mask=0, separator_mask=1): concat = sum((seq + sep for sep, seq in zip(separators, seqs)), []) segment_ids = sum(([i] * (len(seq) + len(sep)) for i, (sep, seq) in enumerate(zip(separators, seqs))), []) if isinstance(seq_mask, int): seq_mask = [[seq_mask] * len(seq) for seq in seqs] if isinstance(separator_mask, int): separator_mask = [[separator_mask] * len(sep) for sep in separators] p_mask = sum((s_mask + mask for sep, seq, s_mask, mask in zip(separators, seqs, seq_mask, separator_mask)), []) return concat, segment_ids, p_mask if not is_test: # `label_list == None` is for regression task label_dtype = "int64" if label_list else "float32" # get the label label = example[-1] example = example[:-1] #create label maps if classification task if label_list: label_map = {} for (i, l) in enumerate(label_list): label_map[l] = i label = label_map[label] label = np.array([label], dtype=label_dtype) # tokenize raw text tokens_raw = [tokenizer(l) for l in example] # truncate to the truncate_length, tokens_trun = _truncate_seqs(tokens_raw, max_seq_length) # concate the sequences with special tokens tokens_trun[0] = [tokenizer.cls_token] + tokens_trun[0] tokens, segment_ids, _ = _concat_seqs(tokens_trun, [[tokenizer.sep_token]] * len(tokens_trun)) # convert the token to ids input_ids = tokenizer.convert_tokens_to_ids(tokens) valid_length = len(input_ids) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. # input_mask = [1] * len(input_ids) if not is_test: return input_ids, segment_ids, valid_length, label else: return input_ids, segment_ids, valid_length def do_train(args): paddle.set_device("gpu" if args.n_gpu else "cpu") if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() set_seed(args) args.task_name = args.task_name.lower() dataset_class, metric_class = TASK_CLASSES[args.task_name] args.model_type = args.model_type.lower() model_class, tokenizer_class = MODEL_CLASSES[args.model_type] train_ds = dataset_class.get_datasets(['train']) tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) trans_func = partial( convert_example, tokenizer=tokenizer, label_list=train_ds.get_labels(), max_seq_length=args.max_seq_length) train_ds = train_ds.apply(trans_func, lazy=True) train_batch_sampler = paddle.io.DistributedBatchSampler( train_ds, batch_size=args.batch_size, shuffle=True) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # input Pad(axis=0, pad_val=tokenizer.pad_token_id), # segment Stack(), # length Stack(dtype="int64" if train_ds.get_labels() else "float32") # label ): [data for i, data in enumerate(fn(samples)) if i != 2] train_data_loader = DataLoader( dataset=train_ds, batch_sampler=train_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True) if args.task_name == "mnli": dev_dataset_matched, dev_dataset_mismatched = dataset_class.get_datasets( ["dev_matched", "dev_mismatched"]) dev_dataset_matched = dev_dataset_matched.apply(trans_func, lazy=True) dev_dataset_mismatched = dev_dataset_mismatched.apply( trans_func, lazy=True) dev_batch_sampler_matched = paddle.io.BatchSampler( dev_dataset_matched, batch_size=args.batch_size, shuffle=False) dev_data_loader_matched = DataLoader( dataset=dev_dataset_matched, batch_sampler=dev_batch_sampler_matched, collate_fn=batchify_fn, num_workers=0, return_list=True) dev_batch_sampler_mismatched = paddle.io.BatchSampler( dev_dataset_mismatched, batch_size=args.batch_size, shuffle=False) dev_data_loader_mismatched = DataLoader( dataset=dev_dataset_mismatched, batch_sampler=dev_batch_sampler_mismatched, collate_fn=batchify_fn, num_workers=0, return_list=True) else: dev_dataset = dataset_class.get_datasets(["dev"]) dev_dataset = dev_dataset.apply(trans_func, lazy=True) dev_batch_sampler = paddle.io.BatchSampler( dev_dataset, batch_size=args.batch_size, shuffle=False) dev_data_loader = DataLoader( dataset=dev_dataset, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True) num_labels = 1 if train_ds.get_labels() == None else len( train_ds.get_labels()) # Step1: Initialize the origin BERT model. model = model_class.from_pretrained( args.model_name_or_path, num_classes=num_labels) if paddle.distributed.get_world_size() > 1: model = paddle.DataParallel(model) # Step2: Convert origin model to supernet. sp_config = supernet(expand_ratio=args.width_mult_list) model = Convert(sp_config).convert(model) # Use weights saved in the dictionary to initialize supernet. weights_path = os.path.join(args.model_name_or_path, 'model_state.pdparams') origin_weights = paddle.load(weights_path) model.set_state_dict(origin_weights) # Step3: Define teacher model. teacher_model = model_class.from_pretrained( args.model_name_or_path, num_classes=num_labels) new_dict = utils.utils.remove_model_fn(teacher_model, origin_weights) teacher_model.set_state_dict(new_dict) del origin_weights, new_dict default_run_config = {'elastic_depth': args.depth_mult_list} run_config = RunConfig(**default_run_config) # Step4: Config about distillation. mapping_layers = ['bert.embeddings'] for idx in range(model.bert.config['num_hidden_layers']): mapping_layers.append('bert.encoder.layers.{}'.format(idx)) default_distill_config = { 'lambda_distill': args.lambda_rep, 'teacher_model': teacher_model, 'mapping_layers': mapping_layers, } distill_config = DistillConfig(**default_distill_config) # Step5: Config in supernet training. ofa_model = OFA(model, run_config=run_config, distill_config=distill_config, elastic_order=['depth']) #elastic_order=['width']) criterion = paddle.nn.loss.CrossEntropyLoss() if train_ds.get_labels( ) else paddle.nn.loss.MSELoss() metric = metric_class() if args.task_name == "mnli": dev_data_loader = (dev_data_loader_matched, dev_data_loader_mismatched) lr_scheduler = paddle.optimizer.lr.LambdaDecay( args.learning_rate, lambda current_step, num_warmup_steps=args.warmup_steps, num_training_steps=args.max_steps if args.max_steps > 0 else (len(train_data_loader) * args.num_train_epochs): float( current_step) / float(max(1, num_warmup_steps)) if current_step < num_warmup_steps else max( 0.0, float(num_training_steps - current_step) / float( max(1, num_training_steps - num_warmup_steps)))) optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, epsilon=args.adam_epsilon, parameters=ofa_model.model.parameters(), weight_decay=args.weight_decay, apply_decay_param_fun=lambda x: x in [ p.name for n, p in ofa_model.model.named_parameters() if not any(nd in n for nd in ["bias", "norm"]) ]) global_step = 0 tic_train = time.time() for epoch in range(args.num_train_epochs): # Step6: Set current epoch and task. ofa_model.set_epoch(epoch) ofa_model.set_task('depth') for step, batch in enumerate(train_data_loader): global_step += 1 input_ids, segment_ids, labels = batch for depth_mult in args.depth_mult_list: for width_mult in args.width_mult_list: # Step7: Broadcast supernet config from width_mult, # and use this config in supernet training. net_config = utils.dynabert_config(ofa_model, width_mult, depth_mult) ofa_model.set_net_config(net_config) logits, teacher_logits = ofa_model( input_ids, segment_ids, attention_mask=[None, None]) rep_loss = ofa_model.calc_distill_loss() if args.task_name == 'sts-b': logit_loss = 0.0 else: logit_loss = soft_cross_entropy(logits, teacher_logits.detach()) loss = rep_loss + args.lambda_logit * logit_loss loss.backward() optimizer.step() lr_scheduler.step() ofa_model.model.clear_gradients() if global_step % args.logging_steps == 0: if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0: logger.info( "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s" % (global_step, epoch, step, loss, args.logging_steps / (time.time() - tic_train))) tic_train = time.time() if global_step % args.save_steps == 0: if args.task_name == "mnli": evaluate( teacher_model, criterion, metric, dev_data_loader_matched, width_mult=100) evaluate( teacher_model, criterion, metric, dev_data_loader_mismatched, width_mult=100) else: evaluate( teacher_model, criterion, metric, dev_data_loader, width_mult=100) for depth_mult in args.depth_mult_list: for width_mult in args.width_mult_list: net_config = utils.dynabert_config( ofa_model, width_mult, depth_mult) ofa_model.set_net_config(net_config) tic_eval = time.time() if args.task_name == "mnli": acc = evaluate(ofa_model, criterion, metric, dev_data_loader_matched, width_mult, depth_mult) evaluate(ofa_model, criterion, metric, dev_data_loader_mismatched, width_mult, depth_mult) print("eval done total : %s s" % (time.time() - tic_eval)) else: acc = evaluate(ofa_model, criterion, metric, dev_data_loader, width_mult, depth_mult) print("eval done total : %s s" % (time.time() - tic_eval)) if (not args.n_gpu > 1 ) or paddle.distributed.get_rank() == 0: output_dir = os.path.join(args.output_dir, "model_%d" % global_step) if not os.path.exists(output_dir): os.makedirs(output_dir) # need better way to get inner model of DataParallel model_to_save = model._layers if isinstance( model, paddle.DataParallel) else model model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) def print_arguments(args): """print arguments""" print('----------- Configuration Arguments -----------') for arg, value in sorted(vars(args).items()): print('%s: %s' % (arg, value)) print('------------------------------------------------') if __name__ == "__main__": args = parse_args() print_arguments(args) if args.n_gpu > 1: paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_gpu) else: do_train(args)