diff --git a/fastNLP/core/callback.py b/fastNLP/core/callback.py index 7bda4c38..9ab4c3ff 100644 --- a/fastNLP/core/callback.py +++ b/fastNLP/core/callback.py @@ -947,13 +947,14 @@ class CheckPointCallback(Callback): model = model.module model.load_state_dict(states['model']) self.optimizer.load_state_dict(states['optimizer']) - self.trainer.epoch = states['epoch'] + 1 # 因为是结束储存的，所以需要从下一个epoch开始 + self.trainer.epoch = states['epoch'] + 1 # 因为是结束储存的，所以需要从下一个epoch开始 self.trainer.step = states['step'] if 'best_dev_epoch' in states: self.trainer.best_dev_perf = states['best_dev_perf'] self.trainer.best_dev_epoch = states['best_dev_epoch'] self.trainer.best_dev_step = states['best_dev_step'] self.trainer.best_metric_indicator = states['best_metric_indicator'] + logger.info("Load checkpoint from {}".format(os.path.expanduser(self.save_path))) def on_epoch_end(self): r""" diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py index b16f5ddb..5fa89e24 100644 --- a/fastNLP/core/trainer.py +++ b/fastNLP/core/trainer.py @@ -645,7 +645,8 @@ class Trainer(object): else: inner_tqdm = tqdm start = time.time() - with inner_tqdm(total=self.n_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar: + with inner_tqdm(total=self.n_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True, + initial=self.step) as pbar: self.pbar = pbar avg_loss = 0 self.batch_per_epoch = self.data_iterator.num_batches diff --git a/fastNLP/core/utils.py b/fastNLP/core/utils.py index ad990e17..0821c6ff 100644 --- a/fastNLP/core/utils.py +++ b/fastNLP/core/utils.py @@ -796,9 +796,12 @@ def _check_forward_error(forward_func, batch_x, dataset, check_level): if len(suggestions) > 1: for idx, sugg in enumerate(suggestions): sugg_str += f'({idx + 1}). {sugg}' - else: + err_str = '\n' + '\n'.join(errs) + '\n\tSuggestion: ' + sugg_str + elif len(suggestions): sugg_str += suggestions[0] - err_str = '\n' + '\n'.join(errs) + '\n\tSuggestion: ' + sugg_str + err_str = '\n' + '\n'.join(errs) + '\n\tSuggestion: ' + sugg_str + else: + err_str = '\n' + '\n'.join(errs) raise NameError(err_str) if _unused: if check_level == WARNING_CHECK_LEVEL: diff --git a/fastNLP/embeddings/gpt2_embedding.py b/fastNLP/embeddings/gpt2_embedding.py index 69a0f0d9..371d9e22 100644 --- a/fastNLP/embeddings/gpt2_embedding.py +++ b/fastNLP/embeddings/gpt2_embedding.py @@ -278,11 +278,11 @@ class GPT2WordPieceEncoder(nn.Module): return output_strs - def generate(self, word_pieces, max_len=40, do_sample=True, num_beams=1, temperature=1, top_k=50, top_p=1.0, + def generate(self, word_pieces=None, max_len=40, do_sample=True, num_beams=1, temperature=1, top_k=50, top_p=1.0, repetition_penalty=1.0, length_penalty=1.0): """ - :param word_pieces: + :param torch.LongTensor,None word_pieces: 如果传入tensor，shape应该为batch_size x start_len; 如果传入None，会随机生成。 :param int max_len: 生成多长的句子 :param bool do_sample: 是否使用采样的方式生成，如果使用采样，相同的参数可能出现不同的句子。 :param int num_beams: 使用多大的beam size @@ -293,7 +293,7 @@ class GPT2WordPieceEncoder(nn.Module): :param float length_penalty: 惩罚过长的句子 :return: """ - pass + raise NotImplemented def get_lm_loss(self, release=True): """ diff --git a/fastNLP/modules/encoder/gpt2.py b/fastNLP/modules/encoder/gpt2.py index 45cc4d24..1bb1bc12 100644 --- a/fastNLP/modules/encoder/gpt2.py +++ b/fastNLP/modules/encoder/gpt2.py @@ -1062,10 +1062,3 @@ class GPT2LMHeadModel(GPT2PreTrainedModel): # all hidden states, tuple: n_layer x batch_size x max_len x embed_size， # attention, tuple: n_layer x batch_size x n_head' x src_len x tgt_len return outputs # (loss), lm_logits, presents, all hidden_states, (attentions) - - - - - -# 输出每个位置的 - diff --git a/fastNLP/modules/generator/seq2seq_generator.py b/fastNLP/modules/generator/seq2seq_generator.py index 1159ccb5..e09de85a 100755 --- a/fastNLP/modules/generator/seq2seq_generator.py +++ b/fastNLP/modules/generator/seq2seq_generator.py @@ -177,7 +177,8 @@ def _no_beam_search_generate(decoder: Decoder, tokens=None, past=None, max_lengt scores = scores / temperature scores = top_k_top_p_filtering(scores, top_k, top_p, min_tokens_to_keep=2) - probs = F.softmax(scores, dim=-1) + # 加上1e-12是为了避免https://github.com/pytorch/pytorch/pull/27523 + probs = F.softmax(scores, dim=-1) + 1e-12 # 保证至少有一个不是eos的值 next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) # batch_size @@ -230,7 +231,7 @@ def _beam_search_generate(decoder: Decoder, tokens=None, past=None, max_length=2 assert vocab_size >= num_beams, "num_beams should be smaller than the number of vocabulary size." if do_sample: - probs = F.softmax(scores, dim=-1) + probs = F.softmax(scores, dim=-1) + 1e-12 next_tokens = torch.multinomial(probs, num_samples=num_beams) # (batch_size, num_beams) logits = probs.log() next_scores = logits.gather(dim=1, index=next_tokens) # (batch_size, num_beams) @@ -276,7 +277,8 @@ def _beam_search_generate(decoder: Decoder, tokens=None, past=None, max_length=2 # 多召回一个防止eos scores = top_k_top_p_filtering(scores, top_k, top_p, min_tokens_to_keep=num_beams + 1) - probs = F.softmax(scores, dim=-1) + # 加上1e-12是为了避免https://github.com/pytorch/pytorch/pull/27523 + probs = F.softmax(scores, dim=-1) + 1e-12 # 保证至少有一个不是eos的值 _tokens = torch.multinomial(probs, num_samples=num_beams + 1) # batch_size' x (num_beams+1)