Fix ernie_gen evaluation bug

modules/text/text_generation/ernie_gen/README.md

@@ -170,9 +170,9 @@ https://github.com/PaddlePaddle/ERNIE/blob/repro/ernie-gen/
 
 ### 依赖
 
-paddlepaddle >= 1.8.2
+paddlepaddle >= 2.0.0
 
-paddlehub >= 1.7.0
+paddlehub >= 2.0.0
 
 paddlenlp >= 2.0.0
 

modules/text/text_generation/ernie_gen/module.py

@@ -42,7 +42,7 @@ from .model import StackModel
     author_email="",
     type="nlp/text_generation",
 )
-class ErnieGen(hub.Module):
+class ErnieGen():
     def __init__(self):
         """
         initialize with the necessary elements
@@ -59,25 +59,25 @@ class ErnieGen(hub.Module):
         return self._model
 
     def finetune(
-            self,
-            train_path,
-            dev_path=None,
-            save_dir="ernie_gen_result",
-            init_ckpt_path=None,
-            use_gpu=True,
-            max_steps=500,
-            batch_size=8,
-            max_encode_len=50,
-            max_decode_len=50,
-            learning_rate=5e-5,
-            warmup_proportion=0.1,
-            weight_decay=0.1,
-            noise_prob=0,
-            label_smooth=0,
-            beam_width=5,
-            length_penalty=1.0,
-            log_interval=100,
-            save_interval=200,
+        self,
+        train_path,
+        dev_path=None,
+        save_dir="ernie_gen_result",
+        init_ckpt_path=None,
+        use_gpu=True,
+        max_steps=500,
+        batch_size=8,
+        max_encode_len=50,
+        max_decode_len=50,
+        learning_rate=5e-5,
+        warmup_proportion=0.1,
+        weight_decay=0.1,
+        noise_prob=0,
+        label_smooth=0,
+        beam_width=5,
+        length_penalty=1.0,
+        log_interval=100,
+        save_interval=200,
     ):
         """
         finetune with the specified dataset.
@@ -119,13 +119,12 @@ class ErnieGen(hub.Module):
 
         train_dataset = self._load_dataset(train_path)
         attn_id = self.tokenizer.vocab['[MASK]']
-        trans_func = convert_example(
-            tokenizer=self.tokenizer,
-            attn_id=attn_id,
-            tgt_type_id=1,
-            max_encode_len=max_encode_len,
-            max_decode_len=max_decode_len,
-            noise_prob=noise_prob)
+        trans_func = convert_example(tokenizer=self.tokenizer,
+                                     attn_id=attn_id,
+                                     tgt_type_id=1,
+                                     max_encode_len=max_encode_len,
+                                     max_decode_len=max_decode_len,
+                                     noise_prob=noise_prob)
 
         train_dataset = train_dataset.map(trans_func)
         train_batch_sampler = paddle.io.BatchSampler(train_dataset, batch_size=batch_size, shuffle=True)
@@ -139,18 +138,20 @@ class ErnieGen(hub.Module):
             Pad(axis=0, pad_val=self.tokenizer.pad_token_id),  # attn_ids
             Pad(axis=0, pad_val=self.tokenizer.pad_token_id),  # tgt_labels
         ): after_padding(fn(samples))
-        train_data_loader = DataLoader(
-            dataset=train_dataset,
-            batch_sampler=train_batch_sampler,
-            collate_fn=batchify_fn,
-            num_workers=0,
-            return_list=True)
+        train_data_loader = DataLoader(dataset=train_dataset,
+                                       batch_sampler=train_batch_sampler,
+                                       collate_fn=batchify_fn,
+                                       num_workers=0,
+                                       return_list=True)
 
         if dev_path:
             dev_dataset = self._load_dataset(dev_path)
             dev_dataset = dev_dataset.map(trans_func)
-            dev_data_loader = DataLoader(
-                dataset=dev_dataset, batch_size=batch_size, collate_fn=batchify_fn, num_workers=0, return_list=True)
+            dev_data_loader = DataLoader(dataset=dev_dataset,
+                                         batch_size=batch_size,
+                                         collate_fn=batchify_fn,
+                                         num_workers=0,
+                                         return_list=True)
 
         label_num = self.model.word_emb.weight.shape[0]
         train_model = StackModel(self.model)
@@ -158,12 +159,11 @@ class ErnieGen(hub.Module):
         # Generate parameter names needed to perform weight decay.
         # All bias and LayerNorm parameters are excluded.
         decay_params = [p.name for n, p in self.model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])]
-        optimizer = paddle.optimizer.AdamW(
-            learning_rate=lr_scheduler,
-            parameters=self.model.parameters(),
-            weight_decay=weight_decay,
-            grad_clip=nn.ClipGradByGlobalNorm(1.0),
-            apply_decay_param_fun=lambda x: x in decay_params)
+        optimizer = paddle.optimizer.AdamW(learning_rate=lr_scheduler,
+                                           parameters=self.model.parameters(),
+                                           weight_decay=weight_decay,
+                                           grad_clip=nn.ClipGradByGlobalNorm(1.0),
+                                           apply_decay_param_fun=lambda x: x in decay_params)
 
         rouge1 = Rouge1()
         rouge2 = Rouge2()
@@ -175,8 +175,8 @@ class ErnieGen(hub.Module):
                 (src_ids, src_tids, src_pids, tgt_ids, tgt_tids, tgt_pids, attn_ids, mask_src_2_src, mask_tgt_2_srctgt,
                  mask_attn_2_srctgtattn, tgt_labels, _) = batch
                 if label_smooth > 0.:
-                    tgt_labels = nn.functional.label_smooth(
-                        nn.functional.one_hot(tgt_labels, label_num), epsilon=label_smooth)
+                    tgt_labels = nn.functional.label_smooth(nn.functional.one_hot(tgt_labels, label_num),
+                                                            epsilon=label_smooth)
 
                 tgt_pos = paddle.nonzero(attn_ids == attn_id)
                 loss = train_model(src_ids, src_tids, src_pids, tgt_ids, tgt_tids, tgt_pids, attn_ids, mask_src_2_src,
@@ -190,8 +190,8 @@ class ErnieGen(hub.Module):
                 if global_step % log_interval == 0 and paddle.distributed.get_rank() == 0:
                     loss_np = loss.numpy()
                     ppl = np.exp(loss_np)
-                    logger.info('[step %d / %d]train loss %.5f, ppl %.5f, elr %.3e' % (global_step, max_steps, loss_np,
-                                                                                       ppl, lr_scheduler.get_lr()))
+                    logger.info('[step %d / %d]train loss %.5f, ppl %.5f, elr %.3e' %
+                                (global_step, max_steps, loss_np, ppl, lr_scheduler.get_lr()))
                 if save_dir and global_step % save_interval == 0 and global_step > 0:
                     loss_np = loss.numpy()
                     ppl = np.exp(loss_np)
@@ -214,8 +214,8 @@ class ErnieGen(hub.Module):
         if global_step % save_interval != 0:
             loss_np = loss.numpy()
             ppl = np.exp(loss_np)
-            logger.info('[final step %d]train loss %.5f, ppl %.5f, elr %.3e' % (global_step, loss_np, ppl,
-                                                                                lr_scheduler.get_lr()))
+            logger.info('[final step %d]train loss %.5f, ppl %.5f, elr %.3e' %
+                        (global_step, loss_np, ppl, lr_scheduler.get_lr()))
             if save_dir:
                 save_name = "step_%s_ppl_%.5f.pdparams" % (global_step, ppl)
                 save_path = os.path.join(save_dir, save_name)
@@ -291,6 +291,7 @@ class ErnieGen(hub.Module):
 
     def _evaluate(self, model, data_loader, tokenizer, rouge1, rouge2, attn_id, max_decode_len, max_encode_len,
                   beam_width, length_penalty):
+        paddle.disable_static()
         model.eval()
 
         vocab = tokenizer.vocab
@@ -305,21 +306,20 @@ class ErnieGen(hub.Module):
         for data in data_loader:
             (src_ids, src_tids, src_pids, _, _, _, _, _, _, _, _, raw_tgt_labels) = data  # never use target when infer
             # Use greedy_search_infilling or beam_search_infilling to get predictions
-            output_ids = beam_search_infilling(
-                model,
-                src_ids,
-                src_tids,
-                eos_id=eos_id,
-                sos_id=sos_id,
-                attn_id=attn_id,
-                pad_id=pad_id,
-                unk_id=unk_id,
-                vocab_size=vocab_size,
-                max_decode_len=max_decode_len,
-                max_encode_len=max_encode_len,
-                beam_width=beam_width,
-                length_penalty=length_penalty,
-                tgt_type_id=1)
+            output_ids = beam_search_infilling(model,
+                                               src_ids,
+                                               src_tids,
+                                               eos_id=eos_id,
+                                               sos_id=sos_id,
+                                               attn_id=attn_id,
+                                               pad_id=pad_id,
+                                               unk_id=unk_id,
+                                               vocab_size=vocab_size,
+                                               max_decode_len=max_decode_len,
+                                               max_encode_len=max_encode_len,
+                                               beam_width=beam_width,
+                                               length_penalty=length_penalty,
+                                               tgt_type_id=1)
 
             for ids in output_ids.tolist():
                 if eos_id in ids:
@@ -361,11 +361,10 @@ class ErnieGen(hub.Module):
 
 if __name__ == "__main__":
     module = ErnieGen()
-    result = module.finetune(
-        train_path='test_data/train.txt',
-        dev_path='test_data/dev.txt',
-        max_steps=30,
-        batch_size=2,
-        log_interval=10,
-        save_interval=20)
+    result = module.finetune(train_path='test_data/train.txt',
+                             dev_path='test_data/dev.txt',
+                             max_steps=30,
+                             batch_size=2,
+                             log_interval=10,
+                             save_interval=20)
     module.export(params_path=result['last_save_path'], module_name="ernie_gen_test", author="test")

modules/text/text_generation/ernie_gen_acrostic_poetry/README.md

@@ -99,9 +99,9 @@ https://github.com/PaddlePaddle/ERNIE/blob/repro/ernie-gen/
 
 ### 依赖
 
-paddlepaddle >= 1.8.2
+paddlepaddle >= 2.0.0
 
-paddlehub >= 1.7.0
+paddlehub >= 2.0.0
 
 paddlenlp >= 2.0.0
 

modules/text/text_generation/ernie_gen_couplet/README.md

@@ -87,9 +87,9 @@ https://github.com/PaddlePaddle/ERNIE/blob/repro/ernie-gen/
 
 ### 依赖
 
-paddlepaddle >= 1.8.2
+paddlepaddle >= 2.0.0
 
-paddlehub >= 1.7.0
+paddlehub >= 2.0.0
 
 paddlenlp >= 2.0.0
 

modules/text/text_generation/ernie_gen_lover_words/README.md

@@ -87,9 +87,9 @@ https://github.com/PaddlePaddle/ERNIE/blob/repro/ernie-gen/
 
 ### 依赖
 
-paddlepaddle >= 1.8.2
+paddlepaddle >= 2.0.0
 
-paddlehub >= 1.7.0
+paddlehub >= 2.0.0
 
 paddlenlp >= 2.0.0
 

modules/text/text_generation/ernie_gen_poetry/README.md

@@ -87,11 +87,11 @@ https://github.com/PaddlePaddle/ERNIE/blob/repro/ernie-gen/
 
 ### 依赖
 
-paddlepaddle >= 1.8.2
+paddlepaddle >= 2.0.0
 
-paddlehub >= 1.7.0
+paddlehub >= 2.0.0
 
-PaddleNLP >= 2.0.0
+paddlenlp >= 2.0.0
 
 
 ## 更新历史