Fix for pd22 (#763)

* fix-load-pretrained model

* update readme

* fix path

README.zh.md

@@ -173,7 +173,7 @@ data/xnli
 - 使用 `动态图` 模型进行finetune:
 
 ```script
-python3 ./ernie_d/demo/finetune_classifier.py \
+python3 ./demo/finetune_classifier.py \
        --from_pretrained ernie-1.0 \
        --data_dir ./data/xnli
 ```

demo/distill/distill.py

@@ -153,7 +153,7 @@ if not os.path.exists('./teacher_model.bin'):
             if step % 100 == 0:
                 f1 = evaluate_teacher(teacher_model, dev_ds)
                 print('teacher f1: %.5f' % f1)
-    P.save(teacher_model.state_dict(), './teacher_model.bin')
+    P.save(teacher_model.state_dict(),str( './teacher_model.bin'))
 else:
     state_dict = P.load('./teacher_model.bin')
     teacher_model.set_state_dict(state_dict)

demo/finetune_classifier.py

@@ -162,7 +162,7 @@ model = ErnieModelForSequenceClassification.from_pretrained(
 
 if args.init_checkpoint is not None:
     log.info('loading checkpoint from %s' % args.init_checkpoint)
-    sd = P.load(args.init_checkpoint)
+    sd = P.load(str(args.init_checkpoint))
     model.set_state_dict(sd)
 
 g_clip = P.nn.ClipGradByGlobalNorm(1.0)  #experimental
@@ -238,9 +238,9 @@ with LogWriter(
                     log_writer.add_scalar('eval/acc', acc, step=step)
                     log.debug('acc %.5f' % acc)
                     if args.save_dir is not None:
-                        P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+                        P.save(model.state_dict(), str(args.save_dir / 'ckpt.bin'))
 if args.save_dir is not None:
-    P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+    P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin'))
 if args.inference_model_dir is not None:
 
     class InferenceModel(ErnieModelForSequenceClassification):

demo/finetune_classifier_distributed.py

@@ -128,7 +128,7 @@ model = ErnieModelForSequenceClassification.from_pretrained(
 
 if args.init_checkpoint is not None:
     log.info('loading checkpoint from %s' % args.init_checkpoint)
-    sd = P.load(args.init_checkpoint)
+    sd = P.load(str(args.init_checkpoint))
     model.set_state_dict(sd)
 
 model = P.DataParallel(model)
@@ -195,11 +195,11 @@ with P.amp.auto_cast(enable=args.use_amp):
             #log_writer.add_scalar('eval/acc', acc, step=step)
             log.debug('acc %.5f' % acc)
             if args.save_dir is not None:
-                P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+                P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin'))
         # exit 
         if step > args.max_steps:
             break
 
 if args.save_dir is not None and env.dev_id == 0:
-    P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+    P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin'))
 log.debug('done')

demo/finetune_mrc.py

@@ -145,7 +145,7 @@ def train(model, train_dataset, dev_dataset, dev_examples, dev_features,
                 log.debug('[step %d] eval result: f1 %.5f em %.5f' %
                           (step, f1, em))
             if env.dev_id == 0 and args.save_dir is not None:
-                P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+                P.save(model.state_dict(), str(args.save_dir / 'ckpt.bin'))
             if step > max_steps:
                 break
 
@@ -244,4 +244,4 @@ if __name__ == "__main__":
                           tokenizer, args)
         log.debug('final eval result: f1 %.5f em %.5f' % (f1, em))
     if env.dev_id == 0 and args.save_dir is not None:
-        P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+        P.save(model.state_dict(), str(args.save_dir / 'ckpt.bin'))

demo/finetune_ner.py

@@ -249,10 +249,10 @@ with LogWriter(
                     log.debug('eval f1: %.5f' % f1)
                     log_writer.add_scalar('eval/f1', f1, step=step)
                     if args.save_dir is not None:
-                        P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+                        P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin'))
 
 f1 = evaluate(model, dev_ds)
 log.debug('final eval f1: %.5f' % f1)
 log_writer.add_scalar('eval/f1', f1, step=step)
 if args.save_dir is not None:
-    P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+    P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin'))

demo/finetune_sentiment_analysis.py

@@ -177,9 +177,9 @@ if not args.eval:
                         log.debug('acc %.5f' % acc)
                         if args.save_dir is not None:
                             P.save(model.state_dict(),
-                                   args.save_dir / 'ckpt.bin')
+                                   str(args.save_dir / 'ckpt.bin'))
         if args.save_dir is not None:
-            P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+            P.save(model.state_dict(), str(args.save_dir / 'ckpt.bin'))
 else:
     feature_column = propeller.data.FeatureColumns([
         propeller.data.TextColumn(
@@ -189,7 +189,7 @@ else:
             tokenizer=tokenizer.tokenize),
     ])
 
-    sd = P.load(args.init_checkpoint)
+    sd = P.load(str(args.init_checkpoint))
     model.set_dict(sd)
     model.eval()
 

demo/pretrain/pretrain.py

@@ -394,7 +394,7 @@ if __name__ == '__main__':
                 log.debug(msg)
             if step % 1000 == 0 and env.dev_id == 0:
                 log.debug('saveing...')
-                P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+                P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin'))
             if step > args.max_steps:
                 break
     log.info('done')

demo/seq2seq/decode.py

@@ -401,7 +401,7 @@ if __name__ == '__main__':
     rev_dict[tokenizer.pad_id] = ''  # replace [PAD]
     rev_dict[tokenizer.unk_id] = ''  # replace [PAD]
 
-    sd = P.load(args.save_dir)
+    sd = P.load(str(args.save_dir))
     ernie.set_state_dict(sd)
 
     def map_fn(src_ids):

demo/seq2seq/finetune_seq2seq.py

@@ -308,7 +308,7 @@ def seq2seq(model, tokenizer, args):
                 log.debug(msg)
 
             if args.save_dir is not None and step % 1000 == 0 and env.dev_id == 0:
-                P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+                P.save(model.state_dict(), str(args.save_dir / 'ckpt.bin'))
 
             if args.predict_output_dir is not None and step > args.skip_eval_steps and step % args.eval_steps == 0:
                 assert  args.predict_output_dir.exists(), \
@@ -320,7 +320,7 @@ def seq2seq(model, tokenizer, args):
         evaluate(model, dev_ds, step, args)
 
     if args.save_dir is not None:
-        P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
+        P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin'))
 
 
 if __name__ == '__main__':
@@ -414,7 +414,7 @@ if __name__ == '__main__':
 
     if args.init_checkpoint is not None:
         log.info('loading checkpoint from %s' % args.init_checkpoint)
-        sd = P.load(args.init_checkpoint)
+        sd = P.load(str(args.init_checkpoint))
         ernie.set_state_dict(sd)
 
     seq2seq(ernie, tokenizer, args)

ernie/modeling_ernie.py

@@ -290,7 +290,7 @@ class PretrainedModel(object):
         #    log.debug('load pretrained weight from program state')
         #    F.io.load_program_state(param_path) #buggy in dygraph.gurad, push paddle to fix
         if state_dict_path.exists():
-            m = P.load(state_dict_path)
+            m = P.load(str(state_dict_path))
             for k, v in model.state_dict().items():
                 if k not in m:
                     log.warn('param:%s not set in pretrained model, skip' % k)