Save PrefixEncoder params only

ptuning/README.md

@@ -39,6 +39,18 @@ bash train.sh
 ```shell
 bash evaluate.sh
 ```
+**[2023/04/10更新]** 在 P-tuning v2 训练时模型只保存 PrefixEncoder 部分的参数，在推理时需要同时载入原 ChatGLM-6B 模型以及 PrefixEncoder 的 Checkpoint，因此需要指定参数（已更新 `evaluate.sh`） ：
+
+```shell
+--model_name_or_path THUDM/chatglm-6b
+--ptuning_checkpoint $CHECKPOINT_PATH
+```
+
+仍然兼容旧版全参保存的 Checkpoint，只需要跟之前一样设定 `model_name_or_path`：
+
+```shell
+--model_name_or_path $CHECKPOINT_PATH
+```
 
 评测指标为中文 Rouge score 和 BLEU-4。生成的结果保存在
 `./output/adgen-chatglm-6b-pt-8-1e-2/generated_predictions.txt`。

ptuning/evaluate.sh

@@ -9,7 +9,8 @@ CUDA_VISIBLE_DEVICES=0 python3 main.py \
     --overwrite_cache \
     --prompt_column content \
     --response_column summary \
-    --model_name_or_path ./output/$CHECKPOINT/checkpoint-$STEP  \
+    --model_name_or_path THUDM/chatglm-6b \
+    --ptuning_checkpoint ./output/$CHECKPOINT/checkpoint-$STEP \
     --output_dir ./output/$CHECKPOINT \
     --overwrite_output_dir \
     --max_source_length 64 \

ptuning/main.py

@@ -28,6 +28,7 @@ from datasets import load_dataset
 import jieba 
 from rouge_chinese import Rouge
 from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+import torch
 
 import transformers
 from transformers import (
@@ -110,13 +111,28 @@ def main():
 
     tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
 
-    model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
+    if model_args.ptuning_checkpoint is not None:
+        # Evaluation
+        # Loading extra state dict of prefix encoder
+        model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
+        prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin"))
+        new_prefix_state_dict = {}
+        for k, v in prefix_state_dict.items():
+            new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
+        model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
+    else:
+        model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
 
     if model_args.quantization_bit is not None:
         print(f"Quantized to {model_args.quantization_bit} bit")
         model = model.quantize(model_args.quantization_bit)
-    model = model.half()
+    if model_args.pre_seq_len is not None:
-    model.transformer.prefix_encoder.float()
+        # P-tuning v2
+        model = model.half()
+        model.transformer.prefix_encoder.float()
+    else:
+        # Finetune
+        model = model.float()
 
     prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
 

ptuning/trainer_seq2seq.py

@@ -19,7 +19,7 @@ from torch import nn
 from torch.utils.data import Dataset
 
 from transformers.deepspeed import is_deepspeed_zero3_enabled
-from transformers.trainer import Trainer
+from trainer import Trainer
 from transformers.trainer_utils import PredictionOutput
 from transformers.utils import logging