added sotopia template

llm_ft/fastchat/train/train.py

@@ -29,7 +29,8 @@
 from transformers.trainer_pt_utils import LabelSmoother
 
 from fastchat.conversation import SeparatorStyle
-from fastchat.model.model_adapter import get_conversation_template
+# from fastchat.model.model_adapter import get_conversation_template
+from fastchat.conversation import get_conv_template
 
 IGNORE_TOKEN_ID = LabelSmoother.ignore_index
 
@@ -50,7 +51,7 @@ class DataArguments:
     )
     lazy_preprocess: bool = False
     shuffle: bool = True
-    abort_long_seq: bool = False
+    drop_long_seq: bool = False
 
 
 @dataclass
@@ -87,9 +88,9 @@ def trainer_save_model_safe(trainer: transformers.Trainer):
 def preprocess(
     sources,
     tokenizer: transformers.PreTrainedTokenizer,
-    abort_long_seq: bool = False,
+    drop_long_seq: bool = False,
 ) -> Dict:
-    conv = get_conversation_template("vicuna")
+    conv = get_conv_template("sotopia")
     roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
 
     # Apply prompt templates
@@ -106,13 +107,13 @@ def preprocess(
             conv.append_message(role, sentence["value"])
         conversations.append(conv.get_prompt())
 
-    if abort_long_seq:
+    if drop_long_seq:
         new_conversation = []
         for temp_conv in conversations:
             token_len = tokenizer(temp_conv, return_tensors="pt", padding=False, truncation=False).input_ids.size()[1]
             if token_len <= tokenizer.model_max_length: new_conversation.append(temp_conv)
         conversation = new_conversation
-        print(f"Aborted conversations longer than {tokenizer.model_max_length}; Now have {len(conversation)} conversations")
+        print(f"Dropping conversations longer than {tokenizer.model_max_length}; Now have {len(conversation)} conversations")
 
     # Tokenize conversations
     input_ids = tokenizer(
@@ -174,12 +175,12 @@ def preprocess(
 class SupervisedDataset(Dataset):
     """Dataset for supervised fine-tuning."""
 
-    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, abort_long_seq: bool = False):
+    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, drop_long_seq: bool = False):
         super(SupervisedDataset, self).__init__()
 
         rank0_print("Formatting inputs...")
         sources = [example["conversations"] for example in raw_data]
-        data_dict = preprocess(sources, tokenizer, abort_long_seq=abort_long_seq)
+        data_dict = preprocess(sources, tokenizer, drop_long_seq=drop_long_seq)
 
         self.input_ids = data_dict["input_ids"]
         self.labels = data_dict["labels"]
@@ -199,10 +200,10 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
 class LazySupervisedDataset(Dataset):
     """Dataset for supervised fine-tuning."""
 
-    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, abort_long_seq: bool = False):
+    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, drop_long_seq: bool = False):
         super(LazySupervisedDataset, self).__init__()
         self.tokenizer = tokenizer
-        self.abort_long_seq = abort_long_seq
+        self.drop_long_seq = drop_long_seq
 
         rank0_print("Formatting inputs...Skip in lazy mode")
         self.tokenizer = tokenizer
@@ -239,13 +240,13 @@ def make_supervised_data_module(
     train_json = json.load(open(data_args.data_path, "r"))
     if data_args.shuffle: random.shuffle(train_json)
 
-    train_dataset = dataset_cls(train_json, tokenizer=tokenizer, abort_long_seq = data_args.abort_long_seq)
+    train_dataset = dataset_cls(train_json, tokenizer=tokenizer, drop_long_seq = data_args.drop_long_seq)
 
     if data_args.eval_data_path:
         eval_json = json.load(open(data_args.eval_data_path, "r"))
         if data_args.shuffle: random.shuffle(train_json)
 
-        eval_dataset = dataset_cls(eval_json, tokenizer=tokenizer, abort_long_seq = data_args.abort_long_seq)
+        eval_dataset = dataset_cls(eval_json, tokenizer=tokenizer, drop_long_seq = data_args.drop_long_seq)
     else:
         eval_dataset = None
 

llm_ft/fastchat/train/train_lora.py

@@ -112,6 +112,8 @@ def train():
         training_args,
         lora_args,
     ) = parser.parse_args_into_dataclasses()
+
+    print(data_args)
 
     device_map = None
     world_size = int(os.environ.get("WORLD_SIZE", 1))

llm_ft/llama2-13b_qlora_train.sh

@@ -1,16 +1,16 @@
 deepspeed --num_gpus=1 fastchat/train/train_lora.py \
-    --model_name_or_path meta-llama/Llama-2-13b-chat-hf \
+    --model_name_or_path meta-llama/Llama-2-13b-hf \
     --lora_r 8 \
     --lora_alpha 16 \
     --lora_dropout 0.05 \
-    --data_path ./data/fastchat-ft-gpt4-gpt4-easy-2-side-partial-speak-drop-long.json \
+    --data_path ./data/fastchat-ft-gpt4-gpt4-easy-2-side-partial.json \
     --shuffle True \
     --bf16 True \
-    --output_dir ./checkpoint-shuffle-speak-drop-long \
-    --num_train_epochs 4 \
+    --output_dir ./checkpoint-shuffle-drop-long \
+    --num_train_epochs 20 \
     --per_device_train_batch_size 1 \
     --per_device_eval_batch_size 1 \
-    --gradient_accumulation_steps 32 \
+    --gradient_accumulation_steps 16 \
     --evaluation_strategy "no" \
     --save_strategy "epoch" \
     --save_total_limit 6 \
@@ -25,12 +25,12 @@ deepspeed --num_gpus=1 fastchat/train/train_lora.py \
     --hf_access_token "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" \
     --tf32 True \
     --flash_attn True \
-    --abort_long_seq True \
+    --drop_long_seq True \
 
 # Possible other options
 # --flash_attn True \
 # --tf32 True \
 # --save_strategy "steps" \ 
 # --save_steps 1200 \
-# --abort_long_seq True \
+# --drop_long_seq True \
 # --lazy_preprocess True \