Update finetune

chatglm/demo.py

@@ -1,5 +1,4 @@
 import sys
-
 sys.path.append("..")
 
 import json

chatglm/test_tokenizer.py

@@ -1,5 +1,4 @@
 import sys
-
 sys.path.append("..")
 
 import json

qwen/__init__.py

@@ -0,0 +1,2 @@
+from qwen.modeling_qwen import QWenLMHeadModel
+from qwen.configuration_qwen import QWenConfig

qwen/finetune.py

@@ -18,13 +18,16 @@ from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
 from accelerate.utils import DistributedType
 from modelscope import snapshot_download
 
+from modeling_qwen import QWenLMHeadModel
+
+
 IGNORE_TOKEN_ID = LabelSmoother.ignore_index
 
 
 @dataclass
 class ModelArguments:
     model_name_or_path: Optional[str] = field(default="qwen/Qwen-1_8B-Chat")
-    
+
 
 @dataclass
 class DataArguments:
@@ -101,12 +104,15 @@ def get_peft_state_maybe_zero_3(named_params, bias):
 
 local_rank = None
 
+
 def rank0_print(*args):
     if local_rank == 0:
         print(*args)
 
 
-def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str, bias="none"):
+def safe_save_model_for_hf_trainer(
+    trainer: transformers.Trainer, output_dir: str, bias="none"
+):
     """Collects the state dict and dump to disk."""
     # check if zero3 mode enabled
     if deepspeed.is_deepspeed_zero3_enabled():
@@ -126,16 +132,16 @@ def preprocess(
     sources,
     tokenizer: transformers.PreTrainedTokenizer,
     max_len: int,
-    system_message: str = "You are a helpful assistant."
+    system_message: str = "You are a helpful assistant.",
 ) -> Dict:
     roles = {"user": "<|im_start|>user", "assistant": "<|im_start|>assistant"}
 
     im_start = tokenizer.im_start_id
     im_end = tokenizer.im_end_id
-    nl_tokens = tokenizer('\n').input_ids
-    _system = tokenizer('system').input_ids + nl_tokens
-    _user = tokenizer('user').input_ids + nl_tokens
-    _assistant = tokenizer('assistant').input_ids + nl_tokens
+    nl_tokens = tokenizer("\n").input_ids
+    _system = tokenizer("system").input_ids + nl_tokens
+    _user = tokenizer("user").input_ids + nl_tokens
+    _assistant = tokenizer("assistant").input_ids + nl_tokens
 
     # Apply prompt templates
     input_ids, targets = [], []
@@ -144,20 +150,43 @@ def preprocess(
             source = source[1:]
 
         input_id, target = [], []
-        system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
+        system = (
+            [im_start]
+            + _system
+            + tokenizer(system_message).input_ids
+            + [im_end]
+            + nl_tokens
+        )
         input_id += system
-        target += [im_start] + [IGNORE_TOKEN_ID] * (len(system)-3) + [im_end] + nl_tokens
+        target += (
+            [im_start] + [IGNORE_TOKEN_ID] * (len(system) - 3) + [im_end] + nl_tokens
+        )
         assert len(input_id) == len(target)
         for j, sentence in enumerate(source):
             role = roles[sentence["from"]]
-            _input_id = tokenizer(role).input_ids + nl_tokens + \
-                tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
+            _input_id = (
+                tokenizer(role).input_ids
+                + nl_tokens
+                + tokenizer(sentence["value"]).input_ids
+                + [im_end]
+                + nl_tokens
+            )
             input_id += _input_id
-            if role == '<|im_start|>user':
-                _target = [im_start] + [IGNORE_TOKEN_ID] * (len(_input_id)-3) + [im_end] + nl_tokens
-            elif role == '<|im_start|>assistant':
-                _target = [im_start] + [IGNORE_TOKEN_ID] * len(tokenizer(role).input_ids) + \
-                    _input_id[len(tokenizer(role).input_ids)+1:-2] + [im_end] + nl_tokens
+            if role == "<|im_start|>user":
+                _target = (
+                    [im_start]
+                    + [IGNORE_TOKEN_ID] * (len(_input_id) - 3)
+                    + [im_end]
+                    + nl_tokens
+                )
+            elif role == "<|im_start|>assistant":
+                _target = (
+                    [im_start]
+                    + [IGNORE_TOKEN_ID] * len(tokenizer(role).input_ids)
+                    + _input_id[len(tokenizer(role).input_ids) + 1 : -2]
+                    + [im_end]
+                    + nl_tokens
+                )
             else:
                 raise NotImplementedError
             target += _target
@@ -179,7 +208,9 @@ def preprocess(
 class SupervisedDataset(Dataset):
     """Dataset for supervised fine-tuning."""
 
-    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int):
+    def __init__(
+        self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int
+    ):
         super(SupervisedDataset, self).__init__()
 
         rank0_print("Formatting inputs...")
@@ -204,7 +235,9 @@ class SupervisedDataset(Dataset):
 class LazySupervisedDataset(Dataset):
     """Dataset for supervised fine-tuning."""
 
-    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int):
+    def __init__(
+        self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int
+    ):
         super(LazySupervisedDataset, self).__init__()
         self.tokenizer = tokenizer
         self.max_len = max_len
@@ -221,7 +254,9 @@ class LazySupervisedDataset(Dataset):
         if i in self.cached_data_dict:
             return self.cached_data_dict[i]
 
-        ret = preprocess([self.raw_data[i]["conversations"]], self.tokenizer, self.max_len)
+        ret = preprocess(
+            [self.raw_data[i]["conversations"]], self.tokenizer, self.max_len
+        )
         ret = dict(
             input_ids=ret["input_ids"][0],
             labels=ret["labels"][0],
@@ -233,7 +268,9 @@ class LazySupervisedDataset(Dataset):
 
 
 def make_supervised_data_module(
-    tokenizer: transformers.PreTrainedTokenizer, data_args, max_len,
+    tokenizer: transformers.PreTrainedTokenizer,
+    data_args,
+    max_len,
 ) -> Dict:
     """Make dataset and collator for supervised fine-tuning."""
     dataset_cls = (
@@ -267,7 +304,10 @@ def train():
     ) = parser.parse_args_into_dataclasses()
 
     # This serves for single-gpu qlora.
-    if getattr(training_args, 'deepspeed', None) and int(os.environ.get("WORLD_SIZE", 1))==1:
+    if (
+        getattr(training_args, "deepspeed", None)
+        and int(os.environ.get("WORLD_SIZE", 1)) == 1
+    ):
         training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED
 
     local_rank = training_args.local_rank
@@ -278,9 +318,7 @@ def train():
     if lora_args.q_lora:
         device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else "auto"
         if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
-            logging.warning(
-                "FSDP or ZeRO3 are incompatible with QLoRA."
-            )
+            logging.warning("FSDP or ZeRO3 are incompatible with QLoRA.")
 
     model_dir = snapshot_download(model_args.model_name_or_path)
 
@@ -294,19 +332,18 @@ def train():
 
     # Load model and tokenizer
 
-
-    model = transformers.AutoModelForCausalLM.from_pretrained(
+    model = QWenLMHeadModel(config)
+    model = model.from_pretrained(
         model_dir,
         config=config,
         cache_dir=training_args.cache_dir,
         device_map=device_map,
         trust_remote_code=True,
-        quantization_config=GPTQConfig(
-            bits=4, disable_exllama=True
-        )
+        quantization_config=GPTQConfig(bits=4, disable_exllama=True)
         if training_args.use_lora and lora_args.q_lora
         else None,
     )
+
     tokenizer = transformers.AutoTokenizer.from_pretrained(
         model_dir,
         cache_dir=training_args.cache_dir,
@@ -318,7 +355,7 @@ def train():
     tokenizer.pad_token_id = tokenizer.eod_id
 
     if training_args.use_lora:
-        if lora_args.q_lora or 'chat' in model_dir.lower():
+        if lora_args.q_lora or "chat" in model_dir.lower():
             modules_to_save = None
         else:
             modules_to_save = ["wte", "lm_head"]
@@ -329,7 +366,7 @@ def train():
             lora_dropout=lora_args.lora_dropout,
             bias=lora_args.lora_bias,
             task_type="CAUSAL_LM",
-            modules_to_save=modules_to_save  # This argument serves for adding new tokens.
+            modules_to_save=modules_to_save,  # This argument serves for adding new tokens.
         )
         if lora_args.q_lora:
             model = prepare_model_for_kbit_training(
@@ -357,7 +394,9 @@ def train():
     trainer.train()
     trainer.save_state()
 
-    safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir, bias=lora_args.lora_bias)
+    safe_save_model_for_hf_trainer(
+        trainer=trainer, output_dir=training_args.output_dir, bias=lora_args.lora_bias
+    )
 
 
 if __name__ == "__main__":

tools/__init__.py

@@ -0,0 +1 @@
+import show