Merge branch 'hiyouga:main' into main

requirements.txt

@@ -1,5 +1,5 @@
 torch>=1.13.1
-transformers>=4.36.2
+transformers>=4.37.2
 datasets>=2.14.3
 accelerate>=0.21.0
 peft>=0.7.0

src/llmtuner/api/app.py

@@ -120,6 +120,9 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
 
     def chat_completion(messages: Sequence[Dict[str, str]], system: str, tools: str, request: ChatCompletionRequest):
         if request.stream:
+            if tools:
+                raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
+
             generate = stream_chat_completion(messages, system, tools, request)
             return EventSourceResponse(generate, media_type="text/event-stream")
 

src/llmtuner/data/loader.py

@@ -155,9 +155,6 @@ def get_dataset(
                 dataset = dataset.to_iterable_dataset()
             return dataset
 
-        if data_args.streaming:
-            raise ValueError("Turn off dataset streaming to save cache files.")
-
     with training_args.main_process_first(desc="load dataset"):
         all_datasets = []
         for dataset_attr in get_dataset_list(data_args):  # TODO: add split

src/llmtuner/data/preprocess.py

@@ -22,12 +22,8 @@ def preprocess_pretrain_dataset(
     examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
 ) -> Dict[str, List[List[int]]]:
     # build grouped texts with format `X1 X2 X3 ...`
-    text_examples = [examples["prompt"][i][0]["content"] for i in range(len(examples["prompt"]))]
+    text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
     tokenized_examples = tokenizer(text_examples, add_special_tokens=False)
-    for i in range(len(tokenized_examples["input_ids"])):
-        tokenized_examples["input_ids"][i] += [tokenizer.eos_token_id]
-        tokenized_examples["attention_mask"][i] += [1]
-
     concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
     total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
     block_size = data_args.cutoff_len
@@ -59,7 +55,12 @@ def preprocess_supervised_dataset(
         input_ids, labels = [], []
         for turn_idx, (source_ids, target_ids) in enumerate(
             template.encode_multiturn(
-                tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+                tokenizer,
+                messages,
+                examples["system"][i],
+                examples["tools"][i],
+                data_args.cutoff_len,
+                data_args.reserved_label_len,
             )
         ):
             if data_args.train_on_prompt:
@@ -147,7 +148,12 @@ def preprocess_unsupervised_dataset(
             messages = examples["prompt"][i] + [{"role": Role.ASSISTANT, "content": ""}]
 
         input_ids, labels = template.encode_oneturn(
-            tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+            tokenizer,
+            messages,
+            examples["system"][i],
+            examples["tools"][i],
+            data_args.cutoff_len,
+            data_args.reserved_label_len,
         )
 
         if template.efficient_eos:
@@ -176,10 +182,20 @@ def preprocess_pairwise_dataset(
         rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
 
         prompt_ids, chosen_ids = template.encode_oneturn(
-            tokenizer, chosen_messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+            tokenizer,
+            chosen_messages,
+            examples["system"][i],
+            examples["tools"][i],
+            data_args.cutoff_len,
+            data_args.reserved_label_len,
         )
         _, rejected_ids = template.encode_oneturn(
-            tokenizer, rejected_messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+            tokenizer,
+            rejected_messages,
+            examples["system"][i],
+            examples["tools"][i],
+            data_args.cutoff_len,
+            data_args.reserved_label_len,
         )
 
         if template.efficient_eos:

src/llmtuner/data/template.py

@@ -37,7 +37,7 @@ class Template:
         system: Optional[str] = None,
         tools: Optional[str] = None,
         cutoff_len: Optional[int] = 1_000_000,
-        reserved_label_len: Optional[int] = 16,
+        reserved_label_len: Optional[int] = 1,
     ) -> Tuple[List[int], List[int]]:
         r"""
         Returns a single pair of token ids representing prompt and response respectively.
@@ -57,7 +57,7 @@ class Template:
         system: Optional[str] = None,
         tools: Optional[str] = None,
         cutoff_len: Optional[int] = 1_000_000,
-        reserved_label_len: Optional[int] = 16,
+        reserved_label_len: Optional[int] = 1,
     ) -> Sequence[Tuple[List[int], List[int]]]:
         r"""
         Returns multiple pairs of token ids representing prompts and responses respectively.
@@ -144,10 +144,10 @@ class Template:
                 max_len=(cutoff_len - total_length),
                 reserved_label_len=reserved_label_len,
             )
-            encoded_messages[i] = encoded_messages[i][:max_source_len]
+            source_ids = encoded_messages[i][:max_source_len]
-            encoded_messages[i + 1] = encoded_messages[i + 1][:max_target_len]
+            target_ids = encoded_messages[i + 1][:max_target_len]
-            total_length += len(encoded_messages[i]) + len(encoded_messages[i + 1])
+            total_length += len(source_ids) + len(target_ids)
-            encoded_pairs.append((encoded_messages[i], encoded_messages[i + 1]))
+            encoded_pairs.append((source_ids, target_ids))
 
         return encoded_pairs
 
@@ -218,7 +218,7 @@ def register_template(
     default_user_formatter = StringFormatter(slots=["{{content}}"])
     default_assistant_formatter = StringFormatter(slots=["{{content}}"] + eos_slots)
     default_function_formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}"] + eos_slots)
-    default_tool_formatter = ToolFormatter(slots="default")
+    default_tool_formatter = ToolFormatter(tool_format="default")
     default_separator_formatter = EmptyFormatter()
     templates[name] = template_class(
         format_user=format_user or default_user_formatter,
@@ -356,6 +356,14 @@ register_template(
 )
 
 
+register_template(
+    name="cpm",
+    format_user=StringFormatter(slots=["<用户>{{content}}<AI>"]),
+    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
+    force_system=True,
+)
+
+
 register_template(
     name="deepseek",
     format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
@@ -464,7 +472,7 @@ register_template(
 
 register_template(
     name="orion",
-    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: </s>"]),
+    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: ", {"eos_token"}]),
     format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
     force_system=True,
 )

src/llmtuner/hparams/data_args.py

@@ -21,10 +21,10 @@ class DataArguments:
         default="train", metadata={"help": "Which dataset split to use for training and evaluation."}
     )
     cutoff_len: Optional[int] = field(
-        default=1024, metadata={"help": "The maximum length of the model inputs after tokenization."}
+        default=1024, metadata={"help": "The cutoff length of the model inputs after tokenization."}
     )
     reserved_label_len: Optional[int] = field(
-        default=1, metadata={"help": "The maximum length reserved for label after tokenization."}
+        default=1, metadata={"help": "The minimum cutoff length reserved for label after tokenization."}
     )
     train_on_prompt: Optional[bool] = field(
         default=False, metadata={"help": "Whether to disable the mask on the prompt or not."}
@@ -57,7 +57,7 @@ class DataArguments:
     ignore_pad_token_for_loss: Optional[bool] = field(
         default=True,
         metadata={
-            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
+            "help": "Whether or not to ignore the tokens corresponding to padded labels in the loss computation."
         },
     )
     val_size: Optional[float] = field(

src/llmtuner/hparams/finetuning_args.py

@@ -17,6 +17,7 @@ class FreezeArguments:
                   BLOOM & Falcon & ChatGLM choices: ["mlp", "self_attention"], \
                   Qwen choices: ["mlp", "attn"], \
                   Phi choices: ["mlp", "mixer"], \
+                  InternLM2 choices: ["feed_forward", "attention"], \
                   Others choices: the same as LLaMA.'
         },
     )

src/llmtuner/hparams/parser.py

@@ -63,13 +63,12 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
         if model_args.adapter_name_or_path is not None and finetuning_args.create_new_adapter:
             raise ValueError("Cannot create new adapter upon a quantized model.")
 
-    if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
+        if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
-        if finetuning_args.finetuning_type != "lora":
-            raise ValueError("Multiple adapters are only available for LoRA tuning.")
-
-        if model_args.quantization_bit is not None:
             raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
 
+    if model_args.adapter_name_or_path is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Only LoRA method has adapters.")
+
 
 def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     parser = HfArgumentParser(_TRAIN_ARGS)

src/llmtuner/model/adapter.py

@@ -52,8 +52,18 @@ def init_adapter(
         else:  # fine-tuning the first n layers if num_layer_trainable < 0
             trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)]  # noqa: C416
 
+        freeze_modules = set()
+        for name, _ in model.named_modules():
+            if "0." in name:
+                freeze_modules.add(name.split("0.")[-1].split(".")[0])
+
         trainable_layers = []
         for module_name in finetuning_args.name_module_trainable:
+            if module_name not in freeze_modules:
+                raise ValueError(
+                    "Module {} is not found, please choose from {}".format(module_name, ", ".join(freeze_modules))
+                )
+
             for idx in trainable_layer_ids:
                 trainable_layers.append("{:d}.{}".format(idx, module_name))
 

src/llmtuner/model/loader.py

@@ -21,7 +21,7 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)
 
 
-require_version("transformers>=4.36.2", "To fix: pip install transformers>=4.36.2")
+require_version("transformers>=4.37.2", "To fix: pip install transformers>=4.37.2")
 require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
 require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
 require_version("peft>=0.7.0", "To fix: pip install peft>=0.7.0")

src/llmtuner/model/utils.py

@@ -41,7 +41,7 @@ def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
         # Make sure tied weights are tied before creating the device map.
         model.tie_weights()
         device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs)
-        device_map_kwargs = {"device_map": device_map}
+        device_map_kwargs = {"device_map": device_map, "offload_dir": "offload"}
         if "skip_keys" in inspect.signature(dispatch_model).parameters:
             device_map_kwargs["skip_keys"] = model._skip_keys_device_placement
         return dispatch_model(model, **device_map_kwargs)

src/llmtuner/train/ppo/utils.py

@@ -1,7 +1,9 @@
 import json
+from contextlib import nullcontext
 from typing import TYPE_CHECKING, Dict, List, Literal, Optional
 
 import torch
+from transformers.integrations import is_deepspeed_zero3_enabled
 
 from ...extras.packages import is_requests_available
 
@@ -23,18 +25,22 @@ def get_rewards_from_server(server_url: str, messages: List[str]) -> List[torch.
 
 
 def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["default", "reward"]) -> None:
-    if target == "reward":  # save default head temporarily
+    if is_deepspeed_zero3_enabled():
-        valuehead_state_dict: Dict[str, torch.Tensor] = model.v_head.state_dict()
+        import deepspeed  # type: ignore
-        setattr(model, "default_head_weight", valuehead_state_dict["summary.weight"].detach().clone())
-        setattr(model, "default_head_bias", valuehead_state_dict["summary.bias"].detach().clone())
 
-    model.pretrained_model.set_adapter(target)  # set the LoRA adapter to be active
+        params = [model.v_head.summary.weight, model.v_head.summary.bias]
-    model.v_head.load_state_dict(
+        context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0)
-        {
+    else:
-            "summary.weight": model.get_buffer("{}_head_weight".format(target)).detach().clone(),
+        context_maybe_zero3 = nullcontext()
-            "summary.bias": model.get_buffer("{}_head_bias".format(target)).detach().clone(),
+
-        }
+    with context_maybe_zero3:
-    )
+        if target == "reward":  # save default head temporarily
+            setattr(model, "default_head_weight", model.v_head.summary.weight.data.detach().clone())
+            setattr(model, "default_head_bias", model.v_head.summary.bias.data.detach().clone())
+
+        model.pretrained_model.set_adapter(target)  # set the LoRA adapter to be active
+        model.v_head.summary.weight.data = model.get_buffer("{}_head_weight".format(target)).detach().clone()
+        model.v_head.summary.bias.data = model.get_buffer("{}_head_bias".format(target)).detach().clone()
 
 
 def dump_layernorm(model: "PreTrainedModel") -> Dict[str, torch.Tensor]: