[misc] fix cli (#7204)

Former-commit-id: 999f57133ca163c7108d2d5ee8194eca9b2109b4

src/llamafactory/cli.py

@@ -88,18 +88,24 @@ def main():
     elif command == Command.TRAIN:
         force_torchrun = is_env_enabled("FORCE_TORCHRUN")
         if force_torchrun or (get_device_count() > 1 and not use_ray()):
+            nnodes = os.getenv("NNODES", "1")
+            node_rank = os.getenv("NODE_RANK", "0")
+            nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
             master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
             master_port = os.getenv("MASTER_PORT", str(random.randint(20001, 29999)))
-            logger.info_rank0(f"Initializing distributed tasks at: {master_addr}:{master_port}")
+            logger.info_rank0(f"Initializing {nproc_per_node} distributed tasks at: {master_addr}:{master_port}")
+            if int(nnodes) > 1:
+                print(f"Multi-node training enabled: num nodes: {nnodes}, node rank: {node_rank}")
+
             process = subprocess.run(
                 (
                     "torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
                     "--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
                 )
                 .format(
-                    nnodes=os.getenv("NNODES", "1"),
-                    node_rank=os.getenv("NODE_RANK", "0"),
-                    nproc_per_node=os.getenv("NPROC_PER_NODE", str(get_device_count())),
+                    nnodes=nnodes,
+                    node_rank=node_rank,
+                    nproc_per_node=nproc_per_node,
                     master_addr=master_addr,
                     master_port=master_port,
                     file_name=launcher.__file__,
@@ -119,7 +125,7 @@ def main():
     elif command == Command.HELP:
         print(USAGE)
     else:
-        raise NotImplementedError(f"Unknown command: {command}.")
+        print(f"Unknown command: {command}.\n{USAGE}")
 
 
 if __name__ == "__main__":

src/llamafactory/hparams/finetuning_args.py

@@ -387,7 +387,7 @@ class SwanLabArguments:
 
 @dataclass
 class FinetuningArguments(
-    FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments, ApolloArguments, BAdamArgument, SwanLabArguments
+    SwanLabArguments, BAdamArgument, ApolloArguments, GaloreArguments, RLHFArguments, LoraArguments, FreezeArguments
 ):
     r"""
     Arguments pertaining to which techniques we are going to fine-tuning with.

src/llamafactory/hparams/model_args.py

@@ -24,6 +24,162 @@ from transformers.training_args import _convert_str_dict
 from typing_extensions import Self
 
 
+@dataclass
+class BaseModelArguments:
+    r"""
+    Arguments pertaining to the model.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."
+        },
+    )
+    adapter_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Path to the adapter weight or identifier from huggingface.co/models. "
+                "Use commas to separate multiple adapters."
+            )
+        },
+    )
+    adapter_folder: Optional[str] = field(
+        default=None,
+        metadata={"help": "The folder containing the adapter weights to load."},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use one of the fast tokenizer (backed by the tokenizers library)."},
+    )
+    resize_vocab: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to resize the tokenizer vocab and the embedding layers."},
+    )
+    split_special_tokens: bool = field(
+        default=False,
+        metadata={"help": "Whether or not the special tokens should be split during the tokenization process."},
+    )
+    new_special_tokens: Optional[str] = field(
+        default=None,
+        metadata={"help": "Special tokens to be added into the tokenizer. Use commas to separate multiple tokens."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    low_cpu_mem_usage: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use memory-efficient model loading."},
+    )
+    rope_scaling: Optional[Literal["linear", "dynamic", "yarn", "llama3"]] = field(
+        default=None,
+        metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
+    )
+    flash_attn: Literal["auto", "disabled", "sdpa", "fa2"] = field(
+        default="auto",
+        metadata={"help": "Enable FlashAttention for faster training and inference."},
+    )
+    shift_attn: bool = field(
+        default=False,
+        metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."},
+    )
+    mixture_of_depths: Optional[Literal["convert", "load"]] = field(
+        default=None,
+        metadata={"help": "Convert the model to mixture-of-depths (MoD) or load the MoD model."},
+    )
+    use_unsloth: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."},
+    )
+    use_unsloth_gc: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use unsloth's gradient checkpointing (no need to install unsloth)."},
+    )
+    enable_liger_kernel: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to enable liger kernel for faster training."},
+    )
+    moe_aux_loss_coef: Optional[float] = field(
+        default=None,
+        metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},
+    )
+    disable_gradient_checkpointing: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to disable gradient checkpointing."},
+    )
+    use_reentrant_gc: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use reentrant gradient checkpointing."},
+    )
+    upcast_layernorm: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to upcast the layernorm weights in fp32."},
+    )
+    upcast_lmhead_output: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to upcast the output of lm_head in fp32."},
+    )
+    train_from_scratch: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to randomly initialize the model weights."},
+    )
+    infer_backend: Literal["huggingface", "vllm"] = field(
+        default="huggingface",
+        metadata={"help": "Backend engine used at inference."},
+    )
+    offload_folder: str = field(
+        default="offload",
+        metadata={"help": "Path to offload model weights."},
+    )
+    use_cache: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use KV cache in generation."},
+    )
+    infer_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
+        default="auto",
+        metadata={"help": "Data type for model weights and activations at inference."},
+    )
+    hf_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Hugging Face Hub."},
+    )
+    ms_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with ModelScope Hub."},
+    )
+    om_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Modelers Hub."},
+    )
+    print_param_status: bool = field(
+        default=False,
+        metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={"help": "Whether to trust the execution of code from datasets/models defined on the Hub or not."},
+    )
+
+    def __post_init__(self):
+        if self.model_name_or_path is None:
+            raise ValueError("Please provide `model_name_or_path`.")
+
+        if self.split_special_tokens and self.use_fast_tokenizer:
+            raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
+
+        if self.adapter_name_or_path is not None:  # support merging multiple lora weights
+            self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]
+
+        if self.new_special_tokens is not None:  # support multiple special tokens
+            self.new_special_tokens = [token.strip() for token in self.new_special_tokens.split(",")]
+
+
 @dataclass
 class QuantizationArguments:
     r"""
@@ -127,6 +283,10 @@ class ExportArguments:
         metadata={"help": "The name of the repository if push the model to the Hugging Face hub."},
     )
 
+    def __post_init__(self):
+        if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
+            raise ValueError("Quantization dataset is necessary for exporting.")
+
 
 @dataclass
 class VllmArguments:
@@ -155,148 +315,19 @@ class VllmArguments:
         metadata={"help": "Config to initialize the vllm engine. Please use JSON strings."},
     )
 
+    def __post_init__(self):
+        if isinstance(self.vllm_config, str) and self.vllm_config.startswith("{"):
+            self.vllm_config = _convert_str_dict(json.loads(self.vllm_config))
+
 
 @dataclass
-class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments, VllmArguments):
+class ModelArguments(VllmArguments, ExportArguments, ProcessorArguments, QuantizationArguments, BaseModelArguments):
     r"""
     Arguments pertaining to which model/config/tokenizer we are going to fine-tune or infer.
+
+    The class on the most right will be displayed first.
     """
 
-    model_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."
-        },
-    )
-    adapter_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": (
-                "Path to the adapter weight or identifier from huggingface.co/models. "
-                "Use commas to separate multiple adapters."
-            )
-        },
-    )
-    adapter_folder: Optional[str] = field(
-        default=None,
-        metadata={"help": "The folder containing the adapter weights to load."},
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether or not to use one of the fast tokenizer (backed by the tokenizers library)."},
-    )
-    resize_vocab: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to resize the tokenizer vocab and the embedding layers."},
-    )
-    split_special_tokens: bool = field(
-        default=False,
-        metadata={"help": "Whether or not the special tokens should be split during the tokenization process."},
-    )
-    new_special_tokens: Optional[str] = field(
-        default=None,
-        metadata={"help": "Special tokens to be added into the tokenizer. Use commas to separate multiple tokens."},
-    )
-    model_revision: str = field(
-        default="main",
-        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
-    )
-    low_cpu_mem_usage: bool = field(
-        default=True,
-        metadata={"help": "Whether or not to use memory-efficient model loading."},
-    )
-    rope_scaling: Optional[Literal["linear", "dynamic", "yarn", "llama3"]] = field(
-        default=None,
-        metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
-    )
-    flash_attn: Literal["auto", "disabled", "sdpa", "fa2"] = field(
-        default="auto",
-        metadata={"help": "Enable FlashAttention for faster training and inference."},
-    )
-    shift_attn: bool = field(
-        default=False,
-        metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."},
-    )
-    mixture_of_depths: Optional[Literal["convert", "load"]] = field(
-        default=None,
-        metadata={"help": "Convert the model to mixture-of-depths (MoD) or load the MoD model."},
-    )
-    use_unsloth: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."},
-    )
-    use_unsloth_gc: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to use unsloth's gradient checkpointing."},
-    )
-    enable_liger_kernel: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to enable liger kernel for faster training."},
-    )
-    moe_aux_loss_coef: Optional[float] = field(
-        default=None,
-        metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},
-    )
-    disable_gradient_checkpointing: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to disable gradient checkpointing."},
-    )
-    use_reentrant_gc: bool = field(
-        default=True,
-        metadata={"help": "Whether or not to use reentrant gradient checkpointing."},
-    )
-    upcast_layernorm: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to upcast the layernorm weights in fp32."},
-    )
-    upcast_lmhead_output: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to upcast the output of lm_head in fp32."},
-    )
-    train_from_scratch: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to randomly initialize the model weights."},
-    )
-    infer_backend: Literal["huggingface", "vllm"] = field(
-        default="huggingface",
-        metadata={"help": "Backend engine used at inference."},
-    )
-    offload_folder: str = field(
-        default="offload",
-        metadata={"help": "Path to offload model weights."},
-    )
-    use_cache: bool = field(
-        default=True,
-        metadata={"help": "Whether or not to use KV cache in generation."},
-    )
-    infer_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
-        default="auto",
-        metadata={"help": "Data type for model weights and activations at inference."},
-    )
-    hf_hub_token: Optional[str] = field(
-        default=None,
-        metadata={"help": "Auth token to log in with Hugging Face Hub."},
-    )
-    ms_hub_token: Optional[str] = field(
-        default=None,
-        metadata={"help": "Auth token to log in with ModelScope Hub."},
-    )
-    om_hub_token: Optional[str] = field(
-        default=None,
-        metadata={"help": "Auth token to log in with Modelers Hub."},
-    )
-    print_param_status: bool = field(
-        default=False,
-        metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
-    )
-    trust_remote_code: bool = field(
-        default=False,
-        metadata={"help": "Whether to trust the execution of code from datasets/models defined on the Hub or not."},
-    )
     compute_dtype: Optional[torch.dtype] = field(
         default=None,
         init=False,
@@ -319,23 +350,9 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
     )
 
     def __post_init__(self):
-        if self.model_name_or_path is None:
-            raise ValueError("Please provide `model_name_or_path`.")
-
-        if self.split_special_tokens and self.use_fast_tokenizer:
-            raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
-
-        if self.adapter_name_or_path is not None:  # support merging multiple lora weights
-            self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]
-
-        if self.new_special_tokens is not None:  # support multiple special tokens
-            self.new_special_tokens = [token.strip() for token in self.new_special_tokens.split(",")]
-
-        if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
-            raise ValueError("Quantization dataset is necessary for exporting.")
-
-        if isinstance(self.vllm_config, str) and self.vllm_config.startswith("{"):
-            self.vllm_config = _convert_str_dict(json.loads(self.vllm_config))
+        BaseModelArguments.__post_init__(self)
+        ExportArguments.__post_init__(self)
+        VllmArguments.__post_init__(self)
 
     @classmethod
     def copyfrom(cls, source: "Self", **kwargs) -> "Self":

src/llamafactory/hparams/parser.py

@@ -382,10 +382,10 @@ def get_train_args(args: Optional[Union[Dict[str, Any], List[str]]] = None) -> _
 
     # Log on each process the small summary
     logger.info(
-        "Process rank: {}, device: {}, n_gpu: {}, distributed training: {}, compute dtype: {}".format(
-            training_args.local_rank,
+        "Process rank: {}, world size: {}, device: {}, distributed training: {}, compute dtype: {}".format(
+            training_args.process_index,
+            training_args.world_size,
             training_args.device,
-            training_args.n_gpu,
             training_args.parallel_mode == ParallelMode.DISTRIBUTED,
             str(model_args.compute_dtype),
         )

src/llamafactory/train/tuner.py

@@ -86,6 +86,9 @@ def _training_function(config: Dict[str, Any]) -> None:
 
 def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None) -> None:
     args = read_args(args)
+    if "-h" in args or "--help" in args:
+        get_train_args(args)
+
     ray_args = get_ray_args(args)
     callbacks = callbacks or []
     if ray_args.use_ray: