imporve log

Former-commit-id: a6abf375975ffea3d51e1b944c9855b5f62ffac8

src/llamafactory/chat/hf_engine.py

@@ -63,7 +63,7 @@ class HuggingfaceEngine(BaseEngine):
         try:
             asyncio.get_event_loop()
         except RuntimeError:
-            logger.warning_once("There is no current event loop, creating a new one.")
+            logger.warning_rank0_once("There is no current event loop, creating a new one.")
             loop = asyncio.new_event_loop()
             asyncio.set_event_loop(loop)
 

src/llamafactory/data/data_utils.py

@@ -56,12 +56,12 @@ def merge_dataset(
         return all_datasets[0]
     elif data_args.mix_strategy == "concat":
         if data_args.streaming:
-            logger.warning_once("The samples between different datasets will not be mixed in streaming mode.")
+            logger.warning_rank0_once("The samples between different datasets will not be mixed in streaming mode.")
 
         return concatenate_datasets(all_datasets)
     elif data_args.mix_strategy.startswith("interleave"):
         if not data_args.streaming:
-            logger.warning_once("We recommend using `mix_strategy=concat` in non-streaming mode.")
+            logger.warning_rank0_once("We recommend using `mix_strategy=concat` in non-streaming mode.")
 
         return interleave_datasets(
             datasets=all_datasets,

src/llamafactory/data/loader.py

@@ -18,11 +18,10 @@ from typing import TYPE_CHECKING, Dict, Literal, Optional, Sequence, Union
 
 import numpy as np
 from datasets import DatasetDict, load_dataset, load_from_disk
-from transformers.utils.versions import require_version
 
 from ..extras import logging
 from ..extras.constants import FILEEXT2TYPE
-from ..extras.misc import has_tokenized_data
+from ..extras.misc import check_version, has_tokenized_data
 from .aligner import align_dataset
 from .data_utils import merge_dataset, split_dataset
 from .parser import get_dataset_list
@@ -84,7 +83,7 @@ def _load_single_dataset(
         raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")
 
     if dataset_attr.load_from == "ms_hub":
-        require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
+        check_version("modelscope>=1.11.0", mandatory=True)
         from modelscope import MsDataset  # type: ignore
         from modelscope.utils.config_ds import MS_DATASETS_CACHE  # type: ignore
 
@@ -103,7 +102,7 @@ def _load_single_dataset(
             dataset = dataset.to_hf_dataset()
 
     elif dataset_attr.load_from == "om_hub":
-        require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
+        check_version("openmind>=0.8.0", mandatory=True)
         from openmind import OmDataset  # type: ignore
         from openmind.utils.hub import OM_DATASETS_CACHE  # type: ignore
 

src/llamafactory/data/mm_plugin.py

@@ -73,10 +73,14 @@ class BasePlugin:
         Validates if this model accepts the input modalities.
         """
         if len(images) != 0 and self.image_token is None:
-            raise ValueError("This model does not support image input.")
+            raise ValueError(
+                "This model does not support image input. Please check whether the correct `template` is used."
+            )
 
         if len(videos) != 0 and self.video_token is None:
-            raise ValueError("This model does not support video input.")
+            raise ValueError(
+                "This model does not support video input. Please check whether the correct `template` is used."
+            )
 
     def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
         r"""

src/llamafactory/data/template.py

@@ -15,10 +15,10 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
 
-from transformers.utils.versions import require_version
 from typing_extensions import override
 
 from ..extras import logging
+from ..extras.misc import check_version
 from .data_utils import Role
 from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
 from .mm_plugin import get_mm_plugin
@@ -365,7 +365,7 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
             raise ValueError(f"Template {data_args.template} does not exist.")
 
     if template.mm_plugin.__class__.__name__ != "BasePlugin":
-        require_version("transformers>=4.45.0", "To fix: pip install transformers>=4.45.0")
+        check_version("transformers>=4.45.0")
 
     if data_args.train_on_prompt and template.efficient_eos:
         raise ValueError("Current template does not support `train_on_prompt`.")

src/llamafactory/extras/logging.py

@@ -68,7 +68,7 @@ class LoggerHandler(logging.Handler):
 
 class _Logger(logging.Logger):
     r"""
-    A logger that supports info_rank0 and warning_once.
+    A logger that supports rank0 logging.
     """
 
     def info_rank0(self, *args, **kwargs) -> None:
@@ -77,7 +77,7 @@ class _Logger(logging.Logger):
     def warning_rank0(self, *args, **kwargs) -> None:
         self.warning(*args, **kwargs)
 
-    def warning_once(self, *args, **kwargs) -> None:
+    def warning_rank0_once(self, *args, **kwargs) -> None:
         self.warning(*args, **kwargs)
 
 
@@ -163,11 +163,11 @@ def warning_rank0(self: "logging.Logger", *args, **kwargs) -> None:
 
 
 @lru_cache(None)
-def warning_once(self: "logging.Logger", *args, **kwargs) -> None:
+def warning_rank0_once(self: "logging.Logger", *args, **kwargs) -> None:
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         self.warning(*args, **kwargs)
 
 
 logging.Logger.info_rank0 = info_rank0
 logging.Logger.warning_rank0 = warning_rank0
-logging.Logger.warning_once = warning_once
+logging.Logger.warning_rank0_once = warning_rank0_once

src/llamafactory/extras/misc.py

@@ -73,19 +73,31 @@ class AverageMeter:
         self.avg = self.sum / self.count
 
 
+def check_version(requirement: str, mandatory: bool = False) -> None:
+    r"""
+    Optionally checks the package version.
+    """
+    if os.getenv("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"] and not mandatory:
+        logger.warning_rank0_once("Version checking has been disabled, may lead to unexpected behaviors.")
+        return
+
+    if mandatory:
+        hint = f"To fix: run `pip install {requirement}`."
+    else:
+        hint = f"To fix: run `pip install {requirement}` or set `DISABLE_VERSION_CHECK=1` to skip this check."
+
+    require_version(requirement, hint)
+
+
 def check_dependencies() -> None:
     r"""
     Checks the version of the required packages.
     """
-    if os.getenv("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
-        logger.warning_once("Version checking has been disabled, may lead to unexpected behaviors.")
-        return
-
-    require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
-    require_version("datasets>=2.16.0,<=3.1.0", "To fix: pip install datasets>=2.16.0,<=3.1.0")
-    require_version("accelerate>=0.34.0,<=1.0.1", "To fix: pip install accelerate>=0.34.0,<=1.0.1")
-    require_version("peft>=0.11.1,<=0.12.0", "To fix: pip install peft>=0.11.1,<=0.12.0")
-    require_version("trl>=0.8.6,<=0.9.6", "To fix: pip install trl>=0.8.6,<=0.9.6")
+    check_version("transformers>=4.41.2,<=4.46.1")
+    check_version("datasets>=2.16.0,<=3.1.0")
+    check_version("accelerate>=0.34.0,<=1.0.1")
+    check_version("peft>=0.11.1,<=0.12.0")
+    check_version("trl>=0.8.6,<=0.9.6")
 
 
 def calculate_tps(dataset: Sequence[Dict[str, Any]], metrics: Dict[str, float], stage: Literal["sft", "rm"]) -> float:
@@ -253,7 +265,7 @@ def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
         return model_args.model_name_or_path
 
     if use_modelscope():
-        require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
+        check_version("modelscope>=1.11.0", mandatory=True)
         from modelscope import snapshot_download  # type: ignore
 
         revision = "master" if model_args.model_revision == "main" else model_args.model_revision
@@ -264,7 +276,7 @@ def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
         )
 
     if use_openmind():
-        require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
+        check_version("openmind>=0.8.0", mandatory=True)
         from openmind.utils.hub import snapshot_download  # type: ignore
 
         return snapshot_download(

src/llamafactory/hparams/parser.py

@@ -29,11 +29,10 @@ from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.training_args import ParallelMode
 from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
-from transformers.utils.versions import require_version
 
 from ..extras import logging
 from ..extras.constants import CHECKPOINT_NAMES
-from ..extras.misc import check_dependencies, get_current_device
+from ..extras.misc import check_dependencies, check_version, get_current_device
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
@@ -124,38 +123,35 @@ def _check_extra_dependencies(
     finetuning_args: "FinetuningArguments",
     training_args: Optional["TrainingArguments"] = None,
 ) -> None:
-    if os.getenv("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
-        logger.warning_once("Version checking has been disabled, may lead to unexpected behaviors.")
-        return
-
     if model_args.use_unsloth:
-        require_version("unsloth", "Please install unsloth: https://github.com/unslothai/unsloth")
+        check_version("unsloth", mandatory=True)
 
     if model_args.enable_liger_kernel:
-        require_version("liger-kernel", "To fix: pip install liger-kernel")
+        check_version("liger-kernel", mandatory=True)
 
     if model_args.mixture_of_depths is not None:
-        require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")
+        check_version("mixture-of-depth>=1.1.6", mandatory=True)
 
     if model_args.infer_backend == "vllm":
-        require_version("vllm>=0.4.3,<0.6.7", "To fix: pip install vllm>=0.4.3,<0.6.7")
+        check_version("vllm>=0.4.3,<0.6.7")
+        check_version("vllm", mandatory=True)
 
     if finetuning_args.use_galore:
-        require_version("galore_torch", "To fix: pip install galore_torch")
+        check_version("galore_torch", mandatory=True)
 
     if finetuning_args.use_badam:
-        require_version("badam>=1.2.1", "To fix: pip install badam>=1.2.1")
+        check_version("badam>=1.2.1", mandatory=True)
 
     if finetuning_args.use_adam_mini:
-        require_version("adam-mini", "To fix: pip install adam-mini")
+        check_version("adam-mini", mandatory=True)
 
     if finetuning_args.plot_loss:
-        require_version("matplotlib", "To fix: pip install matplotlib")
+        check_version("matplotlib", mandatory=True)
 
     if training_args is not None and training_args.predict_with_generate:
-        require_version("jieba", "To fix: pip install jieba")
-        require_version("nltk", "To fix: pip install nltk")
-        require_version("rouge_chinese", "To fix: pip install rouge-chinese")
+        check_version("jieba", mandatory=True)
+        check_version("nltk", mandatory=True)
+        check_version("rouge_chinese", mandatory=True)
 
 
 def _parse_train_args(args: Optional[Union[Dict[str, Any], List[str]]] = None) -> _TRAIN_CLS:

src/llamafactory/model/model_utils/attention.py

@@ -15,9 +15,9 @@
 from typing import TYPE_CHECKING
 
 from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
-from transformers.utils.versions import require_version
 
 from ...extras import logging
+from ...extras.misc import check_version
 
 
 if TYPE_CHECKING:
@@ -35,8 +35,8 @@ def configure_attn_implementation(
     if getattr(config, "model_type", None) == "gemma2" and is_trainable:
         if model_args.flash_attn == "auto" or model_args.flash_attn == "fa2":
             if is_flash_attn_2_available():
-                require_version("transformers>=4.42.4", "To fix: pip install transformers>=4.42.4")
-                require_version("flash_attn>=2.6.3", "To fix: pip install flash_attn>=2.6.3")
+                check_version("transformers>=4.42.4")
+                check_version("flash_attn>=2.6.3")
                 if model_args.flash_attn != "fa2":
                     logger.warning_rank0("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
                     model_args.flash_attn = "fa2"

src/llamafactory/model/model_utils/checkpointing.py

@@ -122,7 +122,7 @@ def _gradient_checkpointing_enable(
     if "value" in inspect.signature(self._set_gradient_checkpointing).parameters:  # old GC format
         self.apply(partial(self._set_gradient_checkpointing, value=True))
         self.enable_input_require_grads()
-        logger.warning_once("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
+        logger.warning_rank0_once("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
     else:  # have already enabled input require gradients
         self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
 

src/llamafactory/model/model_utils/longlora.py

@@ -31,10 +31,10 @@ from transformers.models.llama.modeling_llama import (
     apply_rotary_pos_emb,
     repeat_kv,
 )
-from transformers.utils.versions import require_version
 
 from ...extras import logging
 from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
+from ...extras.misc import check_version
 from ...extras.packages import is_transformers_version_greater_than
 
 
@@ -353,7 +353,7 @@ def llama_sdpa_attention_forward(
 
 
 def _apply_llama_patch() -> None:
-    require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
+    check_version("transformers>=4.41.2,<=4.46.1")
     LlamaAttention.forward = llama_attention_forward
     LlamaFlashAttention2.forward = llama_flash_attention_2_forward
     LlamaSdpaAttention.forward = llama_sdpa_attention_forward

src/llamafactory/model/model_utils/moe.py

@@ -16,7 +16,8 @@ from typing import TYPE_CHECKING, Sequence
 
 import torch
 from transformers.integrations import is_deepspeed_zero3_enabled
-from transformers.utils.versions import require_version
+
+from ...extras.misc import check_version
 
 
 if TYPE_CHECKING:
@@ -26,7 +27,7 @@ if TYPE_CHECKING:
 
 
 def _set_z3_leaf_modules(model: "PreTrainedModel", leaf_modules: Sequence["torch.nn.Module"]) -> None:
-    require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
+    check_version("deepspeed>=0.13.0")
     from deepspeed.utils import set_z3_leaf_modules  # type: ignore
 
     set_z3_leaf_modules(model, leaf_modules)

src/llamafactory/model/model_utils/packing.py

@@ -41,9 +41,9 @@ from typing import TYPE_CHECKING, Tuple
 
 import torch
 import torch.nn.functional as F
-from transformers.utils.versions import require_version
 
 from ...extras import logging
+from ...extras.misc import check_version
 from ...extras.packages import is_transformers_version_greater_than
 
 
@@ -118,6 +118,6 @@ def configure_packing(model_args: "ModelArguments", is_trainable: bool) -> None:
     if not is_trainable or not model_args.block_diag_attn:
         return
 
-    require_version("transformers>=4.43.0,<=4.46.1", "To fix: pip install transformers>=4.43.0,<=4.46.1")
+    check_version("transformers>=4.43.0,<=4.46.1")
     transformers.modeling_flash_attention_utils._get_unpad_data = get_unpad_data
     logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")

src/llamafactory/model/model_utils/quantization.py

@@ -26,11 +26,10 @@ from datasets import load_dataset
 from transformers import BitsAndBytesConfig, EetqConfig, GPTQConfig, HqqConfig
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.modeling_utils import is_fsdp_enabled
-from transformers.utils.versions import require_version
 
 from ...extras import logging
 from ...extras.constants import FILEEXT2TYPE
-from ...extras.misc import get_current_device
+from ...extras.misc import check_version, get_current_device
 
 
 if TYPE_CHECKING:
@@ -118,15 +117,15 @@ def configure_quantization(
         quant_method = quantization_config.get("quant_method", "")
 
         if quant_method == QuantizationMethod.GPTQ:
-            require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
+            check_version("auto_gptq>=0.5.0", mandatory=True)
             quantization_config.pop("disable_exllama", None)  # remove deprecated args
             quantization_config["use_exllama"] = False  # disable exllama
 
         if quant_method == QuantizationMethod.AWQ:
-            require_version("autoawq", "To fix: pip install autoawq")
+            check_version("autoawq", mandatory=True)
 
         if quant_method == QuantizationMethod.AQLM:
-            require_version("aqlm>=1.1.0", "To fix: pip install aqlm[gpu]>=1.1.0")
+            check_version("aqlm>=1.1.0", mandatory=True)
             quantization_config["bits"] = 2
 
         quant_bits = quantization_config.get("bits", "?")
@@ -136,8 +135,8 @@ def configure_quantization(
         if model_args.export_quantization_bit not in [8, 4, 3, 2]:
             raise ValueError("AutoGPTQ only accepts 2/3/4/8-bit quantization.")
 
-        require_version("optimum>=1.17.0", "To fix: pip install optimum>=1.17.0")
-        require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
+        check_version("optimum>=1.17.0", mandatory=True)
+        check_version("auto_gptq>=0.5.0", mandatory=True)
         from accelerate.utils import get_max_memory
 
         if getattr(config, "model_type", None) == "chatglm":
@@ -154,10 +153,10 @@ def configure_quantization(
     elif model_args.quantization_bit is not None:  # on-the-fly
         if model_args.quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
             if model_args.quantization_bit == 8:
-                require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
+                check_version("bitsandbytes>=0.37.0", mandatory=True)
                 init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
             elif model_args.quantization_bit == 4:
-                require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
+                check_version("bitsandbytes>=0.39.0", mandatory=True)
                 init_kwargs["quantization_config"] = BitsAndBytesConfig(
                     load_in_4bit=True,
                     bnb_4bit_compute_dtype=model_args.compute_dtype,
@@ -175,7 +174,7 @@ def configure_quantization(
                 if model_args.quantization_bit != 4:
                     raise ValueError("Only 4-bit quantized model can use fsdp+qlora or auto device map.")
 
-                require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
+                check_version("bitsandbytes>=0.43.0", mandatory=True)
             else:
                 init_kwargs["device_map"] = {"": get_current_device()}  # change auto device map for inference
 
@@ -187,7 +186,7 @@ def configure_quantization(
             if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
                 raise ValueError("HQQ quantization is incompatible with DeepSpeed ZeRO-3 or FSDP.")
 
-            require_version("hqq", "To fix: pip install hqq")
+            check_version("hqq", mandatory=True)
             init_kwargs["quantization_config"] = HqqConfig(
                 nbits=model_args.quantization_bit, quant_zero=False, quant_scale=False, axis=0
             )  # use ATEN kernel (axis=0) for performance
@@ -199,6 +198,6 @@ def configure_quantization(
             if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
                 raise ValueError("EETQ quantization is incompatible with DeepSpeed ZeRO-3 or FSDP.")
 
-            require_version("eetq", "To fix: pip install eetq")
+            check_version("eetq", mandatory=True)
             init_kwargs["quantization_config"] = EetqConfig()
             logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with EETQ.")

src/llamafactory/train/callbacks.py

@@ -239,7 +239,7 @@ class LogCallback(TrainerCallback):
             and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
             and args.overwrite_output_dir
         ):
-            logger.warning_once("Previous trainer log in this folder will be deleted.")
+            logger.warning_rank0_once("Previous trainer log in this folder will be deleted.")
             os.remove(os.path.join(args.output_dir, TRAINER_LOG))
 
     @override

src/llamafactory/train/sft/workflow.py

@@ -122,7 +122,7 @@ def run_sft(
 
     # Predict
     if training_args.do_predict:
-        logger.warning_once("Batch generation can be very slow. Consider using `scripts/vllm_infer.py` instead.")
+        logger.warning_rank0_once("Batch generation can be very slow. Consider using `scripts/vllm_infer.py` instead.")
         predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict", **gen_kwargs)
         trainer.log_metrics("predict", predict_results.metrics)
         trainer.save_metrics("predict", predict_results.metrics)