update examples

Former-commit-id: 369294b31c8a03a1cafcee83eb31a817007d3c49

src/llmtuner/chat/vllm_engine.py

@@ -1,8 +1,6 @@
 import uuid
 from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
 
-from transformers.utils.versions import require_version
-
 from ..data import get_template_and_fix_tokenizer
 from ..extras.misc import get_device_count
 from ..extras.packages import is_vllm_available
@@ -25,7 +23,6 @@ class VllmEngine(BaseEngine):
         finetuning_args: "FinetuningArguments",
         generating_args: "GeneratingArguments",
     ) -> None:
-        require_version("vllm>=0.3.3", "To fix: pip install vllm>=0.3.3")
         self.can_generate = finetuning_args.stage == "sft"
         engine_args = AsyncEngineArgs(
             model=model_args.model_name_or_path,

src/llmtuner/extras/packages.py

@@ -49,10 +49,6 @@ def is_starlette_available():
     return _is_package_available("sse_starlette")
 
 
-def is_unsloth_available():
-    return _is_package_available("unsloth")
-
-
 def is_uvicorn_available():
     return _is_package_available("uvicorn")
 

src/llmtuner/hparams/parser.py

@@ -8,10 +8,10 @@ import transformers
 from transformers import HfArgumentParser, Seq2SeqTrainingArguments
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import is_torch_bf16_gpu_available
+from transformers.utils.versions import require_version
 
 from ..extras.logging import get_logger
 from ..extras.misc import check_dependencies, get_current_device
-from ..extras.packages import is_unsloth_available
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
@@ -74,6 +74,26 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
             raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
 
 
+def _check_extra_dependencies(
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    training_args: Optional["Seq2SeqTrainingArguments"] = None,
+) -> None:
+    if model_args.use_unsloth:
+        require_version("unsloth", "Please install unsloth: https://github.com/unslothai/unsloth")
+
+    if model_args.infer_backend == "vllm":
+        require_version("vllm>=0.3.3", "To fix: pip install vllm>=0.3.3")
+
+    if finetuning_args.use_galore:
+        require_version("galore_torch", "To fix: pip install galore_torch")
+
+    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")
+
+
 def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     parser = HfArgumentParser(_TRAIN_ARGS)
     return _parse_args(parser, args)
@@ -131,9 +151,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if training_args.do_train and training_args.predict_with_generate:
         raise ValueError("`predict_with_generate` cannot be set as True while training.")
 
-    if training_args.do_train and model_args.use_unsloth and not is_unsloth_available():
-        raise ValueError("Unsloth was not installed: https://github.com/unslothai/unsloth")
-
     if finetuning_args.use_dora and model_args.use_unsloth:
         raise ValueError("Unsloth does not support DoRA.")
 
@@ -158,6 +175,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
         raise ValueError("vLLM backend is only available for API, CLI and Web.")
 
     _verify_model_args(model_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args, training_args)
 
     if (
         training_args.do_train
@@ -277,6 +295,7 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
             raise ValueError("vLLM engine does not support RoPE scaling.")
 
     _verify_model_args(model_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args)
 
     if model_args.export_dir is not None:
         model_args.device_map = {"": torch.device(model_args.export_device)}
@@ -298,6 +317,7 @@ def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
         raise ValueError("vLLM backend is only available for API, CLI and Web.")
 
     _verify_model_args(model_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args)
 
     model_args.device_map = "auto"
 

src/llmtuner/model/loader.py

@@ -85,7 +85,9 @@ def load_model(
             logger.warning("Unsloth does not support loading adapters.")
 
     if model is None:
-        model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, config=config, **init_kwargs)
+        init_kwargs["config"] = config
+        init_kwargs["pretrained_model_name_or_path"] = model_args.model_name_or_path
+        model: "PreTrainedModel" = AutoModelForCausalLM.from_pretrained(**init_kwargs)
 
     patch_model(model, tokenizer, model_args, is_trainable)
     register_autoclass(config, model, tokenizer)

src/llmtuner/train/sft/metric.py

@@ -2,7 +2,6 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union
 
 import numpy as np
-from transformers.utils.versions import require_version
 
 from ...extras.constants import IGNORE_INDEX
 from ...extras.packages import is_jieba_available, is_nltk_available, is_rouge_available
@@ -33,10 +32,6 @@ class ComputeMetrics:
         r"""
         Uses the model predictions to compute metrics.
         """
-        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")
-
         preds, labels = eval_preds
         score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
 

src/llmtuner/train/utils.py

@@ -5,7 +5,6 @@ from transformers import Trainer
 from transformers.optimization import get_scheduler
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.trainer_pt_utils import get_parameter_names
-from transformers.utils.versions import require_version
 
 from ..extras.logging import get_logger
 from ..extras.packages import is_galore_available
@@ -168,8 +167,6 @@ def _create_galore_optimizer(
     training_args: "Seq2SeqTrainingArguments",
     finetuning_args: "FinetuningArguments",
 ) -> "torch.optim.Optimizer":
-    require_version("galore_torch", "To fix: pip install galore_torch")
-
     if len(finetuning_args.galore_target) == 1 and finetuning_args.galore_target[0] == "all":
         galore_targets = find_all_linear_modules(model)
     else: