[parser] support omegaconf (#7793)

README.md

@@ -726,7 +726,7 @@ docker exec -it llamafactory bash
 ### Deploy with OpenAI-style API and vLLM
 
 ```bash
-API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
+API_PORT=8000 llamafactory-cli api examples/inference/llama3.yaml infer_backend=vllm vllm_enforce_eager=true
 ```
 
 > [!TIP]

README_zh.md

@@ -730,7 +730,7 @@ docker exec -it llamafactory bash
 ### 利用 vLLM 部署 OpenAI API
 
 ```bash
-API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
+API_PORT=8000 llamafactory-cli api examples/inference/llama3.yaml infer_backend=vllm vllm_enforce_eager=true
 ```
 
 > [!TIP]

examples/README.md

@@ -15,6 +15,18 @@ Use `CUDA_VISIBLE_DEVICES` (GPU) or `ASCEND_RT_VISIBLE_DEVICES` (NPU) to choose
 
 By default, LLaMA-Factory uses all visible computing devices.
 
+Basic usage:
+
+```bash
+llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+```
+
+Advanced usage:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml learning_rate=1e-5 logging_steps=1
+```
+
 ## Examples
 
 ### LoRA Fine-Tuning
@@ -34,7 +46,6 @@ llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 #### Multimodal Supervised Fine-Tuning
 
 ```bash
-llamafactory-cli train examples/train_lora/llava1_5_lora_sft.yaml
 llamafactory-cli train examples/train_lora/qwen2vl_lora_sft.yaml
 ```
 

examples/README_zh.md

@@ -15,6 +15,18 @@
 
 LLaMA-Factory 默认使用所有可见的计算设备。
 
+基础用法：
+
+```bash
+llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+```
+
+高级用法：
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml learning_rate=1e-5 logging_steps=1
+```
+
 ## 示例
 
 ### LoRA 微调
@@ -34,7 +46,6 @@ llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 #### 多模态指令监督微调
 
 ```bash
-llamafactory-cli train examples/train_lora/llava1_5_lora_sft.yaml
 llamafactory-cli train examples/train_lora/qwen2vl_lora_sft.yaml
 ```
 

examples/inference/llama3.yaml

@@ -1,4 +1,4 @@
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 template: llama3
-infer_backend: huggingface  # choices: [huggingface, vllm]
+infer_backend: huggingface  # choices: [huggingface, vllm, sglang]
 trust_remote_code: true

examples/inference/llama3_full_sft.yaml

@@ -1,4 +1,4 @@
 model_name_or_path: saves/llama3-8b/full/sft
 template: llama3
-infer_backend: huggingface  # choices: [huggingface, vllm]
+infer_backend: huggingface  # choices: [huggingface, vllm, sglang]
 trust_remote_code: true

examples/inference/llama3_lora_sft.yaml

@@ -1,5 +1,5 @@
 model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 adapter_name_or_path: saves/llama3-8b/lora/sft
 template: llama3
-infer_backend: huggingface  # choices: [huggingface, vllm]
+infer_backend: huggingface  # choices: [huggingface, vllm, sglang]
 trust_remote_code: true

examples/inference/llama3_sglang.yaml

@@ -1,4 +0,0 @@
-model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-template: llama3
-infer_backend: sglang
-trust_remote_code: true

examples/inference/llama3_vllm.yaml

@@ -1,5 +0,0 @@
-model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
-template: llama3
-infer_backend: vllm
-vllm_enforce_eager: true
-trust_remote_code: true

examples/inference/llava1_5.yaml

@@ -1,4 +0,0 @@
-model_name_or_path: llava-hf/llava-1.5-7b-hf
-template: llava
-infer_backend: huggingface  # choices: [huggingface, vllm]
-trust_remote_code: true

examples/inference/qwen2_vl.yaml

@@ -1,4 +1,4 @@
-model_name_or_path: Qwen/Qwen2-VL-7B-Instruct
+model_name_or_path: Qwen/Qwen2.5-VL-7B-Instruct
 template: qwen2_vl
-infer_backend: huggingface  # choices: [huggingface, vllm]
+infer_backend: huggingface  # choices: [huggingface, vllm, sglang]
 trust_remote_code: true

examples/merge_lora/llama3_full_sft.yaml

@@ -6,5 +6,5 @@ trust_remote_code: true
 ### export
 export_dir: output/llama3_full_sft
 export_size: 5
-export_device: cpu
+export_device: cpu  # choices: [cpu, auto]
 export_legacy_format: false

examples/merge_lora/llama3_gptq.yaml

@@ -6,7 +6,7 @@ trust_remote_code: true
 ### export
 export_dir: output/llama3_gptq
 export_quantization_bit: 4
-export_quantization_dataset: data/c4_demo.json
+export_quantization_dataset: data/c4_demo.jsonl
 export_size: 5
-export_device: cpu
+export_device: cpu  # choices: [cpu, auto]
 export_legacy_format: false

examples/merge_lora/llama3_lora_sft.yaml

@@ -9,5 +9,5 @@ trust_remote_code: true
 ### export
 export_dir: output/llama3_lora_sft
 export_size: 5
-export_device: cpu
+export_device: cpu  # choices: [cpu, auto]
 export_legacy_format: false

examples/merge_lora/qwen2vl_lora_sft.yaml

@@ -1,7 +1,7 @@
 ### Note: DO NOT use quantized model or quantization_bit when merging lora adapters
 
 ### model
-model_name_or_path: Qwen/Qwen2-VL-7B-Instruct
+model_name_or_path: Qwen/Qwen2.5-VL-7B-Instruct
 adapter_name_or_path: saves/qwen2_vl-7b/lora/sft
 template: qwen2_vl
 trust_remote_code: true
@@ -9,5 +9,5 @@ trust_remote_code: true
 ### export
 export_dir: output/qwen2_vl_lora_sft
 export_size: 5
-export_device: cpu
+export_device: cpu  # choices: [cpu, auto]
 export_legacy_format: false

examples/train_lora/llava1_5_lora_sft.yaml

@@ -1,45 +0,0 @@
-### model
-model_name_or_path: llava-hf/llava-1.5-7b-hf
-trust_remote_code: true
-
-### method
-stage: sft
-do_train: true
-finetuning_type: lora
-lora_rank: 8
-lora_target: all
-
-### dataset
-dataset: mllm_demo
-template: llava
-cutoff_len: 2048
-max_samples: 1000
-overwrite_cache: true
-preprocessing_num_workers: 16
-dataloader_num_workers: 4
-
-### output
-output_dir: saves/llava1_5-7b/lora/sft
-logging_steps: 10
-save_steps: 500
-plot_loss: true
-overwrite_output_dir: true
-save_only_model: false
-report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
-
-### train
-per_device_train_batch_size: 1
-gradient_accumulation_steps: 8
-learning_rate: 1.0e-4
-num_train_epochs: 3.0
-lr_scheduler_type: cosine
-warmup_ratio: 0.1
-bf16: true
-ddp_timeout: 180000000
-resume_from_checkpoint: null
-
-### eval
-# val_size: 0.1
-# per_device_eval_batch_size: 1
-# eval_strategy: steps
-# eval_steps: 500

examples/train_lora/qwen2vl_lora_dpo.yaml

@@ -1,5 +1,5 @@
 ### model
-model_name_or_path: Qwen/Qwen2-VL-7B-Instruct
+model_name_or_path: Qwen/Qwen2.5-VL-7B-Instruct
 image_max_pixels: 262144
 video_max_pixels: 16384
 trust_remote_code: true

examples/train_lora/qwen2vl_lora_sft.yaml

@@ -1,5 +1,5 @@
 ### model
-model_name_or_path: Qwen/Qwen2-VL-7B-Instruct
+model_name_or_path: Qwen/Qwen2.5-VL-7B-Instruct
 image_max_pixels: 262144
 video_max_pixels: 16384
 trust_remote_code: true

requirements.txt

@@ -15,6 +15,7 @@ fastapi
 sse-starlette
 matplotlib>=3.7.0
 fire
+omegaconf
 packaging
 pyyaml
 numpy<2.0.0

src/llamafactory/hparams/parser.py

@@ -24,6 +24,7 @@ from typing import Any, Optional, Union
 import torch
 import transformers
 import yaml
+from omegaconf import OmegaConf
 from transformers import HfArgumentParser
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.trainer_utils import get_last_checkpoint
@@ -59,10 +60,14 @@ def read_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> Union[
     if args is not None:
         return args
 
-    if len(sys.argv) == 2 and (sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml")):
+    if sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml"):
-        return yaml.safe_load(Path(sys.argv[1]).absolute().read_text())
+        override_config = OmegaConf.from_cli(sys.argv[2:])
-    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        dict_config = yaml.safe_load(Path(sys.argv[1]).absolute().read_text())
-        return json.loads(Path(sys.argv[1]).absolute().read_text())
+        return OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
+    elif sys.argv[1].endswith(".json"):
+        override_config = OmegaConf.from_cli(sys.argv[2:])
+        dict_config = json.loads(Path(sys.argv[1]).absolute().read_text())
+        return OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
     else:
         return sys.argv[1:]
 
@@ -330,12 +335,20 @@ def get_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _
         logger.warning_rank0("Specify `ref_model` for computing rewards at evaluation.")
 
     # Post-process training arguments
+    training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
+    training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
+    training_args.remove_unused_columns = False  # important for multimodal dataset
+
+    if finetuning_args.finetuning_type == "lora":
+        # https://github.com/huggingface/transformers/blob/v4.50.0/src/transformers/trainer.py#L782
+        training_args.label_names = training_args.label_names or ["labels"]
+
     if (
         training_args.parallel_mode == ParallelMode.DISTRIBUTED
         and training_args.ddp_find_unused_parameters is None
         and finetuning_args.finetuning_type == "lora"
     ):
-        logger.warning_rank0("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
+        logger.info_rank0("Set `ddp_find_unused_parameters` to False in DDP training since LoRA is enabled.")
         training_args.ddp_find_unused_parameters = False
 
     if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:

src/llamafactory/train/dpo/workflow.py

@@ -63,9 +63,6 @@ def run_dpo(
     else:
         ref_model = None
 
-    # Update arguments
-    training_args.remove_unused_columns = False  # important for multimodal and pairwise dataset
-
     # Initialize our Trainer
     trainer = CustomDPOTrainer(
         model=model,

src/llamafactory/train/kto/workflow.py

@@ -59,9 +59,6 @@ def run_kto(
     else:
         ref_model = create_ref_model(model_args, finetuning_args)
 
-    # Update arguments
-    training_args.remove_unused_columns = False  # important for multimodal and pairwise dataset
-
     # Initialize our Trainer
     trainer = CustomKTOTrainer(
         model=model,

src/llamafactory/train/rm/workflow.py

@@ -48,9 +48,6 @@ def run_rm(
         template=template, model=model, pad_to_multiple_of=8, **tokenizer_module
     )
 
-    # Update arguments
-    training_args.remove_unused_columns = False  # important for multimodal and pairwise dataset
-
     # Initialize our Trainer
     trainer = PairwiseTrainer(
         model=model,

src/llamafactory/train/sft/workflow.py

@@ -65,11 +65,6 @@ def run_sft(
         **tokenizer_module,
     )
 
-    # Override the decoding parameters of Seq2SeqTrainer
-    training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
-    training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
-    training_args.remove_unused_columns = False  # important for multimodal dataset
-
     # Metric utils
     metric_module = {}
     if training_args.predict_with_generate:

tests/train/test_sft_trainer.py

@@ -50,6 +50,10 @@ class DataCollatorWithVerbose(DataCollatorWithPadding):
     verbose_list: list[dict[str, Any]] = field(default_factory=list)
 
     def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:
+        features = [
+            {k: v for k, v in feature.items() if k in ["input_ids", "attention_mask", "labels"]}
+            for feature in features
+        ]
         self.verbose_list.extend(features)
         batch = super().__call__(features)
         return {k: v[:, :1] for k, v in batch.items()}  # truncate input length