Merge pull request #5871 from hiyouga/dev

[loss&ui] fix incorrect loss of vlms, add extra args to ui

Former-commit-id: b2d4b9a7a870aba92ed6e74b7805d606ddc0edbc

requirements.txt

@@ -1,5 +1,5 @@
-transformers>=4.41.2,<=4.46.0
+transformers>=4.41.2,<=4.46.1
-datasets>=2.16.0,<=2.21.0
+datasets>=2.16.0,<=3.0.2
 accelerate>=0.34.0,<=1.0.1
 peft>=0.11.1,<=0.12.0
 trl>=0.8.6,<=0.9.6

src/llamafactory/__init__.py

@@ -20,17 +20,17 @@ Level:
 
 Dependency graph:
   main:
-    transformers>=4.41.2,<=4.46.0
+    transformers>=4.41.2,<=4.46.1
-    datasets>=2.16.0,<=2.21.0
+    datasets>=2.16.0,<=3.0.2
     accelerate>=0.34.0,<=1.0.1
     peft>=0.11.1,<=0.12.0
     trl>=0.8.6,<=0.9.6
   attention:
     transformers>=4.42.4 (gemma+fa2)
   longlora:
-    transformers>=4.41.2,<=4.46.0
+    transformers>=4.41.2,<=4.46.1
   packing:
-    transformers>=4.41.2,<=4.46.0
+    transformers>=4.41.2,<=4.46.1
 
 Disable version checking: DISABLE_VERSION_CHECK=1
 Enable VRAM recording: RECORD_VRAM=1

src/llamafactory/data/loader.py

@@ -69,25 +69,24 @@ def _load_single_dataset(
         if os.path.isdir(local_path):  # is directory
             for file_name in os.listdir(local_path):
                 data_files.append(os.path.join(local_path, file_name))
-                if data_path is None:
-                    data_path = FILEEXT2TYPE.get(file_name.split(".")[-1], None)
-                elif data_path != FILEEXT2TYPE.get(file_name.split(".")[-1], None):
-                    raise ValueError("File types should be identical.")
         elif os.path.isfile(local_path):  # is file
             data_files.append(local_path)
-            data_path = FILEEXT2TYPE.get(local_path.split(".")[-1], None)
         else:
             raise ValueError(f"File {local_path} not found.")
 
+        data_path = FILEEXT2TYPE.get(os.path.splitext(data_files[0])[-1][1:], None)
         if data_path is None:
             raise ValueError("Allowed file types: {}.".format(",".join(FILEEXT2TYPE.keys())))
+
+        if any(data_path != FILEEXT2TYPE.get(os.path.splitext(data_file)[-1][1:], None) for data_file in data_files):
+            raise ValueError("File types should be identical.")
     else:
         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")
-        from modelscope import MsDataset
+        from modelscope import MsDataset  # type: ignore
-        from modelscope.utils.config_ds import MS_DATASETS_CACHE
+        from modelscope.utils.config_ds import MS_DATASETS_CACHE  # type: ignore
 
         cache_dir = model_args.cache_dir or MS_DATASETS_CACHE
         dataset = MsDataset.load(
@@ -98,15 +97,15 @@ def _load_single_dataset(
             split=dataset_attr.split,
             cache_dir=cache_dir,
             token=model_args.ms_hub_token,
-            use_streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
+            use_streaming=data_args.streaming,
         )
         if isinstance(dataset, MsDataset):
             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")
-        from openmind import OmDataset
+        from openmind import OmDataset  # type: ignore
-        from openmind.utils.hub import OM_DATASETS_CACHE
+        from openmind.utils.hub import OM_DATASETS_CACHE  # type: ignore
 
         cache_dir = model_args.cache_dir or OM_DATASETS_CACHE
         dataset = OmDataset.load_dataset(
@@ -117,7 +116,7 @@ def _load_single_dataset(
             split=dataset_attr.split,
             cache_dir=cache_dir,
             token=model_args.om_hub_token,
-            streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
+            streaming=data_args.streaming,
         )
     else:
         dataset = load_dataset(
@@ -128,13 +127,10 @@ def _load_single_dataset(
             split=dataset_attr.split,
             cache_dir=model_args.cache_dir,
             token=model_args.hf_hub_token,
-            streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
+            streaming=data_args.streaming,
             trust_remote_code=True,
         )
 
-    if data_args.streaming and (dataset_attr.load_from == "file"):  # faster than specifying streaming=True
-        dataset = dataset.to_iterable_dataset()  # TODO: add num shards parameter
-
     if dataset_attr.num_samples is not None and not data_args.streaming:
         target_num = dataset_attr.num_samples
         indexes = np.random.permutation(len(dataset))[:target_num]  # all samples should be included

src/llamafactory/data/mm_plugin.py

@@ -471,9 +471,7 @@ class PixtralPlugin(BasePlugin):
             content = message["content"]
             while IMAGE_PLACEHOLDER in content:
                 if image_input_sizes is None:
-                    raise ValueError(
+                    raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
-                        "The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER)
-                    )
 
                 image_size = image_input_sizes[0][num_image_tokens]
                 height, width = image_size
@@ -489,7 +487,7 @@ class PixtralPlugin(BasePlugin):
             message["content"] = content
 
         if len(images) != num_image_tokens:
-            raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
+            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
 
         return messages
 

src/llamafactory/data/template.py

@@ -356,10 +356,6 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
     r"""
     Gets chat template and fixes the tokenizer.
     """
-    if data_args.template in ["llava", "paligemma", "qwen2_vl"]:
-        require_version("transformers>=4.45.0", "To fix: pip install transformers>=4.45.0")
-        require_version("accelerate>=0.34.0", "To fix: pip install accelerate>=0.34.0")
-
     if data_args.template is None:
         template = TEMPLATES["empty"]  # placeholder
     else:
@@ -367,6 +363,9 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
         if template is None:
             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")
+
     if data_args.train_on_prompt and template.efficient_eos:
         raise ValueError("Current template does not support `train_on_prompt`.")
 

src/llamafactory/extras/misc.py

@@ -79,8 +79,8 @@ def check_dependencies() -> None:
     if os.environ.get("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
         logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
     else:
-        require_version("transformers>=4.41.2,<=4.46.0", "To fix: pip install transformers>=4.41.2,<=4.46.0")
+        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,<=2.21.0", "To fix: pip install datasets>=2.16.0,<=2.21.0")
+        require_version("datasets>=2.16.0,<=3.0.2", "To fix: pip install datasets>=2.16.0,<=3.0.2")
         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")
@@ -237,7 +237,7 @@ def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
 
     if use_modelscope():
         require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
-        from modelscope import snapshot_download
+        from modelscope import snapshot_download  # type: ignore
 
         revision = "master" if model_args.model_revision == "main" else model_args.model_revision
         return snapshot_download(
@@ -248,7 +248,7 @@ 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")
-        from openmind.utils.hub import snapshot_download
+        from openmind.utils.hub import snapshot_download  # type: ignore
 
         return snapshot_download(
             model_args.model_name_or_path,

src/llamafactory/extras/packages.py

@@ -81,7 +81,7 @@ def is_transformers_version_greater_than_4_43():
 
 @lru_cache
 def is_transformers_version_equal_to_4_46():
-    return _get_package_version("transformers") == version.parse("4.46.0")
+    return version.parse("4.46.0") <= _get_package_version("transformers") <= version.parse("4.46.1")
 
 
 def is_uvicorn_available():

src/llamafactory/model/model_utils/longlora.py

@@ -353,7 +353,7 @@ def llama_sdpa_attention_forward(
 
 
 def _apply_llama_patch() -> None:
-    require_version("transformers>=4.41.2,<=4.46.0", "To fix: pip install transformers>=4.41.2,<=4.46.0")
+    require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install 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/packing.py

@@ -114,7 +114,7 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "tor
 
 
 def _patch_for_block_diag_attn(model_type: str) -> None:
-    require_version("transformers>=4.41.2,<=4.46.0", "To fix: pip install transformers>=4.41.2,<=4.46.0")
+    require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
     if is_transformers_version_greater_than_4_43():
         import transformers.modeling_flash_attention_utils
 

src/llamafactory/train/dpo/trainer.py

@@ -101,7 +101,7 @@ class CustomDPOTrainer(DPOTrainer):
             self.callback_handler.add_callback(PissaConvertCallback)
 
         if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
 
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
@@ -274,7 +274,7 @@ class CustomDPOTrainer(DPOTrainer):
         https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
         """
         loss = super().compute_loss(model, inputs, return_outputs)
-        if kwargs.pop("num_items_in_batch", False) and is_transformers_version_equal_to_4_46():
+        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
             loss /= self.args.gradient_accumulation_steps
 
         return loss

src/llamafactory/train/kto/trainer.py

@@ -96,7 +96,7 @@ class CustomKTOTrainer(KTOTrainer):
             self.add_callback(SaveProcessorCallback(processor))
 
         if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
 
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
@@ -247,7 +247,7 @@ class CustomKTOTrainer(KTOTrainer):
         https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
         """
         loss = super().compute_loss(model, inputs, return_outputs)
-        if kwargs.pop("num_items_in_batch", False) and is_transformers_version_equal_to_4_46():
+        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
             loss /= self.args.gradient_accumulation_steps
 
         return loss

src/llamafactory/train/ppo/trainer.py

@@ -181,7 +181,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             self.add_callback(SaveProcessorCallback(processor))
 
         if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
 
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)

src/llamafactory/train/pt/trainer.py

@@ -19,6 +19,7 @@ from transformers import Trainer
 from typing_extensions import override
 
 from ...extras.logging import get_logger
+from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -51,7 +52,7 @@ class CustomTrainer(Trainer):
             self.add_callback(PissaConvertCallback)
 
         if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
 
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
@@ -68,3 +69,15 @@ class CustomTrainer(Trainer):
     ) -> "torch.optim.lr_scheduler.LRScheduler":
         create_custom_scheduler(self.args, num_training_steps, optimizer)
         return super().create_scheduler(num_training_steps, optimizer)
+
+    @override
+    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
+        r"""
+        Fixes the loss value for transformers 4.46.0.
+        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
+        """
+        loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
+        if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
+            loss /= self.args.gradient_accumulation_steps  # other model should not scale the loss
+
+        return loss

src/llamafactory/train/rm/trainer.py

@@ -60,7 +60,7 @@ class PairwiseTrainer(Trainer):
             self.add_callback(PissaConvertCallback)
 
         if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
 
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
@@ -100,7 +100,7 @@ class PairwiseTrainer(Trainer):
 
         loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
 
-        if kwargs.pop("num_items_in_batch", False) and is_transformers_version_equal_to_4_46():
+        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
             loss /= self.args.gradient_accumulation_steps  # fixes the loss value for transformers 4.46.0
 
         if return_outputs:

src/llamafactory/train/sft/trainer.py

@@ -27,6 +27,7 @@ from typing_extensions import override
 
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
+from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -60,7 +61,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
             self.add_callback(PissaConvertCallback)
 
         if finetuning_args.use_badam:
-            from badam import BAdamCallback, clip_grad_norm_old_version
+            from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore
 
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
@@ -78,6 +79,18 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
         create_custom_scheduler(self.args, num_training_steps, optimizer)
         return super().create_scheduler(num_training_steps, optimizer)
 
+    @override
+    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
+        r"""
+        Fixes the loss value for transformers 4.46.0.
+        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
+        """
+        loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
+        if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
+            loss /= self.args.gradient_accumulation_steps  # other model should not scale the loss
+
+        return loss
+
     @override
     def prediction_step(
         self,

src/llamafactory/webui/components/top.py

@@ -41,7 +41,6 @@ def create_top() -> Dict[str, "Component"]:
         finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1)
         checkpoint_path = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=6)
 
-    with gr.Accordion(open=False) as advanced_tab:
     with gr.Row():
         quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", allow_custom_value=True, scale=2)
         quantization_method = gr.Dropdown(choices=["bitsandbytes", "hqq", "eetq"], value="bitsandbytes", scale=2)
@@ -66,7 +65,6 @@ def create_top() -> Dict[str, "Component"]:
         model_path=model_path,
         finetuning_type=finetuning_type,
         checkpoint_path=checkpoint_path,
-        advanced_tab=advanced_tab,
         quantization_bit=quantization_bit,
         quantization_method=quantization_method,
         template=template,

src/llamafactory/webui/components/train.py

@@ -91,7 +91,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             save_steps = gr.Slider(minimum=10, maximum=5000, value=100, step=10)
             warmup_steps = gr.Slider(minimum=0, maximum=5000, value=0, step=1)
             neftune_alpha = gr.Slider(minimum=0, maximum=10, value=0, step=0.1)
-            optim = gr.Textbox(value="adamw_torch")
+            extra_args = gr.Textbox(value='{"optim": "adamw_torch"}')
 
         with gr.Row():
             with gr.Column():
@@ -116,7 +116,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             save_steps,
             warmup_steps,
             neftune_alpha,
-            optim,
+            extra_args,
             packing,
             neat_packing,
             train_on_prompt,
@@ -134,7 +134,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             save_steps=save_steps,
             warmup_steps=warmup_steps,
             neftune_alpha=neftune_alpha,
-            optim=optim,
+            extra_args=extra_args,
             packing=packing,
             neat_packing=neat_packing,
             train_on_prompt=train_on_prompt,

src/llamafactory/webui/locales.py

@@ -87,20 +87,6 @@ LOCALES = {
             "label": "체크포인트 경로",
         },
     },
-    "advanced_tab": {
-        "en": {
-            "label": "Advanced configurations",
-        },
-        "ru": {
-            "label": "Расширенные конфигурации",
-        },
-        "zh": {
-            "label": "高级设置",
-        },
-        "ko": {
-            "label": "고급 설정",
-        },
-    },
     "quantization_bit": {
         "en": {
             "label": "Quantization bit",
@@ -581,11 +567,11 @@ LOCALES = {
     },
     "neftune_alpha": {
         "en": {
-            "label": "NEFTune Alpha",
+            "label": "NEFTune alpha",
             "info": "Magnitude of noise adding to embedding vectors.",
         },
         "ru": {
-            "label": "NEFTune Alpha",
+            "label": "NEFTune alpha",
             "info": "Величина шума, добавляемого к векторам вложений.",
         },
         "zh": {
@@ -597,22 +583,22 @@ LOCALES = {
             "info": "임베딩 벡터에 추가되는 노이즈의 크기.",
         },
     },
-    "optim": {
+    "extra_args": {
         "en": {
-            "label": "Optimizer",
+            "label": "Extra arguments",
-            "info": "The optimizer to use: adamw_torch, adamw_8bit or adafactor.",
+            "info": "Extra arguments passed to the trainer in JSON format.",
         },
         "ru": {
-            "label": "Оптимизатор",
+            "label": "Дополнительные аргументы",
-            "info": "Оптимизатор для использования: adamw_torch, adamw_8bit или adafactor.",
+            "info": "Дополнительные аргументы, которые передаются тренеру в формате JSON.",
         },
         "zh": {
-            "label": "优化器",
+            "label": "额外参数",
-            "info": "使用的优化器：adamw_torch、adamw_8bit 或 adafactor。",
+            "info": "以 JSON 格式传递给训练器的额外参数。",
         },
         "ko": {
-            "label": "옵티마이저",
+            "label": "추가 인수",
-            "info": "사용할 옵티마이저: adamw_torch, adamw_8bit 또는 adafactor 등.",
+            "info": "JSON 형식으로 트레이너에게 전달할 추가 인수입니다.",
         },
     },
     "packing": {

src/llamafactory/webui/runner.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import os
 from copy import deepcopy
 from subprocess import Popen, TimeoutExpired
@@ -78,6 +79,11 @@ class Runner:
             if not get("train.output_dir"):
                 return ALERTS["err_no_output_dir"][lang]
 
+            try:
+                json.loads(get("train.extra_args"))
+            except json.JSONDecodeError:
+                return ALERTS["err_json_schema"][lang]
+
             stage = TRAINING_STAGES[get("train.training_stage")]
             if stage == "ppo" and not get("train.reward_model"):
                 return ALERTS["err_no_reward_model"][lang]
@@ -130,7 +136,6 @@ class Runner:
             save_steps=get("train.save_steps"),
             warmup_steps=get("train.warmup_steps"),
             neftune_noise_alpha=get("train.neftune_alpha") or None,
-            optim=get("train.optim"),
             packing=get("train.packing") or get("train.neat_packing"),
             neat_packing=get("train.neat_packing"),
             train_on_prompt=get("train.train_on_prompt"),
@@ -148,6 +153,7 @@ class Runner:
             plot_loss=True,
             ddp_timeout=180000000,
             include_num_input_tokens_seen=True,
+            **json.loads(get("train.extra_args")),
         )
 
         # checkpoints