fix mrope

Former-commit-id: 55bee1d333549ca19858b3f5c1b7b86926e5fb09

examples/README.md

@@ -132,8 +132,8 @@ FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
 #### Supervised Fine-Tuning on Multiple Nodes
 
 ```bash
-FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
-FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
 ```
 
 #### Multimodal Supervised Fine-Tuning

examples/README_zh.md

@@ -132,8 +132,8 @@ FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
 #### 在多机上进行指令监督微调
 
 ```bash
-FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
-FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
 ```
 
 #### 多模态指令监督微调

src/llamafactory/data/collator.py

@@ -121,6 +121,15 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                 feature["token_type_ids"] = token_type_ids[i]
 
         features: Dict[str, "torch.Tensor"] = super().__call__(features)
+
+        if self.model is not None and hasattr(self.model, "get_rope_index"):  # for qwen2vl mrope
+            features["position_ids"], _ = self.model.get_rope_index(
+                input_ids=features["input_ids"],
+                image_grid_thw=mm_inputs.get("image_grid_thw", None),
+                video_grid_thw=mm_inputs.get("video_grid_thw", None),
+                attention_mask=features["attention_mask"],
+            )
+
         if "cross_attention_mask" in mm_inputs:  # for mllama inputs when pad_to_multiple_of is enabled
             cross_attention_mask = mm_inputs.pop("cross_attention_mask")
             seq_len = features["input_ids"].size(1)

src/llamafactory/train/dpo/workflow.py

@@ -48,6 +48,7 @@ def run_dpo(
 
     data_collator = PairwiseDataCollatorWithPadding(
         template=template,
+        model=model,
         pad_to_multiple_of=8,
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
         **tokenizer_module,

src/llamafactory/train/kto/workflow.py

@@ -47,6 +47,7 @@ def run_kto(
 
     data_collator = KTODataCollatorWithPadding(
         template=template,
+        model=model,
         pad_to_multiple_of=8,
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
         **tokenizer_module,

src/llamafactory/train/ppo/workflow.py

@@ -46,7 +46,7 @@ def run_ppo(
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
 
     tokenizer.padding_side = "left"  # use left-padding in generation while using right-padding in training
-    data_collator = MultiModalDataCollatorForSeq2Seq(template=template, **tokenizer_module)
+    data_collator = MultiModalDataCollatorForSeq2Seq(template=template, model=model, **tokenizer_module)
 
     # Create reference model and reward model
     ref_model = create_ref_model(model_args, finetuning_args, add_valuehead=True)

src/llamafactory/train/pt/trainer.py

@@ -18,7 +18,7 @@ from typing import TYPE_CHECKING, Optional
 from transformers import Trainer
 from typing_extensions import override
 
-from ...extras.packages import is_transformers_version_equal_to_4_46
+from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -38,6 +38,9 @@ class CustomTrainer(Trainer):
     def __init__(
         self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
     ) -> None:
+        if is_transformers_version_greater_than("4.46"):
+            kwargs["processing_class"] = kwargs.pop("tokenizer")
+
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
 

src/llamafactory/train/rm/trainer.py

@@ -25,7 +25,7 @@ from transformers import Trainer
 from typing_extensions import override
 
 from ...extras import logging
-from ...extras.packages import is_transformers_version_equal_to_4_46
+from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
 from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -48,6 +48,9 @@ class PairwiseTrainer(Trainer):
     def __init__(
         self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
     ) -> None:
+        if is_transformers_version_greater_than("4.46"):
+            kwargs["processing_class"] = kwargs.pop("tokenizer")
+
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
         self.can_return_loss = True  # override property to return eval_loss

src/llamafactory/train/rm/workflow.py

@@ -44,7 +44,9 @@ def run_rm(
     template = get_template_and_fix_tokenizer(tokenizer, data_args)
     dataset_module = get_dataset(template, model_args, data_args, training_args, stage="rm", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
-    data_collator = PairwiseDataCollatorWithPadding(template=template, pad_to_multiple_of=8, **tokenizer_module)
+    data_collator = PairwiseDataCollatorWithPadding(
+        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

src/llamafactory/train/sft/trainer.py

@@ -27,7 +27,7 @@ from typing_extensions import override
 
 from ...extras import logging
 from ...extras.constants import IGNORE_INDEX
-from ...extras.packages import is_transformers_version_equal_to_4_46
+from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -51,6 +51,9 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
     def __init__(
         self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
     ) -> None:
+        if is_transformers_version_greater_than("4.46"):
+            kwargs["processing_class"] = kwargs.pop("tokenizer")
+
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
 

src/llamafactory/train/sft/workflow.py

@@ -56,6 +56,7 @@ def run_sft(
 
     data_collator = SFTDataCollatorWith4DAttentionMask(
         template=template,
+        model=model,
         pad_to_multiple_of=8 if training_args.do_train else None,  # for shift short attention
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
         block_diag_attn=model_args.block_diag_attn,