chore: import upstream snapshot with attribution

2026-07-13 13:39:21 +08:00
commit bc34f6df14
1149 changed files with 328099 additions and 0 deletions
@@ -0,0 +1,134 @@
+import logging
+from typing import Tuple
+from pathlib import Path
+from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer
+
+from FlagEmbedding.abc.finetune.embedder.AbsArguments import AbsEmbedderDataArguments, AbsEmbedderTrainingArguments
+from FlagEmbedding.abc.finetune.embedder import AbsEmbedderRunner, AbsEmbedderModel, EmbedderTrainerCallbackForDataRefresh
+
+from .arguments import DecoderOnlyEmbedderModelArguments
+from .trainer import DecoderOnlyEmbedderTrainer
+from .modeling import BiDecoderOnlyEmbedderModel
+from .load_model import get_model, save_merged_model
+
+logger = logging.getLogger(__name__)
+
+
+class DecoderOnlyEmbedderRunner(AbsEmbedderRunner):
+    """Runner class for decoder only embedding model.
+
+    Args:
+        model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.
+        data_args (AbsEmbedderDataArguments): Data arguments instance.
+        training_args (AbsEmbedderTrainingArguments): Trainer arguments.
+    """
+    def __init__(
+        self,
+        model_args: DecoderOnlyEmbedderModelArguments,
+        data_args: AbsEmbedderDataArguments,
+        training_args: AbsEmbedderTrainingArguments
+    ):
+        super().__init__(model_args, data_args, training_args)
+        self.model_args: DecoderOnlyEmbedderModelArguments
+        self.data_args: AbsEmbedderDataArguments
+        self.training_args: AbsEmbedderTrainingArguments
+
+    def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsEmbedderModel]:
+        """Load tokenizer and model.
+
+        Returns:
+            Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.
+        """
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.model_args.tokenizer_name if self.model_args.tokenizer_name else self.model_args.model_name_or_path,
+            token=self.model_args.token,
+            cache_dir=self.model_args.cache_dir,
+            use_fast=self.model_args.use_fast_tokenizer,
+            add_eos_token=True,
+            trust_remote_code=self.model_args.trust_remote_code,
+        )
+
+        if tokenizer.pad_token is None:
+            if tokenizer.unk_token is not None:
+                tokenizer.pad_token = tokenizer.unk_token
+                tokenizer.pad_token_id = tokenizer.unk_token_id
+            else:
+                tokenizer.pad_token = tokenizer.eos_token
+                tokenizer.pad_token_id = tokenizer.eos_token_id
+        tokenizer.padding_side = 'left'
+
+        resize = False
+        if self.model_args.additional_special_tokens is not None:
+            special_tokens_dict = {'additional_special_tokens': self.model_args.additional_special_tokens}
+            add_num = tokenizer.add_special_tokens(special_tokens_dict)
+            if add_num > 0:
+                resize = True
+                logger.info(f"Add {add_num} special tokens to the tokenizer. Special tokens: {self.model_args.additional_special_tokens}")
+            else:
+                logger.warning(f"Special tokens {self.model_args.additional_special_tokens} already exists in the tokenizer.")
+        base_model = get_model(self.model_args, self.training_args.output_dir, resize, len(tokenizer))
+
+        num_labels = 1
+        config = AutoConfig.from_pretrained(
+            self.model_args.config_name if self.model_args.config_name else self.model_args.model_name_or_path,
+            num_labels=num_labels,
+            cache_dir=self.model_args.cache_dir,
+            token=self.model_args.token,
+            trust_remote_code=self.model_args.trust_remote_code,
+        )
+        logger.info('Config: %s', config)
+
+        model = BiDecoderOnlyEmbedderModel(
+            base_model,
+            tokenizer=tokenizer,
+            negatives_cross_device=self.training_args.negatives_cross_device,
+            temperature=self.training_args.temperature,
+            sub_batch_size=self.training_args.sub_batch_size,
+            kd_loss_type=self.training_args.kd_loss_type,
+            use_mrl=self.training_args.use_mrl,
+            mrl_dims=self.training_args.mrl_dims,
+            sentence_pooling_method=self.training_args.sentence_pooling_method,
+            normalize_embeddings=self.training_args.normalize_embeddings
+        )
+
+        if self.training_args.gradient_checkpointing:
+            model.enable_input_require_grads()
+
+        if self.training_args.fix_position_embedding:
+            for k, v in model.named_parameters():
+                if "position_embeddings" in k:
+                    logging.info(f"Freeze the parameters for {k}")
+                    v.requires_grad = False
+        return tokenizer, model
+
+    def load_trainer(self) -> DecoderOnlyEmbedderTrainer:
+        """Load the trainer.
+
+        Returns:
+            DecoderOnlyEmbedderTrainer: Loaded trainer instance.
+        """
+        trainer = DecoderOnlyEmbedderTrainer(
+            model=self.model,
+            args=self.training_args,
+            train_dataset=self.train_dataset,
+            data_collator=self.data_collator,
+            processing_class=self.tokenizer
+        )
+        if self.data_args.same_dataset_within_batch:
+            trainer.add_callback(EmbedderTrainerCallbackForDataRefresh(self.train_dataset))
+        return trainer
+
+    def run(self):
+        """
+        Run the finetune.
+        """
+        if not self.model_args.only_merge_lora_model:
+            Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)
+
+            # Training
+            self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)
+            self.trainer.save_model()
+
+        # save merged model
+        if self.model_args.save_merged_lora_model and self.training_args.process_index == 0:
+            save_merged_model(self.model_args, self.training_args.output_dir)