init

LLaMA-Factory/src/llamafactory/train/pt/trainer.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from types import MethodType
+from typing import TYPE_CHECKING, Optional
+
+from transformers import Trainer
+from typing_extensions import override
+
+from ...extras.logging import get_logger
+from ..callbacks import PissaConvertCallback, SaveProcessorCallback
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
+
+
+if TYPE_CHECKING:
+    import torch
+    from transformers import ProcessorMixin
+
+    from ...hparams import FinetuningArguments
+
+
+logger = get_logger(__name__)
+
+
+class CustomTrainer(Trainer):
+    r"""
+    Inherits Trainer for custom optimizer.
+    """
+
+    def __init__(
+        self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
+    ) -> None:
+        super().__init__(**kwargs)
+        self.finetuning_args = finetuning_args
+
+        if processor is not None:
+            self.add_callback(SaveProcessorCallback(processor))
+
+        if finetuning_args.pissa_convert:
+            self.add_callback(PissaConvertCallback)
+
+        if finetuning_args.use_badam:
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
+            self.add_callback(BAdamCallback)
+
+    @override
+    def create_optimizer(self) -> "torch.optim.Optimizer":
+        if self.optimizer is None:
+            self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
+        return super().create_optimizer()
+
+    @override
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
+    ) -> "torch.optim.lr_scheduler.LRScheduler":
+        create_custom_scheduler(self.args, num_training_steps, optimizer)
+        return super().create_scheduler(num_training_steps, optimizer)

LLaMA-Factory/src/llamafactory/train/pt/workflow.py

+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, List, Optional
+
+from transformers import DataCollatorForLanguageModeling
+
+from ...data import get_dataset, get_template_and_fix_tokenizer
+from ...extras.ploting import plot_loss
+from ...model import load_model, load_tokenizer
+from ..trainer_utils import create_modelcard_and_push
+from .trainer import CustomTrainer
+
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+
+    from ...hparams import DataArguments, FinetuningArguments, ModelArguments
+
+
+def run_pt(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None,
+):
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="pt", **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+    # Initialize our Trainer
+    trainer = CustomTrainer(
+        model=model,
+        args=training_args,
+        finetuning_args=finetuning_args,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        **dataset_module,
+        **tokenizer_module,
+    )
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval")
+        try:
+            perplexity = math.exp(metrics["eval_loss"])
+        except OverflowError:
+            perplexity = float("inf")
+
+        metrics["perplexity"] = perplexity
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Create model card
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

LLaMA-Factory/src/llamafactory/train/rm/__init__.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .workflow import run_rm
+
+
+__all__ = ["run_rm"]

LLaMA-Factory/src/llamafactory/train/rm/metric.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Dict, Optional
+
+import numpy as np
+
+from ...extras.misc import numpify
+
+
+if TYPE_CHECKING:
+    from transformers import EvalPrediction
+
+
+@dataclass
+class ComputeAccuracy:
+    r"""
+    Computes reward accuracy and supports `batch_eval_metrics`.
+    """
+
+    def _dump(self) -> Optional[Dict[str, float]]:
+        result = None
+        if hasattr(self, "score_dict"):
+            result = {k: float(np.mean(v)) for k, v in self.score_dict.items()}
+
+        self.score_dict = {"accuracy": []}
+        return result
+
+    def __post_init__(self):
+        self._dump()
+
+    def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[Dict[str, float]]:
+        chosen_scores, rejected_scores = numpify(eval_preds.predictions[0]), numpify(eval_preds.predictions[1])
+        if not chosen_scores.shape:
+            self.score_dict["accuracy"].append(chosen_scores > rejected_scores)
+        else:
+            for i in range(len(chosen_scores)):
+                self.score_dict["accuracy"].append(chosen_scores[i] > rejected_scores[i])
+
+        if compute_result:
+            return self._dump()

LLaMA-Factory/src/llamafactory/train/rm/trainer.py

+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from types import MethodType
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import Trainer
+from typing_extensions import override
+
+from ...extras.logging import get_logger
+from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel, ProcessorMixin
+    from transformers.trainer import PredictionOutput
+
+    from ...hparams import FinetuningArguments
+
+
+logger = get_logger(__name__)
+
+
+class PairwiseTrainer(Trainer):
+    r"""
+    Inherits Trainer to compute pairwise loss.
+    """
+
+    def __init__(
+        self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
+    ) -> None:
+        super().__init__(**kwargs)
+        self.finetuning_args = finetuning_args
+        self.can_return_loss = True  # override property to return eval_loss
+        self.add_callback(FixValueHeadModelCallback)
+
+        if processor is not None:
+            self.add_callback(SaveProcessorCallback(processor))
+
+        if finetuning_args.pissa_convert:
+            self.add_callback(PissaConvertCallback)
+
+        if finetuning_args.use_badam:
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
+            self.add_callback(BAdamCallback)
+
+    @override
+    def create_optimizer(self) -> "torch.optim.Optimizer":
+        if self.optimizer is None:
+            self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
+        return super().create_optimizer()
+
+    @override
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
+    ) -> "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: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False
+    ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
+        r"""
+        Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
+
+        Subclass and override to inject custom behavior.
+
+        Note that the first element will be removed from the output tuple.
+        See: https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer.py#L3842
+        """
+        _, _, values = model(**inputs, output_hidden_states=True, return_dict=True, use_cache=False)
+        batch_size = inputs["input_ids"].size(0) // 2
+        chosen_masks, rejected_masks = torch.split(inputs["attention_mask"], batch_size, dim=0)
+        chosen_rewards, rejected_rewards = torch.split(values, batch_size, dim=0)
+        chosen_scores = chosen_rewards.gather(dim=-1, index=(chosen_masks.sum(dim=-1, keepdim=True) - 1))
+        rejected_scores = rejected_rewards.gather(dim=-1, index=(rejected_masks.sum(dim=-1, keepdim=True) - 1))
+        chosen_scores, rejected_scores = chosen_scores.squeeze(), rejected_scores.squeeze()
+
+        loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
+        if return_outputs:
+            return loss, (loss, chosen_scores, rejected_scores)
+        else:
+            return loss
+
+    def save_predictions(self, predict_results: "PredictionOutput") -> None:
+        r"""
+        Saves model predictions to `output_dir`.
+
+        A custom behavior that not contained in Seq2SeqTrainer.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
+        logger.info(f"Saving prediction results to {output_prediction_file}")
+        chosen_scores, rejected_scores = predict_results.predictions
+
+        with open(output_prediction_file, "w", encoding="utf-8") as writer:
+            res: List[str] = []
+            for c_score, r_score in zip(chosen_scores, rejected_scores):
+                res.append(json.dumps({"chosen": round(float(c_score), 2), "rejected": round(float(r_score), 2)}))
+
+            writer.write("\n".join(res))

LLaMA-Factory/src/llamafactory/train/rm/workflow.py

+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, List, Optional
+
+from ...data import PairwiseDataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
+from ...extras.ploting import plot_loss
+from ...model import load_model, load_tokenizer
+from ..callbacks import fix_valuehead_checkpoint
+from ..trainer_utils import create_modelcard_and_push
+from .metric import ComputeAccuracy
+from .trainer import PairwiseTrainer
+
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+
+    from ...hparams import DataArguments, FinetuningArguments, ModelArguments
+
+
+def run_rm(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None,
+):
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    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)
+
+    # Update arguments
+    training_args.remove_unused_columns = False  # important for multimodal and pairwise dataset
+
+    # Initialize our Trainer
+    trainer = PairwiseTrainer(
+        model=model,
+        args=training_args,
+        finetuning_args=finetuning_args,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        compute_metrics=ComputeAccuracy(),
+        **dataset_module,
+        **tokenizer_module,
+    )
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        if training_args.should_save:
+            fix_valuehead_checkpoint(model, training_args.output_dir, training_args.save_safetensors)
+
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "eval_accuracy"])
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval")
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Predict
+    if training_args.do_predict:
+        predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict")
+        trainer.log_metrics("predict", predict_results.metrics)
+        trainer.save_metrics("predict", predict_results.metrics)
+        trainer.save_predictions(predict_results)
+
+    # Create model card
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

LLaMA-Factory/src/llamafactory/train/sft/__init__.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .workflow import run_sft
+
+
+__all__ = ["run_sft"]

LLaMA-Factory/src/llamafactory/train/sft/metric.py

+# Copyright 2024 HuggingFace Inc., THUDM, and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library and the THUDM's ChatGLM implementation.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py
+# https://github.com/THUDM/ChatGLM-6B/blob/main/ptuning/main.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Dict, Optional
+
+import numpy as np
+import torch
+from transformers.utils import is_jieba_available, is_nltk_available
+
+from ...extras.constants import IGNORE_INDEX
+from ...extras.misc import numpify
+from ...extras.packages import is_rouge_available
+
+
+if TYPE_CHECKING:
+    from transformers import EvalPrediction, PreTrainedTokenizer
+
+
+if is_jieba_available():
+    import jieba  # type: ignore
+
+
+if is_nltk_available():
+    from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
+
+
+if is_rouge_available():
+    from rouge_chinese import Rouge
+
+
+def eval_logit_processor(logits: "torch.Tensor", labels: "torch.Tensor") -> "torch.Tensor":
+    r"""
+    Computes the token with the largest likelihood to reduce memory footprint.
+    """
+    if isinstance(logits, (list, tuple)):
+        if logits[0].dim() == 3:  # (batch_size, seq_len, vocab_size)
+            logits = logits[0]
+        else:  # moe models have aux loss
+            logits = logits[1]
+
+    if logits.dim() != 3:
+        raise ValueError("Cannot process the logits.")
+
+    return torch.argmax(logits, dim=-1)
+
+
+@dataclass
+class ComputeAccuracy:
+    r"""
+    Computes accuracy and supports `batch_eval_metrics`.
+    """
+
+    def _dump(self) -> Optional[Dict[str, float]]:
+        result = None
+        if hasattr(self, "score_dict"):
+            result = {k: float(np.mean(v)) for k, v in self.score_dict.items()}
+
+        self.score_dict = {"accuracy": []}
+        return result
+
+    def __post_init__(self):
+        self._dump()
+
+    def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[Dict[str, float]]:
+        preds, labels = numpify(eval_preds.predictions), numpify(eval_preds.label_ids)
+        for i in range(len(preds)):
+            pred, label = preds[i, :-1], labels[i, 1:]
+            label_mask = label != IGNORE_INDEX
+            self.score_dict["accuracy"].append(np.mean(pred[label_mask] == label[label_mask]))
+
+        if compute_result:
+            return self._dump()
+
+
+@dataclass
+class ComputeSimilarity:
+    r"""
+    Computes text similarity scores and supports `batch_eval_metrics`.
+
+    Wraps the tokenizer into metric functions, used in CustomSeq2SeqTrainer.
+    """
+
+    tokenizer: "PreTrainedTokenizer"
+
+    def _dump(self) -> Optional[Dict[str, float]]:
+        result = None
+        if hasattr(self, "score_dict"):
+            result = {k: float(np.mean(v)) for k, v in self.score_dict.items()}
+
+        self.score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
+        return result
+
+    def __post_init__(self):
+        self._dump()
+
+    def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[Dict[str, float]]:
+        preds, labels = numpify(eval_preds.predictions), numpify(eval_preds.label_ids)
+
+        preds = np.where(preds != IGNORE_INDEX, preds, self.tokenizer.pad_token_id)
+        labels = np.where(labels != IGNORE_INDEX, labels, self.tokenizer.pad_token_id)
+
+        decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True)
+        decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        for pred, label in zip(decoded_preds, decoded_labels):
+            hypothesis = list(jieba.cut(pred))
+            reference = list(jieba.cut(label))
+
+            if len(" ".join(hypothesis).split()) == 0 or len(" ".join(reference).split()) == 0:
+                result = {"rouge-1": {"f": 0.0}, "rouge-2": {"f": 0.0}, "rouge-l": {"f": 0.0}}
+            else:
+                rouge = Rouge()
+                scores = rouge.get_scores(" ".join(hypothesis), " ".join(reference))
+                result = scores[0]
+
+            for k, v in result.items():
+                self.score_dict[k].append(round(v["f"] * 100, 4))
+
+            bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
+            self.score_dict["bleu-4"].append(round(bleu_score * 100, 4))
+
+        if compute_result:
+            return self._dump()

LLaMA-Factory/src/llamafactory/train/sft/trainer.py

+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer_seq2seq.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from types import MethodType
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import Seq2SeqTrainer
+from typing_extensions import override
+
+from ...extras.constants import IGNORE_INDEX
+from ...extras.logging import get_logger
+from ..callbacks import PissaConvertCallback, SaveProcessorCallback
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
+
+
+if TYPE_CHECKING:
+    from torch.utils.data import Dataset
+    from transformers import ProcessorMixin
+    from transformers.trainer import PredictionOutput
+
+    from ...hparams import FinetuningArguments
+
+
+logger = get_logger(__name__)
+
+
+class CustomSeq2SeqTrainer(Seq2SeqTrainer):
+    r"""
+    Inherits Seq2SeqTrainer to compute generative metrics such as BLEU and ROUGE.
+    """
+
+    def __init__(
+        self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
+    ) -> None:
+        super().__init__(**kwargs)
+        self.finetuning_args = finetuning_args
+
+        if processor is not None:
+            self.add_callback(SaveProcessorCallback(processor))
+
+        if finetuning_args.pissa_convert:
+            self.add_callback(PissaConvertCallback)
+
+        if finetuning_args.use_badam:
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
+            self.add_callback(BAdamCallback)
+
+    @override
+    def create_optimizer(self) -> "torch.optim.Optimizer":
+        if self.optimizer is None:
+            self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
+        return super().create_optimizer()
+
+    @override
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
+    ) -> "torch.optim.lr_scheduler.LRScheduler":
+        create_custom_scheduler(self.args, num_training_steps, optimizer)
+        return super().create_scheduler(num_training_steps, optimizer)
+
+    @override
+    def prediction_step(
+        self,
+        model: "torch.nn.Module",
+        inputs: Dict[str, Union["torch.Tensor", Any]],
+        prediction_loss_only: bool,
+        ignore_keys: Optional[List[str]] = None,
+    ) -> Tuple[Optional[float], Optional["torch.Tensor"], Optional["torch.Tensor"]]:
+        r"""
+        Removes the prompt part in the generated tokens.
+
+        Subclass and override to inject custom behavior.
+        """
+        labels = inputs["labels"] if "labels" in inputs else None
+        if self.args.predict_with_generate:
+            assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
+            labels = labels.detach().clone() if labels is not None else None  # backup labels
+            prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1)
+            if prompt_len > label_len:
+                inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
+            if label_len > prompt_len:  # truncate the labels instead of padding the inputs (llama2 fp16 compatibility)
+                inputs["labels"] = inputs["labels"][:, :prompt_len]
+
+        loss, generated_tokens, _ = super().prediction_step(  # ignore the returned labels (may be truncated)
+            model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
+        )
+        if generated_tokens is not None and self.args.predict_with_generate:
+            generated_tokens[:, :prompt_len] = self.tokenizer.pad_token_id
+            generated_tokens = generated_tokens.contiguous()
+
+        return loss, generated_tokens, labels
+
+    def _pad_tensors_to_target_len(self, src_tensor: "torch.Tensor", tgt_tensor: "torch.Tensor") -> "torch.Tensor":
+        r"""
+        Pads the tensor to the same length as the target tensor.
+        """
+        assert self.tokenizer.pad_token_id is not None, "Pad token is required."
+        padded_tensor = self.tokenizer.pad_token_id * torch.ones_like(tgt_tensor)
+        padded_tensor[:, -src_tensor.shape[-1] :] = src_tensor  # adopt left-padding
+        return padded_tensor.contiguous()  # in contiguous memory
+
+    def save_predictions(self, dataset: "Dataset", predict_results: "PredictionOutput") -> None:
+        r"""
+        Saves model predictions to `output_dir`.
+
+        A custom behavior that not contained in Seq2SeqTrainer.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
+        logger.info(f"Saving prediction results to {output_prediction_file}")
+
+        labels = np.where(
+            predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id
+        )
+        preds = np.where(
+            predict_results.predictions != IGNORE_INDEX, predict_results.predictions, self.tokenizer.pad_token_id
+        )
+
+        for i in range(len(preds)):
+            pad_len = np.nonzero(preds[i] != self.tokenizer.pad_token_id)[0]
+            if len(pad_len):  # move pad token to last
+                preds[i] = np.concatenate((preds[i][pad_len[0] :], preds[i][: pad_len[0]]), axis=-1)
+
+        decoded_inputs = self.tokenizer.batch_decode(dataset["input_ids"], skip_special_tokens=True)
+        decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True)
+        decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True)
+
+        with open(output_prediction_file, "w", encoding="utf-8") as writer:
+            res: List[str] = []
+            for text, label, pred in zip(decoded_inputs, decoded_labels, decoded_preds):
+                res.append(json.dumps({"prompt": text, "label": label, "predict": pred}, ensure_ascii=False))
+
+            writer.write("\n".join(res))

LLaMA-Factory/src/llamafactory/train/sft/workflow.py

+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, List, Optional
+
+from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
+from ...extras.constants import IGNORE_INDEX
+from ...extras.misc import get_logits_processor
+from ...extras.ploting import plot_loss
+from ...model import load_model, load_tokenizer
+from ..trainer_utils import create_modelcard_and_push
+from .metric import ComputeAccuracy, ComputeSimilarity, eval_logit_processor
+from .trainer import CustomSeq2SeqTrainer
+
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+
+    from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+def run_sft(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[List["TrainerCallback"]] = None,
+):
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
+
+    if getattr(model, "is_quantized", False) and not training_args.do_train:
+        setattr(model, "_hf_peft_config_loaded", True)  # hack here: make model compatible with prediction
+
+    data_collator = SFTDataCollatorWith4DAttentionMask(
+        template=template,
+        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,
+        attn_implementation=getattr(model.config, "_attn_implementation", None),
+        compute_dtype=model_args.compute_dtype,
+        **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:
+        metric_module["compute_metrics"] = ComputeSimilarity(tokenizer=tokenizer)
+    elif finetuning_args.compute_accuracy:
+        metric_module["compute_metrics"] = ComputeAccuracy()
+        metric_module["preprocess_logits_for_metrics"] = eval_logit_processor
+
+    # Initialize our Trainer
+    trainer = CustomSeq2SeqTrainer(
+        model=model,
+        args=training_args,
+        finetuning_args=finetuning_args,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        **dataset_module,
+        **tokenizer_module,
+        **metric_module,
+    )
+
+    # Keyword arguments for `model.generate`
+    gen_kwargs = generating_args.to_dict()
+    gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
+    gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
+    gen_kwargs["logits_processor"] = get_logits_processor()
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "eval_accuracy"])
+
+    if training_args.predict_with_generate:
+        tokenizer.padding_side = "left"  # use left-padding in generation
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs)
+        if training_args.predict_with_generate:  # eval_loss will be wrong if predict_with_generate is enabled
+            metrics.pop("eval_loss", None)
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Predict
+    if training_args.do_predict:
+        predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict", **gen_kwargs)
+        if training_args.predict_with_generate:  # predict_loss will be wrong if predict_with_generate is enabled
+            predict_results.metrics.pop("predict_loss", None)
+        trainer.log_metrics("predict", predict_results.metrics)
+        trainer.save_metrics("predict", predict_results.metrics)
+        trainer.save_predictions(dataset_module["eval_dataset"], predict_results)
+
+    # Create model card
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

LLaMA-Factory/src/llamafactory/train/test_utils.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, Dict, Optional, Sequence, Set, Tuple, Union
+
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+from trl import AutoModelForCausalLMWithValueHead
+
+from ..data import get_dataset, get_template_and_fix_tokenizer
+from ..extras.misc import get_current_device
+from ..hparams import get_infer_args, get_train_args
+from ..model import load_model, load_tokenizer
+
+
+if TYPE_CHECKING:
+    from datasets import Dataset
+    from peft import LoraModel
+    from transformers import PreTrainedModel
+
+
+def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: Sequence[str] = []) -> None:
+    state_dict_a = model_a.state_dict()
+    state_dict_b = model_b.state_dict()
+    assert set(state_dict_a.keys()) == set(state_dict_b.keys())
+    for name in state_dict_a.keys():
+        if any(key in name for key in diff_keys):
+            assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-3, atol=1e-4) is False
+        else:
+            assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-3, atol=1e-4) is True
+
+
+def check_lora_model(model: "LoraModel") -> Tuple[Set[str], Set[str]]:
+    linear_modules, extra_modules = set(), set()
+    for name, param in model.named_parameters():
+        if any(module in name for module in ["lora_A", "lora_B"]):
+            linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
+            assert param.requires_grad is True
+            assert param.dtype == torch.float32
+        elif "modules_to_save" in name:
+            extra_modules.add(name.split(".modules_to_save", maxsplit=1)[0].split(".")[-1])
+            assert param.requires_grad is True
+            assert param.dtype == torch.float32
+        else:
+            assert param.requires_grad is False
+            assert param.dtype == torch.float16
+
+    return linear_modules, extra_modules
+
+
+def load_train_model(add_valuehead: bool = False, **kwargs) -> "PreTrainedModel":
+    model_args, _, _, finetuning_args, _ = get_train_args(kwargs)
+    tokenizer = load_tokenizer(model_args)["tokenizer"]
+    return load_model(tokenizer, model_args, finetuning_args, is_trainable=True, add_valuehead=add_valuehead)
+
+
+def load_infer_model(add_valuehead: bool = False, **kwargs) -> "PreTrainedModel":
+    model_args, _, finetuning_args, _ = get_infer_args(kwargs)
+    tokenizer = load_tokenizer(model_args)["tokenizer"]
+    return load_model(tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead)
+
+
+def load_reference_model(
+    model_path: str,
+    lora_path: Optional[str] = None,
+    use_lora: bool = False,
+    use_pissa: bool = False,
+    is_trainable: bool = False,
+    add_valuehead: bool = False,
+) -> Union["PreTrainedModel", "LoraModel"]:
+    if add_valuehead:
+        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
+            model_path, torch_dtype=torch.float16, device_map=get_current_device()
+        )
+        if not is_trainable:
+            model.v_head = model.v_head.to(torch.float16)
+
+        return model
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path, torch_dtype=torch.float16, device_map=get_current_device()
+    )
+    if use_lora or use_pissa:
+        model = PeftModel.from_pretrained(
+            model, lora_path, subfolder="pissa_init" if use_pissa else None, is_trainable=is_trainable
+        )
+        for param in filter(lambda p: p.requires_grad, model.parameters()):
+            param.data = param.data.to(torch.float32)
+
+    return model
+
+
+def load_train_dataset(**kwargs) -> "Dataset":
+    model_args, data_args, training_args, _, _ = get_train_args(kwargs)
+    tokenizer_module = load_tokenizer(model_args)
+    template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, kwargs["stage"], **tokenizer_module)
+    return dataset_module["train_dataset"]
+
+
+def patch_valuehead_model():
+    def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]) -> None:
+        state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
+        self.v_head.load_state_dict(state_dict, strict=False)
+        del state_dict
+
+    AutoModelForCausalLMWithValueHead.post_init = post_init

LLaMA-Factory/src/llamafactory/train/trainer_utils.py

+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the original GaLore's implementation: https://github.com/jiaweizzhao/GaLore
+# and the original LoRA+'s implementation: https://github.com/nikhil-ghosh-berkeley/loraplus
+# and the original BAdam's implementation: https://github.com/Ledzy/BAdam
+# and the HuggingFace's TRL library: https://github.com/huggingface/trl
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import Trainer
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.modeling_utils import is_fsdp_enabled
+from transformers.optimization import get_scheduler
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.trainer_pt_utils import get_parameter_names
+from typing_extensions import override
+
+from ..extras.constants import IGNORE_INDEX
+from ..extras.logging import get_logger
+from ..extras.packages import is_galore_available
+from ..hparams import FinetuningArguments, ModelArguments
+from ..model import find_all_linear_modules, load_model, load_tokenizer, load_valuehead_params
+
+
+if is_galore_available():
+    from galore_torch import GaLoreAdafactor, GaLoreAdamW, GaLoreAdamW8bit
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel, Seq2SeqTrainingArguments
+    from trl import AutoModelForCausalLMWithValueHead
+
+    from ..hparams import DataArguments
+
+
+logger = get_logger(__name__)
+
+
+class DummyOptimizer(torch.optim.Optimizer):
+    r"""
+    A dummy optimizer used for the GaLore algorithm.
+    """
+
+    def __init__(
+        self, lr: float = 1e-3, optimizer_dict: Optional[Dict["torch.nn.Parameter", "torch.optim.Optimizer"]] = None
+    ) -> None:
+        dummy_tensor = torch.randn(1, 1)
+        self.optimizer_dict = optimizer_dict
+        super().__init__([dummy_tensor], {"lr": lr})
+
+    @override
+    def zero_grad(self, set_to_none: bool = True) -> None:
+        pass
+
+    @override
+    def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
+        pass
+
+
+def create_modelcard_and_push(
+    trainer: "Trainer",
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> None:
+    kwargs = {
+        "tasks": "text-generation",
+        "finetuned_from": model_args.model_name_or_path,
+        "tags": ["llama-factory", finetuning_args.finetuning_type],
+    }
+    if data_args.dataset is not None:
+        kwargs["dataset"] = data_args.dataset
+
+    if model_args.use_unsloth:
+        kwargs["tags"] = kwargs["tags"] + ["unsloth"]
+
+    if not training_args.do_train:
+        pass
+    elif training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(license="other", **kwargs)  # prevent from connecting to hub
+
+
+def create_ref_model(
+    model_args: "ModelArguments", finetuning_args: "FinetuningArguments", add_valuehead: bool = False
+) -> Optional[Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]]:
+    r"""
+    Creates reference model for PPO/DPO training. Evaluation mode is not supported.
+
+    The valuehead parameter is randomly initialized since it is useless for PPO training.
+    """
+    if finetuning_args.ref_model is not None:
+        ref_model_args = ModelArguments.copyfrom(
+            model_args,
+            model_name_or_path=finetuning_args.ref_model,
+            adapter_name_or_path=finetuning_args.ref_model_adapters,
+            quantization_bit=finetuning_args.ref_model_quantization_bit,
+        )
+        ref_finetuning_args = FinetuningArguments()
+        tokenizer = load_tokenizer(ref_model_args)["tokenizer"]
+        ref_model = load_model(
+            tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
+        )
+        logger.info("Created reference model from {}".format(finetuning_args.ref_model))
+    else:
+        if finetuning_args.finetuning_type == "lora":
+            ref_model = None
+        else:
+            ref_model_args = ModelArguments.copyfrom(model_args)
+            ref_finetuning_args = FinetuningArguments()
+            tokenizer = load_tokenizer(ref_model_args)["tokenizer"]
+            ref_model = load_model(
+                tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
+            )
+            logger.info("Created reference model from the model itself.")
+
+    return ref_model
+
+
+def create_reward_model(
+    model: "AutoModelForCausalLMWithValueHead", model_args: "ModelArguments", finetuning_args: "FinetuningArguments"
+) -> Optional["AutoModelForCausalLMWithValueHead"]:
+    r"""
+    Creates reward model for PPO training.
+    """
+    if finetuning_args.reward_model_type == "api":
+        assert finetuning_args.reward_model.startswith("http"), "Please provide full url."
+        logger.info("Use reward server {}".format(finetuning_args.reward_model))
+        return finetuning_args.reward_model
+    elif finetuning_args.reward_model_type == "lora":
+        model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
+        for name, param in model.named_parameters():  # https://github.com/huggingface/peft/issues/1090
+            if "default" in name:
+                param.data = param.data.to(torch.float32)  # trainable params should in fp32
+        vhead_params = load_valuehead_params(finetuning_args.reward_model, model_args)
+        assert vhead_params is not None, "Reward model is not correctly loaded."
+        model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
+        model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
+        model.register_buffer(
+            "default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False
+        )
+        model.register_buffer(
+            "default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False
+        )
+        logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
+        return None
+    else:
+        reward_model_args = ModelArguments.copyfrom(
+            model_args,
+            model_name_or_path=finetuning_args.reward_model,
+            adapter_name_or_path=finetuning_args.reward_model_adapters,
+            quantization_bit=finetuning_args.reward_model_quantization_bit,
+        )
+        reward_finetuning_args = FinetuningArguments()
+        tokenizer = load_tokenizer(reward_model_args)["tokenizer"]
+        reward_model = load_model(
+            tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
+        )
+        logger.info("Loaded full weights of reward model from {}".format(finetuning_args.reward_model))
+        logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
+        return reward_model
+
+
+def _get_decay_parameter_names(model: "PreTrainedModel") -> List[str]:
+    r"""
+    Returns a list of names of parameters with weight decay. (weights in non-layernorm layers)
+    """
+    decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)
+    decay_parameters = [name for name in decay_parameters if "bias" not in name]
+    return decay_parameters
+
+
+def _create_galore_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> "torch.optim.Optimizer":
+    if len(finetuning_args.galore_target) == 1 and finetuning_args.galore_target[0] == "all":
+        galore_targets = find_all_linear_modules(model, finetuning_args.freeze_vision_tower)
+    else:
+        galore_targets = finetuning_args.galore_target
+
+    galore_params: List["torch.nn.Parameter"] = []
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Linear) and any(target in name for target in galore_targets):
+            for param in module.parameters():
+                if param.requires_grad and len(param.shape) > 1:
+                    galore_params.append(param)
+
+    galore_kwargs = {
+        "rank": finetuning_args.galore_rank,
+        "update_proj_gap": finetuning_args.galore_update_interval,
+        "scale": finetuning_args.galore_scale,
+        "proj_type": finetuning_args.galore_proj_type,
+    }
+
+    id_galore_params = {id(param) for param in galore_params}
+    decay_params, nodecay_params = [], []  # they are non-galore parameters
+    trainable_params: List["torch.nn.Parameter"] = []  # galore_params + decay_params + nodecay_params
+    decay_param_names = _get_decay_parameter_names(model)
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            trainable_params.append(param)
+            if id(param) not in id_galore_params:
+                if name in decay_param_names:
+                    decay_params.append(param)
+                else:
+                    nodecay_params.append(param)
+
+    _, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
+
+    if training_args.optim == "adamw_torch":
+        optim_class = GaLoreAdamW
+    elif training_args.optim in ["adamw_bnb_8bit", "adamw_8bit", "paged_adamw_8bit"]:
+        optim_class = GaLoreAdamW8bit
+    elif training_args.optim == "adafactor":
+        optim_class = GaLoreAdafactor
+    else:
+        raise NotImplementedError("Unknow optim: {}".format(training_args.optim))
+
+    if finetuning_args.galore_layerwise:
+        if training_args.gradient_accumulation_steps != 1:
+            raise ValueError("Per-layer GaLore does not support gradient accumulation.")
+
+        optimizer_dict: Dict["torch.Tensor", "torch.optim.Optimizer"] = {}
+        for param in nodecay_params:
+            param_groups = [dict(params=[param], weight_decay=0.0)]
+            optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
+        for param in decay_params:
+            param_groups = [dict(params=[param], weight_decay=training_args.weight_decay)]
+            optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
+        for param in galore_params:  # galore params have weight decay
+            param_groups = [dict(params=[param], weight_decay=training_args.weight_decay, **galore_kwargs)]
+            optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
+
+        def optimizer_hook(param: "torch.nn.Parameter"):
+            if param.grad is not None:
+                optimizer_dict[param].step()
+                optimizer_dict[param].zero_grad()
+
+        for param in trainable_params:
+            param.register_post_accumulate_grad_hook(optimizer_hook)
+
+        optimizer = DummyOptimizer(lr=training_args.learning_rate, optimizer_dict=optimizer_dict)
+    else:
+        param_groups = [
+            dict(params=nodecay_params, weight_decay=0.0),
+            dict(params=decay_params, weight_decay=training_args.weight_decay),
+            dict(params=galore_params, weight_decay=training_args.weight_decay, **galore_kwargs),
+        ]
+        optimizer = optim_class(param_groups, **optim_kwargs)
+
+    logger.info("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
+    return optimizer
+
+
+def _create_loraplus_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> "torch.optim.Optimizer":
+    default_lr = training_args.learning_rate
+    loraplus_lr = training_args.learning_rate * finetuning_args.loraplus_lr_ratio
+    embedding_lr = finetuning_args.loraplus_lr_embedding
+
+    decay_param_names = _get_decay_parameter_names(model)
+    param_dict: Dict[str, List["torch.nn.Parameter"]] = {
+        "lora_a": [],
+        "lora_b": [],
+        "lora_b_nodecay": [],
+        "embedding": [],
+    }
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            if "lora_embedding_B" in name:
+                param_dict["embedding"].append(param)
+            elif "lora_B" in name or param.ndim == 1:
+                if name in decay_param_names:
+                    param_dict["lora_b"].append(param)
+                else:
+                    param_dict["lora_b_nodecay"].append(param)
+            else:
+                param_dict["lora_a"].append(param)
+
+    optim_class, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
+    param_groups = [
+        dict(params=param_dict["lora_a"], lr=default_lr, weight_decay=training_args.weight_decay),
+        dict(params=param_dict["lora_b"], lr=loraplus_lr, weight_decay=training_args.weight_decay),
+        dict(params=param_dict["lora_b_nodecay"], lr=loraplus_lr, weight_decay=0.0),
+        dict(params=param_dict["embedding"], lr=embedding_lr, weight_decay=training_args.weight_decay),
+    ]
+    optimizer = optim_class(param_groups, **optim_kwargs)
+    logger.info("Using LoRA+ optimizer with loraplus lr ratio {:.2f}.".format(finetuning_args.loraplus_lr_ratio))
+    return optimizer
+
+
+def _create_badam_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> "torch.optim.Optimizer":
+    decay_params, nodecay_params = [], []
+    decay_param_names = _get_decay_parameter_names(model)
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            if name in decay_param_names:
+                decay_params.append(param)
+            else:
+                nodecay_params.append(param)
+
+    optim_class, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
+    param_groups = [
+        dict(params=nodecay_params, weight_decay=0.0),
+        dict(params=decay_params, weight_decay=training_args.weight_decay),
+    ]
+
+    if finetuning_args.badam_mode == "layer":
+        from badam import BlockOptimizer
+
+        base_optimizer = optim_class(param_groups, **optim_kwargs)
+        optimizer = BlockOptimizer(
+            base_optimizer=base_optimizer,
+            named_parameters_list=list(model.named_parameters()),
+            block_prefix_list=None,
+            switch_block_every=finetuning_args.badam_switch_interval,
+            start_block=finetuning_args.badam_start_block,
+            switch_mode=finetuning_args.badam_switch_mode,
+            verbose=finetuning_args.badam_verbose,
+            ds_zero3_enabled=is_deepspeed_zero3_enabled(),
+        )
+        logger.info(
+            f"Using BAdam optimizer with layer-wise update, switch mode is {finetuning_args.badam_switch_mode}, "
+            f"switch block every {finetuning_args.badam_switch_interval} steps, "
+            f"default start block is {finetuning_args.badam_start_block}"
+        )
+
+    elif finetuning_args.badam_mode == "ratio":
+        from badam import BlockOptimizerRatio
+
+        assert finetuning_args.badam_update_ratio > 1e-6
+        optimizer = BlockOptimizerRatio(
+            param_groups=param_groups,
+            named_parameters_list=list(model.named_parameters()),
+            update_ratio=finetuning_args.badam_update_ratio,
+            mask_mode=finetuning_args.badam_mask_mode,
+            verbose=finetuning_args.badam_verbose,
+            include_embedding=False,
+            **optim_kwargs,
+        )
+        logger.info(
+            f"Using BAdam optimizer with ratio-based update, update ratio is {finetuning_args.badam_update_ratio}, "
+            f"mask mode is {finetuning_args.badam_mask_mode}"
+        )
+
+    return optimizer
+
+
+def _create_adam_mini_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+) -> "torch.optim.Optimizer":
+    from adam_mini import Adam_mini
+
+    hidden_size = getattr(model.config, "hidden_size", None)
+    num_q_head = getattr(model.config, "num_attention_heads", None)
+    num_kv_head = getattr(model.config, "num_key_value_heads", None)
+
+    optimizer = Adam_mini(
+        named_parameters=model.named_parameters(),
+        lr=training_args.learning_rate,
+        betas=(training_args.adam_beta1, training_args.adam_beta2),
+        eps=training_args.adam_epsilon,
+        weight_decay=training_args.weight_decay,
+        model_sharding=is_fsdp_enabled() or is_deepspeed_zero3_enabled(),
+        dim=hidden_size,
+        n_heads=num_q_head,
+        n_kv_heads=num_kv_head,
+    )
+    logger.info("Using Adam-mini optimizer.")
+    return optimizer
+
+
+def create_custom_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> Optional["torch.optim.Optimizer"]:
+    if finetuning_args.use_galore:
+        return _create_galore_optimizer(model, training_args, finetuning_args)
+
+    if finetuning_args.loraplus_lr_ratio is not None:
+        return _create_loraplus_optimizer(model, training_args, finetuning_args)
+
+    if finetuning_args.use_badam:
+        return _create_badam_optimizer(model, training_args, finetuning_args)
+
+    if finetuning_args.use_adam_mini:
+        return _create_adam_mini_optimizer(model, training_args)
+
+
+def create_custom_scheduler(
+    training_args: "Seq2SeqTrainingArguments",
+    num_training_steps: int,
+    optimizer: Optional["torch.optim.Optimizer"] = None,
+) -> None:
+    if optimizer is not None and isinstance(optimizer, DummyOptimizer):
+        optimizer_dict = optimizer.optimizer_dict
+        scheduler_dict: Dict["torch.nn.Parameter", "torch.optim.lr_scheduler.LRScheduler"] = {}
+
+        for param in optimizer_dict.keys():
+            scheduler_dict[param] = get_scheduler(
+                training_args.lr_scheduler_type,
+                optimizer=optimizer_dict[param],
+                num_warmup_steps=training_args.get_warmup_steps(num_training_steps),
+                num_training_steps=num_training_steps,
+                scheduler_specific_kwargs=training_args.lr_scheduler_kwargs,
+            )
+
+        def scheduler_hook(param: "torch.nn.Parameter"):
+            scheduler_dict[param].step()
+
+        for param in optimizer_dict.keys():
+            param.register_post_accumulate_grad_hook(scheduler_hook)
+
+
+def get_batch_logps(
+    logits: "torch.Tensor", labels: "torch.Tensor", label_pad_token_id: int = IGNORE_INDEX
+) -> Tuple["torch.Tensor", "torch.Tensor"]:
+    r"""
+    Computes the log probabilities of the given labels under the given logits.
+
+    Returns:
+        logps: A tensor of shape (batch_size,) containing the sum of log probabilities.
+        valid_length: A tensor of shape (batch_size,) containing the number of non-masked tokens.
+    """
+    if logits.shape[:-1] != labels.shape:
+        raise ValueError("Logits (batchsize x seqlen) and labels must have the same shape.")
+
+    labels = labels[:, 1:].clone()
+    logits = logits[:, :-1, :]
+    loss_mask = labels != label_pad_token_id
+    labels[labels == label_pad_token_id] = 0  # dummy token
+    per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)
+    return (per_token_logps * loss_mask).sum(-1), loss_mask.sum(-1)

LLaMA-Factory/src/llamafactory/train/tuner.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import shutil
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+import torch
+from transformers import PreTrainedModel
+
+from ..data import get_template_and_fix_tokenizer
+from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
+from ..extras.logging import get_logger
+from ..hparams import get_infer_args, get_train_args
+from ..model import load_model, load_tokenizer
+from .callbacks import LogCallback
+from .dpo import run_dpo
+from .kto import run_kto
+from .ppo import run_ppo
+from .pt import run_pt
+from .rm import run_rm
+from .sft import run_sft
+
+
+if TYPE_CHECKING:
+    from transformers import TrainerCallback
+
+
+logger = get_logger(__name__)
+
+
+def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []) -> None:
+    callbacks.append(LogCallback())
+    model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
+
+    if finetuning_args.stage == "pt":
+        run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif finetuning_args.stage == "sft":
+        run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif finetuning_args.stage == "rm":
+        run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif finetuning_args.stage == "ppo":
+        run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+    elif finetuning_args.stage == "dpo":
+        run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
+    elif finetuning_args.stage == "kto":
+        run_kto(model_args, data_args, training_args, finetuning_args, callbacks)
+    else:
+        raise ValueError("Unknown task: {}.".format(finetuning_args.stage))
+
+
+def export_model(args: Optional[Dict[str, Any]] = None) -> None:
+    model_args, data_args, finetuning_args, _ = get_infer_args(args)
+
+    if model_args.export_dir is None:
+        raise ValueError("Please specify `export_dir` to save model.")
+
+    if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
+        raise ValueError("Please merge adapters before quantizing the model.")
+
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    processor = tokenizer_module["processor"]
+    get_template_and_fix_tokenizer(tokenizer, data_args)
+    model = load_model(tokenizer, model_args, finetuning_args)  # must after fixing tokenizer to resize vocab
+
+    if getattr(model, "quantization_method", None) is not None and model_args.adapter_name_or_path is not None:
+        raise ValueError("Cannot merge adapters to a quantized model.")
+
+    if not isinstance(model, PreTrainedModel):
+        raise ValueError("The model is not a `PreTrainedModel`, export aborted.")
+
+    if getattr(model, "quantization_method", None) is not None:  # quantized model adopts float16 type
+        setattr(model.config, "torch_dtype", torch.float16)
+    else:
+        if model_args.infer_dtype == "auto":
+            output_dtype = getattr(model.config, "torch_dtype", torch.float16)
+        else:
+            output_dtype = getattr(torch, model_args.infer_dtype)
+
+        setattr(model.config, "torch_dtype", output_dtype)
+        model = model.to(output_dtype)
+        logger.info("Convert model dtype to: {}.".format(output_dtype))
+
+    model.save_pretrained(
+        save_directory=model_args.export_dir,
+        max_shard_size="{}GB".format(model_args.export_size),
+        safe_serialization=(not model_args.export_legacy_format),
+    )
+    if model_args.export_hub_model_id is not None:
+        model.push_to_hub(
+            model_args.export_hub_model_id,
+            token=model_args.hf_hub_token,
+            max_shard_size="{}GB".format(model_args.export_size),
+            safe_serialization=(not model_args.export_legacy_format),
+        )
+
+    if finetuning_args.stage == "rm":
+        if model_args.adapter_name_or_path is not None:
+            vhead_path = model_args.adapter_name_or_path[-1]
+        else:
+            vhead_path = model_args.model_name_or_path
+
+        if os.path.exists(os.path.join(vhead_path, V_HEAD_SAFE_WEIGHTS_NAME)):
+            shutil.copy(
+                os.path.join(vhead_path, V_HEAD_SAFE_WEIGHTS_NAME),
+                os.path.join(model_args.export_dir, V_HEAD_SAFE_WEIGHTS_NAME),
+            )
+            logger.info("Copied valuehead to {}.".format(model_args.export_dir))
+        elif os.path.exists(os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME)):
+            shutil.copy(
+                os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME),
+                os.path.join(model_args.export_dir, V_HEAD_WEIGHTS_NAME),
+            )
+            logger.info("Copied valuehead to {}.".format(model_args.export_dir))
+
+    try:
+        tokenizer.padding_side = "left"  # restore padding side
+        tokenizer.init_kwargs["padding_side"] = "left"
+        tokenizer.save_pretrained(model_args.export_dir)
+        if model_args.export_hub_model_id is not None:
+            tokenizer.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
+
+        if processor is not None:
+            getattr(processor, "image_processor").save_pretrained(model_args.export_dir)
+            if model_args.export_hub_model_id is not None:
+                getattr(processor, "image_processor").push_to_hub(
+                    model_args.export_hub_model_id, token=model_args.hf_hub_token
+                )
+
+    except Exception as e:
+        logger.warning("Cannot save tokenizer, please copy the files manually: {}.".format(e))

LLaMA-Factory/src/llamafactory/webui/chatter.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Sequence, Tuple
+
+from ..chat import ChatModel
+from ..data import Role
+from ..extras.constants import PEFT_METHODS
+from ..extras.misc import torch_gc
+from ..extras.packages import is_gradio_available
+from .common import QUANTIZATION_BITS, get_save_dir
+from .locales import ALERTS
+
+
+if TYPE_CHECKING:
+    from ..chat import BaseEngine
+    from .manager import Manager
+
+
+if is_gradio_available():
+    import gradio as gr
+
+
+class WebChatModel(ChatModel):
+    def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
+        self.manager = manager
+        self.demo_mode = demo_mode
+        self.engine: Optional["BaseEngine"] = None
+
+        if not lazy_init:  # read arguments from command line
+            super().__init__()
+
+        if demo_mode and os.environ.get("DEMO_MODEL") and os.environ.get("DEMO_TEMPLATE"):  # load demo model
+            model_name_or_path = os.environ.get("DEMO_MODEL")
+            template = os.environ.get("DEMO_TEMPLATE")
+            infer_backend = os.environ.get("DEMO_BACKEND", "huggingface")
+            super().__init__(
+                dict(model_name_or_path=model_name_or_path, template=template, infer_backend=infer_backend)
+            )
+
+    @property
+    def loaded(self) -> bool:
+        return self.engine is not None
+
+    def load_model(self, data) -> Generator[str, None, None]:
+        get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
+        lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
+        finetuning_type, checkpoint_path = get("top.finetuning_type"), get("top.checkpoint_path")
+        error = ""
+        if self.loaded:
+            error = ALERTS["err_exists"][lang]
+        elif not model_name:
+            error = ALERTS["err_no_model"][lang]
+        elif not model_path:
+            error = ALERTS["err_no_path"][lang]
+        elif self.demo_mode:
+            error = ALERTS["err_demo"][lang]
+
+        if error:
+            gr.Warning(error)
+            yield error
+            return
+
+        if get("top.quantization_bit") in QUANTIZATION_BITS:
+            quantization_bit = int(get("top.quantization_bit"))
+        else:
+            quantization_bit = None
+
+        yield ALERTS["info_loading"][lang]
+        args = dict(
+            model_name_or_path=model_path,
+            finetuning_type=finetuning_type,
+            quantization_bit=quantization_bit,
+            quantization_method=get("top.quantization_method"),
+            template=get("top.template"),
+            flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
+            use_unsloth=(get("top.booster") == "unsloth"),
+            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
+            infer_backend=get("infer.infer_backend"),
+            infer_dtype=get("infer.infer_dtype"),
+        )
+
+        if checkpoint_path:
+            if finetuning_type in PEFT_METHODS:  # list
+                args["adapter_name_or_path"] = ",".join(
+                    [get_save_dir(model_name, finetuning_type, adapter) for adapter in checkpoint_path]
+                )
+            else:  # str
+                args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, checkpoint_path)
+
+        super().__init__(args)
+        yield ALERTS["info_loaded"][lang]
+
+    def unload_model(self, data) -> Generator[str, None, None]:
+        lang = data[self.manager.get_elem_by_id("top.lang")]
+
+        if self.demo_mode:
+            gr.Warning(ALERTS["err_demo"][lang])
+            yield ALERTS["err_demo"][lang]
+            return
+
+        yield ALERTS["info_unloading"][lang]
+        self.engine = None
+        torch_gc()
+        yield ALERTS["info_unloaded"][lang]
+
+    def append(
+        self,
+        chatbot: List[List[Optional[str]]],
+        messages: Sequence[Dict[str, str]],
+        role: str,
+        query: str,
+    ) -> Tuple[List[List[Optional[str]]], List[Dict[str, str]], str]:
+        return chatbot + [[query, None]], messages + [{"role": role, "content": query}], ""
+
+    def stream(
+        self,
+        chatbot: List[List[Optional[str]]],
+        messages: Sequence[Dict[str, str]],
+        system: str,
+        tools: str,
+        image: Optional[Any],
+        video: Optional[Any],
+        max_new_tokens: int,
+        top_p: float,
+        temperature: float,
+    ) -> Generator[Tuple[List[List[Optional[str]]], List[Dict[str, str]]], None, None]:
+        chatbot[-1][1] = ""
+        response = ""
+        for new_text in self.stream_chat(
+            messages, system, tools, image, video, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
+        ):
+            response += new_text
+            if tools:
+                result = self.engine.template.extract_tool(response)
+            else:
+                result = response
+
+            if isinstance(result, list):
+                tool_calls = [{"name": tool[0], "arguments": json.loads(tool[1])} for tool in result]
+                tool_calls = json.dumps(tool_calls, indent=4, ensure_ascii=False)
+                output_messages = messages + [{"role": Role.FUNCTION.value, "content": tool_calls}]
+                bot_text = "```json\n" + tool_calls + "\n```"
+            else:
+                output_messages = messages + [{"role": Role.ASSISTANT.value, "content": result}]
+                bot_text = result
+
+            chatbot[-1][1] = bot_text
+            yield chatbot, output_messages

LLaMA-Factory/src/llamafactory/webui/common.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from collections import defaultdict
+from typing import Any, Dict, Optional, Tuple
+
+from yaml import safe_dump, safe_load
+
+from ..extras.constants import (
+    CHECKPOINT_NAMES,
+    DATA_CONFIG,
+    DEFAULT_TEMPLATE,
+    PEFT_METHODS,
+    STAGES_USE_PAIR_DATA,
+    SUPPORTED_MODELS,
+    TRAINING_STAGES,
+    VISION_MODELS,
+    DownloadSource,
+)
+from ..extras.logging import get_logger
+from ..extras.misc import use_modelscope
+from ..extras.packages import is_gradio_available
+
+
+if is_gradio_available():
+    import gradio as gr
+
+
+logger = get_logger(__name__)
+
+
+DEFAULT_CACHE_DIR = "cache"
+DEFAULT_CONFIG_DIR = "config"
+DEFAULT_DATA_DIR = "data"
+DEFAULT_SAVE_DIR = "saves"
+USER_CONFIG = "user_config.yaml"
+QUANTIZATION_BITS = ["8", "6", "5", "4", "3", "2", "1"]
+GPTQ_BITS = ["8", "4", "3", "2"]
+
+
+def get_save_dir(*paths: str) -> os.PathLike:
+    r"""
+    Gets the path to saved model checkpoints.
+    """
+    if os.path.sep in paths[-1]:
+        logger.warning("Found complex path, some features may be not available.")
+        return paths[-1]
+
+    paths = (path.replace(" ", "").strip() for path in paths)
+    return os.path.join(DEFAULT_SAVE_DIR, *paths)
+
+
+def get_config_path() -> os.PathLike:
+    r"""
+    Gets the path to user config.
+    """
+    return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG)
+
+
+def load_config() -> Dict[str, Any]:
+    r"""
+    Loads user config if exists.
+    """
+    try:
+        with open(get_config_path(), "r", encoding="utf-8") as f:
+            return safe_load(f)
+    except Exception:
+        return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
+
+
+def save_config(lang: str, model_name: Optional[str] = None, model_path: Optional[str] = None) -> None:
+    r"""
+    Saves user config.
+    """
+    os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
+    user_config = load_config()
+    user_config["lang"] = lang or user_config["lang"]
+    if model_name:
+        user_config["last_model"] = model_name
+
+    if model_name and model_path:
+        user_config["path_dict"][model_name] = model_path
+
+    with open(get_config_path(), "w", encoding="utf-8") as f:
+        safe_dump(user_config, f)
+
+
+def get_model_path(model_name: str) -> str:
+    r"""
+    Gets the model path according to the model name.
+    """
+    user_config = load_config()
+    path_dict: Dict["DownloadSource", str] = SUPPORTED_MODELS.get(model_name, defaultdict(str))
+    model_path = user_config["path_dict"].get(model_name, "") or path_dict.get(DownloadSource.DEFAULT, "")
+    if (
+        use_modelscope()
+        and path_dict.get(DownloadSource.MODELSCOPE)
+        and model_path == path_dict.get(DownloadSource.DEFAULT)
+    ):  # replace path
+        model_path = path_dict.get(DownloadSource.MODELSCOPE)
+
+    return model_path
+
+
+def get_model_info(model_name: str) -> Tuple[str, str]:
+    r"""
+    Gets the necessary information of this model.
+
+    Returns:
+        model_path (str)
+        template (str)
+    """
+    return get_model_path(model_name), get_template(model_name)
+
+
+def get_template(model_name: str) -> str:
+    r"""
+    Gets the template name if the model is a chat model.
+    """
+    return DEFAULT_TEMPLATE.get(model_name, "default")
+
+
+def get_visual(model_name: str) -> bool:
+    r"""
+    Judges if the model is a vision language model.
+    """
+    return model_name in VISION_MODELS
+
+
+def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
+    r"""
+    Lists all available checkpoints.
+    """
+    checkpoints = []
+    if model_name:
+        save_dir = get_save_dir(model_name, finetuning_type)
+        if save_dir and os.path.isdir(save_dir):
+            for checkpoint in os.listdir(save_dir):
+                if os.path.isdir(os.path.join(save_dir, checkpoint)) and any(
+                    os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CHECKPOINT_NAMES
+                ):
+                    checkpoints.append(checkpoint)
+
+    if finetuning_type in PEFT_METHODS:
+        return gr.Dropdown(value=[], choices=checkpoints, multiselect=True)
+    else:
+        return gr.Dropdown(value=None, choices=checkpoints, multiselect=False)
+
+
+def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
+    r"""
+    Loads dataset_info.json.
+    """
+    if dataset_dir == "ONLINE" or dataset_dir.startswith("REMOTE:"):
+        logger.info("dataset_dir is {}, using online dataset.".format(dataset_dir))
+        return {}
+
+    try:
+        with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+            return json.load(f)
+    except Exception as err:
+        logger.warning("Cannot open {} due to {}.".format(os.path.join(dataset_dir, DATA_CONFIG), str(err)))
+        return {}
+
+
+def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_STAGES.keys())[0]) -> "gr.Dropdown":
+    r"""
+    Lists all available datasets in the dataset dir for the training stage.
+    """
+    dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
+    ranking = TRAINING_STAGES[training_stage] in STAGES_USE_PAIR_DATA
+    datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking]
+    return gr.Dropdown(choices=datasets)

LLaMA-Factory/src/llamafactory/webui/components/__init__.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .chatbot import create_chat_box
+from .eval import create_eval_tab
+from .export import create_export_tab
+from .infer import create_infer_tab
+from .top import create_top
+from .train import create_train_tab
+
+
+__all__ = [
+    "create_chat_box",
+    "create_eval_tab",
+    "create_export_tab",
+    "create_infer_tab",
+    "create_top",
+    "create_train_tab",
+]

LLaMA-Factory/src/llamafactory/webui/components/chatbot.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, Dict, Tuple
+
+from ...data import Role
+from ...extras.packages import is_gradio_available
+from ..utils import check_json_schema
+
+
+if is_gradio_available():
+    import gradio as gr
+
+
+if TYPE_CHECKING:
+    from gradio.components import Component
+
+    from ..engine import Engine
+
+
+def create_chat_box(
+    engine: "Engine", visible: bool = False
+) -> Tuple["Component", "Component", Dict[str, "Component"]]:
+    with gr.Column(visible=visible) as chat_box:
+        chatbot = gr.Chatbot(show_copy_button=True)
+        messages = gr.State([])
+        with gr.Row():
+            with gr.Column(scale=4):
+                with gr.Row():
+                    with gr.Column():
+                        role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value)
+                        system = gr.Textbox(show_label=False)
+                        tools = gr.Textbox(show_label=False, lines=3)
+
+                    with gr.Column() as mm_box:
+                        with gr.Tab("Image"):
+                            image = gr.Image(sources=["upload"], type="pil")
+
+                        with gr.Tab("Video"):
+                            video = gr.Video(sources=["upload"])
+
+                query = gr.Textbox(show_label=False, lines=8)
+                submit_btn = gr.Button(variant="primary")
+
+            with gr.Column(scale=1):
+                max_new_tokens = gr.Slider(minimum=8, maximum=4096, value=512, step=1)
+                top_p = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.01)
+                temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01)
+                clear_btn = gr.Button()
+
+    tools.input(check_json_schema, inputs=[tools, engine.manager.get_elem_by_id("top.lang")])
+
+    submit_btn.click(
+        engine.chatter.append,
+        [chatbot, messages, role, query],
+        [chatbot, messages, query],
+    ).then(
+        engine.chatter.stream,
+        [chatbot, messages, system, tools, image, video, max_new_tokens, top_p, temperature],
+        [chatbot, messages],
+    )
+    clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
+
+    return (
+        chatbot,
+        messages,
+        dict(
+            chat_box=chat_box,
+            role=role,
+            system=system,
+            tools=tools,
+            mm_box=mm_box,
+            image=image,
+            video=video,
+            query=query,
+            submit_btn=submit_btn,
+            max_new_tokens=max_new_tokens,
+            top_p=top_p,
+            temperature=temperature,
+            clear_btn=clear_btn,
+        ),
+    )

LLaMA-Factory/src/llamafactory/webui/components/data.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple
+
+from ...extras.constants import DATA_CONFIG
+from ...extras.packages import is_gradio_available
+
+
+if is_gradio_available():
+    import gradio as gr
+
+
+if TYPE_CHECKING:
+    from gradio.components import Component
+
+
+PAGE_SIZE = 2
+
+
+def prev_page(page_index: int) -> int:
+    return page_index - 1 if page_index > 0 else page_index
+
+
+def next_page(page_index: int, total_num: int) -> int:
+    return page_index + 1 if (page_index + 1) * PAGE_SIZE < total_num else page_index
+
+
+def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
+    try:
+        with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+            dataset_info = json.load(f)
+    except Exception:
+        return gr.Button(interactive=False)
+
+    if len(dataset) == 0 or "file_name" not in dataset_info[dataset[0]]:
+        return gr.Button(interactive=False)
+
+    data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])
+    if os.path.isfile(data_path) or (os.path.isdir(data_path) and os.listdir(data_path)):
+        return gr.Button(interactive=True)
+    else:
+        return gr.Button(interactive=False)
+
+
+def _load_data_file(file_path: str) -> List[Any]:
+    with open(file_path, "r", encoding="utf-8") as f:
+        if file_path.endswith(".json"):
+            return json.load(f)
+        elif file_path.endswith(".jsonl"):
+            return [json.loads(line) for line in f]
+        else:
+            return list(f)
+
+
+def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int, list, "gr.Column"]:
+    with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+        dataset_info = json.load(f)
+
+    data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])
+    if os.path.isfile(data_path):
+        data = _load_data_file(data_path)
+    else:
+        data = []
+        for file_name in os.listdir(data_path):
+            data.extend(_load_data_file(os.path.join(data_path, file_name)))
+
+    return len(data), data[PAGE_SIZE * page_index : PAGE_SIZE * (page_index + 1)], gr.Column(visible=True)
+
+
+def create_preview_box(dataset_dir: "gr.Textbox", dataset: "gr.Dropdown") -> Dict[str, "Component"]:
+    data_preview_btn = gr.Button(interactive=False, scale=1)
+    with gr.Column(visible=False, elem_classes="modal-box") as preview_box:
+        with gr.Row():
+            preview_count = gr.Number(value=0, interactive=False, precision=0)
+            page_index = gr.Number(value=0, interactive=False, precision=0)
+
+        with gr.Row():
+            prev_btn = gr.Button()
+            next_btn = gr.Button()
+            close_btn = gr.Button()
+
+        with gr.Row():
+            preview_samples = gr.JSON()
+
+    dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn], queue=False).then(
+        lambda: 0, outputs=[page_index], queue=False
+    )
+    data_preview_btn.click(
+        get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
+    )
+    prev_btn.click(prev_page, [page_index], [page_index], queue=False).then(
+        get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
+    )
+    next_btn.click(next_page, [page_index, preview_count], [page_index], queue=False).then(
+        get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
+    )
+    close_btn.click(lambda: gr.Column(visible=False), outputs=[preview_box], queue=False)
+    return dict(
+        data_preview_btn=data_preview_btn,
+        preview_count=preview_count,
+        page_index=page_index,
+        prev_btn=prev_btn,
+        next_btn=next_btn,
+        close_btn=close_btn,
+        preview_samples=preview_samples,
+    )

LLaMA-Factory/src/llamafactory/webui/components/eval.py

+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, Dict
+
+from ...extras.packages import is_gradio_available
+from ..common import DEFAULT_DATA_DIR, list_datasets
+from .data import create_preview_box
+
+
+if is_gradio_available():
+    import gradio as gr
+
+
+if TYPE_CHECKING:
+    from gradio.components import Component
+
+    from ..engine import Engine
+
+
+def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
+    input_elems = engine.manager.get_base_elems()
+    elem_dict = dict()
+
+    with gr.Row():
+        dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
+        dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
+        preview_elems = create_preview_box(dataset_dir, dataset)
+
+    input_elems.update({dataset_dir, dataset})
+    elem_dict.update(dict(dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
+
+    with gr.Row():
+        cutoff_len = gr.Slider(minimum=4, maximum=65536, value=1024, step=1)
+        max_samples = gr.Textbox(value="100000")
+        batch_size = gr.Slider(minimum=1, maximum=1024, value=2, step=1)
+        predict = gr.Checkbox(value=True)
+
+    input_elems.update({cutoff_len, max_samples, batch_size, predict})
+    elem_dict.update(dict(cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict))
+
+    with gr.Row():
+        max_new_tokens = gr.Slider(minimum=8, maximum=4096, value=512, step=1)
+        top_p = gr.Slider(minimum=0.01, maximum=1, value=0.7, step=0.01)
+        temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01)
+        output_dir = gr.Textbox()
+
+    input_elems.update({max_new_tokens, top_p, temperature, output_dir})
+    elem_dict.update(dict(max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, output_dir=output_dir))
+
+    with gr.Row():
+        cmd_preview_btn = gr.Button()
+        start_btn = gr.Button(variant="primary")
+        stop_btn = gr.Button(variant="stop")
+
+    with gr.Row():
+        resume_btn = gr.Checkbox(visible=False, interactive=False)
+        progress_bar = gr.Slider(visible=False, interactive=False)
+
+    with gr.Row():
+        output_box = gr.Markdown()
+
+    elem_dict.update(
+        dict(
+            cmd_preview_btn=cmd_preview_btn,
+            start_btn=start_btn,
+            stop_btn=stop_btn,
+            resume_btn=resume_btn,
+            progress_bar=progress_bar,
+            output_box=output_box,
+        )
+    )
+    output_elems = [output_box, progress_bar]
+
+    cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems, concurrency_limit=None)
+    start_btn.click(engine.runner.run_eval, input_elems, output_elems)
+    stop_btn.click(engine.runner.set_abort)
+    resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
+
+    dataset.focus(list_datasets, [dataset_dir], [dataset], queue=False)
+
+    return elem_dict