[train] KTransformers SFT as backend engine for LLaMA-Factory (#9400)

Co-authored-by: jimmy128 <jimmy128@noreply.gitcode.com>
Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>

src/llamafactory/train/ksft/workflow.py

@@ -0,0 +1,110 @@
+# Copyright 2025 the KVCache.AI team, Approaching AI, and 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, Optional
+
+from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
+from ...extras.constants import IGNORE_INDEX
+from ...extras.logging import get_logger
+from ...extras.misc import calculate_tps
+from ...extras.ploting import plot_loss
+from ...model import load_model, load_tokenizer
+from ..trainer_utils import create_modelcard_and_push
+
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+
+    from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+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)
+
+    from ktransformers.util.globals import GLOBAL_CONFIG
+    GLOBAL_CONFIG._config["mod"] = "sft"
+
+    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,
+        model=model if not training_args.predict_with_generate else None,
+        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,
+    )
+
+    # Metric utils
+    metric_module = {}
+    if training_args.predict_with_generate:
+        raise NotImplementedError("`predict_with_generate` is not supported in KTransformers SFT yet. if you do need it, please open an issue.")
+    elif finetuning_args.compute_accuracy:
+        raise NotImplementedError("`compute_accuracy` is not supported in KTransformers SFT yet. if you do need it, please open an issue.")
+
+    # Initialize our Trainer
+    from ktransformers.sft.lora import KTrainer
+    trainer = KTrainer(
+        model=model,
+        args=training_args,
+        tokenizer=tokenizer_module,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        **dataset_module,
+        **metric_module,
+    )
+
+    # Training
+    if training_args.do_train:
+        model.config.use_cache = False
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        if finetuning_args.include_effective_tokens_per_second:
+            train_result.metrics["effective_tokens_per_sec"] = calculate_tps(
+                dataset_module["train_dataset"], train_result.metrics, stage="sft"
+            )
+
+        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:
+            keys = ["loss"]
+            if isinstance(dataset_module.get("eval_dataset"), dict):
+                keys += sum(
+                    [[f"eval_{key}_loss", f"eval_{key}_accuracy"] for key in dataset_module["eval_dataset"].keys()], []
+                )
+            else:
+                keys += ["eval_loss", "eval_accuracy"]
+
+            plot_loss(training_args.output_dir, keys=keys)
+
+    # Create model card
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)