rename package

Former-commit-id: a07ff0c083558cfe6f474d13027642d3052fee08

src/llamafactory/train/tuner.py

@@ -0,0 +1,101 @@
+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.callbacks import LogCallback
+from ..extras.logging import get_logger
+from ..hparams import get_infer_args, get_train_args
+from ..model import load_model, load_tokenizer
+from .dpo import run_dpo
+from .orpo import run_orpo
+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:
+    model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
+    callbacks.append(LogCallback(training_args.output_dir))
+
+    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 == "orpo":
+        run_orpo(model_args, data_args, training_args, finetuning_args, callbacks)
+    else:
+        raise ValueError("Unknown task.")
+
+
+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.template)
+    model = load_model(tokenizer, model_args, finetuning_args)  # must after fixing tokenizer to resize vocab
+
+    if getattr(model, "quantization_method", 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 None:  # cannot convert dtype of a quantized model
+        output_dtype = getattr(model.config, "torch_dtype", torch.float16)
+        setattr(model.config, "torch_dtype", output_dtype)
+        model = model.to(output_dtype)
+    else:
+        setattr(model.config, "torch_dtype", torch.float16)
+
+    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),
+        )
+
+    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 model_args.visual_inputs and 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:
+        logger.warning("Cannot save tokenizer, please copy the files manually.")