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# 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.template)
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 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.")
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