src/llamafactory/hparams/parser.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 logging
import os
import sys
from typing import Any, Dict, Optional, Tuple

import torch
import transformers
from transformers import HfArgumentParser, Seq2SeqTrainingArguments
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.trainer_utils import get_last_checkpoint
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_bf16_gpu_available
from transformers.utils.versions import require_version

from ..extras.constants import CHECKPOINT_NAMES
from ..extras.logging import get_logger
from ..extras.misc import check_dependencies, get_current_device
from .data_args import DataArguments
from .evaluation_args import EvaluationArguments
from .finetuning_args import FinetuningArguments
from .generating_args import GeneratingArguments
from .model_args import ModelArguments


logger = get_logger(__name__)


check_dependencies()


_TRAIN_ARGS = [ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments]
_TRAIN_CLS = Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments]
_INFER_ARGS = [ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
_INFER_CLS = Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
_EVAL_ARGS = [ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
_EVAL_CLS = Tuple[ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]


def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
    if args is not None:
        return parser.parse_dict(args)

    if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
        return parser.parse_yaml_file(os.path.abspath(sys.argv[1]))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        return parser.parse_json_file(os.path.abspath(sys.argv[1]))

    (*parsed_args, unknown_args) = parser.parse_args_into_dataclasses(return_remaining_strings=True)

    if unknown_args:
        print(parser.format_help())
        print("Got unknown args, potentially deprecated arguments: {}".format(unknown_args))
        raise ValueError("Some specified arguments are not used by the HfArgumentParser: {}".format(unknown_args))

    return (*parsed_args,)


def _set_transformers_logging(log_level: Optional[int] = logging.INFO) -> None:
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()


def _verify_model_args(model_args: "ModelArguments", finetuning_args: "FinetuningArguments") -> None:
    if model_args.adapter_name_or_path is not None and finetuning_args.finetuning_type != "lora":
        raise ValueError("Adapter is only valid for the LoRA method.")

    if model_args.use_unsloth and is_deepspeed_zero3_enabled():
        raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")

    if model_args.quantization_bit is not None:
        if finetuning_args.finetuning_type != "lora":
            raise ValueError("Quantization is only compatible with the LoRA method.")

        if finetuning_args.pissa_init:
            raise ValueError("Please use scripts/pissa_init.py to initialize PiSSA for a quantized model.")

        if model_args.resize_vocab:
            raise ValueError("Cannot resize embedding layers of a quantized model.")

        if model_args.adapter_name_or_path is not None and finetuning_args.create_new_adapter:
            raise ValueError("Cannot create new adapter upon a quantized model.")

        if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
            raise ValueError("Quantized model only accepts a single adapter. Merge them first.")


def _check_extra_dependencies(
    model_args: "ModelArguments",
    finetuning_args: "FinetuningArguments",
    training_args: Optional["Seq2SeqTrainingArguments"] = None,
) -> None:
    if model_args.use_unsloth:
        require_version("unsloth", "Please install unsloth: https://github.com/unslothai/unsloth")

    if model_args.mixture_of_depths is not None:
        require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")

    if model_args.infer_backend == "vllm":
        require_version("vllm>=0.4.3", "To fix: pip install vllm>=0.4.3")

    if finetuning_args.use_galore:
        require_version("galore_torch", "To fix: pip install galore_torch")

    if finetuning_args.use_badam:
        require_version("badam", "To fix: pip install badam")

    if finetuning_args.plot_loss:
        require_version("matplotlib", "To fix: pip install matplotlib")

    if training_args is not None and training_args.predict_with_generate:
        require_version("jieba", "To fix: pip install jieba")
        require_version("nltk", "To fix: pip install nltk")
        require_version("rouge_chinese", "To fix: pip install rouge-chinese")


def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
    parser = HfArgumentParser(_TRAIN_ARGS)
    return _parse_args(parser, args)


def _parse_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
    parser = HfArgumentParser(_INFER_ARGS)
    return _parse_args(parser, args)


def _parse_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
    parser = HfArgumentParser(_EVAL_ARGS)
    return _parse_args(parser, args)


def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
    model_args, data_args, training_args, finetuning_args, generating_args = _parse_train_args(args)

    # Setup logging
    if training_args.should_log:
        _set_transformers_logging()

    # Check arguments
    if finetuning_args.stage != "pt" and data_args.template is None:
        raise ValueError("Please specify which `template` to use.")

    if finetuning_args.stage != "sft" and training_args.predict_with_generate:
        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")

    if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
        raise ValueError("Please enable `predict_with_generate` to save model predictions.")

    if finetuning_args.stage in ["rm", "ppo"] and training_args.load_best_model_at_end:
        raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")

    if finetuning_args.stage == "ppo" and not training_args.do_train:
        raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")

    if finetuning_args.stage == "ppo" and model_args.shift_attn:
        raise ValueError("PPO training is incompatible with S^2-Attn.")

    if finetuning_args.stage == "ppo" and finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
        raise ValueError("Unsloth does not support lora reward model.")

    if (
        finetuning_args.stage == "ppo"
        and training_args.report_to
        and training_args.report_to[0] not in ["wandb", "tensorboard"]
    ):
        raise ValueError("PPO only accepts wandb or tensorboard logger.")

    if training_args.parallel_mode == ParallelMode.NOT_DISTRIBUTED:
        raise ValueError("Please launch distributed training with `llamafactory-cli` or `torchrun`.")

    if training_args.max_steps == -1 and data_args.streaming:
        raise ValueError("Please specify `max_steps` in streaming mode.")

    if training_args.do_train and training_args.predict_with_generate:
        raise ValueError("`predict_with_generate` cannot be set as True while training.")

    if training_args.do_train and model_args.quantization_device_map == "auto":
        raise ValueError("Cannot use device map for quantized models in training.")

    if finetuning_args.pure_bf16:
        if not is_torch_bf16_gpu_available():
            raise ValueError("This device does not support `pure_bf16`.")

        if training_args.fp16 or training_args.bf16:
            raise ValueError("Turn off mixed precision training when using `pure_bf16`.")

    if (
        finetuning_args.use_galore
        and finetuning_args.galore_layerwise
        and training_args.parallel_mode == ParallelMode.DISTRIBUTED
    ):
        raise ValueError("Distributed training does not support layer-wise GaLore.")

    if (
        finetuning_args.use_badam
        and finetuning_args.badam_mode == "layer"
        and training_args.parallel_mode == ParallelMode.DISTRIBUTED
    ):
        raise ValueError("Layer-wise BAdam does not yet support distributed training, use ratio-wise BAdam.")

    if (finetuning_args.use_galore or finetuning_args.use_badam) and training_args.deepspeed is not None:
        raise ValueError("GaLore and BAdam are incompatible with DeepSpeed yet.")

    if model_args.infer_backend == "vllm":
        raise ValueError("vLLM backend is only available for API, CLI and Web.")

    if model_args.visual_inputs and data_args.packing:
        raise ValueError("Cannot use packing in MLLM fine-tuning.")

    _verify_model_args(model_args, finetuning_args)
    _check_extra_dependencies(model_args, finetuning_args, training_args)

    if (
        training_args.do_train
        and finetuning_args.finetuning_type == "lora"
        and model_args.quantization_bit is None
        and model_args.resize_vocab
        and finetuning_args.additional_target is None
    ):
        logger.warning("Remember to add embedding layers to `additional_target` to make the added tokens trainable.")

    if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
        logger.warning("We recommend enable `upcast_layernorm` in quantized training.")

    if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
        logger.warning("We recommend enable mixed precision training.")

    if training_args.do_train and finetuning_args.use_galore and not finetuning_args.pure_bf16:
        logger.warning("Using GaLore with mixed precision training may significantly increases GPU memory usage.")

    if (not training_args.do_train) and model_args.quantization_bit is not None:
        logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")

    if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
        logger.warning("Specify `ref_model` for computing rewards at evaluation.")

    # Post-process training arguments
    if (
        training_args.parallel_mode == ParallelMode.DISTRIBUTED
        and training_args.ddp_find_unused_parameters is None
        and finetuning_args.finetuning_type == "lora"
    ):
        logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
        training_args.ddp_find_unused_parameters = False

    if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:
        can_resume_from_checkpoint = False
        if training_args.resume_from_checkpoint is not None:
            logger.warning("Cannot resume from checkpoint in current stage.")
            training_args.resume_from_checkpoint = None
    else:
        can_resume_from_checkpoint = True

    if (
        training_args.resume_from_checkpoint is None
        and training_args.do_train
        and os.path.isdir(training_args.output_dir)
        and not training_args.overwrite_output_dir
        and can_resume_from_checkpoint
    ):
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and any(
            os.path.isfile(os.path.join(training_args.output_dir, name)) for name in CHECKPOINT_NAMES
        ):
            raise ValueError("Output directory already exists and is not empty. Please set `overwrite_output_dir`.")

        if last_checkpoint is not None:
            training_args.resume_from_checkpoint = last_checkpoint
            logger.info("Resuming training from {}.".format(training_args.resume_from_checkpoint))
            logger.info("Change `output_dir` or use `overwrite_output_dir` to avoid.")

    if (
        finetuning_args.stage in ["rm", "ppo"]
        and finetuning_args.finetuning_type == "lora"
        and training_args.resume_from_checkpoint is not None
    ):
        logger.warning(
            "Add {} to `adapter_name_or_path` to resume training from checkpoint.".format(
                training_args.resume_from_checkpoint
            )
        )

    # Post-process model arguments
    if training_args.bf16 or finetuning_args.pure_bf16:
        model_args.compute_dtype = torch.bfloat16
    elif training_args.fp16:
        model_args.compute_dtype = torch.float16

    model_args.device_map = {"": get_current_device()}
    model_args.model_max_length = data_args.cutoff_len
    data_args.packing = data_args.packing if data_args.packing is not None else finetuning_args.stage == "pt"

    # Log on each process the small summary
    logger.info(
        "Process rank: {}, device: {}, n_gpu: {}, distributed training: {}, compute dtype: {}".format(
            training_args.local_rank,
            training_args.device,
            training_args.n_gpu,
            training_args.parallel_mode == ParallelMode.DISTRIBUTED,
            str(model_args.compute_dtype),
        )
    )

    transformers.set_seed(training_args.seed)

    return model_args, data_args, training_args, finetuning_args, generating_args


def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
    model_args, data_args, finetuning_args, generating_args = _parse_infer_args(args)

    _set_transformers_logging()

    if data_args.template is None:
        raise ValueError("Please specify which `template` to use.")

    if model_args.infer_backend == "vllm":
        if finetuning_args.stage != "sft":
            raise ValueError("vLLM engine only supports auto-regressive models.")

        if model_args.quantization_bit is not None:
            raise ValueError("vLLM engine does not support bnb quantization (GPTQ and AWQ are supported).")

        if model_args.rope_scaling is not None:
            raise ValueError("vLLM engine does not support RoPE scaling.")

        if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
            raise ValueError("vLLM only accepts a single adapter. Merge them first.")

    if finetuning_args.stage == "rm" and model_args.visual_inputs:
        raise ValueError("Reward server does not support MLLM yet. Stay tuned.")

    _verify_model_args(model_args, finetuning_args)
    _check_extra_dependencies(model_args, finetuning_args)

    if model_args.export_dir is not None and model_args.export_device == "cpu":
        model_args.device_map = {"": torch.device("cpu")}
    else:
        model_args.device_map = "auto"

    return model_args, data_args, finetuning_args, generating_args


def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
    model_args, data_args, eval_args, finetuning_args = _parse_eval_args(args)

    _set_transformers_logging()

    if data_args.template is None:
        raise ValueError("Please specify which `template` to use.")

    if model_args.infer_backend == "vllm":
        raise ValueError("vLLM backend is only available for API, CLI and Web.")

    _verify_model_args(model_args, finetuning_args)
    _check_extra_dependencies(model_args, finetuning_args)

    model_args.device_map = "auto"

    transformers.set_seed(eval_args.seed)

    return model_args, data_args, eval_args, finetuning_args