argument.py

# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# 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, field
from typing import List, Optional

from paddlenlp.trainer import TrainingArguments
from paddlenlp.trainer.trainer_utils import IntervalStrategy
from paddlenlp.utils.log import logger


@dataclass
class TrainingArguments(TrainingArguments):
    benchmark: bool = field(default=False, metadata={"help": "Whether runs benchmark"})
    # NOTE(gongenlei): new add autotuner_benchmark
    autotuner_benchmark: bool = field(
        default=False,
        metadata={"help": "Weather to run benchmark by autotuner. True for from_scratch and pad_max_length."},
    )

    def __post_init__(self):
        super().__post_init__()
        # NOTE(gongenlei): new add autotuner_benchmark
        if self.autotuner_benchmark:
            self.max_steps = 5
            self.do_train = True
            self.do_export = False
            self.do_predict = False
            self.do_eval = False
            self.overwrite_output_dir = True
            self.load_best_model_at_end = False
            self.report_to = []
            self.save_strategy = IntervalStrategy.NO
            self.evaluation_strategy = IntervalStrategy.NO


@dataclass
class DataArgument:
    dataset_name_or_path: str = field(default=None, metadata={"help": "Name or path for dataset"})
    task_name: str = field(default=None, metadata={"help": "Additional name to select a more specific task."})
    zero_padding: bool = field(default=False, metadata={"help": "Whether to use Zero Padding data stream"})
    pad_to_multiple_of: int = field(
        default=None, metadata={"help": "If set will pad the sequence to a multiple of the provided value."}
    )
    src_length: int = field(default=1024, metadata={"help": "The maximum length of source(context) tokens."})
    max_length: int = field(
        default=2048,
        metadata={
            "help": "The maximum length that model input tokens can have. When Zero Padding is set to True, it's also the maximum length for Zero Padding data stream"
        },
    )
    eval_with_do_generation: bool = field(default=False, metadata={"help": "Whether to do generation for evaluation"})
    save_generation_output: bool = field(
        default=False,
        metadata={"help": "Whether to save generated text to file when eval_with_do_generation set to True."},
    )
    lazy: bool = field(
        default=False,
        metadata={
            "help": "Weather to return `MapDataset` or an `IterDataset`.True for `IterDataset`. False for `MapDataset`."
        },
    )
    chat_template: str = field(
        default=None,
        metadata={
            "help": "the path of `chat_template.json` file to handle multi-rounds conversation. If is None, it will not use `chat_template.json`; If is equal with `model_name_or_path`, it will use the default loading; If is directory, it will find the `chat_template.json` under the directory; If is file, it will load it."
        },
    )
    # NOTE(gongenlei): deprecated params
    task_name_or_path: str = field(
        default=None,
        metadata={
            "help": "@deprecated Please use `dataset_name_or_path`. Name or path for dataset, same as `dataset_name_or_path`."
        },
    )  # Alias for dataset_name_or_path
    intokens: bool = field(
        default=None,
        metadata={
            "help": "@deprecated Please use `zero_padding`. Whether to use InTokens data stream, same as `zero_padding`."
        },
    )  # Alias for zero_padding

    def __post_init__(self):
        if self.task_name_or_path is not None:
            logger.warning("`--task_name_or_path` is deprecated, please use `--dataset_name_or_path`.")
            self.dataset_name_or_path = self.task_name_or_path

        if self.intokens is not None:
            logger.warning("`--intokens` is deprecated, please use `--zero_padding`.")
            self.zero_padding = self.intokens


@dataclass
class ModelArgument:
    model_name_or_path: str = field(
        default=None, metadata={"help": "Build-in pretrained model name or the path to local model."}
    )
    use_flash_attention: bool = field(default=False, metadata={"help": "Whether to use flash attention"})
    tokenizer_name_or_path: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    use_fused_rms_norm: bool = field(
        default=False,
        metadata={"help": "llama or other model, use_fused_rms_norm"},
    )
    fuse_attention_qkv: bool = field(
        default=False,
        metadata={"help": "whether to fuse attention qkv"},
    )
    fuse_attention_ffn: bool = field(
        default=False,
        metadata={"help": "whether to fuse first up and gate proj in mlp block"},
    )
    recompute_granularity: str = field(
        default="full",
        metadata={"help": "Choose among ['full', 'core_attn', 'full_attn']"},
    )
    virtual_pp_degree: int = field(
        default=1,
        metadata={"help": "virtual_pp_degree"},
    )
    hidden_dropout_prob: float = field(default=0.1, metadata={"help": "The hidden dropout prob."})
    attention_probs_dropout_prob: float = field(default=0.1, metadata={"help": "The attention hidden dropout prob."})

    continue_training: bool = field(
        default=False,
        metadata={
            "help": "Pre-training from existing paddlenlp model weights. Default False and model will train from scratch. If set True, the model_name_or_path argument must exist in the paddlenlp models."
        },
    )
    sequence_parallel: bool = field(
        default=False,
        metadata={"help": "whether to use sequence parallel"},
    )
    fuse_sequence_parallel_allreduce: bool = field(
        default=False,
        metadata={"help": "whether to use fuse sequence parallel allreduce"},
    )
    use_fused_rope: Optional[bool] = field(
        default=False,
        metadata={"help": "Enable rope fusion or not."},
    )
    no_recompute_layers: Optional[List[int]] = field(
        default=None,
        metadata={"help": "Specify the full transformer layers that should not be recomputed."},
    )
    pp_recompute_interval: int = field(
        default=1,
        metadata={
            "help": "The interval for the number of layers at which recomputation occurs. A value of 0 indicates no recomputation. Default is 0."
        },
    )
    recompute_use_reentrant: bool = field(
        default=False,
        metadata={"help": "recompute_use_reentrant"},
    )
    weight_quantize_algo: str = field(
        default=None,
        metadata={
            "help": "Model weight quantization algorithm including 'nf4', 'fp4','weight_only_int4', 'weight_only_int8'."
        },
    )
    weight_blocksize: int = field(
        default=64,
        metadata={"help": "Block size for weight quantization(Only available for nf4 or fp4 quant_scale.)."},
    )
    weight_double_quant: bool = field(
        default=False, metadata={"help": "Whether apply double quant(Only available for nf4 or fp4 quant_scale.)."}
    )
    weight_double_quant_block_size: int = field(
        default=256,
        metadata={
            "help": "Block size for quant_scale of weight quant_scale(Only available for nf4 or fp4 quant_scale.)"
        },
    )

    # LoRA related parameters
    lora: bool = field(default=False, metadata={"help": "Whether to use LoRA technique"})
    lora_path: str = field(default=None, metadata={"help": "Initialize lora state dict."})
    lora_rank: int = field(default=8, metadata={"help": "Lora attention dimension"})
    use_quick_lora: bool = field(
        default=False,
        metadata={
            "help": "Whether to use quick lora, The use of Quick LoRa will only take effect when lora_dropout is set to 0."
        },
    )
    rslora: bool = field(default=False, metadata={"help": "Whether to use RsLoRA"})
    lora_plus_scale: float = field(default=1.0, metadata={"help": "Lora B scale in LoRA+ technique"})
    pissa: bool = field(default=False, metadata={"help": "Whether to use Pissa: https://arxiv.org/pdf/2404.02948.pdf"})

    # prefix tuning related parameters
    prefix_tuning: bool = field(default=False, metadata={"help": "Whether to use Prefix technique"})
    prefix_path: str = field(default=None, metadata={"help": "Initialize prefix state dict."})
    num_prefix_tokens: int = field(default=128, metadata={"help": "Number of prefix tokens"})

    from_aistudio: bool = field(default=False, metadata={"help": "Whether to load model from aistudio"})
    save_to_aistudio: bool = field(default=False, metadata={"help": "Whether to save model to aistudio"})
    aistudio_repo_id: str = field(default=None, metadata={"help": "The id of aistudio repo"})
    aistudio_repo_private: bool = field(default=True, metadata={"help": "Whether to create a private repo"})
    aistudio_repo_license: str = field(default="Apache License 2.0", metadata={"help": "The license of aistudio repo"})
    aistudio_token: str = field(default=None, metadata={"help": "The token of aistudio"})
    neftune: bool = field(default=False, metadata={"help": "Whether to apply NEFT"})
    neftune_noise_alpha: float = field(default=5.0, metadata={"help": "NEFT noise alpha"})


@dataclass
class QuantArgument:
    quant_type: str = field(
        default="a8w8",
        metadata={"help": "Quantization type. Supported values: a8w8, weight_only_int4, weight_only_int8"},
    )

    # QAT related parameters
    # Not Yet support
    do_qat: bool = field(default=False, metadata={"help": "Whether to use QAT technique"})

    # PTQ related parameters
    do_ptq: bool = field(default=False, metadata={"help": "Whether to use PTQ"})
    ptq_step: int = field(default=32, metadata={"help": "Step for PTQ"})

    weight_quant_method: str = field(
        default="abs_max_channel_wise",
        metadata={"help": "Weight quantization method, choosen from ['abs_max_channel_wise', 'groupwise']"},
    )

    # Pre-quant method Shift related parameters
    shift: bool = field(default=False, metadata={"help": "Whether to use Shift"})
    shift_all_linears: bool = field(default=False, metadata={"help": "Whether to shift all linears"})
    shift_sampler: str = field(
        default="ema", metadata={"help": "The name of shift sampler, choosen from ['ema', 'none']"}
    )
    shift_step: int = field(default=32, metadata={"help": "Sample steps when shift"})

    # Pre-quant methos Smooth related parameters
    smooth: bool = field(default=False, metadata={"help": "Whether to use Smooth"})
    smooth_all_linears: bool = field(default=False, metadata={"help": "Whether to smooth all linears"})
    smooth_sampler: str = field(
        default="none", metadata={"help": "The name of smooth sampler, choosen from ['multi_step','none']"}
    )
    smooth_step: int = field(default=32, metadata={"help": "Sample steps when smooth"})
    smooth_piecewise_search: bool = field(
        default=False, metadata={"help": "The number of piece in piecewise search for smooth strategy."}
    )
    smooth_k_piece: int = field(default=3, metadata={"help": "Number of pieces for K-search"})
    smooth_search_piece: bool = field(default=False, metadata={"help": "Whether search k_piece when piecewise search"})

    # GPTQ related parameters
    do_gptq: bool = field(default=False, metadata={"help": "Whether to use GPTQ"})
    gptq_step: int = field(default=8, metadata={"help": "Step for GPTQ"})

    # AWQ related parameters, default for WINT4
    do_awq: bool = field(default=False, metadata={"help": "Whether to use AWQ Search"})
    auto_clip: bool = field(default=False, metadata={"help": "Whether to use AutoClip from AWQ"})
    awq_step: int = field(default=8, metadata={"help": "Step for AWQ Search"})
    autoclip_step: int = field(default=8, metadata={"help": "Step for AutoClip"})


@dataclass
class GenerateArgument:
    top_k: int = field(
        default=1,
        metadata={
            "help": "The number of highest probability tokens to keep for top-k-filtering in the sampling strategy"
        },
    )
    top_p: float = field(
        default=1.0, metadata={"help": "The cumulative probability for top-p-filtering in the sampling strategy."}
    )