278 lines
12 KiB
Python
278 lines
12 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass, field
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from typing import List, Optional
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from paddlenlp.trainer import TrainingArguments
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from paddlenlp.trainer.trainer_utils import IntervalStrategy
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from paddlenlp.utils.log import logger
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@dataclass
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class TrainingArguments(TrainingArguments):
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benchmark: bool = field(default=False, metadata={"help": "Whether runs benchmark"})
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# NOTE(gongenlei): new add autotuner_benchmark
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autotuner_benchmark: bool = field(
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default=False,
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metadata={"help": "Weather to run benchmark by autotuner. True for from_scratch and pad_max_length."},
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)
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def __post_init__(self):
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super().__post_init__()
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# NOTE(gongenlei): new add autotuner_benchmark
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if self.autotuner_benchmark:
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self.max_steps = 5
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self.do_train = True
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self.do_export = False
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self.do_predict = False
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self.do_eval = False
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self.overwrite_output_dir = True
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self.load_best_model_at_end = False
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self.report_to = []
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self.save_strategy = IntervalStrategy.NO
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self.evaluation_strategy = IntervalStrategy.NO
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@dataclass
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class DataArgument:
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dataset_name_or_path: str = field(default=None, metadata={"help": "Name or path for dataset"})
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task_name: str = field(default=None, metadata={"help": "Additional name to select a more specific task."})
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zero_padding: bool = field(default=False, metadata={"help": "Whether to use Zero Padding data stream"})
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pad_to_multiple_of: int = field(
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default=None, metadata={"help": "If set will pad the sequence to a multiple of the provided value."}
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)
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src_length: int = field(default=1024, metadata={"help": "The maximum length of source(context) tokens."})
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max_length: int = field(
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default=2048,
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metadata={
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"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"
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},
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)
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eval_with_do_generation: bool = field(default=False, metadata={"help": "Whether to do generation for evaluation"})
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save_generation_output: bool = field(
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default=False,
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metadata={"help": "Whether to save generated text to file when eval_with_do_generation set to True."},
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)
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lazy: bool = field(
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default=False,
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metadata={
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"help": "Weather to return `MapDataset` or an `IterDataset`.True for `IterDataset`. False for `MapDataset`."
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},
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)
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chat_template: str = field(
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default=None,
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metadata={
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"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."
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},
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)
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# NOTE(gongenlei): deprecated params
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task_name_or_path: str = field(
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default=None,
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metadata={
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"help": "@deprecated Please use `dataset_name_or_path`. Name or path for dataset, same as `dataset_name_or_path`."
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},
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) # Alias for dataset_name_or_path
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intokens: bool = field(
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default=None,
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metadata={
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"help": "@deprecated Please use `zero_padding`. Whether to use InTokens data stream, same as `zero_padding`."
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},
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) # Alias for zero_padding
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def __post_init__(self):
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if self.task_name_or_path is not None:
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logger.warning("`--task_name_or_path` is deprecated, please use `--dataset_name_or_path`.")
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self.dataset_name_or_path = self.task_name_or_path
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if self.intokens is not None:
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logger.warning("`--intokens` is deprecated, please use `--zero_padding`.")
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self.zero_padding = self.intokens
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@dataclass
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class ModelArgument:
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model_name_or_path: str = field(
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default=None, metadata={"help": "Build-in pretrained model name or the path to local model."}
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)
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use_flash_attention: bool = field(default=False, metadata={"help": "Whether to use flash attention"})
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tokenizer_name_or_path: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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use_fused_rms_norm: bool = field(
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default=False,
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metadata={"help": "llama or other model, use_fused_rms_norm"},
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)
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fuse_attention_qkv: bool = field(
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default=False,
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metadata={"help": "whether to fuse attention qkv"},
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)
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fuse_attention_ffn: bool = field(
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default=False,
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metadata={"help": "whether to fuse first up and gate proj in mlp block"},
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)
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recompute_granularity: str = field(
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default="full",
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metadata={"help": "Choose among ['full', 'core_attn', 'full_attn']"},
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)
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virtual_pp_degree: int = field(
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default=1,
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metadata={"help": "virtual_pp_degree"},
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)
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hidden_dropout_prob: float = field(default=0.1, metadata={"help": "The hidden dropout prob."})
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attention_probs_dropout_prob: float = field(default=0.1, metadata={"help": "The attention hidden dropout prob."})
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continue_training: bool = field(
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default=False,
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metadata={
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"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."
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},
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)
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sequence_parallel: bool = field(
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default=False,
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metadata={"help": "whether to use sequence parallel"},
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)
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fuse_sequence_parallel_allreduce: bool = field(
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default=False,
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metadata={"help": "whether to use fuse sequence parallel allreduce"},
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)
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use_fused_rope: Optional[bool] = field(
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default=False,
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metadata={"help": "Enable rope fusion or not."},
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)
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no_recompute_layers: Optional[List[int]] = field(
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default=None,
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metadata={"help": "Specify the full transformer layers that should not be recomputed."},
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)
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pp_recompute_interval: int = field(
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default=1,
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metadata={
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"help": "The interval for the number of layers at which recomputation occurs. A value of 0 indicates no recomputation. Default is 0."
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},
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)
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recompute_use_reentrant: bool = field(
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default=False,
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metadata={"help": "recompute_use_reentrant"},
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)
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weight_quantize_algo: str = field(
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default=None,
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metadata={
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"help": "Model weight quantization algorithm including 'nf4', 'fp4','weight_only_int4', 'weight_only_int8'."
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},
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)
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weight_blocksize: int = field(
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default=64,
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metadata={"help": "Block size for weight quantization(Only available for nf4 or fp4 quant_scale.)."},
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)
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weight_double_quant: bool = field(
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default=False, metadata={"help": "Whether apply double quant(Only available for nf4 or fp4 quant_scale.)."}
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)
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weight_double_quant_block_size: int = field(
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default=256,
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metadata={
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"help": "Block size for quant_scale of weight quant_scale(Only available for nf4 or fp4 quant_scale.)"
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},
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)
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# LoRA related parameters
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lora: bool = field(default=False, metadata={"help": "Whether to use LoRA technique"})
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lora_path: str = field(default=None, metadata={"help": "Initialize lora state dict."})
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lora_rank: int = field(default=8, metadata={"help": "Lora attention dimension"})
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use_quick_lora: bool = field(
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default=False,
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metadata={
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"help": "Whether to use quick lora, The use of Quick LoRa will only take effect when lora_dropout is set to 0."
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},
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)
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rslora: bool = field(default=False, metadata={"help": "Whether to use RsLoRA"})
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lora_plus_scale: float = field(default=1.0, metadata={"help": "Lora B scale in LoRA+ technique"})
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# prefix tuning related parameters
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prefix_tuning: bool = field(default=False, metadata={"help": "Whether to use Prefix technique"})
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prefix_path: str = field(default=None, metadata={"help": "Initialize prefix state dict."})
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num_prefix_tokens: int = field(default=128, metadata={"help": "Number of prefix tokens"})
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from_aistudio: bool = field(default=False, metadata={"help": "Whether to load model from aistudio"})
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save_to_aistudio: bool = field(default=False, metadata={"help": "Whether to save model to aistudio"})
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aistudio_repo_id: str = field(default=None, metadata={"help": "The id of aistudio repo"})
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aistudio_repo_private: bool = field(default=True, metadata={"help": "Whether to create a private repo"})
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aistudio_repo_license: str = field(default="Apache License 2.0", metadata={"help": "The license of aistudio repo"})
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aistudio_token: str = field(default=None, metadata={"help": "The token of aistudio"})
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neftune: bool = field(default=False, metadata={"help": "Whether to apply NEFT"})
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neftune_noise_alpha: float = field(default=5.0, metadata={"help": "NEFT noise alpha"})
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@dataclass
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class QuantArgument:
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quant_type: str = field(
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default="a8w8",
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metadata={"help": "Quantization type. Supported values: a8w8, weight_only_int4, weight_only_int8"},
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)
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# QAT related parameters
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# Not Yet support
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do_qat: bool = field(default=False, metadata={"help": "Whether to use QAT technique"})
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# PTQ related parameters
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do_ptq: bool = field(default=False, metadata={"help": "Whether to use PTQ"})
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ptq_step: int = field(default=32, metadata={"help": "Step for PTQ"})
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weight_quant_method: str = field(
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default="abs_max_channel_wise",
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metadata={"help": "Weight quantization method, choosen from ['abs_max_channel_wise', 'groupwise']"},
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)
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# Pre-quant method Shift related parameters
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shift: bool = field(default=False, metadata={"help": "Whether to use Shift"})
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shift_all_linears: bool = field(default=False, metadata={"help": "Whether to shift all linears"})
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shift_sampler: str = field(
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default="ema", metadata={"help": "The name of shift sampler, choosen from ['ema', 'none']"}
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)
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shift_step: int = field(default=32, metadata={"help": "Sample steps when shift"})
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# Pre-quant methos Smooth related parameters
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smooth: bool = field(default=False, metadata={"help": "Whether to use Smooth"})
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smooth_all_linears: bool = field(default=False, metadata={"help": "Whether to smooth all linears"})
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smooth_sampler: str = field(
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default="none", metadata={"help": "The name of smooth sampler, choosen from ['multi_step','none']"}
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)
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smooth_step: int = field(default=32, metadata={"help": "Sample steps when smooth"})
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smooth_piecewise_search: bool = field(
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default=False, metadata={"help": "The number of piece in piecewise search for smooth strategy."}
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)
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smooth_k_piece: int = field(default=3, metadata={"help": "Number of pieces for K-search"})
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smooth_search_piece: bool = field(default=False, metadata={"help": "Whether search k_piece when piecewise search"})
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# GPTQ related parameters
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do_gptq: bool = field(default=False, metadata={"help": "Whether to use GPTQ"})
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gptq_step: int = field(default=8, metadata={"help": "Step for GPTQ"})
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# AWQ related parameters, default for WINT4
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do_awq: bool = field(default=False, metadata={"help": "Whether to use AWQ Search"})
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auto_clip: bool = field(default=False, metadata={"help": "Whether to use AutoClip from AWQ"})
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awq_step: int = field(default=8, metadata={"help": "Step for AWQ Search"})
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autoclip_step: int = field(default=8, metadata={"help": "Step for AutoClip"})
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@dataclass
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class GenerateArgument:
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top_k: int = field(
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default=1,
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metadata={
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"help": "The number of highest probability tokens to keep for top-k-filtering in the sampling strategy"
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},
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)
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top_p: float = field(
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default=1.0, metadata={"help": "The cumulative probability for top-p-filtering in the sampling strategy."}
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)
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