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CPM-9G-8B/FM_9G/fm9g/arguments.py

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# coding=utf-8
# Copyright 2020 The OpenBMB team. 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.
import argparse
def add_model_config_args(parser: argparse.ArgumentParser):
"""Model arguments"""
group = parser.add_argument_group("model", "model configuration")
group.add_argument("--model-config", type=str, help="model configuration file")
group.add_argument("--vocab", type=str, default=None, help="model vocabulary file")
group.add_argument("--eps", type=float, default=1e-5, help="eps in layernorm")
# group.add_argument("--qk_norm", action="store_true", default=False, help="qk layernorm")
return parser
def add_training_args(parser: argparse.ArgumentParser):
"""Training arguments."""
group = parser.add_argument_group("train", "training configurations")
group.add_argument("--platform-config", type=str, default="platform_config.json", help="Path to platform config")
group.add_argument("--dataset", type=str, default="dataset.json", help="Path to dataset")
group.add_argument("--val-dataset", type=str, default="dataset.json", help="Path to val dataset")
group.add_argument(
"--load",
type=str,
default=None,
help="Path to a directory containing a model checkpoint.",
)
group.add_argument(
"--load-grad",
action="store_true",
default=False,
help="Load the gradient states",
)
group.add_argument(
"--grad-ckpt-num",
type=int,
default=0,
help="grad file num (only work when --load-grad from files less than world-size )",
)
group.add_argument(
"--load-start-step",
action="store_true",
default=False,
help="Load the step state from checkpoints",
)
group.add_argument(
"--save",
type=str,
default=None,
help="Output directory to save checkpoints to.",
)
group.add_argument(
"--save-name",
type=str,
default=None,
help="Output filename to save checkpoints to.",
)
group.add_argument(
"--save-model",
type=str,
default=None,
help="Output directory to save model to.",
)
group.add_argument(
"--tensorboard",
type=str,
default=None,
help="tensorboard directory",
)
group.add_argument("--inspect-iters", type=int, default=1000, help="number of inspecting")
group.add_argument("--batch-size", type=int, default=32, help="Data Loader batch size")
group.add_argument("--num-micro-batches", type=int, default=16)
group.add_argument("--clip-grad", type=float, default=1.0, help="gradient clipping")
group.add_argument("--grad-accum", type=int, default=1, help="gradient accum steps")
group.add_argument(
"--train-iters",
type=int,
default=1000000,
help="total number of iterations to train over all training runs",
)
group.add_argument("--max-length", type=int, default=512, help="max length of input")
group.add_argument("--min-length", type=int, default=None, help="only for speed test")
group.add_argument("--seed", type=int, default=1234, help="random seed for reproducibility")
# Learning rate.
group.add_argument("--lr", type=float, default=1.0e-4, help="initial learning rate")
group.add_argument("--lr_scheduler", type=str, default="cosine", help=" learning rate scheduler")
group.add_argument("--weight-decay", type=float, default=1.0e-2, help="weight decay rate")
group.add_argument("--loss-scale", type=float, default=65536, help="loss scale")
group.add_argument("--max-loss-scale", type=float, default=float("inf"), help="loss scale")
group.add_argument("--min-loss-scale", type=float, default=1, help="loss scale")
group.add_argument("--loss-scale-steps", type=float, default=1024, help="loss scale")
group.add_argument(
"--warmup-iters",
type=float,
default=0.01,
help="percentage of data to warmup on (.01 = 1% of all " "training iters). Default 0.01",
)
group.add_argument(
"--drop-iters",
type=float,
default=0.01,
help="percentage of data to warmup on (.01 = 1% of all " "training iters). Default 0.01",
)
group.add_argument("--lr-decay-iters", type=int, default=None, help="lr decay steps")
group.add_argument("--start-step", type=int, default=0, help="step to start or continue training")
group.add_argument("--concat-data", action="store_true", help="whether we concatenate the dialogues")
group.add_argument("--offload", action="store_true", help="whether we use offload_adam")
group.add_argument("--new-bmt", action="store_true", help="new bmt without ckpt")
group.add_argument("--flash", default="none", choices=["none", "1d", "triton", "cuda"])
group.add_argument("--use-jfs-data", action="store_true", help="whether we use juicefs dataset")
group.add_argument("--tp-size", default=1, type=int)
group.add_argument("--pp-size", default=1, type=int)
group.add_argument("--bf16", action="store_true", help="whether we use bf16")
group.add_argument("--dataloader_num_threads", default=3, type=int, help="Only useful in indexed dataest.")
group.add_argument("--dataloader_prefetch", default=200, type=int, help="Only useful in indexed dataest.")
group.add_argument("--dataloader_num_workers", default=4, type=int, help="Only useful in indexed dataest.")
group.add_argument("--dataloader_prefetch_factor", default=50, type=int, help="Only useful in indexed dataest.")
group.add_argument(
"--dataloader",
default="indexed",
type=str,
help="dataloader type, 'indexed' for indexed dataset, 'normal' for normal dataset",
)
group.add_argument("--stop_when_end", default=0, type=int, help="Whether to stop training when we reach end_iter")
group.add_argument(
"--data_len_threshold",
default=512,
type=int,
help="If the average length of a sequence is less than this int, mean the sample is biased. ",
)
group.add_argument(
"--only_run_dataloader", default=0, type=int, help="Whether to only run dataloader to check data. "
)
group.add_argument(
"--only_load_model", default=0, type=int, help="Whether to only load a model ckpt, without anything else."
)
group.add_argument(
"--load_dataloader_ckpt", default=1, type=int, help="Whether to only load a model ckpt, without anything else."
)
group.add_argument(
"--resume_no_optimze",
default=0,
type=int,
help="The number of steps that does not add optimization after resume",
)
group.add_argument(
"--parallel_load_datastate",
default=256,
type=int,
help="The number of parallel workers to load dataset state",
)
group.add_argument(
"--async_save",
action="store_true",
help="whether to save artifacts asynchronously",
)
group.add_argument(
"--drop_begin",
default=-1,
type=int,
help="The number of steps that starts to drop lr"
)
group.add_argument(
"--drop_rate",
default=0.5,
type=float,
help="The number rate"
)
group.add_argument(
"--use_checkpoint",
default=1,
type=int,
help="Whether to use checkpointing."
)
return parser
def add_pretrain_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("pretrain", "pretrain configurations")
group.add_argument(
"--save-iters",
type=int,
default=1000,
help="number of iterations between saves",
)
group.add_argument(
"--log-dir",
type=str,
default=None,
help="log directory",
)
group.add_argument(
"--worker-name",
type=str,
default=None,
help="worker name",
)
return parser
def add_tokenizer_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("tokenizer", "tokenizer configurations")
group.add_argument(
"--tokenizer_path",
type=str,
default="",
help="tokenizer_path",
)
return parser
def add_finetune_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("finetune", "finetune configurations")
group.add_argument("--epoch", type=int, default=1, help="number of training epochs")
group.add_argument("--task-name", type=str, default="task", help="name of training task")
group.add_argument("--save-epochs", type=int, default=1, help="number of training epochs between saves")
group.add_argument("--save-steps", type=int, default=0, help="number of training steps between saves")
group.add_argument(
"--drop-last",
action="store_true",
default=False,
help="drop data from each epoch that cannot be formed into a complete batch at the end",
)
group.add_argument("--delta-tuning", action="store_true", default=False)
group.add_argument("--each-epoch-save", default=False)
group.add_argument("--train-task-id", type=int, default=-1)
return parser
def add_rhlf_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("rhlf", "rhlf configurations")
group.add_argument(
"--load-reward",
type=str,
default=None,
help="Path to reward model checkpoint.",
)
group.add_argument("--actor-lr", type=float, default=1.0e-5, help="actor learning rate")
group.add_argument("--critic-lr", type=float, default=1.0e-6, help="critic learning rate")
group.add_argument("--actor-loss-scale", type=float, default=65536, help="actor loss scale")
group.add_argument("--critic-loss-scale", type=float, default=65536, help="critic loss scale")
group.add_argument("--avg-reward-bias", type=float, default=0, help="reward bias")
group.add_argument("--actor-delay-step", type=int, default=0, help="actor delay step")
group.add_argument("--entropy-coef", type=float, default=-1.0, help="coef of policy entropy")
##
return parser
def add_simple_rhlf_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("simple_rhlf", "simple rhlf configurations")
group.add_argument("--epoch", type=int, default=1, help="number of training epochs")
group.add_argument("--sample-batch-size", type=int, default=32, help="Data Loader sample batch size")
group.add_argument("--load-reward", type=str, default=None, help="Path to reward model checkpoint")
group.add_argument("--avg-reward-bias", type=float, default=0, help="reward bias")
group.add_argument("--sample-min-length", type=int, default=20, help="sample-min-length")
group.add_argument("--sample-max-inp-length", type=int, default=1024, help="sample-max-inp-length")
group.add_argument("--sample-max-length", type=int, default=64, help="sample-max-length")
group.add_argument("--sample-repetition-penalty", type=float, default=1.05, help="sample-repetition-penalty")
group.add_argument("--sample-temperature", type=float, default=1.0, help="sample-temperature")
group.add_argument("--encode-max-length", type=int, default=1024, help="encode-max-length")
group.add_argument("--generate-max-length", type=int, default=64, help="generate-max-length")
group.add_argument("--value-loss-weight", type=float, default=0.1, help="value-loss-weight")
group.add_argument("--ptx-loss-weight", type=float, default=0.001, help="ptx-loss-weight")
group.add_argument("--save-epochs", type=int, default=1, help="number of training epochs between saves")
##
return parser
def add_feedback_learning_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("rrhf", "rrhf configurations")
group.add_argument("--length-penalty", type=float, default=1.0, help="length_penalty")
group.add_argument("--feedback-weight", type=float, default=1.0, help="feedback_weight")
group.add_argument("--sample-num", type=int, default=6, help="sample_num")
group.add_argument("--dpo-beta", type=float, default=1.0, help="dpo_beta")
group.add_argument("--stable-alignment-margin", type=float, default=1.0, help="stable_alignment_margin")
group.add_argument("--feedback-learning-type", type=str, default="RRHF", help="feedback_learning_type")
group.add_argument("--save-iters", type=int, default=1000, help="number of iterations between saves")
##
return parser
def add_model_change_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("model_change", "model change during pretraining")
group.add_argument("--strict_state_dict", type=int, default=1, help="strict_state_dict")
##
return parser
def add_log_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("log", "log configurations")
group.add_argument("--tensorboard_all_tasks", type=int, default=0, help="log")
return parser
def add_error_handle_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("error_handle", "error_handle configurations")
group.add_argument(
"--ignore_cuda_oom", type=int, default=1, help="continue training by ingore the batch that causes oom"
)
return parser
def add_runtime_eval_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("runtime eval args", "runtime evaluation by submitting a job")
group.add_argument(
"--runtime_eval",
action="store_true",
help="whether to use runtime_eval. Only if this is set to True, the following variables will be useful",
)
group.add_argument("--eval_jeeves_auth", type=str, default="", help="auth, press f12 on jeeves platform to get")
group.add_argument("--eval_project_id", type=str, default=None, help="project id")
group.add_argument("--eval_run_cmd", type=str, default="", help="cmd for eval")
group.add_argument(
"--eval_git_path",
type=str,
default="git@git.in.zhihu.com:luca/llm-bench.git",
help="git path of evaluation code",
)
group.add_argument("--eval_git_branch", type=str, default="master", help="git branch of evaluation code")
group.add_argument("--eval_node_num", type=int, default=1, help="using 1 node to evaluate")
group.add_argument("--eval_gpu_num", type=int, default=1, help="using 1 gpu per node to evaluate")
group.add_argument("--eval_tasks_config", type=str, default="", help="evaluate tasks' config")
group.add_argument("--eval_model_backend", default="torch", type=str, help="model_backend")
group.add_argument(
"--eval_at_start", action="store_true", help="whether to eval at the first epoch, default to false"
)
return parser
def add_reward_args(parser: argparse.ArgumentParser):
group = parser.add_argument_group("reward", "reward configurations")
group.add_argument("--load-all", type=str, default=None, help="Path to a directory containing a model checkpoint.")
##
return parser
def add_long_context_extend_args(parser: argparse.ArgumentParser):
"""long context extending arguments."""
group = parser.add_argument_group("long_context_extend", "long context extend configurations")
group.add_argument("--pose-prob", default=0.0, type=float, help="Sample-level PoSE probability")
group.add_argument(
"--pose-scaling-factor",
default=1.0,
type=float,
help="PoSE scaling factor, simulate input length = max_length * pose_scaling_factor",
)
group.add_argument(
"--rope-scaling-type",
default="",
type=str,
choices=["Linear", "NTK-aware", "Dynamic NTK", "NTK-by-parts", "YaRN", ""],
help="Context scaling type",
)
group.add_argument("--rope-scaling-factor", default=1, type=int, help="Context scaling factor")
group.add_argument(
"--orig-max-length", default=8192, type=int, help="Original context length before context extending"
)
return parser
def get_args(
pretrain: bool = False,
finetune: bool = False,
rhlf: bool = False,
simple_rlhf: bool = False,
feedback_learning: bool = False,
reward: bool = False,
):
parser = argparse.ArgumentParser()
parser = add_model_config_args(parser) # config file need to be exported with model/ckpt
parser = add_training_args(parser)
if pretrain:
parser = add_pretrain_args(parser)
parser = add_runtime_eval_args(parser)
parser = add_tokenizer_args(parser)
parser = add_log_args(parser)
parser = add_error_handle_args(parser)
parser = add_model_change_args(parser)
if finetune:
parser = add_finetune_args(parser)
if rhlf:
parser = add_rhlf_args(parser)
if simple_rlhf:
parser = add_simple_rhlf_args(parser)
if feedback_learning:
parser = add_feedback_learning_args(parser)
if reward:
parser = add_reward_args(parser)
parser = add_long_context_extend_args(parser)
args = parser.parse_args()
return args
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