From 11b930abec40060e59baef81a667a9e68b90bb80 Mon Sep 17 00:00:00 2001 From: HIT-cwh <2892770585@qq.com> Date: Mon, 29 Apr 2024 16:57:15 +0800 Subject: [PATCH 1/3] add internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2 cfg --- ...hat_20b_full_custom_pretrain_e1_32k_sp2.py | 215 ++++++++++++++++++ 1 file changed, 215 insertions(+) create mode 100644 xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py diff --git a/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py b/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py new file mode 100644 index 000000000..d2fbb5f30 --- /dev/null +++ b/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py @@ -0,0 +1,215 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""Data format: + +[ + { + "text": "xxx" + }, + { + "text": "xxx" + }, + ... +] +Please refer to https://xtuner.readthedocs.io/zh-cn/docs/training/custom_pretrain_dataset.html +for more details. +""" # noqa: E501 + +from datasets import load_dataset +from mmengine.dataset import DefaultSampler +from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, + LoggerHook, ParamSchedulerHook) +from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR +from torch.optim import AdamW +from transformers import AutoModelForCausalLM, AutoTokenizer + +from xtuner.dataset import process_hf_dataset +from xtuner.dataset.collate_fns import default_collate_fn +from xtuner.dataset.map_fns import pretrain_map_fn +from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook, + ThroughputHook, + VarlenAttnArgsToMessageHubHook) +from xtuner.engine.runner import TrainLoop +from xtuner.model import SupervisedFinetune +from xtuner.parallel.sequence import SequenceParallelSampler +from xtuner.utils import PROMPT_TEMPLATE + +####################################################################### +# PART 1 Settings # +####################################################################### +# Model +pretrained_model_name_or_path = 'internlm/internlm2-chat-20b' +use_varlen_attn = True + +# Data +data_files = ['/path/to/json/file.json'] +prompt_template = PROMPT_TEMPLATE.internlm2_chat +max_length = 32768 +pack_to_max_length = True + +# parallel +sequence_parallel_size = 2 + +# Scheduler & Optimizer +# batch size per device, set to 1 if `use_varlen_attn` = True +# To clarify, enlarging the batch size essentially enlarges the `max_length`. +# For example, doubling the max length is tantamount to doubling the batch size +batch_size = 1 # per_device +accumulative_counts = 4 # 1bs * 4acc * 16gpu = 64 batchsize +accumulative_counts *= sequence_parallel_size +dataloader_num_workers = 4 +max_epochs = 1 +optim_type = AdamW +lr = 4e-5 +betas = (0.9, 0.95) +weight_decay = 0.01 +max_norm = 1 # grad clip +warm_up_ratio = 0.025 + +# Save +save_steps = 500 +save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited) + +# Evaluate the generation performance during the training +evaluation_freq = 500 +SYSTEM = '' +evaluation_inputs = ['上海是', 'Shanghai is'] + +####################################################################### +# PART 2 Model & Tokenizer # +####################################################################### +tokenizer = dict( + type=AutoTokenizer.from_pretrained, + pretrained_model_name_or_path=pretrained_model_name_or_path, + trust_remote_code=True, + padding_side='right') + +model = dict( + type=SupervisedFinetune, + use_varlen_attn=use_varlen_attn, + llm=dict( + type=AutoModelForCausalLM.from_pretrained, + pretrained_model_name_or_path=pretrained_model_name_or_path, + trust_remote_code=True)) + +####################################################################### +# PART 3 Dataset & Dataloader # +####################################################################### +train_dataset = dict( + type=process_hf_dataset, + dataset=dict(type=load_dataset, path='json', data_files=data_files), + tokenizer=tokenizer, + max_length=max_length, + dataset_map_fn=pretrain_map_fn, + template_map_fn=None, + remove_unused_columns=True, + shuffle_before_pack=True, + pack_to_max_length=pack_to_max_length, + use_varlen_attn=use_varlen_attn) + +sampler = SequenceParallelSampler \ + if sequence_parallel_size > 1 else DefaultSampler + +train_dataloader = dict( + batch_size=batch_size, + num_workers=dataloader_num_workers, + dataset=train_dataset, + sampler=dict(type=sampler, shuffle=True), + collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn)) + +####################################################################### +# PART 4 Scheduler & Optimizer # +####################################################################### +# optimizer +optim_wrapper = dict( + type=AmpOptimWrapper, + optimizer=dict( + type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay), + clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False), + accumulative_counts=accumulative_counts, + loss_scale='dynamic') + +# learning policy +# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501 +param_scheduler = [ + dict( + type=LinearLR, + start_factor=1 / 40, + by_epoch=True, + begin=0, + end=warm_up_ratio * max_epochs, + convert_to_iter_based=True), + dict( + type=CosineAnnealingLR, + eta_min=lr * 0.15, + by_epoch=True, + begin=warm_up_ratio * max_epochs, + end=max_epochs, + convert_to_iter_based=True) +] + +# train, val, test setting +train_cfg = dict(type=TrainLoop, max_epochs=max_epochs) + +####################################################################### +# PART 5 Runtime # +####################################################################### +# Log the dialogue periodically during the training process, optional +custom_hooks = [ + dict(type=DatasetInfoHook, tokenizer=tokenizer), + dict( + type=EvaluateChatHook, + tokenizer=tokenizer, + every_n_iters=evaluation_freq, + evaluation_inputs=evaluation_inputs, + system=SYSTEM), + dict(type=ThroughputHook) +] + +if use_varlen_attn: + custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)] + +# configure default hooks +default_hooks = dict( + # record the time of every iteration. + timer=dict(type=IterTimerHook), + # print log every 1 iterations. + logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=1), + # enable the parameter scheduler. + param_scheduler=dict(type=ParamSchedulerHook), + # save checkpoint per `save_steps`. + checkpoint=dict( + type=CheckpointHook, + by_epoch=False, + interval=save_steps, + max_keep_ckpts=save_total_limit), + # set sampler seed in distributed evrionment. + sampler_seed=dict(type=DistSamplerSeedHook), +) + +# configure environment +env_cfg = dict( + # whether to enable cudnn benchmark + cudnn_benchmark=False, + # set multi process parameters + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), + # set distributed parameters + dist_cfg=dict(backend='nccl'), +) + +# set visualizer +visualizer = None + +# set log level +log_level = 'INFO' + +# load from which checkpoint +load_from = None + +# whether to resume training from the loaded checkpoint +resume = False + +# Defaults to use random seed and disable `deterministic` +randomness = dict(seed=None, deterministic=False) + +# set log processor +log_processor = dict(by_epoch=False, window_size=1) From de446bd0b55c89a94b2dd593b88e382f4bad69b2 Mon Sep 17 00:00:00 2001 From: HIT-cwh <2892770585@qq.com> Date: Mon, 29 Apr 2024 16:59:06 +0800 Subject: [PATCH 2/3] add speed --- .../internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py b/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py index d2fbb5f30..d88576382 100644 --- a/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py +++ b/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py @@ -12,6 +12,8 @@ ] Please refer to https://xtuner.readthedocs.io/zh-cn/docs/training/custom_pretrain_dataset.html for more details. + +Speed: 16 * A100 80G, DeepSpeed ZeRO-3, 1324 tokens per second per GPU """ # noqa: E501 from datasets import load_dataset From 230e282dee258e056a090b2a09e7007d65b26a08 Mon Sep 17 00:00:00 2001 From: HIT-cwh <2892770585@qq.com> Date: Mon, 29 Apr 2024 17:17:20 +0800 Subject: [PATCH 3/3] use base model --- .../internlm2_20b_full_custom_pretrain_e1_32k_sp2.py} | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) rename xtuner/configs/internlm/{internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py => internlm2_20b/internlm2_20b_full_custom_pretrain_e1_32k_sp2.py} (97%) diff --git a/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py b/xtuner/configs/internlm/internlm2_20b/internlm2_20b_full_custom_pretrain_e1_32k_sp2.py similarity index 97% rename from xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py rename to xtuner/configs/internlm/internlm2_20b/internlm2_20b_full_custom_pretrain_e1_32k_sp2.py index d88576382..b518a8b8f 100644 --- a/xtuner/configs/internlm/internlm2_chat_20b/internlm2_chat_20b_full_custom_pretrain_e1_32k_sp2.py +++ b/xtuner/configs/internlm/internlm2_20b/internlm2_20b_full_custom_pretrain_e1_32k_sp2.py @@ -33,18 +33,16 @@ from xtuner.engine.runner import TrainLoop from xtuner.model import SupervisedFinetune from xtuner.parallel.sequence import SequenceParallelSampler -from xtuner.utils import PROMPT_TEMPLATE ####################################################################### # PART 1 Settings # ####################################################################### # Model -pretrained_model_name_or_path = 'internlm/internlm2-chat-20b' +pretrained_model_name_or_path = 'internlm/internlm2-20b' use_varlen_attn = True # Data data_files = ['/path/to/json/file.json'] -prompt_template = PROMPT_TEMPLATE.internlm2_chat max_length = 32768 pack_to_max_length = True