diff --git a/requirements.txt b/requirements.txt index af63eeaaa..d119b17d2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,3 @@ -r requirements/runtime.txt -r requirements/deepspeed.txt +-r requirements/modelscope.txt diff --git a/requirements/modelscope.txt b/requirements/modelscope.txt new file mode 100644 index 000000000..7f3264fc8 --- /dev/null +++ b/requirements/modelscope.txt @@ -0,0 +1 @@ +modelscope diff --git a/requirements/runtime.txt b/requirements/runtime.txt index 8af7336f6..ee7759bc5 100644 --- a/requirements/runtime.txt +++ b/requirements/runtime.txt @@ -1,14 +1,12 @@ bitsandbytes>=0.40.0 datasets einops -fsspec<=2023.6.0 lagent>=0.1.2 -mmengine>=0.9.1 -modelscope +mmengine>=0.10.1 peft>=0.4.0 scipy SentencePiece tiktoken torch -transformers>=4.32.1,<=4.34.0 +transformers>=4.32.1,!=4.34.1,!=4.35.0,!=4.35.1,!=4.35.2 transformers_stream_generator diff --git a/setup.py b/setup.py index 83bf1fdde..b4c0fc5fe 100644 --- a/setup.py +++ b/setup.py @@ -126,7 +126,10 @@ def gen_packages_items(): parse_requirements('requirements.txt'), 'deepspeed': parse_requirements('requirements/runtime.txt') + - parse_requirements('requirements/deepspeed.txt') + parse_requirements('requirements/deepspeed.txt'), + 'modelscope': + parse_requirements('requirements/runtime.txt') + + parse_requirements('requirements/modelscope.txt'), }, zip_safe=False, entry_points={'console_scripts': ['xtuner = xtuner:cli']}) diff --git a/xtuner/configs/mixtral/mixtral_8x7b_full_oasst1_internlm_template_e3.py b/xtuner/configs/mixtral/mixtral_8x7b_full_oasst1_internlm_template_e3.py new file mode 100644 index 000000000..8eddbd7b2 --- /dev/null +++ b/xtuner/configs/mixtral/mixtral_8x7b_full_oasst1_internlm_template_e3.py @@ -0,0 +1,163 @@ +# Copyright (c) OpenMMLab. All rights reserved. +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 +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 oasst1_map_fn, template_map_fn_factory +from xtuner.engine import DatasetInfoHook, EvaluateChatHook, ThroughputHook +from xtuner.model import SupervisedFinetune +from xtuner.utils import PROMPT_TEMPLATE + +####################################################################### +# PART 1 Settings # +####################################################################### +# Model +pretrained_model_name_or_path = 'DiscoResearch/mixtral-7b-8expert' + +# Data +data_path = 'timdettmers/openassistant-guanaco' +prompt_template = PROMPT_TEMPLATE.internlm_chat +max_length = 2048 +pack_to_max_length = True + +# Scheduler & Optimizer +batch_size = 1 # per_device +accumulative_counts = 16 +dataloader_num_workers = 0 +max_epochs = 3 +optim_type = AdamW +lr = 2e-5 +betas = (0.9, 0.999) +weight_decay = 0 +max_norm = 1 # grad clip + +# Evaluate the generation performance during the training +evaluation_freq = 500 +SYSTEM = '' +evaluation_inputs = [ + '请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai' +] + +####################################################################### +# 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, + 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=data_path), + tokenizer=tokenizer, + max_length=max_length, + dataset_map_fn=oasst1_map_fn, + template_map_fn=dict( + type=template_map_fn_factory, template=prompt_template), + remove_unused_columns=True, + shuffle_before_pack=True, + pack_to_max_length=pack_to_max_length) + +train_dataloader = dict( + batch_size=batch_size, + num_workers=dataloader_num_workers, + dataset=train_dataset, + sampler=dict(type=DefaultSampler, shuffle=True), + collate_fn=dict(type=default_collate_fn)) + +####################################################################### +# 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', + dtype='float16') + +# learning policy +# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501 +param_scheduler = dict( + type=CosineAnnealingLR, + eta_min=0.0, + by_epoch=True, + T_max=max_epochs, + convert_to_iter_based=True) + +# train, val, test setting +train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1) + +####################################################################### +# 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, + prompt_template=prompt_template), + dict(type=ThroughputHook) +] + +# configure default hooks +default_hooks = dict( + # record the time of every iteration. + timer=dict(type=IterTimerHook), + # print log every 100 iterations. + logger=dict(type=LoggerHook, interval=10), + # enable the parameter scheduler. + param_scheduler=dict(type=ParamSchedulerHook), + # save checkpoint per epoch. + checkpoint=dict(type=CheckpointHook, interval=1), + # 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) diff --git a/xtuner/configs/mixtral/mixtral_8x7b_qlora_oasst1_internlm_template_e3.py b/xtuner/configs/mixtral/mixtral_8x7b_qlora_oasst1_internlm_template_e3.py new file mode 100644 index 000000000..c76f59661 --- /dev/null +++ b/xtuner/configs/mixtral/mixtral_8x7b_qlora_oasst1_internlm_template_e3.py @@ -0,0 +1,182 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from bitsandbytes.optim import PagedAdamW32bit +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 +from peft import LoraConfig +from transformers import (AutoModelForCausalLM, AutoTokenizer, + BitsAndBytesConfig) + +from xtuner.dataset import process_hf_dataset +from xtuner.dataset.collate_fns import default_collate_fn +from xtuner.dataset.map_fns import oasst1_map_fn, template_map_fn_factory +from xtuner.engine import DatasetInfoHook, EvaluateChatHook +from xtuner.model import SupervisedFinetune +from xtuner.utils import PROMPT_TEMPLATE + +####################################################################### +# PART 1 Settings # +####################################################################### +# Model +pretrained_model_name_or_path = 'DiscoResearch/mixtral-7b-8expert' + +# Data +data_path = 'timdettmers/openassistant-guanaco' +prompt_template = PROMPT_TEMPLATE.internlm_chat +max_length = 2048 +pack_to_max_length = True + +# Scheduler & Optimizer +batch_size = 1 # per_device +accumulative_counts = 16 +dataloader_num_workers = 0 +max_epochs = 3 +optim_type = PagedAdamW32bit +lr = 2e-4 +betas = (0.9, 0.999) +weight_decay = 0 +max_norm = 1 # grad clip + +# Evaluate the generation performance during the training +evaluation_freq = 500 +SYSTEM = '' +evaluation_inputs = [ + '请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai' +] + +####################################################################### +# 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, + llm=dict( + type=AutoModelForCausalLM.from_pretrained, + pretrained_model_name_or_path=pretrained_model_name_or_path, + trust_remote_code=True, + torch_dtype=torch.float16, + quantization_config=dict( + type=BitsAndBytesConfig, + load_in_4bit=True, + load_in_8bit=False, + llm_int8_threshold=6.0, + llm_int8_has_fp16_weight=False, + bnb_4bit_compute_dtype=torch.float16, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type='nf4')), + lora=dict( + type=LoraConfig, + r=64, + lora_alpha=16, + lora_dropout=0.1, + bias='none', + task_type='CAUSAL_LM')) + +####################################################################### +# PART 3 Dataset & Dataloader # +####################################################################### +train_dataset = dict( + type=process_hf_dataset, + dataset=dict(type=load_dataset, path=data_path), + tokenizer=tokenizer, + max_length=max_length, + dataset_map_fn=oasst1_map_fn, + template_map_fn=dict( + type=template_map_fn_factory, template=prompt_template), + remove_unused_columns=True, + shuffle_before_pack=True, + pack_to_max_length=pack_to_max_length) + +train_dataloader = dict( + batch_size=batch_size, + num_workers=dataloader_num_workers, + dataset=train_dataset, + sampler=dict(type=DefaultSampler, shuffle=True), + collate_fn=dict(type=default_collate_fn)) + +####################################################################### +# 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', + dtype='float16') + +# learning policy +# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501 +param_scheduler = dict( + type=CosineAnnealingLR, + eta_min=0.0, + by_epoch=True, + T_max=max_epochs, + convert_to_iter_based=True) + +# train, val, test setting +train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1) + +####################################################################### +# 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, + prompt_template=prompt_template) +] + +# configure default hooks +default_hooks = dict( + # record the time of every iteration. + timer=dict(type=IterTimerHook), + # print log every 100 iterations. + logger=dict(type=LoggerHook, interval=10), + # enable the parameter scheduler. + param_scheduler=dict(type=ParamSchedulerHook), + # save checkpoint per epoch. + checkpoint=dict(type=CheckpointHook, interval=1), + # 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)