本文分享自华为云社区《基于MindSpore的ChatGLM微调》,作者: JeffDing 。
基于 MindSpore 的 ChatGLM 微调
克隆 Hugging Face 模型
克隆 chatglm-6b 代码仓,下载分布式的模型文件
git lfs install
git clone https://huggingface.co/THUDM/chatglm-6b
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准备环境
安装 Transformer
执行 python 脚本,合并模型权重。
from transformers import AutoModel
import torch as pt
pt_ckpt_path="./models/chatglm-6b"
model = AutoModel.from_pretrained(pt_ckpt_path, trust_remote_code=True).half()
pt_pth_path = "models/mindspore/pt_glm_6b.pth"
pt.save(model.state_dict(), pt_pth_path)
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执行转换脚本,得到转换后的输出文件 ms_glm_6b.ckpt
python mindformers/models/glm/convert_weight.py --pt_ckpt_path /home/ma-user/work/models/mindspore/pt_glm_6b.pth --ms_ckpt_path ../models/mindspore/ms_glm_6b.ckpt
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注意可能会遇到以下错误:
执行转换脚本,得到转换后的输出文件ms_glm_6b.ckpt
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解决方法:
export LD_PRELOAD=$LD_PRELOAD:/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/torch/lib/libgomp-d22c30c5.so.1
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原理:找到 torch 中的 libgomp-d22c30c5.so.1 然后赋值给 LD_PRELOAD 环境变量,这个报错好像只有 ARM 平台会有
微调
数据处理
ADGEN 数据集任务为根据输入(content)生成一段广告词(summary)。数据集可选离线生成 Mindrecord 或者实时生成两种方式,两种方式选其一即可。
下载地址:https://cloud.tsinghua.edu.cn/f/b3f119a008264b1cabd1/?dl=1
将任务配置文件 configs/glm/run_glm_6b_*.yaml
中的 ==== dataset config ==== 部分中的 dataset_dir 指向 *.json
文件,vocab_file 指向词表文件
LoRA 低参微调
run_mindformers 脚本启动 LoRA 低参微调
使用 LoRA 算法进行低参微调时,使用 configs/glm/run_glm_6b_lora.yaml 配置文件,该配置文件包含了 lora 低参微调算法所需的配置项
修改数据集/模型权重配置路径
数据集:修改 mindformers/configs/glm/run_glm_6b_lora.yaml 脚本中 train_dataset 的 dataset_dir 为前文生成的数据集路径。
加载预训练模型权重:修改 mindformers/configs/glm/run_glm_6b_lora.yaml 脚本中的 load_checkpoint 为预训练模型权重路径。
** 安装 jieba**
pip install -r requirements.txt
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启动 LoRA 低参微调脚本(1 卡):
python run_mindformer.py --config=./configs/glm/run_glm_6b_lora.yaml --use_parallel=False --run_mode=finetune
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附录 run_glm_6b_lora.yaml
seed: 0
run_mode: 'finetune'
load_checkpoint: "/home/ma-user/work/models/mindspore/ms_glm_6b.ckpt"
src_strategy_path_or_dir: ''
auto_trans_ckpt: False # If true, auto transform load_checkpoint to load in distributed model
only_save_strategy: False
resume_training: False
output_dir: './output' # 当前不支持自定义修改,请勿修改该默认值
# ==== context config ====
context:
mode: 0 #0--Graph Mode; 1--Pynative Mode
device_target: "Ascend"
enable_graph_kernel: False
graph_kernel_flags: "--disable_expand_ops=Softmax,Dropout --enable_parallel_fusion=true --reduce_fuse_depth=8 --enable_auto_tensor_inplace=true"
max_call_depth: 10000
max_device_memory: "30GB"
save_graphs: False
device_id: 0
# aicc
remote_save_url: "Please input obs url on AICC platform."
# ==== model config ====
model:
model_config:
type: GLMConfig
vocab_size: 130528
hidden_size: 4096
num_layers: 28
num_heads: 32
inner_hidden_size: 16384
seq_length: 512 # 推理时, 输入pad到的长度, model里的最大句长
embedding_dropout_prob: 0.0
attention_dropout_rate: 0.0
hidden_dropout_rate: 0.0
hidden_size_per_attention_head: # default "None" means hidden-size/num-attention-heads.
layernorm_order: "post"
layernorm_epsilon: 1.0e-5
use_final_layernorm: True
use_past: False
activation_func: 'GELU'
position_encoding_2d: True
param_init_type: "float16"
layernorm_compute_type: "float32"
softmax_compute_type: "float32"
compute_dtype: "float16"
bos_token_id: 130004
eos_token_id: 130005
mask_token_id: 130000
gmask_token_id: 130001
pad_token_id: 3
max_decode_length: 2048 # The maximum length of the generated words.
is_enhanced_encoder: True
is_sample_acceleration: False
checkpoint_name_or_path: "glm_6b_lora"
top_k: 1
top_p: 1
repetition_penalty: 1
do_sample: True
pet_config:
pet_type: lora
lora_rank: 8
lora_alpha: 32
lora_dropout: 0.1
arch:
type: GLMForPreTrainingWithLora
trainer:
type: CausalLanguageModelingTrainer
model_name: 'glm_6b_lora'
# if True, do evaluate during the training process. if false, do nothing.
# note that the task trainer should support _evaluate_in_training function.
do_eval: False
metric:
type: ADGENMetric
processor:
return_tensors: ms
tokenizer:
type: ChatGLMTokenizer
bos_token: '<sop>'
eos_token: '<eop>'
end_token: '</s>'
mask_token: '[MASK]'
gmask_token: '[gMASK]'
pad_token: '<pad>'
unk_token: '<unk>'
type: GLMProcessor
# ==== dataset config ====
train_dataset: &train_dataset
data_loader:
type: ADGenDataLoader
dataset_dir: "/home/ma-user/work/data/AdvertiseGen/train.json"
shuffle: True
phase: "train"
origin_columns: ["content", "summary"]
tokenizer:
type: ChatGLMTokenizer
vocab_file: "/home/ma-user/work/data/AdvertiseGen/ice_text.model"
input_columns: ["input_ids", "labels", "position_ids", "attention_mask"]
max_source_length: 64
max_target_length: 64
ignore_pad_token_for_loss: True
num_parallel_workers: 8
python_multiprocessing: False
drop_remainder: True
batch_size: 1
repeat: 1
numa_enable: False
prefetch_size: 1
seed: 0
train_dataset_task:
type: KeyWordGenDataset
dataset_config: *train_dataset
eval_dataset: &eval_dataset
data_loader:
type: ADGenDataLoader
dataset_dir: "/home/ma-usr/work/data/AdvertiseGen/dev.json"
shuffle: False
phase: "eval"
origin_columns: ["content", "summary"]
tokenizer:
type: ChatGLMTokenizer
vocab_file: "/home/ma-usr/work/data/AdvertiseGen/ice_text.model"
max_source_length: 256
max_target_length: 256
ignore_pad_token_for_loss: True
input_columns: ["input_ids", "labels"]
num_parallel_workers: 8
python_multiprocessing: False
drop_remainder: True
batch_size: 1
repeat: 1
numa_enable: False
prefetch_size: 1
seed: 0
eval_dataset_task:
type: KeyWordGenDataset
dataset_config: *eval_dataset
# ==== runner config ====
runner_config:
epochs: 1
batch_size: 8
sink_mode: True
sink_size: 4
runner_wrapper:
type: MFTrainOneStepCell
scale_sense:
type: DynamicLossScaleUpdateCell
loss_scale_value: 4294967296
scale_factor: 2
scale_window: 1000
use_clip_grad: True
# lr sechdule
lr_schedule:
type: polynomial
learning_rate: 5.e-5
lr_end: 1.e-6
warmup_steps: 2000
total_steps: -1 # -1 means it will load the total steps of the dataset
# optimizer
optimizer:
type: FusedAdamWeightDecay
beta1: 0.9
beta2: 0.95
eps: 1.e-8
weight_decay: 0.1
layer_scale: False
lr_scale: False
# parallel config
use_parallel: False
parallel:
parallel_mode: 0 # 0-data parallel, 1-semi-auto parallel, 2-auto parallel, 3-hybrid parallel
gradients_mean: False
loss_repeated_mean: True
enable_alltoall: False
full_batch: True
search_mode: "sharding_propagation"
enable_parallel_optimizer: False # optimizer shard
strategy_ckpt_save_file: "./ckpt_strategy.ckpt"
parallel_config:
data_parallel: 1
model_parallel: 1
pipeline_stage: 1
expert_parallel: 1
optimizer_shard: False # optimizer shard
micro_batch_num: 1
vocab_emb_dp: True
gradient_aggregation_group: 4
micro_batch_interleave_num: 1
# moe
moe_config:
expert_num: 1
capacity_factor: 1.05
aux_loss_factor: 0.05
num_experts_chosen: 1
# recompute
recompute_config:
recompute: False
parallel_optimizer_comm_recompute: False
mp_comm_recompute: True
recompute_slice_activation: False
# autotune
auto_tune: False
filepath_prefix: './autotune'
autotune_per_step: 10
# profile
profile: False
profile_start_step: 1
profile_stop_step: 10
init_start_profile: True
profile_communication: True
profile_memory: True
# callbacks
callbacks:
- type: MFLossMonitor
- type: CheckpointMointor
prefix: "glm-6b-lora"
save_checkpoint_steps: 500
keep_checkpoint_max: 2
integrated_save: False
async_save: False
- type: ObsMonitor
keep_last: False
eval_callbacks:
- type: ObsMonitor
keep_last: False
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