Remote return_dict config. Remove unuse files.
This commit is contained in:
Colin
parent
90cb0fe236
commit
a8f2fbbff5
10
qwen/demo.py
10
qwen/demo.py
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@ -14,7 +14,7 @@ model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
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# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
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config, kwargs = AutoConfig.from_pretrained(
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model_dir,
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"./",
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return_unused_kwargs=True,
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trust_remote_code=True,
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code_revision=None,
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@ -25,15 +25,15 @@ model = QWenLMHeadModel(config)
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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model = model.from_pretrained(
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model_dir, device_map="auto", trust_remote_code=True
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model_dir, config=config, device_map="auto", trust_remote_code=True
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).train()
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# model.train()
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# model.zero_grad()
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# 可指定不同的生成长度、top_p等相关超参
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model.generation_config = GenerationConfig.from_pretrained(
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model_dir, trust_remote_code=True
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)
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# model.generation_config = GenerationConfig.from_pretrained(
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# model_dir, trust_remote_code=True
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# )
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# 第一轮对话
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response, history = model.chat(tokenizer, "你好", history=None)
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@ -1,52 +0,0 @@
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{
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"fp16": {
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"enabled": "auto",
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"loss_scale": 0,
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"loss_scale_window": 1000,
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"initial_scale_power": 16,
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"hysteresis": 2,
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"min_loss_scale": 1
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},
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"bf16": {
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"enabled": "auto"
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},
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"optimizer": {
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"type": "AdamW",
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"params": {
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"lr": "auto",
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"betas": "auto",
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"eps": "auto",
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"weight_decay": "auto"
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}
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},
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"scheduler": {
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"type": "WarmupLR",
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"params": {
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"warmup_min_lr": "auto",
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"warmup_max_lr": "auto",
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"warmup_num_steps": "auto"
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}
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},
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"zero_optimization": {
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"stage": 2,
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"offload_optimizer": {
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"device": "none",
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"pin_memory": true
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},
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"allgather_partitions": true,
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"allgather_bucket_size": 2e8,
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"overlap_comm": true,
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"reduce_scatter": true,
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"reduce_bucket_size": 2e8,
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"contiguous_gradients": true
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},
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"gradient_accumulation_steps": "auto",
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"gradient_clipping": "auto",
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"steps_per_print": 100,
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"train_batch_size": "auto",
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"train_micro_batch_size_per_gpu": "auto",
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"wall_clock_breakdown": false
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}
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@ -1,59 +0,0 @@
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{
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"fp16": {
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"enabled": "auto",
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"loss_scale": 0,
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"loss_scale_window": 1000,
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"initial_scale_power": 16,
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"hysteresis": 2,
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"min_loss_scale": 1
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},
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"bf16": {
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"enabled": "auto"
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},
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"optimizer": {
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"type": "AdamW",
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"params": {
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"lr": "auto",
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"betas": "auto",
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"eps": "auto",
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"weight_decay": "auto"
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}
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},
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"scheduler": {
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"type": "WarmupLR",
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"params": {
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"warmup_min_lr": "auto",
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"warmup_max_lr": "auto",
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"warmup_num_steps": "auto"
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}
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},
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"zero_optimization": {
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"stage": 3,
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"offload_optimizer": {
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"device": "none",
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"pin_memory": true
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},
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"offload_param": {
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"device": "none",
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"pin_memory": true
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},
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"overlap_comm": true,
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"contiguous_gradients": true,
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"sub_group_size": 1e9,
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"reduce_bucket_size": "auto",
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"stage3_prefetch_bucket_size": "auto",
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"stage3_param_persistence_threshold": "auto",
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"stage3_max_live_parameters": 1e9,
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"stage3_max_reuse_distance": 1e9,
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"stage3_gather_16bit_weights_on_model_save": true
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},
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"gradient_accumulation_steps": "auto",
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"gradient_clipping": "auto",
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"steps_per_print": 100,
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"train_batch_size": "auto",
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"train_micro_batch_size_per_gpu": "auto",
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"wall_clock_breakdown": false
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}
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@ -1,90 +0,0 @@
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#!/bin/bash
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export CUDA_DEVICE_MAX_CONNECTIONS=1
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DIR=`pwd`
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# Guide:
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# This script supports distributed training on multi-gpu workers (as well as single-worker training).
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# Please set the options below according to the comments.
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# For multi-gpu workers training, these options should be manually set for each worker.
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# After setting the options, please run the script on each worker.
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# Number of GPUs per GPU worker
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GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
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# Number of GPU workers, for single-worker training, please set to 1
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NNODES=${NNODES:-1}
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# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
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NODE_RANK=${NODE_RANK:-0}
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# The ip address of the rank-0 worker, for single-worker training, please set to localhost
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MASTER_ADDR=${MASTER_ADDR:localhost}
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# The port for communication
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MASTER_PORT=${MASTER_PORT:-6001}
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MODEL="Qwen/Qwen-7B" # Set the path if you do not want to load from huggingface directly
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# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
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# See the section for finetuning in README for more information.
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DATA="path_to_data"
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function usage() {
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echo '
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Usage: bash finetune/finetune_ds.sh [-m MODEL_PATH] [-d DATA_PATH]
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'
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}
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while [[ "$1" != "" ]]; do
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case $1 in
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-m | --model )
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shift
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MODEL=$1
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;;
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-d | --data )
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shift
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DATA=$1
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;;
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-h | --help )
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usage
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exit 0
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;;
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* )
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echo "Unknown argument ${1}"
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exit 1
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;;
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esac
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shift
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done
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DISTRIBUTED_ARGS="
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--nproc_per_node $GPUS_PER_NODE \
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--nnodes $NNODES \
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--node_rank $NODE_RANK \
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--master_addr $MASTER_ADDR \
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--master_port $MASTER_PORT
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"
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torchrun $DISTRIBUTED_ARGS finetune.py \
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--model_name_or_path $MODEL \
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--data_path $DATA \
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--bf16 True \
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--output_dir output_qwen \
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--num_train_epochs 5 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 16 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 1000 \
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--save_total_limit 10 \
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--learning_rate 1e-5 \
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--weight_decay 0.1 \
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--adam_beta2 0.95 \
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--warmup_ratio 0.01 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--report_to "none" \
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--model_max_length 512 \
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--gradient_checkpointing True \
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--lazy_preprocess True \
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--deepspeed finetune/ds_config_zero3.json
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@ -1,96 +0,0 @@
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#!/bin/bash
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export CUDA_DEVICE_MAX_CONNECTIONS=1
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DIR=`pwd`
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# Guide:
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# This script supports distributed training on multi-gpu workers (as well as single-worker training).
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# Please set the options below according to the comments.
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# For multi-gpu workers training, these options should be manually set for each worker.
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# After setting the options, please run the script on each worker.
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# Number of GPUs per GPU worker
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GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
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# Number of GPU workers, for single-worker training, please set to 1
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NNODES=${NNODES:-1}
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# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
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NODE_RANK=${NODE_RANK:-0}
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# The ip address of the rank-0 worker, for single-worker training, please set to localhost
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MASTER_ADDR=${MASTER_ADDR:localhost}
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# The port for communication
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MASTER_PORT=${MASTER_PORT:-6001}
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MODEL="Qwen/Qwen-7B" # Set the path if you do not want to load from huggingface directly
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# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
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# See the section for finetuning in README for more information.
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DATA="path_to_data"
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DS_CONFIG_PATH="finetune/ds_config_zero2.json"
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function usage() {
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echo '
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Usage: bash finetune/finetune_lora_ds.sh [-m MODEL_PATH] [-d DATA_PATH] [--deepspeed DS_CONFIG_PATH]
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'
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}
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while [[ "$1" != "" ]]; do
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case $1 in
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-m | --model )
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shift
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MODEL=$1
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;;
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-d | --data )
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shift
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DATA=$1
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;;
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--deepspeed )
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shift
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DS_CONFIG_PATH=$1
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;;
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-h | --help )
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usage
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exit 0
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;;
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* )
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echo "Unknown argument ${1}"
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exit 1
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;;
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esac
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shift
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done
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DISTRIBUTED_ARGS="
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--nproc_per_node $GPUS_PER_NODE \
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--nnodes $NNODES \
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--node_rank $NODE_RANK \
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--master_addr $MASTER_ADDR \
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--master_port $MASTER_PORT
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"
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torchrun $DISTRIBUTED_ARGS finetune.py \
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--model_name_or_path $MODEL \
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--data_path $DATA \
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--bf16 True \
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--output_dir output_qwen \
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--num_train_epochs 5 \
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--per_device_train_batch_size 2 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 8 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 1000 \
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--save_total_limit 10 \
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--learning_rate 3e-4 \
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--weight_decay 0.1 \
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--adam_beta2 0.95 \
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--warmup_ratio 0.01 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--report_to "none" \
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--model_max_length 512 \
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--lazy_preprocess True \
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--use_lora \
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--gradient_checkpointing \
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--deepspeed ${DS_CONFIG_PATH}
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@ -1,93 +0,0 @@
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#!/bin/bash
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export CUDA_DEVICE_MAX_CONNECTIONS=1
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DIR=`pwd`
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# Guide:
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# This script supports distributed training on multi-gpu workers (as well as single-worker training).
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# Please set the options below according to the comments.
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# For multi-gpu workers training, these options should be manually set for each worker.
|
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# After setting the options, please run the script on each worker.
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# Number of GPUs per GPU worker
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GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
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# Number of GPU workers, for single-worker training, please set to 1
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NNODES=${NNODES:-1}
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# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
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NODE_RANK=${NODE_RANK:-0}
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# The ip address of the rank-0 worker, for single-worker training, please set to localhost
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MASTER_ADDR=${MASTER_ADDR:localhost}
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# The port for communication
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MASTER_PORT=${MASTER_PORT:-6001}
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MODEL="Qwen/Qwen-7B-Chat-Int4" # Set the path if you do not want to load from huggingface directly
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# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
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# See the section for finetuning in README for more information.
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DATA="path_to_data"
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function usage() {
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echo '
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Usage: bash finetune/finetune_qlora_ds.sh [-m MODEL_PATH] [-d DATA_PATH]
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'
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}
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while [[ "$1" != "" ]]; do
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case $1 in
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-m | --model )
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shift
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MODEL=$1
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;;
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-d | --data )
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shift
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DATA=$1
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;;
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-h | --help )
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usage
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exit 0
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;;
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* )
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echo "Unknown argument ${1}"
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exit 1
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;;
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esac
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shift
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done
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DISTRIBUTED_ARGS="
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--nproc_per_node $GPUS_PER_NODE \
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--nnodes $NNODES \
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--node_rank $NODE_RANK \
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--master_addr $MASTER_ADDR \
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--master_port $MASTER_PORT
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"
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# Remember to use --fp16 instead of --bf16 due to autogptq
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torchrun $DISTRIBUTED_ARGS finetune.py \
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--model_name_or_path $MODEL \
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--data_path $DATA \
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--fp16 True \
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--output_dir output_qwen \
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--num_train_epochs 5 \
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--per_device_train_batch_size 2 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 8 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 1000 \
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--save_total_limit 10 \
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--learning_rate 3e-4 \
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--weight_decay 0.1 \
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--adam_beta2 0.95 \
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--warmup_ratio 0.01 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--report_to "none" \
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--model_max_length 512 \
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--lazy_preprocess True \
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--use_lora \
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--q_lora \
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--gradient_checkpointing \
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--deepspeed finetune/ds_config_zero2.json
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@ -1,66 +0,0 @@
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#!/bin/bash
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export CUDA_DEVICE_MAX_CONNECTIONS=1
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DIR=`pwd`
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MODEL="Qwen/Qwen-7B-Chat-Int4" # Set the path if you do not want to load from huggingface directly
|
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# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
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# See the section for finetuning in README for more information.
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DATA="path_to_data"
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function usage() {
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echo '
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Usage: bash finetune/finetune_qlora_single_gpu.sh [-m MODEL_PATH] [-d DATA_PATH]
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'
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}
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while [[ "$1" != "" ]]; do
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case $1 in
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-m | --model )
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shift
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MODEL=$1
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;;
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-d | --data )
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shift
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DATA=$1
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;;
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-h | --help )
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usage
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exit 0
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;;
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* )
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echo "Unknown argument ${1}"
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exit 1
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;;
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esac
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shift
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done
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export CUDA_VISIBLE_DEVICES=0
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# Remember to use --fp16 instead of --bf16 due to autogptq
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python finetune.py \
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--model_name_or_path $MODEL \
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--data_path $DATA \
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--fp16 True \
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--output_dir output_qwen \
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--num_train_epochs 5 \
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--per_device_train_batch_size 2 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 8 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 1000 \
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--save_total_limit 10 \
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--learning_rate 3e-4 \
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--weight_decay 0.1 \
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--adam_beta2 0.95 \
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--warmup_ratio 0.01 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--report_to "none" \
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--model_max_length 512 \
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--lazy_preprocess True \
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--gradient_checkpointing \
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--use_lora \
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--q_lora \
|
||||
--deepspeed finetune/ds_config_zero2.json
|
||||
|
|
@ -406,8 +406,7 @@ class QWenModel(QWenPreTrainedModel):
|
|||
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None
|
||||
):
|
||||
output_attentions = (
|
||||
output_attentions
|
||||
|
|
@ -420,9 +419,6 @@ class QWenModel(QWenPreTrainedModel):
|
|||
else self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
return_dict = (
|
||||
return_dict if return_dict is not None else self.config.use_return_dict
|
||||
)
|
||||
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
raise ValueError(
|
||||
|
|
@ -569,11 +565,6 @@ class QWenModel(QWenPreTrainedModel):
|
|||
if output_hidden_states:
|
||||
all_hidden_states = all_hidden_states + (hidden_states,)
|
||||
|
||||
if not return_dict:
|
||||
return tuple(
|
||||
v for v in [hidden_states, presents, all_hidden_states] if v is not None
|
||||
)
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=presents,
|
||||
|
|
@ -639,11 +630,8 @@ class QWenLMHeadModel(QWenPreTrainedModel):
|
|||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||
return_dict = (
|
||||
return_dict if return_dict is not None else self.config.use_return_dict
|
||||
)
|
||||
|
||||
|
||||
transformer_outputs = self.transformer(
|
||||
input_ids,
|
||||
|
|
@ -657,8 +645,7 @@ class QWenLMHeadModel(QWenPreTrainedModel):
|
|||
encoder_attention_mask=encoder_attention_mask,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
output_hidden_states=output_hidden_states
|
||||
)
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
|
|
@ -674,17 +661,13 @@ class QWenLMHeadModel(QWenPreTrainedModel):
|
|||
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
|
||||
)
|
||||
|
||||
shift_labels = torch.ones([1,19]).to(lm_logits.device).to(torch.int64)
|
||||
shift_logits = lm_logits[..., :-1, :].contiguous()
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(
|
||||
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
|
||||
)
|
||||
loss.backward()
|
||||
|
||||
if not return_dict:
|
||||
output = (lm_logits,) + transformer_outputs[1:]
|
||||
return ((loss,) + output) if loss is not None else output
|
||||
# shift_labels = torch.ones([1,19]).to(lm_logits.device).to(torch.int64)
|
||||
# shift_logits = lm_logits[..., :-1, :].contiguous()
|
||||
# loss_fct = CrossEntropyLoss()
|
||||
# loss = loss_fct(
|
||||
# shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
|
||||
# )
|
||||
# loss.backward()
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
|
|
@ -1197,7 +1180,6 @@ class QWenLMHeadModel(QWenPreTrainedModel):
|
|||
# forward pass to get next token
|
||||
outputs = self(
|
||||
**model_inputs,
|
||||
return_dict=True,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in New Issue