Compare commits
4 Commits
f6538c1111
...
a8f2fbbff5
| Author | SHA1 | Date |
|---|---|---|
Colin
|
a8f2fbbff5 | |
|
Colin
|
90cb0fe236 | |
|
Colin
|
611396b656 | |
|
Colin
|
255a2ff71c |
10
qwen/demo.py
10
qwen/demo.py
|
|
@ -14,7 +14,7 @@ model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
|
||||||
# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
|
# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
|
||||||
|
|
||||||
config, kwargs = AutoConfig.from_pretrained(
|
config, kwargs = AutoConfig.from_pretrained(
|
||||||
model_dir,
|
"./",
|
||||||
return_unused_kwargs=True,
|
return_unused_kwargs=True,
|
||||||
trust_remote_code=True,
|
trust_remote_code=True,
|
||||||
code_revision=None,
|
code_revision=None,
|
||||||
|
|
@ -25,15 +25,15 @@ model = QWenLMHeadModel(config)
|
||||||
|
|
||||||
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||||
model = model.from_pretrained(
|
model = model.from_pretrained(
|
||||||
model_dir, device_map="auto", trust_remote_code=True
|
model_dir, config=config, device_map="auto", trust_remote_code=True
|
||||||
).train()
|
).train()
|
||||||
# model.train()
|
# model.train()
|
||||||
# model.zero_grad()
|
# model.zero_grad()
|
||||||
|
|
||||||
# 可指定不同的生成长度、top_p等相关超参
|
# 可指定不同的生成长度、top_p等相关超参
|
||||||
model.generation_config = GenerationConfig.from_pretrained(
|
# model.generation_config = GenerationConfig.from_pretrained(
|
||||||
model_dir, trust_remote_code=True
|
# model_dir, trust_remote_code=True
|
||||||
)
|
# )
|
||||||
|
|
||||||
# 第一轮对话
|
# 第一轮对话
|
||||||
response, history = model.chat(tokenizer, "你好", history=None)
|
response, history = model.chat(tokenizer, "你好", history=None)
|
||||||
|
|
|
||||||
|
|
@ -1,52 +0,0 @@
|
||||||
{
|
|
||||||
"fp16": {
|
|
||||||
"enabled": "auto",
|
|
||||||
"loss_scale": 0,
|
|
||||||
"loss_scale_window": 1000,
|
|
||||||
"initial_scale_power": 16,
|
|
||||||
"hysteresis": 2,
|
|
||||||
"min_loss_scale": 1
|
|
||||||
},
|
|
||||||
"bf16": {
|
|
||||||
"enabled": "auto"
|
|
||||||
},
|
|
||||||
"optimizer": {
|
|
||||||
"type": "AdamW",
|
|
||||||
"params": {
|
|
||||||
"lr": "auto",
|
|
||||||
"betas": "auto",
|
|
||||||
"eps": "auto",
|
|
||||||
"weight_decay": "auto"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
|
|
||||||
"scheduler": {
|
|
||||||
"type": "WarmupLR",
|
|
||||||
"params": {
|
|
||||||
"warmup_min_lr": "auto",
|
|
||||||
"warmup_max_lr": "auto",
|
|
||||||
"warmup_num_steps": "auto"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
|
|
||||||
"zero_optimization": {
|
|
||||||
"stage": 2,
|
|
||||||
"offload_optimizer": {
|
|
||||||
"device": "none",
|
|
||||||
"pin_memory": true
|
|
||||||
},
|
|
||||||
"allgather_partitions": true,
|
|
||||||
"allgather_bucket_size": 2e8,
|
|
||||||
"overlap_comm": true,
|
|
||||||
"reduce_scatter": true,
|
|
||||||
"reduce_bucket_size": 2e8,
|
|
||||||
"contiguous_gradients": true
|
|
||||||
},
|
|
||||||
|
|
||||||
"gradient_accumulation_steps": "auto",
|
|
||||||
"gradient_clipping": "auto",
|
|
||||||
"steps_per_print": 100,
|
|
||||||
"train_batch_size": "auto",
|
|
||||||
"train_micro_batch_size_per_gpu": "auto",
|
|
||||||
"wall_clock_breakdown": false
|
|
||||||
}
|
|
||||||
|
|
@ -1,59 +0,0 @@
|
||||||
{
|
|
||||||
"fp16": {
|
|
||||||
"enabled": "auto",
|
|
||||||
"loss_scale": 0,
|
|
||||||
"loss_scale_window": 1000,
|
|
||||||
"initial_scale_power": 16,
|
|
||||||
"hysteresis": 2,
|
|
||||||
"min_loss_scale": 1
|
|
||||||
},
|
|
||||||
"bf16": {
|
|
||||||
"enabled": "auto"
|
|
||||||
},
|
|
||||||
"optimizer": {
|
|
||||||
"type": "AdamW",
|
|
||||||
"params": {
|
|
||||||
"lr": "auto",
|
|
||||||
"betas": "auto",
|
|
||||||
"eps": "auto",
|
|
||||||
"weight_decay": "auto"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
|
|
||||||
"scheduler": {
|
|
||||||
"type": "WarmupLR",
|
|
||||||
"params": {
|
|
||||||
"warmup_min_lr": "auto",
|
|
||||||
"warmup_max_lr": "auto",
|
|
||||||
"warmup_num_steps": "auto"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
|
|
||||||
"zero_optimization": {
|
|
||||||
"stage": 3,
|
|
||||||
"offload_optimizer": {
|
|
||||||
"device": "none",
|
|
||||||
"pin_memory": true
|
|
||||||
},
|
|
||||||
"offload_param": {
|
|
||||||
"device": "none",
|
|
||||||
"pin_memory": true
|
|
||||||
},
|
|
||||||
"overlap_comm": true,
|
|
||||||
"contiguous_gradients": true,
|
|
||||||
"sub_group_size": 1e9,
|
|
||||||
"reduce_bucket_size": "auto",
|
|
||||||
"stage3_prefetch_bucket_size": "auto",
|
|
||||||
"stage3_param_persistence_threshold": "auto",
|
|
||||||
"stage3_max_live_parameters": 1e9,
|
|
||||||
"stage3_max_reuse_distance": 1e9,
|
|
||||||
"stage3_gather_16bit_weights_on_model_save": true
|
|
||||||
},
|
|
||||||
|
|
||||||
"gradient_accumulation_steps": "auto",
|
|
||||||
"gradient_clipping": "auto",
|
|
||||||
"steps_per_print": 100,
|
|
||||||
"train_batch_size": "auto",
|
|
||||||
"train_micro_batch_size_per_gpu": "auto",
|
|
||||||
"wall_clock_breakdown": false
|
|
||||||
}
|
|
||||||
|
|
@ -1,90 +0,0 @@
|
||||||
#!/bin/bash
|
|
||||||
export CUDA_DEVICE_MAX_CONNECTIONS=1
|
|
||||||
DIR=`pwd`
|
|
||||||
|
|
||||||
# Guide:
|
|
||||||
# This script supports distributed training on multi-gpu workers (as well as single-worker training).
|
|
||||||
# Please set the options below according to the comments.
|
|
||||||
# For multi-gpu workers training, these options should be manually set for each worker.
|
|
||||||
# After setting the options, please run the script on each worker.
|
|
||||||
|
|
||||||
# Number of GPUs per GPU worker
|
|
||||||
GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
|
|
||||||
|
|
||||||
# Number of GPU workers, for single-worker training, please set to 1
|
|
||||||
NNODES=${NNODES:-1}
|
|
||||||
|
|
||||||
# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
|
|
||||||
NODE_RANK=${NODE_RANK:-0}
|
|
||||||
|
|
||||||
# The ip address of the rank-0 worker, for single-worker training, please set to localhost
|
|
||||||
MASTER_ADDR=${MASTER_ADDR:localhost}
|
|
||||||
|
|
||||||
# The port for communication
|
|
||||||
MASTER_PORT=${MASTER_PORT:-6001}
|
|
||||||
|
|
||||||
MODEL="Qwen/Qwen-7B" # Set the path if you do not want to load from huggingface directly
|
|
||||||
# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
|
|
||||||
# See the section for finetuning in README for more information.
|
|
||||||
DATA="path_to_data"
|
|
||||||
|
|
||||||
function usage() {
|
|
||||||
echo '
|
|
||||||
Usage: bash finetune/finetune_ds.sh [-m MODEL_PATH] [-d DATA_PATH]
|
|
||||||
'
|
|
||||||
}
|
|
||||||
|
|
||||||
while [[ "$1" != "" ]]; do
|
|
||||||
case $1 in
|
|
||||||
-m | --model )
|
|
||||||
shift
|
|
||||||
MODEL=$1
|
|
||||||
;;
|
|
||||||
-d | --data )
|
|
||||||
shift
|
|
||||||
DATA=$1
|
|
||||||
;;
|
|
||||||
-h | --help )
|
|
||||||
usage
|
|
||||||
exit 0
|
|
||||||
;;
|
|
||||||
* )
|
|
||||||
echo "Unknown argument ${1}"
|
|
||||||
exit 1
|
|
||||||
;;
|
|
||||||
esac
|
|
||||||
shift
|
|
||||||
done
|
|
||||||
|
|
||||||
DISTRIBUTED_ARGS="
|
|
||||||
--nproc_per_node $GPUS_PER_NODE \
|
|
||||||
--nnodes $NNODES \
|
|
||||||
--node_rank $NODE_RANK \
|
|
||||||
--master_addr $MASTER_ADDR \
|
|
||||||
--master_port $MASTER_PORT
|
|
||||||
"
|
|
||||||
|
|
||||||
torchrun $DISTRIBUTED_ARGS finetune.py \
|
|
||||||
--model_name_or_path $MODEL \
|
|
||||||
--data_path $DATA \
|
|
||||||
--bf16 True \
|
|
||||||
--output_dir output_qwen \
|
|
||||||
--num_train_epochs 5 \
|
|
||||||
--per_device_train_batch_size 1 \
|
|
||||||
--per_device_eval_batch_size 1 \
|
|
||||||
--gradient_accumulation_steps 16 \
|
|
||||||
--evaluation_strategy "no" \
|
|
||||||
--save_strategy "steps" \
|
|
||||||
--save_steps 1000 \
|
|
||||||
--save_total_limit 10 \
|
|
||||||
--learning_rate 1e-5 \
|
|
||||||
--weight_decay 0.1 \
|
|
||||||
--adam_beta2 0.95 \
|
|
||||||
--warmup_ratio 0.01 \
|
|
||||||
--lr_scheduler_type "cosine" \
|
|
||||||
--logging_steps 1 \
|
|
||||||
--report_to "none" \
|
|
||||||
--model_max_length 512 \
|
|
||||||
--gradient_checkpointing True \
|
|
||||||
--lazy_preprocess True \
|
|
||||||
--deepspeed finetune/ds_config_zero3.json
|
|
||||||
|
|
@ -1,96 +0,0 @@
|
||||||
#!/bin/bash
|
|
||||||
export CUDA_DEVICE_MAX_CONNECTIONS=1
|
|
||||||
DIR=`pwd`
|
|
||||||
|
|
||||||
# Guide:
|
|
||||||
# This script supports distributed training on multi-gpu workers (as well as single-worker training).
|
|
||||||
# Please set the options below according to the comments.
|
|
||||||
# For multi-gpu workers training, these options should be manually set for each worker.
|
|
||||||
# After setting the options, please run the script on each worker.
|
|
||||||
|
|
||||||
# Number of GPUs per GPU worker
|
|
||||||
GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
|
|
||||||
|
|
||||||
# Number of GPU workers, for single-worker training, please set to 1
|
|
||||||
NNODES=${NNODES:-1}
|
|
||||||
|
|
||||||
# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
|
|
||||||
NODE_RANK=${NODE_RANK:-0}
|
|
||||||
|
|
||||||
# The ip address of the rank-0 worker, for single-worker training, please set to localhost
|
|
||||||
MASTER_ADDR=${MASTER_ADDR:localhost}
|
|
||||||
|
|
||||||
# The port for communication
|
|
||||||
MASTER_PORT=${MASTER_PORT:-6001}
|
|
||||||
|
|
||||||
MODEL="Qwen/Qwen-7B" # Set the path if you do not want to load from huggingface directly
|
|
||||||
# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
|
|
||||||
# See the section for finetuning in README for more information.
|
|
||||||
DATA="path_to_data"
|
|
||||||
DS_CONFIG_PATH="finetune/ds_config_zero2.json"
|
|
||||||
|
|
||||||
function usage() {
|
|
||||||
echo '
|
|
||||||
Usage: bash finetune/finetune_lora_ds.sh [-m MODEL_PATH] [-d DATA_PATH] [--deepspeed DS_CONFIG_PATH]
|
|
||||||
'
|
|
||||||
}
|
|
||||||
|
|
||||||
while [[ "$1" != "" ]]; do
|
|
||||||
case $1 in
|
|
||||||
-m | --model )
|
|
||||||
shift
|
|
||||||
MODEL=$1
|
|
||||||
;;
|
|
||||||
-d | --data )
|
|
||||||
shift
|
|
||||||
DATA=$1
|
|
||||||
;;
|
|
||||||
--deepspeed )
|
|
||||||
shift
|
|
||||||
DS_CONFIG_PATH=$1
|
|
||||||
;;
|
|
||||||
-h | --help )
|
|
||||||
usage
|
|
||||||
exit 0
|
|
||||||
;;
|
|
||||||
* )
|
|
||||||
echo "Unknown argument ${1}"
|
|
||||||
exit 1
|
|
||||||
;;
|
|
||||||
esac
|
|
||||||
shift
|
|
||||||
done
|
|
||||||
|
|
||||||
DISTRIBUTED_ARGS="
|
|
||||||
--nproc_per_node $GPUS_PER_NODE \
|
|
||||||
--nnodes $NNODES \
|
|
||||||
--node_rank $NODE_RANK \
|
|
||||||
--master_addr $MASTER_ADDR \
|
|
||||||
--master_port $MASTER_PORT
|
|
||||||
"
|
|
||||||
|
|
||||||
torchrun $DISTRIBUTED_ARGS finetune.py \
|
|
||||||
--model_name_or_path $MODEL \
|
|
||||||
--data_path $DATA \
|
|
||||||
--bf16 True \
|
|
||||||
--output_dir output_qwen \
|
|
||||||
--num_train_epochs 5 \
|
|
||||||
--per_device_train_batch_size 2 \
|
|
||||||
--per_device_eval_batch_size 1 \
|
|
||||||
--gradient_accumulation_steps 8 \
|
|
||||||
--evaluation_strategy "no" \
|
|
||||||
--save_strategy "steps" \
|
|
||||||
--save_steps 1000 \
|
|
||||||
--save_total_limit 10 \
|
|
||||||
--learning_rate 3e-4 \
|
|
||||||
--weight_decay 0.1 \
|
|
||||||
--adam_beta2 0.95 \
|
|
||||||
--warmup_ratio 0.01 \
|
|
||||||
--lr_scheduler_type "cosine" \
|
|
||||||
--logging_steps 1 \
|
|
||||||
--report_to "none" \
|
|
||||||
--model_max_length 512 \
|
|
||||||
--lazy_preprocess True \
|
|
||||||
--use_lora \
|
|
||||||
--gradient_checkpointing \
|
|
||||||
--deepspeed ${DS_CONFIG_PATH}
|
|
||||||
|
|
@ -1,93 +0,0 @@
|
||||||
#!/bin/bash
|
|
||||||
export CUDA_DEVICE_MAX_CONNECTIONS=1
|
|
||||||
DIR=`pwd`
|
|
||||||
|
|
||||||
# Guide:
|
|
||||||
# This script supports distributed training on multi-gpu workers (as well as single-worker training).
|
|
||||||
# Please set the options below according to the comments.
|
|
||||||
# For multi-gpu workers training, these options should be manually set for each worker.
|
|
||||||
# After setting the options, please run the script on each worker.
|
|
||||||
|
|
||||||
# Number of GPUs per GPU worker
|
|
||||||
GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
|
|
||||||
|
|
||||||
# Number of GPU workers, for single-worker training, please set to 1
|
|
||||||
NNODES=${NNODES:-1}
|
|
||||||
|
|
||||||
# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
|
|
||||||
NODE_RANK=${NODE_RANK:-0}
|
|
||||||
|
|
||||||
# The ip address of the rank-0 worker, for single-worker training, please set to localhost
|
|
||||||
MASTER_ADDR=${MASTER_ADDR:localhost}
|
|
||||||
|
|
||||||
# The port for communication
|
|
||||||
MASTER_PORT=${MASTER_PORT:-6001}
|
|
||||||
|
|
||||||
MODEL="Qwen/Qwen-7B-Chat-Int4" # Set the path if you do not want to load from huggingface directly
|
|
||||||
# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
|
|
||||||
# See the section for finetuning in README for more information.
|
|
||||||
DATA="path_to_data"
|
|
||||||
|
|
||||||
function usage() {
|
|
||||||
echo '
|
|
||||||
Usage: bash finetune/finetune_qlora_ds.sh [-m MODEL_PATH] [-d DATA_PATH]
|
|
||||||
'
|
|
||||||
}
|
|
||||||
|
|
||||||
while [[ "$1" != "" ]]; do
|
|
||||||
case $1 in
|
|
||||||
-m | --model )
|
|
||||||
shift
|
|
||||||
MODEL=$1
|
|
||||||
;;
|
|
||||||
-d | --data )
|
|
||||||
shift
|
|
||||||
DATA=$1
|
|
||||||
;;
|
|
||||||
-h | --help )
|
|
||||||
usage
|
|
||||||
exit 0
|
|
||||||
;;
|
|
||||||
* )
|
|
||||||
echo "Unknown argument ${1}"
|
|
||||||
exit 1
|
|
||||||
;;
|
|
||||||
esac
|
|
||||||
shift
|
|
||||||
done
|
|
||||||
|
|
||||||
DISTRIBUTED_ARGS="
|
|
||||||
--nproc_per_node $GPUS_PER_NODE \
|
|
||||||
--nnodes $NNODES \
|
|
||||||
--node_rank $NODE_RANK \
|
|
||||||
--master_addr $MASTER_ADDR \
|
|
||||||
--master_port $MASTER_PORT
|
|
||||||
"
|
|
||||||
|
|
||||||
# Remember to use --fp16 instead of --bf16 due to autogptq
|
|
||||||
torchrun $DISTRIBUTED_ARGS finetune.py \
|
|
||||||
--model_name_or_path $MODEL \
|
|
||||||
--data_path $DATA \
|
|
||||||
--fp16 True \
|
|
||||||
--output_dir output_qwen \
|
|
||||||
--num_train_epochs 5 \
|
|
||||||
--per_device_train_batch_size 2 \
|
|
||||||
--per_device_eval_batch_size 1 \
|
|
||||||
--gradient_accumulation_steps 8 \
|
|
||||||
--evaluation_strategy "no" \
|
|
||||||
--save_strategy "steps" \
|
|
||||||
--save_steps 1000 \
|
|
||||||
--save_total_limit 10 \
|
|
||||||
--learning_rate 3e-4 \
|
|
||||||
--weight_decay 0.1 \
|
|
||||||
--adam_beta2 0.95 \
|
|
||||||
--warmup_ratio 0.01 \
|
|
||||||
--lr_scheduler_type "cosine" \
|
|
||||||
--logging_steps 1 \
|
|
||||||
--report_to "none" \
|
|
||||||
--model_max_length 512 \
|
|
||||||
--lazy_preprocess True \
|
|
||||||
--use_lora \
|
|
||||||
--q_lora \
|
|
||||||
--gradient_checkpointing \
|
|
||||||
--deepspeed finetune/ds_config_zero2.json
|
|
||||||
|
|
@ -1,66 +0,0 @@
|
||||||
#!/bin/bash
|
|
||||||
export CUDA_DEVICE_MAX_CONNECTIONS=1
|
|
||||||
DIR=`pwd`
|
|
||||||
|
|
||||||
MODEL="Qwen/Qwen-7B-Chat-Int4" # Set the path if you do not want to load from huggingface directly
|
|
||||||
# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
|
|
||||||
# See the section for finetuning in README for more information.
|
|
||||||
DATA="path_to_data"
|
|
||||||
|
|
||||||
function usage() {
|
|
||||||
echo '
|
|
||||||
Usage: bash finetune/finetune_qlora_single_gpu.sh [-m MODEL_PATH] [-d DATA_PATH]
|
|
||||||
'
|
|
||||||
}
|
|
||||||
|
|
||||||
while [[ "$1" != "" ]]; do
|
|
||||||
case $1 in
|
|
||||||
-m | --model )
|
|
||||||
shift
|
|
||||||
MODEL=$1
|
|
||||||
;;
|
|
||||||
-d | --data )
|
|
||||||
shift
|
|
||||||
DATA=$1
|
|
||||||
;;
|
|
||||||
-h | --help )
|
|
||||||
usage
|
|
||||||
exit 0
|
|
||||||
;;
|
|
||||||
* )
|
|
||||||
echo "Unknown argument ${1}"
|
|
||||||
exit 1
|
|
||||||
;;
|
|
||||||
esac
|
|
||||||
shift
|
|
||||||
done
|
|
||||||
|
|
||||||
export CUDA_VISIBLE_DEVICES=0
|
|
||||||
|
|
||||||
# Remember to use --fp16 instead of --bf16 due to autogptq
|
|
||||||
python finetune.py \
|
|
||||||
--model_name_or_path $MODEL \
|
|
||||||
--data_path $DATA \
|
|
||||||
--fp16 True \
|
|
||||||
--output_dir output_qwen \
|
|
||||||
--num_train_epochs 5 \
|
|
||||||
--per_device_train_batch_size 2 \
|
|
||||||
--per_device_eval_batch_size 1 \
|
|
||||||
--gradient_accumulation_steps 8 \
|
|
||||||
--evaluation_strategy "no" \
|
|
||||||
--save_strategy "steps" \
|
|
||||||
--save_steps 1000 \
|
|
||||||
--save_total_limit 10 \
|
|
||||||
--learning_rate 3e-4 \
|
|
||||||
--weight_decay 0.1 \
|
|
||||||
--adam_beta2 0.95 \
|
|
||||||
--warmup_ratio 0.01 \
|
|
||||||
--lr_scheduler_type "cosine" \
|
|
||||||
--logging_steps 1 \
|
|
||||||
--report_to "none" \
|
|
||||||
--model_max_length 512 \
|
|
||||||
--lazy_preprocess True \
|
|
||||||
--gradient_checkpointing \
|
|
||||||
--use_lora \
|
|
||||||
--q_lora \
|
|
||||||
--deepspeed finetune/ds_config_zero2.json
|
|
||||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue