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| Original file line number | Diff line number | Diff line change |
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| # Supervised Fine-Tuning (SFT) | ||
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| ## Data Preparation | ||
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| Download [Deita 6K](https://huggingface.co/datasets/hkust-nlp/deita-6k-v0) to `data/deita/deita_6k.jsonl`. | ||
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| ## Training | ||
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| Start training in Slurm clusters: `sbatch scripts/sft/2_8.sh`. | ||
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| ## Inference | ||
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| ```python | ||
| from transformers import AutoModelForCausalLM | ||
| from transformers import AutoTokenizer | ||
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| from src.utils.conversation import Conversation | ||
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| conv = Conversation() | ||
| conv.append_message("human", "Give me a three-day plan in Suzhou.") | ||
| conv.append_message("gpt", None) | ||
| prompt = conv.get_prompt() | ||
| print(prompt) | ||
| print(prompt[-1] == " ") | ||
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| model_dir = "llama-moe/LLaMA-MoE-v1-3_5B-2_8-sft" | ||
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| tok = AutoTokenizer.from_pretrained(model_dir) | ||
| m = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True) | ||
| m.eval() | ||
| m.cuda() | ||
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| inputs = tok(prompt, return_tensors="pt") | ||
| input_ids = inputs["input_ids"].cuda() | ||
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| output = m.generate(input_ids, max_length=100, temperature=1.0, do_sample=True, use_cache=True) | ||
| response = tok.decode(output[0], skip_special_tokens=True) | ||
| print(response) | ||
| ``` |
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| #!/usr/bin/bash | ||
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| #SBATCH --job-name=llama_moe_2_16_deita | ||
| #SBATCH --output=logs/%x-%j.log | ||
| #SBATCH --error=logs/%x-%j.log | ||
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| #SBATCH --partition=MoE | ||
| #SBATCH --ntasks-per-node=1 | ||
| #SBATCH --cpus-per-task=16 | ||
| #SBATCH --mem=64G | ||
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| #SBATCH --nodes=1 | ||
| #SBATCH --gres=gpu:4 | ||
| #SBATCH --quotatype=auto | ||
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| export WANDB_PROJECT="llama_moe_sft" | ||
| num_gpus=4 | ||
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| { | ||
| task_name="llama_moe_2_16_deita" | ||
| model_type="auto" | ||
| model_name_or_path="/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_0B-2_16" | ||
| dataset_dir_or_path="data/deita/deita_6k.jsonl" | ||
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| comment="llama-moe 2/16, deita, w/ balance loss, w/ freeze gate, w/ gate noise" | ||
| base_dir="outputs/llama_moe_sft" | ||
| output_dir="${base_dir}/${task_name}/$SLURM_JOB_NAME-$SLURM_JOB_ID" | ||
| mkdir -p $output_dir | ||
| scontrol write batch_script $SLURM_JOBID $output_dir/sbatch.sh | ||
| git diff > $output_dir/diff.patch | ||
| env > $output_dir/env | ||
| echo -e "Job ID: ${SLURM_JOB_ID}\n\nLog: logs/llama_moe_2_16_deita-$SLURM_JOB_ID.log\n\nGit commit: $(git log -1 --oneline)\n\nGit branch: $(git branch | grep "*")\n\nComment: ${comment}" > $output_dir/comment.txt | ||
| ln -snf $(scontrol show job $SLURM_JOB_ID | grep "StdOut=" | cut -d '=' -f 2) $output_dir/log.log | ||
| echo "$SLURM_JOB_ID" > $base_dir/latest.jobid | ||
| ln -snf $output_dir $base_dir/latest.dir | ||
| ln -snf $(scontrol show job $SLURM_JOB_ID | grep "StdOut=" | cut -d '=' -f 2) $base_dir/latest.log | ||
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| nodes=($(scontrol show hostnames $SLURM_JOB_NODELIS)) | ||
| nodes_array=($nodes) | ||
| head_node=${nodes_array[0]} | ||
| echo "Node: $head_node" | ||
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| torchrun \ | ||
| --nnodes 1 \ | ||
| --nproc_per_node $num_gpus \ | ||
| --node_rank $SLURM_NODEID \ | ||
| --rdzv_id $RANDOM \ | ||
| --rdzv_backend c10d \ | ||
| --rdzv_endpoint $head_node:29522 \ | ||
| -m smoe.entrypoint.sft.train_sft \ | ||
| --do_train \ | ||
| --freeze_gate True \ | ||
| --evaluation_strategy no \ | ||
| --run_name $task_name \ | ||
| --model_type $model_type \ | ||
| --model_name_or_path $model_name_or_path \ | ||
| --dataset_dir_or_path $dataset_dir_or_path \ | ||
| --output_dir $output_dir \ | ||
| --deepspeed conf/ds_bf16_zero1.json \ | ||
| --seed 12306 \ | ||
| --bf16 True \ | ||
| --tf32 True \ | ||
| --torch_dtype bfloat16 \ | ||
| --per_device_train_batch_size 4 \ | ||
| --per_device_eval_batch_size 4 \ | ||
| --gradient_accumulation_steps 8 \ | ||
| --num_train_epochs 2 \ | ||
| --save_strategy steps \ | ||
| --save_steps 9999999999999 \ | ||
| --save_total_limit 1 \ | ||
| --learning_rate 2e-5 \ | ||
| --weight_decay 0. \ | ||
| --warmup_ratio 0.03 \ | ||
| --lr_scheduler_type cosine \ | ||
| --logging_steps 1 \ | ||
| --model_max_length 2048 \ | ||
| --gradient_checkpointing True \ | ||
| --report_to wandb | ||
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| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| #!/usr/bin/bash | ||
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| #SBATCH --job-name=llama_moe_2_8_deita | ||
| #SBATCH --output=logs/%x-%j.log | ||
| #SBATCH --error=logs/%x-%j.log | ||
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| #SBATCH --partition=MoE | ||
| #SBATCH --ntasks-per-node=1 | ||
| #SBATCH --cpus-per-task=16 | ||
| #SBATCH --mem=64G | ||
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| #SBATCH --nodes=1 | ||
| #SBATCH --gres=gpu:4 | ||
| #SBATCH --quotatype=auto | ||
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| export WANDB_PROJECT="llama_moe_sft" | ||
| num_gpus=4 | ||
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| { | ||
| task_name="llama_moe_2_8_deita" | ||
| model_type="auto" | ||
| model_name_or_path="/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-2_8-new" | ||
| dataset_dir_or_path="data/deita/deita_6k.jsonl" | ||
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| comment="llama-moe 2/8, deita, w/ balance loss, w/ freeze gate, w/ gate noise" | ||
| base_dir="outputs/llama_moe_sft" | ||
| output_dir="${base_dir}/${task_name}/$SLURM_JOB_NAME-$SLURM_JOB_ID" | ||
| mkdir -p $output_dir | ||
| scontrol write batch_script $SLURM_JOBID $output_dir/sbatch.sh | ||
| git diff > $output_dir/diff.patch | ||
| env > $output_dir/env | ||
| echo -e "Job ID: ${SLURM_JOB_ID}\n\nLog: logs/llama_moe_2_8_deita-$SLURM_JOB_ID.log\n\nGit commit: $(git log -1 --oneline)\n\nGit branch: $(git branch | grep "*")\n\nComment: ${comment}" > $output_dir/comment.txt | ||
| ln -snf $(scontrol show job $SLURM_JOB_ID | grep "StdOut=" | cut -d '=' -f 2) $output_dir/log.log | ||
| echo "$SLURM_JOB_ID" > $base_dir/latest.jobid | ||
| ln -snf $output_dir $base_dir/latest.dir | ||
| ln -snf $(scontrol show job $SLURM_JOB_ID | grep "StdOut=" | cut -d '=' -f 2) $base_dir/latest.log | ||
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| nodes=($(scontrol show hostnames $SLURM_JOB_NODELIS)) | ||
| nodes_array=($nodes) | ||
| head_node=${nodes_array[0]} | ||
| echo "Node: $head_node" | ||
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| torchrun \ | ||
| --nnodes 1 \ | ||
| --nproc_per_node $num_gpus \ | ||
| --node_rank $SLURM_NODEID \ | ||
| --rdzv_id $RANDOM \ | ||
| --rdzv_backend c10d \ | ||
| --rdzv_endpoint $head_node:29522 \ | ||
| -m smoe.entrypoint.sft.train_sft \ | ||
| --do_train \ | ||
| --freeze_gate True \ | ||
| --evaluation_strategy no \ | ||
| --run_name $task_name \ | ||
| --model_type $model_type \ | ||
| --model_name_or_path $model_name_or_path \ | ||
| --dataset_dir_or_path $dataset_dir_or_path \ | ||
| --output_dir $output_dir \ | ||
| --deepspeed conf/deepspeed/bf16_zero1.json \ | ||
| --seed 12306 \ | ||
| --bf16 True \ | ||
| --tf32 True \ | ||
| --torch_dtype bfloat16 \ | ||
| --per_device_train_batch_size 4 \ | ||
| --per_device_eval_batch_size 4 \ | ||
| --gradient_accumulation_steps 8 \ | ||
| --num_train_epochs 2 \ | ||
| --save_strategy steps \ | ||
| --save_steps 9999999999999 \ | ||
| --save_total_limit 1 \ | ||
| --learning_rate 2e-5 \ | ||
| --weight_decay 0. \ | ||
| --warmup_ratio 0.03 \ | ||
| --lr_scheduler_type cosine \ | ||
| --logging_steps 1 \ | ||
| --model_max_length 2048 \ | ||
| --gradient_checkpointing True \ | ||
| --report_to wandb | ||
|
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| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| #!/usr/bin/bash | ||
|
|
||
| #SBATCH --job-name=llama_moe_4_16_deita | ||
| #SBATCH --output=logs/%x-%j.log | ||
| #SBATCH --error=logs/%x-%j.log | ||
|
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||
| #SBATCH --partition=MoE | ||
| #SBATCH --ntasks-per-node=1 | ||
| #SBATCH --cpus-per-task=16 | ||
| #SBATCH --mem=64G | ||
|
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||
| #SBATCH --nodes=1 | ||
| #SBATCH --gres=gpu:4 | ||
| #SBATCH --quotatype=auto | ||
|
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| export WANDB_PROJECT="llama_moe_sft" | ||
| num_gpus=4 | ||
|
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| { | ||
| task_name="llama_moe_4_16_deita" | ||
| model_type="auto" | ||
| model_name_or_path="/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-4_16-new" | ||
| dataset_dir_or_path="data/deita/deita_6k.jsonl" | ||
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| comment="llama-moe 4/16, deita, w/ balance loss, w/ freeze gate, w/ gate noise" | ||
| base_dir="outputs/llama_moe_sft" | ||
| output_dir="${base_dir}/${task_name}/$SLURM_JOB_NAME-$SLURM_JOB_ID" | ||
| mkdir -p $output_dir | ||
| scontrol write batch_script $SLURM_JOBID $output_dir/sbatch.sh | ||
| git diff > $output_dir/diff.patch | ||
| env > $output_dir/env | ||
| echo -e "Job ID: ${SLURM_JOB_ID}\n\nLog: logs/llama_moe_4_16_deita-$SLURM_JOB_ID.log\n\nGit commit: $(git log -1 --oneline)\n\nGit branch: $(git branch | grep "*")\n\nComment: ${comment}" > $output_dir/comment.txt | ||
| ln -snf $(scontrol show job $SLURM_JOB_ID | grep "StdOut=" | cut -d '=' -f 2) $output_dir/log.log | ||
| echo "$SLURM_JOB_ID" > $base_dir/latest.jobid | ||
| ln -snf $output_dir $base_dir/latest.dir | ||
| ln -snf $(scontrol show job $SLURM_JOB_ID | grep "StdOut=" | cut -d '=' -f 2) $base_dir/latest.log | ||
|
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| nodes=($(scontrol show hostnames $SLURM_JOB_NODELIS)) | ||
| nodes_array=($nodes) | ||
| head_node=${nodes_array[0]} | ||
| echo "Node: $head_node" | ||
|
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| torchrun \ | ||
| --nnodes 1 \ | ||
| --nproc_per_node $num_gpus \ | ||
| --node_rank $SLURM_NODEID \ | ||
| --rdzv_id $RANDOM \ | ||
| --rdzv_backend c10d \ | ||
| --rdzv_endpoint $head_node:29522 \ | ||
| -m smoe.entrypoint.sft.train_sft \ | ||
| --do_train \ | ||
| --freeze_gate True \ | ||
| --evaluation_strategy no \ | ||
| --run_name $task_name \ | ||
| --model_type $model_type \ | ||
| --model_name_or_path $model_name_or_path \ | ||
| --dataset_dir_or_path $dataset_dir_or_path \ | ||
| --output_dir $output_dir \ | ||
| --deepspeed conf/ds_bf16_zero1.json \ | ||
| --seed 12306 \ | ||
| --bf16 True \ | ||
| --tf32 True \ | ||
| --torch_dtype bfloat16 \ | ||
| --per_device_train_batch_size 4 \ | ||
| --per_device_eval_batch_size 4 \ | ||
| --gradient_accumulation_steps 8 \ | ||
| --num_train_epochs 2 \ | ||
| --save_strategy steps \ | ||
| --save_steps 9999999999999 \ | ||
| --save_total_limit 1 \ | ||
| --learning_rate 2e-5 \ | ||
| --weight_decay 0. \ | ||
| --warmup_ratio 0.03 \ | ||
| --lr_scheduler_type cosine \ | ||
| --logging_steps 1 \ | ||
| --model_max_length 2048 \ | ||
| --gradient_checkpointing True \ | ||
| --report_to wandb | ||
|
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||
| } |
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