added training code
This commit is contained in:
Saifeddine ALOUI
parent
b4694fee2c
commit
584a1f6f03
26
app.py
26
app.py
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@ -876,6 +876,32 @@ class LoLLMsWebUI(LoLLMsAPPI):
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print(f"Problem with model : {model}")
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return jsonify(models)
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def train(self):
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form_data = request.form
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# Create and populate the config file
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config = {
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'model_name': form_data['model_name'],
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'tokenizer_name': form_data['tokenizer_name'],
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'dataset_path': form_data['dataset_path'],
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'max_length': form_data['max_length'],
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'batch_size': form_data['batch_size'],
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'lr': form_data['lr'],
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'num_epochs': form_data['num_epochs'],
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'output_dir': form_data['output_dir'],
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}
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with open('train/configs/train/local_cfg.yaml', 'w') as f:
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yaml.dump(config, f)
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# Trigger the train.py script
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# Place your code here to run the train.py script with the created config file
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# accelerate launch --dynamo_backend=inductor --num_processes=8 --num_machines=1 --machine_rank=0 --deepspeed_multinode_launcher standard --mixed_precision=bf16 --use_deepspeed --deepspeed_config_file=configs/deepspeed/ds_config_gptj.json train.py --config configs/train/finetune_gptj.yaml
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subprocess.check_call(["accelerate","launch", "--dynamo_backend=inductor", "--num_processes=8", "--num_machines=1", "--machine_rank=0", "--deepspeed_multinode_launcher standard", "--mixed_precision=bf16", "--use_deepspeed", "--deepspeed_config_file=train/configs/deepspeed/ds_config_gptj.json", "train/train.py", "--config", "train/configs/train/local_cfg.yaml"])
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return jsonify({'message': 'Training started'})
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def get_config(self):
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return jsonify(self.config.to_dict())
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@ -0,0 +1,2 @@
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output
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!output/.keep
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@ -0,0 +1,48 @@
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{
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"train_batch_size": "auto",
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"gradient_accumulation_steps": "auto",
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"train_micro_batch_size_per_gpu": "auto",
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"fp16": {
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"enabled": "auto",
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"min_loss_scale": 1,
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"loss_scale_window": 1000,
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"hysteresis": 2,
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"initial_scale_power": 32
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},
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"bf16": {
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"enabled": "auto"
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},
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"gradient_clipping": 1,
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"zero_optimization": {
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"stage": 2,
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"offload_param": {
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"device": "none"
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},
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"offload_optimizer": {
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"device": "none"
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},
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"allgather_partitions": true,
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"allgather_bucket_size": 5e8,
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"contiguous_gradients": true
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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": [
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0.9,
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0.999
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],
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"eps": 1e-08
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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": 0,
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"warmup_max_lr": "auto",
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"warmup_num_steps": "auto",
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"warmup_type": "linear"
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}
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}
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}
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@ -0,0 +1 @@
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local_cfg.yaml
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@ -0,0 +1,29 @@
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# model/tokenizer
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model_name: # add model here
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tokenizer_name: # add model here
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gradient_checkpointing: true
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save_name: # CHANGE
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# dataset
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streaming: false
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num_proc: 64
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dataset_path: # update
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max_length: 1024
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batch_size: 32
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# train dynamics
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lr: 5.0e-5
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eval_every: 800
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eval_steps: 100
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save_every: 800
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output_dir: # CHANGE
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checkpoint: null
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lora: false
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warmup_steps: 100
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num_epochs: 2
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# logging
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wandb: true
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wandb_entity: # update
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wandb_project_name: # update
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seed: 42
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@ -0,0 +1,31 @@
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# model/tokenizer
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model_name: # update
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tokenizer_name: # update
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gradient_checkpointing: false
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save_name: # CHANGE
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# dataset
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streaming: false
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num_proc: 64
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dataset_path: # CHANGE
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max_length: 1024
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batch_size: 4
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# train dynamics
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lr: 5.0e-5
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min_lr: 0
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weight_decay: 0.0
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eval_every: 2000
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eval_steps: 100
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save_every: 2000
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output_dir: # CHANGE
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checkpoint: null
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lora: true
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warmup_steps: 100
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num_epochs: 2
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# logging
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wandb: true
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wandb_entity: # update
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wandb_project_name: # update
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seed: 42
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@ -0,0 +1,31 @@
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# model/tokenizer
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model_name: jondurbin/airoboros-7b-gpt4 # update
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tokenizer_name: jondurbin/airoboros-7b-gpt4 # update
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gradient_checkpointing: false
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save_name: parisneo-7b_gpt42_lora # CHANGE
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# dataset
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streaming: false
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num_proc: 64
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dataset_path: # CHANGE
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max_length: 1024
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batch_size: 4
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# train dynamics
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lr: 5.0e-5
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min_lr: 0
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weight_decay: 0.0
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eval_every: 2000
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eval_steps: 100
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save_every: 2000
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output_dir: output # CHANGE
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checkpoint: null
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lora: true
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warmup_steps: 100
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num_epochs: 2
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# logging
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wandb: false # update if you want to use weights and biases
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wandb_entity: # update
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wandb_project_name: # update
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seed: 42
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@ -0,0 +1,15 @@
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accelerate
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datasets
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torchmetrics
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evaluate
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transformers>=4.28.0
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wandb
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pip
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peft
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nodelist-inflator
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deepspeed
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sentencepiece
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jsonlines
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nomic
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scikit-learn
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matplotlib
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@ -0,0 +1,233 @@
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import os
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from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler, LlamaForCausalLM
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import torch
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from torch.optim import AdamW
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from argparse import ArgumentParser
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from read import read_config
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from accelerate import Accelerator
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from accelerate.utils import DummyScheduler, DummyOptim, set_seed
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from peft import get_peft_model, LoraConfig, TaskType
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from data import load_data
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from torchmetrics import MeanMetric
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from tqdm import tqdm
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import wandb
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torch.backends.cuda.matmul.allow_tf32 = True
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def format_metrics(metrics, split, prefix=""):
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log = f"[{split}]" + prefix
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log += " ".join([f"{key}: {value:.4f}" for key, value in metrics.items()])
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return log
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def evaluate(model, val_dataloader):
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model.eval()
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val_loss = MeanMetric(nan_strategy="error").to(model.device)
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with torch.no_grad():
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for batch in tqdm(val_dataloader):
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loss = model(**batch).loss
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loss_values = accelerator.gather_for_metrics({"loss": loss.detach()})
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val_loss.update(loss_values["loss"])
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return val_loss
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def train(accelerator, config):
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set_seed(config['seed'])
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accelerator.print(config)
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accelerator.print(f"Using {accelerator.num_processes} GPUs")
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tokenizer = AutoTokenizer.from_pretrained(config['tokenizer_name'], model_max_length=config['max_length'])
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# if no pad token, set it to eos
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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with accelerator.main_process_first():
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train_dataloader, val_dataloader = load_data(config, tokenizer)
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checkpoint = config["gradient_checkpointing"]
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model = AutoModelForCausalLM.from_pretrained(config["model_name"],
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use_cache=False if checkpoint else True,
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trust_remote_code=True)
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if checkpoint:
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model.gradient_checkpointing_enable()
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if config["lora"]:
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peft_config = LoraConfig(
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# should R be configurable?
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task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
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)
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model = get_peft_model(model, peft_config)
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model.print_trainable_parameters()
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optimizer_cls = (
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AdamW
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if accelerator.state.deepspeed_plugin is None
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or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
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else DummyOptim
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)
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# karpathy doesn't decay embeddding, maybe we should exclude
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# https://github.com/karpathy/minGPT/commit/bbbdac74fa9b2e55574d70056163ffbae42310c1#diff-2075fa9c224b395be5bda85544dd36572b59c76c54562819eadadbf268602834R157s
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optimizer = optimizer_cls(model.parameters(), lr=config["lr"], weight_decay=config["weight_decay"])
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if accelerator.state.deepspeed_plugin is not None:
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gradient_accumulation_steps = accelerator.state.deepspeed_plugin.deepspeed_config[
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"gradient_accumulation_steps"
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]
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# decay to min_lr instead of 0
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lr_ratio = config["min_lr"] / config["lr"]
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accelerator.print(f"Len of train_dataloader: {len(train_dataloader)}")
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total_num_steps = (len(train_dataloader) / gradient_accumulation_steps) * config["num_epochs"]
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# instead of decaying to zero, decay to ratio of min_lr / lr
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total_num_steps += int(total_num_steps * lr_ratio) + config["warmup_steps"]
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accelerator.print(f"Total training steps: {total_num_steps}")
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# Creates Dummy Scheduler if `scheduler` was specified in the config file else creates `args.lr_scheduler_type` Scheduler
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if (
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accelerator.state.deepspeed_plugin is None
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or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
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):
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scheduler = get_scheduler(
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name="cosine",
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optimizer=optimizer,
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num_warmup_steps=config["warmup_steps"] * accelerator.num_processes,
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num_training_steps=total_num_steps,
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)
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else:
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scheduler = DummyScheduler(
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optimizer, total_num_steps=config["warmup_steps"], warmup_num_steps=config["warmup_steps"]
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)
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model, optimizer, train_dataloader, val_dataloader, scheduler = accelerator.prepare(
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model, optimizer, train_dataloader, val_dataloader, scheduler
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)
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# setup for saving training states in case preemption
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accelerator.register_for_checkpointing(scheduler)
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if config["checkpoint"]:
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accelerator.load_state(config["checkpoint"])
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accelerator.print(f"Resumed from checkpoint: {config['checkpoint']}")
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path = os.path.basename(config["train_args"]["resume_from_checkpoint"])
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training_difference = os.path.splitext(path)[0]
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resume_step = int(training_difference.replace("step_", ""))
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accelerator.skip_first_batches(train_dataloader, resume_step)
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accelerator.print(f"Resuming from step {resume_step}")
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# log gradients
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if accelerator.is_main_process and config["wandb"]:
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wandb.watch(model, log_freq=config["log_grads_every"], log="all")
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for epoch in range(config["num_epochs"]):
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train_loss = MeanMetric(nan_strategy="error").to(model.device)
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for step, batch in enumerate(tqdm(train_dataloader)):
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model.train()
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outputs = model(**batch)
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loss = outputs.loss
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# gather loss before backprop in case of gradient accumulation
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loss_values = accelerator.gather_for_metrics({"loss": loss.detach().float()})
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train_loss.update(loss_values["loss"])
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loss = loss / gradient_accumulation_steps
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accelerator.backward(loss)
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# get gradient norm of all params
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# log LR in case something weird happens
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if step > 0 and step % (config["eval_every"] // 10) == 0:
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if config["wandb"]:
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curr_step = step + epoch * len(train_dataloader)
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accelerator.log({"lr": scheduler.get_last_lr()[0]}, step=curr_step)
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if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
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optimizer.step()
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scheduler.step()
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optimizer.zero_grad()
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if step > 0 and step % config["save_every"] == 0:
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curr_step = step + epoch * len(train_dataloader)
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accelerator.save_state(f"{config['output_dir']}/step_{curr_step}")
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if step > 0 and (step % config["eval_every"] == 0 or step == len(train_dataloader) - 1):
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val_loss = evaluate(model, val_dataloader)
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log_train = {
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"train_loss": train_loss.compute()
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}
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log_val = {
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"val_loss": val_loss.compute()
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}
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if config["wandb"]:
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curr_step = step + epoch * len(train_dataloader)
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accelerator.log({**log_train, **log_val}, step=curr_step)
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accelerator.print(f"Current LR: {scheduler.get_last_lr()[0]}")
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accelerator.print(format_metrics(log_train, "train", f" step {step} "))
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accelerator.print(format_metrics(log_val, "val", f" step {step} "))
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train_loss.reset()
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accelerator.print(f"Epoch {epoch} finished")
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accelerator.print(f"Pushing to HF hub")
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accelerator.wait_for_everyone()
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unwrapped_model = accelerator.unwrap_model(model)
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try:
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if accelerator.is_main_process:
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unwrapped_model.push_to_hub(config["save_name"] + f"-epoch_{epoch}", private=True)
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except Exception as e:
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accelerator.print(e)
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accelerator.print(f"Failed to push to hub")
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unwrapped_model.save_pretrained(
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f"{config['output_dir']}/epoch_{epoch}",
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is_main_process=accelerator.is_main_process,
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save_function=accelerator.save,
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state_dict=accelerator.get_state_dict(model),
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)
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accelerator.wait_for_everyone()
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unwrapped_model = accelerator.unwrap_model(model)
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unwrapped_model.save_pretrained(
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f"{config['output_dir']}/final",
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is_main_process=accelerator.is_main_process,
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save_function=accelerator.save,
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state_dict=accelerator.get_state_dict(model),
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)
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accelerator.end_training()
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if __name__ == "__main__":
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# parse arguments by reading in a config
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parser = ArgumentParser()
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parser.add_argument("--config", type=str, default="config.yaml")
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args = parser.parse_args()
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config = read_config(args.config)
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if config["wandb"]:
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accelerator = Accelerator(log_with="wandb")
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accelerator.init_trackers(
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project_name=config["wandb_project_name"],
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config=config,
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init_kwargs={"wandb": {"entity": config["wandb_entity"]}},
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)
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else:
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accelerator = Accelerator()
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train(accelerator, config=config)
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@ -6,8 +6,8 @@
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>GPT4All - WEBUI</title>
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<script type="module" crossorigin src="/assets/index-0344eb9b.js"></script>
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<link rel="stylesheet" href="/assets/index-488cca87.css">
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<script type="module" crossorigin src="/assets/index-f5f472ed.js"></script>
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<link rel="stylesheet" href="/assets/index-54621153.css">
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</head>
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<body>
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<div id="app"></div>
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@ -1,16 +1,159 @@
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<template>
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<div>
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Training
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<div class="container overflow-y-scroll flex flex-col no-scrollbar shadow-lg p-10 pt-0">
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<form @submit.prevent="submitForm" class="max-w-md mx-auto">
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<!-- Model/Tokenizer -->
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<div class="mb-4">
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<label for="model_name" class="text-sm">Model Name:</label>
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<input
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type="text"
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id="model_name"
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v-model="model_name"
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||||
required
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
>
|
||||
</div>
|
||||
<div class="mb-4">
|
||||
<label for="tokenizer_name" class="text-sm">Tokenizer Name:</label>
|
||||
<input
|
||||
type="text"
|
||||
id="tokenizer_name"
|
||||
v-model="tokenizer_name"
|
||||
required
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
>
|
||||
</div>
|
||||
|
||||
<!-- Dataset -->
|
||||
<div class="mb-4">
|
||||
<label for="dataset_path" class="text-sm">Dataset:</label>
|
||||
<input
|
||||
type="file"
|
||||
id="dataset_path"
|
||||
ref="dataset_path"
|
||||
accept=".parquet"
|
||||
v-on:change="selectDatasetPath"
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
>
|
||||
<p class="mt-2 text-xs">Selected File: {{ selectedDatasetPath }}</p>
|
||||
</div>
|
||||
<div class="mb-4">
|
||||
<label for="max_length" class="text-sm">Max Length:</label>
|
||||
<input
|
||||
type="number"
|
||||
id="max_length"
|
||||
v-model.number="max_length"
|
||||
required
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
>
|
||||
</div>
|
||||
<div class="mb-4">
|
||||
<label for="batch_size" class="text-sm">Batch Size:</label>
|
||||
<input
|
||||
type="number"
|
||||
id="batch_size"
|
||||
v-model.number="batch_size"
|
||||
required
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
>
|
||||
</div>
|
||||
|
||||
<!-- Train Dynamics -->
|
||||
<div class="mb-4">
|
||||
<label for="lr" class="text-sm">Learning Rate:</label>
|
||||
<input
|
||||
type="number"
|
||||
id="lr"
|
||||
v-model.number="lr"
|
||||
required
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
>
|
||||
</div>
|
||||
<div class="mb-4">
|
||||
<label for="num_epochs" class="text-sm">Number of Epochs:</label>
|
||||
<input
|
||||
type="number"
|
||||
id="num_epochs"
|
||||
v-model.number="num_epochs"
|
||||
required
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
>
|
||||
</div>
|
||||
|
||||
<!-- Logging -->
|
||||
<div class="mb-4">
|
||||
<label for="output_dir" class="text-sm">Output Directory:</label>
|
||||
<input
|
||||
type="text"
|
||||
id="output_dir"
|
||||
v-model="selectedFolder"
|
||||
class="w-full mt-1 px-2 py-1 border border-gray-300 rounded"
|
||||
placeholder="Enter or select the output folder"
|
||||
>
|
||||
<input
|
||||
type="file"
|
||||
id="folder_selector"
|
||||
ref="folder_selector"
|
||||
style="display: none"
|
||||
webkitdirectory
|
||||
v-on:change="selectOutputDirectory"
|
||||
>
|
||||
<button type="button" @click="openFolderSelector" class="bg-blue-500 text-white px-4 py-2 rounded">Select Folder</button>
|
||||
</div>
|
||||
|
||||
<button type="submit" class="bg-blue-500 text-white px-4 py-2 rounded">Train LLM</button>
|
||||
</form>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
<script>
|
||||
export default {
|
||||
setup () {
|
||||
|
||||
|
||||
return {}
|
||||
}
|
||||
}
|
||||
</script>
|
||||
|
||||
</template>
|
||||
|
||||
<script>
|
||||
export default {
|
||||
data() {
|
||||
return {
|
||||
model_name: 'jondurbin/airoboros-7b-gpt4',
|
||||
tokenizer_name: 'jondurbin/airoboros-7b-gpt4',
|
||||
dataset_path: '',
|
||||
max_length: 1024,
|
||||
batch_size: 4,
|
||||
lr: 5.0e-5,
|
||||
num_epochs: 2,
|
||||
selectedFolder: '',
|
||||
selectedDatasetPath: '',
|
||||
};
|
||||
},
|
||||
methods: {
|
||||
submitForm() {
|
||||
const formData = {
|
||||
model_name: this.model_name,
|
||||
tokenizer_name: this.tokenizer_name,
|
||||
dataset_path: this.selectedDatasetPath,
|
||||
max_length: this.max_length,
|
||||
batch_size: this.batch_size,
|
||||
lr: this.lr,
|
||||
num_epochs: this.num_epochs,
|
||||
output_dir: this.selectedFolder,
|
||||
};
|
||||
|
||||
// Send the form data to the backend
|
||||
// ...
|
||||
},
|
||||
openFolderSelector() {
|
||||
this.$refs.folder_selector.click();
|
||||
},
|
||||
selectOutputDirectory(event) {
|
||||
console.log("here")
|
||||
const folderPath = event.target.files[0]?.path;
|
||||
console.log(folderPath)
|
||||
if (folderPath) {
|
||||
this.selectedFolder = folderPath;
|
||||
}
|
||||
},
|
||||
selectDatasetPath(event) {
|
||||
const files = event.target.files;
|
||||
if (files.length > 0) {
|
||||
this.selectedDatasetPath = files[0].webkitRelativePath;
|
||||
}
|
||||
},
|
||||
},
|
||||
};
|
||||
</script>
|
||||
|
||||
Loading…
Reference in New Issue