policy.lr, cfg.policy.adam_betas, cfg.policy.adam_eps, cfg.policy.adam_weight_decay ) # TODO(rcadene): modify lr scheduler so that it doesn't depend on epochs but steps # configure lr scheduler lr_scheduler = get_scheduler( cfg.policy.lr_scheduler, optimizer=optimizer, num_warmup_steps...
warmup_lr: 1e-6 weight_decay: 0.05 max_epoch: 1 num_workers: 2 warmup_steps: 1000 iters_per_epoch: 2 seed: 42 output_dir: "Hanh/Test_biomed_clip" amp: True resume_ckpt_path: null evaluate: False train_splits: ["train"] device: "cuda" world_size: 1 dist_url: "env://" ...
Optimizer用的是AdamW,在“训练”过程记录lr。 lr_increment = (peak_lr - initial_lr) / warmup_steps global_step = -1 track_lrs = [] optimizer = torch.optim.AdamW(model.parameters(), weight_decay=0.1) for epoch in range(n_epochs): for input_batch, target_batch in train_loader: ...
importtorchfromprevious_chaptersimportGPTModelGPT_CONFIG_124M={"vocab_size":50257,# Vocabulary size"context_length":256,# Shortened context length (orig: 1024)"emb_dim":768,# Embedding dimension"n_heads":12,# Number of attention heads"n_layers":12,# Number of layers"drop_rate":0.1,# Drop...
the learning rate decays linearly over the training iterations starting at--lrto a minimum set by--min-lrover--lr-decay-itersiterations. The fraction of training iterations used for warmup is set by--lr-warmup-fraction. While this is single GPU training, the batch size specified by--micro...
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from trl import SFTTrainer 我们继续分析导入 torch是我们很熟悉的深度学习库,这里我们不需要torch的那些低级功能,但是它是transformers和trl的依赖,在这里我们需要使用torch来获取dtypes(数据类型),比如torch.Float16以及检查GPU的工具函数。 load_dataset所做的就是加载数据集,但是它从HuggingFace数据集中心下载到本地。
warmup_steps=1_000, lr_scheduler_type="cosine", learning_rate=5e-4, save_steps=5_000, fp16=True, push_to_hub=True, ) trainer = Trainer( model=model, tokenizer=tokenizer, args=args, data_collator=data_collator, train_dataset=tokenized_datasets["train"], ...
optimizer = optim.AdamW(model.parameters(), lr=args.lr, eps=args.eps) scheduler = get_scheduler( args.scheduler, optimizer=optimizer, num_warmup_steps=args.warm_up_steps, num_training_steps=args.num_updates * args.num_train_epochs,
# Adam = lr {args.lr_init} to {args.lr_final}, warmup {args.warmup_steps} steps, beta {args.betas}, eps {args.adam_eps} # # Found torch {torch.__version__}, recommend 1.13.1+cu117 or newer # Found torch {torch.__version__}, recommend 2.4.0 or newer if you use fla #...