Comments (1)
I use generic autoregressive model finetuning from HuggingFace trainer with the following tricks:
- Group texts with this function with the block size of 1024.
- Include end-of-instruction ([EOI]) token at the end of the input/instruction to clarify the separation between input and output.
- Calcluate the loss for the whole part: input and output.
- Most importantly I'm using the following DeepSpeed config for the optimizations and set it in the HuggingFace trainer (I enabled bf16 optimization in the HuggingFace trainer):
{
"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": "cpu",
"pin_memory": true
},
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e12,
"reduce_bucket_size": 2e8,
"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": 50,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
Hope it would help!
from longform.
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from longform.