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# GPT-2 pretraining setup
{
# parallelism settings ( you will want to change these based on your cluster setup, ideally scheduling pipeline stages
# across the node boundaries )
"pipe_parallel_size": 1,
"model_parallel_size": 1,
"attention_config": [[["gmlp"], "all"]],
# model settings
"num_layers": 12,
"hidden_size": 768, # gmlp d_ff defaults to hidden_size * 4
"gmlp_attn_dim": 64,
"num_attention_heads": 12, # this has no effect with gmlp - and amlp defaults to single head attention.
"seq_length": 2048,
"max_position_embeddings": 2048,
"norm": "layernorm",
"pos_emb": "none",
"no_weight_tying": true,
# optimizer settings
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.0006,
"betas": [0.9, 0.999],
"eps": 1.0e_8,
}
},
# batch / data settings
"train_micro_batch_size_per_gpu": 4,
"data_impl": "mmap",
"split": "949,50,1",
# activation checkpointing
"checkpoint_activations": true,
"checkpoint_num_layers": 1,
"partition_activations": false,
"synchronize_each_layer": true,
# regularization
"gradient_clipping": 1.0,
"weight_decay": 0.1,
"hidden_dropout": 0.0,
"attention_dropout": 0.0,
# precision settings
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"hysteresis": 2,
"min_loss_scale": 1
},
# misc. training settings
"train_iters": 320000,
"lr_decay_iters": 320000,
"distributed_backend": "nccl",
"lr_decay_style": "cosine",
"warmup": 0.01,
"checkpoint_factor": 10000,
"eval_interval": 1000,
"eval_iters": 10,
# logging
"log_interval": 100,
"steps_per_print": 10,
"keep_last_n_checkpoints": 4,
"wall_clock_breakdown": true,
}
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