OOM trying to pretrain llama 7b on v4-256 #98
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This is expected. v4-256 is actually only 128 TPU-v4 chips (weird naming convention due to the fact that the two tensorcores on the same chip are viewed as separate devices before v4), so our OpenLLaMA 7B configuration actually uses v4-512. If you want to train on v4-256, consider using batch size 1024 and |
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This results in |
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It appears that since a v4-256 has half the chips of a v4-512, the appropriate mesh topology would be |
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Oh, this is a known problem that by default JAX does not want to split a physical axis into multiple logical axes. However, we can force it to do that by specifying |
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Still not working, even with the parameters you suggested for mesh_dim and batch_size. Full commandexport LIBTPU_INIT_ARGS='--xla_jf_spmd_threshold_for_windowed_einsum_mib=0 --xla_tpu_spmd_threshold_for_allgather_cse=10000 --xla_tpu_spmd_rewrite_einsum_with_reshape=true --xla_enable_async_all_gather=true --jax_enable_async_collective_offload=true --xla_tpu_enable_latency_hiding_scheduler=true TPU_MEGACORE=MEGACORE_DENSE'
python -m EasyLM.models.llama.llama_train \
--mesh_dim='!-1,64,1' \
--dtype='fp32' \
--total_steps=250000 \
--log_freq=50 \
--save_model_freq=0 \
--save_milestone_freq=2500 \
--load_llama_config='7b' \
--update_llama_config='' \
--load_dataset_state='' \
--load_checkpoint='' \
--tokenizer.vocab_file='gs://.../tokenizer.model' \
--optimizer.type='adamw' \
--optimizer.adamw_optimizer.weight_decay=0.1 \
--optimizer.adamw_optimizer.lr=3e-4 \
--optimizer.adamw_optimizer.end_lr=3e-5 \
--optimizer.adamw_optimizer.lr_warmup_steps=2000 \
--optimizer.adamw_optimizer.lr_decay_steps=250000 \
--train_dataset.type='json' \
--train_dataset.text_processor.fields='text' \
--train_dataset.json_dataset.path='gs://.../slimpajama.jsonl' \
--train_dataset.json_dataset.seq_length=2048 \
--train_dataset.json_dataset.batch_size=1024 \
--train_dataset.json_dataset.tokenizer_processes=16 \
--checkpointer.save_optimizer_state=True \
--logger.online=True \
--logger.prefix='devingulliver' \
--logger.project="slender_llama_7b" \
--logger.output_dir="gs://.../output/" \
--logger.wandb_dir="$HOME/experiment_output/slender_llama_7b" \
|& tee $HOME/output.txtFull output log |
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This is quite strange. Maybe XLA is not smart enough for allocating memory. In this case I'd recommend tweaking with batch sizes and mesh size. For example, try a even smaller batch size of 512 or using |
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Command
python -m EasyLM.models.llama.llama_train \ --mesh_dim='-1,32,1' \ --dtype='fp32' \ --total_steps=250000 \ --log_freq=50 \ --save_model_freq=0 \ --save_milestone_freq=2500 \ --load_llama_config='7b' \ --update_llama_config='' \ --load_dataset_state='' \ --load_checkpoint='' \ --tokenizer.vocab_file='gs://.../tokenizer.model' \ --optimizer.type='adamw' \ --optimizer.adamw_optimizer.weight_decay=0.1 \ --optimizer.adamw_optimizer.lr=3e-4 \ --optimizer.adamw_optimizer.end_lr=3e-5 \ --optimizer.adamw_optimizer.lr_warmup_steps=2000 \ --optimizer.adamw_optimizer.lr_decay_steps=250000 \ --train_dataset.type='json' \ --train_dataset.text_processor.fields='text' \ --train_dataset.json_dataset.path='gs://.../dataset.jsonl' \ --train_dataset.json_dataset.seq_length=2048 \ --train_dataset.json_dataset.batch_size=2048 \ --train_dataset.json_dataset.tokenizer_processes=16 \ --checkpointer.save_optimizer_state=True \ --logger.online=True \ --logger.prefix='devingulliver' \ --logger.project="sl_llama_7b" \ --logger.output_dir="gs://.../output/" \ --logger.wandb_dir="$HOME/experiment_output/sl_llama_7b"Log
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