Closed MatejUlcar closed 1 year ago
StellaAthena
commented
2 years ago Hmmm. We haven’t really been using ZeRO 3, as our testing indicates that it’s a big step up in complexity for a minimal increase in performance. It’s possible we broke something without realizing it.
I’ll have to train a model and play with it a bit, can you post the config file? Alternatively, would you be able to share the trained model by any chance? Hopefully we can get this resolved.
MatejUlcar
commented
2 years ago I've figured it's less bother to train from scratch without ZeRO 3, seeing how GPUs were under-utilized in the original run. Either way, I can share the trained model https://drive.google.com/drive/folders/13Z4g4eGFd33yhI2HWj4EM4G6lGw2W24D?usp=sharing The config files included in the archive, the model config here:
{
# parallelism settings ( you will want to change these based on your cluster setup, ideally scheduling pipeline stages
# across the node boundaries )
"pipe-parallel-size": 0,
"model-parallel-size": 1,
# model settings
"num-layers": 12,
"hidden-size": 768,
"num-attention-heads": 12,
"seq-length": 2048,
"max-position-embeddings": 2048,
"norm": "layernorm",
"pos-emb": "rotary",
"no-weight-tying": true,
# this should provide some speedup but takes a while to build, set to true if desired
"scaled-upper-triang-masked-softmax-fusion": false,
"train-iters": 80000,
# optimizer settings
"optimizer": {
"type": "Adam",
"params": {
"lr": 1.0e-5,
#"freeze_step": 5000,
"betas": [0.9, 0.999],
#"cuda_aware": false,
#"comm_backend_name": "nccl"
}
},
"zero_optimization": {
"stage": 3,
"allgather_partitions": True,
"allgather_bucket_size": 100000000,
"overlap_comm": True,
"reduce_scatter": True,
"reduce_bucket_size": 100000000,
"contiguous_gradients": True,
"cpu_offload": False
},
"zero_allow_untested_optimizer": true,
# batch / data settings
"train_micro_batch_size_per_gpu": 8,
"gradient_accumulation_steps": 4,
"data-impl": "mmap",
"split": "949,50,1",
# activation checkpointing
"checkpoint-activations": true,
"checkpoint-num-layers": 1,
"partition-activations": true,
"synchronize-each-layer": true,
# regularization
"gradient_clipping": 1.0,
"weight-decay": 0.05,
"hidden-dropout": 0.1,
"attention-dropout": 0.1,
# precision settings
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 500,
"hysteresis": 2,
"min_loss_scale": 1,
},
# lr decay settings
"lr-decay-iters": 80000,
"lr-decay-style": "cosine",
"warmup": 0.01,
# misc. training settings
"distributed-backend": "nccl",
#"save-interval": 10000,
#"eval-interval": 1000,
"save-interval": 500,
"eval-interval": 100,
"eval-iters": 10,
# logging
#"log-interval": 100,
"log-interval": 10,
"steps_per_print": 10,
"keep-last-n-checkpoints": 4,
"wall_clock_breakdown": true,
# sparse attention
#"attention_config": [[["local", "global"], "all"]],
}We do not currently support ZeRO 3, which seems to be the core source of your issue. Closing for now.
Describe the bug Unable to convert a custom gpt-neox model (with zero stage 3) checkpoints using zero_to_fp32.py script.
To Reproduce Train a model with zero stage 3, pp=0, mp=1 (haven't attempted other combinations). Save checkpoint. Run the zero_to_fp32.py script, attached to the saved checkpoint.
Environment (please complete the following information):
Attempted so far Run as is:
Limit env to 1 GPU:
Adding map_location argument to torch.load as per above error
I have also attempted to convert with deepspeed's latest script, but I got an error that it's not a model state checkpoint, so I figure the differences between current deeperspeed and latest deepspeed are too great to be of any use. Please advice on how I could salvage, ie. actually use the model for inference/evaluation other than training again from scratch.