generate.py not utilizing GPU in full

[frankxu2004]

I tried to run text generation with prompts using generate.py. I provided a large list of prompts, approximately 20K, and tried to run the generation on 10 RTX 8000 GPUs. However, the GPU utilization by nvidia-smi shows that the GPU utilization during generation is averaging at about 50-60%, which is not ideal. Thank you!

My configuration is:

{
  # Text gen type: `input-file`, `unconditional` or `interactive`
  "text-gen-type": "input-file", #"input-file",
 
  # Params for all
  "maximum_tokens": 256,
  "temperature": 0.2,
  "top_p": 0.95,
  "top_k": 0,
  "recompute": false,
  
  # `unconditional`/`input-file`: samples
  "num-samples": 100,

  # input/output file
  "sample-input-file": "0",
  
  "data-path": "data/code/code_text_document",
  
  # or for weighted datasets: 
  # "train-data-paths": ["data/enron/enron_text_document", "data/enron/enron_text_document"],
  # "test-data-paths": ["data/enron/enron_text_document", "data/enron/enron_text_document"],
  # "valid-data-paths": ["data/enron/enron_text_document", "data/enron/enron_text_document"],
  # "train-data-weights": [1., 2.],
  # "test-data-weights": [2., 1.],
  # "valid-data-weights": [0.5, 0.4],

  # If weight_by_num_documents is True, Builds dataset weights from a multinomial distribution over groups of data according to the number of documents in each group. 
  # WARNING: setting this to True will override any user provided weights
  # "weight_by_num_documents": false,
  # "weighted_sampler_alpha": 0.3,

  "vocab-file": "data/code-vocab.json",
  "merge-file": "data/code-merges.txt",

  "save": "checkpoints",
  "load": "checkpoints",
  "checkpoint_validation_with_forward_pass": False,
  
  "tensorboard-dir": "tensorboard",
  "log-dir": "logs",
  "use_wandb": True,
  "wandb_host": "https://api.wandb.ai",
  "wandb_project": "neox",
}

And the model config:

# 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,

   # model settings
   "num-layers": 32,
   "hidden-size": 2560,
   "num-attention-heads": 32,
   "seq-length": 2048,
   "max-position-embeddings": 2048,
   "norm": "layernorm",
   "pos-emb": "rotary",
   "no-weight-tying": true,

   # these should provide some speedup but takes a while to build, set to true if desired
   "scaled-upper-triang-masked-softmax-fusion": true,
   "bias-gelu-fusion": true,

   # optimizer settings
   "zero_allow_untested_optimizer": true,
   "optimizer": {
     "type": "adam",
     "params": {
       "lr": 0.00016,
       "betas": [0.9, 0.999],
       "eps": 1.0e-8,
     }
   },
   "zero_optimization": {
    "stage": 1,
    "allgather_partitions": True,
    "allgather_bucket_size": 500000000,
    "overlap_comm": True,
    "reduce_scatter": True,
    "reduce_bucket_size": 500000000,
    "contiguous_gradients": True,
    "cpu_offload": False
  },

   # batch / data settings
   "train_micro_batch_size_per_gpu": 16,
   "gradient_accumulation_steps": 1,
   "data-impl": "mmap",
   "split": "989,10,1",

   # activation checkpointing
   "checkpoint-activations": true,
   "checkpoint-num-layers": 1,
   "partition-activations": true,
   "synchronize-each-layer": true,

   # regularization
   "gradient_clipping": 1.0,
   "weight-decay": 0,
   "hidden-dropout": 0,
   "attention-dropout": 0,

   # precision settings
   "fp16": { 
     "fp16": true,
     "enabled": true,
     "loss_scale": 0,
     "initial_scale_power": 16,
     "loss_scale_window": 1000,
     "hysteresis": 2,
     "min_loss_scale": 1
   },

   # misc. training settings
   "train-iters": 160000,
   "lr-decay-iters": 160000,
   "distributed-backend": "nccl",
   "lr-decay-style": "cosine",
   "warmup": 0.01,
   "save-interval": 1000,
   "eval-interval": 1000,
   "eval-iters": 10,

   # logging
   "log-interval": 100,
   "steps_per_print": 10,
   "keep-last-n-checkpoints": 1,
   "wall_clock_breakdown": true,
}

[StellaAthena]

Have you tried increasing the batch size?

[frankxu2004]

Thanks for the reply. I wonder how should I increase the batch size during generation? In the configuration file it has batch size for training, such as "train_micro_batch_size_per_gpu"

[sdtblck]

same setting in inference

[psinha30]

@frankxu2004 @sdtblck Changing train_micro_batch_size_per_gpu doesnt work out for Inference mode("Input-file") should I change any other parameter?