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add OnDevice and remove zero-inference #316

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Jul 19, 2022
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add OnDevice and remove zero-inference #316

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63 changes: 4 additions & 59 deletions scripts/inference/bloom-ds-inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,12 +41,12 @@
parser.add_argument("--local_rank", required=False, type=int, help="used by dist launchers")
parser.add_argument("--batch_size", default=1, type=int, help="batch size")
parser.add_argument("--benchmark", action="store_true", help="additionally run benchmark")
parser.add_argument("--cpu_offload", action="store_true", help="whether to activate CPU offload")
args = parser.parse_args()

local_rank = int(os.getenv('LOCAL_RANK', '0'))
world_size = int(os.getenv('WORLD_SIZE', '1'))

deepspeed.init_distributed('nccl')
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### Model loading and instantiating on GPU (via ZeRO)

Expand Down Expand Up @@ -132,44 +132,14 @@ def get_checkpoint_files(pretrained_model_name_or_path):
else:
dtype = torch.bfloat16

#dtype = config.dtype
#print(dtype)

model_hidden_size = config.hidden_size
train_batch_size = 1 * world_size

ds_config = {
"fp16": {
"enabled": dtype == torch.float16,
},
"bf16": {
"enabled": dtype == torch.bfloat16,
},
"zero_optimization": {
"stage": 3,
"overlap_comm": True,
"contiguous_gradients": True,
"reduce_bucket_size": model_hidden_size * model_hidden_size,
"stage3_prefetch_bucket_size": 0.9 * model_hidden_size * model_hidden_size,
"stage3_param_persistence_threshold": 0
},
"steps_per_print": 2000,
"train_batch_size": train_batch_size,
"train_micro_batch_size_per_gpu": 1,
"wall_clock_breakdown": False
}

if args.cpu_offload:
ds_config["zero_optimization"]["offload_param"] = dict(device="cpu", pin_memory=True)

dschf = HfDeepSpeedConfig(ds_config) # this tells from_pretrained to instantiate directly on gpus

if args.benchmark:
torch.cuda.empty_cache()
gc.collect()
deepspeed.runtime.utils.see_memory_usage('pre-from-pretrained', force=True)

model = AutoModelForCausalLM.from_config(config, torch_dtype=torch.bfloat16)
# Construct model with fake meta tensors, later will be replaced during ds-inference ckpt load
with deepspeed.OnDevice(dtype=dtype, device='meta'):
model = AutoModelForCausalLM.from_config(config, torch_dtype=torch.bfloat16)

if args.benchmark:
deepspeed.runtime.utils.see_memory_usage('post-from-pretrained', force=True)
Expand All @@ -178,36 +148,11 @@ def get_checkpoint_files(pretrained_model_name_or_path):

rank = dist.get_rank()

if rank == 0:
print(ds_config)

ds_engine = deepspeed.initialize(model=model, config_params=ds_config)[0]
ds_engine.module.eval()
model = ds_engine.module

### Deepspeed-ZeRO Unloading

# a must to remove ZeRO-installed hooks!
ds_engine.destroy()

# free GPU storage used by ZeRO
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
def ds_clear_params(ds_engine):
for p in ds_engine.parameters():
if hasattr(p, "ds_tensor"):
p.ds_tensor = torch.empty(0, dtype=p.dtype, device=p.device)
p.ds_status = ZeroParamStatus.NOT_AVAILABLE

ds_clear_params(ds_engine)
del ds_engine

if args.benchmark:
torch.cuda.empty_cache()
gc.collect()
deepspeed.runtime.utils.see_memory_usage('post-init-ds-zero-init', force=True)



### Deepspeed-Inference Loading

checkpoints_json = "checkpoints.json"
Expand Down