File size: 3,217 Bytes
0a2e9f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | from .logger import LOGGER as logger
from pprint import pformat
import torch
from .lib import WANDB_ENABLE
def get_deepspeed_config(args):
use_fp16 = args.deepspeed # args.deepspeed_fp16
use_amp = not args.deepspeed # not args.deepspeed_fp16
# use_amp = True
# use_fp16 = False
# by default, if not use deepspeed fp16, will enable deepspeed amp
gradient_accumulate_steps = getattr(args, 'gradient_accumulate_steps', 1)
config_params = {
'train_micro_batch_size_per_gpu': args.local_train_batch_size, # batch size per GPU without grandient accumulation
"gradient_accumulation_steps": gradient_accumulate_steps
}
if use_amp:
config_params['amp'] = {
'enabled': True,
'opt_level': 'O2',
}
if use_fp16:
config_params['fp16'] = {
'enabled': True,
# "auto_cast": True,
}
if args.do_train:
config_params['zero_optimization'] = {
'stage': 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": True},
"contiguous_gradients": True,
"overlap_comm": True,
"reduce_scatter": True, # default
"reduce_bucket_size": 5e8, # default
"allgather_bucket_size": 5e8, # default
"round_robin_gradients": True
}
config_params['zero_allow_untested_optimizer'] = True
if args.do_train:
gradient_clip = getattr(args, 'max_grad_norm', -1)
if gradient_clip > 0:
config_params['gradient_clipping'] = gradient_clip
config_params['flops_profiler'] = {
'enabled': False,
'profile_step': 1,
'module_depth': -1,
'top_modules': 3,
'detailed': True,
}
# if WANDB_ENABLE:
# config_params['wandb'] = {
# "enabled": True,
# "group": args.wandb_project,
# "project": args.project_name
# }
config_params['tensorboard'] = {
"enabled": True,
"output_path": args.log_dir,
"job_name": "tensorboard_log"
}
# if hasattr(args, "zero_opt_stage") and args.zero_opt_stage > 0:
# if args.zero_opt_stage > 0:
# config_params['fp16'] = {
# 'enabled': True
# }
logger.info(pformat(config_params))
return config_params
def fp32_to_fp16(batch):
# deepspeed does not auto cast inputs.
if isinstance(batch, torch.Tensor) and batch.dtype == torch.float32:
return batch.to(dtype=torch.half)
elif isinstance(batch, list):
new_batch = [fp32_to_fp16(t) for t in batch]
elif isinstance(batch, tuple):
new_batch = tuple(fp32_to_fp16(t) for t in batch)
elif isinstance(batch, dict):
new_batch = {n: fp32_to_fp16(t) for n, t in batch.items()}
else:
return batch
return new_batch
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