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fix a bug about the model_avg during finetuning by exchanging the order of loading pre-trained model and initializing avg model

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marcoyang 2023-10-18 17:18:21 +08:00
parent 807816fec0
commit 23375cb64f
1 changed files with 6 additions and 6 deletions
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12
egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py
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@ -1077,12 +1077,6 @@ def run(rank, world_size, args):
num_param = sum([p.numel() for p in model.parameters()]) num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}") logging.info(f"Number of model parameters: {num_param}")
assert params.save_every_n >= params.average_period
model_avg: Optional[nn.Module] = None
if rank == 0:
# model_avg is only used with rank 0
model_avg = copy.deepcopy(model).to(torch.float64)
# load model parameters for model fine-tuning # load model parameters for model fine-tuning
if params.do_finetune: if params.do_finetune:
modules = params.init_modules.split(",") if params.init_modules else None modules = params.init_modules.split(",") if params.init_modules else None
@ -1095,6 +1089,12 @@ def run(rank, world_size, args):
params=params, model=model, model_avg=model_avg params=params, model=model, model_avg=model_avg
) )
assert params.save_every_n >= params.average_period
model_avg: Optional[nn.Module] = None
if rank == 0:
# model_avg is only used with rank 0
model_avg = copy.deepcopy(model).to(torch.float64)
model.to(device) model.to(device)
if world_size > 1: if world_size > 1:
logging.info("Using DDP") logging.info("Using DDP")