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dohe0342 2022-12-26 13:58:38 +09:00
parent 883165bfa1
commit a1b59b8792
2 changed files with 2 additions and 58 deletions
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egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp
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egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
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@ -1528,64 +1528,8 @@ def run_adapter(rank, world_size, args, wb=None):
clipping_scale=None,
parameters_names=[adapter_names],
)
if params.multi_optim:
logging.info("Using seperate optimizers over encoder, decoder ...")
enc_param = []
enc_names = []
dec_names = []
dec_param = []
for n, p in model.named_parameters():
name = n.split('.')[1]
if name == 'encoder' and 'feature_extractor' not in n:
enc_names.append(n)
enc_param.append(p)
elif 'ctc_output' in n:
enc_names.append(n)
enc_param.append(p)
elif 'feature_extractor' not in n:
dec_names.append(n)
dec_param.append(p)
optimizer_enc = ScaledAdam(
enc_param,
lr=params.peak_enc_lr,
clipping_scale=None,
parameters_names=[enc_names],
)
optimizer_dec = ScaledAdam(
dec_param,
lr=params.peak_dec_lr,
clipping_scale=5.0,
parameters_names=[dec_names],
)
scheduler_enc = Eden(optimizer_enc, params.lr_batches, params.lr_epochs)
scheduler_dec = Eden(optimizer_dec, params.lr_batches, params.lr_epochs)
optimizer = [optimizer_enc, optimizer_dec]
scheduler = [scheduler_enc, scheduler_dec]
else:
parameters_names = []
parameters_names.append(
[name_param_pair[0] for name_param_pair in model.named_parameters()]
)
logging.info(f"len name = {len(parameters_names)}")
logging.info(f"len param = {len(list(model.parameters()))}")
optimizer = ScaledAdam(
model.parameters(),
lr=params.base_lr,
clipping_scale=2.0,
parameters_names=parameters_names,
)
scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
scheduler_enc = Eden(optimizer_enc, params.lr_batches, params.lr_epochs)
if checkpoints and ("optimizer" in checkpoints or "optimizer_enc" in checkpoints):
if params.multi_optim:
logging.info("Loading optimizer state dict")