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skip scan_pessimistic_batches_for_oom if params.start_batch > 0

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This commit is contained in:
Fangjun Kuang 2022-07-22 10:20:36 +08:00
parent efdbb98583
commit 728eb5075f
6 changed files with 21 additions and 30 deletions
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15
egs/librispeech/ASR/pruned_transducer_stateless/train.py
View File

@ -873,13 +873,14 @@ def run(rank, world_size, args):
valid_cuts += librispeech.dev_other_cuts() valid_cuts += librispeech.dev_other_cuts()
valid_dl = librispeech.valid_dataloaders(valid_cuts) valid_dl = librispeech.valid_dataloaders(valid_cuts)
scan_pessimistic_batches_for_oom( if params.start_batch <= 0:
model=model, scan_pessimistic_batches_for_oom(
train_dl=train_dl, model=model,
optimizer=optimizer, train_dl=train_dl,
sp=sp, optimizer=optimizer,
params=params, sp=sp,
) params=params,
)
for epoch in range(params.start_epoch, params.num_epochs): for epoch in range(params.start_epoch, params.num_epochs):
fix_random_seed(params.seed + epoch) fix_random_seed(params.seed + epoch)

2
egs/librispeech/ASR/pruned_transducer_stateless2/train.py
View File

@ -924,7 +924,7 @@ def run(rank, world_size, args):
valid_cuts += librispeech.dev_other_cuts() valid_cuts += librispeech.dev_other_cuts()
valid_dl = librispeech.valid_dataloaders(valid_cuts) valid_dl = librispeech.valid_dataloaders(valid_cuts)
if not params.print_diagnostics: if params.start_batch <= 0 and not params.print_diagnostics:
scan_pessimistic_batches_for_oom( scan_pessimistic_batches_for_oom(
model=model, model=model,
train_dl=train_dl, train_dl=train_dl,

17
egs/librispeech/ASR/pruned_transducer_stateless3/train.py
View File

@ -1040,14 +1040,15 @@ def run(rank, world_size, args):
# It's time consuming to include `giga_train_dl` here # It's time consuming to include `giga_train_dl` here
# for dl in [train_dl, giga_train_dl]: # for dl in [train_dl, giga_train_dl]:
for dl in [train_dl]: for dl in [train_dl]:
scan_pessimistic_batches_for_oom( if params.start_batch <= 0:
model=model, scan_pessimistic_batches_for_oom(
train_dl=dl, model=model,
optimizer=optimizer, train_dl=dl,
sp=sp, optimizer=optimizer,
params=params, sp=sp,
warmup=0.0 if params.start_epoch == 0 else 1.0, params=params,
) warmup=0.0 if params.start_epoch == 0 else 1.0,
)
scaler = GradScaler(enabled=params.use_fp16) scaler = GradScaler(enabled=params.use_fp16)
if checkpoints and "grad_scaler" in checkpoints: if checkpoints and "grad_scaler" in checkpoints:

2
egs/librispeech/ASR/pruned_transducer_stateless4/train.py
View File

@ -973,7 +973,7 @@ def run(rank, world_size, args):
valid_cuts += librispeech.dev_other_cuts() valid_cuts += librispeech.dev_other_cuts()
valid_dl = librispeech.valid_dataloaders(valid_cuts) valid_dl = librispeech.valid_dataloaders(valid_cuts)
if not params.print_diagnostics: if params.start_batch <= 0 and not params.print_diagnostics:
scan_pessimistic_batches_for_oom( scan_pessimistic_batches_for_oom(
model=model, model=model,
train_dl=train_dl, train_dl=train_dl,

2
egs/librispeech/ASR/pruned_transducer_stateless5/train.py
View File

@ -962,7 +962,7 @@ def run(rank, world_size, args):
valid_cuts += librispeech.dev_other_cuts() valid_cuts += librispeech.dev_other_cuts()
valid_dl = librispeech.valid_dataloaders(valid_cuts) valid_dl = librispeech.valid_dataloaders(valid_cuts)
if not params.print_diagnostics: if params.start_batch <= 0 and not params.print_diagnostics:
scan_pessimistic_batches_for_oom( scan_pessimistic_batches_for_oom(
model=model, model=model,
train_dl=train_dl, train_dl=train_dl,

13
egs/librispeech/ASR/pruned_transducer_stateless6/train.py
View File

@ -507,9 +507,6 @@ def load_checkpoint_if_available(
if "cur_epoch" in saved_params: if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"] params["start_epoch"] = saved_params["cur_epoch"]
if "cur_batch_idx" in saved_params:
params["cur_batch_idx"] = saved_params["cur_batch_idx"]
return saved_params return saved_params
@ -754,13 +751,7 @@ def train_one_epoch(
tot_loss = MetricsTracker() tot_loss = MetricsTracker()
cur_batch_idx = params.get("cur_batch_idx", 0)
for batch_idx, batch in enumerate(train_dl): for batch_idx, batch in enumerate(train_dl):
if batch_idx < cur_batch_idx:
continue
cur_batch_idx = batch_idx
params.batch_idx_train += 1 params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"]) batch_size = len(batch["supervisions"]["text"])
@ -802,7 +793,6 @@ def train_one_epoch(
params.batch_idx_train > 0 params.batch_idx_train > 0
and params.batch_idx_train % params.save_every_n == 0 and params.batch_idx_train % params.save_every_n == 0
): ):
params.cur_batch_idx = batch_idx
save_checkpoint_with_global_batch_idx( save_checkpoint_with_global_batch_idx(
out_dir=params.exp_dir, out_dir=params.exp_dir,
global_batch_idx=params.batch_idx_train, global_batch_idx=params.batch_idx_train,
@ -815,7 +805,6 @@ def train_one_epoch(
scaler=scaler, scaler=scaler,
rank=rank, rank=rank,
) )
del params.cur_batch_idx
remove_checkpoints( remove_checkpoints(
out_dir=params.exp_dir, out_dir=params.exp_dir,
topk=params.keep_last_k, topk=params.keep_last_k,
@ -990,7 +979,7 @@ def run(rank, world_size, args):
valid_cuts += librispeech.dev_other_cuts() valid_cuts += librispeech.dev_other_cuts()
valid_dl = librispeech.valid_dataloaders(valid_cuts) valid_dl = librispeech.valid_dataloaders(valid_cuts)
if not params.print_diagnostics: if params.start_batch <= 0 and not params.print_diagnostics:
scan_pessimistic_batches_for_oom( scan_pessimistic_batches_for_oom(
model=model, model=model,
train_dl=train_dl, train_dl=train_dl,