From c2a2575f198b1bfd452ea5769bec81bcce3d3a42 Mon Sep 17 00:00:00 2001
From: manyeyes <32889020+manyeyes@users.noreply.github.com>
Date: 星期三, 21 六月 2023 09:31:59 +0800
Subject: [PATCH] add c# assembly for fsmn vad (#650)
---
funasr/train/trainer.py | 72 +++++++++++++++++++++++++++++++-----
1 files changed, 62 insertions(+), 10 deletions(-)
diff --git a/funasr/train/trainer.py b/funasr/train/trainer.py
index 9c4af41..f066909 100644
--- a/funasr/train/trainer.py
+++ b/funasr/train/trainer.py
@@ -39,11 +39,12 @@
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.recursive_op import recursive_average
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
-from funasr.train.abs_espnet_model import AbsESPnetModel
+from funasr.models.base_model import FunASRModel
from funasr.train.distributed_utils import DistributedOption
from funasr.train.reporter import Reporter
from funasr.train.reporter import SubReporter
from funasr.utils.build_dataclass import build_dataclass
+from funasr.utils.kwargs2args import kwargs2args
if torch.distributed.is_available():
from torch.distributed import ReduceOp
@@ -95,6 +96,7 @@
use_pai: bool
oss_bucket: Union[oss2.Bucket, None]
batch_interval: int
+ bias_grad_times: float
class Trainer:
"""Trainer having a optimizer.
@@ -142,11 +144,23 @@
schedulers: Sequence[Optional[AbsScheduler]],
scaler: Optional[GradScaler],
ngpu: int = 0,
+ oss_bucket=None,
):
- states = torch.load(
- checkpoint,
- map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",
- )
+ if oss_bucket is None:
+ if os.path.exists(checkpoint):
+ states = torch.load(
+ checkpoint,
+ map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",
+ )
+
+ else:
+ return 0
+ else:
+ if oss_bucket.object_exists(checkpoint):
+ buffer = BytesIO(oss_bucket.get_object(checkpoint).read())
+ states = torch.load(buffer, map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",)
+ else:
+ return 0
model.load_state_dict(states["model"])
reporter.load_state_dict(states["reporter"])
for optimizer, state in zip(optimizers, states["optimizers"]):
@@ -165,7 +179,7 @@
@classmethod
def run(
cls,
- model: AbsESPnetModel,
+ model: FunASRModel,
optimizers: Sequence[torch.optim.Optimizer],
schedulers: Sequence[Optional[AbsScheduler]],
train_iter_factory: AbsIterFactory,
@@ -205,15 +219,16 @@
else:
scaler = None
- if trainer_options.resume and (output_dir / "checkpoint.pb").exists():
+ if trainer_options.resume:
cls.resume(
- checkpoint=output_dir / "checkpoint.pb",
+ checkpoint=os.path.join(trainer_options.output_dir, "checkpoint.pb") if trainer_options.use_pai else output_dir / "checkpoint.pb",
model=model,
optimizers=optimizers,
schedulers=schedulers,
reporter=reporter,
scaler=scaler,
ngpu=trainer_options.ngpu,
+ oss_bucket=trainer_options.oss_bucket if trainer_options.use_pai else None,
)
start_epoch = reporter.get_epoch() + 1
@@ -546,8 +561,11 @@
no_forward_run = options.no_forward_run
ngpu = options.ngpu
use_wandb = options.use_wandb
+ bias_grad_times = options.bias_grad_times
distributed = distributed_option.distributed
+ if bias_grad_times != 1.0:
+ logging.warning("Using bias_grad_times: {} for gradient scaling".format(bias_grad_times))
if log_interval is None:
try:
log_interval = max(len(iterator) // 20, 10)
@@ -582,10 +600,16 @@
if num_batch_updates % batch_interval == 0:
if options.use_pai and options.oss_bucket is not None:
buffer = BytesIO()
- torch.save(model.state_dict(), buffer)
+ if hasattr(model, "module"):
+ torch.save(model.module.state_dict(), buffer)
+ else:
+ torch.save(model.state_dict(), buffer)
options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}step.pb"), buffer.getvalue())
else:
- torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
+ if hasattr(model, "module"):
+ torch.save(model.module.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
+ else:
+ torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
if distributed:
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
@@ -596,6 +620,24 @@
if no_forward_run:
all_steps_are_invalid = False
continue
+
+ if iiter == 1 and summary_writer is not None:
+ try:
+ args = kwargs2args(model.forward, batch)
+ except (ValueError, TypeError):
+ logging.warning(
+ "inpect.signature() is failed for the model. "
+ "The graph can't be added for tensorboard."
+ )
+ else:
+ try:
+ summary_writer.add_graph(model, args, use_strict_trace=False)
+ except Exception:
+ logging.warning(
+ "summary_writer.add_graph() is failed for the model. "
+ "The graph can't be added for tensorboard."
+ )
+ del args
with autocast(scaler is not None):
with reporter.measure_time("forward_time"):
@@ -684,6 +726,16 @@
scale_factor=0.55,
)
+ # for contextual training
+ if bias_grad_times != 1.0:
+ # contextual related parameter names
+ cr_pnames = ["bias_encoder", "bias_embed", "decoder.bias_decoder", "decoder.bias_output"]
+ for name, param in model.named_parameters():
+ for cr_pname in cr_pnames:
+ if cr_pname in name:
+ param.grad *= bias_grad_times
+ continue
+
# compute the gradient norm to check if it is normal or not
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(),
--
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