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Fixup no_trainer examples scripts and add more tests (#16765)

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* Change tracking to store_true

* Remove step param and use it in the log dictionary directly

* use vars(args) when passing args to init_trackers

* Include tracking tests since tensorboard is already a dep
This commit is contained in:
Zachary Mueller
2022-04-13 14:40:48 -04:00
committed by GitHub
parent 3a16ab25c8
commit be752d12f8
10 changed files with 84 additions and 63 deletions
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16
examples/pytorch/language-modeling/run_clm_no_trainer.py
View File

@@ -200,7 +200,7 @@ def parse_args():
)
parser.add_argument(
"--with_tracking",
required=False,
action="store_true",
help="Whether to load in all available experiment trackers from the environment and use them for logging.",
)
args = parser.parse_args()
@@ -227,7 +227,7 @@ def main():
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will pick up all supported trackers in the environment
accelerator = Accelerator(log_with="all") if args.with_tracking else Accelerator()
accelerator = Accelerator(log_with="all", logging_dir=args.output_dir) if args.with_tracking else Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -485,7 +485,10 @@ def main():
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
accelerator.init_trackers("clm_no_trainer", args)
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("clm_no_trainer", experiment_config)
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -571,12 +574,7 @@ def main():
if args.with_tracking:
accelerator.log(
{
"perplexity": perplexity,
"train_loss": total_loss,
"epoch": epoch,
},
step=completed_steps,
{"perplexity": perplexity, "train_loss": total_loss, "epoch": epoch, "step": completed_steps},
)
if args.push_to_hub and epoch < args.num_train_epochs - 1:

16
examples/pytorch/language-modeling/run_mlm_no_trainer.py
View File

@@ -209,7 +209,7 @@ def parse_args():
)
parser.add_argument(
"--with_tracking",
required=False,
action="store_true",
help="Whether to load in all available experiment trackers from the environment and use them for logging.",
)
args = parser.parse_args()
@@ -238,7 +238,7 @@ def main():
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will pick up all supported trackers in the environment
accelerator = Accelerator(log_with="all") if args.with_tracking else Accelerator()
accelerator = Accelerator(log_with="all", logging_dir=args.output_dir) if args.with_tracking else Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -531,7 +531,10 @@ def main():
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
accelerator.init_trackers("clm_no_trainer", args)
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("mlm_no_trainer", experiment_config)
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -618,12 +621,7 @@ def main():
if args.with_tracking:
accelerator.log(
{
"perplexity": perplexity,
"train_loss": total_loss,
"epoch": epoch,
},
step=completed_steps,
{"perplexity": perplexity, "train_loss": total_loss, "epoch": epoch, "step": completed_steps},
)
if args.push_to_hub and epoch < args.num_train_epochs - 1: