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Refactor out shared LSTM/GGNN training loop #126

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Refactor out shared LSTM/GGNN training loop #126

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2 changes: 2 additions & 0 deletions programl/task/dataflow/BUILD
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,8 @@ py_library(
deps = [
":graph_loader",
"//programl/models:async_batch_builder",
"//programl/models:base_batch_builder",
"//programl/models:model",
"//programl/models/ggnn",
"//programl/proto:checkpoint_py",
"//programl/proto:epoch_py",
Expand Down
71 changes: 70 additions & 1 deletion programl/task/dataflow/dataflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,9 +21,11 @@
import warnings
from typing import Tuple

from labm8.py import app, pbutil
from labm8.py import app, humanize, pbutil
from sklearn.exceptions import UndefinedMetricWarning

from programl.models.base_batch_builder import BaseBatchBuilder
from programl.models.model import Model
from programl.proto import checkpoint_pb2, epoch_pb2

app.DEFINE_string(
Expand Down Expand Up @@ -208,3 +210,70 @@ def CreateLoggingDirectories(
(log_dir / "checkpoints").mkdir()
(log_dir / "graph_loader").mkdir()
return log_dir


def run_training_loop(
log_dir: pathlib.Path,
epochs,
val_batches: BaseBatchBuilder,
start_epoch_step: int,
model: Model,
val_graph_count: int,
) -> pathlib.Path:
"""

Args:
log_dir: The logging directory.
epochs: An epoch batch builder.
val_batches: A batch builder for validation.
start_epoch_step: The initial step count.
model: The model to train.
val_graph_count: The number of validation graphs.

Returns:
The log_dir first argument.
"""
for (
epoch_step,
(train_graph_count, train_graph_cumsum, train_batches),
) in enumerate(epochs, start=start_epoch_step):
start_time = time.time()
hr_graph_cumsum = f"{humanize.Commas(train_graph_cumsum)} graphs"

train_results = model.RunBatches(
epoch_pb2.TRAIN,
train_batches,
log_prefix=f"Train to {hr_graph_cumsum}",
total_graph_count=train_graph_count,
)
val_results = model.RunBatches(
epoch_pb2.VAL,
val_batches.batches,
log_prefix=f"Val at {hr_graph_cumsum}",
total_graph_count=val_graph_count,
)

# Write the epoch to file as an epoch list. This may seem redundant since
# epoch list contains a single item, but it means that we can easily
# concatenate a sequence of these epoch protos to produce a valid epoch
# list using: `cat *.EpochList.pbtxt > epochs.pbtxt`
epoch = epoch_pb2.EpochList(
epoch=[
epoch_pb2.Epoch(
walltime_seconds=time.time() - start_time,
epoch_num=epoch_step,
train_results=train_results,
val_results=val_results,
)
]
)
print(epoch, end="")

epoch_path = log_dir / "epochs" / f"{epoch_step:03d}.EpochList.pbtxt"
pbutil.ToFile(epoch, epoch_path)
app.Log(1, "Wrote %s", epoch_path)

checkpoint_path = log_dir / "checkpoints" / f"{epoch_step:03d}.Checkpoint.pb"
pbutil.ToFile(model.SaveCheckpoint(), checkpoint_path)

return log_dir
47 changes: 3 additions & 44 deletions programl/task/dataflow/ggnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,50 +173,9 @@ def TrainDataflowGGNN(
)
)

for (
epoch_step,
(train_graph_count, train_graph_cumsum, train_batches),
) in enumerate(epochs, start=start_epoch_step):
start_time = time.time()
hr_graph_cumsum = f"{humanize.Commas(train_graph_cumsum)} graphs"

train_results = model.RunBatches(
epoch_pb2.TRAIN,
train_batches,
log_prefix=f"Train to {hr_graph_cumsum}",
total_graph_count=train_graph_count,
)
val_results = model.RunBatches(
epoch_pb2.VAL,
val_batches.batches,
log_prefix=f"Val at {hr_graph_cumsum}",
total_graph_count=val_graph_count,
)

# Write the epoch to file as an epoch list. This may seem redundant since
# epoch list contains a single item, but it means that we can easily
# concatenate a sequence of these epoch protos to produce a valid epoch
# list using: `cat *.EpochList.pbtxt > epochs.pbtxt`
epoch = epoch_pb2.EpochList(
epoch=[
epoch_pb2.Epoch(
walltime_seconds=time.time() - start_time,
epoch_num=epoch_step,
train_results=train_results,
val_results=val_results,
)
]
)
print(epoch, end="")

epoch_path = log_dir / "epochs" / f"{epoch_step:03d}.EpochList.pbtxt"
pbutil.ToFile(epoch, epoch_path)
app.Log(1, "Wrote %s", epoch_path)

checkpoint_path = log_dir / "checkpoints" / f"{epoch_step:03d}.Checkpoint.pb"
pbutil.ToFile(model.SaveCheckpoint(), checkpoint_path)

return log_dir
return dataflow.run_training_loop(
log_dir, epochs, val_batches, start_epoch_step, model, val_graph_count
)


def TestDataflowGGNN(
Expand Down
47 changes: 3 additions & 44 deletions programl/task/dataflow/train_lstm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,50 +160,9 @@ def TrainDataflowLSTM(
)
)

for (
epoch_step,
(train_graph_count, train_graph_cumsum, train_batches),
) in enumerate(epochs, start=start_epoch_step):
start_time = time.time()
hr_graph_cumsum = f"{humanize.Commas(train_graph_cumsum)} graphs"

train_results = model.RunBatches(
epoch_pb2.TRAIN,
train_batches,
log_prefix=f"Train to {hr_graph_cumsum}",
total_graph_count=train_graph_count,
)
val_results = model.RunBatches(
epoch_pb2.VAL,
val_batches.batches,
log_prefix=f"Val at {hr_graph_cumsum}",
total_graph_count=FLAGS.val_graph_count,
)

# Write the epoch to file as an epoch list. This may seem redundant since
# epoch list contains a single item, but it means that we can easily
# concatenate a sequence of these epoch protos to produce a valid epoch
# list using: `cat *.EpochList.pbtxt > epochs.pbtxt`
epoch = epoch_pb2.EpochList(
epoch=[
epoch_pb2.Epoch(
walltime_seconds=time.time() - start_time,
epoch_num=epoch_step,
train_results=train_results,
val_results=val_results,
)
]
)
print(epoch, end="")

epoch_path = log_dir / "epochs" / f"{epoch_step:03d}.EpochList.pbtxt"
pbutil.ToFile(epoch, epoch_path)
app.Log(1, "Wrote %s", epoch_path)

checkpoint_path = log_dir / "checkpoints" / f"{epoch_step:03d}.Checkpoint.pb"
pbutil.ToFile(model.SaveCheckpoint(), checkpoint_path)

return log_dir
return dataflow.run_training_loop(
log_dir, epochs, val_batches, start_epoch_step, model, FLAGS.val_graph_count
)


def TestDataflowLSTM(
Expand Down