def abc_model_config(abc_corpus_config):
"""The proto config for a simple Model."""
architecture = model_pb2.NetworkArchitecture(
backend=model_pb2.NetworkArchitecture.TENSORFLOW,
embedding_size=2,
neuron_type=model_pb2.NetworkArchitecture.LSTM,
neurons_per_layer=4,
num_layers=1,
post_layer_dropout_micros=2000,
)
optimizer = model_pb2.AdamOptimizer(
initial_learning_rate_micros=2000,
learning_rate_decay_per_epoch_micros=5000,
beta_1_micros=900000,
beta_2_micros=999000,
normalized_gradient_clip_micros=5000000,
)
training = model_pb2.TrainingOptions(
num_epochs=1,
sequence_length=10,
batch_size=5,
shuffle_corpus_contentfiles_between_epochs=False,
adam_optimizer=optimizer,
)
return model_pb2.Model(corpus=abc_corpus_config,
architecture=architecture,
training=training)
def MakeClgenInstanceConfig(
working_dir: pathlib.Path,
encoded_db: encoded.EncodedContentFiles,
num_training_epochs: int,
seed_text: str,
neurons_per_layer: int,
num_layers: int,
) -> clgen_pb2.Instance:
"""Construct a CLgen instance.
Args:
working_dir: The directory to cache CLgen working files in.
encoded_db: The directory of encoded content files.
num_training_epochs: The number of epochs to train for.
seed_text: The text to initiate sampling with.
neurons_per_layer: Number of neurons in a layer.
"""
return clgen_pb2.Instance(
working_dir=str(working_dir),
model=model_pb2.Model(
corpus=corpus_pb2.Corpus(pre_encoded_corpus_url=encoded_db.url, ),
architecture=model_pb2.NetworkArchitecture(
backend=model_pb2.NetworkArchitecture.TENSORFLOW,
neuron_type=model_pb2.NetworkArchitecture.LSTM,
neurons_per_layer=neurons_per_layer,
num_layers=num_layers,
post_layer_dropout_micros=0,
),
training=model_pb2.TrainingOptions(
num_epochs=num_training_epochs,
sequence_length=64,
batch_size=64,
shuffle_corpus_contentfiles_between_epochs=True,
adam_optimizer=model_pb2.AdamOptimizer(
initial_learning_rate_micros=2000,
learning_rate_decay_per_epoch_micros=50000,
beta_1_micros=900000,
beta_2_micros=999000,
normalized_gradient_clip_micros=5000000,
),
),
),
sampler=sampler_pb2.Sampler(
start_text=seed_text,
batch_size=64,
sequence_length=1024,
temperature_micros=1000000, # = 1.0 real value
termination_criteria=[
sampler_pb2.SampleTerminationCriterion(
symtok=sampler_pb2.SymmetricalTokenDepth(
depth_increase_token="{",
depth_decrease_token="}",
)),
sampler_pb2.SampleTerminationCriterion(
maxlen=sampler_pb2.MaxTokenLength(
maximum_tokens_in_sample=20000, )),
],
),
)
def CreateModelProtoFromFlags() -> model_pb2.Model:
return model_pb2.Model(
corpus=CreateCorpusProtoFromFlags(),
architecture=model_pb2.NetworkArchitecture(
backend=model_pb2.NetworkArchitecture.TENSORFLOW,
neuron_type=model_pb2.NetworkArchitecture.LSTM,
neurons_per_layer=FLAGS.clgen_layer_size,
num_layers=FLAGS.clgen_num_layers,
post_layer_dropout_micros=0,
),
training=model_pb2.TrainingOptions(
num_epochs=FLAGS.clgen_num_epochs,
sequence_length=FLAGS.clgen_training_sequence_length,
batch_size=FLAGS.clgen_training_batch_size,
shuffle_corpus_contentfiles_between_epochs=True,
adam_optimizer=model_pb2.AdamOptimizer(
initial_learning_rate_micros=2000,
learning_rate_decay_per_epoch_micros=50000,
beta_1_micros=900000,
beta_2_micros=999000,
normalized_gradient_clip_micros=5000000,
),
))
def main(argv: typing.List[str]):
"""Main entry point."""
if len(argv) > 1:
raise app.UsageError("Unknown arguments: '{}'.".format(" ".join(
argv[1:])))
instance = clgen.Instance(
clgen_pb2.Instance(
working_dir=FLAGS.clgen_dir,
model=model_pb2.Model(
corpus=corpus_pb2.Corpus(
local_directory=FLAGS.clgen_corpus_dir,
ascii_character_atomizer=True,
preprocessor=[
"deeplearning.clgen.preprocessors.opencl:ClangPreprocessWithShim",
"deeplearning.clgen.preprocessors.opencl:Compile",
"deeplearning.clgen.preprocessors.opencl:NormalizeIdentifiers",
"deeplearning.clgen.preprocessors.opencl:StripDoubleUnderscorePrefixes",
"deeplearning.clgen.preprocessors.common:StripDuplicateEmptyLines",
"deeplearning.clgen.preprocessors.opencl:SanitizeKernelPrototype",
"deeplearning.clgen.preprocessors.common:StripTrailingWhitespace",
"deeplearning.clgen.preprocessors.opencl:ClangFormat",
"deeplearning.clgen.preprocessors.common:MinimumLineCount3",
"deeplearning.clgen.preprocessors.opencl:Compile",
],
contentfile_separator="\n\n",
),
architecture=model_pb2.NetworkArchitecture(
backend=model_pb2.NetworkArchitecture.TENSORFLOW,
neuron_type=model_pb2.NetworkArchitecture.LSTM,
neurons_per_layer=512,
num_layers=2,
post_layer_dropout_micros=0,
),
training=model_pb2.TrainingOptions(
num_epochs=50,
sequence_length=64,
batch_size=64,
shuffle_corpus_contentfiles_between_epochs=True,
adam_optimizer=model_pb2.AdamOptimizer(
initial_learning_rate_micros=2000,
learning_rate_decay_per_epoch_micros=50000,
beta_1_micros=900000,
beta_2_micros=999000,
normalized_gradient_clip_micros=5000000,
),
),
),
sampler=sampler_pb2.Sampler(
start_text="kernel void ",
batch_size=64,
sequence_length=1024,
temperature_micros=1000000, # = 1.0 real value
termination_criteria=[
sampler_pb2.SampleTerminationCriterion(
symtok=sampler_pb2.SymmetricalTokenDepth(
depth_increase_token="{",
depth_decrease_token="}",
)),
sampler_pb2.SampleTerminationCriterion(
maxlen=sampler_pb2.MaxTokenLength(
maximum_tokens_in_sample=20000, )),
],
),
), )
db = grewe_features_db.Database(FLAGS.db)
profile_dir = pathlib.Path(FLAGS.profile_dir)
profile_dir.mkdir(parents=True, exist_ok=True)
profiler = prof.AutoCsvProfiler(profile_dir)
with instance.Session(), multiprocessing.Pool() as pool:
while True:
Sample(instance, db, profiler, pool)