def LsModels(cache_root: pathlib.Path) -> None:
for model_dir in (cache_root / "model").iterdir():
meta_file = model_dir / "META.pbtxt"
if pbutil.ProtoIsReadable(meta_file, internal_pb2.ModelMeta()):
model = models.Model(
pbutil.FromFile(meta_file, internal_pb2.ModelMeta()).config)
telemetry = list(model.TrainingTelemetry())
num_epochs = model.config.training.num_epochs
n = len(telemetry)
print(f"{model_dir} {n} / {num_epochs} epochs")
elif meta_file.is_file():
app.Warning("Meta file %s cannot be read.", meta_file)
else:
app.Warning("Meta file %s not found.", meta_file)
def parseModelSummary(meta):
m = pbutil.FromString('\n'.join(meta), internal_pb2.ModelMeta())
if m.config.architecture.backend == model_pb2.NetworkArchitecture.TENSORFLOW_BERT:
summary = (
"BERT, hs: {}, nhl: {}, atth: {}, imsz: {}, pemb: {}, preds: {}, dp: {}, mprob: {}, {}"
.format(
m.config.architecture.hidden_size,
m.config.architecture.num_hidden_layers,
m.config.architecture.num_attention_heads,
m.config.architecture.intermediate_size,
m.config.architecture.max_position_embeddings,
m.config.training.max_predictions_per_seq,
m.config.training.dupe_factor,
round(m.config.training.masked_lm_prob, 3),
"mask" if m.config.training.data_generator.HasField("mask")
else "hole-{},{}".format(
(m.config.training.data_generator.hole.absolute
if m.config.training.data_generator.hole.HasField(
"absolute_length") else
m.config.training.data_generator.hole.relative_length),
"unf" if m.config.training.data_generator.hole.HasField(
"uniform_distribution") else "norm-{},{}".format(
round(
m.config.training.data_generator.hole.
normal_distribution.mean, 2),
round(
m.config.training.data_generator.hole.
normal_distribution.variance, 2)))))
else:
raise NotImplementedError
return summary
def test_Model_metafile(clgen_cache_dir, abc_model_config):
"""A newly instantiated model's cache has a metafile."""
del clgen_cache_dir
m = models.Model(abc_model_config)
assert (m.cache.path / 'META.pbtxt').is_file()
assert pbutil.ProtoIsReadable(m.cache.path / 'META.pbtxt',
internal_pb2.ModelMeta())
def __init__(self, path: pathlib.Path):
self.path = path.absolute()
self.cache = cache.FSCache(self.path)
self.corpus = NullCorpus()
self.config = pbutil.FromFile(
self.path / 'META.pbtxt', internal_pb2.ModelMeta()).config
self.atomizer = atomizers.AtomizerBase.FromFile(self.path / 'atomizer')
self.backend = {
model_pb2.NetworkArchitecture.TENSORFLOW: tensorflow_backend.TensorFlowBackend,
model_pb2.NetworkArchitecture.KERAS: keras_backend.KerasBackend,
}[self.config.architecture.backend](self.config, self.cache, self.atomizer)
def __init__(self, config: model_pb2.Model):
"""Instantiate a model.
Args:
config: A Model message.
Raises:
TypeError: If the config argument is not a Model proto.
UserError: In case on an invalid config.
"""
# Error early, so that a cache isn't created.
if not isinstance(config, model_pb2.Model):
t = type(config).__name__
raise TypeError(f"Config must be a Model proto. Received: '{t}'")
# Validate config options.
if config.training.sequence_length < 1:
raise errors.UserError(
'TrainingOptions.sequence_length must be >= 1')
self.config = model_pb2.Model()
self.config.CopyFrom(builders.AssertIsBuildable(config))
self.corpus = corpuses.Corpus(config.corpus)
self.hash = self._ComputeHash(self.corpus, self.config)
self.cache = cache.mkcache('model', self.hash)
# Create the necessary cache directories.
(self.cache.path / 'checkpoints').mkdir(exist_ok=True)
(self.cache.path / 'samples').mkdir(exist_ok=True)
(self.cache.path / 'logs').mkdir(exist_ok=True)
# Create symlink to encoded corpus.
symlink = self.cache.path / 'corpus'
if not symlink.is_symlink():
os.symlink(
os.path.relpath(
pathlib.Path(
self.corpus.encoded.url[len('sqlite:///'):]).parent,
self.cache.path), symlink)
# Create symlink to the atomizer.
symlink = self.cache.path / 'atomizer'
if not symlink.is_symlink():
os.symlink(
os.path.relpath(self.corpus.atomizer_path, self.cache.path),
symlink)
# Validate metadata against cache.
if self.cache.get('META.pbtxt'):
cached_meta = pbutil.FromFile(
pathlib.Path(self.cache['META.pbtxt']),
internal_pb2.ModelMeta())
# Exclude num_epochs and corpus location from metadata comparison.
config_to_compare = model_pb2.Model()
config_to_compare.CopyFrom(self.config)
config_to_compare.corpus.ClearField('contentfiles')
config_to_compare.training.ClearField('num_epochs')
# These fields should have already been cleared, but we'll do it again
# so that metadata comparisons don't fail when the cached meta schema
# is updated.
cached_to_compare = model_pb2.Model()
cached_to_compare.CopyFrom(cached_meta.config)
cached_to_compare.corpus.ClearField('contentfiles')
cached_to_compare.training.ClearField('num_epochs')
if config_to_compare != cached_to_compare:
raise errors.InternalError('Metadata mismatch')
self.meta = cached_meta
else:
self.meta = internal_pb2.ModelMeta()
self.meta.config.CopyFrom(self.config)
self._WriteMetafile()
self.backend = {
model_pb2.NetworkArchitecture.TENSORFLOW:
tensorflow_backend.TensorFlowBackend,
model_pb2.NetworkArchitecture.KERAS: keras_backend.KerasBackend,
}[config.architecture.backend](self.config, self.cache, self.corpus)
def __init__(self, config: model_pb2.Model):
"""Instantiate a model.
Args:
config: A Model message.
Raises:
TypeError: If the config argument is not a Model proto.
UserError: In case on an invalid config.
"""
# Error early, so that a cache isn't created.
if not isinstance(config, model_pb2.Model):
t = type(config).__name__
raise TypeError(f"Config must be a Model proto. Received: '{t}'")
self.config = model_pb2.Model()
# Validate config options.
self.config.CopyFrom(builders.AssertIsBuildable(config))
if FLAGS.num_train_steps:
self.config.training.num_train_steps = FLAGS.num_train_steps
if FLAGS.num_pretrain_steps:
self.config.training.num_pretrain_steps = FLAGS.num_pretrain_steps
if FLAGS.num_epochs:
self.config.training.num_epochs = FLAGS.num_epochs
# Initialize distrib lock path.
if environment.WORLD_SIZE > 1:
if environment.WORLD_RANK == 0:
lock_cache = cache.mkcache("locks")
lock_cache.path.mkdir(exist_ok=True)
else:
while not cache.cachepath("locks").exists():
time.sleep(0.5)
lock_cache = cache.mkcache("locks")
distrib.init(lock_cache.path)
# Initialize corpuses
self.corpus = corpuses.Corpus(config.corpus)
self.pre_train_corpus = None
if config.HasField("pre_train_corpus"):
self.pre_train_corpus = corpuses.Corpus(config.pre_train_corpus)
self.hash = self._ComputeHash(self.pre_train_corpus, self.corpus,
self.config)
self._created = False
distrib.lock()
self.cache = cache.mkcache("model", self.hash)
distrib.unlock()
if environment.WORLD_RANK == 0:
# Create the necessary cache directories.
(self.cache.path / "checkpoints").mkdir(exist_ok=True)
(self.cache.path / "samples").mkdir(exist_ok=True)
# Create symlink to encoded corpus.
symlink = self.cache.path / "corpus"
if not symlink.is_symlink():
os.symlink(
os.path.relpath(
pathlib.Path(self.corpus.encoded.url[len("sqlite:///"
):]).parent,
self.cache.path,
),
symlink,
)
if self.pre_train_corpus:
symlink = self.cache.path / "pre_train_corpus"
if not symlink.is_symlink():
os.symlink(
os.path.relpath(
pathlib.Path(self.pre_train_corpus.encoded.
url[len("sqlite:///"):]).parent,
self.cache.path,
),
symlink,
)
# Create symlink to the tokenizer and create a backup inside checkpoints.
symlink = self.cache.path / "tokenizer"
if not symlink.is_symlink():
os.symlink(
os.path.relpath(self.corpus.tokenizer_path,
self.cache.path), symlink)
if (self.cache.path / "checkpoints" /
"backup_tokenizer.pkl").exists():
shutil.copyfile(
self.cache.path / "checkpoints" / "backup_tokenizer.pkl",
self.corpus.tokenizer_path)
# Validate metadata against cache.
if self.cache.get("META.pbtxt"):
cached_meta = pbutil.FromFile(
pathlib.Path(self.cache["META.pbtxt"]),
internal_pb2.ModelMeta())
# Exclude num_epochs and corpus location from metadata comparison.
config_to_compare = model_pb2.Model()
config_to_compare.CopyFrom(self.config)
config_to_compare.corpus.ClearField("contentfiles")
if config_to_compare.HasField("pre_train_corpus"):
config_to_compare.pre_train_corpus.ClearField(
"contentfiles")
config_to_compare.training.ClearField("num_epochs")
config_to_compare.training.ClearField("num_train_steps")
if config_to_compare.HasField("pre_train_corpus"):
config_to_compare.training.ClearField("num_pretrain_steps")
config_to_compare.training.ClearField("batch_size")
if config_to_compare.training.HasField("data_generator"):
config_to_compare.training.data_generator.ClearField(
"steps_per_epoch")
config_to_compare.training.data_generator.ClearField(
"validation_set")
# These fields should have already been cleared, but we'll do it again
# so that metadata comparisons don't fail when the cached meta schema
# is updated.
cached_to_compare = model_pb2.Model()
cached_to_compare.CopyFrom(cached_meta.config)
cached_to_compare.corpus.ClearField("contentfiles")
if cached_to_compare.HasField("pre_train_corpus"):
cached_to_compare.pre_train_corpus.ClearField(
"contentfiles")
cached_to_compare.training.ClearField("num_epochs")
cached_to_compare.training.ClearField("num_train_steps")
if cached_to_compare.HasField("pre_train_corpus"):
cached_to_compare.training.ClearField("num_pretrain_steps")
cached_to_compare.training.ClearField("batch_size")
if cached_to_compare.training.HasField("data_generator"):
cached_to_compare.training.data_generator.ClearField(
"steps_per_epoch")
cached_to_compare.training.data_generator.ClearField(
"validation_set")
if cached_to_compare.training.sequence_length != config_to_compare.training.sequence_length:
l.logger().warning(
"Mismatch between pre-trained and current config sequence_length!\
This can only be intended in BERT model!")
cached_to_compare.training.ClearField("sequence_length")
config_to_compare.training.ClearField("sequence_length")
if config_to_compare != cached_to_compare:
raise SystemError("Metadata mismatch: {} \n\n {}".format(
config_to_compare, cached_to_compare))
self.meta = cached_meta
else:
self.meta = internal_pb2.ModelMeta()
self.meta.config.CopyFrom(self.config)
self._WriteMetafile()
## Store current commit
commit.saveCommit(self.cache.path)
self.backend = {
model_pb2.NetworkArchitecture.TENSORFLOW_SEQ:
tf_sequential.tfSequential,
model_pb2.NetworkArchitecture.KERAS_SEQ:
keras_sequential.kerasSequential,
model_pb2.NetworkArchitecture.TENSORFLOW_BERT: tf_bert.tfBert,
model_pb2.NetworkArchitecture.TORCH_BERT: torch_bert.torchBert,
}[config.architecture.backend](self.config, self.cache, self.hash)
l.logger().info("Initialized {} in {}".format(self.backend,
self.cache.path))
return