def make_model_tf(path: str, mode: str) -> tf.Tensor:
"""Prepare the model for generation.
Loads the model architecture from the huggingface pre-trained model, then
loads a checkpoint.
TODO: There must be a way to just load from config + checkpoint, no pretrained
weights.
"""
with utils.log_duration(LOGGER, make_model_tf.__name__, "Load model."):
if mode == constants.SaveModeChoices.hfh5:
config_path = os.path.join(path, "config.json")
model_path = os.path.join(path, "tf_model.h5")
utils.check_exists(config_path)
utils.check_exists(model_path)
config = transformers.GPT2Config.from_pretrained(config_path)
return transformers.TFGPT2LMHeadModel.from_pretrained(
model_path, config=config)
elif mode == constants.SaveModeChoices.ckpt:
model = transformers.TFGPT2LMHeadModel.from_pretrained(
_FLAG_HF_MODEL_KEY.value, )
ckpt = tf.train.Checkpoint(model=model)
ckpt.restore(_FLAG_CKPT_MODEL_PATH.value)
else:
raise RuntimeError(f"Unsupported Save Mode: {mode}")
return model
def main(face_root, scene_root, seed, epoch):
mask_path = './checkpoints/multi_view_face_scene/mask_index_file_{}.pickle'.format(
seed)
assert check_exists(mask_path)
with open(mask_path, 'rb') as fin:
face_mask_index, scene_mask_index = pickle.load(fin, encoding='bytes')
model_path = './checkpoints/multi_view_face_scene/demo_arcface_face+scene_nan_{}_model_{:0>4d}.pth' \
.format(seed, epoch)
assert check_exists(model_path)
dataset = IQiYiFaceSceneDataset(
face_root,
scene_root,
'test',
num_frame=40,
transform=sep_cat_qds_select_face_scene_transforms,
face_mask=face_mask_index,
scene_mask=scene_mask_index)
data_loader = DataLoader(dataset,
batch_size=16384,
shuffle=False,
num_workers=4)
model = ArcFaceSceneModel(
len(face_mask_index) + 2,
len(scene_mask_index),
10034 + 1,
)
metric_func = torch.nn.Softmax(-1)
logger.info('load model from {}'.format(model_path))
state_dict = torch.load(model_path, map_location='cpu')
model.load_state_dict(state_dict)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
logger.info('test model on {}'.format(device))
model.eval()
all_outputs = []
all_video_names = []
with torch.no_grad():
for batch_idx, (feats1, feats2, _,
video_names) in enumerate(data_loader):
logger.info('Test Model: {}/{}'.format(batch_idx,
len(data_loader)))
feats1 = feats1.to(device)
feats2 = feats2.to(device)
output = model(feats1, feats2)
output = metric_func(output)
all_outputs.append(output.cpu())
all_video_names += video_names
all_outputs = torch.cat(all_outputs, dim=0)
return all_outputs, all_video_names
def make_model_tf(path):
with utils.log_duration(LOGGER, make_model_tf.__name__,
"Load model."):
if os.path.exists(path):
config_path = os.path.join(path, "config.json")
model_path = os.path.join(path, "tf_model.h5")
utils.check_exists(config_path)
utils.check_exists(model_path)
config = transformers.GPT2Config.from_pretrained(
config_path)
return transformers.TFGPT2LMHeadModel.from_pretrained(
model_path, config=config)
else:
return transformers.TFGPT2LMHeadModel.from_pretrained(
path, )
def process(self):
if not check_exists(self.raw_folder):
raise RuntimeError('Dataset not found')
train_set, test_set, meta = self.make_data()
save(train_set, os.path.join(self.processed_folder, 'train.pt'))
save(test_set, os.path.join(self.processed_folder, 'test.pt'))
save(meta, os.path.join(self.processed_folder, 'meta.pt'))
return
def process(self):
if not check_exists(self.raw_folder):
self.download()
train_set, test_set, meta = self.make_data()
save(train_set, os.path.join(self.processed_folder, 'train.pt'))
save(test_set, os.path.join(self.processed_folder, 'test.pt'))
save(meta, os.path.join(self.processed_folder, 'meta.pt'))
return
def __init__(self, root, split):
self.root = os.path.expanduser(root)
self.split = split
if not check_exists(self.processed_folder):
self.process()
self.token = load(
os.path.join(self.processed_folder, '{}.pt'.format(split)))
self.vocab = load(
os.path.join(self.processed_folder, 'meta.pt'.format(split)))
def read_file():
"""
load the file
"""
file_name = sys.argv[1]
if check_exists(file_name):
print("Reading File")
read_data(file_name)
parse_data()
else:
print("File may not exist so check")
def wrapper(args):
# Check executable requirements
for req in requires:
assert check_exists(req), f"'{req}' is required for {name} step"
# Save previous steps for 'archive' step's postfix
if "prev_steps" not in args:
args.prev_steps = args.postfix
ret = f(args)
args.prev_steps += f"-{name}"
return ret
def __init__(self, root, split, subset, transform=None):
self.root = os.path.expanduser(root)
self.split = split
self.subset = subset
self.transform = transform
if not check_exists(self.processed_folder):
self.process()
self.img, self.target = load(os.path.join(self.processed_folder, '{}.pt'.format(self.split)))
self.target = self.target[self.subset]
self.classes_counts = make_classes_counts(self.target)
self.classes_to_labels, self.classes_size = load(os.path.join(self.processed_folder, 'meta.pt'))
self.classes_to_labels, self.classes_size = self.classes_to_labels[self.subset], self.classes_size[self.subset]
def main(args):
if not check_exists(args.save_dir):
os.makedirs(args.save_dir)
dataset = IQiYiFaceSceneDataset(args.face_root, args.scene_root, 'train+val-noise', num_frame=args.num_frame, )
data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=4)
log_step = len(data_loader) // 10 if len(data_loader) > 10 else 1
model = ArcFaceSceneModel(args.face_feat_dim, args.scene_feat_dim, args.num_classes, )
metric_func = ArcMarginProduct()
loss_func = FocalLoss(gamma=2.)
optimizer = optim.SGD(model.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=1e-5)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epoch)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
for epoch_idx in range(args.epoch):
total_loss = .0
for batch_idx, (face_feats, scene_feats, labels, _) in enumerate(data_loader):
face_feats = face_feats.to(device)
scene_feats = scene_feats.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(face_feats, scene_feats)
outputs_metric = metric_func(outputs, labels)
local_loss = loss_func(outputs_metric, labels)
local_loss.backward()
optimizer.step()
total_loss += local_loss.item()
if batch_idx % log_step == 0 and batch_idx != 0:
print('Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'
.format(epoch_idx, batch_idx * args.batch_size, len(dataset),
100.0 * batch_idx / len(data_loader), local_loss.item()))
log = {'epoch': epoch_idx,
'lr': optimizer.param_groups[0]['lr'],
'loss': total_loss / len(data_loader)}
for key, value in sorted(log.items(), key=lambda item: item[0]):
print(' {:20s}: {:6f}'.format(str(key), value))
lr_scheduler.step()
save_model(model, args.save_dir, 'demo_arcface_face+scene_nan_model', args.epoch)
def gotResult(self, data, kid, keyword, page, totalpage, t, max_interval):
''' '''
tp, feeds = data
if kid in self.key_statuses:
statuses = self.key_statuses[kid]
else:
statuses = [set(), set(), set()]
self.key_statuses[kid] = statuses
log.info("Keyword: %s page: %d/%d result: %d" % (repr(keyword), page,
totalpage, len(feeds)))
fds = []
new_statuses = statuses[0]
collided_feeds = 0
next_request = False
for feed in feeds:
feed['kid'] = str(kid)
if check_exists(feed):
fds.append(feed)
else:
collided_feeds += 1
# 发送消息给kafka队列
send_messages(kid, keyword, fds)
# 判断是否需要继续抓取
if collided_feeds * 3 > len(feeds):
log.info("collided_feeds: %d with total: %d" % (collided_feeds, len(feeds)))
else:
page += 1
totalpage = max(tp, totalpage)
log.info("Page: %d with totalpage: %d" % (page, totalpage))
if page < totalpage:
next_request = True
if next_request:
statuses[1] |= new_statuses
if len(statuses[1]) > 100:
self.key_statuses[kid] = [set(), set(), statuses[1]]
else:
self.key_statuses[kid][0] = set()
#log.info("Keyword: %s result: %d" % (str(kid), len(new_statuses)))
self.key_queue.add_key((kid, page, totalpage, None, None), t)
else:
self.recalcKeywordFreq(kid, len(new_statuses), max_interval)
def main(data_root, epoch):
load_path = './checkpoints/demo_arcface_scene_model_{:0>4d}.pth'.format(
epoch)
assert check_exists(load_path)
dataset = IQiYiSceneFeatDataset(
data_root,
'test',
)
data_loader = DataLoader(dataset,
batch_size=2048,
shuffle=False,
num_workers=0)
model = ArcSceneFeatModel(2048, 10034 + 1)
metric_func = torch.nn.Softmax(-1)
logger.info('load model from {}'.format(load_path))
state_dict = torch.load(load_path, map_location='cpu')
model.load_state_dict(state_dict)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
logger.info('test model on {}'.format(device))
model.eval()
all_outputs = []
all_video_names = []
with torch.no_grad():
for batch_idx, (feats, _, video_names) in enumerate(data_loader):
logger.info('Test Model: {}/{}'.format(batch_idx,
len(data_loader)))
feats = feats.to(device)
output = model(feats)
output = metric_func(output)
all_outputs.append(output.cpu())
all_video_names += video_names
all_outputs = torch.cat(all_outputs, dim=0)
return all_outputs, all_video_names
def main(in_target: utils.PathType, out_target: utils.PathType):
utils.check_exists(in_target)
parent_dir_out = os.path.dirname(os.path.abspath(out_target))
utils.check_exists(parent_dir_out)
utils.check_exists(out_target, inverse=True)
all_code = ""
with open(in_target) as fin:
input_json = json.load(fin)
cells = input_json["cells"]
code_cells = lambda: (c for c in cells if c["cell_type"] == "code")
for cell in code_cells():
all_code += "".join(cell["source"]) + "\n\n"
with open(out_target, "w") as fout:
fout.write(all_code)
def main(face_root, scene_root):
load_path = './checkpoints/demo_arcface_face+scene_nan_model_0100.pth'
assert check_exists(load_path)
dataset = IQiYiFaceSceneDataset(face_root, scene_root, 'test', num_frame=40, )
data_loader = DataLoader(dataset, batch_size=16384, shuffle=False, num_workers=4)
model = ArcFaceSceneModel(512 + 2, 2048, 10034 + 1, )
metric_func = torch.nn.Softmax(-1)
logger.info('load model from {}'.format(load_path))
state_dict = torch.load(load_path, map_location='cpu')
model.load_state_dict(state_dict)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
logger.info('test model on {}'.format(device))
model.eval()
all_outputs = []
all_video_names = []
with torch.no_grad():
for batch_idx, (feats1, feats2, _, video_names) in enumerate(data_loader):
logger.info('Test Model: {}/{}'.format(batch_idx, len(data_loader)))
feats1 = feats1.to(device)
feats2 = feats2.to(device)
output = model(feats1, feats2)
output = metric_func(output)
all_outputs.append(output.cpu())
all_video_names += video_names
all_outputs = torch.cat(all_outputs, dim=0)
return all_outputs, all_video_names
def main(argv):
if len(argv) > 1:
raise RuntimeError(argv[1:])
absl_logging.use_python_logging()
utils.check_contained(_FLAG_APPROACH_TYPE.value, _ACCEPTABLE_APPROACHES)
utils.check_operator(operator.xor, bool(_FLAG_H5_MODEL_PATH.value),
bool(_FLAG_CKPT_MODEL_PATH.value))
if _FLAG_H5_MODEL_PATH.value:
model_path = _FLAG_H5_MODEL_PATH.value
mode = constants.SaveModeChoices.hfh5
elif _FLAG_CKPT_MODEL_PATH.value:
model_path = _FLAG_CKPT_MODEL_PATH.value
mode = constants.SaveModeChoices.ckpt
else:
raise RuntimeError("Logically should never happen.")
utils.check_exists(model_path)
device_type = tf_utils.devices_to_use()[0].device_type
# ONLY GPU IS SUPPORTED
utils.check_equal(device_type, "GPU")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Build the distribution strategy
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if device_type == "TPU":
# ONLY LOCAL TPU IS "SUPPORTED"
utils.check_isinstance(_FLAG_IS_LOCAL_TPU.value, bool)
assert _FLAG_IS_LOCAL_TPU.value
tpu_config = tf_utils.init_tpus(local=True)
utils.check_isinstance(tpu_config, tf_utils.TpuConfigType)
utils.check_not_none(tpu_config)
strategy = tf.distribute.TPUStrategy(tpu_config.resolver)
elif device_type == "GPU":
strategy = tf.distribute.MirroredStrategy(
devices=tf.config.experimental.list_logical_devices('GPU'))
else:
raise RuntimeError(device_type)
# ONLY GPU IS SUPPORTED
print(tf.config.list_logical_devices())
utils.check_isinstance(strategy, tf.distribute.MirroredStrategy)
##############################################################################
# Load Model
##############################################################################
with utils.log_duration(LOGGER, main.__name__, "All of model preparation"):
with strategy.scope():
# HF isn't able to read directly from GCS
if (model_path.startswith("gs://")
and mode == constants.SaveModeChoices.hfh5):
with utils.log_duration(LOGGER, main.__name__,
"Download model from GS"):
with tempfile.TemporaryDirectory() as td:
td += os.path.sep
if os.path.exists("/root/google-cloud-sdk/bin/gsutil"):
exec_ = "/root/google-cloud-sdk/bin/gsutil"
else:
exec_ = "gsutil"
command = [
exec_,
"-m",
"cp",
"-r",
os.path.join(model_path, "*"),
td,
]
LOGGER.debug("Running bash command: %s",
" ".join(command))
subprocess.check_call(command)
LOGGER.debug("Files at the temp dir(%s): %s", td,
str(os.listdir(td)))
model = make_model_tf(td, mode=mode)
else:
model = make_model_tf(model_path, mode=mode)
utils.check_not_none(model)
##############################################################################
# Load Dataset Pipeline
##############################################################################
utils.check_contained(
_FLAG_APPROACH_TYPE.value, {
constants.ApproachTypeChoices.naked_lm,
constants.ApproachTypeChoices.cached_pretok
})
devices = tf_utils.devices_to_use()
num_replicas = (len(devices)
if devices[0].device_type in {"GPU", "TPU"} else 1)
utils.check_equal(devices[0].device_type, "GPU")
# Only a batch size of 1 is currently supported. We need attention masks
batch_size = _FLAG_BATCH_SIZE.value * num_replicas
approach_type = _FLAG_APPROACH_TYPE.value
logging.debug("Loading dataset.")
tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2-xl")
ds = prep_ds_for_generation(
dict(
tokenizer=tokenizer,
context_window_size=1024,
dataset_name="kilt_eli5",
batch_size=1, # >> We set our own batch size elsewhere
db_path=None, # None,
random_seed=0,
use_subset=False,
subset_size=-1,
use_helper_words=True,
approach_type=approach_type,
num_retrievals=5, # Will never change
retrieval_temperature=1.,
retriever=None, # Cached retrievals don't need a retriever
repeat=False, # Will never change
split=_FLAG_SPLIT.value,
enable_debug_checks=False,
retrieval_bank_size=5, # Will never change
dataset_type=_FLAG_DATASET_TYPE.value,
tfr_prefix=_FLAG_TFR_PREFIX.value,
qty_shuffle=1, # Will never change
max_length_generation=350),
tokenizer,
_FLAG_SPLIT.value)
ds = strategy.experimental_distribute_dataset(ds)
##############################################################################
# Generate
##############################################################################
LOGGER.debug("Generating.")
generations = []
num_entries_in_split = (
task_specific.DATASET_CARDINALITIES["kilt_eli5"][_FLAG_SPLIT.value])
entries_counter = tqdm.tqdm(total=num_entries_in_split)
for batch_no, batch in enumerate(ds):
# Calling model.generate. We should make a config file with the
# hyperparameters for generation, or make a facility in the one we already
# have. I feel like a separate one would be better, separating concerns.
output = strategy.run(
model.generate,
kwargs=dict(
input_ids=batch,
max_length=_FLAG_GENERATION_LENGTH_LIMIT.value,
use_cache=True,
attention_mask=tf.cast(batch != tokenizer.eos_token_id,
tf.int32),
repetition_penalty=2.,
num_beams=5,
))
output = tf_utils.process_strat_output(strategy_outputs=output,
current_batch_size=batch_size,
strategy=strategy,
name="generations")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Display the inputs and outputs.
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
rich_console = rich.console.Console(color_system="256")
print_sample = make_print_sample()
with utils.log_duration(LOGGER, "main",
"all of tokenizer.decode for a batch."):
for i in range(batch_size):
input_text = tokenizer.decode(batch.numpy()[i])
output_text = tokenizer.decode(output.numpy()[i])
print("#" * 1000)
print(f"Batch {batch_no} Generation {i}")
print_sample(input_text, f"input batch_no {batch_no}",
rich_console)
print_sample(output_text, f"output batch_no {batch_no}",
rich_console)
generations.append(output_text)
print("#" * 1000)
entries_counter.update(batch.shape[0])
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Save the output to a JSON File.
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
utils.to_json_file(
os.path.join(_FLAG_OUTPUT_PATH.value, _FLAG_SPLIT.value,
_FLAG_APPROACH_TYPE.value,
time.strftime("%Y%m%d-%H%M%S.json")),
dict(flags={
flag.name: flag.value
for flag in flags.FLAGS.flags_by_module_dict()[argv[0]]
},
generations=generations))
logging.debug("Saved to: %s", _FLAG_OUTPUT_PATH.value)
def make_data(self):
if not check_exists(os.path.join(self.raw_folder, 'base')):
train_path = os.path.join(self.raw_folder, 'ILSVRC2012_img_train')
test_path = os.path.join(self.raw_folder, 'ILSVRC2012_img_val')
meta_path = os.path.join(self.raw_folder, 'ILSVRC2012_devkit_t12')
extract_file(
os.path.join(self.raw_folder, 'ILSVRC2012_img_train.tar'),
train_path)
extract_file(
os.path.join(self.raw_folder, 'ILSVRC2012_img_val.tar'),
test_path)
extract_file(
os.path.join(self.raw_folder, 'ILSVRC2012_devkit_t12.tar'),
meta_path)
for archive in [
os.path.join(train_path, archive)
for archive in os.listdir(train_path)
]:
extract_file(archive,
os.path.splitext(archive)[0],
delete=True)
classes_to_labels, classes_size = make_meta(meta_path)
with open(
os.path.join(meta_path, 'data',
'ILSVRC2012_validation_ground_truth.txt'),
'r') as f:
test_id = f.readlines()
test_id = [int(i) for i in test_id]
test_img = sorted([
os.path.join(test_path, file) for file in os.listdir(test_path)
])
test_wnid = []
for test_id_i in test_id:
test_node_i = anytree.find_by_attr(classes_to_labels['label'],
name='id',
value=test_id_i)
test_wnid.append(test_node_i.name)
for test_wnid_i in set(test_wnid):
os.mkdir(os.path.join(test_path, test_wnid_i))
for test_wnid_i, test_img in zip(test_wnid, test_img):
shutil.move(
test_img,
os.path.join(test_path, test_wnid_i,
os.path.basename(test_img)))
shutil.move(
train_path,
os.path.join(self.raw_folder, 'base', 'ILSVRC2012_img_train'))
shutil.move(
test_path,
os.path.join(self.raw_folder, 'base', 'ILSVRC2012_img_val'))
shutil.move(
meta_path,
os.path.join(self.raw_folder, 'base', 'ILSVRC2012_devkit_t12'))
if not check_exists(os.path.join(self.raw_folder, str(self.size))):
raise ValueError('Need to run resizer')
classes_to_labels, classes_size = make_meta(
os.path.join(self.raw_folder, 'base', 'ILSVRC2012_devkit_t12'))
train_img, train_label = make_img(
os.path.join(self.raw_folder, str(self.size),
'ILSVRC2012_img_train'), classes_to_labels['label'])
test_img, test_label = make_img(
os.path.join(self.raw_folder, str(self.size),
'ILSVRC2012_img_val'), classes_to_labels['label'])
train_target = {'label': train_label}
test_target = {'label': test_label}
return (train_img, train_target), (test_img,
test_target), (classes_to_labels,
classes_size)
# Read arguments
specification = sys.argv[1]
container = sys.argv[2]
version = sys.argv[3]
mozart_rest_url = sys.argv[4]
storage = sys.argv[5]
product = utils.get_product_id(specification, version)
# Prepare dataset metadata
metadata = {
"container": container,
"job-version": version,
"resource": "jobspec"
}
if not utils.check_exists(container, mozart_rest_url):
print(
"[ERROR] Container, {0}, does not exist. Cannot create HySDS-IO.".
format(container),
file=sys.stderr)
sys.exit(-2)
# Read specification metadata and merge it
with open(specification, "r") as fp:
payload = json.load(fp)
resolve_dependency_images(payload, storage)
metadata.update(payload)
metadata["id"] = product
endpoint = os.path.join(mozart_rest_url, "job_spec/add")
r = requests.post(endpoint,
def process(self):
if not check_exists(self.raw_folder):
raise RuntimeError('Dataset not found')
data_set = self.make_data()
save(data_set, os.path.join(self.processed_folder, 'data.pt'))
return
# Read arguments
specification = sys.argv[1]
job_spec = sys.argv[2]
version = sys.argv[3]
mozart_rest_url = sys.argv[4]
grq_rest_url = sys.argv[5]
# Generate product name
product = utils.get_product_id(specification, version)
# Prepare dataset metadata
metadata = {
"job-specification": job_spec,
"job-version": version,
"resource": "hysds-io-specification"
}
if not utils.check_exists(job_spec, mozart_rest_url):
print("[ERROR] Job Specification, {0}, does not exist. Cannot create HySDS-IO.".format(job_spec), file=sys.stderr)
sys.exit(-2)
# Read specification metadata and merge it
with open(specification, "r") as fp:
metadata.update(json.load(fp))
metadata["id"] = product
if metadata.get("component", "tosca") == "mozart":
add_hysds_io(mozart_rest_url, metadata)
else:
add_hysds_io(grq_rest_url, metadata)
sys.exit(0)
def main(argv):
#######################################################################
# Initial Setup. Logging, Flags, Random seeds.
#######################################################################
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
absl_logging.use_python_logging()
flags_dict = {
flag.name: flag.value
for flag in FLAGS.flags_by_module_dict()[argv[0]]
}
if FLAGS.use_subset:
message = (f"{colorama.Back.RED}{colorama.Fore.WHITE}"
f"{colorama.Style.BRIGHT}USING A SUBSET OF THE DATASET"
f"{colorama.Style.RESET_ALL}")
LOGGER.warning(message)
utils.log_module_args(LOGGER, argv[0])
if not FLAGS.output_dir.startswith("gs://"):
utils.check_exists(FLAG_OUTPUT_DIR.value)
if not tf.io.gfile.isdir(FLAG_OUTPUT_DIR.value):
raise RuntimeError("Output dir needs to be a directory.")
tf.random.set_seed(FLAG_RANDOM_SEED.value)
np.random.seed(FLAG_RANDOM_SEED.value)
# Prepare the instance output directory path and save the config there
folder_name = time.strftime(
f"{FLAG_RUN_NAME.value}_{FLAG_APPROACH_TYPE.value}_%Y%m%d-%H%M%S")
instance_output_dir = os.path.join(FLAG_OUTPUT_DIR.value,
folder_name).strip()
if not instance_output_dir.endswith("/"):
instance_output_dir += "/"
json_target = os.path.join(instance_output_dir, "training_params.json")
if not json_target.strip().startswith("gs://"):
subprocess.check_call(["mkdir", "-p", instance_output_dir])
utils.to_json_file(json_target, instance_output_dir)
##############################################################################
# Initialization and Configuration of the Devices.
##############################################################################
tpu_setup = None
# current_acelerator_type is always "CPU" in the beginning with TPUs
if tf_utils.current_accelerator_type() == "CPU":
tpu_setup = tf_utils.init_tpus()
LOGGER.debug("Devices we are computing on:\n%s",
utils.wrap_iterable(map(str, tf_utils.devices_to_use())))
LOGGER.debug("All devices:")
LOGGER.debug(tf_utils.device_mapping())
if tf_utils.current_accelerator_type() == "GPU":
tf.config.set_soft_device_placement(True)
if tf_utils.current_accelerator_type() != "TPU":
tf.debugging.set_log_device_placement(True)
if FLAG_DISTRIBUTE_MODE.value in constants.PURE_DATA_PARALLEL_STRATEGIES:
actual_num_replicas = len(tf_utils.devices_to_use())
elif FLAG_DISTRIBUTE_MODE.value in constants.DATA_PARALLEL_DMC:
actual_num_replicas = FLAG_NUM_REPLICAS.value
else:
actual_num_replicas = 1
##############################################################################
# We load the retriever model if it is needed.
##############################################################################
# Not currently used.
retriever = None
# if (FLAG_APPROACH_TYPE.value ==
# constants.ApproachTypeChoices.lm_and_realm):
# raise NotImplementedError("This part needs to be tested anew.")
# config_path = FLAG_RETRIEVER_CONFIG_PATH.value
# realm_save = tf_utils.REALMSave(**utils.from_json_file(config_path))
#
# # Approx 15 min when not in dev mode, on CPU
# with utils.log_duration(LOGGER, "main",
# "whole of BERTScaNNRetriever.__init__",
# logging.INFO):
# scann_config = retrievers.ScannConfig(
# **utils.from_json_file(FLAG_SCANN_CONFIG_PATH.value))
# retriever = retrievers.BERTScaNNRetriever(
# retriever_module_path=realm_save.query_embedder_path,
# block_records_path=realm_save.text_records,
# num_block_records=realm_save.num_block_records,
# mode=tf.estimator.ModeKeys.EVAL,
# scann_config=scann_config)
# elif (FLAG_APPROACH_TYPE.value ==
# constants.ApproachTypeChoices.cached_realm):
# raise NotImplementedError("This part needs to be tested anew.")
# config_path = FLAG_RETRIEVER_CONFIG_PATH.value
# realm_save = tf_utils.REALMSave(**utils.from_json_file(config_path))
#
# # Approx 15 min when not in dev mode, on CPU
# with utils.log_duration(LOGGER, "main",
# "whole of FullyCachedRetriever.__init__",
# logging.INFO):
#
# retriever = retrievers.FullyCachedRetriever(
# db_path=FLAG_FULLYCACHED_H5_PATH.value,
# block_records_path=realm_save.text_records,
# num_block_records=realm_save.num_block_records,
# )
##############################################################################
# Distributed training task
##############################################################################
if FLAG_TASK.value == constants.TaskChoices.train:
with utils.log_duration(LOGGER, "main", "Load model"):
utils.print_mem("before loading model", LOGGER)
model_specific = task_specific.load_model(
FLAG_MODEL_LOAD_PATH.value, FLAG_MODEL_KEY.value,
FLAG_DISTRIBUTE_MODE.value, tpu_setup, FLAG_NUM_REPLICAS.value)
utils.print_mem("after loading model", LOGGER)
model_or_replicas = model_specific.model
if isinstance(model_or_replicas, list):
model_or_replicas: List[transformers.TFGPT2LMHeadModel]
else:
model_or_replicas: transformers.TFGPT2LMHeadModel
tokenizer = model_specific.tokenizer
def make_optimizer():
return tensor2tensor.utils.adafactor.AdafactorOptimizer(
learning_rate=FLAG_LEARNING_RATE.value)
if model_specific.strategy:
with model_specific.strategy.scope():
optimizer = make_optimizer()
else:
optimizer = make_optimizer()
############################################################################
# Prepare the dataset functions
############################################################################
rg = np.random.default_rng(FLAG_RANDOM_SEED.value)
def call_lm_preproc(repeat, split, random_seed):
"""Using functools.partial prevents the linter from doing its job."""
if FLAG_DATASET_NAME.value == constants.DatasetNameChoices.kilt_eli5:
return task_specific.create_lm_ds_kilt_eli5(
tokenizer=tokenizer,
context_window_size=(
model_or_replicas[0].config.n_positions if isinstance(
model_or_replicas,
list) else model_or_replicas.config.n_positions),
dataset_name=FLAG_DATASET_NAME.value,
# Batches are split over the replicas:
batch_size=FLAG_BATCH_SIZE.value * actual_num_replicas,
db_path=FLAG_DB_PATH.value,
random_seed=random_seed,
use_subset=FLAG_USE_SUBSET.value,
subset_size=FLAG_SUBSET_SIZE.value,
use_helper_words=FLAG_USE_HELPER_WORDS.value,
approach_type=FLAG_APPROACH_TYPE.value,
num_retrievals=FLAG_NUM_RETRIEVALS.value,
retrieval_temperature=FLAG_RETRIEVAL_TEMPERATURE.value,
retriever=retriever,
repeat=repeat,
split=split,
enable_debug_checks=FLAG_DATASET_DEBUG.value,
retrieval_bank_size=FLAG_RETRIEVAL_BANK_SIZE.value,
dataset_type=FLAG_DATASET_TYPE.value,
qty_shuffle=FLAG_QTY_SHUFFLE.value,
tfr_prefix=FLAG_TFR_PREFIX.value,
max_length_generation=FLAG_MAX_LENGTH_GENERATION.value,
)
else:
raise NotImplementedError(
f"FLAG_DATASET_NAME.value unsupported: `{FLAG_DATASET_NAME.value}`"
)
make_training_dataset: Callable[Ellipsis,
tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="train",
repeat=False,
)
make_eval_dataset: Callable[Ellipsis,
tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="eval",
repeat=True,
)
############################################################################
# Prepare the step functions
############################################################################
utils.check_contained(FLAG_DISTRIBUTE_MODE.value,
constants.DistributeModeChoices.choices())
tf_function_flags = dict(
experimental_compile=FLAG_EXPERIMENTAL_COMPILE.value,
experimental_relax_shapes=not FLAG_INPUT_FIXED_SIZE.value)
if (FLAG_DISTRIBUTE_MODE.value ==
constants.DistributeModeChoices.split_and_data_parallel):
if not isinstance(model_or_replicas, list):
raise RuntimeError(type(model_or_replicas))
training_step = build_manual_data_parallel_training_step(
model_or_replicas, optimizer, tf_function_flags)
else:
training_step = build_regular_training_step(
model_or_replicas,
optimizer,
strategy=model_specific.strategy,
tf_function_kwargs=tf_function_flags)
evaluation_step = build_evaluation_step(model_or_replicas,
tf_function_flags)
secs_since_last_ckpt = time.time()
# Model checkpoints are saved to the tmp_directory and then rsynced to GCS
##########################################################################
# Prepare the different logging facilities
##########################################################################
train_log_dir = os.path.join(instance_output_dir, "tensorboard",
"train")
eval_log_dir = os.path.join(instance_output_dir, "tensorboard", "eval")
flags_log_dir = os.path.join(instance_output_dir, "tensorboard",
"params")
writers = dict(train=tf.summary.create_file_writer(train_log_dir),
eval=tf.summary.create_file_writer(eval_log_dir),
flags=tf.summary.create_file_writer(flags_log_dir))
with writers["flags"].as_default():
tf.summary.text(
"Flags",
# Tensorboard takes Markdown:
json.dumps(flags_dict, indent=4).replace("\n", "\n\n"),
step=0)
ma_loss = dict(train=utils.MovingAverage(0.9),
eval=utils.MovingAverage(0.9))
step_counters = dict(train=0, eval=0)
batch_counters = dict(train=0, eval=0)
prev_batch_end = time.time()
# The eval ds has no real concept of epoch, repeats forever, shuffling
# each time it reaches its end
with utils.log_duration(LOGGER, "main", "All of make_eval_dataset"):
eval_ds_instance = make_eval_dataset(random_seed=rg.integers(
-2**63, 2**63 - 1), )
LOGGER.debug("Distributing the eval dataset to the replicas.")
if FLAG_DATASET_TYPE.value == "tfr":
eval_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
eval_ds_instance))
LOGGER.debug("Done distributing the eval dataset to the replcias.")
eval_ds_instance = iter(eval_ds_instance)
##########################################################################
# Training Loop
##########################################################################
for epoch in itertools.count():
####################################################################
# Epoch Setup
####################################################################
LOGGER.debug("EPOCH %d START", epoch)
# Shuffle differently every epoch
with utils.log_duration(LOGGER, "main",
"All of make_training_dataset"):
train_ds_instance = make_training_dataset(
random_seed=rg.integers(-2**63, 2**63 - 1), )
LOGGER.debug(
"Attempting to distribute the training dataset to the replicas."
)
if FLAG_DATASET_TYPE.value == "tfr":
train_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
train_ds_instance))
LOGGER.debug(
"Done distributing the training dataset to the replicas.")
train_ds_instance = iter(train_ds_instance)
# This allows us to see if we reached the end of the training iterator,
# in which case "did_at_least_one_training_batch == False".
# We could also test that it did all the batches, to similar results.
did_at_least_one_training_batch = True
split = "eval"
while did_at_least_one_training_batch:
# Invert split
if split == "train":
split = "eval"
else:
split = "train"
# Prepare to test if we did at least one training batch
if split == "train":
did_at_least_one_training_batch = False
if split == "train":
dataset_iterator = itertools.islice(
train_ds_instance, FLAG_BATCHES_BETWEEN_EVALS.value)
else:
# The evaluation DS is tiny, so we reshuffle and take a random
dataset_iterator = itertools.islice(
eval_ds_instance, FLAG_NUMBER_EVAL_BATCHES.value)
LOGGER.debug("Batching")
for batch in dataset_iterator:
# LOGGER.debug("Input sentence:\n\"%s\"",
# tokenizer.decode([x for x in batch["input_ids"][0]
# if x != tokenizer.eos_token_id]))
# LOGGER.debug("Label:\n\"%s\"",
# tokenizer.decode([(x if x != -100 else 0)
# for x in batch["label_ids"][0]]))
if FLAG_DATASET_TYPE.value != "tfr":
batch = (model_specific.strategy.
experimental_distribute_values_from_function(
tf_utils.make_dict_distribute_fn(batch)))
# We only care about training epochs as, obviously, we don't train
# over eval samples; the number of eval samples seen only
# contributes to lowering the variance in the evaluation of when to
# do early stopping.
if split == "train":
did_at_least_one_training_batch = True
input_ids = batch["input_ids"]
label_ids = batch["label_ids"]
####################################################################
# Training Step
####################################################################
step_counters[split] += (FLAG_BATCH_SIZE.value *
actual_num_replicas)
if split == "train":
batch_counters[split] += 1
training_kwargs = dict(
input_ids=input_ids,
label_ids=label_ids,
)
if model_specific.strategy:
utils.print_mem("before running", LOGGER)
LOGGER.debug("Training, Calling strategy.run")
loss = model_specific.strategy.run(
training_step, kwargs=training_kwargs)
LOGGER.debug("Training, Done with strategy.run")
utils.print_mem("after running", LOGGER)
else:
loss = training_step(**training_kwargs) # pytype: disable=wrong-arg-count
# If we are in the strategy-free data parallel mode, we need
# to change the weights of all replicas to those of the model at
# index 0
if (FLAG_DISTRIBUTE_MODE.value ==
constants.DistributeModeChoices.
split_and_data_parallel):
for replica in model_or_replicas[1:]:
replica.set_weights(
model_or_replicas[0].get_weights())
####################################################################
# Evaluation Step
####################################################################
elif split == "eval":
evaluation_kwargs = dict(
input_ids=input_ids,
label_ids=label_ids,
)
if model_specific.strategy:
loss = model_specific.strategy.run(
evaluation_step, kwargs=evaluation_kwargs)
else:
loss = evaluation_step(**evaluation_kwargs)
else:
raise ValueError(
f"Unexpected value for split: {split}")
####################################################################
# Logging
####################################################################
if (FLAG_DISTRIBUTE_MODE.value
in constants.PURE_DATA_PARALLEL_STRATEGIES):
utils.check_equal(len(loss.values),
actual_num_replicas)
LOGGER.debug("Split: %s", split)
LOGGER.debug("Real num replicas: %s",
actual_num_replicas)
LOGGER.debug("Loss: %s", loss)
LOGGER.debug("Loss values: %s", loss.values)
average_loss = float(
tf.math.reduce_mean(loss.values).numpy())
else:
average_loss = float(loss.numpy())
# tf.debugging.check_numerics(loss)
now = time.time()
batch_duration = now - prev_batch_end
prev_batch_end = now
ma_loss[split].update(average_loss)
# Actual logging
LOGGER.info("Epoch: # %d", epoch)
LOGGER.info("Tensorboard_dir: %s", instance_output_dir)
LOGGER.info("Batch: %s # %d", split, batch_counters[split])
LOGGER.info("Step: %s # %d", split, step_counters[split])
if FLAG_USE_SUBSET.value:
LOGGER.warning(">> USING A SUBSET OF THE DATASET <<")
LOGGER.info("%(split)s Batch loss: %(metric)f",
dict(split=split, metric=average_loss))
LOGGER.info(
"%(split)s Moving average loss: %(metric)f",
dict(split=split, metric=ma_loss[split].average))
LOGGER.info(
"%(split)s Moving average ppl: %(metric)f",
dict(split=split,
metric=np.exp(ma_loss[split].average)))
LOGGER.info(
"%(split)s Batch duration: %(duration)s",
dict(split=split,
duration=utils.TimeStamp.from_seconds(
batch_duration).format()))
if FLAG_DISTRIBUTE_MODE.value in constants.DATA_PARALLEL_DMC:
LOGGER.info(
"%(split)s Duration per sample: %(duration)s",
dict(split=split,
duration=utils.TimeStamp.from_seconds(
batch_duration / (FLAG_BATCH_SIZE.value *
actual_num_replicas))))
# Write to Tensorboard
with writers[split].as_default():
tf.summary.scalar(f"Loss/{split}", average_loss,
step_counters[split])
tf.summary.scalar(f"PPL/{split}", np.exp(average_loss),
step_counters[split])
writers[split].flush()
# Save every 5 min
if (time.time() - secs_since_last_ckpt) / (60 * 20) >= 1:
secs_since_last_ckpt = time.time()
save_model(train_steps=step_counters["train"],
model_or_replicas=model_or_replicas,
instance_output_dir=instance_output_dir)
secs_since_last_ckpt = time.time()
save_model(train_steps=step_counters["train"],
model_or_replicas=model_or_replicas,
instance_output_dir=instance_output_dir)
#############################################################
# Post Training Cleanup
#######################################################################
for writer in writers.values():
writer.close()
args = parser.parse_args()
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
SEED = 0
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
log_root = args.log_root
log_path = os.path.join(log_root, 'log.txt')
if check_exists(log_path):
os.remove(log_path)
init_logging(log_path)
main(args)
with open(
os.path.join(args.save_dir,
'scene_infos_{}.pickle'.format(args.tvt)),
'rb') as fin:
scene_infos = pickle.load(fin, encoding='bytes')
assert isinstance(scene_infos, dict)
def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
absl_logging.use_python_logging()
utils.log_module_args(LOGGER, argv[0])
# Some checks for the flags
utils.check_exists(FLAGS.source_text_path)
utils.check_exists(os.path.dirname(FLAGS.subset_text_path))
utils.check_exists(os.path.dirname(FLAGS.subset_embeddings_ds_path))
utils.check_operator(operator.lt, FLAGS.subset_total, FLAGS.source_total)
utils.check_glob_prefix(FLAGS.source_embeddings_prefix)
# Select a random subset
with utils.log_duration(LOGGER, "main", "preparing indices"):
indices = np.random.choice(FLAGS.source_total,
FLAGS.subset_total,
replace=False)
indices.sort()
# Process the textual data
# Much (5 min vs 2 h) faster than iterating through the records and writing
# only those we want. An hypothesis for this is that
# get_single_element would allow to get elements without parsing all of the
# elements along the way, like simply iterating through the records would.
# Or did they get constant time indexing in TFRecords?
# Inspired by the ORQA codebase:
# https://github.com/google-research/language/blob/master/language/orqa/models/orqa_model.py#L147
with utils.log_duration(LOGGER, "main", "preparing data"):
text_ds = tf.data.TFRecordDataset(FLAGS.source_text_path,
buffer_size=512 * 1024 * 1024,
num_parallel_reads=os.cpu_count())
text_ds = text_ds.batch(FLAGS.source_total)
text_ds = tf.data.experimental.get_single_element(text_ds)
subset = tf.gather(text_ds, tf.constant(indices))
with utils.log_duration(LOGGER, "main", "writing text data"):
with tf.io.TFRecordWriter(FLAGS.subset_text_path) as text_writer:
for text in tqdm.tqdm(subset, total=FLAGS.subset_total):
text = text.numpy()
# REALM's data uses no packaging of the data into features, etc.
text_writer.write(text)
with utils.log_duration(LOGGER, "main", "All of the embedding task"):
# Process the embeddings data
with tf.device("/cpu:0"):
with utils.log_duration(LOGGER, "main", "Loading the checkpoint"):
embs = tf.train.load_checkpoint(
FLAGS.source_embeddings_prefix).get_tensor("block_emb")
utils.check_equal(embs.shape[0], FLAGS.source_total)
with utils.log_duration(LOGGER, "main",
"taking a subset of the indices"):
subset = embs[indices]
tf_db = tf.Variable(subset, shape=subset.shape)
ckpt = tf.train.Checkpoint(block_emb=tf_db)
with utils.log_duration(LOGGER, "main", "Saving the checkpoint"):
ckpt.save(FLAGS.subset_embeddings_ds_path)
LOGGER.debug("Done")
def __init__(self, root, transform=None):
self.root = os.path.expanduser(root)
self.transform = transform
if not check_exists(self.processed_folder):
self.process()
self.img = load(os.path.join(self.processed_folder, 'data.pt'))
def main(argv):
# Arguments and logging boilerplate
if len(argv) > 1:
raise RuntimeError(argv)
absl_logging.use_python_logging()
utils.log_module_args(LOGGER, argv[0])
# Load a retriever config.
retriever_config = tf_utils.REALMConfig(
**utils.from_json_file(_FLAG_RETRIEVER_CONFIG_PATH.value))
assert not _FLAG_USE_SUBSET.value
# Preparation of the output path
time_stamp = time.strftime("%Y%m%d-%H%M%S")
target_path = os.path.join(_FLAG_OUTPUT_PATH.value, time_stamp.strip())
if target_path[-1] != "/":
target_path += "/"
##############################################################################
# Setup devices and strategy
##############################################################################
# Duration is pretty much instantaneous
with utils.log_duration(LOGGER, "main", "Initializing devices"):
tpu_config = tf_utils.init_tpus(local=_FLAG_TPU_IS_LOCAL.value,
tpu_name=_FLAG_TPU_NAME.value)
device_type = tf_utils.current_accelerator_type()
LOGGER.debug("Devices: %s", str(tf_utils.devices_to_use()))
if _FLAG_TPU_NAME.value and device_type == "CPU":
raise RuntimeError("Device is CPU and we expected a TPU.")
if device_type == "TPU":
if tpu_config is None:
raise RuntimeError("We should have a tpu_config.")
strategy = tf.distribute.TPUStrategy(tpu_config.resolver)
batch_size = len(
tf_utils.devices_to_use()) * _FLAG_BATCH_SIZE.value
elif device_type == "GPU" or device_type == "CPU":
strategy = tf.distribute.MirroredStrategy()
batch_size = len(
tf_utils.devices_to_use()) * _FLAG_BATCH_SIZE.value
else:
raise RuntimeError(device_type)
##############################################################################
# Load the KILT ELI5 dataset.
##############################################################################
# Takes a while
eli5 = {}
keys = ["train", "validation", "test"]
gpt2_tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2-xl")
gpt2_tokenizer.pad_token = gpt2_tokenizer.eos_token
with utils.log_duration(LOGGER, "main", "Loading the ELI5 datasets."):
if _FLAG_DATASET_ROOT.value:
for split in tqdm.tqdm(keys):
load_path = os.path.join(_FLAG_DATASET_ROOT.value,
"HuggingfaceDatasets",
f"{split}_kilt_eli5.hf")
with tf.device("/job:localhost"):
eli5[split] = datasets.load_from_disk(load_path)
else:
eli5 = datasets.load_dataset("kilt_tasks", "eli5")
##############################################################################
# Load the dataset of the text that will be retrieved.
##############################################################################
# Takes a long time
with utils.log_duration(LOGGER, "Main", "Load the textual dataset"):
# Extract the appropriate text
# The buffer_size is taken from the original ORQA code.
blocks_dataset = tf.data.TFRecordDataset(retriever_config.text_records,
buffer_size=512 * 1024 * 1024)
blocks_dataset = blocks_dataset.batch(
retriever_config.num_block_records, drop_remainder=False)
blocks: tf.Tensor = tf.data.experimental.get_single_element(
blocks_dataset)
############################################################################
# Increase the number of maximum open file descriptors to make space
# for all the shards.
############################################################################
max_num_fd = _FLAG_NUM_SHARDS.value * 3 + _MIN_N_FD
resource.setrlimit(resource.RLIMIT_NOFILE, (max_num_fd, max_num_fd))
############################################################################
# Prepare the output files.
############################################################################
writers = {}
all_paths = {}
for split in keys:
maybe_subset = "_subset" if _FLAG_USE_SUBSET.value else ""
# Prepare paths. They can't be in a generator. A function generator would be
# fine though.
paths = [
os.path.join(target_path + maybe_subset, f"{split}_{i}.tfr")
for i in range(_FLAG_NUM_SHARDS.value)
]
all_paths[split] = paths
writers[split] = []
# Create The TFR writers.
for i, path in enumerate(paths):
writers[split].append(tf.io.TFRecordWriter(path))
# Load the reference DB. We used to accidentally do this once per split :O
with utils.log_duration(LOGGER, "main", "Loading the reference db."):
checkpoint_path = os.path.join(retriever_config.query_embedder_path,
"encoded", "encoded.ckpt")
reference_db_device = tf_utils.device_mapping().CPUs[0].name
with tf.device(reference_db_device):
reference_db = tf_utils.load_reference_db(
checkpoint_path,
variable_name="block_emb",
)
############################################################################
# Prep the encoder and the tokenizer
############################################################################
with utils.log_duration(LOGGER, "main",
"Loading the encoder model and the tokenizer."):
with strategy.scope():
query_encoder = hub.load(retriever_config.query_embedder_path,
tags={})
encode_fn = _make_encode_fn(query_encoder)
encode_fn_strategy_run = make_encode_fn_strategy_run_fn(
strategy=strategy,
encode_fn=encode_fn,
)
vocab_file = os.path.join(retriever_config.query_embedder_path,
"assets", "vocab.txt")
utils.check_exists(vocab_file)
do_lower_case = query_encoder.signatures["tokenization_info"](
)["do_lower_case"]
tokenization_info = dict(vocab_file=vocab_file,
do_lower_case=do_lower_case)
tokenizer, vocab_lookup_table = bert_utils.get_tf_tokenizer(
query_encoder, tokenization_info)
############################################################################
# Preprocess the dataset
############################################################################
cls_token_id = tf.cast(vocab_lookup_table.lookup(tf.constant("[CLS]")),
tf.int32)
sep_token_id = tf.cast(vocab_lookup_table.lookup(tf.constant("[SEP]")),
tf.int32)
transform = _make_transform_fn(
bert_tokenizer=tokenizer,
bert_cls_token_id=cls_token_id,
bert_sep_token_id=sep_token_id,
)
feature_dtypes = {
constants.CTH5Fields.distances: tf.float32,
constants.CTH5Fields.gpt2_retrieved_ids: tf.int32,
constants.CTH5Fields.gpt2_answer_ids_inputs: tf.int32,
constants.CTH5Fields.gpt2_question_ids_inputs: tf.int32,
}
with utils.log_duration(LOGGER, "main", "generating codes"):
for split in keys:
sample_count = 0
eli5: Dict[str, datasets.Dataset]
if split != "test":
for_slices = dict(sample_id=eli5[split]["id"],
question=eli5[split]["input"],
answer=[
sample[0]["answer"]
for sample in eli5[split]["output"]
])
else:
for_slices = dict(
sample_id=eli5[split]["id"],
question=eli5[split]["input"],
)
ds = tf.data.Dataset.from_tensor_slices(for_slices)
ds = ds.map(transform,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.apply(
tf.data.experimental.dense_to_ragged_batch(batch_size))
ds = ds.map(_squeeze,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
tqdm_inner = tqdm.tqdm(enumerate(ds),
total=len(eli5[split]["id"]) //
_FLAG_BATCH_SIZE.value,
desc=f"Split `{split}`: Batches")
for i, batch in tqdm_inner:
features = collections.defaultdict(list)
######################################################################
# Enforce the current real batch size
######################################################################
current_batch_size = batch["sample_id"].shape[0]
for k, v in batch.items():
utils.check_equal(v.shape[0], current_batch_size)
######################################################################
gpt2_question_ids_inputs = _prep_field(batch["question"],
gpt2_tokenizer)
utils.check_equal(gpt2_question_ids_inputs.dtype, np.int32)
utils.check_equal(gpt2_question_ids_inputs.shape[0],
current_batch_size)
if split != "test":
gpt2_answer_ids_inputs = _prep_field(
batch["answer"], gpt2_tokenizer)
utils.check_equal(gpt2_answer_ids_inputs.dtype, np.int32)
utils.check_equal(gpt2_answer_ids_inputs.shape[0],
current_batch_size)
assert len(gpt2_answer_ids_inputs.shape) == 2, (
gpt2_answer_ids_inputs.shape)
######################################################################
# Save the gpt2 tokenized question and answer
######################################################################
features[constants.CTH5Fields.gpt2_question_ids_inputs].extend(
gpt2_question_ids_inputs)
if split != "test":
features[
constants.CTH5Fields.gpt2_answer_ids_inputs].extend(
gpt2_answer_ids_inputs)
######################################################################
# Encode the samples.
######################################################################
batch = strategy.experimental_distribute_values_from_function(
tf_utils.make_dict_distribute_fn(batch))
embeddings = encode_fn_strategy_run(batch)
embeddings = tf_utils.process_strat_output(
embeddings, "embeddings", strategy, current_batch_size)
utils.check_isinstance(embeddings, ops.EagerTensor)
utils.check_equal(embeddings.shape[0], current_batch_size)
# pytype doesn't seem to see that we check the type
utils.check_equal(embeddings.shape[1],
_FLAG_EMBEDDING_DEPTH.value) # pytype: disable=attribute-error
######################################################################
# Retrieve.
######################################################################
# Do exact retrieval
with tf.device(reference_db_device):
top_k, inner_prods = tf_utils.mips_exact_search(
embeddings, _FLAG_NUM_RETRIEVALS.value, reference_db)
# Collate the results
top_k = tf_utils.process_strat_output(top_k, "top_k", strategy,
current_batch_size)
# Check the shapes
utils.check_equal(
inner_prods.shape,
(current_batch_size, _FLAG_NUM_RETRIEVALS.value))
utils.check_equal(
top_k.shape,
(current_batch_size, _FLAG_NUM_RETRIEVALS.value))
# Save the distances
features[constants.CTH5Fields.distances].extend(inner_prods)
# Retrieve the text fields associated to the indices
gathered = tf.gather(blocks, top_k).numpy()
utils.check_equal(gathered.shape[0], current_batch_size)
utils.check_equal(gathered.shape[1],
_FLAG_NUM_RETRIEVALS.value)
retrievals = []
for index_in_batch in range(current_batch_size):
# Put the appropriate byte strings in a list
local_gathered = gathered[index_in_batch].tolist()
utils.check_equal(len(local_gathered),
_FLAG_NUM_RETRIEVALS.value)
# Decode to utf-8
local_gathered = [
sample.decode() for sample in local_gathered
]
# Encode to GPT2 BPE
token_ids = np.array(
gpt2_tokenizer.batch_encode_plus(
local_gathered,
padding="max_length",
truncation=True,
).input_ids)
# Make sure no line is empty
# TODO(julesgm): Maybe optional
for line in token_ids:
assert not np.all(line == 0), line
# Convert the eos_tokens
token_ids[token_ids == gpt2_tokenizer.eos_token_id] = -1
# Save the retrievals
retrievals.append(token_ids)
# Save the feature
features[constants.CTH5Fields.gpt2_retrieved_ids] = retrievals
utils.check_equal(
retrievals[0].shape,
(_FLAG_NUM_RETRIEVALS.value, _FLAG_CONTEXT_SIZE.value))
for k, v in features.items():
utils.check_equal(len(v), current_batch_size)
for index_in_batch in range(current_batch_size):
feature_dict = {}
for feature_k, feature_v in features.items():
# Cast the feature to its appropriate dtype
casted_feats = tf.cast(feature_v[index_in_batch],
feature_dtypes[feature_k])
# Serialize the tensor to bytes
feature_bytes = tf.io.serialize_tensor(casted_feats)
# Build a bytes list tf.train.Feature object,
# the serialization tree node
feature_dict[feature_k] = _bytes_feature(feature_bytes)
# Create the serialization tree root
# Expects a list of features
feature = tf.train.Features(feature=feature_dict)
# Expects a tf.train.Features object
example_obj = tf.train.Example(features=feature)
# Serialize that to bytes
serialized_example = example_obj.SerializeToString()
# Write the bytes
# TODO(julesgm): Parallelize this with a thread or a process pool &
# futures.
writers[split][sample_count %
_FLAG_NUM_SHARDS.value].write(
serialized_example)
sample_count += 1
if sample_count % 1000 == 0:
LOGGER.debug("Paths: %s", str(all_paths[split][0]))
LOGGER.debug("Flushing and closing the `%s` writers", split)
for writer in tqdm.tqdm(writers[split]):
writer.flush()
writer.close()
LOGGER.debug("Done.")
#Read arguments
specification = sys.argv[1]
job_spec = sys.argv[2]
version = sys.argv[3]
#Generate product name
product = utils.get_product_id(specification, version)
#Prepare dataset metadata
metadata = {
"job-specification": job_spec,
"job-version": version,
"resource": "hysds-io-specification"
}
if not utils.check_exists(job_spec):
print(
"[ERROR] Job Specification, {0}, does not exist. Cannot create HySDS-IO."
.format(job_spec),
file=sys.stderr)
sys.exit(-2)
#Read specification metadata and merge it
with open(specification, "r") as fp:
metadata.update(json.load(fp))
metadata["id"] = product
if metadata.get("component", "tosca") == "mozart":
add_hysds_io(app.conf["MOZART_REST_URL"], metadata)
else:
add_hysds_io(app.conf["GRQ_REST_URL"], metadata)
sys.exit(0)
def main(argv):
##############################################################################
# Initial Setup. Logging, Flags, Random seeds.
##############################################################################
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
absl_logging.use_python_logging()
flags_dict = {
flag.name: flag.value
for flag in FLAGS.flags_by_module_dict()[argv[0]]
}
if FLAGS.use_subset:
message = (f"{colorama.Back.RED}{colorama.Fore.WHITE}"
f"{colorama.Style.BRIGHT}USING A SUBSET OF THE DATASET"
f"{colorama.Style.RESET_ALL}")
LOGGER.warning(message)
utils.log_module_args(LOGGER, argv[0])
if not FLAGS.output_dir.startswith("gs://"):
utils.check_exists(FLAG_OUTPUT_DIR.value)
if not tf.io.gfile.isdir(FLAG_OUTPUT_DIR.value):
raise RuntimeError("Output dir needs to be a directory.")
tf.random.set_seed(FLAG_RANDOM_SEED.value)
np.random.seed(FLAG_RANDOM_SEED.value)
# Prepare the instance output directory path and save the config there
# Prepare the path
folder_name = time.strftime(
f"{FLAG_RUN_NAME.value}_{FLAG_APPROACH_TYPE.value}_%Y%m%d-%H%M%S")
instance_output_dir = os.path.join(FLAG_OUTPUT_DIR.value,
folder_name).strip()
if not instance_output_dir.endswith("/"):
instance_output_dir += "/"
json_target = os.path.join(instance_output_dir, "training_params.json")
# Make the folder if we're not on gcloud
if not json_target.strip().startswith("gs://"):
subprocess.check_call(["mkdir", "-p", instance_output_dir])
# Safe the config file
utils.to_json_file(json_target, flags_dict)
##############################################################################
# Initialization and Configuration of the Devices.
##############################################################################
tpu_setup = None
accel = tf_utils.current_accelerator_type()
if FLAG_TPU_IS_LOCAL.value:
assert accel == "TPU", accel
if accel == "TPU":
assert FLAG_TPU_IS_LOCAL.value, FLAG_TPU_IS_LOCAL.value
if tf_utils.current_accelerator_type() in {"CPU", "TPU"}:
tpu_setup = tf_utils.init_tpus(tpu_name=FLAG_TPU_NAME.value,
local=FLAG_TPU_IS_LOCAL.value)
LOGGER.debug("Devices we are computing on:\n%s",
utils.wrap_iterable(map(str, tf_utils.devices_to_use())))
LOGGER.debug("All devices:")
LOGGER.debug(tf_utils.device_mapping())
if tf_utils.current_accelerator_type() == "GPU":
tf.config.set_soft_device_placement(True)
if tf_utils.current_accelerator_type() != "TPU":
tf.debugging.set_log_device_placement(True)
utils.check_operator(operator.ne, tf_utils.current_accelerator_type(),
"CPU")
assert FLAG_TPU_NAME.value == socket.gethostname(), (
"This is a configuration choice. You can remove this. "
"There will be no side effects.")
if FLAG_DISTRIBUTE_MODE.value in constants.PURE_DATA_PARALLEL_STRATEGIES:
actual_num_replicas = len(tf_utils.devices_to_use())
elif FLAG_DISTRIBUTE_MODE.value in constants.DATA_PARALLEL_DMC:
actual_num_replicas = FLAG_NUM_REPLICAS.value
else:
actual_num_replicas = 1
##############################################################################
# We load the retriever model if it is needed.
##############################################################################
# Not currently used. See old commits.
retriever = None
##############################################################################
# Distributed training task
##############################################################################
if FLAG_TASK.value == constants.TaskChoices.train:
with utils.log_duration(LOGGER, "main", "Load model"):
utils.print_mem("before loading model", LOGGER)
model_specific = task_specific.load_model(
FLAG_MODEL_KEY.value, FLAG_DISTRIBUTE_MODE.value, tpu_setup,
FLAG_NUM_REPLICAS.value)
utils.print_mem("after loading model", LOGGER)
model = model_specific.model
if isinstance(model, list):
model: List[transformers.TFGPT2LMHeadModel]
else:
model: transformers.TFGPT2LMHeadModel
tokenizer = model_specific.tokenizer
def make_optimizer():
if FLAG_OPTIMIZER_TYPE.value == constants.OptimizerTypes.adafactor:
return tensor2tensor.utils.adafactor.AdafactorOptimizer(
learning_rate=FLAG_LEARNING_RATE.value)
elif FLAG_OPTIMIZER_TYPE.value == constants.OptimizerTypes.adam:
return tf.keras.optimizers.Adam(
learning_rate=FLAG_LEARNING_RATE.value)
else:
raise ValueError(FLAG_OPTIMIZER_TYPE.value)
if model_specific.strategy:
with model_specific.strategy.scope():
optimizer = make_optimizer()
else:
optimizer = make_optimizer()
############################################################################
# Prepare the dataset functions
############################################################################
rg = np.random.default_rng(FLAG_RANDOM_SEED.value)
def call_lm_preproc(repeat, split, random_seed):
"""Using functools.partial prevents the linter from doing its job."""
if FLAG_DATASET_NAME.value == constants.DatasetNameChoices.kilt_eli5:
return task_specific.create_lm_ds_kilt_eli5(
tokenizer=tokenizer,
context_window_size=model.config.n_positions,
dataset_name=FLAG_DATASET_NAME.value,
# Batches are split over the replicas:
batch_size=FLAG_BATCH_SIZE.value * actual_num_replicas,
db_path=FLAG_DB_PATH.value,
random_seed=random_seed,
use_subset=FLAG_USE_SUBSET.value,
subset_size=FLAG_SUBSET_SIZE.value,
use_helper_words=FLAG_USE_HELPER_WORDS.value,
approach_type=FLAG_APPROACH_TYPE.value,
num_retrievals=FLAG_NUM_RETRIEVALS.value,
retrieval_temperature=FLAG_RETRIEVAL_TEMPERATURE.value,
retriever=retriever,
repeat=repeat,
split=split,
enable_debug_checks=FLAG_DATASET_DEBUG.value,
retrieval_bank_size=FLAG_RETRIEVAL_BANK_SIZE.value,
dataset_type=FLAG_DATASET_TYPE.value,
qty_shuffle=FLAG_QTY_SHUFFLE.value,
tfr_prefix=FLAG_TFR_PREFIX.value,
max_length_generation=FLAG_MAX_LENGTH_GENERATION.value,
)
else:
raise NotImplementedError(
f"FLAG_DATASET_NAME.value unsupported: `{FLAG_DATASET_NAME.value}`"
)
make_training_dataset: Callable[...,
tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="train",
repeat=False,
)
make_eval_dataset: Callable[..., tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="eval",
repeat=True,
)
############################################################################
# Prepare the step functions
############################################################################
utils.check_contained(FLAG_DISTRIBUTE_MODE.value,
constants.DistributeModeChoices.choices())
tf_function_flags = dict(
experimental_compile=FLAG_EXPERIMENTAL_COMPILE.value,
experimental_relax_shapes=not FLAG_INPUT_FIXED_SIZE.value)
training_step = build_regular_training_step(
model,
optimizer,
strategy=model_specific.strategy,
tf_function_kwargs=tf_function_flags)
evaluation_step = build_evaluation_step(model, tf_function_flags)
timestamp_last_ckpt_secs = time.time()
# Model checkpoints are saved to the tmp_directory and then rsynced to GCS
############################################################################
# Prepare the statistics and the logging facilities.
############################################################################
# Tensorboard
with model_specific.strategy.scope():
checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
saver = Saver(instance_output_dir, checkpoint)
train_log_dir = os.path.join(instance_output_dir, "tensorboard",
"train")
eval_log_dir = os.path.join(instance_output_dir, "tensorboard", "eval")
flags_log_dir = os.path.join(instance_output_dir, "tensorboard",
"params")
writers = dict(train=tf.summary.create_file_writer(train_log_dir),
eval=tf.summary.create_file_writer(eval_log_dir),
flags=tf.summary.create_file_writer(flags_log_dir))
with writers["flags"].as_default():
tf.summary.text(
"Flags",
# Tensorboard takes Markdown:
json.dumps(flags_dict, indent=4).replace("\n", "\n\n"),
step=0)
# Different information to log.
ma_loss = dict(train=utils.MovingAverage(0.9),
eval=utils.MovingAverage(0.9))
step_counters = dict(train=0, eval=0)
batch_counters = dict(train=0, eval=0)
prev_batch_end = time.time()
############################################################################
# Create the Eval DS object.
# ==========================================================================
# The eval ds has no real concept of epoch, repeats forever, shuffling
# each time it reaches its end.
############################################################################
# Create
with utils.log_duration(LOGGER, "main", "All of make_eval_dataset"):
eval_ds_instance = make_eval_dataset(random_seed=rg.integers(
-2**63, 2**63 - 1), )
# Maybe distribute
LOGGER.debug("Distributing the eval dataset to the replicas.")
if FLAG_DATASET_TYPE.value == "tfr":
eval_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
eval_ds_instance))
# Start the iteration. We step by calling `next(...)`.
LOGGER.debug("Done distributing the eval dataset to the replicas.")
eval_ds_instance = iter(eval_ds_instance)
step_function = dict(train=training_step, eval=evaluation_step)
############################################################################
# Training Loop
# ==========================================================================
# Create a new training dataset object that lasts for one epoch.
# This is different from the eval training dataset object, which loops
# forever.
############################################################################
for epoch in itertools.count():
##########################################################################
# Epoch Setup
##########################################################################
LOGGER.debug("EPOCH %d START", epoch)
# Shuffle differently every epoch
with utils.log_duration(LOGGER, "main",
"All of make_training_dataset"):
train_ds_instance = make_training_dataset(
random_seed=rg.integers(-2**63, 2**63 - 1), )
LOGGER.debug(
"Attempting to distribute the training dataset to the replicas."
)
if FLAG_DATASET_TYPE.value == "tfr":
train_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
train_ds_instance))
LOGGER.debug(
"Done distributing the training dataset to the replicas.")
train_ds_instance = iter(train_ds_instance)
# To change splits, we use `itertools.islice` over the dataset generator.
# When the training dataset generator is done, a new loop of the following
# while loop occurs, but no training batch is done because we are taking
# an `islice` of a generator that is done.
did_at_least_one_training_batch = True
split = "eval"
while did_at_least_one_training_batch:
utils.check_operator(operator.ne,
tf_utils.current_accelerator_type(),
"CPU")
# Invert split
if split == "train":
split = "eval"
else:
split = "train"
# Prepare to test if we did at least one training batch
if split == "train":
did_at_least_one_training_batch = False
########################################################################
# Take slices from the dataset iterator
# ======================================================================
# We only want to do a certain number of batches before switching splits
# We do this by using an `itertools.islice` of the dataset iterators.
########################################################################
if split == "train":
dataset_iterator = toolz.take(
FLAG_BATCHES_BETWEEN_EVALS.value, train_ds_instance)
else:
# The evaluation dataset generator is infinite, reshuffles everytime
# it gets to its end.
# Still, we take a fixed size slice form that infinite generator.
dataset_iterator = toolz.take(
FLAG_NUMBER_EVAL_BATCHES.value, eval_ds_instance)
LOGGER.debug("Batching")
for batch in dataset_iterator:
if FLAG_LOG_SAMPLES.value:
####################################################################
# Print elements of the dataset
####################################################################
# Make ourselves resistant to values possibly being a PerReplica
# object
LOGGER.warning(
f"%(red)sLOGGING SAMPLES. THIS IS VERY SLOW.%(reset)s",
dict(
red=colorama.Fore.RED,
reset=colorama.Style.RESET_ALL,
))
is_distributed = isinstance(batch["input_ids"],
values.PerReplica)
for in_batch_idx in range(FLAG_BATCH_SIZE.value):
for replica_idx in (range(actual_num_replicas)
if is_distributed else [0]):
if is_distributed:
sample = {
k: batch[k].values[replica_idx]
for k in batch
}
else:
sample = batch
# input_sentence = tokenizer.decode(
# [x for x in sample["input_ids"][i] if x != tokenizer.eos_token_id]
# )
# LOGGER.debug(
# "%sInput [%d / %d]%s:\n\"%s\"",
# colorama.Fore.GREEN,
# replica_idx + 1,
# actual_num_replicas,
# colorama.Style.RESET_ALL,
# input_sentence,
# )
#
# answer = tokenizer.decode(
# [(x if x != -100 else 0) for x in sample["label_ids"][i]]
# )
# LOGGER.debug(
# "%sLabel [%d / %d]%s:\n\"%s\"",
# colorama.Fore.GREEN,
# replica_idx + 1,
# actual_num_replicas,
# colorama.Style.RESET_ALL,
# answer,
# )
cons = console.Console()
sentences = table.Table()
sentences.add_column("BPE Index",
justify="center")
sentences.add_column("Inputs",
justify="center")
sentences.add_column("Labels",
justify="center")
for bpe_idx, (x, y) in enumerate(
itertools.zip_longest(
sample["input_ids"]
[in_batch_idx].numpy(),
sample["label_ids"]
[in_batch_idx].numpy(),
fillvalue=None,
)):
x_w = tokenizer.decode(
[x]) if x >= 0 else f"[ {x} ]"
y_w = tokenizer.decode(
[y]) if y >= 0 else f"[ {y} ]"
sentences.add_row(str(bpe_idx), x_w, y_w)
cons.print(sentences)
# We only care about training epochs as, obviously, we don't train
# over eval samples; the number of eval samples seen only
# contributes to lowering the variance in the evaluation of when to
# do early stopping.
if split == "train":
did_at_least_one_training_batch = True
input_ids = batch["input_ids"]
label_ids = batch["label_ids"]
# Per split step counter
step_counters[
split] += FLAG_BATCH_SIZE.value * actual_num_replicas
batch_counters[split] += 1
######################################################################
# Model step function.
######################################################################
step_function_kwargs = dict(
input_ids=input_ids,
label_ids=label_ids,
)
utils.print_mem(f"[{split}] - Mem before `strategy.run`",
LOGGER)
LOGGER.debug("[%s] - Calling `strategy.run`", split)
loss = model_specific.strategy.run(
step_function[split], kwargs=step_function_kwargs)
LOGGER.debug("[%s] - Done `strategy.run`", split)
utils.print_mem(f"[{split}] - Mem after `strategy.run`",
LOGGER)
####################################################################
# End of logging step code / Logging and saving the model.
####################################################################
if (FLAG_DISTRIBUTE_MODE.value
in constants.PURE_DATA_PARALLEL_STRATEGIES):
utils.check_equal(len(loss.values),
actual_num_replicas)
LOGGER.debug("[%s] - Real num replicas: %s", split,
actual_num_replicas)
average_loss = float(
tf.math.reduce_mean(loss.values).numpy())
LOGGER.debug("[%s] - Loss: %s", str(split),
str(average_loss))
else:
average_loss = float(loss.numpy())
tf.debugging.check_numerics(
loss.values if isinstance(loss, values.PerReplica) else
loss, "Numerics failed.")
now = time.time()
batch_duration = now - prev_batch_end
prev_batch_end = now
ma_loss[split].update(average_loss)
LOGGER.info("[%s] - Epoch: # %d", split, epoch)
LOGGER.info("[%s] - Tensorboard_dir: %s", split,
instance_output_dir)
LOGGER.info("[%s] - Batch: # %d", split,
batch_counters[split])
LOGGER.info("[%s] - Step: # %d", split,
step_counters[split])
if FLAG_USE_SUBSET.value:
LOGGER.warning(">> USING A SUBSET OF THE DATASET <<")
LOGGER.info(
"[%(split)s] - Batch loss: %(metric)f",
dict(split=split, metric=average_loss))
LOGGER.info(
"[%(split)s] - Moving average loss: %(metric)f",
dict(split=split, metric=ma_loss[split].average))
LOGGER.info(
"[%(split)s] - Moving average ppl: %(metric)f",
dict(split=split,
metric=np.exp(ma_loss[split].average)))
LOGGER.info(
"[%(split)s] - Batch duration: %(duration)s",
dict(split=split,
duration=utils.TimeStamp.from_seconds(
batch_duration).format()))
# Write to Tensorboard
with writers[split].as_default():
tf.summary.scalar(f"Loss/{split}", average_loss,
step_counters[split])
tf.summary.scalar(f"PPL/{split}", np.exp(average_loss),
step_counters[split])
writers[split].flush()
######################################################################
# Save every `FLAG_SAVE_PERIOD_MIN.value` minutes.
######################################################################
delta_sec = time.time() - timestamp_last_ckpt_secs
utils.check_operator(operator.gt, delta_sec, 0)
period_sec = 60 * FLAG_SAVE_PERIOD_MIN.value
utils.check_operator(operator.gt, period_sec, 0)
ratio = delta_sec / period_sec
LOGGER.info(
"[%(split)s] - RATIO: %(ratio)s",
dict(split=split, ratio=str(ratio)))
LOGGER.info(
"[%(split)s] - Target: %(target)s, Present: %(present)s",
dict(
split=split,
target=str(period_sec),
present=str(delta_sec),
))
if ratio >= 1:
dur = delta_sec / 60
timestamp_last_ckpt_secs = time.time()
LOGGER.debug(
"SAVING MODEL - CAUSE: DURATION - %0.2f min", dur)
# checkpoint.save(ckpt_prefix)
saver.save_model(
train_steps=step_counters["train"],
model_or_replicas=model,
optimizer=optimizer,
)
############################################################################
# Post Training Cleanup
############################################################################
for writer in writers.values():
writer.close()
def main(argv):
if len(argv) > 1:
raise RuntimeError(argv)
absl_logging.use_python_logging()
retriever_config = tf_utils.REALMSave(
**utils.from_json_file(_FLAG_RETRIEVER_CONFIG_PATH.value))
extra = "_FROM_SUBSET" if _FLAG_USE_SUBSET.value else ""
time_stamp = time.strftime("%Y%m%d-%H%M%S")
target_path = os.path.join(_FLAG_OUTPUT_PATH.value,
time_stamp + extra).strip()
if target_path[-1] != "/":
target_path += "/"
##############################################################################
# Setup devices and strategy
##############################################################################
with utils.log_duration(LOGGER, "main", "Initializing devices"):
tpu_config = tf_utils.init_tpus()
device_type = tf_utils.current_accelerator_type()
LOGGER.debug("Devices: %s", str(tf_utils.devices_to_use()))
if device_type == "TPU":
if tpu_config is None:
raise RuntimeError("We should have a tpu_config.")
strategy = tf.distribute.TPUStrategy(tpu_config.resolver)
batch_size = len(
tf_utils.devices_to_use()) * _FLAG_BATCH_SIZE.value
elif device_type == "GPU" or device_type == "CPU":
strategy = tf.distribute.MirroredStrategy()
batch_size = len(
tf_utils.devices_to_use()) * _FLAG_BATCH_SIZE.value
else:
raise RuntimeError(device_type)
##############################################################################
# Load the dataset.
##############################################################################
eli5 = {}
keys = ["train", "eval", "test"]
gpt2_tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2-xl")
gpt2_tokenizer.pad_token = gpt2_tokenizer.eos_token
with utils.log_duration(LOGGER, "main", "Loading the ELI5 datasets."):
for split in tqdm.tqdm(keys):
load_path = os.path.join(_FLAGS_DATASET_ROOT.value,
"HuggingfaceDatasets",
f"{split}_kilt_eli5.hf")
with tf.device("/job:localhost"):
eli5[split] = datasets.load_from_disk(load_path)
if _FLAG_USE_SUBSET.value:
_warn_subset()
##############################################################################
#
##############################################################################
with utils.log_duration(LOGGER, "Main", "Load the textual dataset"):
# Extract the appropriate text
# The buffer_size is taken from the original ORQA code.
blocks_dataset = tf.data.TFRecordDataset(retriever_config.text_records,
buffer_size=512 * 1024 * 1024)
blocks_dataset = blocks_dataset.batch(
retriever_config.num_block_records, drop_remainder=True)
blocks = tf.data.experimental.get_single_element(blocks_dataset)
with tempfile.TemporaryDirectory() as tmp_dir:
############################################################################
# Prepare the output file.
############################################################################
tmp_dir = pathlib.Path(tmp_dir)
h5_output_path = tmp_dir / "codes.h5"
output_file = h5py.File(h5_output_path, "w")
flags_dict = {
flag.name: flag.value
for flag in flags.FLAGS.flags_by_module_dict()[argv[0]]
}
utils.to_json_file(tmp_dir / "params.json", flags_dict)
for split in keys:
with utils.log_duration(
LOGGER, "main",
"Creating the output hdf5 file, embeddings."):
num_entries = len(eli5[split]["id"])
if _FLAG_USE_SUBSET.value:
num_entries = min(num_entries, _FLAG_SUBSET_AMOUNT.value)
split_group = output_file.create_group(split)
with utils.log_duration(
LOGGER, "main",
"Creating the output hdf5 file, retrieval."):
split_group.create_dataset(
constants.CTH5Fields.distances,
shape=(num_entries, _FLAG_NUM_RETRIEVALS.value),
dtype=np.float32,
)
split_group.create_dataset(
constants.CTH5Fields.gpt2_question_ids_inputs,
shape=(num_entries, _FLAG_CONTEXT_SIZE.value),
dtype=np.int32)
if split != "test":
split_group.create_dataset(
constants.CTH5Fields.gpt2_answer_ids_inputs,
shape=(num_entries, _FLAG_CONTEXT_SIZE.value),
dtype=np.int32)
split_group.create_dataset(
constants.CTH5Fields.gpt2_retrieved_ids,
shape=(
num_entries,
_FLAG_NUM_RETRIEVALS.value,
_FLAG_MAX_LENGTH_RETRIEVALS.value,
),
dtype=np.int32)
with utils.log_duration(LOGGER, "main",
"Loading the reference db."):
checkpoint_path = os.path.join(
retriever_config.query_embedder_path, "encoded",
"encoded.ckpt")
reference_db_device = tf_utils.device_mapping().CPUs[0].name
with tf.device(reference_db_device):
reference_db = tf_utils.load_reference_db(
checkpoint_path,
variable_name="block_emb",
)
############################################################################
# Prep the encoder and the tokenizer
############################################################################
with utils.log_duration(
LOGGER, "main",
"Loading the encoder model and the tokenizer."):
with strategy.scope():
query_encoder = hub.load(retriever_config.query_embedder_path,
tags={})
encode_fn = _make_encode_fn(query_encoder)
encode_fn_strategy_run = _make_encode_fn_strategy_run_fn(
strategy=strategy,
encode_fn=encode_fn,
)
vocab_file = os.path.join(retriever_config.query_embedder_path,
"assets", "vocab.txt")
utils.check_exists(vocab_file)
do_lower_case = query_encoder.signatures["tokenization_info"](
)["do_lower_case"]
tokenization_info = dict(vocab_file=vocab_file,
do_lower_case=do_lower_case)
tokenizer, vocab_lookup_table = bert_utils.get_tf_tokenizer(
query_encoder, tokenization_info)
############################################################################
# Preprocess the dataset
############################################################################
cls_token_id = tf.cast(vocab_lookup_table.lookup(tf.constant("[CLS]")),
tf.int32)
sep_token_id = tf.cast(vocab_lookup_table.lookup(tf.constant("[SEP]")),
tf.int32)
transform = _make_transform_fn(
bert_tokenizer=tokenizer,
bert_cls_token_id=cls_token_id,
bert_sep_token_id=sep_token_id,
)
with utils.log_duration(LOGGER, "main", "generating codes"):
tqdm_splits = tqdm.tqdm(keys)
for split in tqdm_splits:
tqdm_splits.set_description(f"Split `{split}`")
eli5: Dict[str, datasets.Dataset]
write_start = 0
if _FLAG_USE_SUBSET.value:
_warn_subset(tqdm_splits)
eli5[split] = eli5[split][:_FLAG_SUBSET_AMOUNT.value]
utils.check_operator(operator.le, len(eli5[split]["id"]),
_FLAG_SUBSET_AMOUNT.value)
utils.check_operator(operator.le,
len(eli5[split]["input"]),
_FLAG_SUBSET_AMOUNT.value)
else:
utils.check_equal(len(eli5[split]), len(eli5[split]["id"]))
utils.check_equal(len(eli5[split]),
len(eli5[split]["input"]))
if split != "test":
for_slices = dict(sample_id=eli5[split]["id"],
question=eli5[split]["input"],
answer=[
sample["answer"][0]
for sample in eli5[split]["output"]
])
else:
for_slices = dict(
sample_id=eli5[split]["id"],
question=eli5[split]["input"],
)
ds = tf.data.Dataset.from_tensor_slices(for_slices)
ds = ds.map(transform,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.apply(
tf.data.experimental.dense_to_ragged_batch(batch_size))
ds = ds.map(_squeeze,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
tqdm_inner = tqdm.tqdm(enumerate(ds),
total=len(eli5[split]["id"]) //
_FLAG_BATCH_SIZE.value,
desc=f"Split `{split}`: Batches")
for i, batch in tqdm_inner:
######################################################################
# Enforce the current real batch size
######################################################################
current_batch_size = batch["sample_id"].shape[0]
for k, v in batch.items():
utils.check_equal(v.shape[0], current_batch_size)
######################################################################
gpt2_question_ids_inputs = _prep_field(
batch["question"], gpt2_tokenizer)
utils.check_equal(gpt2_question_ids_inputs.dtype, np.int32)
utils.check_equal(gpt2_question_ids_inputs.shape[0],
current_batch_size)
if split != "test":
gpt2_answer_ids_inputs = _prep_field(
batch["answer"], gpt2_tokenizer)
utils.check_equal(gpt2_answer_ids_inputs.dtype,
np.int32)
utils.check_equal(gpt2_answer_ids_inputs.shape[0],
current_batch_size)
assert len(gpt2_answer_ids_inputs.shape) == 2, (
gpt2_answer_ids_inputs.shape)
######################################################################
# Save the gpt2 tokenized question and answer
######################################################################
end = write_start + current_batch_size
utils.check_equal(
output_file[split][
constants.CTH5Fields.gpt2_question_ids_inputs]
[write_start:end].shape[0], current_batch_size)
output_file[split][
constants.CTH5Fields.gpt2_question_ids_inputs][
write_start:end] = gpt2_question_ids_inputs
if split != "test":
output_file[split][
constants.CTH5Fields.gpt2_answer_ids_inputs][
write_start:end] = gpt2_answer_ids_inputs
######################################################################
# Encode the samples.
######################################################################
batch = strategy.experimental_distribute_values_from_function(
tf_utils.make_dict_distribute_fn(batch))
embeddings = encode_fn_strategy_run(batch)
embeddings = tf_utils.process_strat_output(
embeddings, "embeddings", strategy, current_batch_size)
utils.check_isinstance(embeddings, ops.EagerTensor)
utils.check_equal(embeddings.shape[0], current_batch_size)
# pytype doesn't seem to see that we check the type
utils.check_equal(embeddings.shape[1],
_FLAG_EMBEDDING_DEPTH.value) # pytype: disable=attribute-error
######################################################################
# Retrieve.
######################################################################
with tf.device(reference_db_device):
top_k, inner_prods = tf_utils.mips_exact_search(
embeddings, _FLAG_NUM_RETRIEVALS.value,
reference_db)
top_k = tf_utils.process_strat_output(
top_k, "top_k", strategy, current_batch_size)
utils.check_equal(
inner_prods.shape,
(current_batch_size, _FLAG_NUM_RETRIEVALS.value))
utils.check_equal(
top_k.shape,
(current_batch_size, _FLAG_NUM_RETRIEVALS.value))
output_file[split]["distances"][
write_start:end] = inner_prods
gathered = tf.gather(blocks, top_k).numpy()
utils.check_equal(gathered.shape[0], current_batch_size)
utils.check_equal(write_start + gathered.shape[0], end)
for j in range(gathered.shape[0]):
local_gathered = gathered[j].tolist()
utils.check_equal(len(local_gathered),
_FLAG_NUM_RETRIEVALS.value)
local_gathered = [
sample.decode() for sample in local_gathered
]
token_ids = np.array(
gpt2_tokenizer.batch_encode_plus(
local_gathered,
padding="max_length",
truncation=True,
).input_ids)
for line in token_ids:
assert not np.all(line == 0), line
token_ids[token_ids ==
gpt2_tokenizer.eos_token_id] = -1
output_file[split][
constants.CTH5Fields.gpt2_retrieved_ids][
write_start +
j] = token_ids[:, :_FLAG_MAX_LENGTH_RETRIEVALS.
value]
write_start += current_batch_size
############################################################################
# Upload the results to GCS
############################################################################
LOGGER.debug("DONE WITH THE PRODUCTION")
output_file.close()
with utils.log_duration(LOGGER, "main", "gsutil transfer"):
command = [
"/root/google-cloud-sdk/bin/gsutil", "-m", "cp", "-r",
str(tmp_dir / "*"), target_path
]
LOGGER.debug("Command: %s", " ".join(command))
subprocess.check_call(command)
LOGGER.debug("ALL DONE")
def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
absl_logging.use_python_logging()
utils.log_module_args(LOGGER, argv[0])
utils.check_exists(FLAGS.scann_config_path)
utils.check_glob_prefix(FLAGS.embeddings_ckpt_path)
utils.check_exists(FLAGS.output_dir)
if not tf.io.gfile.isdir(FLAGS.output_dir):
raise RuntimeError("Output dir needs to be a directory.")
##############################################################################
# Setup: Build the ScaNN (Scam) searcher
##############################################################################
with utils.log_duration(LOGGER, "main", "load_scann_searcher"):
checkpoint_path = os.path.join(FLAGS.embeddings_ckpt_path)
# The conversion to a ScannConfig object enforces that all the fields we
# expect are present in the json file.
scann_config = retrievers.ScannConfig(
**utils.from_json_file(FLAGS.scann_config_path))
block_emb, scann_searcher = scann_utils.load_scann_searcher(
var_name="block_emb",
checkpoint_path=checkpoint_path,
**vars(scann_config))
utils.check_operator(operator.ge, block_emb.shape[0], FLAGS.test_how_many)
##############################################################################
# Recall Computation
##############################################################################
LOGGER.debug(block_emb.shape)
utils.check_operator(operator.ge, block_emb.shape[0], FLAGS.test_how_many)
with utils.log_duration(LOGGER, "main", "all retrievals & comparisons"):
LOGGER.debug("block_emb.shape: %s", str(block_emb.shape))
LOGGER.debug("FLAGS.test_how_many: %d", FLAGS.test_how_many)
all_indices = np.random.choice(block_emb.shape[0],
FLAGS.test_how_many,
replace=False)
count_total = 0
count_good = 0
for i, idx_start in tqdm.tqdm(
enumerate(range(0, len(all_indices), FLAGS.batch_size))):
indices = all_indices[idx_start:idx_start + FLAGS.batch_size]
vectors = tf.gather(block_emb, indices)
if FLAGS.mode == "all":
with utils.log_duration(LOGGER, "main", "exact_search"):
labels = exact_search(FLAGS.num_neighbors, vectors,
block_emb)
elif FLAGS.mode == "any":
labels = tf.cast(tf.expand_dims(indices, -1), tf.int32)
else:
raise RuntimeError(FLAGS.mode)
with utils.log_duration(LOGGER, "main", "scann_search"):
predictions, _ = scann_searcher.search_batched(vectors)
good = tf.sets.intersection(labels, predictions)
count_good += len(good.values)
count_total += tf.math.reduce_prod(labels.shape)
ratio = count_good / count_total
if i % FLAGS.print_every_n_batches == 0 and i != 0:
LOGGER.debug("Recall so far: %f %%", 100 * ratio)
final_recall = count_good / count_total
LOGGER.debug(
"Final recall for mode `%(mode)s` with `%(num_neighbors)d` "
"neighbors: %(recall)f %%",
dict(mode=FLAGS.mode,
num_neighbors=FLAGS.num_neighbors,
recall=100 * final_recall))
LOGGER.debug("%d true positives over %d points.", count_good, count_total)
##############################################################################
# Build the output object and save it.
##############################################################################
output = {}
output["flags"] = {
flag.name: flag.value
for flag in FLAGS.flags_by_module_dict()[argv[0]]
}
output["recall"] = float(final_recall)
# Redundant but easier to read
output["count_goods"] = int(count_good)
output["count_total"] = int(count_total)
output_path = os.path.join(
FLAGS.output_dir,
"test_recall_" + time.strftime("results_%Y%m%d-%H%M%S.json"))
utils.to_json_file(output_path, output)
def main(argv):
if len(argv) > 1:
raise RuntimeError(argv)
absl_logging.use_python_logging()
utils.log_module_args(LOGGER, argv[0])
retriever_config = tf_utils.REALMSave(
**utils.from_json_file(_FLAG_RETRIEVER_CONFIG_PATH.value))
assert not _FLAG_USE_SUBSET.value
time_stamp = time.strftime("%Y%m%d-%H%M%S")
target_path = os.path.join(_FLAG_OUTPUT_PATH.value, time_stamp.strip())
if target_path[-1] != "/":
target_path += "/"
##############################################################################
# Setup devices and strategy
##############################################################################
with utils.log_duration(LOGGER, "main", "Initializing devices"):
tpu_config = tf_utils.init_tpus()
device_type = tf_utils.current_accelerator_type()
LOGGER.debug("Devices: %s", str(tf_utils.devices_to_use()))
if device_type == "TPU":
if tpu_config is None:
raise RuntimeError("We should have a tpu_config.")
strategy = tf.distribute.TPUStrategy(tpu_config.resolver)
batch_size = len(
tf_utils.devices_to_use()) * _FLAG_BATCH_SIZE.value
elif device_type == "GPU" or device_type == "CPU":
strategy = tf.distribute.MirroredStrategy()
batch_size = len(
tf_utils.devices_to_use()) * _FLAG_BATCH_SIZE.value
else:
raise RuntimeError(device_type)
##############################################################################
# Load the dataset.
##############################################################################
eli5 = {}
keys = ["train", "eval", "test"]
gpt2_tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2-xl")
gpt2_tokenizer.pad_token = gpt2_tokenizer.eos_token
with utils.log_duration(LOGGER, "main", "Loading the ELI5 datasets."):
for split in tqdm.tqdm(keys):
load_path = os.path.join(_FLAG_DATASET_ROOT.value,
"HuggingfaceDatasets",
f"{split}_kilt_eli5.hf")
with tf.device("/job:localhost"):
eli5[split] = datasets.load_from_disk(load_path)
##############################################################################
#
##############################################################################
with utils.log_duration(LOGGER, "Main", "Load the textual dataset"):
# Extract the appropriate text
# The buffer_size is taken from the original ORQA code.
blocks_dataset = tf.data.TFRecordDataset(retriever_config.text_records,
buffer_size=512 * 1024 * 1024)
blocks_dataset = blocks_dataset.batch(
retriever_config.num_block_records, drop_remainder=True)
blocks = tf.data.experimental.get_single_element(blocks_dataset)
############################################################################
# Prepare the output file.
############################################################################
writers = {}
all_paths = {}
for split in keys:
maybe_subset = "_subset" if _FLAG_USE_SUBSET.value else ""
paths = [
os.path.join(target_path + maybe_subset, f"{split}_{i}.tfr")
for i in range(_FLAG_NUM_SHARDS.value)
]
all_paths[split] = paths
writers[split] = [tf.io.TFRecordWriter(filename) for filename in paths]
with utils.log_duration(LOGGER, "main", "Loading the reference db."):
checkpoint_path = os.path.join(
retriever_config.query_embedder_path, "encoded",
"encoded.ckpt")
reference_db_device = tf_utils.device_mapping().CPUs[0].name
with tf.device(reference_db_device):
reference_db = tf_utils.load_reference_db(
checkpoint_path,
variable_name="block_emb",
)
############################################################################
# Prep the encoder and the tokenizer
############################################################################
with utils.log_duration(LOGGER, "main",
"Loading the encoder model and the tokenizer."):
with strategy.scope():
query_encoder = hub.load(retriever_config.query_embedder_path,
tags={})
encode_fn = _make_encode_fn(query_encoder)
encode_fn_strategy_run = make_encode_fn_strategy_run_fn(
strategy=strategy,
encode_fn=encode_fn,
)
vocab_file = os.path.join(retriever_config.query_embedder_path,
"assets", "vocab.txt")
utils.check_exists(vocab_file)
do_lower_case = query_encoder.signatures["tokenization_info"](
)["do_lower_case"]
tokenization_info = dict(vocab_file=vocab_file,
do_lower_case=do_lower_case)
tokenizer, vocab_lookup_table = bert_utils.get_tf_tokenizer(
query_encoder, tokenization_info)
############################################################################
# Preprocess the dataset
############################################################################
cls_token_id = tf.cast(vocab_lookup_table.lookup(tf.constant("[CLS]")),
tf.int32)
sep_token_id = tf.cast(vocab_lookup_table.lookup(tf.constant("[SEP]")),
tf.int32)
transform = _make_transform_fn(
bert_tokenizer=tokenizer,
bert_cls_token_id=cls_token_id,
bert_sep_token_id=sep_token_id,
)
feature_dtypes = {
constants.CTH5Fields.distances: tf.float32,
constants.CTH5Fields.gpt2_retrieved_ids: tf.int32,
constants.CTH5Fields.gpt2_answer_ids_inputs: tf.int32,
constants.CTH5Fields.gpt2_question_ids_inputs: tf.int32,
}
with utils.log_duration(LOGGER, "main", "generating codes"):
for split in keys:
sample_count = 0
eli5: Dict[str, datasets.Dataset]
if split != "test":
for_slices = dict(sample_id=eli5[split]["id"],
question=eli5[split]["input"],
answer=[
sample["answer"][0]
for sample in eli5[split]["output"]
])
else:
for_slices = dict(
sample_id=eli5[split]["id"],
question=eli5[split]["input"],
)
ds = tf.data.Dataset.from_tensor_slices(for_slices)
ds = ds.map(transform,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.apply(
tf.data.experimental.dense_to_ragged_batch(batch_size))
ds = ds.map(_squeeze,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
tqdm_inner = tqdm.tqdm(enumerate(ds),
total=len(eli5[split]["id"]) //
_FLAG_BATCH_SIZE.value,
desc=f"Split `{split}`: Batches")
for i, batch in tqdm_inner:
features = collections.defaultdict(list)
######################################################################
# Enforce the current real batch size
######################################################################
current_batch_size = batch["sample_id"].shape[0]
for k, v in batch.items():
utils.check_equal(v.shape[0], current_batch_size)
######################################################################
gpt2_question_ids_inputs = _prep_field(batch["question"],
gpt2_tokenizer)
utils.check_equal(gpt2_question_ids_inputs.dtype, np.int32)
utils.check_equal(gpt2_question_ids_inputs.shape[0],
current_batch_size)
if split != "test":
gpt2_answer_ids_inputs = _prep_field(
batch["answer"], gpt2_tokenizer)
utils.check_equal(gpt2_answer_ids_inputs.dtype, np.int32)
utils.check_equal(gpt2_answer_ids_inputs.shape[0],
current_batch_size)
assert len(gpt2_answer_ids_inputs.shape) == 2, (
gpt2_answer_ids_inputs.shape)
######################################################################
# Save the gpt2 tokenized question and answer
######################################################################
features[constants.CTH5Fields.gpt2_question_ids_inputs].extend(
gpt2_question_ids_inputs)
if split != "test":
features[
constants.CTH5Fields.gpt2_answer_ids_inputs].extend(
gpt2_answer_ids_inputs)
######################################################################
# Encode the samples.
######################################################################
batch = strategy.experimental_distribute_values_from_function(
tf_utils.make_dict_distribute_fn(batch))
embeddings = encode_fn_strategy_run(batch)
embeddings = tf_utils.process_strat_output(
embeddings, "embeddings", strategy, current_batch_size)
utils.check_isinstance(embeddings, ops.EagerTensor)
utils.check_equal(embeddings.shape[0], current_batch_size)
# pytype doesn't seem to see that we check the type
utils.check_equal(embeddings.shape[1],
_FLAG_EMBEDDING_DEPTH.value) # pytype: disable=attribute-error
######################################################################
# Retrieve.
######################################################################
with tf.device(reference_db_device):
top_k, inner_prods = tf_utils.mips_exact_search(
embeddings, _FLAG_NUM_RETRIEVALS.value, reference_db)
top_k = tf_utils.process_strat_output(top_k, "top_k", strategy,
current_batch_size)
utils.check_equal(
inner_prods.shape,
(current_batch_size, _FLAG_NUM_RETRIEVALS.value))
utils.check_equal(
top_k.shape,
(current_batch_size, _FLAG_NUM_RETRIEVALS.value))
features[constants.CTH5Fields.distances].extend(inner_prods)
gathered = tf.gather(blocks, top_k).numpy()
utils.check_equal(gathered.shape[0], current_batch_size)
retrievals = []
for j in range(gathered.shape[0]):
local_gathered = gathered[j].tolist()
utils.check_equal(len(local_gathered),
_FLAG_NUM_RETRIEVALS.value)
local_gathered = [
sample.decode() for sample in local_gathered
]
token_ids = np.array(
gpt2_tokenizer.batch_encode_plus(
local_gathered,
padding="max_length",
truncation=True,
).input_ids)
for line in token_ids:
assert not np.all(line == 0), line
token_ids[token_ids == gpt2_tokenizer.eos_token_id] = -1
retrievals.append(token_ids)
features[constants.CTH5Fields.gpt2_retrieved_ids] = retrievals
utils.check_equal(
retrievals[0].shape,
(_FLAG_NUM_RETRIEVALS.value, _FLAG_CONTEXT_SIZE.value))
for k, v in features.items():
utils.check_equal(len(v), current_batch_size)
for k in range(current_batch_size):
feature = tf.train.Features(
feature={
k: _bytes_feature(
tf.io.serialize_tensor(
tf.cast(v[k], feature_dtypes[k])))
for k, v in features.items()
})
writers[split][
sample_count % _FLAG_NUM_SHARDS.value].write(
tf.train.Example(
features=feature).SerializeToString())
sample_count += 1
if sample_count % 1000 == 0:
LOGGER.debug("Paths: %s", str(all_paths[split][0]))
LOGGER.debug("Done.")
def main(args):
if not check_exists(args.save_dir):
os.makedirs(args.save_dir)
load_path = './checkpoints/demo_arcface_fine_tune_model_{:0>4d}.pth'.format(
args.epoch)
dataset = IQiYiExtractSceneDataset(args.data_root,
args.tvt,
image_root='/home/dcq/img',
num_frame=1)
if len(dataset) <= 0:
logger.error(
'the size of the dataset for extract scene feat cannot be {}'.
format(len(dataset)))
else:
logger.info(
'the size of the dataset for extract scene feat is {}'.format(
len(dataset)))
data_loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
pin_memory=True)
log_step = len(data_loader) // 100 if len(data_loader) > 100 else 1
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
logger.info('extract scene feat on {}'.format(device))
model = ArcFaceSEResNeXtModel(args.num_classes, include_top=False)
state_dict = torch.load(load_path, map_location='cpu')
model.load_state_dict(state_dict)
model = model.to(device)
all_video_names = []
all_image_index = []
all_scene_feat = []
start = time.time()
with torch.no_grad():
for batch_idx, (image_data, video_names,
image_indexes) in enumerate(data_loader):
image_data = image_data.to(device)
outputs = model(image_data)
all_video_names += list(video_names)
all_image_index += image_indexes.tolist()
all_scene_feat.append(outputs.cpu())
if batch_idx % log_step == 0:
end = time.time()
log_info = '[{}/{} ({:.0f}%)] Time: {}' \
.format(batch_idx * args.batch_size, len(dataset),
100.0 * batch_idx / len(data_loader), (end - start))
logger.info(log_info)
print(log_info)
start = time.time()
all_scene_feat = torch.cat(all_scene_feat, dim=0).numpy()
scene_infos = {}
for idx, video_name in enumerate(all_video_names):
scene_infos.setdefault(video_name, []).append(
(all_image_index[idx], all_scene_feat[idx]))
with open(
os.path.join(args.save_dir,
'scene_infos_{}.pickle'.format(args.tvt)),
'wb') as fout:
pickle.dump(scene_infos, fout)
def main(args):
if not check_exists(args.save_dir):
os.makedirs(args.save_dir)
dataset = IQiYiFineTuneSceneDataset(args.data_root,
'train+val-noise',
image_root='/home/dcq/img')
data_loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
log_step = len(data_loader) // 10 if len(data_loader) > 10 else 1
model = ArcFaceSEResNeXtModel(args.num_classes, include_top=True)
metric_func = ArcMarginProduct()
loss_func = FocalLoss(gamma=2.)
trainable_params = [
{
'params': model.base_model.parameters(),
"lr": args.learning_rate / 100
},
{
'params': model.weight
},
]
optimizer = optim.SGD(trainable_params,
lr=args.learning_rate,
momentum=0.9,
weight_decay=1e-5)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, args.epoch)
device, device_ids = prepare_device()
model = model.to(device)
if len(device_ids) > 1:
model = torch.nn.DataParallel(model, device_ids=device_ids)
for epoch_idx in range(args.epoch):
total_loss = .0
for batch_idx, (images, labels, _) in enumerate(data_loader):
images = images.view(-1, *images.size()[-3:])
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
outputs = outputs.view(outputs.size(0) // 3, 3, -1)
outputs = torch.mean(outputs, dim=1)
outputs_metric = metric_func(outputs, labels)
local_loss = loss_func(outputs_metric, labels)
local_loss.backward()
optimizer.step()
total_loss += local_loss.item()
if batch_idx % log_step == 0 and batch_idx != 0:
print('Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'.format(
epoch_idx, batch_idx * args.batch_size, len(dataset),
100.0 * batch_idx / len(data_loader), local_loss.item()))
log = {
'epoch': epoch_idx,
'lr': optimizer.param_groups[0]['lr'],
'loss': total_loss / len(data_loader)
}
for key, value in sorted(log.items(), key=lambda item: item[0]):
print(' {:20s}: {:6f}'.format(str(key), value))
lr_scheduler.step()
save_model(model.module, args.save_dir, 'demo_arcface_fine_tune_model',
args.epoch)