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.")
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(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")