def __init__(self, args):
self.args = args
self.shard_id = args.local_rank if args.local_rank != -1 else 0
self.distributed_factor = args.distributed_world_size or 1
logger.info("***** Initializing components for training *****")
model_file = get_model_file(self.args, self.args.checkpoint_file_name)
saved_state = None
if model_file:
saved_state = load_states_from_checkpoint(model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
tensorizer, reader, optimizer = init_reader_components(
args.encoder_model_type, args)
reader, optimizer = setup_for_distributed_mode(reader, optimizer,
args.device, args.n_gpu,
args.local_rank,
args.fp16,
args.fp16_opt_level)
self.reader = reader
self.optimizer = optimizer
self.tensorizer = tensorizer
self.start_epoch = 0
self.start_batch = 0
self.scheduler_state = None
self.best_validation_result = None
self.best_cp_name = None
if saved_state:
self._load_saved_state(saved_state)
def main(args):
saved_state = load_states_from_checkpoint(args.model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
print_args(args)
tensorizer, encoder, _ = init_biencoder_components(args.encoder_model_type,
args,
inference_only=True)
encoder = encoder.ctx_model
encoder, _ = setup_for_distributed_mode(encoder, None, args.device,
args.n_gpu, args.local_rank,
args.fp16, args.fp16_opt_level)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info('Loading saved model state ...')
logger.debug('saved model keys =%s', saved_state.model_dict.keys())
prefix_len = len('ctx_model.')
ctx_state = {
key[prefix_len:]: value
for (key, value) in saved_state.model_dict.items()
if key.startswith('ctx_model.')
}
model_to_load.load_state_dict(ctx_state)
logger.info('reading data from file=%s', args.ctx_file)
rows = []
with open(args.ctx_file) as tsvfile:
reader = csv.reader(tsvfile, delimiter='\t')
# file format: doc_id, doc_text, title
rows.extend([(row[0], row[1], row[2]) for row in reader
if row[0] != 'id'])
shard_size = int(len(rows) / args.num_shards)
start_idx = args.shard_id * shard_size
end_idx = start_idx + shard_size
logger.info(
'Producing encodings for passages range: %d to %d (out of total %d)',
start_idx, end_idx, len(rows))
rows = rows[start_idx:end_idx]
data = gen_ctx_vectors(rows, encoder, tensorizer, True)
file = args.out_file + '_' + str(args.shard_id)
pathlib.Path(os.path.dirname(file)).mkdir(parents=True, exist_ok=True)
logger.info('Writing results to %s' % file)
with open(file, mode='wb') as f:
pickle.dump(data, f)
logger.info('Total passages processed %d. Written to %s', len(data), file)
def get_retriever(args):
saved_state = load_states_from_checkpoint(args.model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
tensorizer, encoder, _ = init_biencoder_components(args.encoder_model_type,
args,
inference_only=True)
encoder = encoder.question_model
encoder, _ = setup_for_distributed_mode(encoder, None, args.device,
args.n_gpu, args.local_rank,
args.fp16)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info('Loading saved model state ...')
prefix_len = len('question_model.')
question_encoder_state = {
key[prefix_len:]: value
for (key, value) in saved_state.model_dict.items()
if key.startswith('question_model.')
}
model_to_load.load_state_dict(question_encoder_state)
vector_size = model_to_load.get_out_size()
logger.info('Encoder vector_size=%d', vector_size)
index_buffer_sz = args.index_buffer
if args.hnsw_index:
index = DenseHNSWFlatIndexer(vector_size)
index_buffer_sz = -1 # encode all at once
else:
index = DenseFlatIndexer(vector_size)
retriever = DenseRetriever(encoder, args.batch_size, tensorizer, index,
args.device)
# index all passages
if len(args.encoded_ctx_file) > 0:
ctx_files_pattern = args.encoded_ctx_file
input_paths = glob.glob(ctx_files_pattern)
index_path = "_".join(input_paths[0].split("_")[:-1])
if args.save_or_load_index and os.path.exists(index_path):
retriever.index.deserialize(index_path)
else:
logger.info('Reading all passages data from files: %s',
input_paths)
retriever.index_encoded_data(input_paths,
buffer_size=index_buffer_sz)
if args.save_or_load_index:
retriever.index.serialize(index_path)
# get questions & answers
return retriever
def __init__(self, name, **config):
super().__init__(name)
self.args = argparse.Namespace(**config)
saved_state = load_states_from_checkpoint(self.args.model_file)
set_encoder_params_from_state(saved_state.encoder_params, self.args)
tensorizer, encoder, _ = init_biencoder_components(
self.args.encoder_model_type, self.args, inference_only=True)
encoder = encoder.question_model
encoder, _ = setup_for_distributed_mode(
encoder,
None,
self.args.device,
self.args.n_gpu,
self.args.local_rank,
self.args.fp16,
)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
prefix_len = len("question_model.")
question_encoder_state = {
key[prefix_len:]: value
for (key, value) in saved_state.model_dict.items()
if key.startswith("question_model.")
}
model_to_load.load_state_dict(question_encoder_state)
vector_size = model_to_load.get_out_size()
index_buffer_sz = self.args.index_buffer
if self.args.hnsw_index:
index = DenseHNSWFlatIndexer(vector_size)
index.deserialize_from(self.args.hnsw_index_path)
else:
index = DenseFlatIndexer(vector_size)
self.retriever = DenseRetriever(encoder, self.args.batch_size,
tensorizer, index)
# index all passages
ctx_files_pattern = self.args.encoded_ctx_file
input_paths = glob.glob(ctx_files_pattern)
if not self.args.hnsw_index:
self.retriever.index_encoded_data(input_paths,
buffer_size=index_buffer_sz)
# not needed for now
self.all_passages = load_passages(self.args.ctx_file)
self.KILT_mapping = None
if self.args.KILT_mapping:
self.KILT_mapping = pickle.load(open(self.args.KILT_mapping, "rb"))
def __init__(self, cfg: DictConfig):
self.shard_id = cfg.local_rank if cfg.local_rank != -1 else 0
self.distributed_factor = cfg.distributed_world_size or 1
logger.info("***** Initializing components for training *****")
# if model file is specified, encoder parameters from saved state should be used for initialization
model_file = get_model_file(cfg, cfg.checkpoint_file_name)
saved_state = None
if model_file:
saved_state = load_states_from_checkpoint(model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
gradient_checkpointing = getattr(cfg, "gradient_checkpointing", False)
tensorizer, model, optimizer = init_biencoder_components(
cfg.encoder.encoder_model_type,
cfg,
gradient_checkpointing=gradient_checkpointing,
)
with omegaconf.open_dict(cfg):
cfg.others = DictConfig({
"is_matching":
isinstance(model, (Match_BiEncoder, MatchGated_BiEncoder))
})
model, optimizer = setup_for_distributed_mode(
model,
optimizer,
cfg.device,
cfg.n_gpu,
cfg.local_rank,
cfg.fp16,
cfg.fp16_opt_level,
gradient_checkpointing=gradient_checkpointing,
)
self.biencoder = model
self.optimizer = optimizer
self.tensorizer = tensorizer
self.start_epoch = 0
self.start_batch = 0
self.scheduler_state = None
self.best_validation_result = None
self.best_cp_name = None
self.cfg = cfg
self.ds_cfg = BiencoderDatasetsCfg(cfg)
self.loss_function = init_loss(cfg.encoder.encoder_model_type, cfg)
self.clustering = cfg.clustering
if self.clustering:
cfg.global_loss_buf_sz = 80000000 # this requires a lot of memory
if saved_state:
self._load_saved_state(saved_state)
self.dev_iterator = None
def main(args):
saved_state = load_states_from_checkpoint(args.model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
print_args(args)
tensorizer, encoder, _ = init_biencoder_components(args.encoder_model_type,
args,
inference_only=True)
encoder = encoder.ctx_model
encoder, _ = setup_for_distributed_mode(encoder, None, args.device,
args.n_gpu, args.local_rank,
args.fp16, args.fp16_opt_level)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info('Loading saved model state ...')
logger.debug('saved model keys =%s', saved_state.model_dict.keys())
prefix_len = len('ctx_model.')
ctx_state = {
key[prefix_len:]: value
for (key, value) in saved_state.model_dict.items()
if key.startswith('ctx_model.')
}
model_to_load.load_state_dict(ctx_state)
logger.info('reading data from file=%s', args.ctx_file)
rows = []
with open(args.ctx_file) as jsonfile:
blob = json.load(jsonfile)
for paper in blob.values():
s2_id = paper["paper_id"]
title = paper.get("title") or ""
abstract = paper.get("abstract") or ""
rows.append((s2_id, abstract, title))
data = gen_ctx_vectors(rows, encoder, tensorizer, True)
file = args.out_file
pathlib.Path(os.path.dirname(file)).mkdir(parents=True, exist_ok=True)
logger.info('Writing results to %s' % file)
with open(file, "w+") as f:
for (s2_id, vec) in data:
out = {"paper_id": s2_id, "embedding": vec.tolist()}
f.write(json.dumps(out) + "\n")
logger.info('Total passages processed %d. Written to %s', len(data), file)
def __init__(self, cfg: DictConfig, save_temp_conf=False):
self.shard_id = cfg.local_rank if cfg.local_rank != -1 else 0
self.distributed_factor = cfg.distributed_world_size or 1
if save_temp_conf:
import pickle
with open('/content/drive/MyDrive/conf.pickle', 'wb') as f:
pickle.dump(cfg, f)
logger.info("***** Initializing components for training *****")
# graded dataset settings
self.trainer_type = 'binary' if cfg.binary_trainer else 'graded'
logger.info(f'trainer_type: {self.trainer_type}')
self.relations = cfg.relations
logger.info(f'relations: {self.relations}')
# if model file is specified, encoder parameters from saved state should be used for initialization
model_file = get_model_file(cfg, cfg.checkpoint_file_name)
saved_state = None
if model_file:
saved_state = load_states_from_checkpoint(model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
tensorizer, model, optimizer = init_biencoder_components(
cfg.encoder.encoder_model_type, cfg
)
model, optimizer = setup_for_distributed_mode(
model,
optimizer,
cfg.device,
cfg.n_gpu,
cfg.local_rank,
cfg.fp16,
cfg.fp16_opt_level,
)
self.biencoder = model
self.optimizer = optimizer
self.tensorizer = tensorizer
self.start_epoch = 0
self.start_batch = 0
self.scheduler_state = None
self.best_validation_result = None
self.best_cp_name = None
self.cfg = cfg
self.ds_cfg = BiencoderDatasetsCfg(cfg)
if saved_state:
self._load_saved_state(saved_state)
self.dev_iterator = None
def __init__(self, cfg: DictConfig):
self.cfg = cfg
self.shard_id = cfg.local_rank if cfg.local_rank != -1 else 0
self.distributed_factor = cfg.distributed_world_size or 1
logger.info("***** Initializing components for training *****")
model_file = get_model_file(self.cfg, self.cfg.checkpoint_file_name)
saved_state = None
if model_file:
saved_state = load_states_from_checkpoint(model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
gradient_checkpointing = getattr(self.cfg, "gradient_checkpointing",
False)
tensorizer, reader, optimizer = init_reader_components(
cfg.encoder.encoder_model_type,
cfg,
gradient_checkpointing=gradient_checkpointing,
)
reader, optimizer = setup_for_distributed_mode(
reader,
optimizer,
cfg.device,
cfg.n_gpu,
cfg.local_rank,
cfg.fp16,
cfg.fp16_opt_level,
gradient_checkpointing=gradient_checkpointing,
)
self.reader = reader
self.optimizer = optimizer
self.tensorizer = tensorizer
self.debugging = getattr(self.cfg, "debugging", False)
self.wiki_data = None
self.dev_iterator = None
self.start_epoch = 0
self.start_batch = 0
self.scheduler_state = None
self.best_validation_result = None
self.best_cp_name = None
if saved_state:
self._load_saved_state(saved_state)
def __init__(self, cfg: DictConfig):
self.shard_id = cfg.local_rank if cfg.local_rank != -1 else 0
self.distributed_factor = cfg.distributed_world_size or 1
logger.info("***** Initializing components for training *****")
# if model file is specified, encoder parameters from saved state should be used for initialization
model_file = get_model_file(cfg, cfg.checkpoint_file_name)
saved_state = None
if model_file:
saved_state = load_states_from_checkpoint(model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
tensorizer, model, optimizer = init_biencoder_components(
cfg.encoder.encoder_model_type, cfg
)
model, optimizer = setup_for_distributed_mode(
model,
optimizer,
cfg.device,
cfg.n_gpu,
cfg.local_rank,
cfg.fp16,
cfg.fp16_opt_level,
)
self.biencoder = model
self.optimizer = optimizer
self.tensorizer = tensorizer
self.start_epoch = 0
self.start_batch = 0
self.scheduler_state = None
self.best_validation_result = None
self.best_cp_name = None
self.cfg = cfg
self.ds_cfg = BiencoderDatasetsCfg(cfg)
if saved_state:
self._load_saved_state(saved_state)
self.dev_iterator = None
def main(cfg: DictConfig):
cfg = setup_cfg_gpu(cfg)
logger.info("CFG (after gpu configuration):")
logger.info("%s", OmegaConf.to_yaml(cfg))
saved_state = load_states_from_checkpoint(cfg.model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
tensorizer, encoder, _ = init_biencoder_components(
cfg.encoder.encoder_model_type, cfg, inference_only=True)
encoder_path = cfg.encoder_path
if encoder_path:
logger.info("Selecting encoder: %s", encoder_path)
encoder = getattr(encoder, encoder_path)
else:
logger.info("Selecting standard question encoder")
encoder = encoder.question_model
encoder, _ = setup_for_distributed_mode(encoder, None, cfg.device,
cfg.n_gpu, cfg.local_rank,
cfg.fp16)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info("Loading saved model state ...")
encoder_prefix = (encoder_path if encoder_path else "question_model") + "."
prefix_len = len(encoder_prefix)
logger.info("Encoder state prefix %s", encoder_prefix)
question_encoder_state = {
key[prefix_len:]: value
for (key, value) in saved_state.model_dict.items()
if key.startswith(encoder_prefix)
}
model_to_load.load_state_dict(question_encoder_state)
vector_size = model_to_load.get_out_size()
logger.info("Encoder vector_size=%d", vector_size)
# get questions & answers
questions = []
question_answers = []
if not cfg.qa_dataset:
logger.warning("Please specify qa_dataset to use")
return
ds_key = cfg.qa_dataset
logger.info("qa_dataset: %s", ds_key)
qa_src = hydra.utils.instantiate(cfg.datasets[ds_key])
qa_src.load_data()
for ds_item in qa_src.data:
question, answers = ds_item.query, ds_item.answers
questions.append(question)
question_answers.append(answers)
index = hydra.utils.instantiate(cfg.indexers[cfg.indexer])
logger.info("Index class %s ", type(index))
index_buffer_sz = index.buffer_size
index.init_index(vector_size)
retriever = LocalFaissRetriever(encoder, cfg.batch_size, tensorizer, index)
logger.info("Using special token %s", qa_src.special_query_token)
questions_tensor = retriever.generate_question_vectors(
questions, query_token=qa_src.special_query_token)
if qa_src.selector:
logger.info("Using custom representation token selector")
retriever.selector = qa_src.selector
id_prefixes = []
ctx_sources = []
for ctx_src in cfg.ctx_datatsets:
ctx_src = hydra.utils.instantiate(cfg.ctx_sources[ctx_src])
id_prefixes.append(ctx_src.id_prefix)
ctx_sources.append(ctx_src)
logger.info("id_prefixes per dataset: %s", id_prefixes)
# index all passages
ctx_files_patterns = cfg.encoded_ctx_files
index_path = cfg.index_path
logger.info("ctx_files_patterns: %s", ctx_files_patterns)
if ctx_files_patterns:
assert len(ctx_files_patterns) == len(
id_prefixes), "ctx len={} pref leb={}".format(
len(ctx_files_patterns), len(id_prefixes))
else:
assert (
index_path
), "Either encoded_ctx_files or index_path parameter should be set."
input_paths = []
path_id_prefixes = []
for i, pattern in enumerate(ctx_files_patterns):
pattern_files = glob.glob(pattern)
pattern_id_prefix = id_prefixes[i]
input_paths.extend(pattern_files)
path_id_prefixes.extend([pattern_id_prefix] * len(pattern_files))
logger.info("Embeddings files id prefixes: %s", path_id_prefixes)
if index_path and index.index_exists(index_path):
logger.info("Index path: %s", index_path)
retriever.index.deserialize(index_path)
else:
logger.info("Reading all passages data from files: %s", input_paths)
retriever.index_encoded_data(input_paths,
index_buffer_sz,
path_id_prefixes=path_id_prefixes)
if index_path:
retriever.index.serialize(index_path)
# get top k results
top_ids_and_scores = retriever.get_top_docs(questions_tensor.numpy(),
cfg.n_docs)
# we no longer need the index
retriever = None
all_passages = {}
for ctx_src in ctx_sources:
ctx_src.load_data_to(all_passages)
if len(all_passages) == 0:
raise RuntimeError(
"No passages data found. Please specify ctx_file param properly.")
if cfg.validate_as_tables:
questions_doc_hits = validate_tables(
all_passages,
question_answers,
top_ids_and_scores,
cfg.validation_workers,
cfg.match,
)
else:
questions_doc_hits = validate(
all_passages,
question_answers,
top_ids_and_scores,
cfg.validation_workers,
cfg.match,
)
if cfg.out_file:
save_results(
all_passages,
questions,
question_answers,
top_ids_and_scores,
questions_doc_hits,
cfg.out_file,
)
if cfg.kilt_out_file:
kilt_ctx = next(
iter([
ctx for ctx in ctx_sources if isinstance(ctx, KiltCsvCtxSrc)
]), None)
if not kilt_ctx:
raise RuntimeError("No Kilt compatible context file provided")
assert hasattr(cfg, "kilt_out_file")
kilt_ctx.convert_to_kilt(qa_src.kilt_gold_file, cfg.out_file,
cfg.kilt_out_file)
def main(args):
saved_state = load_states_from_checkpoint(args.model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
tensorizer, encoder, _ = init_biencoder_components(
args.encoder_model_type, args, inference_only=True)
encoder = encoder.question_model
encoder, _ = setup_for_distributed_mode(encoder, None, args.device, args.n_gpu,
args.local_rank,
args.fp16)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info('Loading saved model state ...')
prefix_len = len('question_model.')
question_encoder_state = {key[prefix_len:]: value for (key, value) in saved_state.model_dict.items() if
key.startswith('question_model.')}
model_to_load.load_state_dict(question_encoder_state)
vector_size = model_to_load.get_out_size()
logger.info('Encoder vector_size=%d', vector_size)
if args.hnsw_index:
index = DenseHNSWFlatIndexer(vector_size, args.index_buffer)
else:
index = DenseFlatIndexer(vector_size, args.index_buffer)
retriever = DenseRetriever(encoder, args.batch_size, tensorizer, index)
# index all passages
ctx_files_pattern = args.encoded_ctx_file
input_paths = glob.glob(ctx_files_pattern)
index_path = "_".join(input_paths[0].split("_")[:-1])
if args.save_or_load_index and (os.path.exists(index_path) or os.path.exists(index_path + ".index.dpr")):
retriever.index.deserialize_from(index_path)
else:
logger.info('Reading all passages data from files: %s', input_paths)
retriever.index.index_data(input_paths)
if args.save_or_load_index:
retriever.index.serialize(index_path)
# get questions & answers
questions = []
question_ids = []
for ds_item in parse_qa_csv_file(args.qa_file):
question_id, question = ds_item
question_ids.append(question_id)
questions.append(question)
questions_tensor = retriever.generate_question_vectors(questions)
# get top k results
top_ids_and_scores = retriever.get_top_docs(
questions_tensor.numpy(), args.n_docs)
# all_passages = load_passages(args.ctx_file)
# if len(all_passages) == 0:
# raise RuntimeError(
# 'No passages data found. Please specify ctx_file param properly.')
# questions_doc_hits = validate(all_passages, question_answers, top_ids_and_scores, args.validation_workers,
# args.match)
if args.out_file:
save_results(question_ids,
top_ids_and_scores, args.out_file)
def main(cfg: DictConfig):
assert cfg.model_file, "Please specify encoder checkpoint as model_file param"
assert cfg.ctx_src, "Please specify passages source as ctx_src param"
print(os.getcwd())
cfg = setup_cfg_gpu(cfg)
saved_state = load_states_from_checkpoint(cfg.model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
logger.info("CFG:")
logger.info("%s", OmegaConf.to_yaml(cfg))
tensorizer, encoder, _ = init_biencoder_components(
cfg.encoder.encoder_model_type, cfg, inference_only=True)
encoder = encoder.ctx_model if cfg.encoder_type == "ctx" else encoder.question_model
encoder, _ = setup_for_distributed_mode(
encoder,
None,
cfg.device,
cfg.n_gpu,
cfg.local_rank,
cfg.fp16,
cfg.fp16_opt_level,
)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info("Loading saved model state ...")
logger.debug("saved model keys =%s", saved_state.model_dict.keys())
prefix_len = len("ctx_model.")
ctx_state = {
key[prefix_len:]: value
for (key, value) in saved_state.model_dict.items()
if key.startswith("ctx_model.")
}
model_to_load.load_state_dict(ctx_state)
logger.info("reading data source: %s", cfg.ctx_src)
ctx_src = hydra.utils.instantiate(cfg.ctx_sources[cfg.ctx_src])
all_passages_dict = {}
ctx_src.load_data_to(all_passages_dict)
all_passages = [(k, v) for k, v in all_passages_dict.items()]
shard_size = math.ceil(len(all_passages) / cfg.num_shards)
start_idx = cfg.shard_id * shard_size
end_idx = start_idx + shard_size
logger.info(
"Producing encodings for passages range: %d to %d (out of total %d)",
start_idx,
end_idx,
len(all_passages),
)
shard_passages = all_passages[start_idx:end_idx]
data = gen_ctx_vectors(cfg, shard_passages, encoder, tensorizer, True)
file = cfg.out_file + "_" + str(cfg.shard_id)
pathlib.Path(os.path.dirname(file)).mkdir(parents=True, exist_ok=True)
logger.info("Writing results to %s" % file)
with open(file, mode="wb") as f:
pickle.dump(data, f)
logger.info("Total passages processed %d. Written to %s", len(data), file)
def main(cfg: DictConfig):
cfg = setup_cfg_gpu(cfg)
saved_state = load_states_from_checkpoint(cfg.model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
logger.info("CFG (after gpu configuration):")
logger.info("%s", OmegaConf.to_yaml(cfg))
tensorizer, encoder, _ = init_biencoder_components(
cfg.encoder.encoder_model_type, cfg, inference_only=True)
logger.info("Loading saved model state ...")
encoder.load_state(saved_state, strict=False)
encoder_path = cfg.encoder_path
if encoder_path:
logger.info("Selecting encoder: %s", encoder_path)
encoder = getattr(encoder, encoder_path)
else:
logger.info("Selecting standard question encoder")
encoder = encoder.question_model
encoder, _ = setup_for_distributed_mode(encoder, None, cfg.device,
cfg.n_gpu, cfg.local_rank,
cfg.fp16)
encoder.eval()
model_to_load = get_model_obj(encoder)
vector_size = model_to_load.get_out_size()
logger.info("Encoder vector_size=%d", vector_size)
# get questions & answers
questions = []
questions_text = []
question_answers = []
if not cfg.qa_dataset:
logger.warning("Please specify qa_dataset to use")
return
ds_key = cfg.qa_dataset
logger.info("qa_dataset: %s", ds_key)
qa_src = hydra.utils.instantiate(cfg.datasets[ds_key])
qa_src.load_data()
total_queries = len(qa_src)
for i in range(total_queries):
qa_sample = qa_src[i]
question, answers = qa_sample.query, qa_sample.answers
questions.append(question)
question_answers.append(answers)
logger.info("questions len %d", len(questions))
logger.info("questions_text len %d", len(questions_text))
if cfg.rpc_retriever_cfg_file:
index_buffer_sz = 1000
retriever = DenseRPCRetriever(
encoder,
cfg.batch_size,
tensorizer,
cfg.rpc_retriever_cfg_file,
vector_size,
use_l2_conversion=cfg.use_l2_conversion,
)
else:
index = hydra.utils.instantiate(cfg.indexers[cfg.indexer])
logger.info("Local Index class %s ", type(index))
index_buffer_sz = index.buffer_size
index.init_index(vector_size)
retriever = LocalFaissRetriever(encoder, cfg.batch_size, tensorizer,
index)
logger.info("Using special token %s", qa_src.special_query_token)
questions_tensor = retriever.generate_question_vectors(
questions, query_token=qa_src.special_query_token)
if qa_src.selector:
logger.info("Using custom representation token selector")
retriever.selector = qa_src.selector
index_path = cfg.index_path
if cfg.rpc_retriever_cfg_file and cfg.rpc_index_id:
retriever.load_index(cfg.rpc_index_id)
elif index_path and index.index_exists(index_path):
logger.info("Index path: %s", index_path)
retriever.index.deserialize(index_path)
else:
# send data for indexing
id_prefixes = []
ctx_sources = []
for ctx_src in cfg.ctx_datatsets:
ctx_src = hydra.utils.instantiate(cfg.ctx_sources[ctx_src])
id_prefixes.append(ctx_src.id_prefix)
ctx_sources.append(ctx_src)
logger.info("ctx_sources: %s", type(ctx_src))
logger.info("id_prefixes per dataset: %s", id_prefixes)
# index all passages
ctx_files_patterns = cfg.encoded_ctx_files
logger.info("ctx_files_patterns: %s", ctx_files_patterns)
if ctx_files_patterns:
assert len(ctx_files_patterns) == len(
id_prefixes), "ctx len={} pref leb={}".format(
len(ctx_files_patterns), len(id_prefixes))
else:
assert (
index_path or cfg.rpc_index_id
), "Either encoded_ctx_files or index_path pr rpc_index_id parameter should be set."
input_paths = []
path_id_prefixes = []
for i, pattern in enumerate(ctx_files_patterns):
pattern_files = glob.glob(pattern)
pattern_id_prefix = id_prefixes[i]
input_paths.extend(pattern_files)
path_id_prefixes.extend([pattern_id_prefix] * len(pattern_files))
logger.info("Embeddings files id prefixes: %s", path_id_prefixes)
logger.info("Reading all passages data from files: %s", input_paths)
retriever.index_encoded_data(input_paths,
index_buffer_sz,
path_id_prefixes=path_id_prefixes)
if index_path:
retriever.index.serialize(index_path)
# get top k results
top_results_and_scores = retriever.get_top_docs(questions_tensor.numpy(),
cfg.n_docs)
if cfg.use_rpc_meta:
questions_doc_hits = validate_from_meta(
question_answers,
top_results_and_scores,
cfg.validation_workers,
cfg.match,
cfg.rpc_meta_compressed,
)
if cfg.out_file:
save_results_from_meta(
questions,
question_answers,
top_results_and_scores,
questions_doc_hits,
cfg.out_file,
cfg.rpc_meta_compressed,
)
else:
all_passages = get_all_passages(ctx_sources)
if cfg.validate_as_tables:
questions_doc_hits = validate_tables(
all_passages,
question_answers,
top_results_and_scores,
cfg.validation_workers,
cfg.match,
)
else:
questions_doc_hits = validate(
all_passages,
question_answers,
top_results_and_scores,
cfg.validation_workers,
cfg.match,
)
if cfg.out_file:
save_results(
all_passages,
questions_text if questions_text else questions,
question_answers,
top_results_and_scores,
questions_doc_hits,
cfg.out_file,
)
if cfg.kilt_out_file:
kilt_ctx = next(
iter([
ctx for ctx in ctx_sources if isinstance(ctx, KiltCsvCtxSrc)
]), None)
if not kilt_ctx:
raise RuntimeError("No Kilt compatible context file provided")
assert hasattr(cfg, "kilt_out_file")
kilt_ctx.convert_to_kilt(qa_src.kilt_gold_file, cfg.out_file,
cfg.kilt_out_file)
def setup_dpr(model_file,
ctx_file,
encoded_ctx_file,
hnsw_index=False,
save_or_load_index=False):
global retriever
global all_passages
global answer_cache
global answer_cache_path
parameter_setting = model_file + ctx_file + encoded_ctx_file
answer_cache_path = hashlib.sha1(
parameter_setting.encode("utf-8")).hexdigest()
if os.path.exists(answer_cache_path):
answer_cache = pickle.load(open(answer_cache_path, 'rb'))
else:
answer_cache = {}
parser = argparse.ArgumentParser()
add_encoder_params(parser)
add_tokenizer_params(parser)
add_cuda_params(parser)
args = parser.parse_args()
args.model_file = model_file
args.ctx_file = ctx_file
args.encoded_ctx_file = encoded_ctx_file
args.hnsw_index = hnsw_index
args.save_or_load_index = save_or_load_index
args.batch_size = 1 # TODO
setup_args_gpu(args)
print_args(args)
saved_state = load_states_from_checkpoint(args.model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
tensorizer, encoder, _ = init_biencoder_components(args.encoder_model_type,
args,
inference_only=True)
encoder = encoder.question_model
encoder, _ = setup_for_distributed_mode(encoder, None, args.device,
args.n_gpu, args.local_rank,
args.fp16)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info("Loading saved model state ...")
prefix_len = len("question_model.")
question_encoder_state = {
key[prefix_len:]: value
for (key, value) in saved_state.model_dict.items()
if key.startswith("question_model.")
}
model_to_load.load_state_dict(question_encoder_state)
vector_size = model_to_load.get_out_size()
logger.info("Encoder vector_size=%d", vector_size)
if args.hnsw_index:
index = DenseHNSWFlatIndexer(vector_size, 50000)
else:
index = DenseFlatIndexer(vector_size, 50000,
"IVF65536,PQ64") #IVF65536
retriever = DenseRetriever(encoder, args.batch_size, tensorizer, index)
# index all passages
ctx_files_pattern = args.encoded_ctx_file
input_paths = glob.glob(ctx_files_pattern)
index_path = "_".join(input_paths[0].split("_")[:-1])
if args.save_or_load_index and (os.path.exists(index_path) or
os.path.exists(index_path + ".index.dpr")):
retriever.index.deserialize_from(index_path)
else:
logger.info("Reading all passages data from files: %s", input_paths)
retriever.index.index_data(input_paths)
if args.save_or_load_index:
retriever.index.serialize(index_path)
# get questions & answers
all_passages = load_passages(args.ctx_file)
def main(args):
questions = []
question_answers = []
for i, ds_item in enumerate(parse_qa_csv_file(args.qa_file)):
#if i == 10:
# break
question, answers = ds_item
questions.append(question)
question_answers.append(answers)
if not args.encoder_model_type == 'hf_attention' and not args.encoder_model_type == 'colbert':
saved_state = load_states_from_checkpoint(args.model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
tensorizer, encoder, _ = init_biencoder_components(args.encoder_model_type, args, inference_only=True)
if not args.encoder_model_type == 'hf_attention' and not args.encoder_model_type == 'colbert':
encoder = encoder.question_model
encoder, _ = setup_for_distributed_mode(encoder, None, args.device, args.n_gpu,
args.local_rank,
args.fp16)
encoder.eval()
if not args.encoder_model_type == 'hf_attention' and not args.encoder_model_type == 'colbert':
# load weights from the model file
model_to_load = get_model_obj(encoder)
logger.info('Loading saved model state ...')
prefix_len = len('question_model.')
question_encoder_state = {key[prefix_len:]: value for (key, value) in saved_state.model_dict.items() if
key.startswith('question_model.')}
model_to_load.load_state_dict(question_encoder_state)
vector_size = model_to_load.get_out_size()
logger.info('Encoder vector_size=%d', vector_size)
else:
vector_size = 16
index_buffer_sz = args.index_buffer
if args.index_type == 'hnsw':
index = DenseHNSWFlatIndexer(vector_size, index_buffer_sz)
index_buffer_sz = -1 # encode all at once
elif args.index_type == 'custom':
index = CustomIndexer(vector_size, index_buffer_sz)
else:
index = DenseFlatIndexer(vector_size)
retriever = DenseRetriever(encoder, args.batch_size, tensorizer, index, args.encoder_model_type=='colbert')
# index all passages
ctx_files_pattern = args.encoded_ctx_file
input_paths = glob.glob(ctx_files_pattern)
#logger.info('Reading all passages data from files: %s', input_paths)
#memmap = retriever.index_encoded_data(input_paths, buffer_size=index_buffer_sz, memmap=args.encoder_model_type=='colbert')
print(input_paths)
index_path = "_".join(input_paths[0].split("_")[:-1])
memmap_path = 'memmap.npy'
print(args.save_or_load_index, os.path.exists(index_path), index_path)
if args.save_or_load_index and os.path.exists(index_path+'.index.dpr'):
#if False:
retriever.index.deserialize_from(index_path)
if args.encoder_model_type=='colbert':
memmap = np.memmap(memmap_path, dtype=np.float32, mode='w+', shape=(21015324, 250, 16))
else:
memmap = None
else:
logger.info('Reading all passages data from files: %s', input_paths)
if args.encoder_model_type=='colbert':
memmap = np.memmap(memmap_path, dtype=np.float32, mode='w+', shape=(21015324, 250, 16))
else:
memmap = None
retriever.index_encoded_data(input_paths, buffer_size=index_buffer_sz, memmap=memmap)
if args.save_or_load_index:
retriever.index.serialize(index_path)
# get questions & answers
questions_tensor = retriever.generate_question_vectors(questions)
# get top k results
top_ids_and_scores = retriever.get_top_docs(questions_tensor.numpy(), args.n_docs, is_colbert=args.encoder_model_type=='colbert')
with open('approx_scores.pkl', 'wb') as f:
pickle.dump(top_ids_and_scores, f)
retriever.index.index.reset()
if args.encoder_model_type=='colbert':
logger.info('Colbert score')
top_ids_and_scores_colbert = retriever.colbert_search(questions_tensor.numpy(), memmap, top_ids_and_scores, args.n_docs)
all_passages = load_passages(args.ctx_file)
if len(all_passages) == 0:
raise RuntimeError('No passages data found. Please specify ctx_file param properly.')
with open('colbert_scores.pkl', 'wb') as f:
pickle.dump(top_ids_and_scores_colbert, f)
questions_doc_hits = validate(all_passages, question_answers, top_ids_and_scores, args.validation_workers,
args.match)
if args.encoder_model_type=='colbert':
questions_doc_hits_colbert = validate(all_passages, question_answers, top_ids_and_scores_colbert, args.validation_workers,
args.match)
if args.out_file:
save_results(all_passages, questions, question_answers, top_ids_and_scores, questions_doc_hits, args.out_file)
if args.encoder_model_type=='colbert':
save_results(all_passages, questions, question_answers, top_ids_and_scores_colbert, questions_doc_hits_colbert, args.out_file+'colbert')
psg_ids, scores = ranker.closest_docs(question, args.n_docs)
top_ids_and_scores.append((psg_ids, scores))
else:
from dpr.models import init_biencoder_components
from dpr.utils.data_utils import Tensorizer
from dpr.utils.model_utils import setup_for_distributed_mode, get_model_obj, load_states_from_checkpoint
from dpr.indexer.faiss_indexers import DenseIndexer, DenseHNSWFlatIndexer, DenseFlatIndexer
from dense_retriever import DenseRetriever
saved_state = load_states_from_checkpoint(args.dpr_model_file)
set_encoder_params_from_state(saved_state.encoder_params, args)
tensorizer, encoder, _ = init_biencoder_components(args.encoder_model_type, args, inference_only=True)
encoder = encoder.question_model
setup_args_gpu(args)
encoder, _ = setup_for_distributed_mode(encoder, None, args.device, args.n_gpu,
args.local_rank,
args.fp16)
encoder.eval()
# load weights from the model file
model_to_load = get_model_obj(encoder)
prefix_len = len('question_model.')
question_encoder_state = {key[prefix_len:]: value for (key, value) in saved_state.model_dict.items() if
key.startswith('question_model.')}
model_to_load.load_state_dict(question_encoder_state)
vector_size = model_to_load.get_out_size()
index_buffer_sz = args.index_buffer
if args.hnsw_index:
index = DenseHNSWFlatIndexer(vector_size)
index_buffer_sz = -1 # encode all at once
def __init__(self, cfg: DictConfig):
self.shard_id = cfg.local_rank if cfg.local_rank != -1 else 0
self.distributed_factor = cfg.distributed_world_size or 1
logger.info("***** Initializing components for training *****")
# if model file is specified, encoder parameters from saved state should be used for initialization
model_file = get_model_file(cfg, cfg.checkpoint_file_name)
saved_state = None
if model_file:
saved_state = load_states_from_checkpoint_legacy(model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
if isinstance(saved_state, CheckpointStateOFA):
self.mode = "normal"
# Initialize everything
gradient_checkpointing = getattr(cfg, "gradient_checkpointing", False)
tensorizer, model, biencoder_optimizer, reader_optimizer, forward_fn = init_ofa_model(
cfg.encoder.encoder_model_type, cfg, gradient_checkpointing=gradient_checkpointing,
)
else:
# Only allowed during evaluation-only mode
assert isinstance(saved_state, CheckpointState)
assert cfg.train_datasets is None or len(cfg.train_datasets) == 0
# Convert from old state to OFA state
saved_state, self.mode = convert_from_old_state_to_ofa(saved_state)
if self.mode == "biencoder":
# Sanity check
assert cfg.evaluate_retriever and (not cfg.evaluate_reader)
# Initialize everything
tensorizer, biencoder, _ = init_biencoder_components(
cfg.encoder.encoder_model_type, cfg, inference_only=True,
)
reader = None
else:
# Sanity check
assert cfg.evaluate_reader and (not cfg.evaluate_retriever)
# Initialize everything
tensorizer, reader, _ = init_reader_components(
cfg.encoder.encoder_model_type, cfg, inference_only=True,
)
biencoder = None
# Create a "fake" one-for-all model
model = SimpleOneForAllModel(
biencoder=biencoder, reader=reader, tensorizer=tensorizer,
)
# Modify config
cfg.ignore_checkpoint_optimizer = True
cfg.ignore_checkpoint_offset = True
cfg.gradient_checkpointing = False
cfg.fp16 = False
# Place holder for backward compatibility
gradient_checkpointing = False
biencoder_optimizer = None
reader_optimizer = None
forward_fn = ofa_simple_fw_pass # always the simplest
else:
self.mode = "normal"
# Initialize everything
gradient_checkpointing = getattr(cfg, "gradient_checkpointing", False)
tensorizer, model, biencoder_optimizer, reader_optimizer, forward_fn = init_ofa_model(
cfg.encoder.encoder_model_type, cfg, gradient_checkpointing=gradient_checkpointing,
)
model, (biencoder_optimizer, reader_optimizer) = setup_for_distributed_mode(
model,
[biencoder_optimizer, reader_optimizer],
cfg.device,
cfg.n_gpu,
cfg.local_rank,
cfg.fp16,
cfg.fp16_opt_level,
gradient_checkpointing=gradient_checkpointing,
)
self.forward_fn = forward_fn
self.model = model
self.cfg = cfg
self.ds_cfg = OneForAllDatasetsCfg(cfg)
self.biencoder_optimizer = biencoder_optimizer
self.biencoder_scheduler_state = None
self.reader_optimizer = reader_optimizer
self.reader_scheduler_state = None
self.clustering = cfg.biencoder.clustering
if self.clustering:
cfg.global_loss_buf_sz = 72000000 # this requires a lot of memory
self.tensorizer = tensorizer
self.start_epoch = 0
self.start_batch = 0
self.best_validation_result = None
self.best_cp_name = None
# Biencoder loss function (note that reader loss is automatically computed)
self.biencoder_loss_function: BiEncoderNllLoss = init_loss(cfg.encoder.encoder_model_type, cfg)
if saved_state:
self._load_saved_state(saved_state)
self.dev_iterator = None
def __init__(self, cfg: DictConfig):
self.shard_id = cfg.local_rank if cfg.local_rank != -1 else 0
self.distributed_factor = cfg.distributed_world_size or 1
logger.info("***** Initializing components for training *****")
# if model file is specified, encoder parameters from saved state should be used for initialization
model_file = get_model_file(cfg, cfg.checkpoint_file_name)
saved_state = None
if model_file:
saved_state = load_states_from_checkpoint(model_file)
set_cfg_params_from_state(saved_state.encoder_params, cfg)
tensorizer, model, optimizer = init_biencoder_components(
cfg.encoder.encoder_model_type, cfg
)
if cfg.deepspeed:
model.half()
# XXX
#no_decay = ["bias", "LayerNorm.weight"]
#
#optimizer_grouped_parameters = [
# {
# "params": [
# p
# for n, p in model.named_parameters()
# if not any(nd in n for nd in no_decay)
# ],
# "weight_decay": cfg.train.weight_decay,
# },
# {
# "params": [
# p
# for n, p in model.named_parameters()
# if any(nd in n for nd in no_decay)
# ],
# "weight_decay": 0.0,
# },
#]
optimizer = DeepSpeedCPUAdam(optimizer.param_groups, lr=cfg.train.learning_rate,
weight_decay=cfg.train.weight_decay)
model, optimizer = setup_for_distributed_mode(
model,
optimizer,
cfg.device,
cfg.n_gpu,
cfg.local_rank,
cfg.fp16,
cfg.fp16_opt_level,
)
self.biencoder = model
self.optimizer = optimizer
self.tensorizer = tensorizer
self.start_epoch = 0
self.start_batch = 0
self.scheduler_state = None
self.best_validation_result = None
self.best_cp_name = None
self.cfg = cfg
self.ds_cfg = BiencoderDatasetsCfg(cfg)
if saved_state:
self._load_saved_state(saved_state)
self.dev_iterator = None
