def ann_data_gen(args):
last_checkpoint = args.last_checkpoint_dir
ann_no, ann_path, ndcg_json = get_latest_ann_data(args.output_dir)
output_num = ann_no + 1
logger.info("starting output number %d", output_num)
preloaded_data = None
if is_first_worker():
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
if not os.path.exists(args.cache_dir):
os.makedirs(args.cache_dir)
preloaded_data = load_data(args)
while args.end_output_num == -1 or output_num <= args.end_output_num:
next_checkpoint, latest_step_num = get_latest_checkpoint(args)
if args.only_keep_latest_embedding_file:
latest_step_num = 0
if next_checkpoint == last_checkpoint:
time.sleep(60)
else:
logger.info("start generate ann data number %d", output_num)
logger.info("next checkpoint at " + next_checkpoint)
generate_new_ann(args, output_num, next_checkpoint, preloaded_data, latest_step_num)
logger.info("finished generating ann data number %d", output_num)
output_num += 1
last_checkpoint = next_checkpoint
if args.local_rank != -1:
dist.barrier()
def compute_mrr(D, I, qids, ref_dict):
knn_pkl = {"D": D, "I": I}
all_knn_list = all_gather(knn_pkl)
mrr = 0.0
if is_first_worker():
D_merged = concat_key(all_knn_list, "D", axis=1)
I_merged = concat_key(all_knn_list, "I", axis=1)
print(D_merged.shape, I_merged.shape)
# we pad with negative pids and distance -128 - if they make it to the top we have a problem
idx = np.argsort(D_merged, axis=1)[:, ::-1][:, :10]
sorted_I = np.take_along_axis(I_merged, idx, axis=1)
candidate_dict = {}
for i, qid in enumerate(qids):
seen_pids = set()
if qid not in candidate_dict:
candidate_dict[qid] = [0] * 1000
j = 0
for pid in sorted_I[i]:
if pid >= 0 and pid not in seen_pids:
candidate_dict[qid][j] = pid
j += 1
seen_pids.add(pid)
allowed, message = quality_checks_qids(ref_dict, candidate_dict)
if message != '':
print(message)
mrr_metrics = compute_metrics(ref_dict, candidate_dict)
mrr = mrr_metrics["MRR @10"]
print(mrr)
return mrr
def save_checkpoint(args, model, tokenizer):
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and is_first_worker():
# Create output directory if needed
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
dist.barrier()
def evaluation(args, model, tokenizer):
# Evaluation
results = {}
if args.do_eval:
model_dir = args.model_name_or_path if args.model_name_or_path else args.output_dir
checkpoints = [model_dir]
for checkpoint in checkpoints:
global_step = checkpoint.split(
"-")[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
"/")[-1] if checkpoint.find("checkpoint") != -1 else ""
model.eval()
reranking_mrr, full_ranking_mrr = passage_dist_eval(
args, model, tokenizer)
if is_first_worker():
print("Reranking/Full ranking mrr: {0}/{1}".format(
str(reranking_mrr), str(full_ranking_mrr)))
dist.barrier()
return results
def load_model(args):
# Prepare GLUE task
args.task_name = args.task_name.lower()
args.output_mode = "classification"
label_list = ["0", "1"]
num_labels = len(label_list)
# store args
if args.local_rank != -1:
args.world_size = torch.distributed.get_world_size()
args.rank = dist.get_rank()
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
configObj = MSMarcoConfigDict[args.model_type]
tokenizer = configObj.tokenizer_class.from_pretrained(
"bert-base-uncased",
do_lower_case=True,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if is_first_worker():
# Create output directory if needed
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model = configObj.model_class(args)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
return tokenizer, model
def generate_new_ann(args, output_num, checkpoint_path,
training_query_positive_id, dev_query_positive_id,
latest_step_num):
config, tokenizer, model = load_model(args, checkpoint_path)
logger.info("***** inference of dev query *****")
dev_query_collection_path = os.path.join(args.data_dir, "dev-query")
dev_query_cache = EmbeddingCache(dev_query_collection_path)
with dev_query_cache as emb:
dev_query_embedding, dev_query_embedding2id = StreamInferenceDoc(
args,
model,
GetProcessingFn(args, query=True),
"dev_query_" + str(latest_step_num) + "_",
emb,
is_query_inference=True)
logger.info("***** inference of passages *****")
passage_collection_path = os.path.join(args.data_dir, "passages")
passage_cache = EmbeddingCache(passage_collection_path)
with passage_cache as emb:
passage_embedding, passage_embedding2id = StreamInferenceDoc(
args,
model,
GetProcessingFn(args, query=False),
"passage_" + str(latest_step_num) + "_",
emb,
is_query_inference=False)
logger.info("***** Done passage inference *****")
if args.inference:
return
logger.info("***** inference of train query *****")
train_query_collection_path = os.path.join(args.data_dir, "train-query")
train_query_cache = EmbeddingCache(train_query_collection_path)
with train_query_cache as emb:
query_embedding, query_embedding2id = StreamInferenceDoc(
args,
model,
GetProcessingFn(args, query=True),
"query_" + str(latest_step_num) + "_",
emb,
is_query_inference=True)
if is_first_worker():
dim = passage_embedding.shape[1]
print('passage embedding shape: ' + str(passage_embedding.shape))
top_k = args.topk_training
faiss.omp_set_num_threads(16)
cpu_index = faiss.IndexFlatIP(dim)
cpu_index.add(passage_embedding)
logger.info("***** Done ANN Index *****")
# measure ANN mrr
# I: [number of queries, topk]
_, dev_I = cpu_index.search(dev_query_embedding, 100)
dev_ndcg, num_queries_dev = EvalDevQuery(args, dev_query_embedding2id,
passage_embedding2id,
dev_query_positive_id, dev_I)
# Construct new traing set ==================================
chunk_factor = args.ann_chunk_factor
effective_idx = output_num % chunk_factor
if chunk_factor <= 0:
chunk_factor = 1
num_queries = len(query_embedding)
queries_per_chunk = num_queries // chunk_factor
q_start_idx = queries_per_chunk * effective_idx
q_end_idx = num_queries if (effective_idx == (chunk_factor - 1)) else (
q_start_idx + queries_per_chunk)
query_embedding = query_embedding[q_start_idx:q_end_idx]
query_embedding2id = query_embedding2id[q_start_idx:q_end_idx]
logger.info("Chunked {} query from {}".format(len(query_embedding),
num_queries))
# I: [number of queries, topk]
_, I = cpu_index.search(query_embedding, top_k)
effective_q_id = set(query_embedding2id.flatten())
query_negative_passage = GenerateNegativePassaageID(
args, query_embedding2id, passage_embedding2id,
training_query_positive_id, I, effective_q_id)
logger.info("***** Construct ANN Triplet *****")
train_data_output_path = os.path.join(
args.output_dir, "ann_training_data_" + str(output_num))
with open(train_data_output_path, 'w') as f:
query_range = list(range(I.shape[0]))
random.shuffle(query_range)
for query_idx in query_range:
query_id = query_embedding2id[query_idx]
if query_id not in effective_q_id or query_id not in training_query_positive_id:
continue
pos_pid = training_query_positive_id[query_id]
f.write("{}\t{}\t{}\n".format(
query_id, pos_pid, ','.join(
str(neg_pid)
for neg_pid in query_negative_passage[query_id])))
ndcg_output_path = os.path.join(args.output_dir,
"ann_ndcg_" + str(output_num))
with open(ndcg_output_path, 'w') as f:
json.dump({'ndcg': dev_ndcg, 'checkpoint': checkpoint_path}, f)
return dev_ndcg, num_queries_dev
def train(args, model, tokenizer, f, train_fn):
""" Train the model """
tb_writer = None
if is_first_worker():
tb_writer = SummaryWriter(log_dir=args.log_dir)
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
real_batch_size = args.train_batch_size * args.gradient_accumulation_steps * \
(torch.distributed.get_world_size() if args.local_rank != -1 else 1)
if args.max_steps > 0:
t_total = args.max_steps
else:
t_total = args.expected_train_size // real_batch_size * args.num_train_epochs
# layerwise optimization for lamb
optimizer_grouped_parameters = []
layer_optim_params = set()
for layer_name in [
"roberta.embeddings", "score_out", "downsample1", "downsample2",
"downsample3", "embeddingHead"
]:
layer = getattr_recursive(model, layer_name)
if layer is not None:
optimizer_grouped_parameters.append({"params": layer.parameters()})
for p in layer.parameters():
layer_optim_params.add(p)
if getattr_recursive(model, "roberta.encoder.layer") is not None:
for layer in model.roberta.encoder.layer:
optimizer_grouped_parameters.append({"params": layer.parameters()})
for p in layer.parameters():
layer_optim_params.add(p)
optimizer_grouped_parameters.append({
"params":
[p for p in model.parameters() if p not in layer_optim_params]
})
if args.optimizer.lower() == "lamb":
optimizer = Lamb(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
elif args.optimizer.lower() == "adamw":
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
else:
raise Exception(
"optimizer {0} not recognized! Can only be lamb or adamW".format(
args.optimizer))
if args.scheduler.lower() == "linear":
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
elif args.scheduler.lower() == "cosine":
scheduler = CosineAnnealingLR(optimizer, t_total, 1e-8)
else:
raise Exception(
"Scheduler {0} not recognized! Can only be linear or cosine".
format(args.scheduler))
# Check if saved optimizer or scheduler states exist
if os.path.isfile(os.path.join(
args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
os.path.join(
args.model_name_or_path,
"scheduler.pt")) and args.load_optimizer_scheduler:
# Load in optimizer and scheduler states
optimizer.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
scheduler.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True,
)
# Train!
logger.info("***** Running training *****")
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
# set global_step to gobal_step of last saved checkpoint from model path
try:
global_step = int(
args.model_name_or_path.split("-")[-1].split("/")[0])
epochs_trained = global_step // (args.expected_train_size //
args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (
args.expected_train_size // args.gradient_accumulation_steps)
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d",
global_step)
logger.info(" Will skip the first %d steps in the first epoch",
steps_trained_in_current_epoch)
except:
logger.info(" Start training from a pretrained model")
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(
epochs_trained,
int(args.num_train_epochs),
desc="Epoch",
disable=args.local_rank not in [-1, 0],
)
set_seed(args) # Added here for reproductibility
for m_epoch in train_iterator:
f.seek(0)
sds = StreamingDataset(f, train_fn)
epoch_iterator = DataLoader(sds,
batch_size=args.per_gpu_train_batch_size,
num_workers=1)
for step, batch in tqdm(enumerate(epoch_iterator),
desc="Iteration",
disable=args.local_rank not in [-1, 0]):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device).long() for t in batch)
if (step + 1) % args.gradient_accumulation_steps == 0:
outputs = model(*batch)
else:
with model.no_sync():
outputs = model(*batch)
# model outputs are always tuple in transformers (see doc)
loss = outputs[0]
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
if (step + 1) % args.gradient_accumulation_steps == 0:
loss.backward()
else:
with model.no_sync():
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if is_first_worker(
) and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(
args.output_dir, "checkpoint-{}".format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
torch.save(args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(optimizer.state_dict(),
os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(),
os.path.join(output_dir, "scheduler.pt"))
logger.info("Saving optimizer and scheduler states to %s",
output_dir)
dist.barrier()
if args.logging_steps > 0 and global_step % args.logging_steps == 0:
logs = {}
if args.evaluate_during_training and global_step % (
args.logging_steps_per_eval *
args.logging_steps) == 0:
model.eval()
reranking_mrr, full_ranking_mrr = passage_dist_eval(
args, model, tokenizer)
if is_first_worker():
print("Reranking/Full ranking mrr: {0}/{1}".format(
str(reranking_mrr), str(full_ranking_mrr)))
mrr_dict = {
"reranking": float(reranking_mrr),
"full_raking": float(full_ranking_mrr)
}
tb_writer.add_scalars("mrr", mrr_dict, global_step)
print(args.output_dir)
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
logging_loss = tr_loss
if is_first_worker():
for key, value in logs.items():
print(key, type(value))
tb_writer.add_scalar(key, value, global_step)
tb_writer.add_scalar("epoch", m_epoch, global_step)
print(json.dumps({**logs, **{"step": global_step}}))
dist.barrier()
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
tb_writer.close()
return global_step, tr_loss / global_step
def generate_new_ann(args, output_num, checkpoint_path, preloaded_data, latest_step_num):
model = load_model(args, checkpoint_path)
pid2offset, offset2pid = load_mapping(args.data_dir, "pid2offset")
logger.info("***** inference of train query *****")
train_query_collection_path = os.path.join(args.data_dir, "train-query")
train_query_cache = EmbeddingCache(train_query_collection_path)
with train_query_cache as emb:
query_embedding, query_embedding2id = StreamInferenceDoc(args, model, GetProcessingFn(args, query=True), "query_" + str(latest_step_num)+"_", emb, is_query_inference = True)
logger.info("***** inference of dev query *****")
dev_query_collection_path = os.path.join(args.data_dir, "test-query")
dev_query_cache = EmbeddingCache(dev_query_collection_path)
with dev_query_cache as emb:
dev_query_embedding, dev_query_embedding2id = StreamInferenceDoc(args, model, GetProcessingFn(args, query=True), "dev_query_"+ str(latest_step_num)+"_", emb, is_query_inference = True)
dev_query_collection_path_trivia = os.path.join(args.data_dir, "trivia-test-query")
dev_query_cache_trivia = EmbeddingCache(dev_query_collection_path_trivia)
with dev_query_cache_trivia as emb:
dev_query_embedding_trivia, dev_query_embedding2id_trivia = StreamInferenceDoc(args, model, GetProcessingFn(args, query=True), "dev_query_"+ str(latest_step_num)+"_", emb, is_query_inference = True)
logger.info("***** inference of passages *****")
passage_collection_path = os.path.join(args.data_dir, "passages")
passage_cache = EmbeddingCache(passage_collection_path)
with passage_cache as emb:
passage_embedding, passage_embedding2id = StreamInferenceDoc(args, model, GetProcessingFn(args, query=False), "passage_"+ str(latest_step_num)+"_", emb, is_query_inference = False, load_cache = False)
logger.info("***** Done passage inference *****")
if is_first_worker():
passage_text, train_pos_id, train_answers, test_answers, test_answers_trivia = preloaded_data
dim = passage_embedding.shape[1]
print('passage embedding shape: ' + str(passage_embedding.shape))
top_k = args.topk_training
faiss.omp_set_num_threads(16)
cpu_index = faiss.IndexFlatIP(dim)
cpu_index.add(passage_embedding)
logger.info("***** Done ANN Index *****")
# measure ANN mrr
_, dev_I = cpu_index.search(dev_query_embedding, 100) #I: [number of queries, topk]
top_k_hits = validate(passage_text, test_answers, dev_I, dev_query_embedding2id, passage_embedding2id)
# measure ANN mrr
_, dev_I = cpu_index.search(dev_query_embedding_trivia, 100) #I: [number of queries, topk]
top_k_hits_trivia = validate(passage_text, test_answers_trivia, dev_I, dev_query_embedding2id_trivia, passage_embedding2id)
logger.info("Start searching for query embedding with length %d", len(query_embedding))
_, I = cpu_index.search(query_embedding, top_k) #I: [number of queries, topk]
logger.info("***** GenerateNegativePassaageID *****")
effective_q_id = set(query_embedding2id.flatten())
logger.info("Effective qid length %d, search result length %d", len(effective_q_id), I.shape[0])
query_negative_passage = GenerateNegativePassaageID(args, passage_text, train_answers, query_embedding2id, passage_embedding2id, I, train_pos_id)
logger.info("Done generating negative passages, output length %d", len(query_negative_passage))
logger.info("***** Construct ANN Triplet *****")
train_data_output_path = os.path.join(args.output_dir, "ann_training_data_" + str(output_num))
with open(train_data_output_path, 'w') as f:
query_range = list(range(I.shape[0]))
random.shuffle(query_range)
for query_idx in query_range:
query_id = query_embedding2id[query_idx]
# if not query_id in train_pos_id:
# continue
pos_pid = train_pos_id[query_id]
f.write("{}\t{}\t{}\n".format(query_id, pos_pid, ','.join(str(neg_pid) for neg_pid in query_negative_passage[query_id])))
ndcg_output_path = os.path.join(args.output_dir, "ann_ndcg_" + str(output_num))
with open(ndcg_output_path, 'w') as f:
json.dump({'top20': top_k_hits[19], 'top100': top_k_hits[99], 'top20_trivia': top_k_hits_trivia[19],
'top100_trivia': top_k_hits_trivia[99], 'checkpoint': checkpoint_path}, f)
def train(args, model, tokenizer, query_cache, passage_cache):
""" Train the model """
logger.info("Training/evaluation parameters %s", args)
tb_writer = None
if is_first_worker():
tb_writer = SummaryWriter(log_dir=args.log_dir)
args.train_batch_size = args.per_gpu_train_batch_size * max(
1, args.n_gpu) #nll loss for query
real_batch_size = args.train_batch_size * args.gradient_accumulation_steps * (
torch.distributed.get_world_size() if args.local_rank != -1 else 1)
optimizer = get_optimizer(
args,
model,
weight_decay=args.weight_decay,
)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True,
)
# Train!
logger.info("***** Running training *****")
logger.info(" Max steps = %d", args.max_steps)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
tr_loss = 0.0
model.zero_grad()
model.train()
set_seed(args) # Added here for reproductibility
last_ann_no = -1
train_dataloader = None
train_dataloader_iter = None
dev_ndcg = 0
step = 0
iter_count = 0
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps)
global_step = 0
if args.model_name_or_path != "bert-base-uncased":
saved_state = load_states_from_checkpoint(args.model_name_or_path)
global_step = _load_saved_state(model, optimizer, scheduler,
saved_state)
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from global step %d", global_step)
nq_dev_nll_loss, nq_correct_ratio = evaluate_dev(
args, model, passage_cache)
dev_nll_loss_trivia, correct_ratio_trivia = evaluate_dev(
args, model, passage_cache, "-trivia")
if is_first_worker():
tb_writer.add_scalar("dev_nll_loss/dev_nll_loss", nq_dev_nll_loss,
global_step)
tb_writer.add_scalar("dev_nll_loss/correct_ratio",
nq_correct_ratio, global_step)
tb_writer.add_scalar("dev_nll_loss/dev_nll_loss_trivia",
dev_nll_loss_trivia, global_step)
tb_writer.add_scalar("dev_nll_loss/correct_ratio_trivia",
correct_ratio_trivia, global_step)
while global_step < args.max_steps:
if step % args.gradient_accumulation_steps == 0 and global_step % args.logging_steps == 0:
if args.num_epoch == 0:
# check if new ann training data is availabe
ann_no, ann_path, ndcg_json = get_latest_ann_data(args.ann_dir)
if ann_path is not None and ann_no != last_ann_no:
logger.info("Training on new add data at %s", ann_path)
with open(ann_path, 'r') as f:
ann_training_data = f.readlines()
logger.info("Training data line count: %d",
len(ann_training_data))
ann_training_data = [
l for l in ann_training_data
if len(l.split('\t')[2].split(',')) > 1
]
logger.info("Filtered training data line count: %d",
len(ann_training_data))
ann_checkpoint_path = ndcg_json['checkpoint']
ann_checkpoint_no = get_checkpoint_no(ann_checkpoint_path)
aligned_size = (len(ann_training_data) //
args.world_size) * args.world_size
ann_training_data = ann_training_data[:aligned_size]
logger.info("Total ann queries: %d",
len(ann_training_data))
if args.triplet:
train_dataset = StreamingDataset(
ann_training_data,
GetTripletTrainingDataProcessingFn(
args, query_cache, passage_cache))
train_dataloader = DataLoader(
train_dataset, batch_size=args.train_batch_size)
else:
train_dataset = StreamingDataset(
ann_training_data,
GetTrainingDataProcessingFn(
args, query_cache, passage_cache))
train_dataloader = DataLoader(
train_dataset,
batch_size=args.train_batch_size * 2)
train_dataloader_iter = iter(train_dataloader)
# re-warmup
if not args.single_warmup:
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=len(ann_training_data))
if args.local_rank != -1:
dist.barrier()
if is_first_worker():
# add ndcg at checkpoint step used instead of current step
tb_writer.add_scalar("retrieval_accuracy/top20_nq",
ndcg_json['top20'],
ann_checkpoint_no)
tb_writer.add_scalar("retrieval_accuracy/top100_nq",
ndcg_json['top100'],
ann_checkpoint_no)
if 'top20_trivia' in ndcg_json:
tb_writer.add_scalar(
"retrieval_accuracy/top20_trivia",
ndcg_json['top20_trivia'], ann_checkpoint_no)
tb_writer.add_scalar(
"retrieval_accuracy/top100_trivia",
ndcg_json['top100_trivia'], ann_checkpoint_no)
if last_ann_no != -1:
tb_writer.add_scalar("epoch", last_ann_no,
global_step - 1)
tb_writer.add_scalar("epoch", ann_no, global_step)
last_ann_no = ann_no
elif step == 0:
train_data_path = os.path.join(args.data_dir, "train-data")
with open(train_data_path, 'r') as f:
training_data = f.readlines()
if args.triplet:
train_dataset = StreamingDataset(
training_data,
GetTripletTrainingDataProcessingFn(
args, query_cache, passage_cache))
train_dataloader = DataLoader(
train_dataset, batch_size=args.train_batch_size)
else:
train_dataset = StreamingDataset(
training_data,
GetTrainingDataProcessingFn(args, query_cache,
passage_cache))
train_dataloader = DataLoader(
train_dataset, batch_size=args.train_batch_size * 2)
all_batch = [b for b in train_dataloader]
logger.info("Total batch count: %d", len(all_batch))
train_dataloader_iter = iter(train_dataloader)
try:
batch = next(train_dataloader_iter)
except StopIteration:
logger.info("Finished iterating current dataset, begin reiterate")
if args.num_epoch != 0:
iter_count += 1
if is_first_worker():
tb_writer.add_scalar("epoch", iter_count - 1,
global_step - 1)
tb_writer.add_scalar("epoch", iter_count, global_step)
nq_dev_nll_loss, nq_correct_ratio = evaluate_dev(
args, model, passage_cache)
dev_nll_loss_trivia, correct_ratio_trivia = evaluate_dev(
args, model, passage_cache, "-trivia")
if is_first_worker():
tb_writer.add_scalar("dev_nll_loss/dev_nll_loss",
nq_dev_nll_loss, global_step)
tb_writer.add_scalar("dev_nll_loss/correct_ratio",
nq_correct_ratio, global_step)
tb_writer.add_scalar("dev_nll_loss/dev_nll_loss_trivia",
dev_nll_loss_trivia, global_step)
tb_writer.add_scalar("dev_nll_loss/correct_ratio_trivia",
correct_ratio_trivia, global_step)
train_dataloader_iter = iter(train_dataloader)
batch = next(train_dataloader_iter)
dist.barrier()
if args.num_epoch != 0 and iter_count > args.num_epoch:
break
step += 1
if args.triplet:
loss = triplet_fwd_pass(args, model, batch)
else:
loss, correct_cnt = do_biencoder_fwd_pass(args, model, batch)
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
if step % args.gradient_accumulation_steps == 0:
loss.backward()
else:
with model.no_sync():
loss.backward()
tr_loss += loss.item()
if step % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.logging_steps > 0 and global_step % args.logging_steps == 0:
logs = {}
loss_scalar = tr_loss / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
tr_loss = 0
if is_first_worker():
for key, value in logs.items():
tb_writer.add_scalar(key, value, global_step)
logger.info(json.dumps({**logs, **{"step": global_step}}))
if is_first_worker(
) and args.save_steps > 0 and global_step % args.save_steps == 0:
_save_checkpoint(args, model, optimizer, scheduler,
global_step)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
tb_writer.close()
return global_step
def train(args, model, tokenizer, query_cache, passage_cache):
""" Train the model """
logger.info("Training/evaluation parameters %s", args)
tb_writer = None
if is_first_worker():
tb_writer = SummaryWriter(log_dir=args.log_dir)
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
real_batch_size = args.train_batch_size * args.gradient_accumulation_steps * \
(torch.distributed.get_world_size() if args.local_rank != -1 else 1)
optimizer_grouped_parameters = []
layer_optim_params = set()
for layer_name in [
"roberta.embeddings", "score_out", "downsample1", "downsample2",
"downsample3"
]:
layer = getattr_recursive(model, layer_name)
if layer is not None:
optimizer_grouped_parameters.append({"params": layer.parameters()})
for p in layer.parameters():
layer_optim_params.add(p)
if getattr_recursive(model, "roberta.encoder.layer") is not None:
for layer in model.roberta.encoder.layer:
optimizer_grouped_parameters.append({"params": layer.parameters()})
for p in layer.parameters():
layer_optim_params.add(p)
optimizer_grouped_parameters.append({
"params":
[p for p in model.parameters() if p not in layer_optim_params]
})
if args.optimizer.lower() == "lamb":
optimizer = Lamb(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
elif args.optimizer.lower() == "adamw":
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
else:
raise Exception(
"optimizer {0} not recognized! Can only be lamb or adamW".format(
args.optimizer))
# Check if saved optimizer or scheduler states exist
if os.path.isfile(
os.path.join(args.model_name_or_path,
"optimizer.pt")) and args.load_optimizer_scheduler:
# Load in optimizer and scheduler states
optimizer.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True,
)
# Train
logger.info("***** Running training *****")
logger.info(" Max steps = %d", args.max_steps)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
global_step = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
# set global_step to gobal_step of last saved checkpoint from model
# path
if "-" in args.model_name_or_path:
global_step = int(
args.model_name_or_path.split("-")[-1].split("/")[0])
else:
global_step = 0
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from global step %d", global_step)
tr_loss = 0.0
model.zero_grad()
model.train()
set_seed(args) # Added here for reproductibility
last_ann_no = -1
train_dataloader = None
train_dataloader_iter = None
dev_ndcg = 0
step = 0
if args.single_warmup:
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps)
while global_step < args.max_steps:
if step % args.gradient_accumulation_steps == 0 and global_step % args.logging_steps == 0:
# check if new ann training data is availabe
ann_no, ann_path, ndcg_json = get_latest_ann_data(args.ann_dir)
if ann_path is not None and ann_no != last_ann_no:
logger.info("Training on new add data at %s", ann_path)
with open(ann_path, 'r') as f:
ann_training_data = f.readlines()
dev_ndcg = ndcg_json['ndcg']
ann_checkpoint_path = ndcg_json['checkpoint']
ann_checkpoint_no = get_checkpoint_no(ann_checkpoint_path)
aligned_size = (len(ann_training_data) //
args.world_size) * args.world_size
ann_training_data = ann_training_data[:aligned_size]
logger.info("Total ann queries: %d", len(ann_training_data))
if args.triplet:
train_dataset = StreamingDataset(
ann_training_data,
GetTripletTrainingDataProcessingFn(
args, query_cache, passage_cache))
else:
train_dataset = StreamingDataset(
ann_training_data,
GetTrainingDataProcessingFn(args, query_cache,
passage_cache))
train_dataloader = DataLoader(train_dataset,
batch_size=args.train_batch_size)
train_dataloader_iter = iter(train_dataloader)
# re-warmup
if not args.single_warmup:
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=len(ann_training_data))
if args.local_rank != -1:
dist.barrier()
if is_first_worker():
# add ndcg at checkpoint step used instead of current step
tb_writer.add_scalar("dev_ndcg", dev_ndcg,
ann_checkpoint_no)
if last_ann_no != -1:
tb_writer.add_scalar("epoch", last_ann_no,
global_step - 1)
tb_writer.add_scalar("epoch", ann_no, global_step)
last_ann_no = ann_no
try:
batch = next(train_dataloader_iter)
except StopIteration:
logger.info("Finished iterating current dataset, begin reiterate")
train_dataloader_iter = iter(train_dataloader)
batch = next(train_dataloader_iter)
batch = tuple(t.to(args.device) for t in batch)
step += 1
if args.triplet:
inputs = {
"query_ids": batch[0].long(),
"attention_mask_q": batch[1].long(),
"input_ids_a": batch[3].long(),
"attention_mask_a": batch[4].long(),
"input_ids_b": batch[6].long(),
"attention_mask_b": batch[7].long()
}
else:
inputs = {
"input_ids_a": batch[0].long(),
"attention_mask_a": batch[1].long(),
"input_ids_b": batch[3].long(),
"attention_mask_b": batch[4].long(),
"labels": batch[6]
}
# sync gradients only at gradient accumulation step
if step % args.gradient_accumulation_steps == 0:
outputs = model(**inputs)
else:
with model.no_sync():
outputs = model(**inputs)
# model outputs are always tuple in transformers (see doc)
loss = outputs[0]
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
if step % args.gradient_accumulation_steps == 0:
loss.backward()
else:
with model.no_sync():
loss.backward()
tr_loss += loss.item()
if step % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.logging_steps > 0 and global_step % args.logging_steps == 0:
logs = {}
loss_scalar = tr_loss / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
tr_loss = 0
if is_first_worker():
for key, value in logs.items():
tb_writer.add_scalar(key, value, global_step)
logger.info(json.dumps({**logs, **{"step": global_step}}))
if is_first_worker(
) and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir,
"checkpoint-{}".format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(optimizer.state_dict(),
os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(),
os.path.join(output_dir, "scheduler.pt"))
logger.info("Saving optimizer and scheduler states to %s",
output_dir)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
tb_writer.close()
return global_step
