def main():
log_hardware()
args = parse_args()
log_args(args)
model = NeuMF(nb_users=args.n_users, nb_items=args.n_items, mf_dim=args.factors,
mlp_layer_sizes=args.layers, dropout=args.dropout)
model = model.cuda()
if args.load_checkpoint_path:
state_dict = torch.load(args.load_checkpoint_path)
model.load_state_dict(state_dict)
if args.opt_level == "O2":
model = amp.initialize(model, opt_level=args.opt_level,
keep_batchnorm_fp32=False, loss_scale='dynamic')
users = torch.cuda.LongTensor(args.batch_size).random_(0, args.n_users)
items = torch.cuda.LongTensor(args.batch_size).random_(0, args.n_items)
latencies = []
for _ in range(args.num_batches):
torch.cuda.synchronize()
start = time.time()
predictions = model(users, items, sigmoid=True)
torch.cuda.synchronize()
latencies.append(time.time() - start)
LOGGER.log(key='batch_size', value=args.batch_size)
LOGGER.log(key='best_inference_throughput', value=args.batch_size / min(latencies))
LOGGER.log(key='best_inference_latency', value=min(latencies))
LOGGER.log(key='inference_latencies', value=latencies)
return
def main():
log_hardware()
args = parse_args()
args.distributed, args.world_size = init_distributed(args.local_rank)
log_args(args)
if args.seed is not None:
torch.manual_seed(args.seed)
print("Saving results to {}".format(args.checkpoint_dir))
if not os.path.exists(args.checkpoint_dir) and args.checkpoint_dir != '':
os.makedirs(args.checkpoint_dir, exist_ok=True)
# The default of np.random.choice is replace=True, so does pytorch random_()
LOGGER.log(key=tags.PREPROC_HP_SAMPLE_EVAL_REPLACEMENT, value=True)
LOGGER.log(key=tags.INPUT_HP_SAMPLE_TRAIN_REPLACEMENT, value=True)
LOGGER.log(key=tags.INPUT_STEP_EVAL_NEG_GEN)
# sync workers before timing
if args.distributed:
torch.distributed.broadcast(torch.tensor([1], device="cuda"), 0)
torch.cuda.synchronize()
main_start_time = time.time()
LOGGER.log(key=tags.RUN_START)
train_ratings = torch.load(args.data + '/train_ratings.pt',
map_location=torch.device('cuda:{}'.format(
args.local_rank)))
test_ratings = torch.load(args.data + '/test_ratings.pt',
map_location=torch.device('cuda:{}'.format(
args.local_rank)))
test_negs = torch.load(args.data + '/test_negatives.pt',
map_location=torch.device('cuda:{}'.format(
args.local_rank)))
valid_negative = test_negs.shape[1]
LOGGER.log(key=tags.PREPROC_HP_NUM_EVAL, value=valid_negative)
nb_maxs = torch.max(train_ratings, 0)[0]
nb_users = nb_maxs[0].item() + 1
nb_items = nb_maxs[1].item() + 1
LOGGER.log(key=tags.INPUT_SIZE, value=len(train_ratings))
all_test_users = test_ratings.shape[0]
test_users, test_items, dup_mask, real_indices = dataloading.create_test_data(
test_ratings, test_negs, args)
# make pytorch memory behavior more consistent later
torch.cuda.empty_cache()
LOGGER.log(key=tags.INPUT_BATCH_SIZE, value=args.batch_size)
LOGGER.log(key=tags.INPUT_ORDER) # we shuffled later with randperm
# Create model
model = NeuMF(nb_users,
nb_items,
mf_dim=args.factors,
mlp_layer_sizes=args.layers,
dropout=args.dropout)
optimizer = FusedAdam(model.parameters(),
lr=args.learning_rate,
betas=(args.beta1, args.beta2),
eps=args.eps)
criterion = nn.BCEWithLogitsLoss(
reduction='none'
) # use torch.mean() with dim later to avoid copy to host
# Move model and loss to GPU
model = model.cuda()
criterion = criterion.cuda()
if args.opt_level == "O2":
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.opt_level,
keep_batchnorm_fp32=False,
loss_scale='dynamic')
if args.distributed:
model = DDP(model)
local_batch = args.batch_size // args.world_size
traced_criterion = torch.jit.trace(
criterion.forward,
(torch.rand(local_batch, 1), torch.rand(local_batch, 1)))
print(model)
print("{} parameters".format(admm_utils.count_parameters(model)))
LOGGER.log(key=tags.OPT_LR, value=args.learning_rate)
LOGGER.log(key=tags.OPT_NAME, value="Adam")
LOGGER.log(key=tags.OPT_HP_ADAM_BETA1, value=args.beta1)
LOGGER.log(key=tags.OPT_HP_ADAM_BETA2, value=args.beta2)
LOGGER.log(key=tags.OPT_HP_ADAM_EPSILON, value=args.eps)
LOGGER.log(key=tags.MODEL_HP_LOSS_FN, value=tags.VALUE_BCE)
if args.load_checkpoint_path:
state_dict = torch.load(args.load_checkpoint_path)
state_dict = {
k.replace('module.', ''): v
for k, v in state_dict.items()
}
model.load_state_dict(state_dict)
if args.mode == 'test':
LOGGER.log(key=tags.EVAL_START, value=0)
start = time.time()
hr, ndcg = val_epoch(model,
test_users,
test_items,
dup_mask,
real_indices,
args.topk,
samples_per_user=valid_negative + 1,
num_user=all_test_users,
distributed=args.distributed)
print('HR@{K} = {hit_rate:.4f}, NDCG@{K} = {ndcg:.4f}'.format(
K=args.topk, hit_rate=hr, ndcg=ndcg))
val_time = time.time() - start
eval_size = all_test_users * (valid_negative + 1)
eval_throughput = eval_size / val_time
LOGGER.log(key=tags.EVAL_ACCURACY, value={"epoch": 0, "value": hr})
LOGGER.log(key=tags.EVAL_STOP, value=0)
LOGGER.log(key='best_eval_throughput', value=eval_throughput)
return
success = False
max_hr = 0
train_throughputs, eval_throughputs = [], []
LOGGER.log(key=tags.TRAIN_LOOP)
for epoch in range(args.epochs):
LOGGER.log(key=tags.TRAIN_EPOCH_START, value=epoch)
LOGGER.log(key=tags.INPUT_HP_NUM_NEG, value=args.negative_samples)
LOGGER.log(key=tags.INPUT_STEP_TRAIN_NEG_GEN)
begin = time.time()
epoch_users, epoch_items, epoch_label = dataloading.prepare_epoch_train_data(
train_ratings, nb_items, args)
num_batches = len(epoch_users)
for i in range(num_batches // args.grads_accumulated):
for j in range(args.grads_accumulated):
batch_idx = (args.grads_accumulated * i) + j
user = epoch_users[batch_idx]
item = epoch_items[batch_idx]
label = epoch_label[batch_idx].view(-1, 1)
outputs = model(user, item)
loss = traced_criterion(outputs, label).float()
loss = torch.mean(loss.view(-1), 0)
if args.opt_level == "O2":
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
for p in model.parameters():
p.grad = None
del epoch_users, epoch_items, epoch_label
train_time = time.time() - begin
begin = time.time()
epoch_samples = len(train_ratings) * (args.negative_samples + 1)
train_throughput = epoch_samples / train_time
train_throughputs.append(train_throughput)
LOGGER.log(key='train_throughput', value=train_throughput)
LOGGER.log(key=tags.TRAIN_EPOCH_STOP, value=epoch)
LOGGER.log(key=tags.EVAL_START, value=epoch)
hr, ndcg = val_epoch(model,
test_users,
test_items,
dup_mask,
real_indices,
args.topk,
samples_per_user=valid_negative + 1,
num_user=all_test_users,
epoch=epoch,
distributed=args.distributed)
val_time = time.time() - begin
print(
'Epoch {epoch}: HR@{K} = {hit_rate:.4f}, NDCG@{K} = {ndcg:.4f},'
' train_time = {train_time:.2f}, val_time = {val_time:.2f}'.format(
epoch=epoch,
K=args.topk,
hit_rate=hr,
ndcg=ndcg,
train_time=train_time,
val_time=val_time))
LOGGER.log(key=tags.EVAL_ACCURACY, value={"epoch": epoch, "value": hr})
LOGGER.log(key=tags.EVAL_TARGET, value=args.threshold)
LOGGER.log(key=tags.EVAL_STOP, value=epoch)
eval_size = all_test_users * (valid_negative + 1)
eval_throughput = eval_size / val_time
eval_throughputs.append(eval_throughput)
LOGGER.log(key='eval_throughput', value=eval_throughput)
if hr > max_hr and args.local_rank == 0:
max_hr = hr
save_checkpoint_path = os.path.join(args.checkpoint_dir,
'model.pth')
print("New best hr! Saving the model to: ", save_checkpoint_path)
torch.save(model.state_dict(), save_checkpoint_path)
best_model_timestamp = time.time()
if args.threshold is not None:
if hr >= args.threshold:
print("Hit threshold of {}".format(args.threshold))
success = True
break
if args.local_rank == 0:
LOGGER.log(key='best_train_throughput', value=max(train_throughputs))
LOGGER.log(key='best_eval_throughput', value=max(eval_throughputs))
LOGGER.log(key='best_accuracy', value=max_hr)
LOGGER.log(key='time_to_target', value=time.time() - main_start_time)
LOGGER.log(key='time_to_best_model',
value=best_model_timestamp - main_start_time)
LOGGER.log(key=tags.RUN_STOP, value={"success": success})
LOGGER.log(key=tags.RUN_FINAL)
def main():
log_hardware()
args = parse_args()
args.distributed, args.world_size = init_distributed(args.local_rank)
log_args(args)
main_start_time = time.time()
if args.seed is not None:
torch.manual_seed(args.seed)
# Save configuration to file
timestamp = "{:.0f}".format(datetime.utcnow().timestamp())
run_dir = "./run/neumf/{}.{}".format(timestamp, args.local_rank)
print("Saving results to {}".format(run_dir))
if not os.path.exists(run_dir) and run_dir != '':
os.makedirs(run_dir)
# more like load trigger timer now
LOGGER.log(key=tags.PREPROC_HP_NUM_EVAL, value=args.valid_negative)
# The default of np.random.choice is replace=True, so does pytorch random_()
LOGGER.log(key=tags.PREPROC_HP_SAMPLE_EVAL_REPLACEMENT, value=True)
LOGGER.log(key=tags.INPUT_HP_SAMPLE_TRAIN_REPLACEMENT, value=True)
LOGGER.log(key=tags.INPUT_STEP_EVAL_NEG_GEN)
# sync worker before timing.
if args.distributed:
torch.distributed.broadcast(torch.tensor([1], device="cuda"), 0)
torch.cuda.synchronize()
LOGGER.log(key=tags.RUN_START)
run_start_time = time.time()
# load not converted data, just seperate one for test
train_ratings = torch.load(args.data + '/train_ratings.pt',
map_location=torch.device('cuda:{}'.format(
args.local_rank)))
test_ratings = torch.load(args.data + '/test_ratings.pt',
map_location=torch.device('cuda:{}'.format(
args.local_rank)))
# get input data
# get dims
nb_maxs = torch.max(train_ratings, 0)[0]
nb_users = nb_maxs[0].item() + 1
nb_items = nb_maxs[1].item() + 1
train_users = train_ratings[:, 0]
train_items = train_ratings[:, 1]
del nb_maxs, train_ratings
LOGGER.log(key=tags.INPUT_SIZE, value=len(train_users))
# produce things not change between epoch
# mask for filtering duplicates with real sample
# note: test data is removed before create mask, same as reference
mat = torch.cuda.ByteTensor(nb_users, nb_items).fill_(1)
mat[train_users, train_items] = 0
# create label
train_label = torch.ones_like(train_users, dtype=torch.float32)
neg_label = torch.zeros_like(train_label, dtype=torch.float32)
neg_label = neg_label.repeat(args.negative_samples)
train_label = torch.cat((train_label, neg_label))
del neg_label
if args.fp16:
train_label = train_label.half()
# produce validation negative sample on GPU
all_test_users = test_ratings.shape[0]
test_users = test_ratings[:, 0]
test_pos = test_ratings[:, 1].reshape(-1, 1)
test_negs = generate_neg(test_users, mat, nb_items, args.valid_negative,
True)[1]
# create items with real sample at last position
test_users = test_users.reshape(-1, 1).repeat(1, 1 + args.valid_negative)
test_items = torch.cat(
(test_negs.reshape(-1, args.valid_negative), test_pos), dim=1)
del test_ratings, test_negs
# generate dup mask and real indice for exact same behavior on duplication compare to reference
# here we need a sort that is stable(keep order of duplicates)
# this is a version works on integer
sorted_items, indices = torch.sort(test_items) # [1,1,1,2], [3,1,0,2]
sum_item_indices = sorted_items.float() + indices.float() / len(
indices[0]) #[1.75,1.25,1.0,2.5]
indices_order = torch.sort(sum_item_indices)[1] #[2,1,0,3]
stable_indices = torch.gather(indices, 1, indices_order) #[0,1,3,2]
# produce -1 mask
dup_mask = (sorted_items[:, 0:-1] == sorted_items[:, 1:])
dup_mask = torch.cat(
(torch.zeros_like(test_pos, dtype=torch.uint8), dup_mask), dim=1)
dup_mask = torch.gather(dup_mask, 1, stable_indices.sort()[1])
# produce real sample indices to later check in topk
sorted_items, indices = (test_items != test_pos).sort()
sum_item_indices = sorted_items.float() + indices.float() / len(indices[0])
indices_order = torch.sort(sum_item_indices)[1]
stable_indices = torch.gather(indices, 1, indices_order)
real_indices = stable_indices[:, 0]
del sorted_items, indices, sum_item_indices, indices_order, stable_indices, test_pos
if args.distributed:
test_users = torch.chunk(test_users, args.world_size)[args.local_rank]
test_items = torch.chunk(test_items, args.world_size)[args.local_rank]
dup_mask = torch.chunk(dup_mask, args.world_size)[args.local_rank]
real_indices = torch.chunk(real_indices,
args.world_size)[args.local_rank]
# make pytorch memory behavior more consistent later
torch.cuda.empty_cache()
LOGGER.log(key=tags.INPUT_BATCH_SIZE, value=args.batch_size)
LOGGER.log(key=tags.INPUT_ORDER) # we shuffled later with randperm
print('Load data done [%.1f s]. #user=%d, #item=%d, #train=%d, #test=%d' %
(time.time() - run_start_time, nb_users, nb_items, len(train_users),
nb_users))
# Create model
model = NeuMF(nb_users,
nb_items,
mf_dim=args.factors,
mf_reg=0.,
mlp_layer_sizes=args.layers,
mlp_layer_regs=[0. for i in args.layers],
dropout=args.dropout)
if args.fp16:
model = model.half()
print(model)
print("{} parameters".format(utils.count_parameters(model)))
# Save model text description
with open(os.path.join(run_dir, 'model.txt'), 'w') as file:
file.write(str(model))
# Add optimizer and loss to graph
if args.fp16:
fp_optimizer = Fp16Optimizer(model, args.loss_scale)
params = fp_optimizer.fp32_params
else:
params = model.parameters()
optimizer = FusedAdam(params,
lr=args.learning_rate,
betas=(args.beta1, args.beta2),
eps=args.eps,
eps_inside_sqrt=False)
criterion = nn.BCEWithLogitsLoss(
reduction='none'
) # use torch.mean() with dim later to avoid copy to host
LOGGER.log(key=tags.OPT_LR, value=args.learning_rate)
LOGGER.log(key=tags.OPT_NAME, value="Adam")
LOGGER.log(key=tags.OPT_HP_ADAM_BETA1, value=args.beta1)
LOGGER.log(key=tags.OPT_HP_ADAM_BETA2, value=args.beta2)
LOGGER.log(key=tags.OPT_HP_ADAM_EPSILON, value=args.eps)
LOGGER.log(key=tags.MODEL_HP_LOSS_FN, value=tags.VALUE_BCE)
# Move model and loss to GPU
model = model.cuda()
criterion = criterion.cuda()
if args.distributed:
model = DDP(model)
local_batch = args.batch_size // int(os.environ['WORLD_SIZE'])
else:
local_batch = args.batch_size
traced_criterion = torch.jit.trace(
criterion.forward,
(torch.rand(local_batch, 1), torch.rand(local_batch, 1)))
train_users_per_worker = len(train_label) / int(os.environ['WORLD_SIZE'])
train_users_begin = int(train_users_per_worker * args.local_rank)
train_users_end = int(train_users_per_worker * (args.local_rank + 1))
# Create files for tracking training
valid_results_file = os.path.join(run_dir, 'valid_results.csv')
# Calculate initial Hit Ratio and NDCG
test_x = test_users.view(-1).split(args.valid_batch_size)
test_y = test_items.view(-1).split(args.valid_batch_size)
if args.mode == 'test':
state_dict = torch.load(args.checkpoint_path)
model.load_state_dict(state_dict)
begin = time.time()
LOGGER.log(key=tags.EVAL_START, value=-1)
hr, ndcg = val_epoch(model,
test_x,
test_y,
dup_mask,
real_indices,
args.topk,
samples_per_user=test_items.size(1),
num_user=all_test_users,
distributed=args.distributed)
val_time = time.time() - begin
print(
'Initial HR@{K} = {hit_rate:.4f}, NDCG@{K} = {ndcg:.4f}, valid_time: {val_time:.4f}'
.format(K=args.topk, hit_rate=hr, ndcg=ndcg, val_time=val_time))
LOGGER.log(key=tags.EVAL_ACCURACY, value={"epoch": -1, "value": hr})
LOGGER.log(key=tags.EVAL_TARGET, value=args.threshold)
LOGGER.log(key=tags.EVAL_STOP, value=-1)
if args.mode == 'test':
return
success = False
max_hr = 0
LOGGER.log(key=tags.TRAIN_LOOP)
train_throughputs = []
eval_throughputs = []
for epoch in range(args.epochs):
LOGGER.log(key=tags.TRAIN_EPOCH_START, value=epoch)
LOGGER.log(key=tags.INPUT_HP_NUM_NEG, value=args.negative_samples)
LOGGER.log(key=tags.INPUT_STEP_TRAIN_NEG_GEN)
begin = time.time()
# prepare data for epoch
neg_users, neg_items = generate_neg(train_users, mat, nb_items,
args.negative_samples)
epoch_users = torch.cat((train_users, neg_users))
epoch_items = torch.cat((train_items, neg_items))
del neg_users, neg_items
# shuffle prepared data and split into batches
epoch_indices = torch.randperm(train_users_end - train_users_begin,
device='cuda:{}'.format(
args.local_rank))
epoch_indices += train_users_begin
epoch_users = epoch_users[epoch_indices]
epoch_items = epoch_items[epoch_indices]
epoch_label = train_label[epoch_indices]
epoch_users_list = epoch_users.split(local_batch)
epoch_items_list = epoch_items.split(local_batch)
epoch_label_list = epoch_label.split(local_batch)
# only print progress bar on rank 0
num_batches = len(epoch_users_list)
# handle extremely rare case where last batch size < number of worker
if len(epoch_users) % args.batch_size < args.world_size:
print("epoch_size % batch_size < number of worker!")
exit(1)
for i in range(num_batches // args.grads_accumulated):
for j in range(args.grads_accumulated):
batch_idx = (args.grads_accumulated * i) + j
user = epoch_users_list[batch_idx]
item = epoch_items_list[batch_idx]
label = epoch_label_list[batch_idx].view(-1, 1)
outputs = model(user, item)
loss = traced_criterion(outputs, label).float()
loss = torch.mean(loss.view(-1), 0)
if args.fp16:
fp_optimizer.backward(loss)
else:
loss.backward()
if args.fp16:
fp_optimizer.step(optimizer)
else:
optimizer.step()
for p in model.parameters():
p.grad = None
del epoch_users, epoch_items, epoch_label, epoch_users_list, epoch_items_list, epoch_label_list, user, item, label
train_time = time.time() - begin
begin = time.time()
epoch_samples = len(train_users) * (args.negative_samples + 1)
train_throughput = epoch_samples / train_time
train_throughputs.append(train_throughput)
LOGGER.log(key='train_throughput', value=train_throughput)
LOGGER.log(key=tags.TRAIN_EPOCH_STOP, value=epoch)
LOGGER.log(key=tags.EVAL_START, value=epoch)
hr, ndcg = val_epoch(model,
test_x,
test_y,
dup_mask,
real_indices,
args.topk,
samples_per_user=test_items.size(1),
num_user=all_test_users,
output=valid_results_file,
epoch=epoch,
distributed=args.distributed)
val_time = time.time() - begin
print(
'Epoch {epoch}: HR@{K} = {hit_rate:.4f}, NDCG@{K} = {ndcg:.4f},'
' train_time = {train_time:.2f}, val_time = {val_time:.2f}'.format(
epoch=epoch,
K=args.topk,
hit_rate=hr,
ndcg=ndcg,
train_time=train_time,
val_time=val_time))
LOGGER.log(key=tags.EVAL_ACCURACY, value={"epoch": epoch, "value": hr})
LOGGER.log(key=tags.EVAL_TARGET, value=args.threshold)
LOGGER.log(key=tags.EVAL_STOP, value=epoch)
eval_size = all_test_users * test_items.size(1)
eval_throughput = eval_size / val_time
eval_throughputs.append(eval_throughput)
LOGGER.log(key='eval_throughput', value=eval_throughput)
if hr > max_hr and args.local_rank == 0:
max_hr = hr
print("New best hr! Saving the model to: ", args.checkpoint_path)
torch.save(model.state_dict(), args.checkpoint_path)
if args.threshold is not None:
if hr >= args.threshold:
print("Hit threshold of {}".format(args.threshold))
success = True
break
LOGGER.log(key='best_train_throughput', value=max(train_throughputs))
LOGGER.log(key='best_eval_throughput', value=max(eval_throughputs))
LOGGER.log(key='best_accuracy', value=max_hr)
LOGGER.log(key='time_to_target', value=time.time() - main_start_time)
LOGGER.log(key=tags.RUN_STOP, value={"success": success})
LOGGER.log(key=tags.RUN_FINAL)