def run(args):
if args.do_train:
train_dataloader, processor = load_example(args, 'train')
num_label = len(processor.get_labels())
model = RemBertForSequenceClassification.from_pretrained(
args.model_type, num_classes=num_label)
if nranks > 1:
dist.init_parallel_env()
model = paddle.DataParallel(model)
num_train_steps_per_epoch = len(
train_dataloader) // args.gradient_accumulation_steps
num_train_steps = int(num_train_steps_per_epoch *
args.num_train_epochs)
trainer = Trainer(args,
model=model,
dataloader=train_dataloader,
num_train_steps=num_train_steps,
step_callback=evaluate)
trainer.train()
if args.do_eval:
model = RemBertForSequenceClassification.from_pretrained(
args.output_dir)
evaluate(model, args)
def train(print_result=True):
"""train"""
# 1. initialize parallel environment
train_data_list1 = []
train_data_list2 = []
dist.init_parallel_env()
# 2. create data parallel layer & optimizer
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.MSELoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
# 3. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
assert len(loss) == 1
if print_result is True:
train_data_list1.append(loss.numpy())
assert len(train_data_list1)
loss.backward()
adam.step()
adam.clear_grad()
def run_trainer_func(self, args):
if fluid.core.is_compiled_with_cuda():
device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
place = fluid.CUDAPlace(device_id)
else:
assert ("Only support CUDAPlace for now.")
with fluid.dygraph.guard(place):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
np.random.seed(seed)
random.seed(seed)
model, train_reader, opt = self.get_model()
if args.update_method == "nccl2":
dist.init_parallel_env()
print_to_err(
type(self).__name__,
"begin to prepare context in dygraph with nccl2")
model = paddle.DataParallel(
model, find_unused_parameters=args.find_unused_parameters)
print_to_err(type(self).__name__, "model built in dygraph")
out_losses = self.model_train(args, model, opt, train_reader)
print_to_out(out_losses)
return out_losses
def train():
# init env
dist.init_parallel_env()
# create network
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.CrossEntropyLoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
# create data loader
dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
loader = paddle.io.DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True,
num_workers=1)
# train
for epoch_id in range(EPOCH_NUM):
for batch_id, (image, label) in enumerate(loader()):
out = layer(image)
loss = loss_fn(out, label)
loss.backward()
adam.step()
adam.clear_grad()
if dist.get_rank() == 0:
print("Epoch {} batch {}: loss = {}".format(
epoch_id, batch_id, np.mean(loss.numpy())))
def train():
# 设置支持多卡训练
dist.init_parallel_env()
train_dataset = paddle.vision.datasets.Cifar10(mode='train',
transform=transform)
batch_sampler = paddle.io.DistributedBatchSampler(train_dataset,
batch_size=32,
shuffle=True)
train_loader = DataLoader(dataset=train_dataset,
batch_sampler=batch_sampler)
model = paddle.vision.mobilenet_v2(num_classes=10)
# 设置支持多卡训练
model = paddle.DataParallel(model)
# 设置优化方法
optimizer = paddle.optimizer.SGD(parameters=model.parameters(),
learning_rate=0.1,
weight_decay=5e-4)
# 获取损失函数
loss = paddle.nn.CrossEntropyLoss()
# 开始训练
for epoch in range(10):
for batch_id, (img, label) in enumerate(train_loader()):
output = model(img)
# 计算损失值
los = loss(output, label)
los.backward()
if dist.get_rank() == 0:
print("Epoch {}: batch_id {}, loss {}".format(
epoch, batch_id, los))
optimizer.step()
optimizer.clear_grad()
def train(print_result=False):
# 1. enable dynamic mode
paddle.disable_static()
# 2. initialize parallel environment
dist.init_parallel_env()
# 3. create data parallel layer & optimizer
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.MSELoss()
adam = opt.Adam(
learning_rate=0.001, parameters=dp_layer.parameters())
# 4. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
if print_result is True:
print("loss:", loss.numpy())
loss.backward()
adam.step()
adam.clear_grad()
def train(print_result=False):
# 1. initialize parallel environment
dist.init_parallel_env()
# 2. create data parallel layer & optimizer
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.MSELoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
# 3. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
if print_result is True:
print("Rank:", int(os.getenv("PADDLE_TRAINER_ID")))
loss.backward()
adam.step()
adam.clear_grad()
return int(os.getenv("PADDLE_TRAINER_ID"))
def train():
# 1. enable dynamic mode
paddle.disable_static()
# 2. initialize parallel environment
dist.init_parallel_env()
# 3. create data parallel layer & optimizer
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.MSELoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
# 4. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
loss = dp_layer.scale_loss(loss)
loss.backward()
dp_layer.apply_collective_grads()
adam.step()
adam.clear_grad()
def train(print_result=True):
# 1. enable dynamic mode
# device = paddle.set_device('gpu')
# paddle.disable_static(device)
# 2. initialize parallel environment
dist.init_parallel_env()
# 3. create data parallel layer & optimizer
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.MSELoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
dataset = FakeDataset()
# loader = paddle.io.DataLoader(dataset, batch_size=2, places=device, num_workers=2)
loader = paddle.io.DataLoader(dataset, batch_size=2, num_workers=2)
# 4. run layer
for inputs, labels in loader:
# inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
# labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
if print_result is True:
print("loss:", loss.numpy())
# loss = dp_layer.scale_loss(loss)
loss.backward()
# dp_layer.apply_collective_grads()
adam.step()
adam.clear_grad()
def test_check_env_failed(self):
os.environ['FLAGS_selected_gpus'] = '0'
os.environ['PADDLE_TRAINER_ID'] = '0'
os.environ['PADDLE_CURRENT_ENDPOINT'] = '127.0.0.1:6170'
os.environ['PADDLE_TRAINERS_NUM'] = '2'
with self.assertRaises(ValueError):
dist.init_parallel_env()
def test_multiple_gpus(self):
self.trainer_id = dist.get_rank()
dist.init_parallel_env()
model_a = SimpleNet(self.trainer_id, 0)
model_b = SimpleNet(self.trainer_id, 1)
state_dict = model_a.state_dict()
model_b.set_state_dict(state_dict)
model_a = paddle.DataParallel(model_a)
model_b = paddle.DataParallel(model_b)
for step in range(10):
x_data = np.random.randn(batch, in_dim).astype(np.float32)
x = paddle.to_tensor(x_data)
x.stop_gradient = False
with model_a.no_sync():
y_pred_a = model_a(x)
loss_a = y_pred_a.mean()
loss_a.backward()
fused_allreduce_gradients(list(model_a.parameters()), None)
y_pred_b = model_b(x)
loss_b = y_pred_b.mean()
loss_b.backward()
self.check_gradient(model_a.parameters())
self.check_gradient(model_b.parameters())
self.check_acc(model_a._layers.w.grad, model_b._layers.w.grad)
model_a.clear_gradients()
model_b.clear_gradients()
def finetune(args):
paddle.set_device(args.device)
if dist.get_world_size() > 1:
dist.init_parallel_env()
pos_file = os.path.join(args.data_dir, 'rt-polarity.pos')
neg_file = os.path.join(args.data_dir, 'rt-polarity.neg')
x_text, y = load_data_and_labels(pos_file, neg_file)
x_train, x_test, y_train, y_test = train_test_split(x_text,
y,
test_size=0.1,
random_state=args.seed)
if not args.init_from_ckpt:
raise ValueError('`init_from_ckpt` should be set.')
model = ELMoBowTextClassification(args.init_from_ckpt, args.batch_size,
args.sent_embedding_dim, args.dropout,
args.num_classes)
if dist.get_world_size() > 1:
model = paddle.DataParallel(model)
model.train()
adam = paddle.optimizer.Adam(parameters=model.parameters(),
learning_rate=args.lr,
weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss()
vocab = load_vocab()
train_dataset = SentencePolarityDatasetV1(x_train, y_train, vocab,
args.max_seq_len)
test_dataset = SentencePolarityDatasetV1(x_test, y_test, vocab,
args.max_seq_len)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
return_list=True,
shuffle=True,
collate_fn=lambda batch: generate_batch(batch))
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
return_list=True,
shuffle=False,
collate_fn=lambda batch: generate_batch(batch))
for epoch in range(args.epochs):
print('Epoch {}/{}'.format(epoch + 1, args.epochs))
for step, batch_data in enumerate(train_loader, start=1):
ids, ids_reverse, label = batch_data
output = model((ids, ids_reverse))
loss = criterion(output, label)
loss.backward()
adam.step()
adam.clear_grad()
if step % args.logging_step == 0:
print('step {}, loss {}'.format(step, loss.numpy()[0]))
acc = test(model, test_loader)
print('\ntest acc {}\n'.format(acc))
def train():
# enable dygraph mode
paddle.disable_static()
dist.init_parallel_env()
# create network
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.CrossEntropyLoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
# print(core._get_device_properties(dist.ParallelEnv().device_id))
# create data loader
# loader = paddle.io.DataLoader.from_generator(capacity=5, use_multiprocess=True)
loader = paddle.io.DataLoader.from_generator(capacity=5)
loader.set_batch_generator(random_batch_reader())
for epoch_id in range(EPOCH_NUM):
for batch_id, (image, label) in enumerate(loader()):
out = layer(image)
loss = loss_fn(out, label)
loss = dp_layer.scale_loss(loss)
loss.backward()
dp_layer.apply_collective_grads()
adam.step()
adam.clear_grad()
print("Epoch {} batch {}: loss = {}".format(
epoch_id, batch_id, np.mean(loss.numpy())))
def main(config, device, logger, vdl_writer):
# init dist environment
if config['Global']['distributed']:
dist.init_parallel_env()
global_config = config['Global']
# build dataloader
train_dataloader = build_dataloader(config, 'Train', device, logger)
if len(train_dataloader) == 0:
logger.error(
"No Images in train dataset, please ensure\n" +
"\t1. The images num in the train label_file_list should be larger than or equal with batch size.\n"
+
"\t2. The annotation file and path in the configuration file are provided normally."
)
return
if config['Eval']:
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
else:
valid_dataloader = None
# build post process
post_process_class = build_post_process(config['PostProcess'],
global_config)
# build model
# for rec algorithm
if hasattr(post_process_class, 'character'):
char_num = len(getattr(post_process_class, 'character'))
config['Architecture']["Head"]['out_channels'] = char_num
model = build_model(config['Architecture'])
if config['Global']['distributed']:
model = paddle.DataParallel(model)
# build loss
loss_class = build_loss(config['Loss'])
# build optim
optimizer, lr_scheduler = build_optimizer(
config['Optimizer'],
epochs=config['Global']['epoch_num'],
step_each_epoch=len(train_dataloader),
parameters=model.parameters())
# build metric
eval_class = build_metric(config['Metric'])
# load pretrain model
pre_best_model_dict = init_model(config, model, logger, optimizer)
logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
if valid_dataloader is not None:
logger.info('valid dataloader has {} iters'.format(
len(valid_dataloader)))
# start train
program.train(config, train_dataloader, valid_dataloader, device, model,
loss_class, optimizer, lr_scheduler, post_process_class,
eval_class, pre_best_model_dict, logger, vdl_writer)
def train():
# 1. initialize parallel environment
dist.init_parallel_env()
# 2. get current ParallelEnv
parallel_env = dist.ParallelEnv()
print("rank: ", parallel_env.rank)
print("world_size: ", parallel_env.world_size)
def test_init_parallel_env_break(self):
os.environ['FLAGS_selected_gpus'] = '0'
os.environ['PADDLE_TRAINER_ID'] = '0'
os.environ['PADDLE_CURRENT_ENDPOINT'] = '127.0.0.1:6170'
os.environ['PADDLE_TRAINERS_NUM'] = '1'
os.environ['PADDLE_TRAINER_ENDPOINTS'] = '127.0.0.1:6170'
# coverage success branch
dist.init_parallel_env()
self.assertFalse(parallel_helper._is_parallel_ctx_initialized())
def train():
"""parallelenv"""
# 1. initialize parallel env
dist.init_parallel_env()
# 2. get current ParallelEnv
parallel_env = dist.ParallelEnv()
assert parallel_env.rank == 0
assert parallel_env.world_size == 2
print("test_ParallelEnv ... ok")
def do_train():
dist.init_parallel_env()
net = paddle.nn.Linear(2, 2)
net = paddle.DataParallel(net)
x = paddle.to_tensor(np.random.random(size=(2, 2)).astype('float32'))
j = []
y = net(x)
dist.all_gather(j, y)
print(j)
def main(config, device, logger, vdl_writer):
# init dist environment
if config['Global']['distributed']:
dist.init_parallel_env()
global_config = config['Global']
# build dataloader
train_dataloader = build_dataloader(config, 'Train', device, logger)
if config['Eval']:
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
else:
valid_dataloader = None
# build post process
post_process_class = build_post_process(config['PostProcess'],
global_config)
# build model
# for rec algorithm
if hasattr(post_process_class, 'character'):
char_num = len(getattr(post_process_class, 'character'))
config['Architecture']["Head"]['out_channels'] = char_num
model = build_model(config['Architecture'])
if config['Global']['distributed']:
model = paddle.DataParallel(model)
# build loss
loss_class = build_loss(config['Loss'])
# build optim
optimizer, lr_scheduler = build_optimizer(
config['Optimizer'],
epochs=config['Global']['epoch_num'],
step_each_epoch=len(train_dataloader),
parameters=model.parameters())
# build metric
eval_class = build_metric(config['Metric'])
# load pretrain model
pre_best_model_dict = init_model(config, model, logger, optimizer)
logger.info(
'train dataloader has {} iters, valid dataloader has {} iters'.format(
len(train_dataloader), len(valid_dataloader)))
quanter = QAT(config=quant_config, act_preprocess=PACT)
quanter.quantize(model)
# start train
program.train(config, train_dataloader, valid_dataloader, device, model,
loss_class, optimizer, lr_scheduler, post_process_class,
eval_class, pre_best_model_dict, logger, vdl_writer)
def train():
"""bergin train"""
arr1 = []
arr2 = []
dist.init_parallel_env()
set_seed(2021)
layer = LinearNet()
if dist.get_world_size() > 1:
dp_layer = paddle.DataParallel(layer)
else:
dp_layer = layer
layer2 = LinearNet()
if dist.get_world_size() > 1:
dp_layer2 = paddle.DataParallel(layer2)
else:
dp_layer2 = layer2
dp_layer2.set_state_dict(dp_layer.state_dict())
loss_fn = nn.MSELoss()
adam = opt.Adam(
learning_rate=0.001, parameters=dp_layer.parameters())
adam2 = opt.Adam(
learning_rate=0.001, parameters=dp_layer2.parameters())
for i in range(2):
batch_size = 10
shard = int(batch_size / dist.get_world_size())
start_no = shard * dist.get_rank()
end_no = start_no + shard
inputs = paddle.randn([10, 10], 'float32')[start_no:end_no]
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')[start_no:end_no]
loss = loss_fn(outputs, labels)
if dist.get_rank() == 0:
arr1.append(loss.numpy()[0])
loss.backward()
adam.step()
adam.clear_grad()
outputs = dp_layer2(inputs)
loss = loss_fn(outputs, labels)
loss.backward()
if dist.get_rank() == 0:
arr2.append(loss.numpy()[0])
adam2.step()
adam2.clear_grad()
check_data(arr1, arr2)
def run_trainer(self, args):
if fluid.core.is_compiled_with_cuda():
device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
place = fluid.CUDAPlace(device_id)
else:
assert ("Only support CUDAPlace for now.")
with fluid.dygraph.guard(place):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
np.random.seed(seed)
random.seed(seed)
model, train_reader, opt = self.get_model()
if args.update_method == "nccl2":
dist.init_parallel_env()
print_to_err(
type(self).__name__,
"begin to prepare context in dygraph with nccl2")
if not args.find_unused_parameters:
model = paddle.DataParallel(model,
find_unused_parameters=False)
else:
model = paddle.DataParallel(model,
find_unused_parameters=True)
print_to_err(type(self).__name__, "model built in dygraph")
out_losses = []
print_to_err(type(self).__name__, "begin to run dygraph training")
for step_id, data in enumerate(train_reader()):
data = self._get_data(data, args)
if step_id == RUN_STEP:
break
if step_id % 3 != 0:
if args.update_method == "nccl2":
with model.no_sync():
loss = self.run_one_loop(model, opt, data)
loss.backward()
else:
loss = self.run_one_loop(model, opt, data)
loss.backward()
else:
loss = self.run_one_loop(model, opt, data)
loss.backward()
opt.minimize(loss)
print_to_err(
type(self).__name__,
"loss at step %d: %f" % (step_id, loss.numpy()))
out_losses.append(loss.numpy())
if not args.accumulate_gradient:
model.clear_gradients()
print_to_out(out_losses)
def train():
dist.init_parallel_env()
# 1. initialize parallel environment
set_seed(2021)
# 2. create data parallel layer & optimizer
layer = LinearNet()
if dist.get_world_size() > 1:
dp_layer = paddle.DataParallel(layer)
else:
dp_layer = layer
layer2 = LinearNet()
if dist.get_world_size() > 1:
dp_layer2 = paddle.DataParallel(layer2)
else:
dp_layer2 = layer2
dp_layer2.set_state_dict(dp_layer.state_dict())
loss_fn = nn.MSELoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
adam2 = opt.Adam(learning_rate=0.001, parameters=dp_layer2.parameters())
# 3. run layer
print("Start")
for i in range(10):
batch_size = 10
shard = int(batch_size / dist.get_world_size())
start_no = shard * dist.get_rank()
end_no = start_no + shard
inputs = paddle.randn([10, 10], 'float32')[start_no:end_no]
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')[start_no:end_no]
loss = loss_fn(outputs, labels)
if dist.get_rank() == 0:
print("Loss1", loss.numpy()[0])
print(dp_layer.parameters())
loss.backward()
adam.step()
adam.clear_grad()
outputs = dp_layer2(inputs)
loss = loss_fn(outputs, labels)
loss.backward()
if dist.get_rank() == 0:
print("Loss2", loss.numpy()[0])
print(dp_layer2.parameters())
adam2.step()
adam2.clear_grad()
def test_send1():
"""test_send1"""
init_parallel_env()
if paddle.distributed.ParallelEnv().rank == 0:
data = paddle.to_tensor([7, 8, 9])
paddle.distributed.send(data, dst=1)
else:
data = paddle.to_tensor([1, 2, 3])
paddle.distributed.recv(data, src=0)
out = data.numpy()
assert out[0] == 7
assert out[1] == 8
assert out[2] == 9
def test_send3():
"""test_send3"""
init_parallel_env()
if paddle.distributed.ParallelEnv().rank == 0:
data = paddle.to_tensor([7, 8, 9])
paddle.distributed.send(data, dst=1, group=None, use_calc_stream=False)
else:
data = paddle.to_tensor([1, 2, 3])
paddle.distributed.recv(data, src=0)
out = data.numpy()
assert out[0] == 7
assert out[1] == 8
assert out[2] == 9
def train(config):
logging.info('training arguments: %s', config)
if config.train.use_data_parallel:
logging.info("parallel mode. init env...")
dist.init_parallel_env()
dataset_config = {
'db_file': config.data.db,
'input_encoder': g_input_encoder,
'label_encoder': g_label_encoder,
'is_cached': True
}
train_set = DatasetClass(
name='train', data_file=config.data.train_set, **dataset_config)
dev_set = DatasetClass(
name='dev', data_file=config.data.dev_set, **dataset_config)
shuf_train = True if not config.general.is_debug else False
train_reader = DataLoaderClass(
config,
train_set,
batch_size=config.general.batch_size,
shuffle=shuf_train)
#dev_reader = dataproc.DataLoader(config, dev_set, batch_size=config.general.batch_size, shuffle=False)
dev_reader = DataLoaderClass(config, dev_set, batch_size=1, shuffle=False)
max_train_steps = config.train.epochs * (
len(train_set) // config.general.batch_size // config.train.trainer_num)
model = ModelClass(config.model, g_label_encoder)
if config.model.init_model_params is not None:
logging.info("loading model param from %s",
config.model.init_model_params)
model.set_state_dict(paddle.load(config.model.init_model_params))
if config.train.use_data_parallel:
logging.info("parallel mode. init model...")
model = paddle.DataParallel(model)
optimizer = text2sql.optim.init_optimizer(model, config.train,
max_train_steps)
if config.model.init_model_optim is not None:
logging.info("loading model optim from %s",
config.model.init_model_optim)
optimizer.set_state_dict(paddle.load(config.model.init_model_optim))
logging.info("start of training...")
launch.trainer.train(config, model, optimizer, config.train.epochs,
train_reader, dev_reader)
logging.info("end of training...")
def init_process_group(strategy=None):
nranks = ParallelEnv().nranks
rank = ParallelEnv().local_rank
is_master = True if rank == 0 else False
pg_group = dist.init_parallel_env()
return pg_group.process_group
def test_multiple_gpus(self):
dist.init_parallel_env()
self.trainer_id = dist.get_rank()
model_a = SimpleNet(self.trainer_id)
model_b = SimpleNet(self.trainer_id)
state_dict = model_a.state_dict()
model_b.set_state_dict(state_dict)
model_a = paddle.DataParallel(model_a, find_unused_parameters=True)
model_b = paddle.DataParallel(model_b, find_unused_parameters=True)
ones_input = paddle.ones(shape=(batch, in_dim))
ones_input.stop_gradient = True
w1_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
w2_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
for step_id in range(5):
random_input = paddle.rand(shape=(batch, in_dim))
random_input.stop_gradient = True
if step_id % 2 == 0:
out_a = model_a(random_input)
out_b = model_b(random_input)
else:
out_a = model_a(ones_input)
out_b = model_b(ones_input)
out_a.sum().backward()
out_b.sum().backward()
self.check_gradient(model_a.parameters())
self.check_gradient(model_b.parameters())
# test acc gradient
w1_grad_sum = self.check_acc(model_a._layers.w1.grad, w1_grad_sum,
model_b._layers.w1.grad)
w2_grad_sum = self.check_acc(model_a._layers.w2.grad, w2_grad_sum,
model_b._layers.w2.grad)
model_a.clear_gradients()
def run_trainer_with_spawn(self, args):
paddle.disable_static()
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
np.random.seed(seed)
random.seed(seed)
args.trainer_id = dist.get_rank()
if args.update_method == "nccl2":
dist.init_parallel_env()
model, train_reader, opt = self.get_model()
if args.update_method == "nccl2":
if args.find_unused_parameters:
model = paddle.DataParallel(model, find_unused_parameters=True)
else:
model = paddle.DataParallel(model,
find_unused_parameters=False)
out_losses = []
for step_id, data in enumerate(train_reader()):
data = self._get_data(data, args)
if step_id == RUN_STEP:
break
if step_id % 3 != 0:
if args.update_method == "nccl2":
with model.no_sync():
loss = self.run_one_loop(model, opt, data)
loss.backward()
else:
loss = self.run_one_loop(model, opt, data)
loss.backward()
else:
loss = self.run_one_loop(model, opt, data)
loss.backward()
opt.minimize(loss)
print_to_err(
type(self).__name__,
"loss at step %d: %f" % (step_id, loss.numpy()))
out_losses.append(loss.numpy())
model.clear_gradients()
print_to_out(out_losses)
return out_losses
def main(config, device, logger, vdl_writer):
# init dist environment
if config['Global']['distributed']:
dist.init_parallel_env()
global_config = config['Global']
# build dataloader
config['Train']['loader']['num_workers'] = 0
train_dataloader = build_dataloader(config, 'Train', device, logger)
if config['Eval']:
config['Eval']['loader']['num_workers'] = 0
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
else:
valid_dataloader = None
paddle.enable_static()
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
if 'inference_model' in global_config.keys(): # , 'inference_model'):
inference_model_dir = global_config['inference_model']
else:
inference_model_dir = os.path.dirname(
global_config['pretrained_model'])
if not (os.path.exists(os.path.join(inference_model_dir, "inference.pdmodel")) and \
os.path.exists(os.path.join(inference_model_dir, "inference.pdiparams")) ):
raise ValueError(
"Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantazition"
)
paddleslim.quant.quant_post_static(
executor=exe,
model_dir=inference_model_dir,
model_filename='inference.pdmodel',
params_filename='inference.pdiparams',
quantize_model_path=global_config['save_inference_dir'],
sample_generator=sample_generator(train_dataloader),
save_model_filename='inference.pdmodel',
save_params_filename='inference.pdiparams',
batch_size=1,
batch_nums=None)
def test_scatter():
"""scatter"""
paddle.set_device('gpu:%d' % paddle.distributed.ParallelEnv().dev_id)
init_parallel_env()
for t in types:
if paddle.distributed.ParallelEnv().local_rank == 0:
np_data1 = np.array([7, 8, 9]).astype(t)
np_data2 = np.array([10, 11, 12]).astype(t)
else:
np_data1 = np.array([1, 2, 3]).astype(t)
np_data2 = np.array([4, 5, 6]).astype(t)
data1 = paddle.to_tensor(np_data1)
data2 = paddle.to_tensor(np_data2)
if paddle.distributed.ParallelEnv().local_rank == 0:
paddle.distributed.scatter(data1, src=1)
else:
paddle.distributed.scatter(data1,
tensor_list=[data1, data2],
src=1)
out = data1.numpy()
assert len(out) == 3
print("test_scatter %s ... ok" % t)
