def get_model(self, place, batch_size=32, image_shape=[224, 224, 3]):
image = paddle.static.data(
shape=[batch_size] + image_shape, dtype='float32', name='image')
model = BatchNormActNet()
pred_out = model(image)
loss = paddle.mean(pred_out)
optimizer = paddle.optimizer.Adam(learning_rate=1e-3)
dist_strategy = fleet.DistributedStrategy()
dist_strategy.fuse_all_reduce_ops = False
dist_strategy.without_graph_optimization = True
dist_strategy.amp = True
dist_strategy.amp_configs = {
"init_loss_scaling": 32768,
"use_dynamic_loss_scaling": True,
}
fleet.init(is_collective=True, strategy=dist_strategy)
optimizer = fleet.distributed_optimizer(optimizer)
optimizer.minimize(loss)
rank = paddle.distributed.get_rank()
def reader():
seed = int(os.environ.get("SEED", 0))
np.random.seed(seed + rank)
for _ in range(10):
image_np = np.random.random(size=image.shape).astype('float32')
yield image_np,
main_program = paddle.static.default_main_program()
startup_program = paddle.static.default_startup_program()
return main_program, startup_program, [image], [loss], reader
def init_distributed_infer_env(self,
exe,
loss,
role_maker=None,
dirname=None):
import paddle.distributed.fleet as fleet
if fleet.fleet._runtime_handle is None:
fleet.init(role_maker=role_maker)
fake_optimizer = paddle.optimizer.SGD()
strategy = fleet.DistributedStrategy()
strategy.a_sync = True
optimizer = fleet.distributed_optimizer(
fake_optimizer, strategy=strategy)
optimizer.minimize(
loss, startup_program=self.origin_startup_program)
if fleet.is_server():
fleet.init_server(dirname=dirname)
fleet.run_server()
else:
exe.run(paddle.static.default_startup_program())
fleet.init_worker()
self._init_dense_params(exe, dirname)
global_startup_program = paddle.static.default_startup_program()
global_startup_program = self.origin_startup_program
global_main_program = paddle.static.default_main_program()
global_main_program = self.origin_main_program
def get_model(self, batch_size=2, use_dgc=False, dist_strategy=None):
# Input data
device_id = 0
if dist_strategy:
fleet.init(is_collective=True)
with fluid.device_guard("gpu:0"):
images = fluid.layers.data(
name='pixel', shape=[1, 28, 28], dtype=DTYPE)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if dist_strategy:
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[images, label],
capacity=64,
use_double_buffer=False,
iterable=False)
# Train program
predict = cnn_model(images)
with fluid.device_guard("gpu:0"):
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
# Evaluator
with fluid.device_guard("gpu:0"):
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(
input=predict, label=label, total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
base_lr = self.lr
passes = [30, 60, 80, 90]
steps_per_pass = 10
bd = [steps_per_pass * p for p in passes]
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
lr_val = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
opt = fluid.optimizer.Momentum(learning_rate=lr_val, momentum=0.9)
# Reader
train_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
if dist_strategy:
strategy = fleet.DistributedStrategy()
strategy.pipeline = True
strategy.pipeline_configs = {
'schedule_mode': 'F-then-B',
'micro_batch_size': batch_size
}
dist_opt = fleet.distributed_optimizer(
optimizer=opt, strategy=strategy)
dist_opt.minimize(avg_cost)
else:
opt.minimize(avg_cost)
if dist_strategy:
return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict, data_loader
else:
return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
def test_fleet_get_applied_optimizer(self):
input_x = paddle.fluid.layers.data(name="x",
shape=[32],
dtype='float32')
input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
prediction = paddle.fluid.layers.fc(input=[fc_2],
size=2,
act='softmax')
cost = paddle.fluid.layers.cross_entropy(input=prediction,
label=input_y)
avg_cost = paddle.fluid.layers.mean(x=cost)
fleet.init(is_collective=True)
meta_list = fleet._get_applied_meta_list()
graph_list = fleet._get_applied_graph_list()
# not called minimize function
self.assertEqual(len(meta_list), 0)
self.assertEqual(len(graph_list), 0)
strategy = fleet.DistributedStrategy()
optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.001)
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
optimizer.minimize(avg_cost)
meta_list = fleet._get_applied_meta_list()
graph_list = fleet._get_applied_graph_list()
self.assertEqual(len(meta_list), 0)
self.assertEqual(len(graph_list), 1)
def _init_distributed_strategy(self):
"""Initialize distributed strategy."""
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = True
exec_strategy.num_threads = 4
exec_strategy.num_iteration_per_drop_scope = 1
dist_strategy = fleet.DistributedStrategy()
dist_strategy.execution_strategy = exec_strategy
dist_strategy.nccl_comm_num = 1
dist_strategy.fuse_all_reduce_ops = True
if self.use_recompute:
dist_strategy.recompute = True
if self.use_amp:
dist_strategy.amp = True
dist_strategy.amp_configs = {
"custom_white_list": ["softmax", "layer_norm", "gelu"],
"init_loss_scaling": self.amp_loss_scaling
}
if self.use_sharding:
dist_strategy.sharding = True
dist_strategy.sharding_configs = {
"segment_broadcast_MB": 32,
"dp_degree": self.dp_degree,
"sharding_degree": self.sharding_degree,
"mp_degree": self.mp_degree,
"pp_degree": self.pp_degree
}
self.dist_strategy = dist_strategy
self._init_build_strategy()
print(self.dist_strategy)
return
def __init__(self,
model=None,
inputs_spec=None,
labels_spec=None,
cluster=None,
strategy=None):
self.model = model
self.inputs_spec = self._validate_spec(inputs_spec)
self.labels_spec = self._validate_spec(labels_spec)
self.cluster = cluster
# if self.cluster is None:
# self.cluster = get_default_cluster()
self.strategy = strategy
if self.strategy is None:
self.strategy = fleet.DistributedStrategy()
self._executor = None
self._cur_rank = paddle.distributed.get_rank()
self._nranks = paddle.distributed.get_world_size()
self._saver = DistributedSaver()
self._logger = get_logger(logging.INFO)
self._default_strategy = None
self._orig_main_prog = static.default_main_program()
self._orig_startup_prog = static.default_startup_program()
self._orig_dist_context = get_default_distributed_context()
self._dist_contexts = {}
self._serial_main_progs = {}
self._serial_startup_progs = {}
self._dist_main_progs = defaultdict(dict) # dist main programs
self._dist_startup_progs = defaultdict(dict) # dist startup programs
self._feed_vars = {}
self._fetch_vars = {}
def get_dist_prog(train_program, startup_program, dist_context, rank_id):
loss, train_program, startup_program = mlp_forward(train_program,
startup_program)
fleet._user_defined_strategy = fleet.DistributedStrategy()
fleet.user_defined_optimizer = paddle.fluid.optimizer.AdamOptimizer()
parallelizer = AutoParallelizer(fleet)
parallelizer._dist_context = dist_context
# serial forward & backward completion
complete_train_program = auto.complete_annotation(train_program,
dist_context)
params_grads = parallelizer._generate_backward(complete_train_program,
startup_program,
loss,
parameter_list=None,
no_grad_set=None,
callbacks=None)
# logical partition
partitioner = Partitioner(dist_context, rank_id)
auto_parallel_main_prog, auto_parallel_startup_prog, dist_params_grads = partitioner.partition(
complete_train_program, startup_program, params_grads)
partitioned_optimize_ops = parallelizer._apply_optimize(
auto_parallel_main_prog, auto_parallel_startup_prog, dist_params_grads)
return auto_parallel_main_prog, auto_parallel_startup_prog
def train():
global _global_process_mesh
_global_process_mesh = auto.ProcessMesh(mesh=[0, 1])
dist_strategy = fleet.DistributedStrategy()
dist_strategy.amp = False
dist_strategy.pipeline = False
dist_strategy.recompute = False
# init parallel optimizer
dist_strategy.semi_auto = True
fleet.init(is_collective=True, strategy=dist_strategy)
train_program = static.Program()
start_program = static.Program()
loss, train_program, start_program, loader = mlp_pretrain_forward(
train_program, start_program)
optimizer = paddle.fluid.optimizer.AdamOptimizer(learning_rate=0.00001,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
grad_clip=None)
optimizer = fleet.distributed_optimizer(optimizer)
_, _, distributed_startup_program, distributed_main_program = optimizer.minimize(
loss, start_program)
places = static.cuda_places()
loader.set_batch_generator(batch_generator_creator(), places=places)
exe = paddle.static.Executor(places[0])
exe.run(distributed_startup_program)
for data in loader():
exe.run(distributed_main_program, feed=data, fetch_list=[loss])
def get_dist_prog(train_program, startup_program, dist_context, rank_id):
loss, train_program, startup_program = mlp_forward(train_program,
startup_program)
fleet._user_defined_strategy = fleet.DistributedStrategy()
fleet.user_defined_optimizer = paddle.fluid.optimizer.AdamOptimizer()
parallelizer = AutoParallelizer(fleet)
parallelizer._dist_context = dist_context
# auto completion
completer = Completer(dist_context)
complete_train_program = completer.complete_forward_annotation(
train_program)
dist_context.block_state.parse_forward_blocks(complete_train_program)
params_grads = parallelizer._generate_backward(
complete_train_program,
startup_program,
loss,
parameter_list=None,
no_grad_set=None,
callbacks=None)
partitioner = Partitioner(dist_context, rank_id)
dist_train_program, dist_startup_prog, dist_params_grads = partitioner.partition(
complete_train_program, startup_program, params_grads)
partitioned_optimize_ops = parallelizer._apply_optimize(
dist_train_program, dist_startup_prog, dist_params_grads)
resharder = Resharder(dist_train_program, dist_startup_prog, rank_id,
dist_context, dist_params_grads)
resharder.reshard()
return dist_train_program, dist_startup_prog
def dist_optimizer(args, optimizer):
"""
Create a distributed optimizer based on a normal optimizer
Args:
args:
optimizer: a normal optimizer
Returns:
optimizer: a distributed optimizer
"""
build_strategy, exec_strategy = create_strategy()
dist_strategy = fleet.DistributedStrategy()
dist_strategy.execution_strategy = exec_strategy
dist_strategy.build_strategy = build_strategy
dist_strategy.fuse_grad_size_in_MB = 16
if args.use_amp:
dist_strategy.amp = True
dist_strategy.amp_configs = {
'custom_white_list': ['softmax', 'layer_norm', 'gelu'],
'init_loss_scaling': args.scale_loss,
}
optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
return optimizer
def test_single_gpu(self):
paddle.enable_static()
fleet.init(is_collective=True)
sharding_program = paddle.static.Program()
sharding_startup_program = paddle.static.Program()
strategy = fleet.DistributedStrategy()
strategy.without_graph_optimization = True
with fluid.program_guard(sharding_program, sharding_startup_program):
with fluid.unique_name.guard():
input_x = paddle.static.data(name="x",
shape=[None, 32],
dtype='float32')
input_y = paddle.static.data(name="y",
shape=[None, 1],
dtype='int64')
cost = self.mlp(input_x=input_x, input_y=input_y)
output_name = cost.name
optimizer = fleet.distributed_optimizer(
fluid.optimizer.Adam(), strategy)
optimizer.minimize(cost)
trainer_id = fleet.worker_index()
exe = paddle.static.Executor(paddle.CUDAPlace(trainer_id))
rank = fleet.worker_index()
exe.run(sharding_startup_program)
exe.run(program=sharding_program, feed=self.gen_data())
def test_util_base(self):
import paddle.distributed.fleet as fleet
util = fleet.UtilBase()
strategy = fleet.DistributedStrategy()
util._set_strategy(strategy)
role_maker = None # should be fleet.PaddleCloudRoleMaker()
util._set_role_maker(role_maker)
def get_dist_prog_with_parallelizer(train_program, startup_program,
dist_context):
global _global_process_mesh
dist_strategy = fleet.DistributedStrategy()
dist_strategy.amp = False
dist_strategy.pipeline = False
dist_strategy.recompute = False
# init parallel optimizer
dist_strategy.semi_auto = True
fleet.init(is_collective=True, strategy=dist_strategy)
loss, train_program, startup_program = mlp_forward(train_program,
startup_program)
optimizer = paddle.fluid.optimizer.AdamOptimizer(learning_rate=0.00001,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
grad_clip=None)
optimizer = fleet.distributed_optimizer(optimizer)
_, _, distributed_startup_program, distributed_main_program = optimizer.minimize(
loss, startup_program)
return distributed_main_program, distributed_startup_program
def dist_optimizer(args, optimizer):
"""
Create a distributed optimizer based on a normal optimizer
"""
build_strategy, exec_strategy = create_strategy(args)
dist_strategy = fleet.DistributedStrategy()
dist_strategy.execution_strategy = exec_strategy
dist_strategy.build_strategy = build_strategy
dist_strategy.fuse_grad_size_in_MB = 16
if args.use_amp:
dist_strategy.amp = True
custom_black_list = ['lookup_table', 'lookup_table_v2'
] if args.use_pure_fp16 else None
dist_strategy.amp_configs = {
'custom_white_list': ['softmax', 'layer_norm', 'gelu'],
'init_loss_scaling': args.scale_loss,
'custom_black_list': custom_black_list,
'use_pure_fp16': args.use_pure_fp16
}
if args.gradient_merge_steps > 1:
dist_strategy.gradient_merge = True
dist_strategy.gradient_merge_configs = {
'k_steps': args.gradient_merge_steps
}
optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
return optimizer
def apply_passes(self):
dist_strategy = fleet.DistributedStrategy()
dist_strategy.semi_auto = True
dist_strategy.sharding = True
dist_strategy.sharding_configs = {
"sharding_degree": 2,
"stage": 2,
}
fleet.init(is_collective=True, strategy=dist_strategy)
def setUp(self):
strategy = fleet.DistributedStrategy()
self.model_parallel_size = 2
strategy.hybrid_configs = {
"dp_degree": 1,
"mp_degree": self.model_parallel_size,
"pp_degree": 1
}
fleet.init(is_collective=True, strategy=strategy)
def dist_optimizer(args, topo):
default_global_batch_size = topo.data_info.size * args.micro_batch_size
if args.global_batch_size is None:
args.global_batch_size = default_global_batch_size
bsz_per_dp = args.global_batch_size // topo.data_info.size
micro_batch_size = args.micro_batch_size
assert args.global_batch_size % micro_batch_size == 0, "cannot do gradient accumulate, global_batch_size: {} micro_batch_size: {}".format(
args.global_batch_size, micro_batch_size)
acc_steps = bsz_per_dp // micro_batch_size
exec_strategy = paddle.fluid.ExecutionStrategy()
exec_strategy.num_threads = 2
exec_strategy.num_iteration_per_drop_scope = 1
dist_strategy = fleet.DistributedStrategy()
dist_strategy.execution_strategy = exec_strategy
dist_strategy.nccl_comm_num = 3
dist_strategy.recompute = args.use_recompute
dist_strategy.pipeline = args.pp_degree > 1
if args.use_amp:
dist_strategy.amp = True
dist_strategy.amp_configs = {
"custom_white_list": [
'softmax', 'layer_norm', 'gelu',
"fused_softmax_mask_upper_triangle", "elementwise_add"
],
"custom_black_list":
["reduce_sum", "c_softmax_with_cross_entropy", "elementwise_div"],
"init_loss_scaling": 32768,
"use_dynamic_loss_scaling": True,
"use_pure_fp16": args.amp_level == "O2",
"use_fp16_guard": False
}
if args.use_sharding:
dist_strategy.sharding = True
dist_strategy.sharding_configs = {
"segment_broadcast_MB": 32,
"sharding_degree": args.sharding_degree,
"mp_degree": args.mp_degree,
"pp_degree": args.pp_degree,
"dp_degree": args.dp_degree,
"optimize_offload": False,
}
if args.pp_degree > 1:
dist_strategy.pipeline_configs = {
"schedule_mode": "1F1B",
"micro_micro_batch_size": micro_batch_size,
"accumulate_steps": acc_steps,
}
else:
assert acc_steps == 1, "Only support accumulate steps in piplinemode. Please set you global_batch_size={}".format(
default_global_batch_size)
return dist_strategy
def setUp(self):
strategy = fleet.DistributedStrategy()
self.pipeline_parallel_size = 2
strategy.hybrid_configs = {
"dp_degree": 1,
"mp_degree": 1,
"pp_degree": self.pipeline_parallel_size
}
fleet.init(is_collective=True, strategy=strategy)
self.hcg = fleet.get_hybrid_communicate_group()
def get_model(self, place, gradient_merge, batch_size, max_step):
paddle.seed(2021)
random.seed(2021)
np.random.seed(2021)
hidden_size = 128
global _global_parallel_strategy
global _global_process_mesh
world_size = paddle.distributed.get_world_size()
if world_size == 1:
_global_parallel_strategy = "dp"
_global_process_mesh = auto.ProcessMesh([0])
elif world_size == 2:
_global_parallel_strategy = "dp"
_global_process_mesh = auto.ProcessMesh([0, 1])
train_program = static.Program()
startup_program = static.Program()
dist_strategy = fleet.DistributedStrategy()
dist_strategy.semi_auto = True
#if gradient_merge:
# dist_strategy.gradient_merge = True
# dist_strategy.gradient_merge_configs = {"k_steps": 4, "avg": True}
fleet.init(is_collective=True, strategy=dist_strategy)
with static.program_guard(train_program, startup_program), \
utils.unique_name.guard():
input = static.data(name="input",
shape=[batch_size, hidden_size],
dtype='float32')
label = static.data(name="label",
shape=[batch_size, 1],
dtype='float32')
input.stop_gradient = False
loss = mlp_forward(input, label, hidden_size)
optimizer = paddle.fluid.optimizer.SGDOptimizer(learning_rate=0.01)
#optimizer = paddle.fluid.optimizer.Adam(learning_rate=0.01)
optimizer = fleet.distributed_optimizer(optimizer)
_, self._params_grads, dist_startup_prog, dist_main_prog = optimizer.minimize(
loss, startup_program)
input_data = np.random.random(size=(128,
hidden_size)).astype('float32')
label_data = np.random.random(size=(128, 1)).astype('float32')
def reader():
for i in range(max_step):
x_data = input_data[i * batch_size:(i + 1) * batch_size, :]
y_data = label_data[i * batch_size:(i + 1) * batch_size, :]
yield x_data, y_data
return dist_main_prog, dist_startup_prog, [input,
label], [loss], reader
def test_util_factory(self):
import paddle.distributed.fleet as fleet
factory = fleet.base.util_factory.UtilFactory()
strategy = fleet.DistributedStrategy()
role_maker = None # should be fleet.PaddleCloudRoleMaker()
optimize_ops = []
params_grads = []
context = {}
context["role_maker"] = role_maker
context["valid_strategy"] = strategy
util = factory._create_util(context)
self.assertEqual(util.role_maker, None)
def apply_passes(self, main_prog, startup_prog):
#self._config["params_grads"] = self._params_grads
#pass_context = PassContext()
#auto_parallel_gradient_merge_pass = new_pass(
# "auto_parallel_gradient_merge_pass", self._config)
#auto_parallel_gradient_merge_pass.apply([main_prog], [startup_prog],
# pass_context)
dist_strategy = fleet.DistributedStrategy()
dist_strategy.gradient_merge = True
dist_strategy.gradient_merge_configs = {"k_steps": 4, "avg": True}
dist_strategy.semi_auto = True
fleet.init(is_collective=True, strategy=dist_strategy)
def train():
dist_strategy = fleet.DistributedStrategy()
# init parallel optimizer
dist_strategy.auto_search = True
fleet.init(is_collective=True, strategy=dist_strategy)
train_program = static.Program()
start_program = static.Program()
place = paddle.set_device("gpu")
gpus = [0, 1]
batch_size = 8
sequence_len = 512
vocab_size = 1000
train_program, start_program, loss, gen_data = get_gpt_model(
train_program, start_program, place, batch_size, sequence_len,
vocab_size)
optimizer = paddle.fluid.optimizer.AdamOptimizer(learning_rate=0.00001,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
grad_clip=None)
optimizer = fleet.distributed_optimizer(optimizer)
_, _, distributed_startup_program, distributed_main_program = optimizer.minimize(
loss, start_program)
places = static.cuda_places()
exe = paddle.static.Executor(places[0])
exe.run(distributed_startup_program)
for step in range(10):
tokens, position_ids, attention_mask, labels, loss_mask = gen_data()
if loss.name in distributed_main_program.global_block().vars:
loss_print, = exe.run(distributed_main_program,
feed={
"tokens": tokens,
"position_ids": position_ids,
"attention_mask": attention_mask,
"labels": labels,
"loss_mask": loss_mask
},
fetch_list=[loss])
print("step: %s, loss: %f" % (step, loss_print[0]))
else:
exe.run(distributed_main_program,
feed={
"tokens": tokens,
"position_ids": position_ids,
"attention_mask": attention_mask,
"labels": labels,
"loss_mask": loss_mask
})
print("step: %s, loss: %s" % (step, "None"))
def _set_strategy(self, args):
"""配置运行的distributed_strategy,
build_strategy 配置在do_training中"""
self.dist_strategy = fleet.DistributedStrategy()
if args.run_params["mode"] == "sync":
self.dist_strategy.a_sync = False
elif args.run_params["mode"] == "async":
self.dist_strategy.a_sync = True
elif args.run_params["mode"] == "geo_async":
self.dist_strategy.a_sync = True
self.dist_strategy.a_sync_configs = {"k_steps": 2}
elif args.run_params["mode"] == "auto":
self.dist_strategy.auto = True
def boundary_net(self, main_prog, startup_prog):
with fluid.program_guard(main_prog, startup_prog):
fleet.init(is_collective=True)
x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
with paddle.static.device_guard('gpu:0'):
linear = fluid.Linear(4, 8, bias_attr=False)
out = linear(x)
with paddle.static.device_guard('gpu:1'):
linear = fluid.Linear(8, 5, bias_attr=False)
out = linear(out)
avg_cost = paddle.mean(out)
strategy = fleet.DistributedStrategy()
return avg_cost, strategy
def test_dygraph_fleet_api(self):
import paddle.distributed.fleet as fleet
import paddle.distributed as dist
strategy = fleet.DistributedStrategy()
strategy.amp = True
strategy.recompute = True
fleet.init(is_collective=True, strategy=strategy)
net = paddle.nn.Sequential(
paddle.nn.Linear(10, 1), paddle.nn.Linear(1, 2))
net = dist.fleet.distributed_model(net)
data = np.random.uniform(-1, 1, [30, 10]).astype('float32')
data = paddle.to_tensor(data)
net(data)
def test_allgather(self):
train_program = paddle.static.Program()
startup_program = paddle.static.Program()
process_mesh = auto.ProcessMesh(mesh=[0, 3])
with static.program_guard(train_program, startup_program):
x = paddle.static.data(name="x", shape=[4, 4], dtype='float32')
x = auto.shard_tensor(x,
dist_attr={
"process_mesh": process_mesh,
"dims_mapping": [0, -1]
})
w = paddle.static.data(name="w", shape=[4, 4], dtype='float32')
w = auto.shard_tensor(w,
dist_attr={
"process_mesh": process_mesh,
"dims_mapping": [-1, -1]
})
# y = paddle.distributed.shard_op(paddle.matmul, process_mesh, {
# x.name: [-1, -1],
# w.name: [-1, -1]
# }, **{"x": x,
# "y": w})[0]
y = paddle.distributed.shard_op(paddle.matmul,
dist_attr={
"process_mesh": process_mesh,
x: {
"dims_mapping": [-1, -1]
},
w: {
"dims_mapping": [-1, -1]
}
})(x, w)[0]
rank_id = 0
dist_context = DistributedContext()
dist_strategy = fleet.DistributedStrategy()
partitioner = Partitioner(dist_context, rank_id)
completer = Completer(dist_context)
complete_train_program = completer.complete_forward_annotation(
train_program)
dist_context.block_state.parse_forward_blocks(complete_train_program)
partitioned_main_prog, partitioned_startup_prog, partitioned_params_grads = partitioner.partition(
complete_train_program, startup_program, [])
resharder = Resharder(partitioned_main_prog, partitioned_startup_prog,
rank_id, dist_context, partitioned_params_grads)
resharder.reshard()
# the x should not be slice
self.assertTrue(check_allgather(partitioned_main_prog))
def main(args):
paddle.set_device("cpu")
paddle.enable_static()
fleet.init()
fake_num_nodes = 1
py_reader, loss = StaticSkipGramModel(
fake_num_nodes,
args.neg_num,
args.embed_size,
sparse_embedding=True,
shared_embedding=args.shared_embedding)
optimizer = F.optimizer.Adam(args.learning_rate, lazy_mode=True)
dist_strategy = fleet.DistributedStrategy()
dist_strategy.a_sync = True
optimizer = fleet.distributed_optimizer(optimizer, dist_strategy)
optimizer.minimize(loss)
# init and run server or worker
if fleet.is_server():
fleet.init_server()
fleet.run_server()
if fleet.is_worker():
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
fleet.init_worker()
graph = build_graph(args)
# bind gen
train_ds = ShardedDataset(graph.nodes, args.epoch)
collate_fn = BatchRandWalk(graph, args.walk_len, args.win_size,
args.neg_num, args.neg_sample_type)
data_loader = Dataloader(train_ds,
batch_size=args.cpu_batch_size,
shuffle=True,
num_workers=args.sample_workers,
collate_fn=collate_fn)
py_reader.set_batch_generator(lambda: data_loader)
train_loss = train(exe, paddle.static.default_main_program(),
py_reader, loss)
fleet.stop_worker()
if fleet.is_first_worker():
fleet.save_persistables(exe, "./model",
paddle.static.default_main_program())
def get_distributed_program():
train_program = static.Program()
startup_program = static.Program()
dist_strategy = fleet.DistributedStrategy()
dist_strategy.semi_auto = True
fleet.init(is_collective=True, strategy=dist_strategy)
loss, train_program, startup_program = mlp_forward(train_program,
startup_program)
optimizer = paddle.fluid.optimizer.SGDOptimizer(learning_rate=0.01)
optimizer = fleet.distributed_optimizer(optimizer)
_, _, dist_startup_prog, dist_main_prog = optimizer.minimize(
loss, startup_program)
return dist_main_prog, dist_startup_prog, loss
def main(args):
paddle.set_device("cpu")
paddle.enable_static()
role = role_maker.PaddleCloudRoleMaker()
fleet.init(role)
if args.num_nodes is None:
num_nodes = load(args.dataset).num_nodes
else:
num_nodes = args.num_nodes
loss = StaticSkipGramModel(
num_nodes, args.neg_num, args.embed_size, sparse=True)
optimizer = F.optimizer.Adam(args.learning_rate, lazy_mode=True)
dist_strategy = fleet.DistributedStrategy()
dist_strategy.a_sync = True
optimizer = fleet.distributed_optimizer(optimizer, dist_strategy)
optimizer.minimize(loss)
# init and run server or worker
if fleet.is_server():
fleet.init_server()
fleet.run_server()
if fleet.is_worker():
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
fleet.init_worker()
graph = load(args.dataset)
# bind gen
train_ds = ShardedDataset(graph.nodes)
collate_fn = BatchRandWalk(graph, args.walk_len, args.win_size,
args.neg_num, args.neg_sample_type)
data_loader = Dataloader(
train_ds,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.sample_workers,
collate_fn=collate_fn)
for epoch in range(args.epoch):
train_loss = train(exe,
paddle.static.default_main_program(),
data_loader, loss)
log.info("Runing epoch:%s\t train_loss:%.6f", epoch, train_loss)
fleet.stop_worker()
def main(args):
paddle.enable_static()
paddle.set_device('gpu:%d' % paddle.distributed.ParallelEnv().dev_id)
fleet.init(is_collective=True)
graph = load(args.dataset)
loss = StaticSkipGramModel(graph.num_nodes,
args.neg_num,
args.embed_size,
num_emb_part=args.num_emb_part,
shared_embedding=args.shared_embedding)
optimizer = F.optimizer.Adam(args.learning_rate)
dist_strategy = fleet.DistributedStrategy()
dist_strategy.sharding = True
dist_strategy.sharding_configs = {
"segment_anchors": None,
"sharding_segment_strategy": "segment_broadcast_MB",
"segment_broadcast_MB": 32,
"sharding_degree": int(paddle.distributed.get_world_size()),
}
optimizer = fleet.distributed_optimizer(optimizer, dist_strategy)
optimizer.minimize(loss)
place = paddle.CUDAPlace(paddle.distributed.ParallelEnv().dev_id)
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
# bind gen
train_ds = ShardedDataset(graph.nodes)
collate_fn = BatchRandWalk(graph, args.walk_len, args.win_size,
args.neg_num, args.neg_sample_type)
data_loader = Dataloader(train_ds,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.sample_workers,
collate_fn=collate_fn)
for epoch in range(args.epoch):
train_loss = train(exe, paddle.static.default_main_program(),
data_loader, loss)
log.info("Runing epoch:%s\t train_loss:%.6f", epoch, train_loss)
fleet.stop_worker()
if fleet.is_first_worker():
fleet.save_persistables(exe, "./model",
paddle.static.default_main_program())
