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fix optimizer parallel problems

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pull/3405/head
Ziyan 5 years ago
parent 7cb567ebbe
commit 98e2ee90de
9 changed files with 52 additions and 20 deletions
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6
mindspore/ccsrc/backend/kernel_compiler/hccl/hccl_kernel_metadata.cc
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@ -26,13 +26,13 @@ namespace mindspore {
namespace kernel {
namespace {
std::string GetKernelFormat(const CNodePtr &kernel_node, size_t index) {
const std::set<std::string> kReduceNoSupportedSet = {kOpFormat_FRAC_Z, kOpFormat_FRACTAL_Z_C04, kOpFormat_C1HWNCoC0};
auto op_name = AnfAlgo::GetCNodeName(kernel_node);
auto parallel_context_instance = parallel::ParallelContext::GetInstance();
MS_EXCEPTION_IF_NULL(parallel_context_instance);
if (parallel_context_instance->enable_parallel_optimizer()) {
if (parallel_context_instance->enable_parallel_optimizer() && op_name == kBroadcast) {
return kOpFormat_DEFAULT;
}
const std::set<std::string> kReduceNoSupportedSet = {kOpFormat_FRAC_Z, kOpFormat_FRACTAL_Z_C04, kOpFormat_C1HWNCoC0};
auto op_name = AnfAlgo::GetCNodeName(kernel_node);
auto format = AnfAlgo::GetPrevNodeOutputFormat(kernel_node, index);
if (op_name != kReduceScatter && op_name != kAllGatherOpName) {
return format;

2
mindspore/ccsrc/frontend/parallel/context.cc
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@ -65,6 +65,8 @@ void ParallelContext::Reset() {
strategy_ckpt_load_file_ = "";
strategy_ckpt_save_file_ = "";
enable_parallel_optimizer_ = false;
all_reduce_fusion_split_indices_.clear();
all_reduce_fusion_split_sizes_.clear();
}
void ParallelContext::set_device_num(int32_t device_num) {

2
mindspore/nn/optim/adam.py
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@ -371,5 +371,5 @@ class AdamWeightDecay(Optimizer):
self.parameters, self.moments1, self.moments2,
gradients, self.decay_flags, self.optim_filter)
if self.use_parallel:
optim_result = self.broadcast_params(optim_result)
self.broadcast_params(optim_result)
return optim_result

2
mindspore/nn/optim/lamb.py
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@ -312,7 +312,7 @@ class Lamb(Optimizer):
self.decay_flags, self.optim_filter)
if self.use_parallel:
optim_result = self.broadcast_params(optim_result)
self.broadcast_params(optim_result)
if not self.dynamic_lr:
F.control_depend(lr, self.assignadd(self.global_step, 1))

6
mindspore/nn/optim/optimizer.py
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@ -466,7 +466,7 @@ class Optimizer(Cell):
param_group.append(F.make_tuple())
key_group.append(F.make_tuple())
for i in range(self.param_length):
param_group[self.param_rank[i]] = param_group[self.param_rank[i]] + (optim_result[i],)
param_group[self.param_rank[i]] = param_group[self.param_rank[i]] + (self.parameters[i],)
key = P.MakeRefKey(self.param_names[i])()
key_group[self.param_rank[i]] = key_group[self.param_rank[i]] + (key,)
new_param_group = []
@ -476,9 +476,9 @@ class Optimizer(Cell):
new_param_group.append(next_params)
for i in range(F.tuple_len(next_params)):
F.assign(key_group[root][i], next_params[i])
status = True
status = F.control_depend(optim_result, new_param_group[0][0])
for i in range(self.dev_num - 1):
status = F.control_depend(new_param_group[i][0], new_param_group[i+1])
status = F.depend(F.control_depend(new_param_group[i], new_param_group[i+1][0]), status)
return status

8
mindspore/nn/wrap/grad_reducer.py
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@ -25,7 +25,7 @@ import mindspore.common.dtype as mstype
reduce_opt = C.MultitypeFuncGraph("reduce_opt")
def _init_allreduce_operators(length):
def _init_allreduce_operators(length, split_indices):
""" initialize allreduce communication operators"""
group = 1
fusion = ()
@ -318,7 +318,7 @@ class DistributedGradReducer(Cell):
split_indices = auto_parallel_context().get_all_reduce_fusion_split_indices()
if is_parallel_optimizer and split_indices:
self.split_fusion = True
self.op_list = _init_allreduce_operators(len(parameters))
self.op_list = _init_allreduce_operators(len(parameters), split_indices)
else:
self.split_fusion = False
self.allreduce = AllReduce().add_prim_attr('fusion', 1)
@ -344,10 +344,10 @@ class DistributedGradReducer(Cell):
if self.split_fusion:
if self.enable_parameter_server:
new_grad = self.map_(F.partial(reduce_opt, self.degree, self.mean, self.allgather),
self.opt_list, self.allreduce_filter, grads, self.ps_parameters)
self.op_list, self.allreduce_filter, grads, self.ps_parameters)
else:
new_grad = self.map_(F.partial(reduce_opt, self.degree, self.mean, self.allgather),
self.opt_list, self.allreduce_filter, grads)
self.op_list, self.allreduce_filter, grads)
else:
if self.enable_parameter_server:
new_grad = self.map_(F.partial(reduce_opt, self.degree, self.mean, self.allgather,

15
mindspore/parallel/_dp_allreduce_fusion.py
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@ -16,8 +16,6 @@
import ctypes
from mindspore import log as logger
_MAX_GROUP_NAME_LEN = 127
_HCCL_LIB = 'libhccl.so'
@ -25,8 +23,8 @@ _HCCL_LIB = 'libhccl.so'
def _load_lib():
try:
hccl_lib = ctypes.CDLL(_HCCL_LIB)
except RuntimeError:
logger.error('Get hccl lib error')
except Exception:
raise RuntimeError('Get hccl lib error')
return hccl_lib
@ -69,8 +67,9 @@ def _set_fusion_strategy_by_idx(idxList, group="hccl_world_group"):
try:
lib_ctype = _load_lib()
except RuntimeError:
logger.error('Load HCCL lib failed')
import hccl_test.manage.api as hccl
hccl.set_fusion_strategy_by_idx()
return
if isinstance(group, (str)):
group_len = len(group)
if (group_len > _MAX_GROUP_NAME_LEN or group_len == 0):
@ -126,7 +125,9 @@ def _set_fusion_strategy_by_size(dataSizeList, group="hccl_world_group"):
try:
lib_ctype = _load_lib()
except RuntimeError:
logger.error('Load HCCL lib failed')
import hccl_test.manage.api as hccl
hccl.set_fusion_strategy_by_size()
return
if isinstance(group, (str)):
group_len = len(group)
if group_len > _MAX_GROUP_NAME_LEN or group_len == 0:

10
tests/ut/python/hccl_test/manage/api.py
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@ -86,3 +86,13 @@ def create_group(group, rank_size, rank_ids):
# pylint: disable=unused-argument
def destroy_group(group):
pass
# pylint: disable=unused-argument
def set_fusion_strategy_by_idx():
pass
# pylint: disable=unused-argument
def set_fusion_strategy_by_size():
pass

21
tests/ut/python/parallel/test_parallel_optimizer.py
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@ -23,7 +23,7 @@ from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Adam, AdamWeightDecay, Lamb
from mindspore.ops import operations as P
from mindspore import context
from mindspore.parallel._auto_parallel_context import auto_parallel_context
class Net(nn.Cell):
"""Net definition"""
@ -64,6 +64,7 @@ def test_AdamWeightDecay():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
context.reset_auto_parallel_context()
def test_lamb_compile():
@ -79,7 +80,24 @@ def test_lamb_compile():
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
context.reset_auto_parallel_context()
def test_lamb_split_fusion():
""" test_Lamb_split_fusion """
context.set_auto_parallel_context(parallel_mode="data_parallel", device_num=2, enable_parallel_optimizer=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([2, 4, 6, 8])
inputs = Tensor(np.ones([32, 128]).astype(np.float32))
label = Tensor(np.zeros([32, 768]).astype(np.float32))
net = Net()
net.set_train()
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = Lamb(net.trainable_params(), learning_rate=0.1)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)
context.reset_auto_parallel_context()
def test_edge_case():
""" test_edge_case """
@ -93,3 +111,4 @@ def test_edge_case():
with pytest.raises(RuntimeError):
context.set_auto_parallel_context(device_num=16)
Lamb(net.trainable_params(), learning_rate=0.1)
context.reset_auto_parallel_context()

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