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!19848 [AutoParallel]Fix_auto_parallel_bug_master 

Merge pull request !19848 from lichen/fix_auto_parallel_bug_master
This commit is contained in:
i-robot 2021-07-10 03:34:57 +00:00 committed by Gitee
parent de635448af cb91e606ac
commit 1260700865
7 changed files with 133 additions and 5 deletions
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14
mindspore/ccsrc/frontend/parallel/graph_util/pipeline_split_utils.cc
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@ -29,6 +29,18 @@
namespace mindspore {
namespace parallel {
const std::set<PrimitivePtr> END_NODE_BLACK_LIST = {prim::kPrimDepend, prim::kPrimTupleGetItem,
prim::kPrimSoftmaxCrossEntropyWithLogits};
static bool IsInEndNodeBlackList(const CNodePtr &cnode) {
for (auto &prim : END_NODE_BLACK_LIST) {
if (IsPrimitiveCNode(cnode, prim)) {
return true;
}
}
return false;
}
AnfNodePtr FindAccuGrad(const CNodePtr &cnode) {
auto pre_node = cnode->input(1);
while (true) {
@ -392,7 +404,7 @@ void BroadCastMicroBatch(const CNodePtr &node, NodeUsersMap *node_users_map, con
AnfNodePtr GetPreNode(const AnfNodePtr &node) {
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (IsPrimitiveCNode(node, prim::kPrimDepend)) {
if (IsInEndNodeBlackList(cnode)) {
return GetPreNode(cnode->input(1));
}
return cnode;

10
mindspore/ccsrc/frontend/parallel/pipeline_transformer/pipeline_transformer.cc
View File

@ -134,6 +134,11 @@ void PipelineTransformer::LabelMicroBatch() {
for (auto &node_user : node_users) {
if (IsPrimitiveCNode(node_user.first, prim::kPrimTupleGetItem)) {
auto data_users = manager_->node_users()[node_user.first];
auto node_first = data_users.front().first;
if (!IsPrimitiveCNode(node_first, prim::kPrimStridedSlice)) {
data_users.clear();
data_users = node_user_map[node_first];
}
auto micro_size = int64_t(data_users.size());
micro_size_ = micro_size;
MS_LOG(INFO) << "Micro Size is: " << micro_size;
@ -690,7 +695,10 @@ std::pair<std::vector<AnfNodePtr>, std::vector<AnfNodePtr>> PipelineTransformer:
auto depend_op = CreatOpInstance(depend_attrs, DEPEND, DEPEND);
std::vector<AnfNodePtr> receive_ops;
std::vector<AnfNodePtr> send_ops;
auto all_nodes = graph->nodes();
auto ret = graph->get_return();
MS_EXCEPTION_IF_NULL(ret);
std::vector<AnfNodePtr> all_nodes = DeepScopedGraphSearch(ret);
std::reverse(all_nodes.begin(), all_nodes.end());
auto stage_num = g_device_manager->stage_num();
if (root_->has_flag(TRAINING) && (stage_num > micro_size_)) {
MS_LOG(EXCEPTION) << "MicroBatch size: " << micro_size_ << " can't less than stage num: " << stage_num;

3
mindspore/ccsrc/frontend/parallel/step_parallel.cc
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@ -3045,6 +3045,9 @@ void CheckpointStrategy(const std::vector<AnfNodePtr> &all_nodes, const FuncGrap
}
std::string cloned_param_name = cloned_parameter_node->cast<ParameterPtr>()->name();
auto cloned_param_layout = cloned_parameter_node->user_data<TensorLayout>();
if (cloned_param_layout == nullptr) {
continue;
}
tensor_info_map[cloned_param_name] = cloned_param_layout;
}
if (StrategyCheckpoint::GetInstance().Save(stra_map, tensor_info_map, &manual_shape_map) != SUCCESS) {

1
mindspore/ccsrc/frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.cc
View File

@ -161,6 +161,7 @@ Status StrategyCheckpoint::Save(const StrategyMap &strategy_map, const TensorInf
}
for (auto &node_tensor_info : tensor_info_map) {
TensorLayoutPtr tensor_layout = node_tensor_info.second;
MS_EXCEPTION_IF_NULL(tensor_layout);
straspb::ParallelLayoutItem *parallel_layout_item = parallel_strategy_map.add_parallel_layout_item();
MS_EXCEPTION_IF_NULL(parallel_layout_item);
parallel_layout_item->set_param_name(node_tensor_info.first);

3
mindspore/nn/wrap/__init__.py
View File

@ -18,7 +18,7 @@ Wrap cells for networks.
Use the Wrapper to combine the loss or build the training steps.
"""
from .cell_wrapper import ForwardValueAndGrad, TrainOneStepCell, WithLossCell, WithGradCell, WithEvalCell, \
ParameterUpdate, GetNextSingleOp, VirtualDatasetCellTriple
ParameterUpdate, GetNextSingleOp, VirtualDatasetCellTriple, PipelineCell
from .loss_scale import TrainOneStepWithLossScaleCell, DynamicLossScaleUpdateCell, FixedLossScaleUpdateCell
from .grad_reducer import DistributedGradReducer
from ..layer.timedistributed import TimeDistributed
@ -29,6 +29,7 @@ __all__ = [
"TrainOneStepCell",
"WithLossCell",
"WithGradCell",
"PipelineCell",
"WithEvalCell",
"GetNextSingleOp",
"TrainOneStepWithLossScaleCell",

98
mindspore/nn/wrap/loss_scale.py
View File

@ -15,6 +15,7 @@
"""Loss scale cell for loss scale training."""
import mindspore.context as context
from mindspore.context import ParallelMode
from mindspore.parallel._utils import _get_enable_parallel_optimizer
from .cell_wrapper import TrainOneStepCell
from ..cell import Cell
from ...common import Tensor, RowTensor
@ -430,3 +431,100 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
if self.loss_scaling_manager is not None:
return self.loss_scaling_manager(self.scale_sense, overflow)
return overflow
grad_scale = C.MultitypeFuncGraph("grad_scale")
shard_grad_scale = C.MultitypeFuncGraph("shard_grad_scale")
reciprocal = P.Reciprocal()
@grad_scale.register("Tensor", "Tensor", "Tensor")
def tensor_grad_scale_pipeline(scale, grad, accu_grad):
accu_grad = F.depend(accu_grad, grad)
new_grad = accu_grad * reciprocal(scale)
accu_grad = F.depend(accu_grad, new_grad)
zeros = F.tensor_mul(accu_grad, 0.0)
new_grad = F.depend(new_grad, F.assign(accu_grad, zeros))
return new_grad
@shard_grad_scale.register("Tensor", "Tensor", "Tensor")
def tensor_shard_grad_scale_pipeline(scale, grad, accu_grad):
new_grad = grad * reciprocal(scale)
accu_grad = F.depend(accu_grad, new_grad)
new_grad = F.depend(new_grad, F.assign(accu_grad, F.zeros_like(accu_grad)))
return new_grad
class _TrainPipelineWithLossScaleCell(TrainOneStepCell):
"""
Append an optimizer to the training network after that the construct
function can be called to create the backward graph.
Args:
network (Cell): The training network. Note that loss function should have been added.
optimizer (Optimizer): Optimizer for updating the weights.
scale_sense (Cell): Cell to do the loss scale.
"""
def __init__(self, network, optimizer, scale_sense):
super(_TrainPipelineWithLossScaleCell, self).__init__(network, optimizer, sens=None)
self.network = network
self.network.add_flags(defer_inline=True)
self.weights = optimizer.parameters
self.accu_grads = self.weights.clone(prefix="accu_grads", init="zeros")
self.optimizer = optimizer
self.grad = C.GradOperation(get_by_list=True, sens_param=True)
self.grad_reducer = F.identity
self.degree = 1
self.cast = P.Cast()
self.alloc_status = P.NPUAllocFloatStatus()
self.get_status = P.NPUGetFloatStatus()
self.clear_before_grad = P.NPUClearFloatStatus()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.base = Tensor(1, mstype.float32)
self.less_equal = P.LessEqual()
self.hyper_map = C.HyperMap()
self.reshape = P.Reshape()
self.loss_scaling_manager = None
if isinstance(scale_sense, Cell):
self.loss_scaling_manager = scale_sense
self.scale_sense = Parameter(Tensor(scale_sense.get_loss_scale(), dtype=mstype.float32),
name="scale_sense")
elif isinstance(scale_sense, Tensor):
if scale_sense.shape == (1,) or scale_sense.shape == ():
self.scale_sense = Parameter(scale_sense, name='scale_sense')
else:
raise ValueError("The shape of scale_sense must be (1,) or (), but got {}".format(scale_sense.shape))
else:
raise TypeError("The scale_sense must be Cell or Tensor, but got {}".format(type(scale_sense)))
self.opt_shard = _get_enable_parallel_optimizer()
def construct(self, *inputs):
weights = self.weights
loss = self.network(*inputs)
scaling_sens = self.scale_sense
init = self.alloc_status()
status_clear = self.clear_before_grad(init)
scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss))
grads = self.grad(self.network, weights)(*inputs, scaling_sens_filled)
init = F.depend(init, grads)
get_status = self.get_status(init)
init = F.depend(init, get_status)
flag_sum = self.reduce_sum(init, (0,))
loss = F.depend(loss, status_clear)
if self.opt_shard:
grads = self.grad_reducer(grads)
grads = self.hyper_map(F.partial(shard_grad_scale, scaling_sens * self.degree), grads, self.accu_grads)
else:
accu_grads = self.grad_reducer(self.accu_grads)
grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads, accu_grads)
cond = self.less_equal(self.base, flag_sum)
overflow = cond
if self.loss_scaling_manager is not None:
overflow = self.loss_scaling_manager(self.scale_sense, cond)
if overflow:
succ = False
else:
succ = self.optimizer(grads)
ret = (loss, overflow, scaling_sens)
return F.depend(ret, succ)

9
mindspore/train/amp.py
View File

@ -19,6 +19,7 @@ from .._checkparam import Rel
from ..common import dtype as mstype
from ..nn import acc
from ..nn.wrap.cell_wrapper import _VirtualDatasetCell, _TrainPipelineAccuStepCell
from ..nn.wrap.loss_scale import _TrainPipelineWithLossScaleCell
from ..ops import functional as F
from ..parallel._utils import _get_parallel_mode, _get_pipeline_stages
from .loss_scale_manager import DynamicLossScaleManager, LossScaleManager
@ -184,8 +185,12 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
raise ValueError("Only `loss_scale_manager=None` or "
"`loss_scale_manager=FixedLossScaleManager(drop_overflow_update=False)`"
"are supported on device `CPU`. ")
network = nn.TrainOneStepWithLossScaleCell(network, optimizer,
scale_sense=update_cell).set_train()
if _get_pipeline_stages() > 1:
network = _TrainPipelineWithLossScaleCell(network, optimizer,
scale_sense=update_cell).set_train()
else:
network = nn.TrainOneStepWithLossScaleCell(network, optimizer,
scale_sense=update_cell).set_train()
return network
if _get_pipeline_stages() > 1:
network = _TrainPipelineAccuStepCell(network, optimizer).set_train()