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fix two cast bug in auto parallel

This commit is contained in:
lichenever 2020-03-31 18:43:42 +08:00 committed by chang zherui
parent b27129c9da
commit 07449cd1cc
3 changed files with 32 additions and 7 deletions
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2
mindspore/ccsrc/parallel/step_auto_parallel.cc
View File

@ -350,6 +350,8 @@ bool IsAutoParallelCareNode(const CNodePtr &cnode) {
}
OperatorInfoPtr CreateTheOperatorInfo(const PrimitivePtr &prim, const CNodePtr &cnode) {
MS_EXCEPTION_IF_NULL(prim);
MS_EXCEPTION_IF_NULL(cnode);
auto attrs = prim->attrs();
std::vector<Shapes> shape_list = ExtractShape(cnode);
if (shape_list.empty()) {

8
mindspore/ccsrc/parallel/step_parallel.cc
View File

@ -374,7 +374,6 @@ bool IsParallelCareNode(const CNodePtr& cnode) {
if (prim == nullptr) {
return false;
}
auto attrs = prim->attrs();
if (IsInBlackList(prim)) {
MS_LOG(INFO) << "Parallel don't care node: " << prim->name();
return false;
@ -1971,11 +1970,7 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
MS_EXCEPTION_IF_NULL(current_value);
PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
MS_EXCEPTION_IF_NULL(current_prim);
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> fix_cast_bug
// return -> cast
if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
@ -1983,8 +1978,7 @@ CNodePtr FindLossCNode(const FuncGraphPtr& func_graph) {
current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
}
=======
>>>>>>> 回退 'Pull Request !17 : [AutoParallel]Fix bug in the case of two cast'
// notice: the GetNext op has not input
if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
MS_LOG(INFO) << "The loss is: " << current_prim->name();

29
tests/ut/python/parallel/test_element_wise_function.py
View File

@ -268,3 +268,32 @@ def test_cast_before_mirror3():
y = Tensor(np.ones([32, 64]), dtype=ms.float16)
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
_executor.compile(net, x, y, b)
def test_mul_two_cast():
class Net(nn.Cell):
def __init__(self, strategy1, strategy2, strategy3):
super().__init__()
self.mul = P.Mul().set_strategy(strategy1)
self.mul2 = P.Mul().set_strategy(strategy2)
self.cast = P.Cast().set_strategy(strategy3)
self.cast2 = P.Cast().set_strategy(strategy3)
def construct(self, x, y, b):
out = self.mul(x, y)
out = self.mul2(out, b)
out = self.cast(out, ms.int32)
out = self.cast2(out, ms.bool_)
return out
context.set_auto_parallel_context(device_num=8, global_rank=0)
strategy1 = ((2, 2), (2, 2))
strategy2 = ((8, 1), (8, 1))
strategy3 = ((8, 1), )
net = GradWrap(Net(strategy1, strategy2, strategy3))
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
y = Tensor(np.ones([128, 32]), dtype=ms.float32)
b = Tensor(np.ones([128, 32]), dtype=ms.float32)
_executor.compile(net, x, y, b)