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!177 prelu operator support parallel on the channel 

Merge pull request !177 from yao_yf/fix_auto_parallel_prelu
This commit is contained in:
mindspore-ci-bot 2020-04-09 14:08:41 +08:00 committed by Gitee
parent 03ba579d4f b5e3fa9593
commit 2e6e94b2b6
3 changed files with 20 additions and 5 deletions
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2
mindspore/ccsrc/parallel/ops_info/prelu_info.cc
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@ -52,7 +52,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr& strategy) {
}
return FAILED;
}
if ((stra[0][PRELU_CHANNEL_INDEX] != PRELU_CHANNEL_STRATEGY) || (stra[1][0] != PRELU_CHANNEL_STRATEGY)) {
if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0]) {
if (is_auto_parallel_) {
MS_LOG(DEBUG) << name_ << ": Invalid channel strategy.";
} else {

6
tests/ut/cpp/parallel/ops_info/prelu_test.cc
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@ -146,11 +146,10 @@ TEST_F(TestPReLUInfo, CheckStrategy1) {
}
TEST_F(TestPReLUInfo, CheckStrategy2) {
// Success: {{2,1,8,16},{1}}
std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {4}};
StrategyPtr strategy = NewStrategy(0, inputs);
Status ret = prelu->Init(strategy);
ASSERT_EQ(ret, FAILED);
ASSERT_EQ(ret, SUCCESS);
}
TEST_F(TestPReLUInfo, AutoStrategy1) {
@ -252,11 +251,10 @@ TEST_F(TestPReLUInfo, CheckStrategy_2d1) {
}
TEST_F(TestPReLUInfo, CheckStrategy_2d2) {
// Success: {{2,1,8,16},{1}}
std::vector<Dimensions> inputs = {{128, 4}, {4}};
StrategyPtr strategy = NewStrategy(0, inputs);
Status ret = prelu_2d->Init(strategy);
ASSERT_EQ(ret, FAILED);
ASSERT_EQ(ret, SUCCESS);
}
TEST_F(TestPReLUInfo, AutoStrategy_2d1) {

17
tests/ut/python/parallel/test_prelu.py
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@ -149,3 +149,20 @@ def test_prelu_parallel_success3():
w = Tensor(np.random.rand(16),dtype=ms.float32)
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
_executor.compile(net, x, y, w)
def test_prelu_parallel_success4():
class Net(nn.Cell):
def __init__(self, strategy):
super().__init__()
self.prelu = P.PReLU().set_strategy(strategy)
def construct(self, x, y):
out = self.prelu(x, y)
return out
context.reset_auto_parallel_context()
context.set_auto_parallel_context(device_num=64, global_rank=0)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
strategy = ((2, 4, 4, 2), (4, ))
x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32)
w = Tensor(np.random.rand(16),dtype=ms.float32)
net = GradWrap(NetWithLoss(Net(strategy)))
_executor.compile(net, x, w)