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!645 auto parallel prelu operator support broadcast 

Merge pull request !645 from yao_yf/auto_parallel_prelu_support_broadcast
This commit is contained in:
mindspore-ci-bot 2020-04-24 17:32:37 +08:00 committed by Gitee
parent e23a663f52 425276d43d
commit ce71c17933
2 changed files with 25 additions and 3 deletions
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10
mindspore/ccsrc/parallel/ops_info/prelu_info.cc
View File

@ -32,7 +32,7 @@ namespace parallel {
* prelu has 2 input
* A: A float tensor of shape [NCHW] representing the output of the preview layer.
* w: Float Tensor, w > 0: there is only two shapes are legitimate: 1, or the number of channels at input.
* the strategy of w should equal to the channel dimension of strategy of A
* the strategy of w should equal to the channel dimension of strategy of A, or equal to 1
*/
Status PReLUInfo::CheckStrategy(const StrategyPtr &strategy) {
if (CheckStrategyValue(strategy, inputs_shape_, is_auto_parallel_) != SUCCESS) {
@ -52,7 +52,7 @@ Status PReLUInfo::CheckStrategy(const StrategyPtr &strategy) {
}
return FAILED;
}
if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0]) {
if (stra[0][PRELU_CHANNEL_INDEX] != stra[1][0] && inputs_shape_[1][0] != 1) {
if (is_auto_parallel_) {
MS_LOG(DEBUG) << name_ << ": Invalid channel strategy.";
} else {
@ -107,7 +107,11 @@ Status PReLUInfo::InferTensorMap() {
}
TensorMap param_tensor_map;
param_tensor_map.push_back(input_tensor_map.at(1));
if (inputs_shape_[1][0] == 1) {
param_tensor_map.push_back(-1);
} else {
param_tensor_map.push_back(input_tensor_map.at(1));
}
inputs_tensor_map_.push_back(input_tensor_map);
inputs_tensor_map_.push_back(param_tensor_map);
outputs_tensor_map_.push_back(input_tensor_map);

18
tests/ut/python/parallel/test_prelu.py
View File

@ -166,3 +166,21 @@ def test_prelu_parallel_success4():
w = Tensor(np.random.rand(16),dtype=ms.float32)
net = GradWrap(NetWithLoss(Net(strategy)))
_executor.compile(net, x, w)
def test_prelu_parallel_success5():
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), (1, ))
x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32)
w = Tensor(np.random.rand(1),dtype=ms.float32)
net = GradWrap(NetWithLoss(Net(strategy)))
_executor.compile(net, x, w)