Nigel
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!1600 [AutoParallel]Fix GatherV2 bug 

Merge pull request !1600 from lichen/fix_auto_parallel_gatherv2_bug
This commit is contained in:
mindspore-ci-bot 2020-05-29 10:27:14 +08:00 committed by Gitee
parent 0b4de00176 c223fde566
commit f523a0f83c
2 changed files with 21 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
mindspore/ccsrc/parallel/ops_info/gather_v2_p_info.cc
View File

@ -57,6 +57,15 @@ Status GatherV2PInfo::CheckStrategy(const StrategyPtr &strategy) {
return FAILED;
}
// param slice shape need 32Byte aligned
auto param_shape = inputs_shape_.at(0);
auto param_strategy = strategy->GetInputDim().at(0);
auto slice_shape = param_shape.at(param_shape.size() - 1) / param_strategy.at(param_strategy.size() - 1);
if (slice_shape % 8 != 0) {
MS_LOG(ERROR) << name_ << ": Last dim of param slice shape need 32Byte aligned.";
return FAILED;
}
// only support 1-dim and 2-dim param
if (inputs_shape_.at(0).size() != 1 && inputs_shape_.at(0).size() != 2) {
MS_LOG(ERROR) << name_ << ": Don't support param dim " << inputs_shape_.at(0).size();
@ -71,14 +80,12 @@ Status GatherV2PInfo::CheckStrategy(const StrategyPtr &strategy) {
// axis=0, index_shape(0)%param_strategy(0) must be 0
Shape index_shape = inputs_shape_.at(1);
auto param_strategy = strategy->GetInputDim().at(0);
if ((axis_ == 0) && (index_shape.at(0) % param_strategy.at(0) != 0)) {
MS_LOG(ERROR) << name_ << ": index_shape(0) can't be divided by param_strategy(0).";
return FAILED;
}
// axis != 0, param_shape(0)%(param_strategy(0)*param_strategy(axis)) must be 0
Shape param_shape = inputs_shape_.at(0);
if (axis_ != 0 && param_shape.at(0) % (param_strategy.at(0) * param_strategy.at(IntToSize(axis_))) != 0) {
MS_LOG(ERROR) << name_ << ": index_shape(0) can't be divided by (param_strategy(0)*param_strategy(axis)).";
return FAILED;
@ -158,12 +165,12 @@ Status GatherV2PInfo::InferDevMatrixShape() {
} else {
out_dev_matrix_shape_ = dev_matrix_shape_;
}
auto product_out =
std::accumulate(out_dev_matrix_shape_.begin(), out_dev_matrix_shape_.end(), 1, std::multiplies<int>());
CheckGlobalDeviceManager();
size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
if (product_out == 1) {
out_dev_matrix_shape_.insert(out_dev_matrix_shape_.begin(), dev_num);
auto param_product = std::accumulate(param_strategy.begin(), param_strategy.end(), 1, std::multiplies<int>());
auto index_product = std::accumulate(index_strategy.begin(), index_strategy.end(), 1, std::multiplies<int>());
if (param_product * index_product < SizeToInt(dev_num)) {
out_dev_matrix_shape_.insert(out_dev_matrix_shape_.begin(), SizeToInt(dev_num / (param_product * index_product)));
}
return SUCCESS;
@ -174,7 +181,7 @@ Status GatherV2PInfo::InferTensorMap() {
// param_strategy(axis) != 1
size_t param_size = inputs_shape_.at(0).size();
size_t index_size = inputs_shape_.at(1).size();
size_t total_size = dev_matrix_shape_.size();
size_t total_size = param_size + index_size;
std::vector<int32_t> tensor_map_index;
std::vector<int32_t> tensor_map_params;
auto param_strategy = strategy_->GetInputDim().at(0);

14
tests/ut/python/parallel/test_gather_v2.py
View File

@ -67,8 +67,8 @@ def test_gatherv2_semi_auto0():
net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
net.set_auto_parallel()
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
_executor.compile(net, x, y)
@ -79,8 +79,8 @@ def test_gatherv2_semi_auto1():
net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
net.set_auto_parallel()
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
_executor.compile(net, x, y)
@ -91,8 +91,8 @@ def test_gatherv2_semi_auto2():
net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
net.set_auto_parallel()
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
_executor.compile(net, x, y)
@ -103,7 +103,7 @@ def test_gatherv2_semi_auto3():
net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
net.set_auto_parallel()
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
_executor.compile(net, x, y)