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fix bug in reshape strategy search when reshape is first operator

This commit is contained in:
yao_yf 2020-10-21 17:18:36 +08:00
parent f887618662
commit f7189adb91
3 changed files with 40 additions and 23 deletions
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15
mindspore/ccsrc/frontend/parallel/ops_info/reshape_info.cc
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@ -39,7 +39,7 @@ Status ReshapeInfo::CheckStrategy(const StrategyPtr &strategy) { return CheckStr
Status ReshapeInfo::InferDevMatrixShape() {
Strategys stra = strategy_->GetInputDim();
input_strategy_ = stra.at(0);
dev_matrix_shape_.push_back(input_strategy_[0]);
dev_matrix_shape_ = stra.at(0);
return SUCCESS;
}
@ -162,17 +162,13 @@ Status ReshapeInfo::InferTensorMap() {
}
Shape tensor_map_index_input;
tensor_map_index_input.push_back(0);
for (size_t j = 1; j < inputs_shape_[0].size(); ++j) {
tensor_map_index_input.push_back(MAP_NONE);
for (size_t j = 0; j < inputs_shape_[0].size(); ++j) {
tensor_map_index_input.push_back((int64_t)(inputs_shape_[0].size() - j - 1));
}
inputs_tensor_map_.push_back(tensor_map_index_input);
Shape tensor_map_index_output;
tensor_map_index_output.push_back(0);
for (size_t j = 1; j < outputs_shape_[0].size(); ++j) {
for (size_t j = 0; j < outputs_shape_[0].size(); ++j) {
tensor_map_index_output.push_back(MAP_NONE);
}
outputs_tensor_map_.push_back(tensor_map_index_output);
@ -186,8 +182,7 @@ Status ReshapeInfo::InferTensorMap() {
Strategys ReshapeInfo::GetOutputsStrategy() {
Strategys outputs_strategy;
Dimensions strategy;
strategy.push_back(input_strategy_[0]);
for (size_t j = 1; j < outputs_shape_[0].size(); ++j) {
for (size_t j = 0; j < outputs_shape_[0].size(); ++j) {
strategy.push_back(1);
}
outputs_strategy.push_back(strategy);

16
tests/ut/cpp/parallel/ops_info/reshape_test.cc
View File

@ -74,7 +74,7 @@ TEST_F(TestReshapeInfo, InferDevMatrixShape1) {
reshape->Init(strategy);
Shape dev_matrix_shape = reshape->dev_matrix_shape();
Shape expect = {4, 8};
Shape expect = {4, 1, 1, 1, 8};
ASSERT_EQ(dev_matrix_shape, expect);
}
@ -85,7 +85,7 @@ TEST_F(TestReshapeInfo, InferDevMatrixShape2) {
reshape->Init(strategy);
Shape dev_matrix_shape = reshape->dev_matrix_shape();
Shape expect = {32};
Shape expect = {32, 1, 1, 1};
ASSERT_EQ(dev_matrix_shape, expect);
}
@ -98,7 +98,7 @@ TEST_F(TestReshapeInfo, InferSliceShape1) {
std::vector<TensorInfo> outputs = reshape->outputs_tensor_info();
Shape input_slice_shape_expect = {8, 512, 7, 7};
Shape output_slice_shape_expect = {8, 25088};
Shape output_slice_shape_expect = {32, 25088};
TensorInfo input_tensor_info = inputs.at(0);
TensorInfo output_tensor_info = outputs.at(0);
@ -119,7 +119,7 @@ TEST_F(TestReshapeInfo, InferSliceShape2) {
std::vector<TensorInfo> outputs = reshape->outputs_tensor_info();
Shape input_slice_shape_expect = {1, 512, 7, 7};
Shape output_slice_shape_expect = {1, 25088};
Shape output_slice_shape_expect = {32, 25088};
TensorInfo input_tensor_info = inputs.at(0);
TensorInfo output_tensor_info = outputs.at(0);
@ -139,8 +139,8 @@ TEST_F(TestReshapeInfo, GetTensorLayout1) {
std::vector<TensorInfo> inputs = reshape->inputs_tensor_info();
std::vector<TensorInfo> outputs = reshape->outputs_tensor_info();
TensorMap input_expect = {1, -1, -1, -1};
TensorMap output_expect = {1, -1};
TensorMap input_expect = {4, 3, 2, 1};
TensorMap output_expect = {-1, -1};
TensorInfo input_tensor_info = inputs.at(0);
TensorInfo output_tensor_info = outputs.at(0);
@ -160,8 +160,8 @@ TEST_F(TestReshapeInfo, GetTensorLayout2) {
std::vector<TensorInfo> inputs = reshape->inputs_tensor_info();
std::vector<TensorInfo> outputs = reshape->outputs_tensor_info();
TensorMap input_expect = {0, -1, -1, -1};
TensorMap output_expect = {0, -1};
TensorMap input_expect = {3, 2, 1, 0};
TensorMap output_expect = {-1, -1};
TensorInfo input_tensor_info = inputs.at(0);
TensorInfo output_tensor_info = outputs.at(0);

32
tests/ut/python/parallel/test_reshape_unexpand.py
View File

@ -74,12 +74,12 @@ def test_reshape_unexpand_1():
def __init__(self):
super().__init__()
self.reshape = P.Reshape()
self.mul = P.Mul().shard(((1, 8), (1, 1, 8)))
self.mul_weight = Parameter(Tensor(np.ones([96, 128]), dtype=ms.float32), name="weight")
self.mul = P.Mul().shard(((1, 1, 8), (1, 8)))
self.mul_weight = Parameter(Tensor(np.ones([128, 96]), dtype=ms.float32), name="weight")
def construct(self, x):
weight = self.reshape(self.mul_weight, (1, 128, 96))
out = self.mul(x, weight)
def construct(self, data):
x = self.reshape(self.mul_weight, (1, 128, 96))
out = self.mul(x, self.mul_weight)
return out
size = 8
@ -236,3 +236,25 @@ def test_reshape_unexpand_7():
net = GradWrap(NetWithLoss(Net()))
net.set_auto_parallel()
_executor.compile(net, x)
def test_reshape_unexpand_8():
class Net(nn.Cell):
def __init__(self):
super().__init__()
self.reshape = P.Reshape()
self.mul = P.Mul().shard(((1, 4, 2), (4, 2)))
self.mul_weight = Parameter(Tensor(np.ones([128, 96]), dtype=ms.float32), name="weight")
def construct(self, data):
x = self.reshape(self.mul_weight, (1, 128, 96))
out = self.mul(x, self.mul_weight)
return out
size = 8
context.set_auto_parallel_context(device_num=size, global_rank=0)
x = Tensor(np.ones([128, 96]), dtype=ms.float32)
net = GradWrap(NetWithLoss(Net()))
context.set_auto_parallel_context(parallel_mode="auto_parallel")
net.set_auto_parallel()
_executor.compile(net, x)