m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge pull request #7624 from tonyyang-svail/7450

Browse Source 
Make merge and split support lodtensor
add_depthwiseConv_op_gpu
Yu Yang 7 years ago committed by GitHub
parent 259fcc3026 d002f60af9
commit 47622d7f5c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 111 additions and 22 deletions
Show Stats Download Patch File Download Diff File

68
paddle/framework/lod_tensor.cc
Unescape View File

@ -286,18 +286,18 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor,
DeserializeFromStream(is, static_cast<Tensor *>(tensor), dev_ctx);
}
// TODO(tonyyang-svail): make this function support LoD
std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
const std::vector<platform::Place> places) const {
check_memory_size();
PADDLE_ENFORCE(lod().empty(), "Disable parallel lod for now");
size_t result_size = std::min(static_cast<size_t>(dims()[0]), places.size());
size_t remainder = dims()[0] % places.size();
int batch_size =
lod().empty() ? dims()[0] : static_cast<int>(lod()[0].size()) - 1;
size_t result_size = std::min(static_cast<size_t>(batch_size), places.size());
size_t remainder = batch_size % places.size();
std::vector<LoDTensor> results;
results.reserve(result_size);
int step_width = static_cast<int>(dims()[0] / result_size);
int step_width = static_cast<int>(batch_size / result_size);
for (size_t i = 0; i < result_size; ++i) {
int begin = static_cast<int>(i * step_width);
int end = static_cast<int>((i + 1) * step_width);
@ -305,13 +305,28 @@ std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
end += remainder;
}
auto src = Slice(begin, end);
auto &dst_place = places[i];
LoDTensor dst;
if (!(dst_place == place())) {
if (lod().empty()) {
auto src = Slice(begin, end);
auto &dst_place = places[i];
framework::Copy(src, dst_place, &dst);
} else { // It is no need to copy if src_place and dst_place are same.
dst.ShareDataWith(src);
} else {
auto lod_and_offset = GetSubLoDAndAbsoluteOffset(lod(), begin, end, 0);
auto &offset = lod_and_offset.second;
auto src = Slice(offset.first, offset.second);
auto &dst_place = places[i];
framework::Copy(src, dst_place, &dst);
LoD my_lod;
for (auto &l : lod_and_offset.first) {
std::vector<size_t> v{0};
for (auto &ll : l) {
v.push_back(ll + v.back());
}
my_lod.emplace_back(v);
}
dst.set_lod(my_lod);
}
results.emplace_back(dst);
}
@ -319,29 +334,38 @@ std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
return results;
}
// TODO(tonyyang-svail): make this function support LoD
void LoDTensor::MergeLoDTensor(
const std::vector<const LoDTensor *> &lod_tensors,
platform::Place dst_place) {
PADDLE_ENFORCE(!lod_tensors.empty());
framework::DDim new_dim = lod_tensors[0]->dims();
std::type_index new_type = lod_tensors[0]->type();
auto new_layout = lod_tensors[0]->layout();
int64_t new_height = 0;
for (auto *lod : lod_tensors) {
new_height += lod->dims()[0];
for (int i = 1; i < new_dim.size(); ++i) {
PADDLE_ENFORCE_EQ(new_dim[i], lod->dims()[i]);
framework::DataLayout new_layout = lod_tensors[0]->layout();
LoD new_lod = lod_tensors[0]->lod();
for (size_t i = 1; i < lod_tensors.size(); ++i) {
auto *t = lod_tensors[i];
PADDLE_ENFORCE_EQ(new_type.hash_code(), t->type().hash_code());
PADDLE_ENFORCE_EQ(new_layout, t->layout());
PADDLE_ENFORCE_EQ(framework::product(new_dim) / new_dim[0],
framework::product(t->dims()) / t->dims()[0]);
new_dim[0] += t->dims()[0];
auto &lod = t->lod();
for (size_t j = 0; j < lod.size(); ++j) {
auto &sub_lod = new_lod[j];
auto &offset = sub_lod.back();
for (size_t k = 1; k < lod[j].size(); ++k) {
sub_lod.push_back(lod[j][k] + offset);
}
}
PADDLE_ENFORCE_EQ(new_type, lod->type());
PADDLE_ENFORCE_EQ(new_layout, lod->layout());
}
new_dim[0] = new_height;
Resize(new_dim);
set_layout(new_layout);
set_lod(new_lod);
mutable_data(dst_place, new_type);
int begin = 0;
for (auto *src : lod_tensors) {
int end = begin + src->dims()[0];

65
paddle/framework/lod_tensor_test.cc
Unescape View File

@ -100,6 +100,71 @@ TEST(LoD, ToAbsOffset) {
EXPECT_EQ(abs_lod, expected);
}
TEST(LoD, SplitLoDTensor) {
LoD lod;
lod.push_back(std::vector<size_t>({0, 2, 4, 5, 6}));
lod.push_back(std::vector<size_t>({0, 1, 6, 8, 13, 15, 20}));
platform::CPUPlace place;
LoDTensor lod_tensor;
lod_tensor.Resize({20, 1});
float* dst_ptr = lod_tensor.mutable_data<float>(place);
for (int i = 0; i < lod_tensor.numel(); ++i) {
dst_ptr[i] = i;
}
lod_tensor.set_lod(lod);
std::vector<platform::Place> places{platform::CPUPlace(),
platform::CPUPlace()};
LoD lod0;
lod0.push_back(std::vector<size_t>({0, 2, 4}));
lod0.push_back(std::vector<size_t>({0, 1, 6, 8, 13}));
LoD lod1;
lod1.push_back(std::vector<size_t>({0, 1, 2}));
lod1.push_back(std::vector<size_t>({0, 2, 7}));
auto lods = lod_tensor.SplitLoDTensor(places);
EXPECT_EQ(lods[0].lod(), lod0);
EXPECT_EQ(lods[1].lod(), lod1);
}
TEST(LoD, MergeLoDTensor) {
LoD lod;
lod.push_back(std::vector<size_t>({0, 2, 4, 5, 6}));
lod.push_back(std::vector<size_t>({0, 1, 6, 8, 13, 15, 20}));
platform::CPUPlace place;
LoDTensor lod_tensor0;
LoD lod0;
lod0.push_back(std::vector<size_t>({0, 2, 4}));
lod0.push_back(std::vector<size_t>({0, 1, 6, 8, 13}));
lod_tensor0.set_lod(lod0);
lod_tensor0.Resize({13, 1});
float* dst_ptr = lod_tensor0.mutable_data<float>(place);
for (int i = 0; i < lod_tensor0.numel(); ++i) {
dst_ptr[i] = i;
}
LoDTensor lod_tensor1;
LoD lod1;
lod1.push_back(std::vector<size_t>({0, 1, 2}));
lod1.push_back(std::vector<size_t>({0, 2, 7}));
lod_tensor1.set_lod(lod1);
lod_tensor1.Resize({7, 1});
dst_ptr = lod_tensor1.mutable_data<float>(place);
for (int i = 0; i < lod_tensor1.numel(); ++i) {
dst_ptr[i] = i;
}
std::vector<const LoDTensor*> lods{&lod_tensor0, &lod_tensor1};
LoDTensor lod_tensor;
lod_tensor.MergeLoDTensor(lods, place);
EXPECT_EQ(lod_tensor.lod(), lod);
}
TEST(LoD, CheckLoD) {
LoD relative_lod;
relative_lod.push_back(std::vector<size_t>({0, 2}));

Write Preview
Loading…
Cancel
Save