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Unverified Commit cded5b80 authored by Xin Yao's avatar Xin Yao Committed by GitHub
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[Feature] Bump DLPack to v0.7 and decouple DLPack from the core library (#4454) 

* rename `DLContext` to `DGLContext`

* rename `kDLGPU` to `kDLCUDA`

* replace DLTensor with DGLArray

* fix linting

* Unify DGLType and DLDataType to DGLDataType

* Fix FFI

* rename DLDeviceType to DGLDeviceType

* decouple dlpack from the core library

* fix bug

* fix lint

* fix merge

* fix build

* address comments

* rename dl_converter to dlpack_convert

* remove redundant comments
parent f1689ad0
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Showing with 125 additions and 154 deletions
src/graph/sampling/randomwalks/node2vec_randomwalk.h
View file @ cded5b80
...@@ -65,7 +65,7 @@ bool has_edge_between(const CSRMatrix &csr, dgl_id_t u, ...@@ -65,7 +65,7 @@ bool has_edge_between(const CSRMatrix &csr, dgl_id_t u,
* as well as whether to terminate. * as well as whether to terminate.
*/ */
template <DLDeviceType XPU, typename IdxType> template <DGLDeviceType XPU, typename IdxType>
std::tuple<dgl_id_t, dgl_id_t, bool> Node2vecRandomWalkStep( std::tuple<dgl_id_t, dgl_id_t, bool> Node2vecRandomWalkStep(
IdxType *data, dgl_id_t curr, dgl_id_t pre, const double p, const double q, IdxType *data, dgl_id_t curr, dgl_id_t pre, const double p, const double q,
int64_t len, const CSRMatrix &csr, bool csr_has_data, const FloatArray &probs, int64_t len, const CSRMatrix &csr, bool csr_has_data, const FloatArray &probs,
...@@ -146,7 +146,7 @@ std::tuple<dgl_id_t, dgl_id_t, bool> Node2vecRandomWalkStep( ...@@ -146,7 +146,7 @@ std::tuple<dgl_id_t, dgl_id_t, bool> Node2vecRandomWalkStep(
return std::make_tuple(next_node, eid, terminate(data, next_node, len)); return std::make_tuple(next_node, eid, terminate(data, next_node, len));
} }
template <DLDeviceType XPU, typename IdxType> template <DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> Node2vecRandomWalk( std::pair<IdArray, IdArray> Node2vecRandomWalk(
const HeteroGraphPtr g, const IdArray seeds, const HeteroGraphPtr g, const IdArray seeds,
const double p, const double q, const double p, const double q,
......
src/graph/sampling/randomwalks/randomwalk_cpu.cc
View file @ cded5b80
...@@ -23,7 +23,7 @@ namespace sampling { ...@@ -23,7 +23,7 @@ namespace sampling {
namespace impl { namespace impl {
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> RandomWalk( std::pair<IdArray, IdArray> RandomWalk(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -37,13 +37,13 @@ std::pair<IdArray, IdArray> RandomWalk( ...@@ -37,13 +37,13 @@ std::pair<IdArray, IdArray> RandomWalk(
return MetapathBasedRandomWalk<XPU, IdxType>(hg, seeds, metapath, prob, terminate); return MetapathBasedRandomWalk<XPU, IdxType>(hg, seeds, metapath, prob, terminate);
} }
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors( std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors(
const IdArray src, const IdArray src,
const IdArray dst, const IdArray dst,
const int64_t num_samples_per_node, const int64_t num_samples_per_node,
const int64_t k) { const int64_t k) {
CHECK(src->ctx.device_type == kDLCPU) << "IdArray needs be on CPU!"; CHECK(src->ctx.device_type == kDGLCPU) << "IdArray needs be on CPU!";
int64_t len = src->shape[0] / num_samples_per_node; int64_t len = src->shape[0] / num_samples_per_node;
IdxType* src_data = src.Ptr<IdxType>(); IdxType* src_data = src.Ptr<IdxType>();
const IdxType* dst_data = dst.Ptr<IdxType>(); const IdxType* dst_data = dst.Ptr<IdxType>();
...@@ -90,43 +90,43 @@ std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors( ...@@ -90,43 +90,43 @@ std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors(
device->CopyDataFromTo(static_cast<IdxType*>(res_src_vec.data()), 0, device->CopyDataFromTo(static_cast<IdxType*>(res_src_vec.data()), 0,
res_src.Ptr<IdxType>(), 0, res_src.Ptr<IdxType>(), 0,
sizeof(IdxType) * res_src_vec.size(), sizeof(IdxType) * res_src_vec.size(),
DGLContext{kDLCPU, 0}, res_src->ctx, DGLContext{kDGLCPU, 0}, res_src->ctx,
res_src->dtype); res_src->dtype);
device->CopyDataFromTo(static_cast<IdxType*>(res_dst_vec.data()), 0, device->CopyDataFromTo(static_cast<IdxType*>(res_dst_vec.data()), 0,
res_dst.Ptr<IdxType>(), 0, res_dst.Ptr<IdxType>(), 0,
sizeof(IdxType) * res_dst_vec.size(), sizeof(IdxType) * res_dst_vec.size(),
DGLContext{kDLCPU, 0}, res_dst->ctx, DGLContext{kDGLCPU, 0}, res_dst->ctx,
res_dst->dtype); res_dst->dtype);
device->CopyDataFromTo(static_cast<IdxType*>(res_cnt_vec.data()), 0, device->CopyDataFromTo(static_cast<IdxType*>(res_cnt_vec.data()), 0,
res_cnt.Ptr<IdxType>(), 0, res_cnt.Ptr<IdxType>(), 0,
sizeof(IdxType) * res_cnt_vec.size(), sizeof(IdxType) * res_cnt_vec.size(),
DGLContext{kDLCPU, 0}, res_cnt->ctx, DGLContext{kDGLCPU, 0}, res_cnt->ctx,
res_cnt->dtype); res_cnt->dtype);
return std::make_tuple(res_src, res_dst, res_cnt); return std::make_tuple(res_src, res_dst, res_cnt);
} }
template template
std::pair<IdArray, IdArray> RandomWalk<kDLCPU, int32_t>( std::pair<IdArray, IdArray> RandomWalk<kDGLCPU, int32_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob); const std::vector<FloatArray> &prob);
template template
std::pair<IdArray, IdArray> RandomWalk<kDLCPU, int64_t>( std::pair<IdArray, IdArray> RandomWalk<kDGLCPU, int64_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob); const std::vector<FloatArray> &prob);
template template
std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDLCPU, int32_t>( std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDGLCPU, int32_t>(
const IdArray src, const IdArray src,
const IdArray dst, const IdArray dst,
const int64_t num_samples_per_node, const int64_t num_samples_per_node,
const int64_t k); const int64_t k);
template template
std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDLCPU, int64_t>( std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDGLCPU, int64_t>(
const IdArray src, const IdArray src,
const IdArray dst, const IdArray dst,
const int64_t num_samples_per_node, const int64_t num_samples_per_node,
......
src/graph/sampling/randomwalks/randomwalk_gpu.cu
View file @ cded5b80
...@@ -168,7 +168,7 @@ __global__ void _RandomWalkBiasedKernel( ...@@ -168,7 +168,7 @@ __global__ void _RandomWalkBiasedKernel(
} // namespace } // namespace
// random walk for uniform choice // random walk for uniform choice
template<DLDeviceType XPU, typename IdType> template<DGLDeviceType XPU, typename IdType>
std::pair<IdArray, IdArray> RandomWalkUniform( std::pair<IdArray, IdArray> RandomWalkUniform(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -205,7 +205,7 @@ std::pair<IdArray, IdArray> RandomWalkUniform( ...@@ -205,7 +205,7 @@ std::pair<IdArray, IdArray> RandomWalkUniform(
// copy graph metadata pointers to GPU // copy graph metadata pointers to GPU
device->CopyDataFromTo(h_graphs.data(), 0, d_graphs, 0, device->CopyDataFromTo(h_graphs.data(), 0, d_graphs, 0,
(num_etypes) * sizeof(GraphKernelData<IdType>), (num_etypes) * sizeof(GraphKernelData<IdType>),
DGLContext{kDLCPU, 0}, DGLContext{kDGLCPU, 0},
ctx, ctx,
hg->GetCSRMatrix(0).indptr->dtype); hg->GetCSRMatrix(0).indptr->dtype);
// copy metapath to GPU // copy metapath to GPU
...@@ -218,7 +218,7 @@ std::pair<IdArray, IdArray> RandomWalkUniform( ...@@ -218,7 +218,7 @@ std::pair<IdArray, IdArray> RandomWalkUniform(
dim3 grid((num_seeds + TILE_SIZE - 1) / TILE_SIZE); dim3 grid((num_seeds + TILE_SIZE - 1) / TILE_SIZE);
const uint64_t random_seed = RandomEngine::ThreadLocal()->RandInt(1000000000); const uint64_t random_seed = RandomEngine::ThreadLocal()->RandInt(1000000000);
ATEN_FLOAT_TYPE_SWITCH(restart_prob->dtype, FloatType, "random walk GPU kernel", { ATEN_FLOAT_TYPE_SWITCH(restart_prob->dtype, FloatType, "random walk GPU kernel", {
CHECK(restart_prob->ctx.device_type == kDLGPU) << "restart prob should be in GPU."; CHECK(restart_prob->ctx.device_type == kDGLCUDA) << "restart prob should be in GPU.";
CHECK(restart_prob->ndim == 1) << "restart prob dimension should be 1."; CHECK(restart_prob->ndim == 1) << "restart prob dimension should be 1.";
const FloatType *restart_prob_data = restart_prob.Ptr<FloatType>(); const FloatType *restart_prob_data = restart_prob.Ptr<FloatType>();
const int64_t restart_prob_size = restart_prob->shape[0]; const int64_t restart_prob_size = restart_prob->shape[0];
...@@ -246,7 +246,7 @@ std::pair<IdArray, IdArray> RandomWalkUniform( ...@@ -246,7 +246,7 @@ std::pair<IdArray, IdArray> RandomWalkUniform(
* \brief Random walk for biased choice. We use inverse transform sampling to * \brief Random walk for biased choice. We use inverse transform sampling to
* choose the next step. * choose the next step.
*/ */
template <DLDeviceType XPU, typename FloatType, typename IdType> template <DGLDeviceType XPU, typename FloatType, typename IdType>
std::pair<IdArray, IdArray> RandomWalkBiased( std::pair<IdArray, IdArray> RandomWalkBiased(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -321,7 +321,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased( ...@@ -321,7 +321,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased(
device->AllocWorkspace(ctx, (num_etypes) * sizeof(GraphKernelData<IdType>))); device->AllocWorkspace(ctx, (num_etypes) * sizeof(GraphKernelData<IdType>)));
device->CopyDataFromTo(h_graphs.data(), 0, d_graphs, 0, device->CopyDataFromTo(h_graphs.data(), 0, d_graphs, 0,
(num_etypes) * sizeof(GraphKernelData<IdType>), (num_etypes) * sizeof(GraphKernelData<IdType>),
DGLContext{kDLCPU, 0}, DGLContext{kDGLCPU, 0},
ctx, ctx,
hg->GetCSRMatrix(0).indptr->dtype); hg->GetCSRMatrix(0).indptr->dtype);
// copy probs pointers to GPU // copy probs pointers to GPU
...@@ -329,7 +329,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased( ...@@ -329,7 +329,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased(
device->AllocWorkspace(ctx, num_etypes * sizeof(FloatType *))); device->AllocWorkspace(ctx, num_etypes * sizeof(FloatType *)));
device->CopyDataFromTo(probs.get(), 0, probs_dev, 0, device->CopyDataFromTo(probs.get(), 0, probs_dev, 0,
(num_etypes) * sizeof(FloatType *), (num_etypes) * sizeof(FloatType *),
DGLContext{kDLCPU, 0}, DGLContext{kDGLCPU, 0},
ctx, ctx,
prob[0]->dtype); prob[0]->dtype);
// copy probs_sum pointers to GPU // copy probs_sum pointers to GPU
...@@ -337,7 +337,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased( ...@@ -337,7 +337,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased(
device->AllocWorkspace(ctx, num_etypes * sizeof(FloatType *))); device->AllocWorkspace(ctx, num_etypes * sizeof(FloatType *)));
device->CopyDataFromTo(prob_sums.get(), 0, prob_sums_dev, 0, device->CopyDataFromTo(prob_sums.get(), 0, prob_sums_dev, 0,
(num_etypes) * sizeof(FloatType *), (num_etypes) * sizeof(FloatType *),
DGLContext{kDLCPU, 0}, DGLContext{kDGLCPU, 0},
ctx, ctx,
prob[0]->dtype); prob[0]->dtype);
// copy metapath to GPU // copy metapath to GPU
...@@ -349,7 +349,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased( ...@@ -349,7 +349,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased(
dim3 block(256); dim3 block(256);
dim3 grid((num_seeds + TILE_SIZE - 1) / TILE_SIZE); dim3 grid((num_seeds + TILE_SIZE - 1) / TILE_SIZE);
const uint64_t random_seed = RandomEngine::ThreadLocal()->RandInt(1000000000); const uint64_t random_seed = RandomEngine::ThreadLocal()->RandInt(1000000000);
CHECK(restart_prob->ctx.device_type == kDLGPU) << "restart prob should be in GPU."; CHECK(restart_prob->ctx.device_type == kDGLCUDA) << "restart prob should be in GPU.";
CHECK(restart_prob->ndim == 1) << "restart prob dimension should be 1."; CHECK(restart_prob->ndim == 1) << "restart prob dimension should be 1.";
const FloatType *restart_prob_data = restart_prob.Ptr<FloatType>(); const FloatType *restart_prob_data = restart_prob.Ptr<FloatType>();
const int64_t restart_prob_size = restart_prob->shape[0]; const int64_t restart_prob_size = restart_prob->shape[0];
...@@ -376,7 +376,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased( ...@@ -376,7 +376,7 @@ std::pair<IdArray, IdArray> RandomWalkBiased(
return std::make_pair(traces, eids); return std::make_pair(traces, eids);
} }
template<DLDeviceType XPU, typename IdType> template<DGLDeviceType XPU, typename IdType>
std::pair<IdArray, IdArray> RandomWalk( std::pair<IdArray, IdArray> RandomWalk(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -392,7 +392,7 @@ std::pair<IdArray, IdArray> RandomWalk( ...@@ -392,7 +392,7 @@ std::pair<IdArray, IdArray> RandomWalk(
} }
auto restart_prob = NDArray::Empty( auto restart_prob = NDArray::Empty(
{0}, DLDataType{kDLFloat, 32, 1}, DGLContext{XPU, 0}); {0}, DGLDataType{kDGLFloat, 32, 1}, DGLContext{XPU, 0});
if (!isUniform) { if (!isUniform) {
std::pair<IdArray, IdArray> ret; std::pair<IdArray, IdArray> ret;
ATEN_FLOAT_TYPE_SWITCH(prob[0]->dtype, FloatType, "probability", { ATEN_FLOAT_TYPE_SWITCH(prob[0]->dtype, FloatType, "probability", {
...@@ -404,7 +404,7 @@ std::pair<IdArray, IdArray> RandomWalk( ...@@ -404,7 +404,7 @@ std::pair<IdArray, IdArray> RandomWalk(
} }
} }
template<DLDeviceType XPU, typename IdType> template<DGLDeviceType XPU, typename IdType>
std::pair<IdArray, IdArray> RandomWalkWithRestart( std::pair<IdArray, IdArray> RandomWalkWithRestart(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -422,7 +422,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart( ...@@ -422,7 +422,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart(
auto device_ctx = seeds->ctx; auto device_ctx = seeds->ctx;
auto restart_prob_array = NDArray::Empty( auto restart_prob_array = NDArray::Empty(
{1}, DLDataType{kDLFloat, 64, 1}, device_ctx); {1}, DGLDataType{kDGLFloat, 64, 1}, device_ctx);
auto device = dgl::runtime::DeviceAPI::Get(device_ctx); auto device = dgl::runtime::DeviceAPI::Get(device_ctx);
// use cuda stream from local thread // use cuda stream from local thread
...@@ -430,7 +430,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart( ...@@ -430,7 +430,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart(
device->CopyDataFromTo( device->CopyDataFromTo(
&restart_prob, 0, restart_prob_array.Ptr<double>(), 0, &restart_prob, 0, restart_prob_array.Ptr<double>(), 0,
sizeof(double), sizeof(double),
DGLContext{kDLCPU, 0}, device_ctx, DGLContext{kDGLCPU, 0}, device_ctx,
restart_prob_array->dtype); restart_prob_array->dtype);
device->StreamSync(device_ctx, stream); device->StreamSync(device_ctx, stream);
...@@ -446,7 +446,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart( ...@@ -446,7 +446,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart(
} }
} }
template<DLDeviceType XPU, typename IdType> template<DGLDeviceType XPU, typename IdType>
std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart( std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -473,13 +473,13 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart( ...@@ -473,13 +473,13 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart(
} }
} }
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors( std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors(
const IdArray src, const IdArray src,
const IdArray dst, const IdArray dst,
const int64_t num_samples_per_node, const int64_t num_samples_per_node,
const int64_t k) { const int64_t k) {
CHECK(src->ctx.device_type == kDLGPU) << CHECK(src->ctx.device_type == kDGLCUDA) <<
"IdArray needs be on GPU!"; "IdArray needs be on GPU!";
const IdxType* src_data = src.Ptr<IdxType>(); const IdxType* src_data = src.Ptr<IdxType>();
const IdxType* dst_data = dst.Ptr<IdxType>(); const IdxType* dst_data = dst.Ptr<IdxType>();
...@@ -495,27 +495,27 @@ std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors( ...@@ -495,27 +495,27 @@ std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors(
} }
template template
std::pair<IdArray, IdArray> RandomWalk<kDLGPU, int32_t>( std::pair<IdArray, IdArray> RandomWalk<kDGLCUDA, int32_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob); const std::vector<FloatArray> &prob);
template template
std::pair<IdArray, IdArray> RandomWalk<kDLGPU, int64_t>( std::pair<IdArray, IdArray> RandomWalk<kDGLCUDA, int64_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob); const std::vector<FloatArray> &prob);
template template
std::pair<IdArray, IdArray> RandomWalkWithRestart<kDLGPU, int32_t>( std::pair<IdArray, IdArray> RandomWalkWithRestart<kDGLCUDA, int32_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob, const std::vector<FloatArray> &prob,
double restart_prob); double restart_prob);
template template
std::pair<IdArray, IdArray> RandomWalkWithRestart<kDLGPU, int64_t>( std::pair<IdArray, IdArray> RandomWalkWithRestart<kDGLCUDA, int64_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
...@@ -523,14 +523,14 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart<kDLGPU, int64_t>( ...@@ -523,14 +523,14 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart<kDLGPU, int64_t>(
double restart_prob); double restart_prob);
template template
std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDLGPU, int32_t>( std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDGLCUDA, int32_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob, const std::vector<FloatArray> &prob,
FloatArray restart_prob); FloatArray restart_prob);
template template
std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDLGPU, int64_t>( std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDGLCUDA, int64_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
...@@ -538,13 +538,13 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDLGPU, int64_t>( ...@@ -538,13 +538,13 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDLGPU, int64_t>(
FloatArray restart_prob); FloatArray restart_prob);
template template
std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDLGPU, int32_t>( std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDGLCUDA, int32_t>(
const IdArray src, const IdArray src,
const IdArray dst, const IdArray dst,
const int64_t num_samples_per_node, const int64_t num_samples_per_node,
const int64_t k); const int64_t k);
template template
std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDLGPU, int64_t>( std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors<kDGLCUDA, int64_t>(
const IdArray src, const IdArray src,
const IdArray dst, const IdArray dst,
const int64_t num_samples_per_node, const int64_t num_samples_per_node,
......
src/graph/sampling/randomwalks/randomwalk_with_restart_cpu.cc
View file @ cded5b80
...@@ -22,7 +22,7 @@ namespace sampling { ...@@ -22,7 +22,7 @@ namespace sampling {
namespace impl { namespace impl {
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> RandomWalkWithRestart( std::pair<IdArray, IdArray> RandomWalkWithRestart(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -37,21 +37,21 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart( ...@@ -37,21 +37,21 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart(
} }
template template
std::pair<IdArray, IdArray> RandomWalkWithRestart<kDLCPU, int32_t>( std::pair<IdArray, IdArray> RandomWalkWithRestart<kDGLCPU, int32_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob, const std::vector<FloatArray> &prob,
double restart_prob); double restart_prob);
template template
std::pair<IdArray, IdArray> RandomWalkWithRestart<kDLCPU, int64_t>( std::pair<IdArray, IdArray> RandomWalkWithRestart<kDGLCPU, int64_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob, const std::vector<FloatArray> &prob,
double restart_prob); double restart_prob);
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart( std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -73,14 +73,14 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart( ...@@ -73,14 +73,14 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart(
} }
template template
std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDLCPU, int32_t>( std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDGLCPU, int32_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
const std::vector<FloatArray> &prob, const std::vector<FloatArray> &prob,
FloatArray restart_prob); FloatArray restart_prob);
template template
std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDLCPU, int64_t>( std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart<kDGLCPU, int64_t>(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
const TypeArray metapath, const TypeArray metapath,
......
src/graph/sampling/randomwalks/randomwalks.cc
View file @ cded5b80
...@@ -36,7 +36,7 @@ void CheckRandomWalkInputs( ...@@ -36,7 +36,7 @@ void CheckRandomWalkInputs(
// CHECK_SAME_CONTEXT(seeds, metapath); // CHECK_SAME_CONTEXT(seeds, metapath);
if (hg->IsPinned()) { if (hg->IsPinned()) {
CHECK_EQ(seeds->ctx.device_type, kDLGPU) << "Expected seeds (" << seeds->ctx << ")" \ CHECK_EQ(seeds->ctx.device_type, kDGLCUDA) << "Expected seeds (" << seeds->ctx << ")" \
<< " to be on the GPU when the graph is pinned."; << " to be on the GPU when the graph is pinned.";
} else if (hg->Context() != seeds->ctx) { } else if (hg->Context() != seeds->ctx) {
LOG(FATAL) << "Expected seeds (" << seeds->ctx << ")" << " to have the same " \ LOG(FATAL) << "Expected seeds (" << seeds->ctx << ")" << " to have the same " \
......
src/graph/sampling/randomwalks/randomwalks_cpu.h
View file @ cded5b80
...@@ -35,7 +35,7 @@ namespace { ...@@ -35,7 +35,7 @@ namespace {
* \return A 2D array of shape (len(seeds), max_num_steps + 1) with node IDs. * \return A 2D array of shape (len(seeds), max_num_steps + 1) with node IDs.
* \note The graph itself should be bounded in the closure of \c step. * \note The graph itself should be bounded in the closure of \c step.
*/ */
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> GenericRandomWalk( std::pair<IdArray, IdArray> GenericRandomWalk(
const IdArray seeds, const IdArray seeds,
int64_t max_num_steps, int64_t max_num_steps,
......
src/graph/sampling/randomwalks/randomwalks_impl.h
View file @ cded5b80
...@@ -38,7 +38,7 @@ using StepFunc = std::function< ...@@ -38,7 +38,7 @@ using StepFunc = std::function<
* \brief Get the node types traversed by the metapath. * \brief Get the node types traversed by the metapath.
* \return A 1D array of shape (len(metapath) + 1,) with node type IDs. * \return A 1D array of shape (len(metapath) + 1,) with node type IDs.
*/ */
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
TypeArray GetNodeTypesFromMetapath( TypeArray GetNodeTypesFromMetapath(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const TypeArray metapath); const TypeArray metapath);
...@@ -58,7 +58,7 @@ TypeArray GetNodeTypesFromMetapath( ...@@ -58,7 +58,7 @@ TypeArray GetNodeTypesFromMetapath(
* \note This function should be called together with GetNodeTypesFromMetapath to * \note This function should be called together with GetNodeTypesFromMetapath to
* determine the node type of each node in the random walk traces. * determine the node type of each node in the random walk traces.
*/ */
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> RandomWalk( std::pair<IdArray, IdArray> RandomWalk(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -81,7 +81,7 @@ std::pair<IdArray, IdArray> RandomWalk( ...@@ -81,7 +81,7 @@ std::pair<IdArray, IdArray> RandomWalk(
* \note This function should be called together with GetNodeTypesFromMetapath to * \note This function should be called together with GetNodeTypesFromMetapath to
* determine the node type of each node in the random walk traces. * determine the node type of each node in the random walk traces.
*/ */
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> RandomWalkWithRestart( std::pair<IdArray, IdArray> RandomWalkWithRestart(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -107,7 +107,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart( ...@@ -107,7 +107,7 @@ std::pair<IdArray, IdArray> RandomWalkWithRestart(
* \note This function should be called together with GetNodeTypesFromMetapath to * \note This function should be called together with GetNodeTypesFromMetapath to
* determine the node type of each node in the random walk traces. * determine the node type of each node in the random walk traces.
*/ */
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart( std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart(
const HeteroGraphPtr hg, const HeteroGraphPtr hg,
const IdArray seeds, const IdArray seeds,
...@@ -115,7 +115,7 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart( ...@@ -115,7 +115,7 @@ std::pair<IdArray, IdArray> RandomWalkWithStepwiseRestart(
const std::vector<FloatArray> &prob, const std::vector<FloatArray> &prob,
FloatArray restart_prob); FloatArray restart_prob);
template<DLDeviceType XPU, typename IdxType> template<DGLDeviceType XPU, typename IdxType>
std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors( std::tuple<IdArray, IdArray, IdArray> SelectPinSageNeighbors(
const IdArray src, const IdArray src,
const IdArray dst, const IdArray dst,
......
src/graph/serialize/zerocopy_serializer.cc
View file @ cded5b80
/*! /*!
* Copyright (c) 2020 by Contributors * Copyright (c) 2020-2022 by Contributors
* \file graph/serailize/zerocopy_serializer.cc * \file graph/serailize/zerocopy_serializer.cc
* \brief serializer implementation. * \brief serializer implementation.
*/ */
...@@ -13,38 +13,9 @@ namespace dgl { ...@@ -13,38 +13,9 @@ namespace dgl {
using dgl::runtime::NDArray; using dgl::runtime::NDArray;
struct RawDataTensorCtx { NDArray CreateNDArrayFromRawData(std::vector<int64_t> shape, DGLDataType dtype,
std::vector<int64_t> shape; DGLContext ctx, void* raw) {
std::vector<int64_t> stride; return NDArray::CreateFromRaw(shape, dtype, ctx, raw, true);
DLManagedTensor tensor;
};
void RawDataTensoDLPackDeleter(DLManagedTensor* tensor) {
auto ctx = static_cast<RawDataTensorCtx*>(tensor->manager_ctx);
delete[] ctx->tensor.dl_tensor.data;
delete ctx;
}
NDArray CreateNDArrayFromRawData(std::vector<int64_t> shape, DLDataType dtype,
DLContext ctx, void* raw) {
auto dlm_tensor_ctx = new RawDataTensorCtx();
DLManagedTensor* dlm_tensor = &dlm_tensor_ctx->tensor;
dlm_tensor_ctx->shape = shape;
dlm_tensor->manager_ctx = dlm_tensor_ctx;
dlm_tensor->dl_tensor.shape = dmlc::BeginPtr(dlm_tensor_ctx->shape);
dlm_tensor->dl_tensor.ctx = ctx;
dlm_tensor->dl_tensor.ndim = static_cast<int>(shape.size());
dlm_tensor->dl_tensor.dtype = dtype;
dlm_tensor_ctx->stride.resize(dlm_tensor->dl_tensor.ndim, 1);
for (int i = dlm_tensor->dl_tensor.ndim - 2; i >= 0; --i) {
dlm_tensor_ctx->stride[i] =
dlm_tensor_ctx->shape[i + 1] * dlm_tensor_ctx->stride[i + 1];
}
dlm_tensor->dl_tensor.strides = dmlc::BeginPtr(dlm_tensor_ctx->stride);
dlm_tensor->dl_tensor.data = raw;
dlm_tensor->deleter = RawDataTensoDLPackDeleter;
return NDArray::FromDLPack(dlm_tensor);
} }
void StreamWithBuffer::PushNDArray(const NDArray& tensor) { void StreamWithBuffer::PushNDArray(const NDArray& tensor) {
...@@ -89,18 +60,18 @@ void StreamWithBuffer::PushNDArray(const NDArray& tensor) { ...@@ -89,18 +60,18 @@ void StreamWithBuffer::PushNDArray(const NDArray& tensor) {
NDArray StreamWithBuffer::PopNDArray() { NDArray StreamWithBuffer::PopNDArray() {
#ifndef _WIN32 #ifndef _WIN32
int ndim; int ndim;
DLDataType dtype; DGLDataType dtype;
CHECK(this->Read(&ndim)) << "Invalid DLTensor file format"; CHECK(this->Read(&ndim)) << "Invalid DGLArray file format";
CHECK(this->Read(&dtype)) << "Invalid DLTensor file format"; CHECK(this->Read(&dtype)) << "Invalid DGLArray file format";
std::vector<int64_t> shape(ndim); std::vector<int64_t> shape(ndim);
if (ndim != 0) { if (ndim != 0) {
CHECK(this->ReadArray(&shape[0], ndim)) << "Invalid DLTensor file format"; CHECK(this->ReadArray(&shape[0], ndim)) << "Invalid DGLArray file format";
} }
DLContext cpu_ctx; DGLContext cpu_ctx;
cpu_ctx.device_type = kDLCPU; cpu_ctx.device_type = kDGLCPU;
cpu_ctx.device_id = 0; cpu_ctx.device_id = 0;
bool is_shared_mem; bool is_shared_mem;
......
src/graph/shared_mem_manager.cc
View file @ cded5b80
...@@ -33,7 +33,7 @@ namespace dgl { ...@@ -33,7 +33,7 @@ namespace dgl {
template <> template <>
NDArray SharedMemManager::CopyToSharedMem<NDArray>(const NDArray &data, NDArray SharedMemManager::CopyToSharedMem<NDArray>(const NDArray &data,
std::string name) { std::string name) {
DLContext ctx = {kDLCPU, 0}; DGLContext ctx = {kDGLCPU, 0};
std::vector<int64_t> shape(data->shape, data->shape + data->ndim); std::vector<int64_t> shape(data->shape, data->shape + data->ndim);
strm_->Write(data->ndim); strm_->Write(data->ndim);
strm_->Write(data->dtype); strm_->Write(data->dtype);
...@@ -83,15 +83,15 @@ template <> ...@@ -83,15 +83,15 @@ template <>
bool SharedMemManager::CreateFromSharedMem<NDArray>(NDArray *nd, bool SharedMemManager::CreateFromSharedMem<NDArray>(NDArray *nd,
std::string name) { std::string name) {
int ndim; int ndim;
DLContext ctx = {kDLCPU, 0}; DGLContext ctx = {kDGLCPU, 0};
DLDataType dtype; DGLDataType dtype;
CHECK(this->Read(&ndim)) << "Invalid DLTensor file format"; CHECK(this->Read(&ndim)) << "Invalid DGLArray file format";
CHECK(this->Read(&dtype)) << "Invalid DLTensor file format"; CHECK(this->Read(&dtype)) << "Invalid DGLArray file format";
std::vector<int64_t> shape(ndim); std::vector<int64_t> shape(ndim);
if (ndim != 0) { if (ndim != 0) {
CHECK(this->ReadArray(&shape[0], ndim)) << "Invalid DLTensor file format"; CHECK(this->ReadArray(&shape[0], ndim)) << "Invalid DGLArray file format";
} }
bool is_null; bool is_null;
this->Read(&is_null); this->Read(&is_null);
......
src/graph/subgraph.cc
View file @ cded5b80
...@@ -13,7 +13,7 @@ HeteroSubgraph InEdgeGraphRelabelNodes( ...@@ -13,7 +13,7 @@ HeteroSubgraph InEdgeGraphRelabelNodes(
CHECK_EQ(vids.size(), graph->NumVertexTypes()) CHECK_EQ(vids.size(), graph->NumVertexTypes())
<< "Invalid input: the input list size must be the same as the number of vertex types."; << "Invalid input: the input list size must be the same as the number of vertex types.";
std::vector<IdArray> eids(graph->NumEdgeTypes()); std::vector<IdArray> eids(graph->NumEdgeTypes());
DLContext ctx = aten::GetContextOf(vids); DGLContext ctx = aten::GetContextOf(vids);
for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) { for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
auto pair = graph->meta_graph()->FindEdge(etype); auto pair = graph->meta_graph()->FindEdge(etype);
const dgl_type_t dst_vtype = pair.second; const dgl_type_t dst_vtype = pair.second;
...@@ -34,7 +34,7 @@ HeteroSubgraph InEdgeGraphNoRelabelNodes( ...@@ -34,7 +34,7 @@ HeteroSubgraph InEdgeGraphNoRelabelNodes(
<< "Invalid input: the input list size must be the same as the number of vertex types."; << "Invalid input: the input list size must be the same as the number of vertex types.";
std::vector<HeteroGraphPtr> subrels(graph->NumEdgeTypes()); std::vector<HeteroGraphPtr> subrels(graph->NumEdgeTypes());
std::vector<IdArray> induced_edges(graph->NumEdgeTypes()); std::vector<IdArray> induced_edges(graph->NumEdgeTypes());
DLContext ctx = aten::GetContextOf(vids); DGLContext ctx = aten::GetContextOf(vids);
for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) { for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
auto pair = graph->meta_graph()->FindEdge(etype); auto pair = graph->meta_graph()->FindEdge(etype);
const dgl_type_t src_vtype = pair.first; const dgl_type_t src_vtype = pair.first;
...@@ -79,7 +79,7 @@ HeteroSubgraph OutEdgeGraphRelabelNodes( ...@@ -79,7 +79,7 @@ HeteroSubgraph OutEdgeGraphRelabelNodes(
CHECK_EQ(vids.size(), graph->NumVertexTypes()) CHECK_EQ(vids.size(), graph->NumVertexTypes())
<< "Invalid input: the input list size must be the same as the number of vertex types."; << "Invalid input: the input list size must be the same as the number of vertex types.";
std::vector<IdArray> eids(graph->NumEdgeTypes()); std::vector<IdArray> eids(graph->NumEdgeTypes());
DLContext ctx = aten::GetContextOf(vids); DGLContext ctx = aten::GetContextOf(vids);
for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) { for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
auto pair = graph->meta_graph()->FindEdge(etype); auto pair = graph->meta_graph()->FindEdge(etype);
const dgl_type_t src_vtype = pair.first; const dgl_type_t src_vtype = pair.first;
...@@ -100,7 +100,7 @@ HeteroSubgraph OutEdgeGraphNoRelabelNodes( ...@@ -100,7 +100,7 @@ HeteroSubgraph OutEdgeGraphNoRelabelNodes(
<< "Invalid input: the input list size must be the same as the number of vertex types."; << "Invalid input: the input list size must be the same as the number of vertex types.";
std::vector<HeteroGraphPtr> subrels(graph->NumEdgeTypes()); std::vector<HeteroGraphPtr> subrels(graph->NumEdgeTypes());
std::vector<IdArray> induced_edges(graph->NumEdgeTypes()); std::vector<IdArray> induced_edges(graph->NumEdgeTypes());
DLContext ctx = aten::GetContextOf(vids); DGLContext ctx = aten::GetContextOf(vids);
for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) { for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
auto pair = graph->meta_graph()->FindEdge(etype); auto pair = graph->meta_graph()->FindEdge(etype);
const dgl_type_t src_vtype = pair.first; const dgl_type_t src_vtype = pair.first;
......
src/graph/transform/compact.cc
View file @ cded5b80
...@@ -140,7 +140,7 @@ CompactGraphsCPU( ...@@ -140,7 +140,7 @@ CompactGraphsCPU(
template<> template<>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>> std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphs<kDLCPU, int32_t>( CompactGraphs<kDGLCPU, int32_t>(
const std::vector<HeteroGraphPtr> &graphs, const std::vector<HeteroGraphPtr> &graphs,
const std::vector<IdArray> &always_preserve) { const std::vector<IdArray> &always_preserve) {
return CompactGraphsCPU<int32_t>(graphs, always_preserve); return CompactGraphsCPU<int32_t>(graphs, always_preserve);
...@@ -148,7 +148,7 @@ CompactGraphs<kDLCPU, int32_t>( ...@@ -148,7 +148,7 @@ CompactGraphs<kDLCPU, int32_t>(
template<> template<>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>> std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphs<kDLCPU, int64_t>( CompactGraphs<kDGLCPU, int64_t>(
const std::vector<HeteroGraphPtr> &graphs, const std::vector<HeteroGraphPtr> &graphs,
const std::vector<IdArray> &always_preserve) { const std::vector<IdArray> &always_preserve) {
return CompactGraphsCPU<int64_t>(graphs, always_preserve); return CompactGraphsCPU<int64_t>(graphs, always_preserve);
......
src/graph/transform/compact.h
View file @ cded5b80
...@@ -41,7 +41,7 @@ namespace transform { ...@@ -41,7 +41,7 @@ namespace transform {
* *
* @return The vector of compacted graphs and the vector of induced nodes. * @return The vector of compacted graphs and the vector of induced nodes.
*/ */
template<DLDeviceType XPU, typename IdType> template<DGLDeviceType XPU, typename IdType>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>> std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphs( CompactGraphs(
const std::vector<HeteroGraphPtr> &graphs, const std::vector<HeteroGraphPtr> &graphs,
......
src/graph/transform/cpu/knn.cc
View file @ cded5b80
...@@ -304,7 +304,7 @@ void BruteForceKNN(const NDArray& data_points, const IdArray& data_offsets, ...@@ -304,7 +304,7 @@ void BruteForceKNN(const NDArray& data_points, const IdArray& data_offsets,
} }
} // namespace impl } // namespace impl
template <DLDeviceType XPU, typename FloatType, typename IdType> template <DGLDeviceType XPU, typename FloatType, typename IdType>
void KNN(const NDArray& data_points, const IdArray& data_offsets, void KNN(const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm) { const int k, IdArray result, const std::string& algorithm) {
...@@ -319,7 +319,7 @@ void KNN(const NDArray& data_points, const IdArray& data_offsets, ...@@ -319,7 +319,7 @@ void KNN(const NDArray& data_points, const IdArray& data_offsets,
} }
} }
template <DLDeviceType XPU, typename FloatType, typename IdType> template <DGLDeviceType XPU, typename FloatType, typename IdType>
void NNDescent(const NDArray& points, const IdArray& offsets, void NNDescent(const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta) { const int num_candidates, const double delta) {
...@@ -567,36 +567,36 @@ void NNDescent(const NDArray& points, const IdArray& offsets, ...@@ -567,36 +567,36 @@ void NNDescent(const NDArray& points, const IdArray& offsets,
device->FreeWorkspace(ctx, flags); device->FreeWorkspace(ctx, flags);
} }
template void KNN<kDLCPU, float, int32_t>( template void KNN<kDGLCPU, float, int32_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void KNN<kDLCPU, float, int64_t>( template void KNN<kDGLCPU, float, int64_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void KNN<kDLCPU, double, int32_t>( template void KNN<kDGLCPU, double, int32_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void KNN<kDLCPU, double, int64_t>( template void KNN<kDGLCPU, double, int64_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void NNDescent<kDLCPU, float, int32_t>( template void NNDescent<kDGLCPU, float, int32_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
template void NNDescent<kDLCPU, float, int64_t>( template void NNDescent<kDGLCPU, float, int64_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
template void NNDescent<kDLCPU, double, int32_t>( template void NNDescent<kDGLCPU, double, int32_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
template void NNDescent<kDLCPU, double, int64_t>( template void NNDescent<kDGLCPU, double, int64_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
......
src/graph/transform/cuda/cuda_compact_graph.cu
View file @ cded5b80
...@@ -70,7 +70,7 @@ void BuildNodeMaps( ...@@ -70,7 +70,7 @@ void BuildNodeMaps(
for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) { for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
const IdArray& nodes = input_nodes[ntype]; const IdArray& nodes = input_nodes[ntype];
if (nodes->shape[0] > 0) { if (nodes->shape[0] > 0) {
CHECK_EQ(nodes->ctx.device_type, kDLGPU); CHECK_EQ(nodes->ctx.device_type, kDGLCUDA);
node_maps->LhsHashTable(ntype).FillWithDuplicates( node_maps->LhsHashTable(ntype).FillWithDuplicates(
nodes.Ptr<IdType>(), nodes.Ptr<IdType>(),
nodes->shape[0], nodes->shape[0],
...@@ -92,7 +92,7 @@ CompactGraphsGPU( ...@@ -92,7 +92,7 @@ CompactGraphsGPU(
auto device = runtime::DeviceAPI::Get(ctx); auto device = runtime::DeviceAPI::Get(ctx);
cudaStream_t stream = runtime::getCurrentCUDAStream(); cudaStream_t stream = runtime::getCurrentCUDAStream();
CHECK_EQ(ctx.device_type, kDLGPU); CHECK_EQ(ctx.device_type, kDGLCUDA);
// Step 1: Collect the nodes that has connections for each type. // Step 1: Collect the nodes that has connections for each type.
const uint64_t num_ntypes = graphs[0]->NumVertexTypes(); const uint64_t num_ntypes = graphs[0]->NumVertexTypes();
...@@ -206,8 +206,8 @@ CompactGraphsGPU( ...@@ -206,8 +206,8 @@ CompactGraphsGPU(
num_induced_nodes.data(), 0, num_induced_nodes.data(), 0,
sizeof(*num_induced_nodes.data())*num_ntypes, sizeof(*num_induced_nodes.data())*num_ntypes,
ctx, ctx,
DGLContext{kDLCPU, 0}, DGLContext{kDGLCPU, 0},
DGLType{kDLInt, 64, 1}); DGLDataType{kDGLInt, 64, 1});
device->StreamSync(ctx, stream); device->StreamSync(ctx, stream);
// wait for the node counts to finish transferring // wait for the node counts to finish transferring
...@@ -255,7 +255,7 @@ CompactGraphsGPU( ...@@ -255,7 +255,7 @@ CompactGraphsGPU(
template<> template<>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>> std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphs<kDLGPU, int32_t>( CompactGraphs<kDGLCUDA, int32_t>(
const std::vector<HeteroGraphPtr> &graphs, const std::vector<HeteroGraphPtr> &graphs,
const std::vector<IdArray> &always_preserve) { const std::vector<IdArray> &always_preserve) {
return CompactGraphsGPU<int32_t>(graphs, always_preserve); return CompactGraphsGPU<int32_t>(graphs, always_preserve);
...@@ -263,7 +263,7 @@ CompactGraphs<kDLGPU, int32_t>( ...@@ -263,7 +263,7 @@ CompactGraphs<kDLGPU, int32_t>(
template<> template<>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>> std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphs<kDLGPU, int64_t>( CompactGraphs<kDGLCUDA, int64_t>(
const std::vector<HeteroGraphPtr> &graphs, const std::vector<HeteroGraphPtr> &graphs,
const std::vector<IdArray> &always_preserve) { const std::vector<IdArray> &always_preserve) {
return CompactGraphsGPU<int64_t>(graphs, always_preserve); return CompactGraphsGPU<int64_t>(graphs, always_preserve);
......
src/graph/transform/cuda/cuda_map_edges.cuh
View file @ cded5b80
...@@ -250,9 +250,9 @@ MapEdges( ...@@ -250,9 +250,9 @@ MapEdges(
node_map.RhsHashTable(dst_type).DeviceHandle()); node_map.RhsHashTable(dst_type).DeviceHandle());
} else { } else {
new_lhs.emplace_back( new_lhs.emplace_back(
aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx)); aten::NullArray(DGLDataType{kDGLInt, sizeof(IdType)*8, 1}, ctx));
new_rhs.emplace_back( new_rhs.emplace_back(
aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx)); aten::NullArray(DGLDataType{kDGLInt, sizeof(IdType)*8, 1}, ctx));
} }
} }
......
src/graph/transform/cuda/cuda_to_block.cu
View file @ cded5b80
...@@ -82,7 +82,7 @@ class DeviceNodeMapMaker { ...@@ -82,7 +82,7 @@ class DeviceNodeMapMaker {
for (int64_t ntype = 0; ntype < lhs_num_ntypes; ++ntype) { for (int64_t ntype = 0; ntype < lhs_num_ntypes; ++ntype) {
const IdArray& nodes = lhs_nodes[ntype]; const IdArray& nodes = lhs_nodes[ntype];
if (nodes->shape[0] > 0) { if (nodes->shape[0] > 0) {
CHECK_EQ(nodes->ctx.device_type, kDLGPU); CHECK_EQ(nodes->ctx.device_type, kDGLCUDA);
node_maps->LhsHashTable(ntype).FillWithDuplicates( node_maps->LhsHashTable(ntype).FillWithDuplicates(
nodes.Ptr<IdType>(), nodes.Ptr<IdType>(),
nodes->shape[0], nodes->shape[0],
...@@ -127,7 +127,7 @@ class DeviceNodeMapMaker { ...@@ -127,7 +127,7 @@ class DeviceNodeMapMaker {
for (int64_t ntype = 0; ntype < lhs_num_ntypes; ++ntype) { for (int64_t ntype = 0; ntype < lhs_num_ntypes; ++ntype) {
const IdArray& nodes = lhs_nodes[ntype]; const IdArray& nodes = lhs_nodes[ntype];
if (nodes->shape[0] > 0) { if (nodes->shape[0] > 0) {
CHECK_EQ(nodes->ctx.device_type, kDLGPU); CHECK_EQ(nodes->ctx.device_type, kDGLCUDA);
node_maps->LhsHashTable(ntype).FillWithUnique( node_maps->LhsHashTable(ntype).FillWithUnique(
nodes.Ptr<IdType>(), nodes.Ptr<IdType>(),
nodes->shape[0], nodes->shape[0],
...@@ -154,7 +154,7 @@ class DeviceNodeMapMaker { ...@@ -154,7 +154,7 @@ class DeviceNodeMapMaker {
// Since partial specialization is not allowed for functions, use this as an // Since partial specialization is not allowed for functions, use this as an
// intermediate for ToBlock where XPU = kDLGPU. // intermediate for ToBlock where XPU = kDGLCUDA.
template<typename IdType> template<typename IdType>
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlockGPU( ToBlockGPU(
...@@ -170,7 +170,7 @@ ToBlockGPU( ...@@ -170,7 +170,7 @@ ToBlockGPU(
auto device = runtime::DeviceAPI::Get(ctx); auto device = runtime::DeviceAPI::Get(ctx);
cudaStream_t stream = runtime::getCurrentCUDAStream(); cudaStream_t stream = runtime::getCurrentCUDAStream();
CHECK_EQ(ctx.device_type, kDLGPU); CHECK_EQ(ctx.device_type, kDGLCUDA);
for (const auto& nodes : rhs_nodes) { for (const auto& nodes : rhs_nodes) {
CHECK_EQ(ctx.device_type, nodes->ctx.device_type); CHECK_EQ(ctx.device_type, nodes->ctx.device_type);
} }
...@@ -296,8 +296,8 @@ ToBlockGPU( ...@@ -296,8 +296,8 @@ ToBlockGPU(
num_nodes_per_type.data(), 0, num_nodes_per_type.data(), 0,
sizeof(*num_nodes_per_type.data())*num_ntypes, sizeof(*num_nodes_per_type.data())*num_ntypes,
ctx, ctx,
DGLContext{kDLCPU, 0}, DGLContext{kDGLCPU, 0},
DGLType{kDLInt, 64, 1}); DGLDataType{kDGLInt, 64, 1});
device->StreamSync(ctx, stream); device->StreamSync(ctx, stream);
// wait for the node counts to finish transferring // wait for the node counts to finish transferring
...@@ -321,7 +321,7 @@ ToBlockGPU( ...@@ -321,7 +321,7 @@ ToBlockGPU(
induced_edges.push_back(edge_arrays[etype].id); induced_edges.push_back(edge_arrays[etype].id);
} else { } else {
induced_edges.push_back( induced_edges.push_back(
aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx)); aten::NullArray(DGLDataType{kDGLInt, sizeof(IdType)*8, 1}, ctx));
} }
} }
...@@ -358,8 +358,8 @@ ToBlockGPU( ...@@ -358,8 +358,8 @@ ToBlockGPU(
// No rhs nodes are given for this edge type. Create an empty graph. // No rhs nodes are given for this edge type. Create an empty graph.
rel_graphs.push_back(CreateFromCOO( rel_graphs.push_back(CreateFromCOO(
2, lhs_nodes[srctype]->shape[0], rhs_nodes[dsttype]->shape[0], 2, lhs_nodes[srctype]->shape[0], rhs_nodes[dsttype]->shape[0],
aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx), aten::NullArray(DGLDataType{kDGLInt, sizeof(IdType)*8, 1}, ctx),
aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx))); aten::NullArray(DGLDataType{kDGLInt, sizeof(IdType)*8, 1}, ctx)));
} else { } else {
rel_graphs.push_back(CreateFromCOO( rel_graphs.push_back(CreateFromCOO(
2, 2,
...@@ -383,7 +383,7 @@ ToBlockGPU( ...@@ -383,7 +383,7 @@ ToBlockGPU(
// functions are the same. // functions are the same.
// Using template<> fails to export the symbols. // Using template<> fails to export the symbols.
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
// ToBlock<kDLGPU, int32_t> // ToBlock<kDGLCUDA, int32_t>
ToBlockGPU32( ToBlockGPU32(
HeteroGraphPtr graph, HeteroGraphPtr graph,
const std::vector<IdArray> &rhs_nodes, const std::vector<IdArray> &rhs_nodes,
...@@ -393,7 +393,7 @@ ToBlockGPU32( ...@@ -393,7 +393,7 @@ ToBlockGPU32(
} }
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
// ToBlock<kDLGPU, int64_t> // ToBlock<kDGLCUDA, int64_t>
ToBlockGPU64( ToBlockGPU64(
HeteroGraphPtr graph, HeteroGraphPtr graph,
const std::vector<IdArray> &rhs_nodes, const std::vector<IdArray> &rhs_nodes,
......
src/graph/transform/cuda/knn.cu
View file @ cded5b80
...@@ -528,11 +528,11 @@ void BruteForceKNNSharedCuda(const NDArray& data_points, const IdArray& data_off ...@@ -528,11 +528,11 @@ void BruteForceKNNSharedCuda(const NDArray& data_points, const IdArray& data_off
int64_t num_blocks = 0, final_elem = 0, copyoffset = (batch_size - 1) * sizeof(IdType); int64_t num_blocks = 0, final_elem = 0, copyoffset = (batch_size - 1) * sizeof(IdType);
device->CopyDataFromTo( device->CopyDataFromTo(
num_block_prefixsum, copyoffset, &num_blocks, 0, num_block_prefixsum, copyoffset, &num_blocks, 0,
sizeof(IdType), ctx, DLContext{kDLCPU, 0}, sizeof(IdType), ctx, DGLContext{kDGLCPU, 0},
query_offsets->dtype); query_offsets->dtype);
device->CopyDataFromTo( device->CopyDataFromTo(
num_block_per_segment, copyoffset, &final_elem, 0, num_block_per_segment, copyoffset, &final_elem, 0,
sizeof(IdType), ctx, DLContext{kDLCPU, 0}, sizeof(IdType), ctx, DGLContext{kDGLCPU, 0},
query_offsets->dtype); query_offsets->dtype);
num_blocks += final_elem; num_blocks += final_elem;
device->FreeWorkspace(ctx, num_block_per_segment); device->FreeWorkspace(ctx, num_block_per_segment);
...@@ -815,7 +815,7 @@ __global__ void UpdateNeighborsKernel(const FloatType* points, const IdType* off ...@@ -815,7 +815,7 @@ __global__ void UpdateNeighborsKernel(const FloatType* points, const IdType* off
} // namespace impl } // namespace impl
template <DLDeviceType XPU, typename FloatType, typename IdType> template <DGLDeviceType XPU, typename FloatType, typename IdType>
void KNN(const NDArray& data_points, const IdArray& data_offsets, void KNN(const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm) { const int k, IdArray result, const std::string& algorithm) {
...@@ -830,7 +830,7 @@ void KNN(const NDArray& data_points, const IdArray& data_offsets, ...@@ -830,7 +830,7 @@ void KNN(const NDArray& data_points, const IdArray& data_offsets,
} }
} }
template <DLDeviceType XPU, typename FloatType, typename IdType> template <DGLDeviceType XPU, typename FloatType, typename IdType>
void NNDescent(const NDArray& points, const IdArray& offsets, void NNDescent(const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta) { const int num_candidates, const double delta) {
...@@ -905,7 +905,7 @@ void NNDescent(const NDArray& points, const IdArray& offsets, ...@@ -905,7 +905,7 @@ void NNDescent(const NDArray& points, const IdArray& offsets,
stream)); stream));
device->CopyDataFromTo( device->CopyDataFromTo(
total_num_updates_d, 0, &total_num_updates, 0, total_num_updates_d, 0, &total_num_updates, 0,
sizeof(IdType), ctx, DLContext{kDLCPU, 0}, sizeof(IdType), ctx, DGLContext{kDGLCPU, 0},
offsets->dtype); offsets->dtype);
if (total_num_updates <= static_cast<IdType>(delta * k * num_nodes)) { if (total_num_updates <= static_cast<IdType>(delta * k * num_nodes)) {
...@@ -922,36 +922,36 @@ void NNDescent(const NDArray& points, const IdArray& offsets, ...@@ -922,36 +922,36 @@ void NNDescent(const NDArray& points, const IdArray& offsets,
device->FreeWorkspace(ctx, sum_temp_storage); device->FreeWorkspace(ctx, sum_temp_storage);
} }
template void KNN<kDLGPU, float, int32_t>( template void KNN<kDGLCUDA, float, int32_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void KNN<kDLGPU, float, int64_t>( template void KNN<kDGLCUDA, float, int64_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void KNN<kDLGPU, double, int32_t>( template void KNN<kDGLCUDA, double, int32_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void KNN<kDLGPU, double, int64_t>( template void KNN<kDGLCUDA, double, int64_t>(
const NDArray& data_points, const IdArray& data_offsets, const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
template void NNDescent<kDLGPU, float, int32_t>( template void NNDescent<kDGLCUDA, float, int32_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
template void NNDescent<kDLGPU, float, int64_t>( template void NNDescent<kDGLCUDA, float, int64_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
template void NNDescent<kDLGPU, double, int32_t>( template void NNDescent<kDGLCUDA, double, int32_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
template void NNDescent<kDLGPU, double, int64_t>( template void NNDescent<kDGLCUDA, double, int64_t>(
const NDArray& points, const IdArray& offsets, const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
......
src/graph/transform/knn.h
View file @ cded5b80
...@@ -27,7 +27,7 @@ namespace transform { ...@@ -27,7 +27,7 @@ namespace transform {
* relation between \a query_points and \a data_points. * relation between \a query_points and \a data_points.
* \param algorithm algorithm used to compute the k-nearest neighbors. * \param algorithm algorithm used to compute the k-nearest neighbors.
*/ */
template <DLDeviceType XPU, typename FloatType, typename IdType> template <DGLDeviceType XPU, typename FloatType, typename IdType>
void KNN(const NDArray& data_points, const IdArray& data_offsets, void KNN(const NDArray& data_points, const IdArray& data_offsets,
const NDArray& query_points, const IdArray& query_offsets, const NDArray& query_points, const IdArray& query_offsets,
const int k, IdArray result, const std::string& algorithm); const int k, IdArray result, const std::string& algorithm);
...@@ -44,7 +44,7 @@ void KNN(const NDArray& data_points, const IdArray& data_offsets, ...@@ -44,7 +44,7 @@ void KNN(const NDArray& data_points, const IdArray& data_offsets,
* \param num_candidates The maximum number of candidates to be considered during one iteration. * \param num_candidates The maximum number of candidates to be considered during one iteration.
* \param delta A value controls the early abort. * \param delta A value controls the early abort.
*/ */
template <DLDeviceType XPU, typename FloatType, typename IdType> template <DGLDeviceType XPU, typename FloatType, typename IdType>
void NNDescent(const NDArray& points, const IdArray& offsets, void NNDescent(const NDArray& points, const IdArray& offsets,
IdArray result, const int k, const int num_iters, IdArray result, const int k, const int num_iters,
const int num_candidates, const double delta); const int num_candidates, const double delta);
......
src/graph/transform/partition_hetero.cc
View file @ cded5b80
...@@ -164,10 +164,10 @@ HaloHeteroSubgraph GetSubgraphWithHalo(std::shared_ptr<HeteroGraph> hg, ...@@ -164,10 +164,10 @@ HaloHeteroSubgraph GetSubgraphWithHalo(std::shared_ptr<HeteroGraph> hg,
} }
num_edges = edge_src.size(); num_edges = edge_src.size();
IdArray new_src = IdArray::Empty({num_edges}, DLDataType{kDLInt, 64, 1}, IdArray new_src = IdArray::Empty({num_edges}, DGLDataType{kDGLInt, 64, 1},
DLContext{kDLCPU, 0}); DGLContext{kDGLCPU, 0});
IdArray new_dst = IdArray::Empty({num_edges}, DLDataType{kDLInt, 64, 1}, IdArray new_dst = IdArray::Empty({num_edges}, DGLDataType{kDGLInt, 64, 1},
DLContext{kDLCPU, 0}); DGLContext{kDGLCPU, 0});
dgl_id_t *new_src_data = static_cast<dgl_id_t *>(new_src->data); dgl_id_t *new_src_data = static_cast<dgl_id_t *>(new_src->data);
dgl_id_t *new_dst_data = static_cast<dgl_id_t *>(new_dst->data); dgl_id_t *new_dst_data = static_cast<dgl_id_t *>(new_dst->data);
for (size_t i = 0; i < edge_src.size(); i++) { for (size_t i = 0; i < edge_src.size(); i++) {
......
src/graph/transform/to_bipartite.cc
View file @ cded5b80
...@@ -41,7 +41,7 @@ namespace transform { ...@@ -41,7 +41,7 @@ namespace transform {
namespace { namespace {
// Since partial specialization is not allowed for functions, use this as an // Since partial specialization is not allowed for functions, use this as an
// intermediate for ToBlock where XPU = kDLCPU. // intermediate for ToBlock where XPU = kDGLCPU.
template<typename IdType> template<typename IdType>
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlockCPU(HeteroGraphPtr graph, const std::vector<IdArray> &rhs_nodes, ToBlockCPU(HeteroGraphPtr graph, const std::vector<IdArray> &rhs_nodes,
...@@ -143,7 +143,7 @@ ToBlockCPU(HeteroGraphPtr graph, const std::vector<IdArray> &rhs_nodes, ...@@ -143,7 +143,7 @@ ToBlockCPU(HeteroGraphPtr graph, const std::vector<IdArray> &rhs_nodes,
template<> template<>
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlock<kDLCPU, int32_t>(HeteroGraphPtr graph, ToBlock<kDGLCPU, int32_t>(HeteroGraphPtr graph,
const std::vector<IdArray> &rhs_nodes, const std::vector<IdArray> &rhs_nodes,
bool include_rhs_in_lhs, bool include_rhs_in_lhs,
std::vector<IdArray>* const lhs_nodes) { std::vector<IdArray>* const lhs_nodes) {
...@@ -152,7 +152,7 @@ ToBlock<kDLCPU, int32_t>(HeteroGraphPtr graph, ...@@ -152,7 +152,7 @@ ToBlock<kDLCPU, int32_t>(HeteroGraphPtr graph,
template<> template<>
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlock<kDLCPU, int64_t>(HeteroGraphPtr graph, ToBlock<kDGLCPU, int64_t>(HeteroGraphPtr graph,
const std::vector<IdArray> &rhs_nodes, const std::vector<IdArray> &rhs_nodes,
bool include_rhs_in_lhs, bool include_rhs_in_lhs,
std::vector<IdArray>* const lhs_nodes) { std::vector<IdArray>* const lhs_nodes) {
...@@ -172,7 +172,7 @@ ToBlockGPU64(HeteroGraphPtr, const std::vector<IdArray>&, bool, std::vector<IdAr ...@@ -172,7 +172,7 @@ ToBlockGPU64(HeteroGraphPtr, const std::vector<IdArray>&, bool, std::vector<IdAr
template<> template<>
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlock<kDLGPU, int32_t>(HeteroGraphPtr graph, ToBlock<kDGLCUDA, int32_t>(HeteroGraphPtr graph,
const std::vector<IdArray> &rhs_nodes, const std::vector<IdArray> &rhs_nodes,
bool include_rhs_in_lhs, bool include_rhs_in_lhs,
std::vector<IdArray>* const lhs_nodes) { std::vector<IdArray>* const lhs_nodes) {
...@@ -181,7 +181,7 @@ ToBlock<kDLGPU, int32_t>(HeteroGraphPtr graph, ...@@ -181,7 +181,7 @@ ToBlock<kDLGPU, int32_t>(HeteroGraphPtr graph,
template<> template<>
std::tuple<HeteroGraphPtr, std::vector<IdArray>> std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlock<kDLGPU, int64_t>(HeteroGraphPtr graph, ToBlock<kDGLCUDA, int64_t>(HeteroGraphPtr graph,
const std::vector<IdArray> &rhs_nodes, const std::vector<IdArray> &rhs_nodes,
bool include_rhs_in_lhs, bool include_rhs_in_lhs,
std::vector<IdArray>* const lhs_nodes) { std::vector<IdArray>* const lhs_nodes) {
......
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