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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
#include <mxnet/io.h>
#include <mxnet/base.h>
#include <mxnet/ndarray.h>
#include <mxnet/operator.h>
#include <mxnet/operator_util.h>
#include <dmlc/logging.h>
#include <dmlc/optional.h>
#include <algorithm>
#include <random>
#include <utility>
#include "../elemwise_op_common.h"
#include "../../imperative/imperative_utils.h"
#include "../subgraph_op_common.h"
#include "./dgl_graph-inl.h"
namespace mxnet {
namespace op {
typedef int64_t dgl_id_t;
////////////////////////////// Graph Sampling ///////////////////////////////
/*
* ArrayHeap is used to sample elements from vector
*/
class ArrayHeap {
public:
explicit ArrayHeap(const std::vector<float>& prob, unsigned int seed) {
generator_ = std::mt19937(seed);
distribution_ = std::uniform_real_distribution<float>(0.0, 1.0);
vec_size_ = prob.size();
bit_len_ = ceil(log2(vec_size_));
limit_ = 1 << bit_len_;
// allocate twice the size
heap_.resize(limit_ << 1, 0);
// allocate the leaves
for (int i = limit_; i < vec_size_ + limit_; ++i) {
heap_[i] = prob[i - limit_];
}
// iterate up the tree (this is O(m))
for (int i = bit_len_ - 1; i >= 0; --i) {
for (int j = (1 << i); j < (1 << (i + 1)); ++j) {
heap_[j] = heap_[j << 1] + heap_[(j << 1) + 1];
}
}
}
~ArrayHeap() = default;
/*
* Remove term from index (this costs O(log m) steps)
*/
void Delete(size_t index) {
size_t i = index + limit_;
float w = heap_[i];
for (int j = bit_len_; j >= 0; --j) {
heap_[i] -= w;
i = i >> 1;
}
}
/*
* Add value w to index (this costs O(log m) steps)
*/
void Add(size_t index, float w) {
size_t i = index + limit_;
for (int j = bit_len_; j >= 0; --j) {
heap_[i] += w;
i = i >> 1;
}
}
/*
* Sample from arrayHeap
*/
size_t Sample() {
float xi = heap_[1] * distribution_(generator_);
int i = 1;
while (i < limit_) {
i = i << 1;
if (xi >= heap_[i]) {
xi -= heap_[i];
i += 1;
}
}
return i - limit_;
}
/*
* Sample a vector by given the size n
*/
void SampleWithoutReplacement(size_t n, std::vector<size_t>* samples) {
// sample n elements
for (size_t i = 0; i < n; ++i) {
samples->at(i) = this->Sample();
this->Delete(samples->at(i));
}
}
private:
int vec_size_; // sample size
int bit_len_; // bit size
int limit_;
std::vector<float> heap_;
std::mt19937 generator_;
std::uniform_real_distribution<float> distribution_;
};
struct NeighborSampleParam : public dmlc::Parameter<NeighborSampleParam> {
int num_args;
dgl_id_t num_hops;
dgl_id_t num_neighbor;
dgl_id_t max_num_vertices;
DMLC_DECLARE_PARAMETER(NeighborSampleParam) {
DMLC_DECLARE_FIELD(num_args).set_lower_bound(2).describe("Number of input NDArray.");
DMLC_DECLARE_FIELD(num_hops).set_default(1).describe("Number of hops.");
DMLC_DECLARE_FIELD(num_neighbor).set_default(2).describe("Number of neighbor.");
DMLC_DECLARE_FIELD(max_num_vertices).set_default(100).describe("Max number of vertices.");
}
};
DMLC_REGISTER_PARAMETER(NeighborSampleParam);
/*
* Check uniform Storage Type
*/
static bool CSRNeighborUniformSampleStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 1;
CHECK_EQ(out_attrs->size(), 3 * num_subgraphs);
// input[0] is csr_graph
CHECK_EQ(in_attrs->at(0), mxnet::kCSRStorage);
// the rest input ndarray is seed_vector
for (size_t i = 0; i < num_subgraphs; i++)
CHECK_EQ(in_attrs->at(1 + i), mxnet::kDefaultStorage);
bool success = true;
// sample_id
for (size_t i = 0; i < num_subgraphs; i++) {
if (!type_assign(&(*out_attrs)[i], mxnet::kDefaultStorage)) {
success = false;
}
}
// sub_graph
for (size_t i = 0; i < num_subgraphs; i++) {
if (!type_assign(&(*out_attrs)[i + num_subgraphs], mxnet::kCSRStorage)) {
success = false;
}
}
// sub_layer
for (size_t i = 0; i < num_subgraphs; i++) {
if (!type_assign(&(*out_attrs)[i + 2 * num_subgraphs], mxnet::kDefaultStorage)) {
success = false;
}
}
*dispatch_mode = DispatchMode::kFComputeEx;
return success;
}
/*
* Check non-uniform Storage Type
*/
static bool CSRNeighborNonUniformSampleStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 2;
CHECK_EQ(out_attrs->size(), 4 * num_subgraphs);
// input[0] is csr_graph
CHECK_EQ(in_attrs->at(0), mxnet::kCSRStorage);
// input[1] is probability
CHECK_EQ(in_attrs->at(1), mxnet::kDefaultStorage);
// the rest input ndarray is seed_vector
for (size_t i = 0; i < num_subgraphs; i++)
CHECK_EQ(in_attrs->at(2 + i), mxnet::kDefaultStorage);
bool success = true;
// sample_id
for (size_t i = 0; i < num_subgraphs; i++) {
if (!type_assign(&(*out_attrs)[i], mxnet::kDefaultStorage)) {
success = false;
}
}
// sub_graph
for (size_t i = 0; i < num_subgraphs; i++) {
if (!type_assign(&(*out_attrs)[i + num_subgraphs], mxnet::kCSRStorage)) {
success = false;
}
}
// sub_probability
for (size_t i = 0; i < num_subgraphs; i++) {
if (!type_assign(&(*out_attrs)[i + 2 * num_subgraphs], mxnet::kDefaultStorage)) {
success = false;
}
}
// sub_layer
for (size_t i = 0; i < num_subgraphs; i++) {
if (!type_assign(&(*out_attrs)[i + 3 * num_subgraphs], mxnet::kDefaultStorage)) {
success = false;
}
}
*dispatch_mode = DispatchMode::kFComputeEx;
return success;
}
/*
* Check uniform Shape
*/
static bool CSRNeighborUniformSampleShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 1;
CHECK_EQ(out_attrs->size(), 3 * num_subgraphs);
// input[0] is csr graph
CHECK_EQ(in_attrs->at(0).ndim(), 2U);
CHECK_EQ(in_attrs->at(0)[0], in_attrs->at(0)[1]);
// the rest input ndarray is seed vector
for (size_t i = 0; i < num_subgraphs; i++) {
CHECK_EQ(in_attrs->at(1 + i).ndim(), 1U);
}
// Output
bool success = true;
mxnet::TShape out_shape(1, -1);
// We use the last element to store the actual
// number of vertices in the subgraph.
out_shape[0] = params.max_num_vertices + 1;
for (size_t i = 0; i < num_subgraphs; i++) {
SHAPE_ASSIGN_CHECK(*out_attrs, i, out_shape);
success = success && !mxnet::op::shape_is_none(out_attrs->at(i));
}
// sub_csr
mxnet::TShape out_csr_shape(2, -1);
out_csr_shape[0] = params.max_num_vertices;
out_csr_shape[1] = in_attrs->at(0)[1];
for (size_t i = 0; i < num_subgraphs; i++) {
SHAPE_ASSIGN_CHECK(*out_attrs, i + num_subgraphs, out_csr_shape);
success = success && !mxnet::op::shape_is_none(out_attrs->at(i + num_subgraphs));
}
// sub_layer
mxnet::TShape out_layer_shape(1, -1);
out_layer_shape[0] = params.max_num_vertices;
for (size_t i = 0; i < num_subgraphs; i++) {
SHAPE_ASSIGN_CHECK(*out_attrs, i + 2 * num_subgraphs, out_layer_shape);
success = success && !mxnet::op::shape_is_none(out_attrs->at(i + 2 * num_subgraphs));
}
return success;
}
/*
* Check non-uniform Shape
*/
static bool CSRNeighborNonUniformSampleShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 2;
CHECK_EQ(out_attrs->size(), 4 * num_subgraphs);
// input[0] is csr graph
CHECK_EQ(in_attrs->at(0).ndim(), 2U);
CHECK_EQ(in_attrs->at(0)[0], in_attrs->at(0)[1]);
// input[1] is probability
CHECK_EQ(in_attrs->at(1).ndim(), 1U);
// the rest ndarray is seed vector
for (size_t i = 0; i < num_subgraphs; i++) {
CHECK_EQ(in_attrs->at(2 + i).ndim(), 1U);
}
// Output
bool success = true;
mxnet::TShape out_shape(1, -1);
// We use the last element to store the actual
// number of vertices in the subgraph.
out_shape[0] = params.max_num_vertices + 1;
for (size_t i = 0; i < num_subgraphs; i++) {
SHAPE_ASSIGN_CHECK(*out_attrs, i, out_shape);
success = success && !mxnet::op::shape_is_none(out_attrs->at(i));
}
// sub_csr
mxnet::TShape out_csr_shape(2, -1);
out_csr_shape[0] = params.max_num_vertices;
out_csr_shape[1] = in_attrs->at(0)[1];
for (size_t i = 0; i < num_subgraphs; i++) {
SHAPE_ASSIGN_CHECK(*out_attrs, i + num_subgraphs, out_csr_shape);
success = success && !mxnet::op::shape_is_none(out_attrs->at(i + num_subgraphs));
}
// sub_probability
mxnet::TShape out_prob_shape(1, -1);
out_prob_shape[0] = params.max_num_vertices;
for (size_t i = 0; i < num_subgraphs; i++) {
SHAPE_ASSIGN_CHECK(*out_attrs, i + 2 * num_subgraphs, out_prob_shape);
success = success && !mxnet::op::shape_is_none(out_attrs->at(i + 2 * num_subgraphs));
}
// sub_layer
mxnet::TShape out_layer_shape(1, -1);
out_layer_shape[0] = params.max_num_vertices;
for (size_t i = 0; i < num_subgraphs; i++) {
SHAPE_ASSIGN_CHECK(*out_attrs, i + 3 * num_subgraphs, out_prob_shape);
success = success && !mxnet::op::shape_is_none(out_attrs->at(i + 3 * num_subgraphs));
}
return success;
}
/*
* Check uniform Type
*/
static bool CSRNeighborUniformSampleType(const nnvm::NodeAttrs& attrs,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 1;
CHECK_EQ(out_attrs->size(), 3 * num_subgraphs);
bool success = true;
for (size_t i = 0; i < num_subgraphs; i++) {
TYPE_ASSIGN_CHECK(*out_attrs, i, in_attrs->at(1));
TYPE_ASSIGN_CHECK(*out_attrs, i + num_subgraphs, in_attrs->at(0));
TYPE_ASSIGN_CHECK(*out_attrs, i + 2 * num_subgraphs, in_attrs->at(1));
success = success && out_attrs->at(i) != -1 && out_attrs->at(i + num_subgraphs) != -1 &&
out_attrs->at(i + 2 * num_subgraphs) != -1;
}
return success;
}
/*
* Check non-uniform Type
*/
static bool CSRNeighborNonUniformSampleType(const nnvm::NodeAttrs& attrs,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 2;
CHECK_EQ(out_attrs->size(), 4 * num_subgraphs);
bool success = true;
for (size_t i = 0; i < num_subgraphs; i++) {
TYPE_ASSIGN_CHECK(*out_attrs, i, in_attrs->at(2));
TYPE_ASSIGN_CHECK(*out_attrs, i + num_subgraphs, in_attrs->at(0));
TYPE_ASSIGN_CHECK(*out_attrs, i + 2 * num_subgraphs, in_attrs->at(1));
TYPE_ASSIGN_CHECK(*out_attrs, i + 3 * num_subgraphs, in_attrs->at(2));
success = success && out_attrs->at(i) != -1 && out_attrs->at(i + num_subgraphs) != -1 &&
out_attrs->at(i + 2 * num_subgraphs) != -1 &&
out_attrs->at(i + 3 * num_subgraphs) != -1;
}
return success;
}
static void RandomSample(size_t set_size, size_t num, std::vector<size_t>* out, unsigned int seed) {
std::mt19937 generator(seed);
std::unordered_set<size_t> sampled_idxs;
std::uniform_int_distribution<size_t> distribution(0, set_size - 1);
while (sampled_idxs.size() < num) {
sampled_idxs.insert(distribution(generator));
}
out->clear();
for (size_t sampled_idx : sampled_idxs) {
out->push_back(sampled_idx);
}
}
static void NegateSet(const std::vector<size_t>& idxs, size_t set_size, std::vector<size_t>* out) {
// idxs must have been sorted.
auto it = idxs.begin();
size_t i = 0;
CHECK_GT(set_size, idxs.back());
for (; i < set_size && it != idxs.end(); i++) {
if (*it == i) {
it++;
continue;
}
out->push_back(i);
}
for (; i < set_size; i++) {
out->push_back(i);
}
}
/*
* Uniform sample
*/
static void GetUniformSample(const dgl_id_t* val_list,
const dgl_id_t* col_list,
const size_t ver_len,
const size_t max_num_neighbor,
std::vector<dgl_id_t>* out_ver,
std::vector<dgl_id_t>* out_edge,
unsigned int seed) {
// Copy ver_list to output
if (ver_len <= max_num_neighbor) {
for (size_t i = 0; i < ver_len; ++i) {
out_ver->push_back(col_list[i]);
out_edge->push_back(val_list[i]);
}
return;
}
// If we just sample a small number of elements from a large neighbor list.
std::vector<size_t> sorted_idxs;
if (ver_len > max_num_neighbor * 2) {
sorted_idxs.reserve(max_num_neighbor);
RandomSample(ver_len, max_num_neighbor, &sorted_idxs, seed);
std::sort(sorted_idxs.begin(), sorted_idxs.end());
} else {
std::vector<size_t> negate;
negate.reserve(ver_len - max_num_neighbor);
RandomSample(ver_len, ver_len - max_num_neighbor, &negate, seed);
std::sort(negate.begin(), negate.end());
NegateSet(negate, ver_len, &sorted_idxs);
}
// verify the result.
CHECK_EQ(sorted_idxs.size(), max_num_neighbor);
for (size_t i = 1; i < sorted_idxs.size(); i++) {
CHECK_GT(sorted_idxs[i], sorted_idxs[i - 1]);
}
for (auto idx : sorted_idxs) {
out_ver->push_back(col_list[idx]);
out_edge->push_back(val_list[idx]);
}
}
/*
* Non-uniform sample via ArrayHeap
*/
static void GetNonUniformSample(const float* probability,
const dgl_id_t* val_list,
const dgl_id_t* col_list,
const size_t ver_len,
const size_t max_num_neighbor,
std::vector<dgl_id_t>* out_ver,
std::vector<dgl_id_t>* out_edge,
unsigned int seed) {
// Copy ver_list to output
if (ver_len <= max_num_neighbor) {
for (size_t i = 0; i < ver_len; ++i) {
out_ver->push_back(col_list[i]);
out_edge->push_back(val_list[i]);
}
return;
}
// Make sample
std::vector<size_t> sp_index(max_num_neighbor);
std::vector<float> sp_prob(ver_len);
for (size_t i = 0; i < ver_len; ++i) {
sp_prob[i] = probability[col_list[i]];
}
ArrayHeap arrayHeap(sp_prob, seed);
arrayHeap.SampleWithoutReplacement(max_num_neighbor, &sp_index);
out_ver->resize(max_num_neighbor);
out_edge->resize(max_num_neighbor);
for (size_t i = 0; i < max_num_neighbor; ++i) {
size_t idx = sp_index[i];
out_ver->at(i) = col_list[idx];
out_edge->at(i) = val_list[idx];
}
sort(out_ver->begin(), out_ver->end());
sort(out_edge->begin(), out_edge->end());
}
/*
* Used for subgraph sampling
*/
struct neigh_list {
std::vector<dgl_id_t> neighs;
std::vector<dgl_id_t> edges;
neigh_list(std::vector<dgl_id_t> _neighs, std::vector<dgl_id_t> _edges)
: neighs(std::move(_neighs)), edges(std::move(_edges)) {}
};
/*
* Sample sub-graph from csr graph
*/
static void SampleSubgraph(const NDArray& csr,
const NDArray& seed_arr,
const NDArray& sampled_ids,
const NDArray& sub_csr,
float* sub_prob,
const NDArray& sub_layer,
const float* probability,
int num_hops,
size_t num_neighbor,
size_t max_num_vertices,
unsigned int random_seed) {
size_t num_seeds = seed_arr.shape().Size();
CHECK_GE(max_num_vertices, num_seeds);
const dgl_id_t* val_list = csr.data().dptr<dgl_id_t>();
const dgl_id_t* col_list = csr.aux_data(csr::kIdx).dptr<dgl_id_t>();
const dgl_id_t* indptr = csr.aux_data(csr::kIndPtr).dptr<dgl_id_t>();
const dgl_id_t* seed = seed_arr.data().dptr<dgl_id_t>();
dgl_id_t* out = sampled_ids.data().dptr<dgl_id_t>();
dgl_id_t* out_layer = sub_layer.data().dptr<dgl_id_t>();
// BFS traverse the graph and sample vertices
// <vertex_id, layer_id>
std::unordered_set<dgl_id_t> sub_ver_mp;
std::vector<std::pair<dgl_id_t, dgl_id_t> > sub_vers;
sub_vers.reserve(num_seeds * 10);
// add seed vertices
for (size_t i = 0; i < num_seeds; ++i) {
auto ret = sub_ver_mp.insert(seed[i]);
// If the vertex is inserted successfully.
if (ret.second) {
sub_vers.emplace_back(seed[i], 0);
}
}
std::vector<dgl_id_t> tmp_sampled_src_list;
std::vector<dgl_id_t> tmp_sampled_edge_list;
// ver_id, position
std::vector<std::pair<dgl_id_t, size_t> > neigh_pos;
neigh_pos.reserve(num_seeds);
std::vector<dgl_id_t> neighbor_list;
size_t num_edges = 0;
// sub_vers is used both as a node collection and a queue.
// In the while loop, we iterate over sub_vers and new nodes are added to the vector.
// A vertex in the vector only needs to be accessed once. If there is a vertex behind idx
// isn't in the last level, we will sample its neighbors. If not, the while loop terminates.
size_t idx = 0;
while (idx < sub_vers.size() && sub_ver_mp.size() < max_num_vertices) {
dgl_id_t dst_id = sub_vers[idx].first;
int cur_node_level = sub_vers[idx].second;
idx++;
// If the node is in the last level, we don't need to sample neighbors
// from this node.
if (cur_node_level >= num_hops)
continue;
tmp_sampled_src_list.clear();
tmp_sampled_edge_list.clear();
dgl_id_t ver_len = *(indptr + dst_id + 1) - *(indptr + dst_id);
if (probability == nullptr) { // uniform-sample
GetUniformSample(val_list + *(indptr + dst_id),
col_list + *(indptr + dst_id),
ver_len,
num_neighbor,
&tmp_sampled_src_list,
&tmp_sampled_edge_list,
random_seed);
} else { // non-uniform-sample
GetNonUniformSample(probability,
val_list + *(indptr + dst_id),
col_list + *(indptr + dst_id),
ver_len,
num_neighbor,
&tmp_sampled_src_list,
&tmp_sampled_edge_list,
random_seed);
}
CHECK_EQ(tmp_sampled_src_list.size(), tmp_sampled_edge_list.size());
size_t pos = neighbor_list.size();
neigh_pos.emplace_back(dst_id, pos);
// First we push the size of neighbor vector
neighbor_list.push_back(tmp_sampled_edge_list.size());
// Then push the vertices
for (dgl_id_t& i : tmp_sampled_src_list) {
neighbor_list.push_back(i);
}
// Finally we push the edge list
for (dgl_id_t& i : tmp_sampled_edge_list) {
neighbor_list.push_back(i);
}
num_edges += tmp_sampled_src_list.size();
for (dgl_id_t& i : tmp_sampled_src_list) {
// If we have sampled the max number of vertices, we have to stop.
if (sub_ver_mp.size() >= max_num_vertices)
break;
// We need to add the neighbor in the hashtable here. This ensures that
// the vertex in the queue is unique. If we see a vertex before, we don't
// need to add it to the queue again.
auto ret = sub_ver_mp.insert(i);
// If the sampled neighbor is inserted to the map successfully.
if (ret.second)
sub_vers.emplace_back(i, cur_node_level + 1);
}
}
// Let's check if there is a vertex that we haven't sampled its neighbors.
for (; idx < sub_vers.size(); idx++) {
if (sub_vers[idx].second < num_hops) {
LOG(WARNING)
<< "The sampling is truncated because we have reached the max number of vertices\n"
<< "Please use a smaller number of seeds or a small neighborhood";
break;
}
}
// Copy sub_ver_mp to output[0]
// Copy layer
size_t num_vertices = sub_ver_mp.size();
std::sort(sub_vers.begin(),
sub_vers.end(),
[](const std::pair<dgl_id_t, dgl_id_t>& a1, const std::pair<dgl_id_t, dgl_id_t>& a2) {
return a1.first < a2.first;
});
for (size_t i = 0; i < sub_vers.size(); i++) {
out[i] = sub_vers[i].first;
out_layer[i] = sub_vers[i].second;
}
// The last element stores the actual
// number of vertices in the subgraph.
out[max_num_vertices] = sub_ver_mp.size();
// Copy sub_probability
if (sub_prob != nullptr) {
for (size_t i = 0; i < sub_ver_mp.size(); ++i) {
dgl_id_t idx = out[i];
sub_prob[i] = probability[idx];
}
}
// Construct sub_csr_graph
mxnet::TShape shape_1(1, -1);
mxnet::TShape shape_2(1, -1);
shape_1[0] = num_edges;
shape_2[0] = max_num_vertices + 1;
sub_csr.CheckAndAllocData(shape_1);
sub_csr.CheckAndAllocAuxData(csr::kIdx, shape_1);
sub_csr.CheckAndAllocAuxData(csr::kIndPtr, shape_2);
dgl_id_t* val_list_out = sub_csr.data().dptr<dgl_id_t>();
dgl_id_t* col_list_out = sub_csr.aux_data(1).dptr<dgl_id_t>();
dgl_id_t* indptr_out = sub_csr.aux_data(0).dptr<dgl_id_t>();
indptr_out[0] = 0;
size_t collected_nedges = 0;
// Both the out array and neigh_pos are sorted. By scanning the two arrays, we can see
// which vertices have neighbors and which don't.
std::sort(neigh_pos.begin(),
neigh_pos.end(),
[](const std::pair<dgl_id_t, size_t>& a1, const std::pair<dgl_id_t, size_t>& a2) {
return a1.first < a2.first;
});
size_t idx_with_neigh = 0;
for (size_t i = 0; i < num_vertices; i++) {
dgl_id_t dst_id = *(out + i);
// If a vertex is in sub_ver_mp but not in neigh_pos, this vertex must not
// have edges.
size_t edge_size = 0;
if (idx_with_neigh < neigh_pos.size() && dst_id == neigh_pos[idx_with_neigh].first) {
size_t pos = neigh_pos[idx_with_neigh].second;
CHECK_LT(pos, neighbor_list.size());
edge_size = neighbor_list[pos];
CHECK_LE(pos + edge_size * 2 + 1, neighbor_list.size());
std::copy_n(neighbor_list.begin() + pos + 1, edge_size, col_list_out + collected_nedges);
std::copy_n(
neighbor_list.begin() + pos + edge_size + 1, edge_size, val_list_out + collected_nedges);
collected_nedges += edge_size;
idx_with_neigh++;
}
indptr_out[i + 1] = indptr_out[i] + edge_size;
}
for (size_t i = num_vertices + 1; i <= max_num_vertices; ++i) {
indptr_out[i] = indptr_out[i - 1];
}
}
/*
* Operator: contrib_csr_neighbor_uniform_sample
*/
static void CSRNeighborUniformSampleComputeExCPU(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
int num_subgraphs = inputs.size() - 1;
CHECK_EQ(outputs.size(), 3 * num_subgraphs);
mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
mshadow::Random<cpu, unsigned int>* prnd = ctx.requested[0].get_random<cpu, unsigned int>(s);
unsigned int seed = prnd->GetRandInt();
#pragma omp parallel for
for (int i = 0; i < num_subgraphs; i++) {
SampleSubgraph(inputs[0], // graph_csr
inputs[i + 1], // seed vector
outputs[i], // sample_id
outputs[i + 1 * num_subgraphs], // sub_csr
nullptr, // sample_id_probability
outputs[i + 2 * num_subgraphs], // sample_id_layer
nullptr, // probability
params.num_hops,
params.num_neighbor,
params.max_num_vertices,
#if defined(_OPENMP)
seed + omp_get_thread_num());
#else
seed);
#endif
}
}
NNVM_REGISTER_OP(_contrib_dgl_csr_neighbor_uniform_sample)
.describe(R"code(This operator samples sub-graphs from a csr graph via an
uniform probability. The operator is designed for DGL.
The operator outputs three sets of NDArrays to represent the sampled results
(the number of NDArrays in each set is the same as the number of seed NDArrays):
1) a set of 1D NDArrays containing the sampled vertices, 2) a set of CSRNDArrays representing
the sampled edges, 3) a set of 1D NDArrays indicating the layer where a vertex is sampled.
The first set of 1D NDArrays have a length of max_num_vertices+1. The last element in an NDArray
indicate the acutal number of vertices in a subgraph. The third set of NDArrays have a length
of max_num_vertices, and the valid number of vertices is the same as the ones in the first set.
Example:
.. code:: python
shape = (5, 5)
data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64)
indices_np = np.array([1,2,3,4,0,2,3,4,0,1,3,4,0,1,2,4,0,1,2,3], dtype=np.int64)
indptr_np = np.array([0,4,8,12,16,20], dtype=np.int64)
a = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
a.asnumpy()
seed = mx.nd.array([0,1,2,3,4], dtype=np.int64)
out = mx.nd.contrib.dgl_csr_neighbor_uniform_sample(a, seed, num_args=2, num_hops=1, num_neighbor=2, max_num_vertices=5)
out[0]
[0 1 2 3 4 5]
<NDArray 6 @cpu(0)>
out[1].asnumpy()
array([[ 0, 1, 0, 3, 0],
[ 5, 0, 0, 7, 0],
[ 9, 0, 0, 11, 0],
[13, 0, 15, 0, 0],
[17, 0, 19, 0, 0]])
out[2]
[0 0 0 0 0]
<NDArray 5 @cpu(0)>
)code" ADD_FILELINE)
.set_attr_parser(ParamParser<NeighborSampleParam>)
.set_num_inputs([](const NodeAttrs& attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
return params.num_args;
})
.set_num_outputs([](const NodeAttrs& attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 1;
return num_subgraphs * 3;
})
.set_attr<FInferStorageType>("FInferStorageType", CSRNeighborUniformSampleStorageType)
.set_attr<mxnet::FInferShape>("FInferShape", CSRNeighborUniformSampleShape)
.set_attr<nnvm::FInferType>("FInferType", CSRNeighborUniformSampleType)
.set_attr<FComputeEx>("FComputeEx<cpu>", CSRNeighborUniformSampleComputeExCPU)
.set_attr<FResourceRequest>("FResourceRequest",
[](const NodeAttrs& attrs) {
return std::vector<ResourceRequest>{ResourceRequest::kRandom};
})
.add_argument("csr_matrix", "NDArray-or-Symbol", "csr matrix")
.add_argument("seed_arrays", "NDArray-or-Symbol[]", "seed vertices")
.set_attr<std::string>("key_var_num_args", "num_args")
.add_arguments(NeighborSampleParam::__FIELDS__());
/*
* Operator: contrib_csr_neighbor_non_uniform_sample
*/
static void CSRNeighborNonUniformSampleComputeExCPU(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
int num_subgraphs = inputs.size() - 2;
CHECK_EQ(outputs.size(), 4 * num_subgraphs);
const float* probability = inputs[1].data().dptr<float>();
mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
mshadow::Random<cpu, unsigned int>* prnd = ctx.requested[0].get_random<cpu, unsigned int>(s);
unsigned int seed = prnd->GetRandInt();
#pragma omp parallel for
for (int i = 0; i < num_subgraphs; i++) {
float* sub_prob = outputs[i + 2 * num_subgraphs].data().dptr<float>();
SampleSubgraph(inputs[0], // graph_csr
inputs[i + 2], // seed vector
outputs[i], // sample_id
outputs[i + 1 * num_subgraphs], // sub_csr
sub_prob, // sample_id_probability
outputs[i + 3 * num_subgraphs], // sample_id_layer
probability,
params.num_hops,
params.num_neighbor,
params.max_num_vertices,
#if defined(_OPENMP)
seed + omp_get_thread_num());
#else
seed);
#endif
}
}
NNVM_REGISTER_OP(_contrib_dgl_csr_neighbor_non_uniform_sample)
.describe(R"code(This operator samples sub-graph from a csr graph via an
non-uniform probability. The operator is designed for DGL.
The operator outputs four sets of NDArrays to represent the sampled results
(the number of NDArrays in each set is the same as the number of seed NDArrays):
1) a set of 1D NDArrays containing the sampled vertices, 2) a set of CSRNDArrays representing
the sampled edges, 3) a set of 1D NDArrays with the probability that vertices are sampled,
4) a set of 1D NDArrays indicating the layer where a vertex is sampled.
The first set of 1D NDArrays have a length of max_num_vertices+1. The last element in an NDArray
indicate the acutal number of vertices in a subgraph. The third and fourth set of NDArrays have a length
of max_num_vertices, and the valid number of vertices is the same as the ones in the first set.
Example:
.. code:: python
shape = (5, 5)
prob = mx.nd.array([0.9, 0.8, 0.2, 0.4, 0.1], dtype=np.float32)
data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64)
indices_np = np.array([1,2,3,4,0,2,3,4,0,1,3,4,0,1,2,4,0,1,2,3], dtype=np.int64)
indptr_np = np.array([0,4,8,12,16,20], dtype=np.int64)
a = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
seed = mx.nd.array([0,1,2,3,4], dtype=np.int64)
out = mx.nd.contrib.dgl_csr_neighbor_non_uniform_sample(a, prob, seed, num_args=3, num_hops=1, num_neighbor=2, max_num_vertices=5)
out[0]
[0 1 2 3 4 5]
<NDArray 6 @cpu(0)>
out[1].asnumpy()
array([[ 0, 1, 2, 0, 0],
[ 5, 0, 6, 0, 0],
[ 9, 10, 0, 0, 0],
[13, 14, 0, 0, 0],
[ 0, 18, 19, 0, 0]])
out[2]
[0.9 0.8 0.2 0.4 0.1]
<NDArray 5 @cpu(0)>
out[3]
[0 0 0 0 0]
<NDArray 5 @cpu(0)>
)code" ADD_FILELINE)
.set_attr_parser(ParamParser<NeighborSampleParam>)
.set_num_inputs([](const NodeAttrs& attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
return params.num_args;
})
.set_num_outputs([](const NodeAttrs& attrs) {
const NeighborSampleParam& params = nnvm::get<NeighborSampleParam>(attrs.parsed);
size_t num_subgraphs = params.num_args - 2;
return num_subgraphs * 4;
})
.set_attr<FInferStorageType>("FInferStorageType", CSRNeighborNonUniformSampleStorageType)
.set_attr<mxnet::FInferShape>("FInferShape", CSRNeighborNonUniformSampleShape)
.set_attr<nnvm::FInferType>("FInferType", CSRNeighborNonUniformSampleType)
.set_attr<FComputeEx>("FComputeEx<cpu>", CSRNeighborNonUniformSampleComputeExCPU)
.set_attr<FResourceRequest>("FResourceRequest",
[](const NodeAttrs& attrs) {
return std::vector<ResourceRequest>{ResourceRequest::kRandom};
})
.add_argument("csr_matrix", "NDArray-or-Symbol", "csr matrix")
.add_argument("probability", "NDArray-or-Symbol", "probability vector")
.add_argument("seed_arrays", "NDArray-or-Symbol[]", "seed vertices")
.set_attr<std::string>("key_var_num_args", "num_args")
.add_arguments(NeighborSampleParam::__FIELDS__());
///////////////////////// Create induced subgraph ///////////////////////////
struct DGLSubgraphParam : public dmlc::Parameter<DGLSubgraphParam> {
int num_args;
bool return_mapping;
DMLC_DECLARE_PARAMETER(DGLSubgraphParam) {
DMLC_DECLARE_FIELD(num_args).set_lower_bound(2).describe(
"Number of input arguments, including all symbol inputs.");
DMLC_DECLARE_FIELD(return_mapping)
.describe("Return mapping of vid and eid between the subgraph and the parent graph.");
}
}; // struct DGLSubgraphParam
DMLC_REGISTER_PARAMETER(DGLSubgraphParam);
static bool DGLSubgraphStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->at(0), kCSRStorage);
for (size_t i = 1; i < in_attrs->size(); i++)
CHECK_EQ(in_attrs->at(i), kDefaultStorage);
bool success = true;
*dispatch_mode = DispatchMode::kFComputeEx;
for (int& out_attr : *out_attrs) {
if (!type_assign(&out_attr, mxnet::kCSRStorage))
success = false;
}
return success;
}
static bool DGLSubgraphShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
const DGLSubgraphParam& params = nnvm::get<DGLSubgraphParam>(attrs.parsed);
CHECK_EQ(in_attrs->at(0).ndim(), 2U);
for (size_t i = 1; i < in_attrs->size(); i++)
CHECK_EQ(in_attrs->at(i).ndim(), 1U);
size_t num_g = params.num_args - 1;
for (size_t i = 0; i < num_g; i++) {
mxnet::TShape gshape(2, -1);
gshape[0] = in_attrs->at(i + 1)[0];
gshape[1] = in_attrs->at(i + 1)[0];
out_attrs->at(i) = gshape;
}
for (size_t i = num_g; i < out_attrs->size(); i++) {
mxnet::TShape gshape(2, -1);
gshape[0] = in_attrs->at(i - num_g + 1)[0];
gshape[1] = in_attrs->at(i - num_g + 1)[0];
out_attrs->at(i) = gshape;
}
return true;
}
static bool DGLSubgraphType(const nnvm::NodeAttrs& attrs,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
const DGLSubgraphParam& params = nnvm::get<DGLSubgraphParam>(attrs.parsed);
size_t num_g = params.num_args - 1;
for (size_t i = 0; i < num_g; i++) {
CHECK_EQ(in_attrs->at(i + 1), mshadow::kInt64);
}
for (int& out_attr : *out_attrs) {
out_attr = in_attrs->at(0);
}
return true;
}
class Bitmap {
const size_t size = 1024 * 1024 * 4;
const size_t mask = size - 1;
std::vector<bool> map;
size_t hash(dgl_id_t id) const {
return id & mask;
}
public:
Bitmap(const dgl_id_t* vid_data, int64_t len) : map(size) {
for (int64_t i = 0; i < len; ++i) {
map[hash(vid_data[i])] = true;
}
}
bool test(dgl_id_t id) const {
return map[hash(id)];
}
};
/*
* This uses a hashtable to check if a node is in the given node list.
*/
class HashTableChecker {
std::unordered_map<dgl_id_t, dgl_id_t> oldv2newv;
Bitmap map;
public:
HashTableChecker(const dgl_id_t* vid_data, int64_t len) : map(vid_data, len) {
oldv2newv.reserve(len);
for (int64_t i = 0; i < len; ++i) {
oldv2newv[vid_data[i]] = i;
}
}
void CollectOnRow(const dgl_id_t col_idx[],
const dgl_id_t eids[],
size_t row_len,
std::vector<dgl_id_t>* new_col_idx,
std::vector<dgl_id_t>* orig_eids) {
// TODO(zhengda) I need to make sure the column index in each row is sorted.
for (size_t j = 0; j < row_len; ++j) {
const dgl_id_t oldsucc = col_idx[j];
const dgl_id_t eid = eids[j];
Collect(oldsucc, eid, new_col_idx, orig_eids);
}
}
void Collect(const dgl_id_t old_id,
const dgl_id_t old_eid,
std::vector<dgl_id_t>* col_idx,
std::vector<dgl_id_t>* orig_eids) {
if (!map.test(old_id))
return;
auto it = oldv2newv.find(old_id);
if (it != oldv2newv.end()) {
const dgl_id_t new_id = it->second;
col_idx->push_back(new_id);
if (orig_eids)
orig_eids->push_back(old_eid);
}
}
};
static void GetSubgraph(const NDArray& csr_arr,
const NDArray& varr,
const NDArray& sub_csr,
const NDArray* old_eids) {
const TBlob& data = varr.data();
int64_t num_vertices = csr_arr.shape()[0];
const size_t len = varr.shape()[0];
const dgl_id_t* vid_data = data.dptr<dgl_id_t>();
HashTableChecker def_check(vid_data, len);
// check if varr is sorted.
CHECK(std::is_sorted(vid_data, vid_data + len)) << "The input vertex list has to be sorted";
// Collect the non-zero entries in from the original graph.
std::vector<dgl_id_t> row_idx(len + 1);
std::vector<dgl_id_t> col_idx;
std::vector<dgl_id_t> orig_eids;
col_idx.reserve(len * 50);
orig_eids.reserve(len * 50);
const dgl_id_t* eids = csr_arr.data().dptr<dgl_id_t>();
const dgl_id_t* indptr = csr_arr.aux_data(csr::kIndPtr).dptr<dgl_id_t>();
const dgl_id_t* indices = csr_arr.aux_data(csr::kIdx).dptr<dgl_id_t>();
for (size_t i = 0; i < len; ++i) {
const dgl_id_t oldvid = vid_data[i];
CHECK_LT(oldvid, num_vertices)
<< "Vertex Id " << oldvid << " isn't in a graph of " << num_vertices << " vertices";
size_t row_start = indptr[oldvid];
size_t row_len = indptr[oldvid + 1] - indptr[oldvid];
def_check.CollectOnRow(indices + row_start,
eids + row_start,
row_len,
&col_idx,
old_eids == nullptr ? nullptr : &orig_eids);
row_idx[i + 1] = col_idx.size();
}
mxnet::TShape nz_shape(1, -1);
nz_shape[0] = col_idx.size();
mxnet::TShape indptr_shape(1, -1);
indptr_shape[0] = row_idx.size();
// Store the non-zeros in a subgraph with edge attributes of new edge ids.
sub_csr.CheckAndAllocData(nz_shape);
sub_csr.CheckAndAllocAuxData(csr::kIdx, nz_shape);
sub_csr.CheckAndAllocAuxData(csr::kIndPtr, indptr_shape);
dgl_id_t* indices_out = sub_csr.aux_data(csr::kIdx).dptr<dgl_id_t>();
dgl_id_t* indptr_out = sub_csr.aux_data(csr::kIndPtr).dptr<dgl_id_t>();
std::copy(col_idx.begin(), col_idx.end(), indices_out);
std::copy(row_idx.begin(), row_idx.end(), indptr_out);
dgl_id_t* sub_eids = sub_csr.data().dptr<dgl_id_t>();
for (int64_t i = 0; i < nz_shape[0]; i++)
sub_eids[i] = i;
// Store the non-zeros in a subgraph with edge attributes of old edge ids.
if (old_eids) {
old_eids->CheckAndAllocData(nz_shape);
old_eids->CheckAndAllocAuxData(csr::kIdx, nz_shape);
old_eids->CheckAndAllocAuxData(csr::kIndPtr, indptr_shape);
dgl_id_t* indices_out = old_eids->aux_data(csr::kIdx).dptr<dgl_id_t>();
dgl_id_t* indptr_out = old_eids->aux_data(csr::kIndPtr).dptr<dgl_id_t>();
dgl_id_t* sub_eids = old_eids->data().dptr<dgl_id_t>();
std::copy(col_idx.begin(), col_idx.end(), indices_out);
std::copy(row_idx.begin(), row_idx.end(), indptr_out);
std::copy(orig_eids.begin(), orig_eids.end(), sub_eids);
}
}
static void DGLSubgraphComputeExCPU(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
const DGLSubgraphParam& params = nnvm::get<DGLSubgraphParam>(attrs.parsed);
int num_g = params.num_args - 1;
#pragma omp parallel for
for (int i = 0; i < num_g; i++) {
const NDArray* old_eids = params.return_mapping ? &outputs[i + num_g] : nullptr;
GetSubgraph(inputs[0], inputs[i + 1], outputs[i], old_eids);
}
}
NNVM_REGISTER_OP(_contrib_dgl_subgraph)
.describe(R"code(This operator constructs an induced subgraph for
a given set of vertices from a graph. The operator accepts multiple
sets of vertices as input. For each set of vertices, it returns a pair
of CSR matrices if return_mapping is True: the first matrix contains edges
with new edge Ids, the second matrix contains edges with the original
edge Ids.
Example:
.. code:: python
x=[[1, 0, 0, 2],
[3, 0, 4, 0],
[0, 5, 0, 0],
[0, 6, 7, 0]]
v = [0, 1, 2]
dgl_subgraph(x, v, return_mapping=True) =
[[1, 0, 0],
[2, 0, 3],
[0, 4, 0]],
[[1, 0, 0],
[3, 0, 4],
[0, 5, 0]]
)code" ADD_FILELINE)
.set_attr_parser(ParamParser<DGLSubgraphParam>)
.set_num_inputs([](const NodeAttrs& attrs) {
const DGLSubgraphParam& params = nnvm::get<DGLSubgraphParam>(attrs.parsed);
return params.num_args;
})
.set_num_outputs([](const NodeAttrs& attrs) {
const DGLSubgraphParam& params = nnvm::get<DGLSubgraphParam>(attrs.parsed);
int num_varray = params.num_args - 1;
if (params.return_mapping)
return num_varray * 2;
else
return num_varray;
})
.set_attr<nnvm::FListInputNames>("FListInputNames",
[](const NodeAttrs& attrs) {
const DGLSubgraphParam& params =
nnvm::get<DGLSubgraphParam>(attrs.parsed);
std::vector<std::string> names;
names.reserve(params.num_args);
names.emplace_back("graph");
for (int i = 1; i < params.num_args; ++i)
names.push_back("varray" + std::to_string(i - 1));
return names;
})
.set_attr<FInferStorageType>("FInferStorageType", DGLSubgraphStorageType)
.set_attr<mxnet::FInferShape>("FInferShape", DGLSubgraphShape)
.set_attr<nnvm::FInferType>("FInferType", DGLSubgraphType)
.set_attr<FComputeEx>("FComputeEx<cpu>", DGLSubgraphComputeExCPU)
.set_attr<std::string>("key_var_num_args", "num_args")
.add_argument("graph", "NDArray-or-Symbol", "Input graph where we sample vertices.")
.add_argument("data",
"NDArray-or-Symbol[]",
"The input arrays that include data arrays and states.")
.add_arguments(DGLSubgraphParam::__FIELDS__());
///////////////////////// Edge Id ///////////////////////////
inline bool EdgeIDShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
CHECK_EQ(in_attrs->size(), 3U);
CHECK_EQ(out_attrs->size(), 1U);
CHECK_EQ(in_attrs->at(1).ndim(), 1U);
CHECK_EQ(in_attrs->at(2).ndim(), 1U);
CHECK_EQ(in_attrs->at(1)[0], in_attrs->at(2)[0]);
SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
SHAPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
SHAPE_ASSIGN_CHECK(*in_attrs, 2, out_attrs->at(0));
return !mxnet::op::shape_is_none(out_attrs->at(0));
}
inline bool EdgeIDType(const nnvm::NodeAttrs& attrs,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 3U);
CHECK_EQ(out_attrs->size(), 1U);
TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
return out_attrs->at(0) != -1;
}
inline bool EdgeIDStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 3U) << "Only works for 2d arrays";
CHECK_EQ(out_attrs->size(), 1U);
int& in_stype = in_attrs->at(0);
int& out_stype = out_attrs->at(0);
bool dispatched = false;
if (!dispatched && in_stype == kCSRStorage) {
// csr -> dns
dispatched =
storage_type_assign(&out_stype, kDefaultStorage, dispatch_mode, DispatchMode::kFComputeEx);
}
if (!dispatched) {
LOG(ERROR) << "Cannot dispatch edge_id storage type, only works for csr matrices";
}
return dispatched;
}
struct edge_id_csr_forward {
template <typename DType, typename IType, typename CType>
MSHADOW_XINLINE static void Map(int i,
DType* out_data,
const DType* in_data,
const IType* in_indices,
const IType* in_indptr,
const CType* u,
const CType* v) {
const int64_t target_row_id = static_cast<int64_t>(u[i]);
const IType target_col_id = static_cast<IType>(v[i]);
auto ptr = std::find(in_indices + in_indptr[target_row_id],
in_indices + in_indptr[target_row_id + 1],
target_col_id);
if (ptr == in_indices + in_indptr[target_row_id + 1]) {
// does not exist in the range
out_data[i] = DType(-1);
} else {
out_data[i] = *(in_data + (ptr - in_indices));
}
}
};
template <typename xpu>
void EdgeIDForwardCsrImpl(const OpContext& ctx,
const std::vector<NDArray>& inputs,
const OpReqType req,
const NDArray& output) {
using namespace mshadow;
using namespace mxnet_op;
using namespace csr;
if (req == kNullOp)
return;
CHECK_EQ(inputs.size(), 3U);
CHECK_EQ(req, kWriteTo) << "EdgeID with CSR only supports kWriteTo";
Stream<xpu>* s = ctx.get_stream<xpu>();
const NDArray& u = inputs[1];
const dim_t out_elems = u.shape().Size();
if (!inputs[0].storage_initialized()) {
MSHADOW_TYPE_SWITCH(output.dtype(), DType, {
Kernel<mxnet_op::op_with_req<mshadow_op::identity, kWriteTo>, xpu>::Launch(
s, out_elems, output.data().dptr<DType>(), DType(-1));
});
return;
}
const NDArray& data = inputs[0];
const TBlob& in_data = data.data();
const TBlob& in_indices = data.aux_data(kIdx);
const TBlob& in_indptr = data.aux_data(kIndPtr);
const NDArray& v = inputs[2];
CHECK_EQ(data.aux_type(kIdx), data.aux_type(kIndPtr))
<< "The dtypes of indices and indptr don't match";
MSHADOW_TYPE_SWITCH(data.dtype(), DType, {
MSHADOW_IDX_TYPE_SWITCH(data.aux_type(kIdx), IType, {
MSHADOW_TYPE_SWITCH(u.dtype(), CType, {
Kernel<edge_id_csr_forward, xpu>::Launch(s,
out_elems,
output.data().dptr<DType>(),
in_data.dptr<DType>(),
in_indices.dptr<IType>(),
in_indptr.dptr<IType>(),
u.data().dptr<CType>(),
v.data().dptr<CType>());
});
});
});
}
template <typename xpu>
void EdgeIDForwardEx(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
CHECK_EQ(inputs.size(), 3U);
CHECK_EQ(outputs.size(), 1U);
CHECK_EQ(req.size(), 1U);
const auto in_stype = inputs[0].storage_type();
const auto out_stype = outputs[0].storage_type();
if (in_stype == kCSRStorage && out_stype == kDefaultStorage) {
EdgeIDForwardCsrImpl<xpu>(ctx, inputs, req[0], outputs[0]);
} else {
LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
}
}
NNVM_REGISTER_OP(_contrib_edge_id)
.describe(R"code(This operator implements the edge_id function for a graph
stored in a CSR matrix (the value of the CSR stores the edge Id of the graph).
output[i] = input[u[i], v[i]] if there is an edge between u[i] and v[i]],
otherwise output[i] will be -1. Both u and v should be 1D vectors.
Example:
.. code:: python
x = [[ 1, 0, 0 ],
[ 0, 2, 0 ],
[ 0, 0, 3 ]]
u = [ 0, 0, 1, 1, 2, 2 ]
v = [ 0, 1, 1, 2, 0, 2 ]
edge_id(x, u, v) = [ 1, -1, 2, -1, -1, 3 ]
The storage type of ``edge_id`` output depends on storage types of inputs
- edge_id(csr, default, default) = default
- default and rsp inputs are not supported
)code" ADD_FILELINE)
.set_num_inputs(3)
.set_num_outputs(1)
.set_attr<nnvm::FListInputNames>("FListInputNames",
[](const NodeAttrs& attrs) {
return std::vector<std::string>{"data", "u", "v"};
})
.set_attr<mxnet::FInferShape>("FInferShape", EdgeIDShape)
.set_attr<nnvm::FInferType>("FInferType", EdgeIDType)
.set_attr<FInferStorageType>("FInferStorageType", EdgeIDStorageType)
.set_attr<FComputeEx>("FComputeEx<cpu>", EdgeIDForwardEx<cpu>)
.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
.add_argument("u", "NDArray-or-Symbol", "u ndarray")
.add_argument("v", "NDArray-or-Symbol", "v ndarray");
///////////////////////// DGL Adjacency ///////////////////////////
inline bool DGLAdjacencyShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
SHAPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
return !mxnet::op::shape_is_none(out_attrs->at(0));
}
inline bool DGLAdjacencyType(const nnvm::NodeAttrs& attrs,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
CHECK_EQ(in_attrs->at(0), mshadow::kInt64);
TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat32);
return out_attrs->at(0) != -1;
}
inline bool DGLAdjacencyStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 1U) << "Only works for 2d arrays";
CHECK_EQ(out_attrs->size(), 1U);
int& out_stype = out_attrs->at(0);
bool dispatched =
storage_type_assign(&out_stype, kCSRStorage, dispatch_mode, DispatchMode::kFComputeEx);
if (!dispatched) {
LOG(ERROR) << "Cannot dispatch output storage type: " << common::stype_string(out_stype)
<< ". dgl_adjacency only works for csr matrices";
}
return dispatched;
}
NNVM_REGISTER_OP(_contrib_dgl_adjacency)
.describe(R"code(This operator converts a CSR matrix whose values are edge Ids
to an adjacency matrix whose values are ones. The output CSR matrix always has
the data value of float32.
Example:
.. code:: python
x = [[ 1, 0, 0 ],
[ 0, 2, 0 ],
[ 0, 0, 3 ]]
dgl_adjacency(x) =
[[ 1, 0, 0 ],
[ 0, 1, 0 ],
[ 0, 0, 1 ]]
)code" ADD_FILELINE)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr<nnvm::FListInputNames>("FListInputNames",
[](const NodeAttrs& attrs) {
return std::vector<std::string>{"data"};
})
.set_attr<mxnet::FInferShape>("FInferShape", DGLAdjacencyShape)
.set_attr<nnvm::FInferType>("FInferType", DGLAdjacencyType)
.set_attr<FInferStorageType>("FInferStorageType", DGLAdjacencyStorageType)
.set_attr<FComputeEx>("FComputeEx<cpu>", DGLAdjacencyForwardEx<cpu>)
.add_argument("data", "NDArray-or-Symbol", "Input ndarray");
///////////////////////// Compact subgraphs ///////////////////////////
struct SubgraphCompactParam : public dmlc::Parameter<SubgraphCompactParam> {
int num_args;
bool return_mapping;
mxnet::Tuple<dim_t> graph_sizes;
DMLC_DECLARE_PARAMETER(SubgraphCompactParam) {
DMLC_DECLARE_FIELD(num_args).set_lower_bound(2).describe("Number of input arguments.");
DMLC_DECLARE_FIELD(return_mapping)
.describe("Return mapping of vid and eid between the subgraph and the parent graph.");
DMLC_DECLARE_FIELD(graph_sizes).describe("the number of vertices in each graph.");
}
}; // struct SubgraphCompactParam
DMLC_REGISTER_PARAMETER(SubgraphCompactParam);
static inline size_t get_num_graphs(const SubgraphCompactParam& params) {
// Each CSR needs a 1D array to store the original vertex Id for each row.
return params.num_args / 2;
}
static void CompactSubgraph(const NDArray& csr,
const NDArray& vids,
const NDArray& out_csr,
size_t graph_size) {
TBlob in_idx_data = csr.aux_data(csr::kIdx);
TBlob in_ptr_data = csr.aux_data(csr::kIndPtr);
const dgl_id_t* indices_in = in_idx_data.dptr<dgl_id_t>();
const dgl_id_t* indptr_in = in_ptr_data.dptr<dgl_id_t>();
const dgl_id_t* row_ids = vids.data().dptr<dgl_id_t>();
size_t num_elems = csr.aux_data(csr::kIdx).shape_.Size();
// The last element in vids is the actual number of vertices in the subgraph.
CHECK_EQ(vids.shape()[0], in_ptr_data.shape_[0]);
CHECK_EQ(static_cast<size_t>(row_ids[vids.shape()[0] - 1]), graph_size);
// Prepare the Id map from the original graph to the subgraph.
std::unordered_map<dgl_id_t, dgl_id_t> id_map;
id_map.reserve(graph_size);
for (size_t i = 0; i < graph_size; i++) {
id_map.insert(std::pair<dgl_id_t, dgl_id_t>(row_ids[i], i));
CHECK_NE(row_ids[i], -1);
}
mxnet::TShape nz_shape(1, -1);
nz_shape[0] = num_elems;
mxnet::TShape indptr_shape(1, -1);
CHECK_EQ(out_csr.shape()[0], graph_size);
indptr_shape[0] = graph_size + 1;
CHECK_GE(in_ptr_data.shape_[0], indptr_shape[0]);
out_csr.CheckAndAllocData(nz_shape);
out_csr.CheckAndAllocAuxData(csr::kIdx, nz_shape);
out_csr.CheckAndAllocAuxData(csr::kIndPtr, indptr_shape);
dgl_id_t* indices_out = out_csr.aux_data(csr::kIdx).dptr<dgl_id_t>();
dgl_id_t* indptr_out = out_csr.aux_data(csr::kIndPtr).dptr<dgl_id_t>();
dgl_id_t* sub_eids = out_csr.data().dptr<dgl_id_t>();
std::copy(indptr_in, indptr_in + indptr_shape[0], indptr_out);
for (int64_t i = 0; i < nz_shape[0]; i++) {
dgl_id_t old_id = indices_in[i];
auto it = id_map.find(old_id);
CHECK(it != id_map.end());
indices_out[i] = it->second;
sub_eids[i] = i;
}
}
static void SubgraphCompactComputeExCPU(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
const SubgraphCompactParam& params = nnvm::get<SubgraphCompactParam>(attrs.parsed);
int num_g = get_num_graphs(params);
#pragma omp parallel for
for (int i = 0; i < num_g; i++) {
CompactSubgraph(inputs[i], inputs[i + num_g], outputs[i], params.graph_sizes[i]);
}
}
static bool SubgraphCompactStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
const SubgraphCompactParam& params = nnvm::get<SubgraphCompactParam>(attrs.parsed);
size_t num_g = get_num_graphs(params);
CHECK_EQ(num_g * 2, in_attrs->size());
// These are the input subgraphs.
for (size_t i = 0; i < num_g; i++)
CHECK_EQ(in_attrs->at(i), kCSRStorage);
// These are the vertex Ids in the original graph.
for (size_t i = 0; i < num_g; i++)
CHECK_EQ(in_attrs->at(i + num_g), kDefaultStorage);
bool success = true;
*dispatch_mode = DispatchMode::kFComputeEx;
for (int& out_attr : *out_attrs) {
if (!type_assign(&out_attr, mxnet::kCSRStorage))
success = false;
}
return success;
}
static bool SubgraphCompactShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
const SubgraphCompactParam& params = nnvm::get<SubgraphCompactParam>(attrs.parsed);
size_t num_g = get_num_graphs(params);
CHECK_EQ(num_g * 2, in_attrs->size());
// These are the input subgraphs.
for (size_t i = 0; i < num_g; i++) {
CHECK_EQ(in_attrs->at(i).ndim(), 2U);
CHECK_GE(in_attrs->at(i)[0], params.graph_sizes[i]);
CHECK_GE(in_attrs->at(i)[1], params.graph_sizes[i]);
}
// These are the vertex Ids in the original graph.
for (size_t i = 0; i < num_g; i++) {
CHECK_EQ(in_attrs->at(i + num_g).ndim(), 1U);
CHECK_GE(in_attrs->at(i + num_g)[0], params.graph_sizes[i]);
}
for (size_t i = 0; i < num_g; i++) {
mxnet::TShape gshape(2, -1);
gshape[0] = params.graph_sizes[i];
gshape[1] = params.graph_sizes[i];
out_attrs->at(i) = gshape;
if (params.return_mapping)
out_attrs->at(i + num_g) = gshape;
}
return true;
}
static bool SubgraphCompactType(const nnvm::NodeAttrs& attrs,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
for (int& in_attr : *in_attrs) {
CHECK_EQ(in_attr, mshadow::kInt64);
}
for (int& out_attr : *out_attrs) {
out_attr = mshadow::kInt64;
}
return true;
}
NNVM_REGISTER_OP(_contrib_dgl_graph_compact)
.describe(R"code(This operator compacts a CSR matrix generated by
dgl_csr_neighbor_uniform_sample and dgl_csr_neighbor_non_uniform_sample.
The CSR matrices generated by these two operators may have many empty
rows at the end and many empty columns. This operator removes these
empty rows and empty columns.
Example:
.. code:: python
shape = (5, 5)
data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64)
indices_np = np.array([1,2,3,4,0,2,3,4,0,1,3,4,0,1,2,4,0,1,2,3], dtype=np.int64)
indptr_np = np.array([0,4,8,12,16,20], dtype=np.int64)
a = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape)
seed = mx.nd.array([0,1,2,3,4], dtype=np.int64)
out = mx.nd.contrib.dgl_csr_neighbor_uniform_sample(a, seed, num_args=2, num_hops=1,
num_neighbor=2, max_num_vertices=6)
subg_v = out[0]
subg = out[1]
compact = mx.nd.contrib.dgl_graph_compact(subg, subg_v,
graph_sizes=(subg_v[-1].asnumpy()[0]), return_mapping=False)
compact.asnumpy()
array([[0, 0, 0, 1, 0],
[2, 0, 3, 0, 0],
[0, 4, 0, 0, 5],
[0, 6, 0, 0, 7],
[8, 9, 0, 0, 0]])
)code" ADD_FILELINE)
.set_attr_parser(ParamParser<SubgraphCompactParam>)
.set_num_inputs([](const NodeAttrs& attrs) {
const SubgraphCompactParam& params = nnvm::get<SubgraphCompactParam>(attrs.parsed);
return params.num_args;
})
.set_num_outputs([](const NodeAttrs& attrs) {
const SubgraphCompactParam& params = nnvm::get<SubgraphCompactParam>(attrs.parsed);
int num_varray = get_num_graphs(params);
if (params.return_mapping)
return num_varray * 2;
else
return num_varray;
})
.set_attr<nnvm::FListInputNames>("FListInputNames",
[](const NodeAttrs& attrs) {
const SubgraphCompactParam& params =
nnvm::get<SubgraphCompactParam>(attrs.parsed);
std::vector<std::string> names;
names.reserve(params.num_args);
size_t num_graphs = get_num_graphs(params);
for (size_t i = 0; i < num_graphs; i++)
names.push_back("graph" + std::to_string(i));
for (size_t i = 0; i < num_graphs; ++i)
names.push_back("varray" + std::to_string(i));
return names;
})
.set_attr<FInferStorageType>("FInferStorageType", SubgraphCompactStorageType)
.set_attr<mxnet::FInferShape>("FInferShape", SubgraphCompactShape)
.set_attr<nnvm::FInferType>("FInferType", SubgraphCompactType)
.set_attr<FComputeEx>("FComputeEx<cpu>", SubgraphCompactComputeExCPU)
.set_attr<std::string>("key_var_num_args", "num_args")
.add_argument("graph_data", "NDArray-or-Symbol[]", "Input graphs and input vertex Ids.")
.add_arguments(SubgraphCompactParam::__FIELDS__());
} // namespace op
} // namespace mxnet