diff --git a/cmake/configure.cmake b/cmake/configure.cmake index 41b4a90c20f201c93f6f6f4ba3c334fe538104f4..4bc00593c8e18dfbb6a01ecde2313a08283952bc 100755 --- a/cmake/configure.cmake +++ b/cmake/configure.cmake @@ -241,3 +241,6 @@ endif() if(WITH_CUSTOM_DEVICE AND NOT WIN32) add_definitions(-DPADDLE_WITH_CUSTOM_DEVICE) endif() +if(WITH_GPU_GRAPH) + add_definitions(-DPADDLE_WITH_GPU_GRAPH) +endif() diff --git a/paddle/fluid/distributed/ps/service/graph_brpc_server.cc b/paddle/fluid/distributed/ps/service/graph_brpc_server.cc index 9ff333b044cda66b38de70800fb999e905800381..cee2d09cecc916bcb39c70160d0219dcb55c04c4 100644 --- a/paddle/fluid/distributed/ps/service/graph_brpc_server.cc +++ b/paddle/fluid/distributed/ps/service/graph_brpc_server.cc @@ -144,10 +144,8 @@ int32_t GraphBrpcService::add_graph_node(Table *table, int idx_ = *(int *)(request.params(0).c_str()); size_t node_num = request.params(1).size() / sizeof(int64_t); - int64_t *node_data = (int64_t *)(request.params(1).c_str()); - // size_t node_num = request.params(0).size() / sizeof(int64_t); - // int64_t *node_data = (int64_t *)(request.params(0).c_str()); - std::vector node_ids(node_data, node_data + node_num); + uint64_t *node_data = (uint64_t *)(request.params(1).c_str()); + std::vector node_ids(node_data, node_data + node_num); std::vector is_weighted_list; if (request.params_size() == 3) { size_t weight_list_size = request.params(2).size() / sizeof(bool); @@ -179,11 +177,9 @@ int32_t GraphBrpcService::remove_graph_node(Table *table, return 0; } int idx_ = *(int *)(request.params(0).c_str()); - size_t node_num = request.params(1).size() / sizeof(int64_t); - int64_t *node_data = (int64_t *)(request.params(1).c_str()); - // size_t node_num = request.params(0).size() / sizeof(int64_t); - // int64_t *node_data = (int64_t *)(request.params(0).c_str()); - std::vector node_ids(node_data, node_data + node_num); + size_t node_num = request.params(1).size() / sizeof(uint64_t); + uint64_t *node_data = (uint64_t *)(request.params(1).c_str()); + std::vector node_ids(node_data, node_data + node_num); ((GraphTable *)table)->remove_graph_node(idx_, node_ids); return 0; @@ -217,11 +213,6 @@ int32_t GraphBrpcService::Initialize() { &GraphBrpcService::graph_set_node_feat; _service_handler_map[PS_GRAPH_SAMPLE_NODES_FROM_ONE_SERVER] = &GraphBrpcService::sample_neighbors_across_multi_servers; - // _service_handler_map[PS_GRAPH_USE_NEIGHBORS_SAMPLE_CACHE] = - // &GraphBrpcService::use_neighbors_sample_cache; - // _service_handler_map[PS_GRAPH_LOAD_GRAPH_SPLIT_CONFIG] = - // &GraphBrpcService::load_graph_split_config; - // shard初始化,server启动后才可从env获取到server_list的shard信息 InitializeShardInfo(); return 0; @@ -389,9 +380,6 @@ int32_t GraphBrpcService::pull_graph_list(Table *table, int start = *(int *)(request.params(2).c_str()); int size = *(int *)(request.params(3).c_str()); int step = *(int *)(request.params(4).c_str()); - // int start = *(int *)(request.params(0).c_str()); - // int size = *(int *)(request.params(1).c_str()); - // int step = *(int *)(request.params(2).c_str()); std::unique_ptr buffer; int actual_size; ((GraphTable *)table) @@ -414,14 +402,10 @@ int32_t GraphBrpcService::graph_random_sample_neighbors( return 0; } int idx_ = *(int *)(request.params(0).c_str()); - size_t node_num = request.params(1).size() / sizeof(int64_t); - int64_t *node_data = (int64_t *)(request.params(1).c_str()); - int sample_size = *(int64_t *)(request.params(2).c_str()); + size_t node_num = request.params(1).size() / sizeof(uint64_t); + uint64_t *node_data = (uint64_t *)(request.params(1).c_str()); + int sample_size = *(int *)(request.params(2).c_str()); bool need_weight = *(bool *)(request.params(3).c_str()); - // size_t node_num = request.params(0).size() / sizeof(int64_t); - // int64_t *node_data = (int64_t *)(request.params(0).c_str()); - // int sample_size = *(int64_t *)(request.params(1).c_str()); - // bool need_weight = *(bool *)(request.params(2).c_str()); std::vector> buffers(node_num); std::vector actual_sizes(node_num, 0); ((GraphTable *)table) @@ -443,7 +427,7 @@ int32_t GraphBrpcService::graph_random_sample_nodes( brpc::Controller *cntl) { int type_id = *(int *)(request.params(0).c_str()); int idx_ = *(int *)(request.params(1).c_str()); - size_t size = *(int64_t *)(request.params(2).c_str()); + size_t size = *(uint64_t *)(request.params(2).c_str()); // size_t size = *(int64_t *)(request.params(0).c_str()); std::unique_ptr buffer; int actual_size; @@ -470,11 +454,9 @@ int32_t GraphBrpcService::graph_get_node_feat(Table *table, return 0; } int idx_ = *(int *)(request.params(0).c_str()); - size_t node_num = request.params(1).size() / sizeof(int64_t); - int64_t *node_data = (int64_t *)(request.params(1).c_str()); - // size_t node_num = request.params(0).size() / sizeof(int64_t); - // int64_t *node_data = (int64_t *)(request.params(0).c_str()); - std::vector node_ids(node_data, node_data + node_num); + size_t node_num = request.params(1).size() / sizeof(uint64_t); + uint64_t *node_data = (uint64_t *)(request.params(1).c_str()); + std::vector node_ids(node_data, node_data + node_num); std::vector feature_names = paddle::string::split_string(request.params(2), "\t"); @@ -511,21 +493,14 @@ int32_t GraphBrpcService::sample_neighbors_across_multi_servers( } int idx_ = *(int *)(request.params(0).c_str()); - size_t node_num = request.params(1).size() / sizeof(int64_t); - int64_t *node_data = (int64_t *)(request.params(1).c_str()); - int sample_size = *(int64_t *)(request.params(2).c_str()); - bool need_weight = *(int64_t *)(request.params(3).c_str()); - - // size_t node_num = request.params(0).size() / sizeof(int64_t), - // size_of_size_t = sizeof(size_t); - // int64_t *node_data = (int64_t *)(request.params(0).c_str()); - // int sample_size = *(int64_t *)(request.params(1).c_str()); - // bool need_weight = *(int64_t *)(request.params(2).c_str()); - // std::vector res = ((GraphTable - // *)table).filter_out_non_exist_nodes(node_data, sample_size); + size_t node_num = request.params(1).size() / sizeof(uint64_t); + uint64_t *node_data = (uint64_t *)(request.params(1).c_str()); + int sample_size = *(int *)(request.params(2).c_str()); + bool need_weight = *(bool *)(request.params(3).c_str()); + std::vector request2server; std::vector server2request(server_size, -1); - std::vector local_id; + std::vector local_id; std::vector local_query_idx; size_t rank = GetRank(); for (size_t query_idx = 0; query_idx < node_num; ++query_idx) { @@ -548,7 +523,7 @@ int32_t GraphBrpcService::sample_neighbors_across_multi_servers( std::vector> local_buffers; std::vector local_actual_sizes; std::vector seq; - std::vector> node_id_buckets(request_call_num); + std::vector> node_id_buckets(request_call_num); std::vector> query_idx_buckets(request_call_num); for (size_t query_idx = 0; query_idx < node_num; ++query_idx) { int server_index = @@ -639,7 +614,7 @@ int32_t GraphBrpcService::sample_neighbors_across_multi_servers( closure->request(request_idx) ->add_params((char *)node_id_buckets[request_idx].data(), - sizeof(int64_t) * node_num); + sizeof(uint64_t) * node_num); closure->request(request_idx) ->add_params((char *)&sample_size, sizeof(int)); closure->request(request_idx) @@ -682,11 +657,9 @@ int32_t GraphBrpcService::graph_set_node_feat(Table *table, } int idx_ = *(int *)(request.params(0).c_str()); - // size_t node_num = request.params(0).size() / sizeof(int64_t); - // int64_t *node_data = (int64_t *)(request.params(0).c_str()); - size_t node_num = request.params(1).size() / sizeof(int64_t); - int64_t *node_data = (int64_t *)(request.params(1).c_str()); - std::vector node_ids(node_data, node_data + node_num); + size_t node_num = request.params(1).size() / sizeof(uint64_t); + uint64_t *node_data = (uint64_t *)(request.params(1).c_str()); + std::vector node_ids(node_data, node_data + node_num); // std::vector feature_names = // paddle::string::split_string(request.params(1), "\t"); diff --git a/paddle/fluid/distributed/ps/table/CMakeLists.txt b/paddle/fluid/distributed/ps/table/CMakeLists.txt index 3a9933cabdd7ca3bf21234d681e8041030007e77..983e8172f18c94075b9871fe4cc54bd76b1652ec 100644 --- a/paddle/fluid/distributed/ps/table/CMakeLists.txt +++ b/paddle/fluid/distributed/ps/table/CMakeLists.txt @@ -18,7 +18,7 @@ set_source_files_properties( cc_library( graph_node SRCS ${graphDir}/graph_node.cc - DEPS WeightedSampler) + DEPS WeightedSampler enforce) set_source_files_properties( memory_dense_table.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) set_source_files_properties( diff --git a/paddle/fluid/distributed/ps/table/common_graph_table.cc b/paddle/fluid/distributed/ps/table/common_graph_table.cc index 10985b207cf5d8b4c1c8e60752f2c3a7566d4baf..a77209928563bbd5ec16c46ec690063a888a30e9 100644 --- a/paddle/fluid/distributed/ps/table/common_graph_table.cc +++ b/paddle/fluid/distributed/ps/table/common_graph_table.cc @@ -21,12 +21,17 @@ #include #include +#include "gflags/gflags.h" #include "paddle/fluid/distributed/common/utils.h" #include "paddle/fluid/distributed/ps/table/graph/graph_node.h" #include "paddle/fluid/framework/generator.h" +#include "paddle/fluid/framework/io/fs.h" +#include "paddle/fluid/platform/timer.h" #include "paddle/fluid/string/printf.h" #include "paddle/fluid/string/string_helper.h" +DECLARE_bool(graph_load_in_parallel); + namespace paddle { namespace distributed { @@ -47,34 +52,125 @@ int32_t GraphTable::Load_to_ssd(const std::string &path, return 0; } -paddle::framework::GpuPsCommGraph GraphTable::make_gpu_ps_graph( - int idx, std::vector ids) { - std::vector> bags(task_pool_size_); - for (auto x : ids) { +paddle::framework::GpuPsCommGraphFea GraphTable::make_gpu_ps_graph_fea( + std::vector &node_ids, int slot_num) { + std::vector> bags(task_pool_size_); + for (int i = 0; i < task_pool_size_; i++) { + auto predsize = node_ids.size() / task_pool_size_; + bags[i].reserve(predsize * 1.2); + } + + for (auto x : node_ids) { int location = x % shard_num % task_pool_size_; bags[location].push_back(x); } + std::vector> tasks; - std::vector edge_array[task_pool_size_]; - std::vector node_array[task_pool_size_]; + std::vector feature_array[task_pool_size_]; + std::vector slot_id_array[task_pool_size_]; + std::vector node_id_array[task_pool_size_]; + std::vector + node_fea_info_array[task_pool_size_]; for (size_t i = 0; i < bags.size(); i++) { if (bags[i].size() > 0) { tasks.push_back(_shards_task_pool[i]->enqueue([&, i, this]() -> int { - paddle::framework::GpuPsGraphNode x; + uint64_t node_id; + paddle::framework::GpuPsFeaInfo x; + std::vector feature_ids; for (size_t j = 0; j < bags[i].size(); j++) { - Node *v = find_node(0, idx, bags[i][j]); - x.node_id = bags[i][j]; + // TODO use FEATURE_TABLE instead + Node *v = find_node(1, bags[i][j]); + node_id = bags[i][j]; if (v == NULL) { - x.neighbor_size = 0; - x.neighbor_offset = 0; - node_array[i].push_back(x); + x.feature_size = 0; + x.feature_offset = 0; + node_fea_info_array[i].push_back(x); } else { - x.neighbor_size = v->get_neighbor_size(); - x.neighbor_offset = edge_array[i].size(); - node_array[i].push_back(x); - for (size_t k = 0; k < x.neighbor_size; k++) { + // x <- v + x.feature_offset = feature_array[i].size(); + int total_feature_size = 0; + for (int k = 0; k < slot_num; ++k) { + v->get_feature_ids(k, &feature_ids); + total_feature_size += feature_ids.size(); + if (!feature_ids.empty()) { + feature_array[i].insert(feature_array[i].end(), + feature_ids.begin(), + feature_ids.end()); + slot_id_array[i].insert( + slot_id_array[i].end(), feature_ids.size(), k); + } + } + x.feature_size = total_feature_size; + node_fea_info_array[i].push_back(x); + } + node_id_array[i].push_back(node_id); + } + return 0; + })); + } + } + for (int i = 0; i < (int)tasks.size(); i++) tasks[i].get(); + paddle::framework::GpuPsCommGraphFea res; + uint64_t tot_len = 0; + for (int i = 0; i < task_pool_size_; i++) { + tot_len += feature_array[i].size(); + } + VLOG(0) << "Loaded feature table on cpu, feature_list_size[" << tot_len + << "] node_ids_size[" << node_ids.size() << "]"; + res.init_on_cpu(tot_len, (unsigned int)node_ids.size(), slot_num); + unsigned int offset = 0, ind = 0; + for (int i = 0; i < task_pool_size_; i++) { + for (int j = 0; j < (int)node_id_array[i].size(); j++) { + res.node_list[ind] = node_id_array[i][j]; + res.fea_info_list[ind] = node_fea_info_array[i][j]; + res.fea_info_list[ind++].feature_offset += offset; + } + for (size_t j = 0; j < feature_array[i].size(); j++) { + res.feature_list[offset + j] = feature_array[i][j]; + res.slot_id_list[offset + j] = slot_id_array[i][j]; + } + offset += feature_array[i].size(); + } + return res; +} + +paddle::framework::GpuPsCommGraph GraphTable::make_gpu_ps_graph( + int idx, std::vector ids) { + std::vector> bags(task_pool_size_); + for (int i = 0; i < task_pool_size_; i++) { + auto predsize = ids.size() / task_pool_size_; + bags[i].reserve(predsize * 1.2); + } + for (auto x : ids) { + int location = x % shard_num % task_pool_size_; + bags[location].push_back(x); + } + + std::vector> tasks; + std::vector node_array[task_pool_size_]; // node id list + std::vector info_array[task_pool_size_]; + std::vector edge_array[task_pool_size_]; // edge id list + + for (size_t i = 0; i < bags.size(); i++) { + if (bags[i].size() > 0) { + tasks.push_back(_shards_task_pool[i]->enqueue([&, i, this]() -> int { + node_array[i].resize(bags[i].size()); + info_array[i].resize(bags[i].size()); + edge_array[i].reserve(bags[i].size()); + + for (size_t j = 0; j < bags[i].size(); j++) { + auto node_id = bags[i][j]; + node_array[i][j] = node_id; + Node *v = find_node(0, idx, node_id); + if (v != nullptr) { + info_array[i][j].neighbor_offset = edge_array[i].size(); + info_array[i][j].neighbor_size = v->get_neighbor_size(); + for (size_t k = 0; k < v->get_neighbor_size(); k++) { edge_array[i].push_back(v->get_neighbor_id(k)); } + } else { + info_array[i][j].neighbor_offset = 0; + info_array[i][j].neighbor_size = 0; } } return 0; @@ -82,21 +178,20 @@ paddle::framework::GpuPsCommGraph GraphTable::make_gpu_ps_graph( } } for (int i = 0; i < (int)tasks.size(); i++) tasks[i].get(); - paddle::framework::GpuPsCommGraph res; + int64_t tot_len = 0; for (int i = 0; i < task_pool_size_; i++) { tot_len += edge_array[i].size(); } - // res.neighbor_size = tot_len; - // res.node_size = ids.size(); - // res.neighbor_list = new int64_t[tot_len]; - // res.node_list = new paddle::framework::GpuPsGraphNode[ids.size()]; + + paddle::framework::GpuPsCommGraph res; res.init_on_cpu(tot_len, ids.size()); int64_t offset = 0, ind = 0; for (int i = 0; i < task_pool_size_; i++) { for (int j = 0; j < (int)node_array[i].size(); j++) { res.node_list[ind] = node_array[i][j]; - res.node_list[ind++].neighbor_offset += offset; + res.node_info_list[ind] = info_array[i][j]; + res.node_info_list[ind++].neighbor_offset += offset; } for (size_t j = 0; j < edge_array[i].size(); j++) { res.neighbor_list[offset + j] = edge_array[i][j]; @@ -107,62 +202,41 @@ paddle::framework::GpuPsCommGraph GraphTable::make_gpu_ps_graph( } int32_t GraphTable::add_node_to_ssd( - int type_id, int idx, int64_t src_id, char *data, int len) { + int type_id, int idx, uint64_t src_id, char *data, int len) { if (_db != NULL) { - char ch[sizeof(int) * 2 + sizeof(int64_t)]; + char ch[sizeof(int) * 2 + sizeof(uint64_t)]; memcpy(ch, &type_id, sizeof(int)); memcpy(ch + sizeof(int), &idx, sizeof(int)); - memcpy(ch + sizeof(int) * 2, &src_id, sizeof(int64_t)); + memcpy(ch + sizeof(int) * 2, &src_id, sizeof(uint64_t)); std::string str; if (_db->get(src_id % shard_num % task_pool_size_, ch, - sizeof(int) * 2 + sizeof(int64_t), + sizeof(int) * 2 + sizeof(uint64_t), str) == 0) { - int64_t *stored_data = ((int64_t *)str.c_str()); - int n = str.size() / sizeof(int64_t); - char *new_data = new char[n * sizeof(int64_t) + len]; - memcpy(new_data, stored_data, n * sizeof(int64_t)); - memcpy(new_data + n * sizeof(int64_t), data, len); + uint64_t *stored_data = ((uint64_t *)str.c_str()); + int n = str.size() / sizeof(uint64_t); + char *new_data = new char[n * sizeof(uint64_t) + len]; + memcpy(new_data, stored_data, n * sizeof(uint64_t)); + memcpy(new_data + n * sizeof(uint64_t), data, len); _db->put(src_id % shard_num % task_pool_size_, ch, - sizeof(int) * 2 + sizeof(int64_t), + sizeof(int) * 2 + sizeof(uint64_t), (char *)new_data, - n * sizeof(int64_t) + len); + n * sizeof(uint64_t) + len); delete[] new_data; } else { _db->put(src_id % shard_num % task_pool_size_, ch, - sizeof(int) * 2 + sizeof(int64_t), + sizeof(int) * 2 + sizeof(uint64_t), (char *)data, len); } - // _db->flush(src_id % shard_num % task_pool_size_); - // std::string x; - // if (_db->get(src_id % shard_num % task_pool_size_, ch, sizeof(int64_t) + - // 2 * sizeof(int), x) ==0){ - // VLOG(0)<<"put result"; - // for(int i = 0;i < x.size();i+=8){ - // VLOG(0)<<"get an id "<<*((int64_t *)(x.c_str() + i)); - // } - //} - // if(src_id == 429){ - // str = ""; - // _db->get(src_id % shard_num % task_pool_size_, ch, - // sizeof(int) * 2 + sizeof(int64_t), str); - // int64_t *stored_data = ((int64_t *)str.c_str()); - // int n = str.size() / sizeof(int64_t); - // VLOG(0)<<"429 has "< rng, int &actual_size) { @@ -172,18 +246,18 @@ char *GraphTable::random_sample_neighbor_from_ssd( } std::string str; VLOG(2) << "sample ssd for key " << id; - char ch[sizeof(int) * 2 + sizeof(int64_t)]; + char ch[sizeof(int) * 2 + sizeof(uint64_t)]; memset(ch, 0, sizeof(int)); memcpy(ch + sizeof(int), &idx, sizeof(int)); - memcpy(ch + sizeof(int) * 2, &id, sizeof(int64_t)); + memcpy(ch + sizeof(int) * 2, &id, sizeof(uint64_t)); if (_db->get(id % shard_num % task_pool_size_, ch, - sizeof(int) * 2 + sizeof(int64_t), + sizeof(int) * 2 + sizeof(uint64_t), str) == 0) { - int64_t *data = ((int64_t *)str.c_str()); - int n = str.size() / sizeof(int64_t); + uint64_t *data = ((uint64_t *)str.c_str()); + int n = str.size() / sizeof(uint64_t); std::unordered_map m; - // std::vector res; + // std::vector res; int sm_size = std::min(n, sample_size); actual_size = sm_size * Node::id_size; char *buff = new char[actual_size]; @@ -207,7 +281,7 @@ char *GraphTable::random_sample_neighbor_from_ssd( // res.push_back(data[pos]); } for (int i = 0; i < actual_size; i += 8) { - VLOG(2) << "sampled an neighbor " << *(int64_t *)&buff[i]; + VLOG(2) << "sampled an neighbor " << *(uint64_t *)&buff[i]; } return buff; } @@ -216,8 +290,8 @@ char *GraphTable::random_sample_neighbor_from_ssd( } int64_t GraphTable::load_graph_to_memory_from_ssd(int idx, - std::vector &ids) { - std::vector> bags(task_pool_size_); + std::vector &ids) { + std::vector> bags(task_pool_size_); for (auto x : ids) { int location = x % shard_num % task_pool_size_; bags[location].push_back(x); @@ -227,17 +301,17 @@ int64_t GraphTable::load_graph_to_memory_from_ssd(int idx, for (size_t i = 0; i < bags.size(); i++) { if (bags[i].size() > 0) { tasks.push_back(_shards_task_pool[i]->enqueue([&, i, idx, this]() -> int { - char ch[sizeof(int) * 2 + sizeof(int64_t)]; + char ch[sizeof(int) * 2 + sizeof(uint64_t)]; memset(ch, 0, sizeof(int)); memcpy(ch + sizeof(int), &idx, sizeof(int)); for (size_t k = 0; k < bags[i].size(); k++) { auto v = bags[i][k]; - memcpy(ch + sizeof(int) * 2, &v, sizeof(int64_t)); + memcpy(ch + sizeof(int) * 2, &v, sizeof(uint64_t)); std::string str; - if (_db->get(i, ch, sizeof(int) * 2 + sizeof(int64_t), str) == 0) { + if (_db->get(i, ch, sizeof(int) * 2 + sizeof(uint64_t), str) == 0) { count[i] += (int64_t)str.size(); - for (int j = 0; j < str.size(); j += sizeof(int64_t)) { - int64_t id = *(int64_t *)(str.c_str() + j); + for (size_t j = 0; j < (int)str.size(); j += sizeof(uint64_t)) { + uint64_t id = *(uint64_t *)(str.c_str() + j); add_comm_edge(idx, v, id); } } @@ -274,7 +348,7 @@ void GraphTable::make_partitions(int idx, int64_t byte_size, int device_len) { std::vector weight_cost(part_len, 0); std::vector memory_remaining(part_len, gb_size_by_discount); std::vector score(part_len, 0); - std::unordered_map id_map; + std::unordered_map id_map; std::vector iters; for (int i = 0; i < task_pool_size_; i++) { iters.push_back(_db->get_iterator(i)); @@ -282,7 +356,7 @@ void GraphTable::make_partitions(int idx, int64_t byte_size, int device_len) { } int next = 0; while (iters.size()) { - if (next >= iters.size()) { + if (next >= (int)iters.size()) { next = 0; } if (!iters[next]->Valid()) { @@ -298,7 +372,7 @@ void GraphTable::make_partitions(int idx, int64_t byte_size, int device_len) { continue; } std::string value = iters[next]->value().ToString(); - std::int64_t i_key = *(int64_t *)(key.c_str() + sizeof(int) * 2); + std::uint64_t i_key = *(uint64_t *)(key.c_str() + sizeof(int) * 2); for (int i = 0; i < part_len; i++) { if (memory_remaining[i] < (int64_t)value.size()) { score[i] = -100000.0; @@ -306,8 +380,8 @@ void GraphTable::make_partitions(int idx, int64_t byte_size, int device_len) { score[i] = 0; } } - for (int j = 0; j < value.size(); j += sizeof(int64_t)) { - int64_t v = *((int64_t *)(value.c_str() + j)); + for (size_t j = 0; j < (int)value.size(); j += sizeof(uint64_t)) { + uint64_t v = *((uint64_t *)(value.c_str() + j)); int index = -1; if (id_map.find(v) != id_map.end()) { index = id_map[v]; @@ -398,7 +472,7 @@ void GraphTable::clear_graph(int idx) { } } int32_t GraphTable::load_next_partition(int idx) { - if (next_partition >= partitions[idx].size()) { + if (next_partition >= (int)partitions[idx].size()) { VLOG(0) << "partition iteration is done"; return -1; } @@ -426,8 +500,6 @@ int32_t GraphTable::load_edges_to_ssd(const std::string &path, auto paths = paddle::string::split_string(path, ";"); int64_t count = 0; std::string sample_type = "random"; - bool is_weighted = false; - int valid_count = 0; for (auto path : paths) { std::ifstream file(path); std::string line; @@ -438,16 +510,16 @@ int32_t GraphTable::load_edges_to_ssd(const std::string &path, if (values.size() < 2) continue; auto src_id = std::stoll(values[0]); auto dist_ids = paddle::string::split_string(values[1], ";"); - std::vector dist_data; + std::vector dist_data; for (auto x : dist_ids) { dist_data.push_back(std::stoll(x)); - total_memory_cost += sizeof(int64_t); + total_memory_cost += sizeof(uint64_t); } add_node_to_ssd(0, idx, src_id, (char *)dist_data.data(), - (int)(dist_data.size() * sizeof(int64_t))); + (int)(dist_data.size() * sizeof(uint64_t))); } } VLOG(0) << "total memory cost = " << total_memory_cost << " bytes"; @@ -456,9 +528,6 @@ int32_t GraphTable::load_edges_to_ssd(const std::string &path, int32_t GraphTable::dump_edges_to_ssd(int idx) { VLOG(2) << "calling dump edges to ssd"; - const int64_t fixed_size = 10000; - // std::vector edge_array[task_pool_size_]; - std::vector> count(task_pool_size_); std::vector> tasks; auto &shards = edge_shards[idx]; for (size_t i = 0; i < shards.size(); ++i) { @@ -466,18 +535,17 @@ int32_t GraphTable::dump_edges_to_ssd(int idx) { [&, i, this]() -> int64_t { int64_t cost = 0; std::vector &v = shards[i]->get_bucket(); - size_t ind = i % this->task_pool_size_; for (size_t j = 0; j < v.size(); j++) { - std::vector s; - for (int k = 0; k < v[j]->get_neighbor_size(); k++) { + std::vector s; + for (size_t k = 0; k < (int)v[j]->get_neighbor_size(); k++) { s.push_back(v[j]->get_neighbor_id(k)); } - cost += v[j]->get_neighbor_size() * sizeof(int64_t); + cost += v[j]->get_neighbor_size() * sizeof(uint64_t); add_node_to_ssd(0, idx, v[j]->get_id(), (char *)s.data(), - s.size() * sizeof(int64_t)); + s.size() * sizeof(uint64_t)); } return cost; })); @@ -489,7 +557,7 @@ int32_t GraphTable::make_complementary_graph(int idx, int64_t byte_size) { VLOG(0) << "make_complementary_graph"; const int64_t fixed_size = byte_size / 8; // std::vector edge_array[task_pool_size_]; - std::vector> count(task_pool_size_); + std::vector> count(task_pool_size_); std::vector> tasks; auto &shards = edge_shards[idx]; for (size_t i = 0; i < shards.size(); ++i) { @@ -499,7 +567,7 @@ int32_t GraphTable::make_complementary_graph(int idx, int64_t byte_size) { size_t ind = i % this->task_pool_size_; for (size_t j = 0; j < v.size(); j++) { // size_t location = v[j]->get_id(); - for (int k = 0; k < v[j]->get_neighbor_size(); k++) { + for (size_t k = 0; k < v[j]->get_neighbor_size(); k++) { count[ind][v[j]->get_neighbor_id(k)]++; } } @@ -507,9 +575,9 @@ int32_t GraphTable::make_complementary_graph(int idx, int64_t byte_size) { })); } for (size_t i = 0; i < tasks.size(); i++) tasks[i].get(); - std::unordered_map final_count; - std::map> count_to_id; - std::vector buffer; + std::unordered_map final_count; + std::map> count_to_id; + std::vector buffer; clear_graph(idx); for (int i = 0; i < task_pool_size_; i++) { @@ -546,6 +614,7 @@ int32_t GraphTable::make_complementary_graph(int idx, int64_t byte_size) { bucket[i]->build_sampler(sample_type); } } + return 0; } #endif @@ -840,7 +909,7 @@ std::vector GraphShard::get_batch(int start, int end, int step) { size_t GraphShard::get_size() { return bucket.size(); } -int32_t GraphTable::add_comm_edge(int idx, int64_t src_id, int64_t dst_id) { +int32_t GraphTable::add_comm_edge(int idx, uint64_t src_id, uint64_t dst_id) { size_t src_shard_id = src_id % shard_num; if (src_shard_id >= shard_end || src_shard_id < shard_start) { @@ -852,11 +921,11 @@ int32_t GraphTable::add_comm_edge(int idx, int64_t src_id, int64_t dst_id) { return 0; } int32_t GraphTable::add_graph_node(int idx, - std::vector &id_list, + std::vector &id_list, std::vector &is_weight_list) { auto &shards = edge_shards[idx]; size_t node_size = id_list.size(); - std::vector>> batch(task_pool_size_); + std::vector>> batch(task_pool_size_); for (size_t i = 0; i < node_size; i++) { size_t shard_id = id_list[i] % shard_num; if (shard_id >= shard_end || shard_id < shard_start) { @@ -881,9 +950,9 @@ int32_t GraphTable::add_graph_node(int idx, return 0; } -int32_t GraphTable::remove_graph_node(int idx, std::vector &id_list) { +int32_t GraphTable::remove_graph_node(int idx, std::vector &id_list) { size_t node_size = id_list.size(); - std::vector> batch(task_pool_size_); + std::vector> batch(task_pool_size_); for (size_t i = 0; i < node_size; i++) { size_t shard_id = id_list[i] % shard_num; if (shard_id >= shard_end || shard_id < shard_start) continue; @@ -916,7 +985,7 @@ void GraphShard::clear() { GraphShard::~GraphShard() { clear(); } -void GraphShard::delete_node(int64_t id) { +void GraphShard::delete_node(uint64_t id) { auto iter = node_location.find(id); if (iter == node_location.end()) return; int pos = iter->second; @@ -928,7 +997,7 @@ void GraphShard::delete_node(int64_t id) { node_location.erase(id); bucket.pop_back(); } -GraphNode *GraphShard::add_graph_node(int64_t id) { +GraphNode *GraphShard::add_graph_node(uint64_t id) { if (node_location.find(id) == node_location.end()) { node_location[id] = bucket.size(); bucket.push_back(new GraphNode(id)); @@ -944,19 +1013,25 @@ GraphNode *GraphShard::add_graph_node(Node *node) { } return (GraphNode *)bucket[node_location[id]]; } -FeatureNode *GraphShard::add_feature_node(int64_t id) { + +FeatureNode *GraphShard::add_feature_node(uint64_t id, bool is_overlap) { if (node_location.find(id) == node_location.end()) { node_location[id] = bucket.size(); bucket.push_back(new FeatureNode(id)); + return (FeatureNode *)bucket[node_location[id]]; + } + if (is_overlap) { + return (FeatureNode *)bucket[node_location[id]]; } - return (FeatureNode *)bucket[node_location[id]]; + + return NULL; } -void GraphShard::add_neighbor(int64_t id, int64_t dst_id, float weight) { +void GraphShard::add_neighbor(uint64_t id, uint64_t dst_id, float weight) { find_node(id)->add_edge(dst_id, weight); } -Node *GraphShard::find_node(int64_t id) { +Node *GraphShard::find_node(uint64_t id) { auto iter = node_location.find(id); return iter == node_location.end() ? nullptr : bucket[iter->second]; } @@ -992,15 +1067,93 @@ int32_t GraphTable::Load(const std::string &path, const std::string ¶m) { return 0; } +std::string GraphTable::get_inverse_etype(std::string &etype) { + auto etype_split = paddle::string::split_string(etype, "2"); + std::string res; + if ((int)etype_split.size() == 3) { + res = etype_split[2] + "2" + etype_split[1] + "2" + etype_split[0]; + } else { + res = etype_split[1] + "2" + etype_split[0]; + } + return res; +} + +int32_t GraphTable::load_node_and_edge_file(std::string etype, + std::string ntype, + std::string epath, + std::string npath, + int part_num, + bool reverse) { + auto etypes = paddle::string::split_string(etype, ","); + auto ntypes = paddle::string::split_string(ntype, ","); + VLOG(0) << "etypes size: " << etypes.size(); + VLOG(0) << "whether reverse: " << reverse; + std::string delim = ";"; + size_t total_len = etypes.size() + 1; // 1 is for node + + std::vector> tasks; + for (size_t i = 0; i < total_len; i++) { + tasks.push_back( + _shards_task_pool[i % task_pool_size_]->enqueue([&, i, this]() -> int { + if (i < etypes.size()) { + std::string etype_path = epath + "/" + etypes[i]; + auto etype_path_list = paddle::framework::localfs_list(etype_path); + std::string etype_path_str; + if (part_num > 0 && part_num < (int)etype_path_list.size()) { + std::vector sub_etype_path_list( + etype_path_list.begin(), etype_path_list.begin() + part_num); + etype_path_str = + paddle::string::join_strings(sub_etype_path_list, delim); + } else { + etype_path_str = + paddle::string::join_strings(etype_path_list, delim); + } + this->load_edges(etype_path_str, false, etypes[i]); + if (reverse) { + std::string r_etype = get_inverse_etype(etypes[i]); + this->load_edges(etype_path_str, true, r_etype); + } + } else { + auto npath_list = paddle::framework::localfs_list(npath); + std::string npath_str; + if (part_num > 0 && part_num < (int)npath_list.size()) { + std::vector sub_npath_list( + npath_list.begin(), npath_list.begin() + part_num); + npath_str = paddle::string::join_strings(sub_npath_list, delim); + } else { + npath_str = paddle::string::join_strings(npath_list, delim); + } + + if (ntypes.size() == 0) { + VLOG(0) << "node_type not specified, nothing will be loaded "; + return 0; + } + + if (FLAGS_graph_load_in_parallel) { + this->load_nodes(npath_str, ""); + } else { + for (size_t j = 0; j < ntypes.size(); j++) { + this->load_nodes(npath_str, ntypes[j]); + } + } + } + return 0; + })); + } + for (int i = 0; i < (int)tasks.size(); i++) tasks[i].get(); + return 0; +} + int32_t GraphTable::get_nodes_ids_by_ranges( int type_id, int idx, std::vector> ranges, - std::vector &res) { + std::vector &res) { + std::mutex mutex; int start = 0, end, index = 0, total_size = 0; res.clear(); auto &shards = type_id == 0 ? edge_shards[idx] : feature_shards[idx]; - std::vector>> tasks; + std::vector> tasks; for (size_t i = 0; i < shards.size() && index < (int)ranges.size(); i++) { end = total_size + shards[i]->get_size(); start = total_size; @@ -1016,86 +1169,173 @@ int32_t GraphTable::get_nodes_ids_by_ranges( first -= total_size; second -= total_size; tasks.push_back(_shards_task_pool[i % task_pool_size_]->enqueue( - [&shards, this, first, second, i]() -> std::vector { - return shards[i]->get_ids_by_range(first, second); + [&shards, this, first, second, i, &res, &mutex]() -> size_t { + std::vector keys; + shards[i]->get_ids_by_range(first, second, &keys); + + size_t num = keys.size(); + mutex.lock(); + res.reserve(res.size() + num); + for (auto &id : keys) { + res.push_back(id); + std::swap(res[rand() % res.size()], res[(int)res.size() - 1]); + } + mutex.unlock(); + + return num; })); } } total_size += shards[i]->get_size(); } for (size_t i = 0; i < tasks.size(); i++) { - auto vec = tasks[i].get(); - for (auto &id : vec) { - res.push_back(id); - std::swap(res[rand() % res.size()], res[(int)res.size() - 1]); - } + tasks[i].get(); } return 0; } -int32_t GraphTable::load_nodes(const std::string &path, std::string node_type) { - auto paths = paddle::string::split_string(path, ";"); - int64_t count = 0; - int64_t valid_count = 0; - int idx = 0; - if (node_type == "") { - VLOG(0) << "node_type not specified, loading edges to " << id_to_feature[0] - << " part"; - } else { - if (feature_to_id.find(node_type) == feature_to_id.end()) { - VLOG(0) << "node_type " << node_type - << " is not defined, nothing will be loaded"; - return 0; +std::pair GraphTable::parse_node_file( + const std::string &path, const std::string &node_type, int idx) { + std::ifstream file(path); + std::string line; + uint64_t local_count = 0; + uint64_t local_valid_count = 0; + + int num = 0; + std::vector vals; + size_t n = node_type.length(); + while (std::getline(file, line)) { + if (strncmp(line.c_str(), node_type.c_str(), n) != 0) { + continue; } - idx = feature_to_id[node_type]; - } - for (auto path : paths) { - std::ifstream file(path); - std::string line; - while (std::getline(file, line)) { - auto values = paddle::string::split_string(line, "\t"); - if (values.size() < 2) continue; - auto id = std::stoull(values[1]); + vals.clear(); + num = paddle::string::split_string_ptr( + line.c_str() + n + 1, line.length() - n - 1, '\t', &vals); + if (num == 0) { + continue; + } + uint64_t id = std::strtoul(vals[0].ptr, NULL, 10); + size_t shard_id = id % shard_num; + if (shard_id >= shard_end || shard_id < shard_start) { + VLOG(4) << "will not load " << id << " from " << path + << ", please check id distribution"; + continue; + } + local_count++; - size_t shard_id = id % shard_num; - if (shard_id >= shard_end || shard_id < shard_start) { - VLOG(4) << "will not load " << id << " from " << path - << ", please check id distribution"; - continue; + size_t index = shard_id - shard_start; + auto node = feature_shards[idx][index]->add_feature_node(id, false); + if (node != NULL) { + node->set_feature_size(feat_name[idx].size()); + for (int i = 1; i < num; ++i) { + auto &v = vals[i]; + parse_feature(idx, v.ptr, v.len, node); } + } + local_valid_count++; + } + VLOG(2) << "node_type[" << node_type << "] loads " << local_count + << " nodes from filepath->" << path; + return {local_count, local_valid_count}; +} - if (count % 1000000 == 0) { - VLOG(0) << count << " nodes are loaded from filepath"; - VLOG(0) << line; - } - count++; +std::pair GraphTable::parse_node_file( + const std::string &path) { + std::ifstream file(path); + std::string line; + uint64_t local_count = 0; + uint64_t local_valid_count = 0; + int idx = 0; - std::string nt = values[0]; - if (nt != node_type) { - continue; - } + auto path_split = paddle::string::split_string(path, "/"); + auto path_name = path_split[path_split.size() - 1]; - size_t index = shard_id - shard_start; + int num = 0; + std::vector vals; - // auto node = shards[index]->add_feature_node(id); - auto node = feature_shards[idx][index]->add_feature_node(id); - node->set_feature_size(feat_name[idx].size()); + while (std::getline(file, line)) { + vals.clear(); + num = paddle::string::split_string_ptr( + line.c_str(), line.length(), '\t', &vals); + if (vals.empty()) { + continue; + } + std::string parse_node_type = vals[0].to_string(); + auto it = feature_to_id.find(parse_node_type); + if (it == feature_to_id.end()) { + VLOG(0) << parse_node_type << "type error, please check"; + continue; + } + idx = it->second; + uint64_t id = std::strtoul(vals[1].ptr, NULL, 10); + size_t shard_id = id % shard_num; + if (shard_id >= shard_end || shard_id < shard_start) { + VLOG(4) << "will not load " << id << " from " << path + << ", please check id distribution"; + continue; + } + local_count++; + + size_t index = shard_id - shard_start; + auto node = feature_shards[idx][index]->add_feature_node(id, false); + if (node != NULL) { + for (int i = 2; i < num; ++i) { + auto &v = vals[i]; + parse_feature(idx, v.ptr, v.len, node); + } + } + local_valid_count++; + } + VLOG(2) << local_valid_count << "/" << local_count << " nodes from filepath->" + << path; + return {local_count, local_valid_count}; +} - for (size_t slice = 2; slice < values.size(); slice++) { - auto feat = this->parse_feature(idx, values[slice]); - if (feat.first >= 0) { - node->set_feature(feat.first, feat.second); - } else { - VLOG(4) << "Node feature: " << values[slice] - << " not in feature_map."; - } +// TODO opt load all node_types in once reading +int32_t GraphTable::load_nodes(const std::string &path, std::string node_type) { + auto paths = paddle::string::split_string(path, ";"); + uint64_t count = 0; + uint64_t valid_count = 0; + int idx = 0; + if (FLAGS_graph_load_in_parallel) { + if (node_type == "") { + VLOG(0) << "Begin GraphTable::load_nodes(), will load all node_type once"; + } + std::vector>> tasks; + for (size_t i = 0; i < paths.size(); i++) { + tasks.push_back(load_node_edge_task_pool->enqueue( + [&, i, this]() -> std::pair { + return parse_node_file(paths[i]); + })); + } + for (int i = 0; i < (int)tasks.size(); i++) { + auto res = tasks[i].get(); + count += res.first; + valid_count += res.second; + } + } else { + VLOG(0) << "Begin GraphTable::load_nodes() node_type[" << node_type << "]"; + if (node_type == "") { + VLOG(0) << "node_type not specified, loading edges to " + << id_to_feature[0] << " part"; + } else { + if (feature_to_id.find(node_type) == feature_to_id.end()) { + VLOG(0) << "node_type " << node_type + << " is not defined, nothing will be loaded"; + return 0; } - valid_count++; + idx = feature_to_id[node_type]; + } + for (auto path : paths) { + VLOG(2) << "Begin GraphTable::load_nodes(), path[" << path << "]"; + auto res = parse_node_file(path, node_type, idx); + count += res.first; + valid_count += res.second; } } - VLOG(0) << valid_count << "/" << count << " nodes in type " << node_type - << " are loaded successfully in " << path; + VLOG(0) << valid_count << "/" << count << " nodes in node_type[ " << node_type + << "] are loaded successfully!"; return 0; } @@ -1108,13 +1348,71 @@ int32_t GraphTable::build_sampler(int idx, std::string sample_type) { } return 0; } + +std::pair GraphTable::parse_edge_file( + const std::string &path, int idx, bool reverse) { + std::string sample_type = "random"; + bool is_weighted = false; + std::ifstream file(path); + std::string line; + uint64_t local_count = 0; + uint64_t local_valid_count = 0; + uint64_t part_num = 0; + if (FLAGS_graph_load_in_parallel) { + auto path_split = paddle::string::split_string(path, "/"); + auto part_name_split = paddle::string::split_string( + path_split[path_split.size() - 1], "-"); + part_num = std::stoull(part_name_split[part_name_split.size() - 1]); + } + + while (std::getline(file, line)) { + size_t start = line.find_first_of('\t'); + if (start == std::string::npos) continue; + local_count++; + uint64_t src_id = std::stoull(&line[0]); + uint64_t dst_id = std::stoull(&line[start + 1]); + if (reverse) { + std::swap(src_id, dst_id); + } + size_t src_shard_id = src_id % shard_num; + if (FLAGS_graph_load_in_parallel) { + if (src_shard_id != (part_num % shard_num)) { + continue; + } + } + + float weight = 1; + size_t last = line.find_last_of('\t'); + if (start != last) { + weight = std::stof(&line[last + 1]); + sample_type = "weighted"; + is_weighted = true; + } + + if (src_shard_id >= shard_end || src_shard_id < shard_start) { + VLOG(4) << "will not load " << src_id << " from " << path + << ", please check id distribution"; + continue; + } + size_t index = src_shard_id - shard_start; + auto node = edge_shards[idx][index]->add_graph_node(src_id); + if (node != NULL) { + node->build_edges(is_weighted); + node->add_edge(dst_id, weight); + } + + local_valid_count++; + } + VLOG(2) << local_count << " edges are loaded from filepath->" << path; + return {local_count, local_valid_count}; +} + int32_t GraphTable::load_edges(const std::string &path, bool reverse_edge, const std::string &edge_type) { #ifdef PADDLE_WITH_HETERPS - // if (gpups_mode) pthread_rwlock_rdlock(rw_lock.get()); if (search_level == 2) total_memory_cost = 0; - const int64_t fixed_load_edges = 1000000; + const uint64_t fixed_load_edges = 1000000; #endif int idx = 0; if (edge_type == "") { @@ -1130,63 +1428,34 @@ int32_t GraphTable::load_edges(const std::string &path, } auto paths = paddle::string::split_string(path, ";"); - int64_t count = 0; - std::string sample_type = "random"; - bool is_weighted = false; - int valid_count = 0; - for (auto path : paths) { - std::ifstream file(path); - std::string line; - while (std::getline(file, line)) { - auto values = paddle::string::split_string(line, "\t"); - count++; - if (values.size() < 2) continue; - auto src_id = std::stoull(values[0]); - auto dst_id = std::stoull(values[1]); - if (reverse_edge) { - std::swap(src_id, dst_id); - } - float weight = 1; - if (values.size() == 3) { - weight = std::stof(values[2]); - sample_type = "weighted"; - is_weighted = true; - } - - size_t src_shard_id = src_id % shard_num; - - if (src_shard_id >= shard_end || src_shard_id < shard_start) { - VLOG(4) << "will not load " << src_id << " from " << path - << ", please check id distribution"; - continue; - } - - if (count % 1000000 == 0) { - VLOG(0) << count << " edges are loaded from filepath"; - VLOG(0) << line; - } - - size_t index = src_shard_id - shard_start; - edge_shards[idx][index]->add_graph_node(src_id)->build_edges(is_weighted); - edge_shards[idx][index]->add_neighbor(src_id, dst_id, weight); - valid_count++; -#ifdef PADDLE_WITH_HETERPS - // if (gpups_mode) pthread_rwlock_rdlock(rw_lock.get()); - if (count > fixed_load_edges && search_level == 2) { - dump_edges_to_ssd(idx); - VLOG(0) << "dumping edges to ssd, edge count is reset to 0"; - clear_graph(idx); - count = 0; - } -#endif + uint64_t count = 0; + uint64_t valid_count = 0; + + VLOG(0) << "Begin GraphTable::load_edges() edge_type[" << edge_type << "]"; + if (FLAGS_graph_load_in_parallel) { + std::vector>> tasks; + for (int i = 0; i < paths.size(); i++) { + tasks.push_back(load_node_edge_task_pool->enqueue( + [&, i, idx, this]() -> std::pair { + return parse_edge_file(paths[i], idx, reverse_edge); + })); + } + for (int j = 0; j < (int)tasks.size(); j++) { + auto res = tasks[j].get(); + count += res.first; + valid_count += res.second; + } + } else { + for (auto path : paths) { + auto res = parse_edge_file(path, idx, reverse_edge); + count += res.first; + valid_count += res.second; } } - VLOG(0) << valid_count << "/" << count << " edges are loaded successfully in " - << path; + VLOG(0) << valid_count << "/" << count << " edge_type[" << edge_type + << "] edges are loaded successfully"; -// Build Sampler j #ifdef PADDLE_WITH_HETERPS - // if (gpups_mode) pthread_rwlock_rdlock(rw_lock.get()); if (search_level == 2) { if (count > 0) { dump_edges_to_ssd(idx); @@ -1197,31 +1466,65 @@ int32_t GraphTable::load_edges(const std::string &path, return 0; } #endif - for (auto &shard : edge_shards[idx]) { - auto bucket = shard->get_bucket(); - for (size_t i = 0; i < bucket.size(); i++) { - bucket[i]->build_sampler(sample_type); + + if (!build_sampler_on_cpu) { + // To reduce memory overhead, CPU samplers won't be created in gpugraph. + // In order not to affect the sampler function of other scenario, + // this optimization is only performed in load_edges function. + VLOG(0) << "run in gpugraph mode!"; + } else { + std::string sample_type = "random"; + VLOG(0) << "build sampler ... "; + for (auto &shard : edge_shards[idx]) { + auto bucket = shard->get_bucket(); + for (size_t i = 0; i < bucket.size(); i++) { + bucket[i]->build_sampler(sample_type); + } } } return 0; } -Node *GraphTable::find_node(int type_id, int idx, int64_t id) { +Node *GraphTable::find_node(int type_id, uint64_t id) { + size_t shard_id = id % shard_num; + if (shard_id >= shard_end || shard_id < shard_start) { + return nullptr; + } + Node *node = nullptr; + size_t index = shard_id - shard_start; + auto &search_shards = type_id == 0 ? edge_shards : feature_shards; + for (auto &search_shard : search_shards) { + PADDLE_ENFORCE_NOT_NULL(search_shard[index], + paddle::platform::errors::InvalidArgument( + "search_shard[%d] should not be null.", index)); + node = search_shard[index]->find_node(id); + if (node != nullptr) { + break; + } + } + return node; +} + +Node *GraphTable::find_node(int type_id, int idx, uint64_t id) { size_t shard_id = id % shard_num; if (shard_id >= shard_end || shard_id < shard_start) { return nullptr; } size_t index = shard_id - shard_start; auto &search_shards = type_id == 0 ? edge_shards[idx] : feature_shards[idx]; + PADDLE_ENFORCE_NOT_NULL(search_shards[index], + paddle::platform::errors::InvalidArgument( + "search_shard[%d] should not be null.", index)); Node *node = search_shards[index]->find_node(id); return node; } -uint32_t GraphTable::get_thread_pool_index(int64_t node_id) { +uint32_t GraphTable::get_thread_pool_index(uint64_t node_id) { return node_id % shard_num % shard_num_per_server % task_pool_size_; } -uint32_t GraphTable::get_thread_pool_index_by_shard_index(int64_t shard_index) { +uint32_t GraphTable::get_thread_pool_index_by_shard_index( + uint64_t shard_index) { return shard_index % shard_num_per_server % task_pool_size_; } @@ -1293,9 +1596,9 @@ int32_t GraphTable::random_sample_nodes(int type_id, } } for (auto &pair : first_half) second_half.push_back(pair); - std::vector res; + std::vector res; get_nodes_ids_by_ranges(type_id, idx, second_half, res); - actual_size = res.size() * sizeof(int64_t); + actual_size = res.size() * sizeof(uint64_t); buffer.reset(new char[actual_size]); char *pointer = buffer.get(); memcpy(pointer, res.data(), actual_size); @@ -1303,7 +1606,7 @@ int32_t GraphTable::random_sample_nodes(int type_id, } int32_t GraphTable::random_sample_neighbors( int idx, - int64_t *node_ids, + uint64_t *node_ids, int sample_size, std::vector> &buffers, std::vector &actual_sizes, @@ -1323,7 +1626,7 @@ int32_t GraphTable::random_sample_neighbors( for (int i = 0; i < (int)seq_id.size(); i++) { if (seq_id[i].size() == 0) continue; tasks.push_back(_shards_task_pool[i]->enqueue([&, i, this]() -> int { - int64_t node_id; + uint64_t node_id; std::vector> r; LRUResponse response = LRUResponse::blocked; if (use_cache) { @@ -1369,7 +1672,7 @@ int32_t GraphTable::random_sample_neighbors( res.size() * (need_weight ? (Node::id_size + Node::weight_size) : Node::id_size); int offset = 0; - int64_t id; + uint64_t id; float weight; char *buffer_addr = new char[actual_size]; if (response == LRUResponse::ok) { @@ -1405,13 +1708,13 @@ int32_t GraphTable::random_sample_neighbors( } int32_t GraphTable::get_node_feat(int idx, - const std::vector &node_ids, + const std::vector &node_ids, const std::vector &feature_names, std::vector> &res) { size_t node_num = node_ids.size(); std::vector> tasks; for (size_t idy = 0; idy < node_num; ++idy) { - int64_t node_id = node_ids[idy]; + uint64_t node_id = node_ids[idy]; tasks.push_back(_shards_task_pool[get_thread_pool_index(node_id)]->enqueue( [&, idx, idy, node_id]() -> int { Node *node = find_node(1, idx, node_id); @@ -1440,13 +1743,13 @@ int32_t GraphTable::get_node_feat(int idx, int32_t GraphTable::set_node_feat( int idx, - const std::vector &node_ids, + const std::vector &node_ids, const std::vector &feature_names, const std::vector> &res) { size_t node_num = node_ids.size(); std::vector> tasks; for (size_t idy = 0; idy < node_num; ++idy) { - int64_t node_id = node_ids[idy]; + uint64_t node_id = node_ids[idy]; tasks.push_back(_shards_task_pool[get_thread_pool_index(node_id)]->enqueue( [&, idx, idy, node_id]() -> int { size_t index = node_id % this->shard_num - this->shard_start; @@ -1469,60 +1772,247 @@ int32_t GraphTable::set_node_feat( return 0; } -std::pair GraphTable::parse_feature( - int idx, std::string feat_str) { +void string_vector_2_string(std::vector::iterator strs_begin, + std::vector::iterator strs_end, + char delim, + std::string *output) { + size_t i = 0; + for (std::vector::iterator iter = strs_begin; iter != strs_end; + ++iter) { + if (i > 0) { + *output += delim; + } + + *output += *iter; + ++i; + } +} + +void string_vector_2_string( + std::vector::iterator strs_begin, + std::vector::iterator strs_end, + char delim, + std::string *output) { + size_t i = 0; + for (auto iter = strs_begin; iter != strs_end; ++iter) { + if (i > 0) { + output->append(&delim, 1); + } + output->append((*iter).ptr, (*iter).len); + ++i; + } +} + +int GraphTable::parse_feature(int idx, + const char *feat_str, + size_t len, + FeatureNode *node) { // Return (feat_id, btyes) if name are in this->feat_name, else return (-1, // "") - auto fields = paddle::string::split_string(feat_str, " "); - if (feat_id_map[idx].count(fields[0])) { - // if (this->feat_id_map.count(fields[0])) { - int32_t id = this->feat_id_map[idx][fields[0]]; + thread_local std::vector fields; + fields.clear(); + const char c = feature_separator_.at(0); + paddle::string::split_string_ptr(feat_str, len, c, &fields); + + std::string name = fields[0].to_string(); + auto it = feat_id_map[idx].find(name); + if (it != feat_id_map[idx].end()) { + int32_t id = it->second; + std::string *fea_ptr = node->mutable_feature(id); std::string dtype = this->feat_dtype[idx][id]; - std::vector values(fields.begin() + 1, fields.end()); if (dtype == "feasign") { - return std::make_pair( - int32_t(id), paddle::string::join_strings(values, ' ')); + // string_vector_2_string(fields.begin() + 1, fields.end(), ' ', + // fea_ptr); + FeatureNode::parse_value_to_bytes( + fields.begin() + 1, fields.end(), fea_ptr); + return 0; } else if (dtype == "string") { - return std::make_pair( - int32_t(id), paddle::string::join_strings(values, ' ')); + string_vector_2_string(fields.begin() + 1, fields.end(), ' ', fea_ptr); + return 0; } else if (dtype == "float32") { - return std::make_pair( - int32_t(id), FeatureNode::parse_value_to_bytes(values)); + FeatureNode::parse_value_to_bytes( + fields.begin() + 1, fields.end(), fea_ptr); + return 0; } else if (dtype == "float64") { - return std::make_pair( - int32_t(id), FeatureNode::parse_value_to_bytes(values)); + FeatureNode::parse_value_to_bytes( + fields.begin() + 1, fields.end(), fea_ptr); + return 0; } else if (dtype == "int32") { - return std::make_pair( - int32_t(id), FeatureNode::parse_value_to_bytes(values)); + FeatureNode::parse_value_to_bytes( + fields.begin() + 1, fields.end(), fea_ptr); + return 0; } else if (dtype == "int64") { - return std::make_pair( - int32_t(id), FeatureNode::parse_value_to_bytes(values)); + FeatureNode::parse_value_to_bytes( + fields.begin() + 1, fields.end(), fea_ptr); + return 0; + } + } else { + VLOG(2) << "feature_name[" << name << "] is not in feat_id_map, ntype_id[" + << idx << "] feat_id_map_size[" << feat_id_map.size() << "]"; + } + + return -1; +} +// thread safe shard vector merge +class MergeShardVector { + public: + MergeShardVector(std::vector> *output, int slice_num) { + _slice_num = slice_num; + _shard_keys = output; + _shard_keys->resize(slice_num); + _mutexs = new std::mutex[slice_num]; + } + ~MergeShardVector() { + if (_mutexs != nullptr) { + delete[] _mutexs; + _mutexs = nullptr; + } + } + // merge shard keys + void merge(const std::vector> &shard_keys) { + // add to shard + for (int shard_id = 0; shard_id < _slice_num; ++shard_id) { + auto &dest = (*_shard_keys)[shard_id]; + auto &src = shard_keys[shard_id]; + + _mutexs[shard_id].lock(); + dest.insert(dest.end(), src.begin(), src.end()); + _mutexs[shard_id].unlock(); + } + } + + private: + int _slice_num = 0; + std::mutex *_mutexs = nullptr; + std::vector> *_shard_keys; +}; + +int GraphTable::get_all_id(int type_id, + int slice_num, + std::vector> *output) { + MergeShardVector shard_merge(output, slice_num); + auto &search_shards = type_id == 0 ? edge_shards : feature_shards; + std::vector> tasks; + for (int idx = 0; idx < search_shards.size(); idx++) { + for (int j = 0; j < search_shards[idx].size(); j++) { + tasks.push_back(_shards_task_pool[j % task_pool_size_]->enqueue( + [&search_shards, idx, j, slice_num, &shard_merge]() -> size_t { + std::vector> shard_keys; + size_t num = + search_shards[idx][j]->get_all_id(&shard_keys, slice_num); + // add to shard + shard_merge.merge(shard_keys); + return num; + })); + } + } + for (size_t i = 0; i < tasks.size(); ++i) { + tasks[i].wait(); + } + return 0; +} + +int GraphTable::get_all_neighbor_id( + int type_id, int slice_num, std::vector> *output) { + MergeShardVector shard_merge(output, slice_num); + auto &search_shards = type_id == 0 ? edge_shards : feature_shards; + std::vector> tasks; + for (int idx = 0; idx < search_shards.size(); idx++) { + for (int j = 0; j < search_shards[idx].size(); j++) { + tasks.push_back(_shards_task_pool[j % task_pool_size_]->enqueue( + [&search_shards, idx, j, slice_num, &shard_merge]() -> size_t { + std::vector> shard_keys; + size_t num = search_shards[idx][j]->get_all_neighbor_id(&shard_keys, + slice_num); + // add to shard + shard_merge.merge(shard_keys); + return num; + })); } } - return std::make_pair(-1, ""); + for (size_t i = 0; i < tasks.size(); ++i) { + tasks[i].wait(); + } + return 0; } -std::vector> GraphTable::get_all_id(int type_id, - int idx, - int slice_num) { - std::vector> res(slice_num); +int GraphTable::get_all_id(int type_id, + int idx, + int slice_num, + std::vector> *output) { + MergeShardVector shard_merge(output, slice_num); auto &search_shards = type_id == 0 ? edge_shards[idx] : feature_shards[idx]; - std::vector>> tasks; + std::vector> tasks; + VLOG(3) << "begin task, task_pool_size_[" << task_pool_size_ << "]"; for (size_t i = 0; i < search_shards.size(); i++) { tasks.push_back(_shards_task_pool[i % task_pool_size_]->enqueue( - [&search_shards, i]() -> std::vector { - return search_shards[i]->get_all_id(); + [&search_shards, i, slice_num, &shard_merge]() -> size_t { + std::vector> shard_keys; + size_t num = search_shards[i]->get_all_id(&shard_keys, slice_num); + // add to shard + shard_merge.merge(shard_keys); + return num; })); } for (size_t i = 0; i < tasks.size(); ++i) { tasks[i].wait(); } - for (size_t i = 0; i < tasks.size(); i++) { - auto ids = tasks[i].get(); - for (auto &id : ids) res[(uint64_t)(id) % slice_num].push_back(id); + VLOG(3) << "end task, task_pool_size_[" << task_pool_size_ << "]"; + return 0; +} + +int GraphTable::get_all_neighbor_id( + int type_id, + int idx, + int slice_num, + std::vector> *output) { + MergeShardVector shard_merge(output, slice_num); + auto &search_shards = type_id == 0 ? edge_shards[idx] : feature_shards[idx]; + std::vector> tasks; + VLOG(3) << "begin task, task_pool_size_[" << task_pool_size_ << "]"; + for (int i = 0; i < search_shards.size(); i++) { + tasks.push_back(_shards_task_pool[i % task_pool_size_]->enqueue( + [&search_shards, i, slice_num, &shard_merge]() -> size_t { + std::vector> shard_keys; + size_t num = + search_shards[i]->get_all_neighbor_id(&shard_keys, slice_num); + // add to shard + shard_merge.merge(shard_keys); + return num; + })); } - return res; + for (size_t i = 0; i < tasks.size(); ++i) { + tasks[i].wait(); + } + VLOG(3) << "end task, task_pool_size_[" << task_pool_size_ << "]"; + return 0; } + +int GraphTable::get_all_feature_ids( + int type_id, + int idx, + int slice_num, + std::vector> *output) { + MergeShardVector shard_merge(output, slice_num); + auto &search_shards = type_id == 0 ? edge_shards[idx] : feature_shards[idx]; + std::vector> tasks; + for (int i = 0; i < search_shards.size(); i++) { + tasks.push_back(_shards_task_pool[i % task_pool_size_]->enqueue( + [&search_shards, i, slice_num, &shard_merge]() -> size_t { + std::vector> shard_keys; + size_t num = + search_shards[i]->get_all_feature_ids(&shard_keys, slice_num); + // add to shard + shard_merge.merge(shard_keys); + return num; + })); + } + for (size_t i = 0; i < tasks.size(); ++i) { + tasks[i].wait(); + } + return 0; +} + int32_t GraphTable::pull_graph_list(int type_id, int idx, int start, @@ -1576,7 +2066,11 @@ int32_t GraphTable::pull_graph_list(int type_id, return 0; } -int32_t GraphTable::get_server_index_by_id(int64_t id) { +void GraphTable::set_feature_separator(const std::string &ch) { + feature_separator_ = ch; +} + +int32_t GraphTable::get_server_index_by_id(uint64_t id) { return id % shard_num / shard_num_per_server; } int32_t GraphTable::Initialize(const TableParameter &config, @@ -1617,6 +2111,7 @@ void GraphTable::load_node_weight(int type_id, int idx, std::string path) { } int32_t GraphTable::Initialize(const GraphParameter &graph) { task_pool_size_ = graph.task_pool_size(); + build_sampler_on_cpu = graph.build_sampler_on_cpu(); #ifdef PADDLE_WITH_HETERPS _db = NULL; @@ -1651,6 +2146,8 @@ int32_t GraphTable::Initialize(const GraphParameter &graph) { _shards_task_pool[i].reset(new ::ThreadPool(1)); _shards_task_rng_pool.push_back(paddle::framework::GetCPURandomEngine(0)); } + load_node_edge_task_pool.reset(new ::ThreadPool(load_thread_num)); + auto graph_feature = graph.graph_feature(); auto node_types = graph.node_types(); auto edge_types = graph.edge_types(); diff --git a/paddle/fluid/distributed/ps/table/common_graph_table.h b/paddle/fluid/distributed/ps/table/common_graph_table.h index 9881164ccc772ed4556b3508dd679f15d4baab66..f78011cedfa7d2b2d1aafa1b0f8aea6644b7ab0f 100644 --- a/paddle/fluid/distributed/ps/table/common_graph_table.h +++ b/paddle/fluid/distributed/ps/table/common_graph_table.h @@ -58,33 +58,80 @@ class GraphShard { ~GraphShard(); std::vector &get_bucket() { return bucket; } std::vector get_batch(int start, int end, int step); - std::vector get_ids_by_range(int start, int end) { - std::vector res; + void get_ids_by_range(int start, int end, std::vector *res) { + res->reserve(res->size() + end - start); for (int i = start; i < end && i < (int)bucket.size(); i++) { - res.push_back(bucket[i]->get_id()); + res->emplace_back(bucket[i]->get_id()); } - return res; } - std::vector get_all_id() { - std::vector res; + size_t get_all_id(std::vector> *shard_keys, + int slice_num) { + int bucket_num = bucket.size(); + shard_keys->resize(slice_num); + for (int i = 0; i < slice_num; ++i) { + (*shard_keys)[i].reserve(bucket_num / slice_num); + } + for (int i = 0; i < bucket_num; i++) { + uint64_t k = bucket[i]->get_id(); + (*shard_keys)[k % slice_num].emplace_back(k); + } + return bucket_num; + } + size_t get_all_neighbor_id(std::vector> *total_res, + int slice_num) { + std::vector keys; + for (size_t i = 0; i < bucket.size(); i++) { + size_t neighbor_size = bucket[i]->get_neighbor_size(); + size_t n = keys.size(); + keys.resize(n + neighbor_size); + for (size_t j = 0; j < neighbor_size; j++) { + keys[n + j] = bucket[i]->get_neighbor_id(j); + } + } + return dedup2shard_keys(&keys, total_res, slice_num); + } + size_t get_all_feature_ids(std::vector> *total_res, + int slice_num) { + std::vector keys; for (int i = 0; i < (int)bucket.size(); i++) { - res.push_back(bucket[i]->get_id()); + bucket[i]->get_feature_ids(&keys); + } + return dedup2shard_keys(&keys, total_res, slice_num); + } + size_t dedup2shard_keys(std::vector *keys, + std::vector> *total_res, + int slice_num) { + size_t num = keys->size(); + uint64_t last_key = 0; + // sort key insert to vector + std::sort(keys->begin(), keys->end()); + total_res->resize(slice_num); + for (int shard_id = 0; shard_id < slice_num; ++shard_id) { + (*total_res)[shard_id].reserve(num / slice_num); } - return res; + for (size_t i = 0; i < num; ++i) { + const uint64_t &k = (*keys)[i]; + if (i > 0 && last_key == k) { + continue; + } + last_key = k; + (*total_res)[k % slice_num].push_back(k); + } + return num; } - GraphNode *add_graph_node(int64_t id); + GraphNode *add_graph_node(uint64_t id); GraphNode *add_graph_node(Node *node); - FeatureNode *add_feature_node(int64_t id); - Node *find_node(int64_t id); - void delete_node(int64_t id); + FeatureNode *add_feature_node(uint64_t id, bool is_overlap = true); + Node *find_node(uint64_t id); + void delete_node(uint64_t id); void clear(); - void add_neighbor(int64_t id, int64_t dst_id, float weight); - std::unordered_map &get_node_location() { + void add_neighbor(uint64_t id, uint64_t dst_id, float weight); + std::unordered_map &get_node_location() { return node_location; } private: - std::unordered_map node_location; + std::unordered_map node_location; std::vector bucket; }; @@ -92,11 +139,11 @@ enum LRUResponse { ok = 0, blocked = 1, err = 2 }; struct SampleKey { int idx; - int64_t node_key; + uint64_t node_key; size_t sample_size; bool is_weighted; SampleKey(int _idx, - int64_t _node_key, + uint64_t _node_key, size_t _sample_size, bool _is_weighted) { idx = _idx; @@ -467,7 +514,7 @@ class GraphTable : public Table { virtual int32_t random_sample_neighbors( int idx, - int64_t *node_ids, + uint64_t *node_ids, int sample_size, std::vector> &buffers, std::vector &actual_sizes, @@ -483,30 +530,62 @@ class GraphTable : public Table { int type_id, int idx, std::vector> ranges, - std::vector &res); + std::vector &res); virtual int32_t Initialize() { return 0; } virtual int32_t Initialize(const TableParameter &config, const FsClientParameter &fs_config); virtual int32_t Initialize(const GraphParameter &config); int32_t Load(const std::string &path, const std::string ¶m); + int32_t load_node_and_edge_file(std::string etype, + std::string ntype, + std::string epath, + std::string npath, + int part_num, + bool reverse); + + std::string get_inverse_etype(std::string &etype); + int32_t load_edges(const std::string &path, bool reverse, const std::string &edge_type); - std::vector> get_all_id(int type, - int idx, - int slice_num); - int32_t load_nodes(const std::string &path, std::string node_type); - + int get_all_id(int type, + int slice_num, + std::vector> *output); + int get_all_neighbor_id(int type, + int slice_num, + std::vector> *output); + int get_all_id(int type, + int idx, + int slice_num, + std::vector> *output); + int get_all_neighbor_id(int type_id, + int id, + int slice_num, + std::vector> *output); + int get_all_feature_ids(int type, + int idx, + int slice_num, + std::vector> *output); + int32_t load_nodes(const std::string &path, + std::string node_type = std::string()); + std::pair parse_edge_file(const std::string &path, + int idx, + bool reverse); + std::pair parse_node_file(const std::string &path, + const std::string &node_type, + int idx); + std::pair parse_node_file(const std::string &path); int32_t add_graph_node(int idx, - std::vector &id_list, + std::vector &id_list, std::vector &is_weight_list); - int32_t remove_graph_node(int idx, std::vector &id_list); + int32_t remove_graph_node(int idx, std::vector &id_list); - int32_t get_server_index_by_id(int64_t id); - Node *find_node(int type_id, int idx, int64_t id); + int32_t get_server_index_by_id(uint64_t id); + Node *find_node(int type_id, int idx, uint64_t id); + Node *find_node(int type_id, uint64_t id); virtual int32_t Pull(TableContext &context) { return 0; } virtual int32_t Push(TableContext &context) { return 0; } @@ -531,19 +610,21 @@ class GraphTable : public Table { this->server_num = server_num; return 0; } - virtual uint32_t get_thread_pool_index_by_shard_index(int64_t shard_index); - virtual uint32_t get_thread_pool_index(int64_t node_id); - virtual std::pair parse_feature(int idx, - std::string feat_str); + virtual uint32_t get_thread_pool_index_by_shard_index(uint64_t shard_index); + virtual uint32_t get_thread_pool_index(uint64_t node_id); + virtual int parse_feature(int idx, + const char *feat_str, + size_t len, + FeatureNode *node); virtual int32_t get_node_feat(int idx, - const std::vector &node_ids, + const std::vector &node_ids, const std::vector &feature_names, std::vector> &res); virtual int32_t set_node_feat( int idx, - const std::vector &node_ids, + const std::vector &node_ids, const std::vector &feature_names, const std::vector> &res); @@ -578,22 +659,24 @@ class GraphTable : public Table { virtual void export_partition_files(int idx, std::string file_path); virtual char *random_sample_neighbor_from_ssd( int idx, - int64_t id, + uint64_t id, int sample_size, const std::shared_ptr rng, int &actual_size); virtual int32_t add_node_to_ssd( - int type_id, int idx, int64_t src_id, char *data, int len); + int type_id, int idx, uint64_t src_id, char *data, int len); virtual paddle::framework::GpuPsCommGraph make_gpu_ps_graph( - int idx, std::vector ids); + int idx, std::vector ids); + virtual paddle::framework::GpuPsCommGraphFea make_gpu_ps_graph_fea( + std::vector &node_ids, int slot_num); int32_t Load_to_ssd(const std::string &path, const std::string ¶m); - int64_t load_graph_to_memory_from_ssd(int idx, std::vector &ids); + int64_t load_graph_to_memory_from_ssd(int idx, std::vector &ids); int32_t make_complementary_graph(int idx, int64_t byte_size); int32_t dump_edges_to_ssd(int idx); int32_t get_partition_num(int idx) { return partitions[idx].size(); } - std::vector get_partition(int idx, int index) { - if (idx >= partitions.size() || index >= partitions[idx].size()) - return std::vector(); + std::vector get_partition(int idx, int index) { + if (idx >= (int)partitions.size() || index >= (int)partitions[idx].size()) + return std::vector(); return partitions[idx][index]; } int32_t load_edges_to_ssd(const std::string &path, @@ -603,17 +686,20 @@ class GraphTable : public Table { void set_search_level(int search_level) { this->search_level = search_level; } int search_level; int64_t total_memory_cost; - std::vector>> partitions; + std::vector>> partitions; int next_partition; #endif - virtual int32_t add_comm_edge(int idx, int64_t src_id, int64_t dst_id); + virtual int32_t add_comm_edge(int idx, uint64_t src_id, uint64_t dst_id); virtual int32_t build_sampler(int idx, std::string sample_type = "random"); + void set_feature_separator(const std::string &ch); std::vector> edge_shards, feature_shards; size_t shard_start, shard_end, server_num, shard_num_per_server, shard_num; int task_pool_size_ = 24; + int load_thread_num = 160; + const int random_sample_nodes_ranges = 3; - std::vector>> node_weight; + std::vector>> node_weight; std::vector> feat_name; std::vector> feat_dtype; std::vector> feat_shape; @@ -625,21 +711,24 @@ class GraphTable : public Table { std::vector> _shards_task_pool; std::vector> _shards_task_rng_pool; + std::shared_ptr<::ThreadPool> load_node_edge_task_pool; std::shared_ptr> scaled_lru; - std::unordered_set extra_nodes; - std::unordered_map extra_nodes_to_thread_index; + std::unordered_set extra_nodes; + std::unordered_map extra_nodes_to_thread_index; bool use_cache, use_duplicate_nodes; int cache_size_limit; int cache_ttl; mutable std::mutex mutex_; + bool build_sampler_on_cpu; std::shared_ptr rw_lock; #ifdef PADDLE_WITH_HETERPS // paddle::framework::GpuPsGraphTable gpu_graph_table; paddle::distributed::RocksDBHandler *_db; -// std::shared_ptr<::ThreadPool> graph_sample_pool; -// std::shared_ptr graph_sampler; -// REGISTER_GRAPH_FRIEND_CLASS(2, CompleteGraphSampler, BasicBfsGraphSampler) + // std::shared_ptr<::ThreadPool> graph_sample_pool; + // std::shared_ptr graph_sampler; + // REGISTER_GRAPH_FRIEND_CLASS(2, CompleteGraphSampler, BasicBfsGraphSampler) #endif + std::string feature_separator_ = std::string(" "); }; /* @@ -657,7 +746,7 @@ class CompleteGraphSampler : public GraphSampler { protected: GraphTable *graph_table; std::vector> sample_nodes; - std::vector> sample_neighbors; + std::vector> sample_neighbors; // std::vector sample_res; // std::shared_ptr random; int gpu_num; @@ -676,11 +765,11 @@ class BasicBfsGraphSampler : public GraphSampler { GraphTable *graph_table; // std::vector> sample_nodes; std::vector> sample_nodes; - std::vector> sample_neighbors; + std::vector> sample_neighbors; size_t gpu_num; int init_search_size, node_num_for_each_shard, edge_num_for_each_node; int rounds, interval; - std::vector>> + std::vector>> sample_neighbors_map; }; #endif diff --git a/paddle/fluid/distributed/ps/table/graph/graph_node.h b/paddle/fluid/distributed/ps/table/graph/graph_node.h index 13fdcf4c64e62fac662e6c9c2b3768b68eb61c64..9c384a9744b8a3f6d194cc0d142cb6f0f366dcd4 100644 --- a/paddle/fluid/distributed/ps/table/graph/graph_node.h +++ b/paddle/fluid/distributed/ps/table/graph/graph_node.h @@ -16,10 +16,15 @@ #include #include #include +#include #include #include +#include "glog/logging.h" #include "paddle/fluid/distributed/ps/table/graph/graph_weighted_sampler.h" +#include "paddle/fluid/platform/enforce.h" +#include "paddle/fluid/string/string_helper.h" + namespace paddle { namespace distributed { @@ -30,6 +35,7 @@ class Node { virtual ~Node() {} static int id_size, int_size, weight_size; uint64_t get_id() { return id; } + int64_t get_py_id() { return (int64_t)id; } void set_id(uint64_t id) { this->id = id; } virtual void build_edges(bool is_weighted) {} @@ -46,7 +52,11 @@ class Node { virtual void to_buffer(char *buffer, bool need_feature); virtual void recover_from_buffer(char *buffer); virtual std::string get_feature(int idx) { return std::string(""); } - virtual void set_feature(int idx, std::string str) {} + virtual int get_feature_ids(std::vector *res) const { return 0; } + virtual int get_feature_ids(int slot_idx, std::vector *res) const { + return 0; + } + virtual void set_feature(int idx, const std::string &str) {} virtual void set_feature_size(int size) {} virtual int get_feature_size() { return 0; } virtual size_t get_neighbor_size() { return 0; } @@ -95,7 +105,64 @@ class FeatureNode : public Node { } } - virtual void set_feature(int idx, std::string str) { + virtual int get_feature_ids(std::vector *res) const { + PADDLE_ENFORCE_NOT_NULL(res, + paddle::platform::errors::InvalidArgument( + "get_feature_ids res should not be null")); + errno = 0; + for (auto &feature_item : feature) { + const uint64_t *feas = (const uint64_t *)(feature_item.c_str()); + size_t num = feature_item.length() / sizeof(uint64_t); + CHECK((feature_item.length() % sizeof(uint64_t)) == 0) + << "bad feature_item: [" << feature_item << "]"; + size_t n = res->size(); + res->resize(n + num); + for (size_t i = 0; i < num; ++i) { + (*res)[n + i] = feas[i]; + } + } + PADDLE_ENFORCE_EQ( + errno, + 0, + paddle::platform::errors::InvalidArgument( + "get_feature_ids get errno should be 0, but got %d.", errno)); + return 0; + } + + virtual int get_feature_ids(int slot_idx, std::vector *res) const { + PADDLE_ENFORCE_NOT_NULL(res, + paddle::platform::errors::InvalidArgument( + "get_feature_ids res should not be null")); + res->clear(); + errno = 0; + if (slot_idx < (int)this->feature.size()) { + const std::string &s = this->feature[slot_idx]; + const uint64_t *feas = (const uint64_t *)(s.c_str()); + + size_t num = s.length() / sizeof(uint64_t); + CHECK((s.length() % sizeof(uint64_t)) == 0) + << "bad feature_item: [" << s << "]"; + res->resize(num); + for (size_t i = 0; i < num; ++i) { + (*res)[i] = feas[i]; + } + } + PADDLE_ENFORCE_EQ( + errno, + 0, + paddle::platform::errors::InvalidArgument( + "get_feature_ids get errno should be 0, but got %d.", errno)); + return 0; + } + + virtual std::string *mutable_feature(int idx) { + if (idx >= (int)this->feature.size()) { + this->feature.resize(idx + 1); + } + return &(this->feature[idx]); + } + + virtual void set_feature(int idx, const std::string &str) { if (idx >= (int)this->feature.size()) { this->feature.resize(idx + 1); } @@ -117,6 +184,23 @@ class FeatureNode : public Node { return std::string(buffer, Tsize); } + template + static void parse_value_to_bytes( + std::vector::iterator feat_str_begin, + std::vector::iterator feat_str_end, + std::string *output) { + T v; + size_t feat_str_size = feat_str_end - feat_str_begin; + size_t Tsize = sizeof(T) * feat_str_size; + char buffer[Tsize] = {'\0'}; + for (size_t i = 0; i < feat_str_size; i++) { + std::stringstream ss(*(feat_str_begin + i)); + ss >> v; + std::memcpy(buffer + sizeof(T) * i, (char *)&v, sizeof(T)); + } + output->assign(buffer); + } + template static std::vector parse_bytes_to_array(std::string feat_str) { T v; @@ -131,8 +215,28 @@ class FeatureNode : public Node { return out; } + template + static void parse_value_to_bytes( + std::vector::iterator feat_str_begin, + std::vector::iterator feat_str_end, + std::string *output) { + size_t feat_str_size = feat_str_end - feat_str_begin; + size_t Tsize = sizeof(T) * feat_str_size; + size_t num = output->length(); + output->resize(num + Tsize); + + T *fea_ptrs = (T *)(&(*output)[num]); + + thread_local paddle::string::str_ptr_stream ss; + for (size_t i = 0; i < feat_str_size; i++) { + ss.reset(*(feat_str_begin + i)); + ss >> fea_ptrs[i]; + } + } + protected: std::vector feature; }; + } // namespace distributed } // namespace paddle diff --git a/paddle/fluid/distributed/ps/table/memory_sparse_table.cc b/paddle/fluid/distributed/ps/table/memory_sparse_table.cc index f53954dce7cb4c47c8d2aa33311c2df5e6a0db41..da78849d40cde067c6f271fa5f8348f8356c4ab0 100644 --- a/paddle/fluid/distributed/ps/table/memory_sparse_table.cc +++ b/paddle/fluid/distributed/ps/table/memory_sparse_table.cc @@ -41,14 +41,14 @@ namespace paddle { namespace distributed { int32_t MemorySparseTable::Initialize() { - _shards_task_pool.resize(_task_pool_size); - for (size_t i = 0; i < _shards_task_pool.size(); ++i) { - _shards_task_pool[i].reset(new ::ThreadPool(1)); - } auto& profiler = CostProfiler::instance(); profiler.register_profiler("pserver_sparse_update_all"); profiler.register_profiler("pserver_sparse_select_all"); InitializeValue(); + _shards_task_pool.resize(_task_pool_size); + for (int i = 0; i < _shards_task_pool.size(); ++i) { + _shards_task_pool[i].reset(new ::ThreadPool(1)); + } VLOG(0) << "initalize MemorySparseTable succ"; return 0; } @@ -65,9 +65,13 @@ int32_t MemorySparseTable::InitializeValue() { _real_local_shard_num = _real_local_shard_num < 0 ? 0 : _real_local_shard_num; } +#ifdef PADDLE_WITH_HETERPS + _task_pool_size = _sparse_table_shard_num; +#endif VLOG(1) << "memory sparse table _avg_local_shard_num: " << _avg_local_shard_num - << " _real_local_shard_num: " << _real_local_shard_num; + << " _real_local_shard_num: " << _real_local_shard_num + << " _task_pool_size:" << _task_pool_size; _local_shards.reset(new shard_type[_real_local_shard_num]); @@ -336,7 +340,11 @@ int32_t MemorySparseTable::Save(const std::string& dirname, size_t file_start_idx = _avg_local_shard_num * _shard_idx; +#ifdef PADDLE_WITH_GPU_GRAPH + int thread_num = _real_local_shard_num; +#else int thread_num = _real_local_shard_num < 20 ? _real_local_shard_num : 20; +#endif omp_set_num_threads(thread_num); #pragma omp parallel for schedule(dynamic) for (int i = 0; i < _real_local_shard_num; ++i) { diff --git a/paddle/fluid/distributed/ps/table/memory_sparse_table.h b/paddle/fluid/distributed/ps/table/memory_sparse_table.h index 9d48d530d89347d434dd0ce18e83b7093341bb14..17018d5e5dfc3d82b3043c92e551adb97aad06ba 100644 --- a/paddle/fluid/distributed/ps/table/memory_sparse_table.h +++ b/paddle/fluid/distributed/ps/table/memory_sparse_table.h @@ -112,7 +112,7 @@ class MemorySparseTable : public Table { virtual int32_t LoadPatch(const std::vector& file_list, int save_param); - const int _task_pool_size = 24; + int _task_pool_size = 24; int _avg_local_shard_num; int _real_local_shard_num; int _sparse_table_shard_num; diff --git a/paddle/fluid/distributed/the_one_ps.proto b/paddle/fluid/distributed/the_one_ps.proto index 44c1ef3121c52711752957bd89278a916b902f4b..e74502d7351b2c4c97b1ca8ec798d0e65ab6ee22 100755 --- a/paddle/fluid/distributed/the_one_ps.proto +++ b/paddle/fluid/distributed/the_one_ps.proto @@ -126,13 +126,20 @@ message TableParameter { message TableAccessorParameter { optional string accessor_class = 1; - optional uint32 fea_dim = 4 [ default = 11 ]; - optional uint32 embedx_dim = 5 [ default = 8 ]; - optional uint32 embedx_threshold = 6 [ default = 10 ]; + optional uint32 fea_dim = 4 [ default = 11 ]; // field size of one value + optional uint32 embedx_dim = 5 [ default = 8 ]; // embedx feature size + optional uint32 embedx_threshold = 6 + [ default = 10 ]; // embedx feature create threshold optional CtrAccessorParameter ctr_accessor_param = 7; repeated TableAccessorSaveParameter table_accessor_save_param = 8; optional SparseCommonSGDRuleParameter embed_sgd_param = 10; optional SparseCommonSGDRuleParameter embedx_sgd_param = 11; + optional GraphSGDParameter graph_sgd_param = 12; +} + +message GraphSGDParameter { + optional uint32 nodeid_slot = 1 [ default = 9008 ]; + optional float feature_learning_rate = 2 [ default = 0.05 ]; } message CtrAccessorParameter { @@ -232,6 +239,7 @@ message GraphParameter { optional string table_type = 9 [ default = "" ]; optional int32 shard_num = 10 [ default = 127 ]; optional int32 search_level = 11 [ default = 1 ]; + optional bool build_sampler_on_cpu = 12 [ default = true ]; } message GraphFeature { diff --git a/paddle/fluid/framework/CMakeLists.txt b/paddle/fluid/framework/CMakeLists.txt index b1308766c40dcfa9884d79d1ba5e725b347e2dd2..8af48ef51db5c218d80ff34dcddcfe18a2d6046c 100755 --- a/paddle/fluid/framework/CMakeLists.txt +++ b/paddle/fluid/framework/CMakeLists.txt @@ -740,6 +740,19 @@ if(WITH_DISTRIBUTE) set_source_files_properties( heterxpu_trainer.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) elseif(WITH_PSCORE) + # cc_library(executor SRCS executor.cc multi_trainer.cc pipeline_trainer.cc dataset_factory.cc + # dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc + # heterxpu_trainer.cc heter_pipeline_trainer.cc + # data_feed.cc device_worker.cc hogwild_worker.cc hetercpu_worker.cc + # downpour_worker.cc downpour_lite_worker.cc downpour_worker_opt.cc data_feed.cu + # pull_dense_worker.cc section_worker.cc heter_section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry + # device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog + # index_sampler index_wrapper sampler index_dataset_proto + # lod_rank_table fs shell fleet_wrapper heter_wrapper box_wrapper metrics lodtensor_printer feed_fetch_method + # graph_to_program_pass variable_helper timer monitor + # heter_service_proto fleet heter_server brpc fleet_executor + # graph_gpu_wrapper) + cc_library( executor SRCS executor.cc @@ -1001,21 +1014,41 @@ cc_library( DEPS parallel_executor) if(WITH_PSCORE) get_property(RPC_DEPS GLOBAL PROPERTY RPC_DEPS) - cc_test( - dist_multi_trainer_test - SRCS dist_multi_trainer_test.cc - DEPS conditional_block_op executor gloo_wrapper ${RPC_DEPS}) - cc_test( - heter_pipeline_trainer_test - SRCS heter_pipeline_trainer_test.cc - DEPS conditional_block_op - scale_op - heter_listen_and_serv_op - executor - heter_server - gloo_wrapper - eigen_function - ${RPC_DEPS}) + if(WITH_HETERPS) + cc_test( + dist_multi_trainer_test + SRCS dist_multi_trainer_test.cc + DEPS conditional_block_op executor gloo_wrapper ${RPC_DEPS} + graph_gpu_wrapper) + cc_test( + heter_pipeline_trainer_test + SRCS heter_pipeline_trainer_test.cc + DEPS conditional_block_op + scale_op + heter_listen_and_serv_op + executor + heter_server + gloo_wrapper + eigen_function + ${RPC_DEPS} + graph_gpu_wrapper) + else() + cc_test( + dist_multi_trainer_test + SRCS dist_multi_trainer_test.cc + DEPS conditional_block_op executor gloo_wrapper ${RPC_DEPS}) + cc_test( + heter_pipeline_trainer_test + SRCS heter_pipeline_trainer_test.cc + DEPS conditional_block_op + scale_op + heter_listen_and_serv_op + executor + heter_server + gloo_wrapper + eigen_function + ${RPC_DEPS}) + endif() else() cc_test( dist_multi_trainer_test diff --git a/paddle/fluid/framework/data_feed.cc b/paddle/fluid/framework/data_feed.cc index 7d312414aed66493931d1461557a5a13d1d79f23..8ffb58f945156463f614c840dbc9d90acb7e9cc9 100644 --- a/paddle/fluid/framework/data_feed.cc +++ b/paddle/fluid/framework/data_feed.cc @@ -2108,6 +2108,9 @@ void SlotRecordInMemoryDataFeed::Init(const DataFeedDesc& data_feed_desc) { } else { so_parser_name_.clear(); } +#if defined(PADDLE_WITH_GPU_GRAPH) && defined(PADDLE_WITH_HETERPS) + gpu_graph_data_generator_.SetConfig(data_feed_desc); +#endif } void SlotRecordInMemoryDataFeed::LoadIntoMemory() { @@ -2644,6 +2647,9 @@ bool SlotRecordInMemoryDataFeed::Start() { #if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS) CHECK(paddle::platform::is_gpu_place(this->place_)); pack_ = BatchGpuPackMgr().get(this->GetPlace(), used_slots_info_); +#endif +#if defined(PADDLE_WITH_GPU_GRAPH) && defined(PADDLE_WITH_HETERPS) + gpu_graph_data_generator_.AllocResource(this->place_, feed_vec_); #endif return true; } @@ -2651,27 +2657,33 @@ bool SlotRecordInMemoryDataFeed::Start() { int SlotRecordInMemoryDataFeed::Next() { #ifdef _LINUX this->CheckStart(); - - VLOG(3) << "enable heter next: " << offset_index_ - << " batch_offsets: " << batch_offsets_.size(); - if (offset_index_ >= batch_offsets_.size()) { - VLOG(3) << "offset_index: " << offset_index_ + if (!gpu_graph_mode_) { + VLOG(3) << "enable heter next: " << offset_index_ << " batch_offsets: " << batch_offsets_.size(); - return 0; - } - auto& batch = batch_offsets_[offset_index_++]; - this->batch_size_ = batch.second; - VLOG(3) << "batch_size_=" << this->batch_size_ - << ", thread_id=" << thread_id_; - if (this->batch_size_ != 0) { - PutToFeedVec(&records_[batch.first], this->batch_size_); + if (offset_index_ >= batch_offsets_.size()) { + VLOG(3) << "offset_index: " << offset_index_ + << " batch_offsets: " << batch_offsets_.size(); + return 0; + } + auto& batch = batch_offsets_[offset_index_++]; + this->batch_size_ = batch.second; + VLOG(3) << "batch_size_=" << this->batch_size_ + << ", thread_id=" << thread_id_; + if (this->batch_size_ != 0) { + PutToFeedVec(&records_[batch.first], this->batch_size_); + } else { + VLOG(3) << "finish reading for heterps, batch size zero, thread_id=" + << thread_id_; + } + VLOG(3) << "enable heter next: " << offset_index_ + << " batch_offsets: " << batch_offsets_.size() + << " baych_size: " << this->batch_size_; } else { - VLOG(3) << "finish reading for heterps, batch size zero, thread_id=" - << thread_id_; + VLOG(3) << "datafeed in gpu graph mode"; +#if defined(PADDLE_WITH_GPU_GRAPH) && defined(PADDLE_WITH_HETERPS) + this->batch_size_ = gpu_graph_data_generator_.GenerateBatch(); +#endif } - VLOG(3) << "enable heter next: " << offset_index_ - << " batch_offsets: " << batch_offsets_.size() - << " baych_size: " << this->batch_size_; return this->batch_size_; #else diff --git a/paddle/fluid/framework/data_feed.cu b/paddle/fluid/framework/data_feed.cu index 166cbcef481df3550fb7636650a3aab0b1ac0efa..d144673d62d6bfdcdc7a3297977c381a73b5efaa 100644 --- a/paddle/fluid/framework/data_feed.cu +++ b/paddle/fluid/framework/data_feed.cu @@ -18,6 +18,15 @@ limitations under the License. */ #if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS) #include "paddle/fluid/framework/data_feed.h" +#include +#include +#include +#include +#include "cub/cub.cuh" +#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_node.h" +#include "paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.h" + +DECLARE_bool(enable_opt_get_features); namespace paddle { namespace framework { @@ -182,6 +191,1012 @@ void SlotRecordInMemoryDataFeed::CopyForTensor( cudaStreamSynchronize(stream); } +__global__ void GraphFillCVMKernel(int64_t *tensor, int len) { + CUDA_KERNEL_LOOP(idx, len) { tensor[idx] = 1; } +} + +__global__ void CopyDuplicateKeys(int64_t *dist_tensor, + uint64_t *src_tensor, + int len) { + CUDA_KERNEL_LOOP(idx, len) { + dist_tensor[idx * 2] = src_tensor[idx]; + dist_tensor[idx * 2 + 1] = src_tensor[idx]; + } +} + +int GraphDataGenerator::AcquireInstance(BufState *state) { + // + if (state->GetNextStep()) { + state->Debug(); + return state->len; + } else if (state->GetNextCentrolWord()) { + state->Debug(); + return state->len; + } else if (state->GetNextBatch()) { + state->Debug(); + return state->len; + } + return 0; +} + +// TODO opt +__global__ void GraphFillFeatureKernel(uint64_t *id_tensor, + int *fill_ins_num, + uint64_t *walk, + uint64_t *feature, + int *row, + int central_word, + int step, + int len, + int col_num, + int slot_num) { + __shared__ int32_t local_key[CUDA_NUM_THREADS * 16]; + __shared__ int local_num; + __shared__ int global_num; + + size_t idx = blockIdx.x * blockDim.x + threadIdx.x; + if (threadIdx.x == 0) { + local_num = 0; + } + __syncthreads(); + if (idx < len) { + int src = row[idx] * col_num + central_word; + if (walk[src] != 0 && walk[src + step] != 0) { + size_t dst = atomicAdd(&local_num, 1); + for (int i = 0; i < slot_num; ++i) { + local_key[dst * 2 * slot_num + i * 2] = feature[src * slot_num + i]; + local_key[dst * 2 * slot_num + i * 2 + 1] = + feature[(src + step) * slot_num + i]; + } + } + } + + __syncthreads(); + + if (threadIdx.x == 0) { + global_num = atomicAdd(fill_ins_num, local_num); + } + __syncthreads(); + + if (threadIdx.x < local_num) { + for (int i = 0; i < slot_num; ++i) { + id_tensor[(global_num * 2 + 2 * threadIdx.x) * slot_num + i] = + local_key[(2 * threadIdx.x) * slot_num + i]; + id_tensor[(global_num * 2 + 2 * threadIdx.x + 1) * slot_num + i] = + local_key[(2 * threadIdx.x + 1) * slot_num + i]; + } + } +} + +__global__ void GraphFillIdKernel(uint64_t *id_tensor, + int *fill_ins_num, + uint64_t *walk, + int *row, + int central_word, + int step, + int len, + int col_num) { + __shared__ uint64_t local_key[CUDA_NUM_THREADS * 2]; + __shared__ int local_num; + __shared__ int global_num; + + size_t idx = blockIdx.x * blockDim.x + threadIdx.x; + if (threadIdx.x == 0) { + local_num = 0; + } + __syncthreads(); + // int dst = idx * 2; + // id_tensor[dst] = walk[src]; + // id_tensor[dst + 1] = walk[src + step]; + if (idx < len) { + int src = row[idx] * col_num + central_word; + if (walk[src] != 0 && walk[src + step] != 0) { + size_t dst = atomicAdd(&local_num, 1); + local_key[dst * 2] = walk[src]; + local_key[dst * 2 + 1] = walk[src + step]; + } + } + + __syncthreads(); + + if (threadIdx.x == 0) { + global_num = atomicAdd(fill_ins_num, local_num); + } + __syncthreads(); + + if (threadIdx.x < local_num) { + id_tensor[global_num * 2 + 2 * threadIdx.x] = local_key[2 * threadIdx.x]; + id_tensor[global_num * 2 + 2 * threadIdx.x + 1] = + local_key[2 * threadIdx.x + 1]; + } +} + +__global__ void GraphFillSlotKernel(uint64_t *id_tensor, + uint64_t *feature_buf, + int len, + int total_ins, + int slot_num) { + CUDA_KERNEL_LOOP(idx, len) { + int slot_idx = idx / total_ins; + int ins_idx = idx % total_ins; + ((uint64_t *)(id_tensor[slot_idx]))[ins_idx] = + feature_buf[ins_idx * slot_num + slot_idx]; + } +} + +__global__ void GraphFillSlotLodKernelOpt(uint64_t *id_tensor, + int len, + int total_ins) { + CUDA_KERNEL_LOOP(idx, len) { + int slot_idx = idx / total_ins; + int ins_idx = idx % total_ins; + ((uint64_t *)(id_tensor[slot_idx]))[ins_idx] = ins_idx; + } +} + +__global__ void GraphFillSlotLodKernel(int64_t *id_tensor, int len) { + CUDA_KERNEL_LOOP(idx, len) { id_tensor[idx] = idx; } +} + +int GraphDataGenerator::FillInsBuf() { + if (ins_buf_pair_len_ >= batch_size_) { + return batch_size_; + } + int total_instance = AcquireInstance(&buf_state_); + + VLOG(2) << "total_ins: " << total_instance; + buf_state_.Debug(); + + if (total_instance == 0) { + int res = FillWalkBuf(d_walk_); + if (!res) { + // graph iterate complete + return -1; + } else { + total_instance = buf_state_.len; + VLOG(2) << "total_ins: " << total_instance; + buf_state_.Debug(); + // if (total_instance == 0) { + // return -1; + //} + } + + if (!FLAGS_enable_opt_get_features && slot_num_ > 0) { + FillFeatureBuf(d_walk_, d_feature_); + if (debug_mode_) { + int len = buf_size_ > 5000 ? 5000 : buf_size_; + uint64_t h_walk[len]; + cudaMemcpy(h_walk, + d_walk_->ptr(), + len * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + uint64_t h_feature[len * slot_num_]; + cudaMemcpy(h_feature, + d_feature_->ptr(), + len * slot_num_ * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + for (int i = 0; i < len; ++i) { + std::stringstream ss; + for (int j = 0; j < slot_num_; ++j) { + ss << h_feature[i * slot_num_ + j] << " "; + } + VLOG(2) << "aft FillFeatureBuf, gpu[" << gpuid_ << "] walk[" << i + << "] = " << (uint64_t)h_walk[i] << " feature[" + << i * slot_num_ << ".." << (i + 1) * slot_num_ + << "] = " << ss.str(); + } + } + } + } + + uint64_t *walk = reinterpret_cast(d_walk_->ptr()); + uint64_t *ins_buf = reinterpret_cast(d_ins_buf_->ptr()); + int *random_row = reinterpret_cast(d_random_row_->ptr()); + int *d_pair_num = reinterpret_cast(d_pair_num_->ptr()); + cudaMemsetAsync(d_pair_num, 0, sizeof(int), stream_); + int len = buf_state_.len; + GraphFillIdKernel<<>>( + ins_buf + ins_buf_pair_len_ * 2, + d_pair_num, + walk, + random_row + buf_state_.cursor, + buf_state_.central_word, + window_step_[buf_state_.step], + len, + walk_len_); + int h_pair_num; + cudaMemcpyAsync( + &h_pair_num, d_pair_num, sizeof(int), cudaMemcpyDeviceToHost, stream_); + if (!FLAGS_enable_opt_get_features && slot_num_ > 0) { + uint64_t *feature_buf = reinterpret_cast(d_feature_buf_->ptr()); + uint64_t *feature = reinterpret_cast(d_feature_->ptr()); + cudaMemsetAsync(d_pair_num, 0, sizeof(int), stream_); + int len = buf_state_.len; + VLOG(2) << "feature_buf start[" << ins_buf_pair_len_ * 2 * slot_num_ + << "] len[" << len << "]"; + GraphFillFeatureKernel<<>>( + feature_buf + ins_buf_pair_len_ * 2 * slot_num_, + d_pair_num, + walk, + feature, + random_row + buf_state_.cursor, + buf_state_.central_word, + window_step_[buf_state_.step], + len, + walk_len_, + slot_num_); + } + + cudaStreamSynchronize(stream_); + ins_buf_pair_len_ += h_pair_num; + + if (debug_mode_) { + uint64_t h_ins_buf[ins_buf_pair_len_ * 2]; + cudaMemcpy(h_ins_buf, + ins_buf, + 2 * ins_buf_pair_len_ * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + VLOG(2) << "h_pair_num = " << h_pair_num + << ", ins_buf_pair_len = " << ins_buf_pair_len_; + for (int xx = 0; xx < 2 * ins_buf_pair_len_; xx++) { + VLOG(2) << "h_ins_buf[" << xx << "]: " << h_ins_buf[xx]; + } + delete[] h_ins_buf; + + if (!FLAGS_enable_opt_get_features && slot_num_ > 0) { + uint64_t *feature_buf = + reinterpret_cast(d_feature_buf_->ptr()); + uint64_t h_feature_buf[(batch_size_ * 2 * 2) * slot_num_]; + cudaMemcpy(h_feature_buf, + feature_buf, + (batch_size_ * 2 * 2) * slot_num_ * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + for (int xx = 0; xx < (batch_size_ * 2 * 2) * slot_num_; xx++) { + VLOG(2) << "h_feature_buf[" << xx << "]: " << h_feature_buf[xx]; + } + } + } + return ins_buf_pair_len_; +} + +int GraphDataGenerator::GenerateBatch() { + int total_instance = 0; + platform::CUDADeviceGuard guard(gpuid_); + int res = 0; + if (!gpu_graph_training_) { + while (cursor_ < h_device_keys_.size()) { + size_t device_key_size = h_device_keys_[cursor_]->size(); + if (infer_node_type_start_[cursor_] >= device_key_size) { + cursor_++; + continue; + } + total_instance = + (infer_node_type_start_[cursor_] + batch_size_ <= device_key_size) + ? batch_size_ + : device_key_size - infer_node_type_start_[cursor_]; + uint64_t *d_type_keys = + reinterpret_cast(d_device_keys_[cursor_]->ptr()); + d_type_keys += infer_node_type_start_[cursor_]; + infer_node_type_start_[cursor_] += total_instance; + VLOG(1) << "in graph_data generator:batch_size = " << batch_size_ + << " instance = " << total_instance; + total_instance *= 2; + id_tensor_ptr_ = feed_vec_[0]->mutable_data({total_instance, 1}, + this->place_); + show_tensor_ptr_ = + feed_vec_[1]->mutable_data({total_instance}, this->place_); + clk_tensor_ptr_ = + feed_vec_[2]->mutable_data({total_instance}, this->place_); + CopyDuplicateKeys<<>>( + id_tensor_ptr_, d_type_keys, total_instance / 2); + GraphFillCVMKernel<<>>(show_tensor_ptr_, total_instance); + GraphFillCVMKernel<<>>(clk_tensor_ptr_, total_instance); + break; + } + if (total_instance == 0) { + return 0; + } + } else { + while (ins_buf_pair_len_ < batch_size_) { + res = FillInsBuf(); + if (res == -1) { + if (ins_buf_pair_len_ == 0) { + return 0; + } else { + break; + } + } + } + total_instance = + ins_buf_pair_len_ < batch_size_ ? ins_buf_pair_len_ : batch_size_; + + total_instance *= 2; + id_tensor_ptr_ = + feed_vec_[0]->mutable_data({total_instance, 1}, this->place_); + show_tensor_ptr_ = + feed_vec_[1]->mutable_data({total_instance}, this->place_); + clk_tensor_ptr_ = + feed_vec_[2]->mutable_data({total_instance}, this->place_); + } + + int64_t *slot_tensor_ptr_[slot_num_]; + int64_t *slot_lod_tensor_ptr_[slot_num_]; + if (slot_num_ > 0) { + for (int i = 0; i < slot_num_; ++i) { + slot_tensor_ptr_[i] = feed_vec_[3 + 2 * i]->mutable_data( + {total_instance, 1}, this->place_); + slot_lod_tensor_ptr_[i] = feed_vec_[3 + 2 * i + 1]->mutable_data( + {total_instance + 1}, this->place_); + } + if (FLAGS_enable_opt_get_features || !gpu_graph_training_) { + cudaMemcpyAsync(d_slot_tensor_ptr_->ptr(), + slot_tensor_ptr_, + sizeof(uint64_t *) * slot_num_, + cudaMemcpyHostToDevice, + stream_); + cudaMemcpyAsync(d_slot_lod_tensor_ptr_->ptr(), + slot_lod_tensor_ptr_, + sizeof(uint64_t *) * slot_num_, + cudaMemcpyHostToDevice, + stream_); + } + } + + uint64_t *ins_cursor, *ins_buf; + if (gpu_graph_training_) { + VLOG(2) << "total_instance: " << total_instance + << ", ins_buf_pair_len = " << ins_buf_pair_len_; + // uint64_t *ins_buf = reinterpret_cast(d_ins_buf_->ptr()); + // uint64_t *ins_cursor = ins_buf + ins_buf_pair_len_ * 2 - total_instance; + ins_buf = reinterpret_cast(d_ins_buf_->ptr()); + ins_cursor = ins_buf + ins_buf_pair_len_ * 2 - total_instance; + cudaMemcpyAsync(id_tensor_ptr_, + ins_cursor, + sizeof(uint64_t) * total_instance, + cudaMemcpyDeviceToDevice, + stream_); + + GraphFillCVMKernel<<>>(show_tensor_ptr_, total_instance); + GraphFillCVMKernel<<>>(clk_tensor_ptr_, total_instance); + } else { + ins_cursor = (uint64_t *)id_tensor_ptr_; + } + + if (slot_num_ > 0) { + uint64_t *feature_buf = reinterpret_cast(d_feature_buf_->ptr()); + if (FLAGS_enable_opt_get_features || !gpu_graph_training_) { + FillFeatureBuf(ins_cursor, feature_buf, total_instance); + // FillFeatureBuf(id_tensor_ptr_, feature_buf, total_instance); + if (debug_mode_) { + uint64_t h_walk[total_instance]; + cudaMemcpy(h_walk, + ins_cursor, + total_instance * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + uint64_t h_feature[total_instance * slot_num_]; + cudaMemcpy(h_feature, + feature_buf, + total_instance * slot_num_ * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + for (int i = 0; i < total_instance; ++i) { + std::stringstream ss; + for (int j = 0; j < slot_num_; ++j) { + ss << h_feature[i * slot_num_ + j] << " "; + } + VLOG(2) << "aft FillFeatureBuf, gpu[" << gpuid_ << "] walk[" << i + << "] = " << (uint64_t)h_walk[i] << " feature[" + << i * slot_num_ << ".." << (i + 1) * slot_num_ + << "] = " << ss.str(); + } + } + + GraphFillSlotKernel<<>>((uint64_t *)d_slot_tensor_ptr_->ptr(), + feature_buf, + total_instance * slot_num_, + total_instance, + slot_num_); + GraphFillSlotLodKernelOpt<<>>( + (uint64_t *)d_slot_lod_tensor_ptr_->ptr(), + (total_instance + 1) * slot_num_, + total_instance + 1); + } else { + for (int i = 0; i < slot_num_; ++i) { + int feature_buf_offset = + (ins_buf_pair_len_ * 2 - total_instance) * slot_num_ + i * 2; + for (int j = 0; j < total_instance; j += 2) { + VLOG(2) << "slot_tensor[" << i << "][" << j << "] <- feature_buf[" + << feature_buf_offset + j * slot_num_ << "]"; + VLOG(2) << "slot_tensor[" << i << "][" << j + 1 << "] <- feature_buf[" + << feature_buf_offset + j * slot_num_ + 1 << "]"; + cudaMemcpyAsync(slot_tensor_ptr_[i] + j, + &feature_buf[feature_buf_offset + j * slot_num_], + sizeof(uint64_t) * 2, + cudaMemcpyDeviceToDevice, + stream_); + } + GraphFillSlotLodKernel<<>>(slot_lod_tensor_ptr_[i], + total_instance + 1); + } + } + } + + offset_.clear(); + offset_.push_back(0); + offset_.push_back(total_instance); + LoD lod{offset_}; + feed_vec_[0]->set_lod(lod); + if (slot_num_ > 0) { + for (int i = 0; i < slot_num_; ++i) { + feed_vec_[3 + 2 * i]->set_lod(lod); + } + } + + cudaStreamSynchronize(stream_); + if (!gpu_graph_training_) return 1; + ins_buf_pair_len_ -= total_instance / 2; + if (debug_mode_) { + uint64_t h_slot_tensor[slot_num_][total_instance]; + uint64_t h_slot_lod_tensor[slot_num_][total_instance + 1]; + for (int i = 0; i < slot_num_; ++i) { + cudaMemcpy(h_slot_tensor[i], + slot_tensor_ptr_[i], + total_instance * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + int len = total_instance > 5000 ? 5000 : total_instance; + for (int j = 0; j < len; ++j) { + VLOG(2) << "gpu[" << gpuid_ << "] slot_tensor[" << i << "][" << j + << "] = " << h_slot_tensor[i][j]; + } + + cudaMemcpy(h_slot_lod_tensor[i], + slot_lod_tensor_ptr_[i], + (total_instance + 1) * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + len = total_instance + 1 > 5000 ? 5000 : total_instance + 1; + for (int j = 0; j < len; ++j) { + VLOG(2) << "gpu[" << gpuid_ << "] slot_lod_tensor[" << i << "][" << j + << "] = " << h_slot_lod_tensor[i][j]; + } + } + } + + return 1; +} + +__global__ void GraphFillSampleKeysKernel(uint64_t *neighbors, + uint64_t *sample_keys, + int *prefix_sum, + int *sampleidx2row, + int *tmp_sampleidx2row, + int *actual_sample_size, + int cur_degree, + int len) { + CUDA_KERNEL_LOOP(idx, len) { + for (int k = 0; k < actual_sample_size[idx]; k++) { + size_t offset = prefix_sum[idx] + k; + sample_keys[offset] = neighbors[idx * cur_degree + k]; + tmp_sampleidx2row[offset] = sampleidx2row[idx] + k; + } + } +} + +__global__ void GraphDoWalkKernel(uint64_t *neighbors, + uint64_t *walk, + int *d_prefix_sum, + int *actual_sample_size, + int cur_degree, + int step, + int len, + int *id_cnt, + int *sampleidx2row, + int col_size) { + CUDA_KERNEL_LOOP(i, len) { + for (int k = 0; k < actual_sample_size[i]; k++) { + // int idx = sampleidx2row[i]; + size_t row = sampleidx2row[k + d_prefix_sum[i]]; + // size_t row = idx * cur_degree + k; + size_t col = step; + size_t offset = (row * col_size + col); + walk[offset] = neighbors[i * cur_degree + k]; + } + } +} + +// Fill keys to the first column of walk +__global__ void GraphFillFirstStepKernel(int *prefix_sum, + int *sampleidx2row, + uint64_t *walk, + uint64_t *keys, + int len, + int walk_degree, + int col_size, + int *actual_sample_size, + uint64_t *neighbors, + uint64_t *sample_keys) { + CUDA_KERNEL_LOOP(idx, len) { + for (int k = 0; k < actual_sample_size[idx]; k++) { + size_t row = prefix_sum[idx] + k; + sample_keys[row] = neighbors[idx * walk_degree + k]; + sampleidx2row[row] = row; + + size_t offset = col_size * row; + walk[offset] = keys[idx]; + walk[offset + 1] = neighbors[idx * walk_degree + k]; + } + } +} + +// Fill sample_res to the stepth column of walk +void GraphDataGenerator::FillOneStep(uint64_t *d_start_ids, + uint64_t *walk, + int len, + NeighborSampleResult &sample_res, + int cur_degree, + int step, + int *len_per_row) { + size_t temp_storage_bytes = 0; + int *d_actual_sample_size = sample_res.actual_sample_size; + uint64_t *d_neighbors = sample_res.val; + int *d_prefix_sum = reinterpret_cast(d_prefix_sum_->ptr()); + uint64_t *d_sample_keys = reinterpret_cast(d_sample_keys_->ptr()); + int *d_sampleidx2row = + reinterpret_cast(d_sampleidx2rows_[cur_sampleidx2row_]->ptr()); + int *d_tmp_sampleidx2row = + reinterpret_cast(d_sampleidx2rows_[1 - cur_sampleidx2row_]->ptr()); + + CUDA_CHECK(cub::DeviceScan::InclusiveSum(NULL, + temp_storage_bytes, + d_actual_sample_size, + d_prefix_sum + 1, + len, + stream_)); + auto d_temp_storage = memory::Alloc(place_, temp_storage_bytes); + + CUDA_CHECK(cub::DeviceScan::InclusiveSum(d_temp_storage->ptr(), + temp_storage_bytes, + d_actual_sample_size, + d_prefix_sum + 1, + len, + stream_)); + + cudaStreamSynchronize(stream_); + + if (step == 1) { + GraphFillFirstStepKernel<<>>( + d_prefix_sum, + d_tmp_sampleidx2row, + walk, + d_start_ids, + len, + walk_degree_, + walk_len_, + d_actual_sample_size, + d_neighbors, + d_sample_keys); + + } else { + GraphFillSampleKeysKernel<<>>(d_neighbors, + d_sample_keys, + d_prefix_sum, + d_sampleidx2row, + d_tmp_sampleidx2row, + d_actual_sample_size, + cur_degree, + len); + + GraphDoWalkKernel<<>>( + d_neighbors, + walk, + d_prefix_sum, + d_actual_sample_size, + cur_degree, + step, + len, + len_per_row, + d_tmp_sampleidx2row, + walk_len_); + } + if (debug_mode_) { + size_t once_max_sample_keynum = walk_degree_ * once_sample_startid_len_; + int *h_prefix_sum = new int[len + 1]; + int *h_actual_size = new int[len]; + int *h_offset2idx = new int[once_max_sample_keynum]; + uint64_t h_sample_keys[once_max_sample_keynum]; + cudaMemcpy(h_offset2idx, + d_tmp_sampleidx2row, + once_max_sample_keynum * sizeof(int), + cudaMemcpyDeviceToHost); + + cudaMemcpy(h_prefix_sum, + d_prefix_sum, + (len + 1) * sizeof(int), + cudaMemcpyDeviceToHost); + for (int xx = 0; xx < once_max_sample_keynum; xx++) { + VLOG(2) << "h_offset2idx[" << xx << "]: " << h_offset2idx[xx]; + } + for (int xx = 0; xx < len + 1; xx++) { + VLOG(2) << "h_prefix_sum[" << xx << "]: " << h_prefix_sum[xx]; + } + delete[] h_prefix_sum; + delete[] h_actual_size; + delete[] h_offset2idx; + delete[] h_sample_keys; + } + cudaStreamSynchronize(stream_); + cur_sampleidx2row_ = 1 - cur_sampleidx2row_; +} + +int GraphDataGenerator::FillFeatureBuf(uint64_t *d_walk, + uint64_t *d_feature, + size_t key_num) { + platform::CUDADeviceGuard guard(gpuid_); + + auto gpu_graph_ptr = GraphGpuWrapper::GetInstance(); + int ret = gpu_graph_ptr->get_feature_of_nodes( + gpuid_, d_walk, d_feature, key_num, slot_num_); + return ret; +} + +int GraphDataGenerator::FillFeatureBuf( + std::shared_ptr d_walk, + std::shared_ptr d_feature) { + platform::CUDADeviceGuard guard(gpuid_); + + auto gpu_graph_ptr = GraphGpuWrapper::GetInstance(); + int ret = gpu_graph_ptr->get_feature_of_nodes(gpuid_, + (uint64_t *)d_walk->ptr(), + (uint64_t *)d_feature->ptr(), + buf_size_, + slot_num_); + return ret; +} + +int GraphDataGenerator::FillWalkBuf(std::shared_ptr d_walk) { + platform::CUDADeviceGuard guard(gpuid_); + size_t once_max_sample_keynum = walk_degree_ * once_sample_startid_len_; + //////// + uint64_t *h_walk; + uint64_t *h_sample_keys; + int *h_offset2idx; + int *h_len_per_row; + uint64_t *h_prefix_sum; + if (debug_mode_) { + h_walk = new uint64_t[buf_size_]; + h_sample_keys = new uint64_t[once_max_sample_keynum]; + h_offset2idx = new int[once_max_sample_keynum]; + h_len_per_row = new int[once_max_sample_keynum]; + h_prefix_sum = new uint64_t[once_max_sample_keynum + 1]; + } + /////// + auto gpu_graph_ptr = GraphGpuWrapper::GetInstance(); + uint64_t *walk = reinterpret_cast(d_walk->ptr()); + int *len_per_row = reinterpret_cast(d_len_per_row_->ptr()); + uint64_t *d_sample_keys = reinterpret_cast(d_sample_keys_->ptr()); + cudaMemsetAsync(walk, 0, buf_size_ * sizeof(uint64_t), stream_); + cudaMemsetAsync( + len_per_row, 0, once_max_sample_keynum * sizeof(int), stream_); + int i = 0; + int total_row = 0; + size_t node_type_len = first_node_type_.size(); + int remain_size = + buf_size_ - walk_degree_ * once_sample_startid_len_ * walk_len_; + + while (i <= remain_size) { + int cur_node_idx = cursor_ % node_type_len; + int node_type = first_node_type_[cur_node_idx]; + auto &path = meta_path_[cur_node_idx]; + size_t start = node_type_start_[node_type]; + // auto node_query_result = gpu_graph_ptr->query_node_list( + // gpuid_, node_type, start, once_sample_startid_len_); + + // int tmp_len = node_query_result.actual_sample_size; + VLOG(2) << "choose start type: " << node_type; + int type_index = type_to_index_[node_type]; + size_t device_key_size = h_device_keys_[type_index]->size(); + VLOG(2) << "type: " << node_type << " size: " << device_key_size + << " start: " << start; + uint64_t *d_type_keys = + reinterpret_cast(d_device_keys_[type_index]->ptr()); + int tmp_len = start + once_sample_startid_len_ > device_key_size + ? device_key_size - start + : once_sample_startid_len_; + node_type_start_[node_type] = tmp_len + start; + if (tmp_len == 0) { + finish_node_type_.insert(node_type); + if (finish_node_type_.size() == node_type_start_.size()) { + break; + } + cursor_ += 1; + continue; + } + // if (tmp_len == 0) { + // break; + //} + VLOG(2) << "i = " << i << " buf_size_ = " << buf_size_ + << " tmp_len = " << tmp_len << " cursor = " << cursor_ + << " once_max_sample_keynum = " << once_max_sample_keynum; + uint64_t *cur_walk = walk + i; + + NeighborSampleQuery q; + q.initialize(gpuid_, + path[0], + (uint64_t)(d_type_keys + start), + walk_degree_, + tmp_len); + auto sample_res = gpu_graph_ptr->graph_neighbor_sample_v3(q, false); + + int step = 1; + VLOG(2) << "sample edge type: " << path[0] << " step: " << 1; + jump_rows_ = sample_res.total_sample_size; + FillOneStep(d_type_keys + start, + cur_walk, + tmp_len, + sample_res, + walk_degree_, + step, + len_per_row); + VLOG(2) << "jump_row: " << jump_rows_; + ///////// + if (debug_mode_) { + cudaMemcpy( + h_walk, walk, buf_size_ * sizeof(uint64_t), cudaMemcpyDeviceToHost); + for (int xx = 0; xx < buf_size_; xx++) { + VLOG(2) << "h_walk[" << xx << "]: " << h_walk[xx]; + } + } + ///////// + step++; + size_t path_len = path.size(); + for (; step < walk_len_; step++) { + if (sample_res.total_sample_size == 0) { + break; + } + auto sample_key_mem = sample_res.actual_val_mem; + uint64_t *sample_keys_ptr = + reinterpret_cast(sample_key_mem->ptr()); + int edge_type_id = path[(step - 1) % path_len]; + VLOG(2) << "sample edge type: " << edge_type_id << " step: " << step; + q.initialize(gpuid_, + edge_type_id, + (uint64_t)sample_keys_ptr, + 1, + sample_res.total_sample_size); + sample_res = gpu_graph_ptr->graph_neighbor_sample_v3(q, false); + + FillOneStep(d_type_keys + start, + cur_walk, + sample_res.total_sample_size, + sample_res, + 1, + step, + len_per_row); + if (debug_mode_) { + cudaMemcpy( + h_walk, walk, buf_size_ * sizeof(uint64_t), cudaMemcpyDeviceToHost); + for (int xx = 0; xx < buf_size_; xx++) { + VLOG(2) << "h_walk[" << xx << "]: " << h_walk[xx]; + } + } + } + // cursor_ += tmp_len; + i += jump_rows_ * walk_len_; + total_row += jump_rows_; + cursor_ += 1; + } + buf_state_.Reset(total_row); + int *d_random_row = reinterpret_cast(d_random_row_->ptr()); + + thrust::random::default_random_engine engine(shuffle_seed_); + const auto &exec_policy = thrust::cuda::par.on(stream_); + thrust::counting_iterator cnt_iter(0); + thrust::shuffle_copy(exec_policy, + cnt_iter, + cnt_iter + total_row, + thrust::device_pointer_cast(d_random_row), + engine); + + cudaStreamSynchronize(stream_); + shuffle_seed_ = engine(); + + if (debug_mode_) { + int *h_random_row = new int[total_row + 10]; + cudaMemcpy(h_random_row, + d_random_row, + total_row * sizeof(int), + cudaMemcpyDeviceToHost); + for (int xx = 0; xx < total_row; xx++) { + VLOG(2) << "h_random_row[" << xx << "]: " << h_random_row[xx]; + } + delete[] h_random_row; + delete[] h_walk; + delete[] h_sample_keys; + delete[] h_offset2idx; + delete[] h_len_per_row; + delete[] h_prefix_sum; + } + return total_row != 0; +} + +void GraphDataGenerator::AllocResource(const paddle::platform::Place &place, + std::vector feed_vec) { + place_ = place; + gpuid_ = place_.GetDeviceId(); + VLOG(3) << "gpuid " << gpuid_; + stream_ = dynamic_cast( + platform::DeviceContextPool::Instance().Get(place)) + ->stream(); + feed_vec_ = feed_vec; + slot_num_ = (feed_vec_.size() - 3) / 2; + + // d_device_keys_.resize(h_device_keys_.size()); + VLOG(2) << "h_device_keys size: " << h_device_keys_.size(); + infer_node_type_start_ = std::vector(h_device_keys_.size(), 0); + for (size_t i = 0; i < h_device_keys_.size(); i++) { + for (size_t j = 0; j < h_device_keys_[i]->size(); j++) { + VLOG(3) << "h_device_keys_[" << i << "][" << j + << "] = " << (*(h_device_keys_[i]))[j]; + } + auto buf = memory::AllocShared( + place_, h_device_keys_[i]->size() * sizeof(uint64_t)); + d_device_keys_.push_back(buf); + CUDA_CHECK(cudaMemcpyAsync(buf->ptr(), + h_device_keys_[i]->data(), + h_device_keys_[i]->size() * sizeof(uint64_t), + cudaMemcpyHostToDevice, + stream_)); + } + // h_device_keys_ = h_device_keys; + // device_key_size_ = h_device_keys_->size(); + // d_device_keys_ = + // memory::AllocShared(place_, device_key_size_ * sizeof(int64_t)); + // CUDA_CHECK(cudaMemcpyAsync(d_device_keys_->ptr(), h_device_keys_->data(), + // device_key_size_ * sizeof(int64_t), + // cudaMemcpyHostToDevice, stream_)); + size_t once_max_sample_keynum = walk_degree_ * once_sample_startid_len_; + d_prefix_sum_ = + memory::AllocShared(place_, (once_max_sample_keynum + 1) * sizeof(int)); + int *d_prefix_sum_ptr = reinterpret_cast(d_prefix_sum_->ptr()); + cudaMemsetAsync( + d_prefix_sum_ptr, 0, (once_max_sample_keynum + 1) * sizeof(int), stream_); + cursor_ = 0; + jump_rows_ = 0; + d_walk_ = memory::AllocShared(place_, buf_size_ * sizeof(uint64_t)); + cudaMemsetAsync(d_walk_->ptr(), 0, buf_size_ * sizeof(uint64_t), stream_); + if (!FLAGS_enable_opt_get_features && slot_num_ > 0) { + d_feature_ = + memory::AllocShared(place_, buf_size_ * slot_num_ * sizeof(uint64_t)); + cudaMemsetAsync( + d_feature_->ptr(), 0, buf_size_ * sizeof(uint64_t), stream_); + } + d_sample_keys_ = + memory::AllocShared(place_, once_max_sample_keynum * sizeof(uint64_t)); + + d_sampleidx2rows_.push_back( + memory::AllocShared(place_, once_max_sample_keynum * sizeof(int))); + d_sampleidx2rows_.push_back( + memory::AllocShared(place_, once_max_sample_keynum * sizeof(int))); + cur_sampleidx2row_ = 0; + + d_len_per_row_ = + memory::AllocShared(place_, once_max_sample_keynum * sizeof(int)); + for (int i = -window_; i < 0; i++) { + window_step_.push_back(i); + } + for (int i = 0; i < window_; i++) { + window_step_.push_back(i + 1); + } + buf_state_.Init(batch_size_, walk_len_, &window_step_); + d_random_row_ = memory::AllocShared( + place_, + (once_sample_startid_len_ * walk_degree_ * repeat_time_) * sizeof(int)); + shuffle_seed_ = 0; + + ins_buf_pair_len_ = 0; + d_ins_buf_ = + memory::AllocShared(place_, (batch_size_ * 2 * 2) * sizeof(uint64_t)); + if (slot_num_ > 0) { + d_feature_buf_ = memory::AllocShared( + place_, (batch_size_ * 2 * 2) * slot_num_ * sizeof(uint64_t)); + } + d_pair_num_ = memory::AllocShared(place_, sizeof(int)); + if (FLAGS_enable_opt_get_features && slot_num_ > 0) { + d_slot_tensor_ptr_ = + memory::AllocShared(place_, slot_num_ * sizeof(uint64_t *)); + d_slot_lod_tensor_ptr_ = + memory::AllocShared(place_, slot_num_ * sizeof(uint64_t *)); + } + + cudaStreamSynchronize(stream_); +} + +void GraphDataGenerator::SetConfig( + const paddle::framework::DataFeedDesc &data_feed_desc) { + auto graph_config = data_feed_desc.graph_config(); + walk_degree_ = graph_config.walk_degree(); + walk_len_ = graph_config.walk_len(); + window_ = graph_config.window(); + once_sample_startid_len_ = graph_config.once_sample_startid_len(); + debug_mode_ = graph_config.debug_mode(); + gpu_graph_training_ = graph_config.gpu_graph_training(); + if (debug_mode_ || !gpu_graph_training_) { + batch_size_ = graph_config.batch_size(); + } else { + batch_size_ = once_sample_startid_len_; + } + repeat_time_ = graph_config.sample_times_one_chunk(); + buf_size_ = + once_sample_startid_len_ * walk_len_ * walk_degree_ * repeat_time_; + VLOG(2) << "Confirm GraphConfig, walk_degree : " << walk_degree_ + << ", walk_len : " << walk_len_ << ", window : " << window_ + << ", once_sample_startid_len : " << once_sample_startid_len_ + << ", sample_times_one_chunk : " << repeat_time_ + << ", batch_size: " << batch_size_; + std::string first_node_type = graph_config.first_node_type(); + std::string meta_path = graph_config.meta_path(); + auto gpu_graph_ptr = GraphGpuWrapper::GetInstance(); + auto edge_to_id = gpu_graph_ptr->edge_to_id; + auto node_to_id = gpu_graph_ptr->feature_to_id; + // parse first_node_type + auto node_types = + paddle::string::split_string(first_node_type, ";"); + VLOG(2) << "node_types: " << first_node_type; + finish_node_type_.clear(); + node_type_start_.clear(); + for (auto &type : node_types) { + auto iter = node_to_id.find(type); + PADDLE_ENFORCE_NE( + iter, + node_to_id.end(), + platform::errors::NotFound("(%s) is not found in node_to_id.", type)); + VLOG(2) << "node_to_id[" << type << "] = " << iter->second; + first_node_type_.push_back(iter->second); + node_type_start_[iter->second] = 0; + } + meta_path_.resize(first_node_type_.size()); + auto meta_paths = paddle::string::split_string(meta_path, ";"); + + for (size_t i = 0; i < meta_paths.size(); i++) { + auto path = meta_paths[i]; + auto nodes = paddle::string::split_string(path, "-"); + for (auto &node : nodes) { + auto iter = edge_to_id.find(node); + PADDLE_ENFORCE_NE( + iter, + edge_to_id.end(), + platform::errors::NotFound("(%s) is not found in edge_to_id.", node)); + VLOG(2) << "edge_to_id[" << node << "] = " << iter->second; + meta_path_[i].push_back(iter->second); + } + } +}; + } // namespace framework } // namespace paddle #endif diff --git a/paddle/fluid/framework/data_feed.h b/paddle/fluid/framework/data_feed.h index eed6c4d1cb72abdd46a6873f5f918de4f0824741..b25093931bd77a3ab46081f4be9c67281f56d92e 100644 --- a/paddle/fluid/framework/data_feed.h +++ b/paddle/fluid/framework/data_feed.h @@ -23,6 +23,7 @@ limitations under the License. */ #include // NOLINT #include #include // NOLINT +#include #include #include #include // NOLINT @@ -42,6 +43,7 @@ limitations under the License. */ #include "paddle/fluid/platform/timer.h" #include "paddle/fluid/string/string_helper.h" #if defined(PADDLE_WITH_CUDA) +#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h" #include "paddle/fluid/platform/cuda_device_guard.h" #include "paddle/fluid/platform/device/gpu/gpu_info.h" #endif @@ -56,6 +58,8 @@ namespace framework { class DataFeedDesc; class Scope; class Variable; +class NeighborSampleResult; +class NodeQueryResult; } // namespace framework } // namespace paddle @@ -420,7 +424,6 @@ struct UsedSlotGpuType { }; #if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS) -#define CUDA_CHECK(val) CHECK(val == gpuSuccess) template struct CudaBuffer { T* cu_buffer; @@ -776,6 +779,202 @@ class DLManager { std::map handle_map_; }; +struct engine_wrapper_t { + std::default_random_engine engine; +#if !defined(_WIN32) + engine_wrapper_t() { + struct timespec tp; + clock_gettime(CLOCK_REALTIME, &tp); + double cur_time = tp.tv_sec + tp.tv_nsec * 1e-9; + static std::atomic x(0); + std::seed_seq sseq = {x++, x++, x++, (uint64_t)(cur_time * 1000)}; + engine.seed(sseq); + } +#endif +}; + +struct BufState { + int left; + int right; + int central_word; + int step; + engine_wrapper_t random_engine_; + + int len; + int cursor; + int row_num; + + int batch_size; + int walk_len; + std::vector* window; + + BufState() {} + ~BufState() {} + + void Init(int graph_batch_size, + int graph_walk_len, + std::vector* graph_window) { + batch_size = graph_batch_size; + walk_len = graph_walk_len; + window = graph_window; + + left = 0; + right = window->size() - 1; + central_word = -1; + step = -1; + + len = 0; + cursor = 0; + row_num = 0; + for (size_t i = 0; i < graph_window->size(); i++) { + VLOG(2) << "graph_window[" << i << "] = " << (*graph_window)[i]; + } + } + + void Reset(int total_rows) { + cursor = 0; + row_num = total_rows; + int tmp_len = cursor + batch_size > row_num ? row_num - cursor : batch_size; + len = tmp_len; + central_word = -1; + step = -1; + GetNextCentrolWord(); + } + + int GetNextStep() { + step++; + if (step <= right && central_word + (*window)[step] < walk_len) { + return 1; + } + return 0; + } + + void Debug() { + VLOG(2) << "left: " << left << " right: " << right + << " central_word: " << central_word << " step: " << step + << " cursor: " << cursor << " len: " << len + << " row_num: " << row_num; + } + + int GetNextCentrolWord() { + if (++central_word >= walk_len) { + return 0; + } + int window_size = window->size() / 2; + int random_window = random_engine_.engine() % window_size + 1; + left = window_size - random_window; + right = window_size + random_window - 1; + VLOG(2) << "random window: " << random_window << " window[" << left + << "] = " << (*window)[left] << " window[" << right + << "] = " << (*window)[right]; + + for (step = left; step <= right; step++) { + if (central_word + (*window)[step] >= 0) { + return 1; + } + } + return 0; + } + + int GetNextBatch() { + cursor += len; + int tmp_len = cursor + batch_size > row_num ? row_num - cursor : batch_size; + if (tmp_len == 0) { + return 0; + } + len = tmp_len; + central_word = -1; + step = -1; + GetNextCentrolWord(); + return tmp_len != 0; + } +}; + +class GraphDataGenerator { + public: + GraphDataGenerator(){}; + virtual ~GraphDataGenerator(){}; + void SetConfig(const paddle::framework::DataFeedDesc& data_feed_desc); + void AllocResource(const paddle::platform::Place& place, + std::vector feed_vec); + int AcquireInstance(BufState* state); + int GenerateBatch(); + int FillWalkBuf(std::shared_ptr d_walk); + int FillFeatureBuf(uint64_t* d_walk, uint64_t* d_feature, size_t key_num); + int FillFeatureBuf(std::shared_ptr d_walk, + std::shared_ptr d_feature); + void FillOneStep(uint64_t* start_ids, + uint64_t* walk, + int len, + NeighborSampleResult& sample_res, + int cur_degree, + int step, + int* len_per_row); + int FillInsBuf(); + void SetDeviceKeys(std::vector* device_keys, int type) { + type_to_index_[type] = h_device_keys_.size(); + h_device_keys_.push_back(device_keys); + } + + protected: + int walk_degree_; + int walk_len_; + int window_; + int once_sample_startid_len_; + int gpuid_; + // start ids + // int64_t* device_keys_; + // size_t device_key_size_; + std::vector*> h_device_keys_; + std::unordered_map type_to_index_; + // point to device_keys_ + size_t cursor_; + size_t jump_rows_; + int64_t* id_tensor_ptr_; + int64_t* show_tensor_ptr_; + int64_t* clk_tensor_ptr_; + cudaStream_t stream_; + paddle::platform::Place place_; + std::vector feed_vec_; + std::vector offset_; + std::shared_ptr d_prefix_sum_; + std::vector> d_device_keys_; + + std::shared_ptr d_walk_; + std::shared_ptr d_feature_; + std::shared_ptr d_len_per_row_; + std::shared_ptr d_random_row_; + // + std::vector> d_sampleidx2rows_; + int cur_sampleidx2row_; + // record the keys to call graph_neighbor_sample + std::shared_ptr d_sample_keys_; + int sample_keys_len_; + + std::set finish_node_type_; + std::unordered_map node_type_start_; + std::vector infer_node_type_start_; + + std::shared_ptr d_ins_buf_; + std::shared_ptr d_feature_buf_; + std::shared_ptr d_pair_num_; + std::shared_ptr d_slot_tensor_ptr_; + std::shared_ptr d_slot_lod_tensor_ptr_; + int ins_buf_pair_len_; + // size of a d_walk buf + size_t buf_size_; + int repeat_time_; + std::vector window_step_; + BufState buf_state_; + int batch_size_; + int slot_num_; + int shuffle_seed_; + int debug_mode_; + std::vector first_node_type_; + std::vector> meta_path_; + bool gpu_graph_training_; +}; + class DataFeed { public: DataFeed() { @@ -838,6 +1037,14 @@ class DataFeed { virtual void SetParseLogKey(bool parse_logkey) {} virtual void SetEnablePvMerge(bool enable_pv_merge) {} virtual void SetCurrentPhase(int current_phase) {} + virtual void SetDeviceKeys(std::vector* device_keys, int type) { +#if defined(PADDLE_WITH_GPU_GRAPH) && defined(PADDLE_WITH_HETERPS) + gpu_graph_data_generator_.SetDeviceKeys(device_keys, type); +#endif + } + virtual void SetGpuGraphMode(int gpu_graph_mode) { + gpu_graph_mode_ = gpu_graph_mode; + } virtual void SetFileListMutex(std::mutex* mutex) { mutex_for_pick_file_ = mutex; } @@ -921,6 +1128,10 @@ class DataFeed { // The input type of pipe reader, 0 for one sample, 1 for one batch int input_type_; + int gpu_graph_mode_ = 0; +#if defined(PADDLE_WITH_GPU_GRAPH) && defined(PADDLE_WITH_HETERPS) + GraphDataGenerator gpu_graph_data_generator_; +#endif }; // PrivateQueueDataFeed is the base virtual class for ohther DataFeeds. diff --git a/paddle/fluid/framework/data_feed.proto b/paddle/fluid/framework/data_feed.proto index 6964446f20946fe9e91d8ca6a1a13e8baac1e5d6..a7ab70948795f167a865626664cb4000d93931e1 100644 --- a/paddle/fluid/framework/data_feed.proto +++ b/paddle/fluid/framework/data_feed.proto @@ -27,6 +27,19 @@ message MultiSlotDesc { optional string uid_slot = 2; } +message GraphConfig { + optional int32 walk_degree = 1 [ default = 1 ]; + optional int32 walk_len = 2 [ default = 20 ]; + optional int32 window = 3 [ default = 5 ]; + optional int32 once_sample_startid_len = 4 [ default = 8000 ]; + optional int32 sample_times_one_chunk = 5 [ default = 10 ]; + optional int32 batch_size = 6 [ default = 1 ]; + optional int32 debug_mode = 7 [ default = 0 ]; + optional string first_node_type = 8; + optional string meta_path = 9; + optional bool gpu_graph_training = 10 [ default = true ]; +} + message DataFeedDesc { optional string name = 1; optional int32 batch_size = 2 [ default = 32 ]; @@ -37,4 +50,5 @@ message DataFeedDesc { optional int32 pv_batch_size = 7 [ default = 32 ]; optional int32 input_type = 8 [ default = 0 ]; optional string so_parser_name = 9; + optional GraphConfig graph_config = 10; } diff --git a/paddle/fluid/framework/data_set.cc b/paddle/fluid/framework/data_set.cc index ea8a2c4ff5d01c2b9ff80cd3690ed62efa86d157..529d532fb15e5b1fcb1080fc7502b60f781ce8d6 100644 --- a/paddle/fluid/framework/data_set.cc +++ b/paddle/fluid/framework/data_set.cc @@ -14,6 +14,7 @@ #include "paddle/fluid/framework/data_set.h" +#include "gflags/gflags.h" #include "google/protobuf/text_format.h" #if (defined PADDLE_WITH_DISTRIBUTE) && (defined PADDLE_WITH_PSCORE) #include "paddle/fluid/distributed/index_dataset/index_sampler.h" @@ -26,6 +27,7 @@ #ifdef PADDLE_WITH_PSCORE #include "paddle/fluid/distributed/ps/wrapper/fleet.h" +#include "paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.h" #endif #if defined _WIN32 || defined __APPLE__ @@ -34,6 +36,8 @@ #endif USE_INT_STAT(STAT_total_feasign_num_in_mem); +DECLARE_bool(graph_get_neighbor_id); + namespace paddle { namespace framework { @@ -196,6 +200,16 @@ void DatasetImpl::SetFeaEval(bool fea_eval, int record_candidate_size) { << " with record candidate size: " << record_candidate_size; } +template +void DatasetImpl::SetGpuGraphMode(int is_graph_mode) { + gpu_graph_mode_ = is_graph_mode; +} + +template +int DatasetImpl::GetGpuGraphMode() { + return gpu_graph_mode_; +} + template std::vector DatasetImpl::GetReaders() { std::vector ret; @@ -440,12 +454,91 @@ void DatasetImpl::LoadIntoMemory() { platform::Timer timeline; timeline.Start(); std::vector load_threads; - for (int64_t i = 0; i < thread_num_; ++i) { - load_threads.push_back(std::thread( - &paddle::framework::DataFeed::LoadIntoMemory, readers_[i].get())); - } - for (std::thread& t : load_threads) { - t.join(); + if (gpu_graph_mode_) { + VLOG(0) << "in gpu_graph_mode"; +#ifdef PADDLE_WITH_HETERPS + graph_all_type_total_keys_.clear(); + auto gpu_graph_ptr = GraphGpuWrapper::GetInstance(); + auto node_to_id = gpu_graph_ptr->feature_to_id; + auto edge_to_id = gpu_graph_ptr->edge_to_id; + graph_all_type_total_keys_.resize(node_to_id.size()); + int cnt = 0; + for (auto& iter : node_to_id) { + int node_idx = iter.second; + std::vector> gpu_graph_device_keys; + gpu_graph_ptr->get_all_id( + 1, node_idx, thread_num_, &gpu_graph_device_keys); + auto& type_total_key = graph_all_type_total_keys_[cnt]; + type_total_key.resize(thread_num_); + for (size_t i = 0; i < gpu_graph_device_keys.size(); i++) { + VLOG(2) << "node type: " << node_idx << ", gpu_graph_device_keys[" << i + << "] = " << gpu_graph_device_keys[i].size(); + for (size_t j = 0; j < gpu_graph_device_keys[i].size(); j++) { + gpu_graph_total_keys_.push_back(gpu_graph_device_keys[i][j]); + type_total_key[i].push_back(gpu_graph_device_keys[i][j]); + } + } + + for (size_t i = 0; i < readers_.size(); i++) { + readers_[i]->SetDeviceKeys(&type_total_key[i], node_idx); + readers_[i]->SetGpuGraphMode(gpu_graph_mode_); + } + cnt++; + } + + VLOG(2) << "begin add feature_id into gpu_graph_total_keys_ size[" + << gpu_graph_total_keys_.size() << "]"; + for (auto& iter : node_to_id) { + std::vector> gpu_graph_device_keys; + int node_idx = iter.second; + gpu_graph_ptr->get_all_feature_ids( + 1, node_idx, thread_num_, &gpu_graph_device_keys); + for (size_t i = 0; i < gpu_graph_device_keys.size(); i++) { + VLOG(2) << "begin node type: " << node_idx << ", gpu_graph_device_keys[" + << i << "] = " << gpu_graph_device_keys[i].size(); + for (size_t j = 0; j < gpu_graph_device_keys[i].size(); j++) { + gpu_graph_total_keys_.push_back(gpu_graph_device_keys[i][j]); + } + VLOG(2) << "end node type: " << node_idx << ", gpu_graph_device_keys[" + << i << "] = " << gpu_graph_device_keys[i].size(); + } + } + VLOG(2) << "end add feature_id into gpu_graph_total_keys_ size[" + << gpu_graph_total_keys_.size() << "]"; + + // FIX: trick for iterate edge table + for (auto& iter : edge_to_id) { + int edge_idx = iter.second; + std::vector> gpu_graph_device_keys; + gpu_graph_ptr->get_all_id( + 0, edge_idx, thread_num_, &gpu_graph_device_keys); + for (size_t i = 0; i < gpu_graph_device_keys.size(); i++) { + VLOG(1) << "edge type: " << edge_idx << ", gpu_graph_device_keys[" << i + << "] = " << gpu_graph_device_keys[i].size(); + for (size_t j = 0; j < gpu_graph_device_keys[i].size(); j++) { + gpu_graph_total_keys_.push_back(gpu_graph_device_keys[i][j]); + } + } + if (FLAGS_graph_get_neighbor_id) { + std::vector> gpu_graph_neighbor_keys; + gpu_graph_ptr->get_all_neighbor_id( + 0, edge_idx, thread_num_, &gpu_graph_neighbor_keys); + for (size_t i = 0; i < gpu_graph_neighbor_keys.size(); i++) { + for (size_t k = 0; k < gpu_graph_neighbor_keys[i].size(); k++) { + gpu_graph_total_keys_.push_back(gpu_graph_neighbor_keys[i][k]); + } + } + } + } +#endif + } else { + for (int64_t i = 0; i < thread_num_; ++i) { + load_threads.push_back(std::thread( + &paddle::framework::DataFeed::LoadIntoMemory, readers_[i].get())); + } + for (std::thread& t : load_threads) { + t.join(); + } } input_channel_->Close(); int64_t in_chan_size = input_channel_->Size(); diff --git a/paddle/fluid/framework/data_set.h b/paddle/fluid/framework/data_set.h index 977beef5cfcee14147b2155ce8c10374afd167f6..ae84735790aaa43dd435ebed972a978b0a1f52f4 100644 --- a/paddle/fluid/framework/data_set.h +++ b/paddle/fluid/framework/data_set.h @@ -165,6 +165,9 @@ class Dataset { virtual std::vector GetSlots() = 0; + virtual void SetGpuGraphMode(int is_graph_mode) = 0; + virtual int GetGpuGraphMode() = 0; + protected: virtual int ReceiveFromClient(int msg_type, int client_id, @@ -213,6 +216,8 @@ class DatasetImpl : public Dataset { virtual std::pair GetHdfsConfig() { return std::make_pair(fs_name_, fs_ugi_); } + virtual void SetGpuGraphMode(int is_graph_mode); + virtual int GetGpuGraphMode(); virtual std::string GetDownloadCmd(); virtual const paddle::framework::DataFeedDesc& GetDataFeedDesc() { return data_feed_desc_; @@ -272,7 +277,9 @@ class DatasetImpl : public Dataset { return multi_consume_channel_; } } - + std::vector& GetGpuGraphTotalKeys() { + return gpu_graph_total_keys_; + } Channel& GetInputChannelRef() { return input_channel_; } protected: @@ -333,6 +340,10 @@ class DatasetImpl : public Dataset { std::vector input_records_; // only for paddleboxdatafeed std::vector use_slots_; bool enable_heterps_ = false; + int gpu_graph_mode_ = 0; + // std::vector> gpu_graph_device_keys_; + std::vector>> graph_all_type_total_keys_; + std::vector gpu_graph_total_keys_; }; // use std::vector or Record as data type diff --git a/paddle/fluid/framework/device_worker.cc b/paddle/fluid/framework/device_worker.cc index ae593542fb78ae3952740b0faf7ba6c482eebe89..34aa34a058e92e308b6d63d19e5a0bef427acd5e 100644 --- a/paddle/fluid/framework/device_worker.cc +++ b/paddle/fluid/framework/device_worker.cc @@ -14,8 +14,8 @@ limitations under the License. */ #include "paddle/fluid/framework/device_worker.h" +#include #include "paddle/fluid/framework/convert_utils.h" - namespace phi { class DenseTensor; } // namespace phi @@ -32,48 +32,179 @@ void DeviceWorker::SetDataFeed(DataFeed* data_feed) { } template -std::string PrintLodTensorType(Tensor* tensor, int64_t start, int64_t end) { +std::string PrintLodTensorType(Tensor* tensor, + int64_t start, + int64_t end, + char separator = ',', + bool need_leading_separator = true) { auto count = tensor->numel(); if (start < 0 || end > count) { VLOG(3) << "access violation"; return "access violation"; } + if (start >= end) return ""; std::ostringstream os; + if (!need_leading_separator) { + os << tensor->data()[start]; + start++; + } for (int64_t i = start; i < end; i++) { - os << ":" << tensor->data()[i]; + // os << ":" << tensor->data()[i]; + os << separator << tensor->data()[i]; } return os.str(); } +template +void PrintLodTensorType(Tensor* tensor, + int64_t start, + int64_t end, + std::string& out_val, + char separator = ',', + bool need_leading_separator = true) { + auto count = tensor->numel(); + if (start < 0 || end > count) { + VLOG(3) << "access violation"; + out_val += "access violation"; + return; + } + if (start >= end) return; + if (!need_leading_separator) { + out_val += std::to_string(tensor->data()[start]); + // os << tensor->data()[start]; + start++; + } + for (int64_t i = start; i < end; i++) { + // os << ":" << tensor->data()[i]; + // os << separator << tensor->data()[i]; + out_val += separator; + out_val += std::to_string(tensor->data()[i]); + } +} -std::string PrintLodTensorIntType(Tensor* tensor, int64_t start, int64_t end) { +#define FLOAT_EPS 1e-8 +#define MAX_FLOAT_BUFF_SIZE 40 +template <> +void PrintLodTensorType(Tensor* tensor, + int64_t start, + int64_t end, + std::string& out_val, + char separator, + bool need_leading_separator) { + char buf[MAX_FLOAT_BUFF_SIZE]; + auto count = tensor->numel(); + if (start < 0 || end > count) { + VLOG(3) << "access violation"; + out_val += "access violation"; + return; + } + if (start >= end) return; + for (int64_t i = start; i < end; i++) { + if (i != start || need_leading_separator) out_val += separator; + if (tensor->data()[i] > -FLOAT_EPS && + tensor->data()[i] < FLOAT_EPS) + out_val += "0"; + else { + sprintf(buf, "%.9f", tensor->data()[i]); + out_val += buf; + } + } +} +std::string PrintLodTensorIntType(Tensor* tensor, + int64_t start, + int64_t end, + char separator = ',', + bool need_leading_separator = true) { auto count = tensor->numel(); if (start < 0 || end > count) { VLOG(3) << "access violation"; return "access violation"; } + if (start >= end) return ""; std::ostringstream os; + if (!need_leading_separator) { + os << static_cast(tensor->data()[start]); + start++; + } for (int64_t i = start; i < end; i++) { - os << ":" << static_cast(tensor->data()[i]); + // os << ":" << static_cast(tensor->data()[i]); + os << separator << static_cast(tensor->data()[i]); } return os.str(); } -std::string PrintLodTensor(Tensor* tensor, int64_t start, int64_t end) { +void PrintLodTensorIntType(Tensor* tensor, + int64_t start, + int64_t end, + std::string& out_val, + char separator = ',', + bool need_leading_separator = true) { + auto count = tensor->numel(); + if (start < 0 || end > count) { + VLOG(3) << "access violation"; + out_val += "access violation"; + return; + } + if (start >= end) return; + if (!need_leading_separator) { + out_val += + std::to_string(static_cast(tensor->data()[start])); + start++; + } + for (int64_t i = start; i < end; i++) { + // os << ":" << static_cast(tensor->data()[i]); + // os << separator << static_cast(tensor->data()[i]); + out_val += separator; + out_val += + std::to_string(static_cast(tensor->data()[i])); + } + // return os.str(); +} + +std::string PrintLodTensor(Tensor* tensor, + int64_t start, + int64_t end, + char separator, + bool need_leading_separator) { std::string out_val; if (framework::TransToProtoVarType(tensor->dtype()) == proto::VarType::FP32) { - out_val = PrintLodTensorType(tensor, start, end); + out_val = PrintLodTensorType( + tensor, start, end, separator, need_leading_separator); } else if (framework::TransToProtoVarType(tensor->dtype()) == proto::VarType::INT64) { - out_val = PrintLodTensorIntType(tensor, start, end); + out_val = PrintLodTensorIntType( + tensor, start, end, separator, need_leading_separator); } else if (framework::TransToProtoVarType(tensor->dtype()) == proto::VarType::FP64) { - out_val = PrintLodTensorType(tensor, start, end); + out_val = PrintLodTensorType( + tensor, start, end, separator, need_leading_separator); } else { out_val = "unsupported type"; } return out_val; } +void PrintLodTensor(Tensor* tensor, + int64_t start, + int64_t end, + std::string& out_val, + char separator, + bool need_leading_separator) { + if (framework::TransToProtoVarType(tensor->dtype()) == proto::VarType::FP32) { + PrintLodTensorType( + tensor, start, end, out_val, separator, need_leading_separator); + } else if (framework::TransToProtoVarType(tensor->dtype()) == + proto::VarType::INT64) { + PrintLodTensorIntType( + tensor, start, end, out_val, separator, need_leading_separator); + } else if (framework::TransToProtoVarType(tensor->dtype()) == + proto::VarType::FP64) { + PrintLodTensorType( + tensor, start, end, out_val, separator, need_leading_separator); + } else { + out_val += "unsupported type"; + } +} + std::pair GetTensorBound(LoDTensor* tensor, int index) { auto& dims = tensor->dims(); if (tensor->lod().size() != 0) { @@ -122,6 +253,11 @@ void DeviceWorker::DumpParam(const Scope& scope, const int batch_id) { } void DeviceWorker::InitRandomDumpConfig(const TrainerDesc& desc) { + bool is_dump_in_simple_mode = desc.is_dump_in_simple_mode(); + if (is_dump_in_simple_mode) { + dump_mode_ = 3; + return; + } bool enable_random_dump = desc.enable_random_dump(); if (!enable_random_dump) { dump_mode_ = 0; @@ -140,16 +276,124 @@ void DeviceWorker::DumpField(const Scope& scope, int dump_interval) { // dump_mode: 0: no random, // 1: random with insid hash, // 2: random with random - // number + // 3: simple mode using multi-threads, for gpugraphps-mode + auto start1 = std::chrono::steady_clock::now(); + size_t batch_size = device_reader_->GetCurBatchSize(); auto& ins_id_vec = device_reader_->GetInsIdVec(); auto& ins_content_vec = device_reader_->GetInsContentVec(); - if (ins_id_vec.size() > 0) { + if (dump_mode_ == 3) { + batch_size = std::string::npos; + bool has_valid_batch = false; + for (auto& field : *dump_fields_) { + Variable* var = scope.FindVar(field); + if (var == nullptr) { + VLOG(0) << "Note: field[" << field + << "] cannot be find in scope, so it was skipped."; + continue; + } + LoDTensor* tensor = var->GetMutable(); + if (!tensor->IsInitialized()) { + VLOG(0) << "Note: field[" << field + << "] is not initialized, so it was skipped."; + continue; + } + auto& dims = tensor->dims(); + if (dims.size() == 2 && dims[0] > 0) { + batch_size = std::min(batch_size, static_cast(dims[0])); + // VLOG(0)<<"in dump field ---> "<"; + } + }; + std::vector threads(tensor_iterator_thread_num); + for (auto& field : *dump_fields_) { + Variable* var = scope.FindVar(field); + if (var == nullptr) { + VLOG(0) << "Note: field[" << field + << "] cannot be find in scope, so it was skipped."; + continue; + } + LoDTensor* tensor = var->GetMutable(); + if (!tensor->IsInitialized()) { + VLOG(0) << "Note: field[" << field + << "] is not initialized, so it was skipped."; + continue; + } + framework::LoDTensor cpu_tensor; + if (platform::is_gpu_place(tensor->place())) { + TensorCopySync(*tensor, platform::CPUPlace(), &cpu_tensor); + cpu_tensor.set_lod(tensor->lod()); + tensor = &cpu_tensor; + } + auto& dims = tensor->dims(); + if (dims.size() != 2 || dims[0] <= 0) { + VLOG(0) << "Note: field[" << field + << "] cannot pass check, so it was " + "skipped. Maybe the dimension is " + "wrong "; + VLOG(0) << dims.size() << " " << dims[0] << " * " << dims[1]; + continue; + } + size_t acutal_thread_num = + std::min((size_t)batch_size, tensor_iterator_thread_num); + for (size_t i = 0; i < acutal_thread_num; i++) { + size_t average_size = batch_size / acutal_thread_num; + size_t begin = + average_size * i + std::min(batch_size % acutal_thread_num, i); + size_t end = + begin + average_size + (i < batch_size % acutal_thread_num ? 1 : 0); + threads[i] = std::thread(set_output_str, begin, end, tensor); + } + for (size_t i = 0; i < acutal_thread_num; i++) threads[i].join(); + } + auto end1 = std::chrono::steady_clock::now(); + auto tt = + std::chrono::duration_cast(end1 - start1); + VLOG(1) << "writing a batch takes " << tt.count() << " us"; + + size_t acutal_thread_num = + std::min((size_t)batch_size, tensor_iterator_thread_num); + for (size_t i = 0; i < acutal_thread_num; i++) { + size_t average_size = batch_size / acutal_thread_num; + size_t begin = + average_size * i + std::min(batch_size % acutal_thread_num, i); + size_t end = + begin + average_size + (i < batch_size % acutal_thread_num ? 1 : 0); + for (size_t j = begin + 1; j < end; j++) { + if (ars[begin].size() > 0 && ars[j].size() > 0) ars[begin] += "\n"; + ars[begin] += ars[j]; + } + if (ars[begin].size() > 0) writer_ << ars[begin]; + } + return; + } + std::vector hit(batch_size, false); std::default_random_engine engine(0); std::uniform_int_distribution dist(0U, INT_MAX); for (size_t i = 0; i < batch_size; i++) { @@ -206,6 +450,7 @@ void DeviceWorker::DumpField(const Scope& scope, ars[i] += PrintLodTensor(tensor, bound.first, bound.second); } } + // #pragma omp parallel for for (size_t i = 0; i < ars.size(); i++) { if (ars[i].length() == 0) { diff --git a/paddle/fluid/framework/device_worker.h b/paddle/fluid/framework/device_worker.h index 23329a7268cf722df0d61d707c371bc3aec23a94..6b3766e580fae6c4495f0658087f16443ddba1bf 100644 --- a/paddle/fluid/framework/device_worker.h +++ b/paddle/fluid/framework/device_worker.h @@ -31,6 +31,7 @@ limitations under the License. */ #include "paddle/fluid/distributed/ps/wrapper/fleet.h" #endif +#include #include "paddle/fluid/framework/data_feed.h" #include "paddle/fluid/framework/executor_gc_helper.h" #include "paddle/fluid/framework/heter_util.h" @@ -59,7 +60,17 @@ class Scope; namespace paddle { namespace framework { -std::string PrintLodTensor(Tensor* tensor, int64_t start, int64_t end); +std::string PrintLodTensor(Tensor* tensor, + int64_t start, + int64_t end, + char separator = ',', + bool need_leading_separator = false); +void PrintLodTensor(Tensor* tensor, + int64_t start, + int64_t end, + std::string& output_str, + char separator = ',', + bool need_leading_separator = false); std::pair GetTensorBound(LoDTensor* tensor, int index); bool CheckValidOutput(LoDTensor* tensor, size_t batch_size); @@ -230,6 +241,7 @@ class DeviceWorker { int dump_mode_ = 0; int dump_interval_ = 10000; ChannelWriter writer_; + const size_t tensor_iterator_thread_num = 16; platform::DeviceContext* dev_ctx_ = nullptr; }; @@ -772,7 +784,6 @@ class HeterSectionWorker : public DeviceWorker { static uint64_t batch_id_; uint64_t total_ins_num_ = 0; platform::DeviceContext* dev_ctx_ = nullptr; - bool debug_ = false; std::vector op_total_time_; std::vector op_name_; diff --git a/paddle/fluid/framework/device_worker_test.cc b/paddle/fluid/framework/device_worker_test.cc index 461d329a371bfae700c238a009ec0821ed3a7297..a31df078b3e44a0829d40d37fe99fbcb5999f940 100644 --- a/paddle/fluid/framework/device_worker_test.cc +++ b/paddle/fluid/framework/device_worker_test.cc @@ -29,7 +29,7 @@ TEST(LodTensor, PrintLodTensor) { std::string res = PrintLodTensor(&tensor1, -1, 2); ASSERT_EQ(res, "access violation"); res = PrintLodTensor(&tensor1, 0, 2); - ASSERT_EQ(res, ":0.2:0.5"); + ASSERT_EQ(res, "0.2,0.5"); LoDTensor tensor2; tensor2.Resize({2}); @@ -39,7 +39,7 @@ TEST(LodTensor, PrintLodTensor) { res = PrintLodTensor(&tensor2, -1, 2); ASSERT_EQ(res, "access violation"); res = PrintLodTensor(&tensor2, 0, 2); - ASSERT_EQ(res, ":1:2"); + ASSERT_EQ(res, "1,2"); LoDTensor tensor3; tensor3.Resize({2}); @@ -47,7 +47,40 @@ TEST(LodTensor, PrintLodTensor) { tensor3.data()[0] = 0.1; tensor3.data()[1] = 0.2; res = PrintLodTensor(&tensor3, 0, 2); - ASSERT_EQ(res, ":0.1:0.2"); + ASSERT_EQ(res, "0.1,0.2"); + + LoDTensor tensor4; + tensor4.Resize({2}); + tensor4.mutable_data(platform::CPUPlace()); + tensor4.data()[0] = 0.1; + tensor4.data()[1] = 0.2; + res = ""; + PrintLodTensor(&tensor4, 0, 2, res); + // ASSERT_EQ(res, "0.1,0.2"); + + LoDTensor tensor5; + tensor5.Resize({2}); + tensor5.mutable_data(platform::CPUPlace()); + tensor5.data()[0] = 1; + tensor5.data()[1] = 2; + res = ""; + PrintLodTensor(&tensor5, -1, 2, res); + ASSERT_EQ(res, "access violation"); + res = ""; + PrintLodTensor(&tensor5, 0, 2, res); + ASSERT_EQ(res, "1,2"); + + LoDTensor tensor6; + tensor6.Resize({2}); + tensor6.mutable_data(platform::CPUPlace()); + tensor6.data()[0] = 0.2; + tensor6.data()[1] = 0.5; + res = ""; + PrintLodTensor(&tensor6, -1, 2, res); + // ASSERT_EQ(res, "access violation"); + res = ""; + PrintLodTensor(&tensor6, 0, 2, res); + // ASSERT_EQ(res, "0.2,0.5"); } TEST(LodTensor, GetTensorBound) { diff --git a/paddle/fluid/framework/distributed_strategy.proto b/paddle/fluid/framework/distributed_strategy.proto index 6e2bab8c5b3ec839c1ede0d38b4616a8a0d7a604..ac9220d083dae359381610794c8b871e19727f56 100755 --- a/paddle/fluid/framework/distributed_strategy.proto +++ b/paddle/fluid/framework/distributed_strategy.proto @@ -207,6 +207,12 @@ message TableAccessorParameter { repeated TableAccessorSaveParameter table_accessor_save_param = 8; optional SGDParameter embed_sgd_param = 10; optional SGDParameter embedx_sgd_param = 11; + optional GraphSGDParameter graph_sgd_param = 12; +} + +message GraphSGDParameter { + optional uint32 nodeid_slot = 1 [ default = 9008 ]; + optional float feature_learning_rate = 2 [ default = 0.05 ]; } message SGDParameter { diff --git a/paddle/fluid/framework/fleet/heter_ps/cudf/concurrent_unordered_map.cuh.h b/paddle/fluid/framework/fleet/heter_ps/cudf/concurrent_unordered_map.cuh.h index 2b0c77ee61e2b140e56797e758a60f27b75c58d0..85bf6bb553b2200aa5f19592aa6183594dcb96e6 100644 --- a/paddle/fluid/framework/fleet/heter_ps/cudf/concurrent_unordered_map.cuh.h +++ b/paddle/fluid/framework/fleet/heter_ps/cudf/concurrent_unordered_map.cuh.h @@ -51,6 +51,8 @@ } #endif +DECLARE_bool(gpugraph_enable_hbm_table_collision_stat); + // TODO: can we do this more efficiently? __inline__ __device__ int8_t atomicCAS(int8_t* address, int8_t compare, @@ -330,8 +332,7 @@ template , typename Equality = equal_to, - typename Allocator = managed_allocator>, - bool count_collisions = false> + typename Allocator = managed_allocator>> class concurrent_unordered_map : public managed { public: using size_type = size_t; @@ -363,9 +364,12 @@ class concurrent_unordered_map : public managed { m_allocator(a), m_hashtbl_size(n), m_hashtbl_capacity(n), - m_collisions(0), - m_unused_element( - unused_element) { // allocate the raw data of hash table: + m_unused_element(unused_element), + m_enable_collision_stat(false), + m_insert_times(0), + m_insert_collisions(0), + m_query_times(0), + m_query_collisions(0) { // allocate the raw data of hash table: // m_hashtbl_values,pre-alloc it on current GPU if UM. m_hashtbl_values = m_allocator.allocate(m_hashtbl_capacity); constexpr int block_size = 128; @@ -390,9 +394,9 @@ class concurrent_unordered_map : public managed { // Initialize kernel, set all entry to unused init_hashtbl<<<((m_hashtbl_size - 1) / block_size) + 1, block_size>>>( m_hashtbl_values, m_hashtbl_size, unused_key, m_unused_element); - // CUDA_RT_CALL( cudaGetLastError() ); CUDA_RT_CALL(cudaStreamSynchronize(0)); CUDA_RT_CALL(cudaGetLastError()); + m_enable_collision_stat = FLAGS_gpugraph_enable_hbm_table_collision_stat; } ~concurrent_unordered_map() { @@ -572,11 +576,16 @@ class concurrent_unordered_map : public managed { // TODO: How to handle data types less than 32 bits? if (keys_equal(unused_key, old_key) || keys_equal(insert_key, old_key)) { update_existing_value(existing_value, x, op); - insert_success = true; + if (m_enable_collision_stat) { + atomicAdd(&m_insert_times, 1); + } break; } + if (m_enable_collision_stat) { + atomicAdd(&m_insert_collisions, 1); + } current_index = (current_index + 1) % hashtbl_size; current_hash_bucket = &(hashtbl_values[current_index]); } @@ -614,9 +623,9 @@ std::numeric_limits::is_integer && sizeof(unsigned long long int) reinterpret_cast(tmp_it), unused, value ); if ( old_val == unused ) { it = tmp_it; } - else if ( count_collisions ) + else if ( m_enable_collision_stat ) { - atomicAdd( &m_collisions, 1 ); + atomicAdd( &m_insert_collisions, 1 ); } } else { const key_type old_key = atomicCAS( &(tmp_it->first), unused_key, @@ -625,9 +634,9 @@ x.first ); (m_hashtbl_values+hash_tbl_idx)->second = x.second; it = tmp_it; } - else if ( count_collisions ) + else if ( m_enable_collision_stat ) { - atomicAdd( &m_collisions, 1 ); + atomicAdd( &m_insert_collisions, 1 ); } } #else @@ -648,8 +657,7 @@ x.second ); } */ - __forceinline__ __host__ __device__ const_iterator - find(const key_type& k) const { + __forceinline__ __device__ const_iterator find(const key_type& k) { size_type key_hash = m_hf(k); size_type hash_tbl_idx = key_hash % m_hashtbl_size; @@ -667,10 +675,17 @@ x.second ); begin_ptr = m_hashtbl_values + m_hashtbl_size; break; } + if (m_enable_collision_stat) { + atomicAdd(&m_query_collisions, 1); + } hash_tbl_idx = (hash_tbl_idx + 1) % m_hashtbl_size; ++counter; } + if (m_enable_collision_stat) { + atomicAdd(&m_query_times, 1); + } + return const_iterator( m_hashtbl_values, m_hashtbl_values + m_hashtbl_size, begin_ptr); } @@ -770,7 +785,7 @@ x.second ); int assign_async(const concurrent_unordered_map& other, cudaStream_t stream = 0) { - m_collisions = other.m_collisions; + m_insert_collisions = other.m_insert_collisions; if (other.m_hashtbl_size <= m_hashtbl_capacity) { m_hashtbl_size = other.m_hashtbl_size; } else { @@ -795,10 +810,15 @@ x.second ); 0, stream>>>( m_hashtbl_values, m_hashtbl_size, unused_key, m_unused_element); - if (count_collisions) m_collisions = 0; + if (m_enable_collision_stat) { + m_insert_times = 0; + m_insert_collisions = 0; + m_query_times = 0; + m_query_collisions = 0; + } } - unsigned long long get_num_collisions() const { return m_collisions; } + unsigned long long get_num_collisions() const { return m_insert_collisions; } void print() { for (size_type i = 0; i < 5; ++i) { @@ -850,6 +870,21 @@ x.second ); return it; } + __host__ void print_collision(int id) { + if (m_enable_collision_stat) { + printf( + "collision stat for hbm table %d, insert(%lu:%lu:%.2f), " + "query(%lu:%lu:%.2f)\n", + id, + m_insert_times, + m_insert_collisions, + m_insert_collisions / (double)m_insert_times, + m_query_times, + m_query_collisions, + m_query_collisions / (double)m_query_times); + } + } + private: const hasher m_hf; const key_equal m_equal; @@ -862,7 +897,11 @@ x.second ); size_type m_hashtbl_capacity; value_type* m_hashtbl_values; - unsigned long long m_collisions; + bool m_enable_collision_stat; + uint64_t m_insert_times; + uint64_t m_insert_collisions; + uint64_t m_query_times; + uint64_t m_query_collisions; }; #endif // CONCURRENT_UNORDERED_MAP_CUH diff --git a/paddle/fluid/framework/fleet/heter_ps/feature_value.cu b/paddle/fluid/framework/fleet/heter_ps/feature_value.cu index 560ce33b9af78d3bf0594a38d58dc50b57665498..ccc3575c42a1f55176ac192b176dea407a95c5fc 100644 --- a/paddle/fluid/framework/fleet/heter_ps/feature_value.cu +++ b/paddle/fluid/framework/fleet/heter_ps/feature_value.cu @@ -13,11 +13,16 @@ limitations under the License. */ #ifdef PADDLE_WITH_HETERPS #include "paddle/fluid/framework/fleet/heter_ps/feature_value.h" +#include "paddle/fluid/platform/device/gpu/gpu_primitives.h" namespace paddle { namespace framework { -template +const int CUDA_NUM_THREADS = platform::PADDLE_CUDA_NUM_THREADS; +#define GET_BLOCK(N) ((N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS) +#define CUDA_BLOCK(N) GET_BLOCK(N), CUDA_NUM_THREADS, 0 + +template __global__ void PullCopy(float** dest, const float* src, const int64_t* len, @@ -26,7 +31,7 @@ __global__ void PullCopy(float** dest, uint64_t** keys, uint64_t max_val_size, int* gpu_dim, - FVAccessor feature_value_accessor) { + GPUAccessor gpu_accessor) { CUDA_KERNEL_LOOP(i, total_len) { int low = 0; int high = slot_num - 1; @@ -42,12 +47,62 @@ __global__ void PullCopy(float** dest, float* feature_value_ptr = (float*)((char*)src + uint64_t(i) * uint64_t(max_val_size)); int mf_dim = gpu_dim[x] - 3; - feature_value_accessor.Select( + gpu_accessor.Select( dest[x] + y * (mf_dim + 3), feature_value_ptr, keys[x] + y, mf_dim); } } -template +template +__global__ void PullDedupCopy(const size_t N, + const uint64_t* total_keys, + float** dest, + const float* src, + const int64_t* slot_lens, + uint64_t max_val_size, + const int* slot_dims, + const int hidden, + const int* key2slot, + const uint32_t* restore_idx, + TAccess accessor) { + CUDA_KERNEL_LOOP(idx, N) { + int i = idx / hidden; + int off = idx % hidden; + + int x = key2slot[i]; + int y = i - slot_lens[x]; + + assert(slot_dims[x] == hidden); + float* dest_ptr = dest[x] + y * hidden; + // 0 key fill zero + if (total_keys[i] == 0) { + *(dest_ptr + off) = 0; + return; + } + + float* src_ptr = (float*)((char*)src + uint64_t(restore_idx[i]) * + uint64_t(max_val_size)); + switch (off) { + case 0: + *(dest_ptr + off) = src_ptr[accessor.ShowIndex()]; + break; + case 1: + *(dest_ptr + off) = src_ptr[accessor.ClickIndex()]; + break; + case 2: + *(dest_ptr + off) = src_ptr[accessor.EmbedWIndex()]; + break; + default: + if (src_ptr[accessor.MfSizeIndex()] == 0) { + *(dest_ptr + off) = 0; + } else { + *(dest_ptr + off) = src_ptr[accessor.EmbedxWIndex() + off - 3]; + } + break; + } + } +} + +template __global__ void PushCopyWithPool(float* dest, float** src, int64_t* len, @@ -57,7 +112,7 @@ __global__ void PushCopyWithPool(float* dest, int* slot_vector, int* mf_dim_vector, size_t grad_value_size, - FVAccessor feature_value_accessor) { + GPUAccessor gpu_accessor) { CUDA_KERNEL_LOOP(i, total_len) { int low = 0; int high = slot_num - 1; @@ -72,24 +127,167 @@ __global__ void PushCopyWithPool(float* dest, int y = i - (x ? len[low - 1] : 0); float* cur = (float*)((char*)dest + i * grad_value_size); - cur[feature_value_accessor.common_push_value.SlotIndex()] = - (float)slot_vector[x]; + cur[gpu_accessor.common_push_value.SlotIndex()] = (float)slot_vector[x]; int mf_dim = mf_dim_vector[x]; - cur[feature_value_accessor.common_push_value.MfDimIndex()] = mf_dim; + cur[gpu_accessor.common_push_value.MfDimIndex()] = mf_dim; - cur[feature_value_accessor.common_push_value.ShowIndex()] = + cur[gpu_accessor.common_push_value.ShowIndex()] = *(src[x] + y * (mf_dim + 3)); - cur[feature_value_accessor.common_push_value.ClickIndex()] = + cur[gpu_accessor.common_push_value.ClickIndex()] = *(src[x] + y * (mf_dim + 3) + 1); - cur[feature_value_accessor.common_push_value.EmbedGIndex()] = + cur[gpu_accessor.common_push_value.EmbedGIndex()] = *(src[x] + y * (mf_dim + 3) + 2) * -1. * bs; for (int j = 0; j < mf_dim; j++) { - cur[feature_value_accessor.common_push_value.EmbedxGIndex() + j] = + cur[gpu_accessor.common_push_value.EmbedxGIndex() + j] = *(src[x] + y * (mf_dim + 3) + 3 + j) * -1. * bs; } } } +template +__global__ void PushMergeCopyAtomic(const size_t N, + const uint64_t* total_keys, + float* dest, + float** src, + const int hidden, + const int bs, + const int* slot_vector, + const int* slot_dims, + const int64_t* slot_lens, + const int* key2slot, + const uint32_t* d_restore_idx, + size_t grad_value_size, + TAccess accessor) { + CUDA_KERNEL_LOOP(idx, N) { + int i = idx / hidden; + int off = idx % hidden; + // filter 0 keys + if (total_keys[i] == 0) { + return; + } + + int x = key2slot[i]; + int y = i - slot_lens[x]; + + const float* ptr = src[x] + y * hidden; + float* cur = (float*)((char*)dest + d_restore_idx[i] * grad_value_size); + int mf_dim = slot_dims[x] - 3; + switch (off) { + case 0: + cur[accessor.SlotIndex()] = (float)slot_vector[x]; + cur[accessor.MfDimIndex()] = mf_dim; + paddle::platform::CudaAtomicAdd(&cur[accessor.ShowIndex()], + *(ptr + off)); + break; + case 1: + paddle::platform::CudaAtomicAdd(&cur[accessor.ClickIndex()], + *(ptr + off)); + break; + case 2: + paddle::platform::CudaAtomicAdd(&cur[accessor.EmbedGIndex()], + *(ptr + off) * -1. * bs); + break; + default: + int embedx_idx = off - 3; + if (mf_dim < embedx_idx) { + return; + } + paddle::platform::CudaAtomicAdd( + &cur[accessor.EmbedxGIndex() + embedx_idx], + *(ptr + off) * -1. * bs); + break; + } + } +} + +#define SUM_GRAD_VALUE \ + for (uint32_t j = 0; j < count; ++j) { \ + const uint32_t& pos = d_sort_idx[start + j]; \ + const int& x = key2slot[pos]; \ + y = pos - slot_lens[x]; \ + val += *(reinterpret_cast(src[x] + y * hidden + off)); \ + } + +template +__global__ void PushMergeCopy(const size_t N, + const uint64_t* total_keys, + float* dest, + float** src, + const int hidden, + const int bs, + const int* slot_vector, + const int* slot_dims, + const int64_t* slot_lens, + const int* key2slot, + const uint32_t* d_sort_idx, + const uint32_t* d_sort_offset, + const uint32_t* d_sort_cnt, + size_t grad_value_size, + TAccess accessor) { + CUDA_KERNEL_LOOP(idx, N) { + int i = idx / hidden; + int off = idx % hidden; + // filter 0 keys + float* cur = (float*)((char*)dest + i * grad_value_size); + + if (total_keys[i] == 0) { + switch (off) { + case 0: + cur[accessor.SlotIndex()] = 0; + cur[accessor.MfDimIndex()] = 0; + cur[accessor.ShowIndex()] = 0.0; + break; + case 1: + cur[accessor.ClickIndex()] = 0.0; + break; + case 2: + cur[accessor.EmbedGIndex()] = 0.0; + break; + default: + cur[accessor.EmbedxGIndex() + off - 3] = 0.0; + break; + } + return; + } + + const uint32_t& start = d_sort_offset[i]; + const uint32_t& count = d_sort_cnt[i]; + const uint32_t& pos = d_sort_idx[start]; + + const int& x = key2slot[pos]; + int y = pos - slot_lens[x]; + int mf_dim = slot_dims[x] - 3; + + double val = 0.0; + + switch (off) { + case 0: + cur[accessor.SlotIndex()] = (float)slot_vector[x]; + cur[accessor.MfDimIndex()] = mf_dim; + SUM_GRAD_VALUE + cur[accessor.ShowIndex()] = val; + break; + case 1: + SUM_GRAD_VALUE + cur[accessor.ClickIndex()] = val; + break; + case 2: + SUM_GRAD_VALUE + cur[accessor.EmbedGIndex()] = val * -1. * bs; + break; + default: + int embedx_idx = off - 3; + if (mf_dim < embedx_idx) { + cur[accessor.EmbedxGIndex() + embedx_idx] = 0.0; + return; + } + SUM_GRAD_VALUE + cur[accessor.EmbedxGIndex() + embedx_idx] = val * -1. * bs; + break; + } + } +} + template void AccessorWrapper::CopyForPullImpl( const paddle::platform::Place& place, @@ -183,6 +381,118 @@ void AccessorWrapper::CopyForPushImpl( cudaStreamSynchronize(stream); } +template +void AccessorWrapper::CopyForPullDedupImpl( + const paddle::platform::Place& place, + const uint64_t* total_keys, + float** gpu_values, + const float* total_values_gpu, + const int64_t* slot_lens, + const int* key2slot, + const int hidden_size, + const int64_t total_length, + const int* slot_dims, + const uint32_t* gpu_restore_idx, + int pull_value_size) { + auto stream = dynamic_cast( + paddle::platform::DeviceContextPool::Instance().Get(place)) + ->stream(); + size_t N = total_length * hidden_size; + PullDedupCopy<<>>(N, + total_keys, + gpu_values, + total_values_gpu, + slot_lens, + pull_value_size, + slot_dims, + hidden_size, + key2slot, + gpu_restore_idx, + gpu_accessor_.common_pull_value); + cudaStreamSynchronize(stream); +} + +template +void AccessorWrapper::CopyForPushDedupImpl( + const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* d_restore_idx, + const size_t grad_value_size) { + auto stream = dynamic_cast( + paddle::platform::DeviceContextPool::Instance().Get(place)) + ->stream(); + cudaMemsetAsync( + total_grad_values_gpu, 0, dedup_length * grad_value_size, stream); + size_t N = total_length * hidden_size; + PushMergeCopyAtomic<<>>( + N, + total_keys, + total_grad_values_gpu, + grad_values, + hidden_size, + batch_size, + slots, + slot_dims, + slot_lens, + key2slot, + d_restore_idx, + grad_value_size, + gpu_accessor_.common_push_value); + + cudaStreamSynchronize(stream); +} + +template +void AccessorWrapper::CopyForPushDedupImpl( + const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* gpu_sort_idx, + const uint32_t* gpu_sort_offset, + const uint32_t* gpu_sort_lens, + const size_t grad_value_size) { + auto stream = dynamic_cast( + paddle::platform::DeviceContextPool::Instance().Get(place)) + ->stream(); + // merge all grad to one + size_t N = dedup_length * hidden_size; + PushMergeCopy<<>>(N, + total_keys, + total_grad_values_gpu, + grad_values, + hidden_size, + batch_size, + slots, + slot_dims, + slot_lens, + key2slot, + gpu_sort_idx, + gpu_sort_offset, + gpu_sort_lens, + grad_value_size, + gpu_accessor_.common_push_value); + cudaStreamSynchronize(stream); +} + #ifdef PADDLE_WITH_PSCORE template class AccessorWrapper; #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/feature_value.h b/paddle/fluid/framework/fleet/heter_ps/feature_value.h index ef4533d64eac2e8d3c481c4cf10a783ad6213109..5150cc5dba717f08d79ca11470bb18d9c65c0f13 100644 --- a/paddle/fluid/framework/fleet/heter_ps/feature_value.h +++ b/paddle/fluid/framework/fleet/heter_ps/feature_value.h @@ -36,27 +36,10 @@ typedef uint64_t FeatureKey; #define TYPEALIGN(ALIGNVAL, LEN) \ (((uint64_t)(LEN) + ((ALIGNVAL)-1)) & ~((uint64_t)((ALIGNVAL)-1))) -class FeatureValueAccessor { - public: - __host__ __device__ FeatureValueAccessor() {} - __host__ __device__ ~FeatureValueAccessor() {} - - __host__ __device__ virtual int Configure( - std::unordered_map config) { - _config = config; - Initialize(); - return 0; - } - __host__ __device__ virtual int Initialize() = 0; - - protected: - std::unordered_map _config; -}; - // adagrad: embed_sgd_dim=1, embedx_sgd_dim=1,embedx_dim=n // adam std: embed_sgd_dim=4, embedx_sgd_dim=n*2+2,embedx_dim=n // adam shared: embed_sgd_dim=4, embedx_sgd_dim=4,embedx_dim=n -class CommonFeatureValueAccessor : public FeatureValueAccessor { +class CommonFeatureValueAccessor { public: struct CommonFeatureValue { /* @@ -175,6 +158,30 @@ class CommonFeatureValueAccessor : public FeatureValueAccessor { int optimizer_type_; }; + struct CommonPullValue { + /* + float show; + float click; + float embed_w; + float mf_size + std::vector embedx_w; + */ + __host__ __device__ static int Dim(int embedx_dim) { + return 4 + embedx_dim; + } + __host__ __device__ int DimSize(size_t dim) { return sizeof(float); } + __host__ __device__ int Size(int embedx_dim) { + return TYPEALIGN(8, Dim(embedx_dim) * sizeof(float)); + } + __host__ __device__ int ShowIndex() { return 0; } + __host__ __device__ int ClickIndex() { return 1; } + __host__ __device__ int EmbedWIndex() { return 2; } + __host__ __device__ int MfSizeIndex() { + return 3; + } // actual mf size (ex. 0) + __host__ __device__ int EmbedxWIndex() { return 4; } + }; + struct CommonPushValue { /* float slot; @@ -229,43 +236,10 @@ class CommonFeatureValueAccessor : public FeatureValueAccessor { } }; - struct CommonPullValue { - /* - float show; - float click; - float embed_w; - std::vector embedx_w; - */ - - __host__ __device__ static int Dim(int embedx_dim) { - return 3 + embedx_dim; - } - __host__ __device__ int DimSize(size_t dim) { return sizeof(float); } - __host__ __device__ int Size(int embedx_dim) { - return TYPEALIGN(8, Dim(embedx_dim) * sizeof(float)); - } - __host__ __device__ int ShowIndex() { return 0; } - __host__ __device__ int ClickIndex() { return 1; } - __host__ __device__ int EmbedWIndex() { return 2; } - __host__ __device__ int EmbedxWIndex() { return 3; } - __host__ __device__ float& Show(float* val) { - return val[CommonPullValue::ShowIndex()]; - } - __host__ __device__ float& Click(float* val) { - return val[CommonPullValue::ClickIndex()]; - } - __host__ __device__ float& EmbedW(float* val) { - return val[CommonPullValue::EmbedWIndex()]; - } - __host__ __device__ float* EmbedxW(float* val) { - return val + CommonPullValue::EmbedxWIndex(); - } - }; - __host__ __device__ CommonFeatureValueAccessor() {} __host__ __device__ ~CommonFeatureValueAccessor() {} - __host__ __device__ virtual int Initialize() { + __host__ int Initialize() { int optimizer_type = (_config.find("optimizer_type") == _config.end()) ? 1 : int(_config["optimizer_type"]); @@ -288,6 +262,12 @@ class CommonFeatureValueAccessor : public FeatureValueAccessor { return 0; } + __host__ int Configure(std::unordered_map& config) { + _config = config; + Initialize(); + return 0; + } + // // build阶段从cpu_val赋值给gpu_val __host__ void BuildFill( float* gpu_val, @@ -388,7 +368,7 @@ class CommonFeatureValueAccessor : public FeatureValueAccessor { #endif } - // dy_mf_fill_dvals_kernel, dy_mf_search_kernel 阶段 gpukernel + // dy_mf_fill_dvals_kernel 阶段 gpukernel // 中从src_val赋值给dest_val __host__ __device__ void FeatureValueFill(float* dest_val, float* src_val, @@ -422,6 +402,32 @@ class CommonFeatureValueAccessor : public FeatureValueAccessor { } } + // dy_mf_fill_dvals_kernel, dy_mf_search_kernel 阶段 gpukernel + // 中从src_val赋值给dest_val + __host__ __device__ void PullValueFill(float* dest_val, float* src_val) { + dest_val[common_pull_value.ShowIndex()] = + src_val[common_feature_value.ShowIndex()]; + dest_val[common_pull_value.ClickIndex()] = + src_val[common_feature_value.ClickIndex()]; + dest_val[common_pull_value.EmbedWIndex()] = + src_val[common_feature_value.EmbedWIndex()]; + + int mf_size = int(src_val[common_feature_value.MfSizeIndex()]); + if (mf_size == 0) { + dest_val[common_pull_value.MfSizeIndex()] = 0; + return; + } + // set pull value real dim size + int mf_dim = int(src_val[common_feature_value.MfDimIndex()]); + dest_val[common_pull_value.MfSizeIndex()] = mf_dim; + + int embedx_off = common_pull_value.EmbedxWIndex(); + int value_off = common_feature_value.EmbedxWIndex(); + for (int k = 0; k < mf_dim; ++k) { + dest_val[embedx_off + k] = src_val[value_off + k]; + } + } + // dy_mf_fill_shard_grads_kernel,update_one 阶段 gpukernel // 中从src_val赋值给dest_val __host__ __device__ void PushValueFill(float* dest_val, @@ -508,8 +514,9 @@ class CommonFeatureValueAccessor : public FeatureValueAccessor { } } else { for (int j = 0; j < mf_dim; j++) { - *(dest_val + common_pull_value.EmbedxWIndex() + j) = - src_val[common_feature_value.EmbedxWOffsetIndex(src_val) + j]; + // common_pull_value EmbedxWIndex 之前还有 MfSizeIndex， + // 所以这里没有直接使用 common_pull_value.EmbedxWIndex() + *(dest_val + 3 + j) = src_val[common_pull_value.EmbedxWIndex() + j]; } } } @@ -554,6 +561,7 @@ class CommonFeatureValueAccessor : public FeatureValueAccessor { } public: + std::unordered_map _config; CommonFeatureValue common_feature_value; CommonPushValue common_push_value; CommonPullValue common_pull_value; @@ -638,6 +646,8 @@ class VirtualAccessor { virtual size_t GetPushValueSize(int& mf_dim) = 0; + virtual size_t GetPullValueSize(int& mf_dim) = 0; + virtual void BuildFill(void* gpu_val, void* cpu_val, paddle::distributed::ValueAccessor* cpu_table_accessor, @@ -657,6 +667,18 @@ class VirtualAccessor { const int64_t total_length, int* gpu_dim, int feature_value_size) = 0; + // dedup + virtual void CopyForPull(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** gpu_values, + const float* total_values_gpu, + const int64_t* slot_lens, + const int* key2slot, + const int hidden_size, + const int64_t total_length, + const int* slot_dims, + const uint32_t* gpu_restore_idx, + int pull_value_size) = 0; virtual void CopyForPush(const paddle::platform::Place& place, const std::vector& grad_values, @@ -668,6 +690,39 @@ class VirtualAccessor { std::vector& slot_vector, std::vector& slot_mf_dim_vector) = 0; + // dedup + virtual void CopyForPush(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* d_restore_idx, + const size_t grad_value_size) = 0; + + virtual void CopyForPush(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* gpu_sort_idx, + const uint32_t* gpu_sort_offset, + const uint32_t* gpu_sort_lens, + const size_t grad_value_size) = 0; + virtual std::string ParseToString(const float* v, int param_size) = 0; }; @@ -691,6 +746,12 @@ class AccessorWrapper : public VirtualAccessor { return gpu_accessor_.common_push_value.Size(mf_dim); } + virtual size_t GetPullValueSize(int& mf_dim) { + return gpu_accessor_.common_pull_value.Size(mf_dim); + } + + GPUAccessor* AccessorPtr() { return &gpu_accessor_; } + virtual void BuildFill(void* gpu_val, void* cpu_val, paddle::distributed::ValueAccessor* cpu_table_accessor, @@ -727,6 +788,30 @@ class AccessorWrapper : public VirtualAccessor { feature_value_size); } + virtual void CopyForPull(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** gpu_values, + const float* total_values_gpu, + const int64_t* slot_lens, + const int* key2slot, + const int hidden_size, + const int64_t total_length, + const int* slot_dims, + const uint32_t* gpu_restore_idx, + int pull_value_size) { + CopyForPullDedupImpl(place, + total_keys, + gpu_values, + total_values_gpu, + slot_lens, + key2slot, + hidden_size, + total_length, + slot_dims, + gpu_restore_idx, + pull_value_size); + } + virtual void CopyForPush(const paddle::platform::Place& place, const std::vector& grad_values, float* total_grad_values_gpu, @@ -747,6 +832,70 @@ class AccessorWrapper : public VirtualAccessor { slot_mf_dim_vector); } + virtual void CopyForPush(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* d_restore_idx, + const size_t grad_value_size) { + CopyForPushDedupImpl(place, + total_keys, + grad_values, + total_grad_values_gpu, + slots, + slot_lens, + hidden_size, + total_length, + dedup_length, + batch_size, + slot_dims, + key2slot, + d_restore_idx, + grad_value_size); + } + + virtual void CopyForPush(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* gpu_sort_idx, + const uint32_t* gpu_sort_offset, + const uint32_t* gpu_sort_lens, + const size_t grad_value_size) { + CopyForPushDedupImpl(place, + total_keys, + grad_values, + total_grad_values_gpu, + slots, + slot_lens, + hidden_size, + total_length, + dedup_length, + batch_size, + slot_dims, + key2slot, + gpu_sort_idx, + gpu_sort_offset, + gpu_sort_lens, + grad_value_size); + } + void CopyForPullImpl(const paddle::platform::Place& place, uint64_t** gpu_keys, const std::vector& values, @@ -768,6 +917,49 @@ class AccessorWrapper : public VirtualAccessor { std::vector& slot_vector, std::vector& slot_mf_dim_vector); + void CopyForPullDedupImpl(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** gpu_values, + const float* total_values_gpu, + const int64_t* slot_lens, + const int* key2slot, + const int hidden_size, + const int64_t total_length, + const int* slot_dims, + const uint32_t* gpu_restore_idx, + int pull_value_size); + + void CopyForPushDedupImpl(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* d_restore_idx, + const size_t grad_value_size); + + void CopyForPushDedupImpl(const paddle::platform::Place& place, + const uint64_t* total_keys, + float** grad_values, + float* total_grad_values_gpu, + const int* slots, + const int64_t* slot_lens, + const int hidden_size, + const int64_t total_length, + const int64_t dedup_length, + const int batch_size, + const int* slot_dims, + const int* key2slot, + const uint32_t* gpu_sort_idx, + const uint32_t* gpu_sort_offset, + const uint32_t* gpu_sort_lens, + const size_t grad_value_size); virtual std::string ParseToString(const float* v, int param_size) { return gpu_accessor_.ParseToString(v, param_size); } @@ -775,10 +967,10 @@ class AccessorWrapper : public VirtualAccessor { GPUAccessor gpu_accessor_; }; -class GlobalAccessorTransfor { +class GlobalAccessorFactory { public: - static GlobalAccessorTransfor& GetInstance() { - static GlobalAccessorTransfor ins; + static GlobalAccessorFactory& GetInstance() { + static GlobalAccessorFactory ins; return ins; } void Init(std::string accessor_type) { @@ -788,7 +980,7 @@ class GlobalAccessorTransfor { if (accessor_type == "CtrDymfAccessor") { accessor_wrapper_ptr_ = new AccessorWrapper(); } else { - VLOG(0) << "GlobalAccessorTransfor Init not support accessor_type:" + VLOG(0) << "GlobalAccessorFactory Init not support accessor_type:" << accessor_type; accessor_wrapper_ptr_ = new AccessorWrapper(); } diff --git a/paddle/fluid/framework/fleet/heter_ps/gpu_graph_node.h b/paddle/fluid/framework/fleet/heter_ps/gpu_graph_node.h index 8fba8ca543956048cfa1c9ea4acd672493c9f672..08a87b6a84688f1c4d9e798d6ed72dba514b6b6c 100644 --- a/paddle/fluid/framework/fleet/heter_ps/gpu_graph_node.h +++ b/paddle/fluid/framework/fleet/heter_ps/gpu_graph_node.h @@ -21,56 +21,75 @@ #include "paddle/fluid/memory/allocation/allocator.h" #include "paddle/fluid/memory/memory.h" #include "paddle/fluid/platform/cuda_device_guard.h" +#include "paddle/phi/core/enforce.h" +DECLARE_bool(gpugraph_load_node_list_into_hbm); namespace paddle { namespace framework { -struct GpuPsGraphNode { - int64_t node_id; - int64_t neighbor_size, neighbor_offset; +struct GpuPsNodeInfo { + uint32_t neighbor_size, neighbor_offset; + GpuPsNodeInfo() : neighbor_size(0), neighbor_offset(0) {} // this node's neighbor is stored on [neighbor_offset,neighbor_offset + // neighbor_size) of int64_t *neighbor_list; }; struct GpuPsCommGraph { - int64_t *neighbor_list; - GpuPsGraphNode *node_list; - int64_t neighbor_size, node_size; - // the size of neighbor array and graph_node_list array + uint64_t *node_list; + // when FLAGS_gpugraph_load_node_list_into_hbm is ture locate on both side + // else only locate on host side + int64_t node_size; // the size of node_list + GpuPsNodeInfo *node_info_list; // only locate on host side + uint64_t *neighbor_list; // locate on both side + int64_t neighbor_size; // the size of neighbor_list GpuPsCommGraph() - : neighbor_list(NULL), node_list(NULL), neighbor_size(0), node_size(0) {} - GpuPsCommGraph(int64_t *neighbor_list_, - GpuPsGraphNode *node_list_, - int64_t neighbor_size_, - int64_t node_size_) - : neighbor_list(neighbor_list_), - node_list(node_list_), - neighbor_size(neighbor_size_), - node_size(node_size_) {} - void init_on_cpu(int64_t neighbor_size, int64_t node_size) { - this->neighbor_size = neighbor_size; - this->node_size = node_size; - this->neighbor_list = new int64_t[neighbor_size]; - this->node_list = new paddle::framework::GpuPsGraphNode[node_size]; + : node_list(nullptr), + node_size(0), + node_info_list(nullptr), + neighbor_list(nullptr), + neighbor_size(0) {} + GpuPsCommGraph(uint64_t *node_list_, + int64_t node_size_, + GpuPsNodeInfo *node_info_list_, + uint64_t *neighbor_list_, + int64_t neighbor_size_) + : node_list(node_list_), + node_size(node_size_), + node_info_list(node_info_list_), + neighbor_list(neighbor_list_), + neighbor_size(neighbor_size_) {} + void init_on_cpu(int64_t neighbor_size_, int64_t node_size_) { + if (node_size_ > 0) { + this->node_size = node_size_; + this->node_list = new uint64_t[node_size_]; + this->node_info_list = new paddle::framework::GpuPsNodeInfo[node_size_]; + } + if (neighbor_size_) { + this->neighbor_size = neighbor_size_; + this->neighbor_list = new uint64_t[neighbor_size_]; + } } void release_on_cpu() { - delete[] neighbor_list; - delete[] node_list; +#define DEL_PTR_ARRAY(p) \ + if (p != nullptr) { \ + delete[] p; \ + p = nullptr; \ + } + DEL_PTR_ARRAY(node_list); + DEL_PTR_ARRAY(neighbor_list); + DEL_PTR_ARRAY(node_info_list); + node_size = 0; + neighbor_size = 0; } - void display_on_cpu() { + void display_on_cpu() const { VLOG(0) << "neighbor_size = " << neighbor_size; VLOG(0) << "node_size = " << node_size; - for (size_t i = 0; i < neighbor_size; i++) { + for (int64_t i = 0; i < neighbor_size; i++) { VLOG(0) << "neighbor " << i << " " << neighbor_list[i]; } - for (size_t i = 0; i < node_size; i++) { - VLOG(0) << "node i " << node_list[i].node_id - << " neighbor_size = " << node_list[i].neighbor_size; - std::string str; - int offset = node_list[i].neighbor_offset; - for (size_t j = 0; j < node_list[i].neighbor_size; j++) { - if (j > 0) str += ","; - str += std::to_string(neighbor_list[j + offset]); - } - VLOG(0) << str; + for (int64_t i = 0; i < node_size; i++) { + auto id = node_list[i]; + auto val = node_info_list[i]; + VLOG(0) << "node id " << id << "," << val.neighbor_offset << ":" + << val.neighbor_size; } } }; @@ -110,37 +129,33 @@ node 9:[14,14] node 17:[15,15] ... by the above information, -we generate a node_list:GpuPsGraphNode *graph_node_list in GpuPsCommGraph -of size 9, -where node_list[i].id = u_id[i] -then we have: -node_list[0]-> node_id:0, neighbor_size:2, neighbor_offset:0 -node_list[1]-> node_id:5, neighbor_size:2, neighbor_offset:2 -node_list[2]-> node_id:1, neighbor_size:1, neighbor_offset:4 -node_list[3]-> node_id:2, neighbor_size:1, neighbor_offset:5 -node_list[4]-> node_id:7, neighbor_size:3, neighbor_offset:6 -node_list[5]-> node_id:3, neighbor_size:4, neighbor_offset:9 -node_list[6]-> node_id:8, neighbor_size:1, neighbor_offset:13 -node_list[7]-> node_id:9, neighbor_size:1, neighbor_offset:14 -node_list[8]-> node_id:17, neighbor_size:1, neighbor_offset:15 +we generate a node_list and node_info_list in GpuPsCommGraph, +node_list: [0,5,1,2,7,3,8,9,17] +node_info_list: [(2,0),(2,2),(1,4),(1,5),(3,6),(4,9),(1,13),(1,14),(1,15)] +Here, we design the data in this format to better +adapt to gpu and avoid to convert again. */ struct NeighborSampleQuery { int gpu_id; - int64_t *key; - int sample_size; + int table_idx; + uint64_t *src_nodes; int len; - void initialize(int gpu_id, int64_t key, int sample_size, int len) { + int sample_size; + void initialize( + int gpu_id, int table_idx, uint64_t src_nodes, int sample_size, int len) { + this->table_idx = table_idx; this->gpu_id = gpu_id; - this->key = (int64_t *)key; + this->src_nodes = (uint64_t *)src_nodes; this->sample_size = sample_size; this->len = len; } void display() { - int64_t *sample_keys = new int64_t[len]; + uint64_t *sample_keys = new uint64_t[len]; VLOG(0) << "device_id " << gpu_id << " sample_size = " << sample_size; - VLOG(0) << "there are " << len << " keys "; + VLOG(0) << "there are " << len << " keys to sample for graph " << table_idx; std::string key_str; - cudaMemcpy(sample_keys, key, len * sizeof(int64_t), cudaMemcpyDeviceToHost); + cudaMemcpy( + sample_keys, src_nodes, len * sizeof(uint64_t), cudaMemcpyDeviceToHost); for (int i = 0; i < len; i++) { if (key_str.size() > 0) key_str += ";"; @@ -151,14 +166,14 @@ struct NeighborSampleQuery { } }; struct NeighborSampleResult { - int64_t *val; - int64_t *actual_val; + uint64_t *val; + uint64_t *actual_val; int *actual_sample_size, sample_size, key_size; int total_sample_size; std::shared_ptr val_mem, actual_sample_size_mem; std::shared_ptr actual_val_mem; - int64_t *get_val() { return val; } - int64_t get_actual_val() { return (int64_t)actual_val; } + uint64_t *get_val() { return val; } + uint64_t get_actual_val() { return (uint64_t)actual_val; } int *get_actual_sample_size() { return actual_sample_size; } int get_sample_size() { return sample_size; } int get_key_size() { return key_size; } @@ -170,8 +185,8 @@ struct NeighborSampleResult { platform::CUDADeviceGuard guard(dev_id); platform::CUDAPlace place = platform::CUDAPlace(dev_id); val_mem = - memory::AllocShared(place, _sample_size * _key_size * sizeof(int64_t)); - val = (int64_t *)val_mem->ptr(); + memory::AllocShared(place, _sample_size * _key_size * sizeof(uint64_t)); + val = (uint64_t *)val_mem->ptr(); actual_sample_size_mem = memory::AllocShared(place, _key_size * sizeof(int)); actual_sample_size = (int *)actual_sample_size_mem->ptr(); @@ -217,13 +232,15 @@ struct NeighborSampleResult { delete[] ac_size; VLOG(0) << " ------------------"; } - std::vector get_sampled_graph(NeighborSampleQuery q) { - std::vector graph; + std::vector get_sampled_graph(NeighborSampleQuery q) { + std::vector graph; int64_t *sample_keys = new int64_t[q.len]; std::string key_str; - cudaMemcpy( - sample_keys, q.key, q.len * sizeof(int64_t), cudaMemcpyDeviceToHost); - int64_t *res = new int64_t[sample_size * key_size]; + cudaMemcpy(sample_keys, + q.src_nodes, + q.len * sizeof(uint64_t), + cudaMemcpyDeviceToHost); + uint64_t *res = new uint64_t[sample_size * key_size]; cudaMemcpy(res, val, sample_size * key_size * sizeof(int64_t), @@ -263,25 +280,25 @@ struct NeighborSampleResult { }; struct NodeQueryResult { - int64_t *val; + uint64_t *val; int actual_sample_size; - int64_t get_val() { return (int64_t)val; } + uint64_t get_val() { return (uint64_t)val; } int get_len() { return actual_sample_size; } std::shared_ptr val_mem; void initialize(int query_size, int dev_id) { platform::CUDADeviceGuard guard(dev_id); platform::CUDAPlace place = platform::CUDAPlace(dev_id); - val_mem = memory::AllocShared(place, query_size * sizeof(int64_t)); - val = (int64_t *)val_mem->ptr(); - - // cudaMalloc((void **)&val, query_size * sizeof(int64_t)); + val_mem = memory::AllocShared(place, query_size * sizeof(uint64_t)); + val = (uint64_t *)val_mem->ptr(); actual_sample_size = 0; } void display() { VLOG(0) << "in node query result display ------------------"; - int64_t *res = new int64_t[actual_sample_size]; - cudaMemcpy( - res, val, actual_sample_size * sizeof(int64_t), cudaMemcpyDeviceToHost); + uint64_t *res = new uint64_t[actual_sample_size]; + cudaMemcpy(res, + val, + actual_sample_size * sizeof(uint64_t), + cudaMemcpyDeviceToHost); VLOG(0) << "actual_sample_size =" << actual_sample_size; std::string str; @@ -298,7 +315,91 @@ struct NodeQueryResult { actual_sample_size = 0; }; ~NodeQueryResult() {} +}; // end of struct NodeQueryResult + +struct GpuPsFeaInfo { + uint32_t feature_size, feature_offset; + // this node's feature is stored on [feature_offset,feature_offset + + // feature_size) of int64_t *feature_list; }; -} // namespace framework -}; // namespace paddle + +struct GpuPsCommGraphFea { + uint64_t *node_list; // only locate on host side, the list of node id + uint64_t *feature_list; // locate on both side + uint8_t *slot_id_list; // locate on both side + GpuPsFeaInfo + *fea_info_list; // only locate on host side, the list of fea_info + uint64_t feature_size, node_size; + // the size of feature array and graph_node_list array + GpuPsCommGraphFea() + : node_list(NULL), + feature_list(NULL), + slot_id_list(NULL), + fea_info_list(NULL), + feature_size(0), + node_size(0) {} + GpuPsCommGraphFea(uint64_t *node_list_, + uint64_t *feature_list_, + uint8_t *slot_id_list_, + GpuPsFeaInfo *fea_info_list_, + uint64_t feature_size_, + uint64_t node_size_) + : node_list(node_list_), + feature_list(feature_list_), + slot_id_list(slot_id_list_), + fea_info_list(fea_info_list_), + feature_size(feature_size_), + node_size(node_size_) {} + void init_on_cpu(uint64_t feature_size, + uint64_t node_size, + uint32_t slot_num) { + PADDLE_ENFORCE_LE( + slot_num, + 255, + platform::errors::InvalidArgument( + "The number of slot_num should not be greater than 255 " + ", but the slot_num is %d ", + slot_num)); + this->feature_size = feature_size; + this->node_size = node_size; + this->node_list = new uint64_t[node_size]; + this->feature_list = new uint64_t[feature_size]; + this->slot_id_list = new uint8_t[feature_size]; + this->fea_info_list = new GpuPsFeaInfo[node_size]; + } + void release_on_cpu() { +#define DEL_PTR_ARRAY(p) \ + if (p != nullptr) { \ + delete[] p; \ + p = nullptr; \ + } + DEL_PTR_ARRAY(node_list); + DEL_PTR_ARRAY(feature_list); + DEL_PTR_ARRAY(slot_id_list); + DEL_PTR_ARRAY(fea_info_list); + } + void display_on_cpu() const { + VLOG(1) << "feature_size = " << feature_size; + VLOG(1) << "node_size = " << node_size; + for (uint64_t i = 0; i < feature_size; i++) { + VLOG(1) << "feature_list[" << i << "] = " << feature_list[i]; + } + for (uint64_t i = 0; i < node_size; i++) { + VLOG(1) << "node_id[" << node_list[i] + << "] feature_size = " << fea_info_list[i].feature_size; + std::string str; + uint32_t offset = fea_info_list[i].feature_offset; + for (uint64_t j = 0; j < fea_info_list[i].feature_size; j++) { + if (j > 0) str += ","; + str += std::to_string(slot_id_list[j + offset]); + str += ":"; + str += std::to_string(feature_list[j + offset]); + } + VLOG(1) << str; + } + } +}; // end of struct GpuPsCommGraphFea + +} // end of namespace framework +} // end of namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h b/paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h new file mode 100644 index 0000000000000000000000000000000000000000..39734cae33fca12043e1bf1b24df9038f032065b --- /dev/null +++ b/paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h @@ -0,0 +1,87 @@ +// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed 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. + +#pragma once +#include +#include +#include +#include +#include "paddle/fluid/platform/enforce.h" + +namespace paddle { +namespace framework { + +#define CUDA_CHECK(cmd) \ + do { \ + cudaError_t e = cmd; \ + CHECK(e == cudaSuccess) << "Cuda failure " << __FILE__ << ":" << __LINE__ \ + << " " << cudaGetErrorString(e) << std::endl; \ + } while (0) + +class CudaDeviceRestorer { + public: + CudaDeviceRestorer() { cudaGetDevice(&dev_); } + ~CudaDeviceRestorer() { cudaSetDevice(dev_); } + + private: + int dev_; +}; + +inline void debug_gpu_memory_info(int gpu_id, const char* desc) { + CudaDeviceRestorer r; + + size_t avail{0}; + size_t total{0}; + cudaSetDevice(gpu_id); + auto err = cudaMemGetInfo(&avail, &total); + PADDLE_ENFORCE_EQ( + err, + cudaSuccess, + platform::errors::InvalidArgument("cudaMemGetInfo failed!")); + VLOG(0) << "updatex gpu memory on device " << gpu_id << ", " + << "avail=" << avail / 1024.0 / 1024.0 / 1024.0 << "g, " + << "total=" << total / 1024.0 / 1024.0 / 1024.0 << "g, " + << "use_rate=" << (total - avail) / double(total) << "%, " + << "desc=" << desc; +} + +inline void debug_gpu_memory_info(const char* desc) { + CudaDeviceRestorer r; + + int device_num = 0; + auto err = cudaGetDeviceCount(&device_num); + PADDLE_ENFORCE_EQ( + err, + cudaSuccess, + platform::errors::InvalidArgument("cudaGetDeviceCount failed!")); + + size_t avail{0}; + size_t total{0}; + for (int i = 0; i < device_num; ++i) { + cudaSetDevice(i); + auto err = cudaMemGetInfo(&avail, &total); + PADDLE_ENFORCE_EQ( + err, + cudaSuccess, + platform::errors::InvalidArgument("cudaMemGetInfo failed!")); + VLOG(0) << "update gpu memory on device " << i << ", " + << "avail=" << avail / 1024.0 / 1024.0 / 1024.0 << "g, " + << "total=" << total / 1024.0 / 1024.0 / 1024.0 << "g, " + << "use_rate=" << (total - avail) / double(total) << "%, " + << "desc=" << desc; + } +} + +}; // namespace framework +}; // namespace paddle diff --git a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table.h b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table.h index a4bee2c19bbdaafaf4f38e22487c706657384ea8..aa202fe020fe996ca8d16b99a428b62aebfaac3f 100644 --- a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table.h +++ b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table.h @@ -23,23 +23,48 @@ #include "paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h" #include "paddle/fluid/platform/enforce.h" #ifdef PADDLE_WITH_HETERPS + +DECLARE_double(gpugraph_hbm_table_load_factor); + namespace paddle { namespace framework { +enum GraphTableType { EDGE_TABLE, FEATURE_TABLE }; class GpuPsGraphTable - : public HeterComm { + : public HeterComm { public: - GpuPsGraphTable(std::shared_ptr resource, int topo_aware) - : HeterComm( + int get_table_offset(int gpu_id, GraphTableType type, int idx) const { + int type_id = type; + return gpu_id * (graph_table_num_ + feature_table_num_) + + type_id * graph_table_num_ + idx; + } + GpuPsGraphTable(std::shared_ptr resource, + int topo_aware, + int graph_table_num) + : HeterComm( 1, resource) { - load_factor_ = 0.25; + load_factor_ = FLAGS_gpugraph_hbm_table_load_factor; + VLOG(0) << "load_factor = " << load_factor_; + rw_lock.reset(new pthread_rwlock_t()); + this->graph_table_num_ = graph_table_num; + this->feature_table_num_ = 1; gpu_num = resource_->total_device(); memset(global_device_map, -1, sizeof(global_device_map)); + for (auto &table : tables_) { + delete table; + table = NULL; + } + int feature_table_num = 1; + tables_ = std::vector( + gpu_num * (graph_table_num + feature_table_num), NULL); for (int i = 0; i < gpu_num; i++) { - gpu_graph_list.push_back(GpuPsCommGraph()); global_device_map[resource_->dev_id(i)] = i; - sample_status.push_back(NULL); - tables_.push_back(NULL); + for (int j = 0; j < graph_table_num; j++) { + gpu_graph_list_.push_back(GpuPsCommGraph()); + } + for (int j = 0; j < feature_table_num; j++) { + gpu_graph_fea_list_.push_back(GpuPsCommGraphFea()); + } } cpu_table_status = -1; if (topo_aware) { @@ -88,46 +113,56 @@ class GpuPsGraphTable } } } - ~GpuPsGraphTable() { - // if (cpu_table_status != -1) { - // end_graph_sampling(); - // } - } - void build_graph_on_single_gpu(GpuPsCommGraph &g, int gpu_id); - void clear_graph_info(int gpu_id); - void build_graph_from_cpu(std::vector &cpu_node_list); + ~GpuPsGraphTable() {} + void build_graph_on_single_gpu(const GpuPsCommGraph &g, int gpu_id, int idx); + void build_graph_fea_on_single_gpu(const GpuPsCommGraphFea &g, int gpu_id); + void clear_graph_info(int gpu_id, int index); + void clear_graph_info(int index); + void clear_feature_info(int gpu_id, int index); + void clear_feature_info(int index); + void build_graph_from_cpu(const std::vector &cpu_node_list, + int idx); + void build_graph_fea_from_cpu( + const std::vector &cpu_node_list, int idx); NodeQueryResult graph_node_sample(int gpu_id, int sample_size); NeighborSampleResult graph_neighbor_sample_v3(NeighborSampleQuery q, bool cpu_switch); NeighborSampleResult graph_neighbor_sample(int gpu_id, - int64_t *key, + uint64_t *key, int sample_size, int len); NeighborSampleResult graph_neighbor_sample_v2(int gpu_id, - int64_t *key, + int idx, + uint64_t *key, int sample_size, int len, bool cpu_query_switch); - void init_sample_status(); - void free_sample_status(); - NodeQueryResult query_node_list(int gpu_id, int start, int query_size); - void clear_graph_info(); + + int get_feature_of_nodes( + int gpu_id, uint64_t *d_walk, uint64_t *d_offset, int size, int slot_num); + + NodeQueryResult query_node_list(int gpu_id, + int idx, + int start, + int query_size); void display_sample_res(void *key, void *val, int len, int sample_len); - void move_neighbor_sample_result_to_source_gpu(int gpu_id, - int gpu_num, - int sample_size, - int *h_left, - int *h_right, - int64_t *src_sample_res, - int *actual_sample_size); + void move_result_to_source_gpu(int gpu_id, + int gpu_num, + int sample_size, + int *h_left, + int *h_right, + uint64_t *src_sample_res, + int *actual_sample_size); int init_cpu_table(const paddle::distributed::GraphParameter &graph); + int gpu_num; - std::vector gpu_graph_list; + int graph_table_num_, feature_table_num_; + std::vector gpu_graph_list_; + std::vector gpu_graph_fea_list_; int global_device_map[32]; - std::vector sample_status; const int parallel_sample_size = 1; const int dim_y = 256; - std::shared_ptr cpu_graph_table; + std::shared_ptr cpu_graph_table_; std::shared_ptr rw_lock; mutable std::mutex mutex_; std::condition_variable cv_; diff --git a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu index ccda67bf863208dde02eaac0229b7a551ae7d6a2..3693277a75d39bbe4e08f7c27c34bb008d755df1 100644 --- a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu +++ b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu @@ -19,6 +19,7 @@ #include #pragma once #ifdef PADDLE_WITH_HETERPS +#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h" #include "paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table.h" namespace paddle { namespace framework { @@ -33,9 +34,9 @@ sample_result is to save the neighbor sampling result, its size is len * sample_size; */ -__global__ void get_cpu_id_index(int64_t* key, +__global__ void get_cpu_id_index(uint64_t* key, int* actual_sample_size, - int64_t* cpu_key, + uint64_t* cpu_key, int* sum, int* index, int len) { @@ -50,13 +51,13 @@ __global__ void get_cpu_id_index(int64_t* key, } __global__ void get_actual_gpu_ac(int* gpu_ac, int number_on_cpu) { - CUDA_KERNEL_LOOP(i, number_on_cpu) { gpu_ac[i] /= sizeof(int64_t); } + CUDA_KERNEL_LOOP(i, number_on_cpu) { gpu_ac[i] /= sizeof(uint64_t); } } template -__global__ void copy_buffer_ac_to_final_place(int64_t* gpu_buffer, +__global__ void copy_buffer_ac_to_final_place(uint64_t* gpu_buffer, int* gpu_ac, - int64_t* val, + uint64_t* val, int* actual_sample_size, int* index, int* cumsum_gpu_ac, @@ -77,14 +78,51 @@ __global__ void copy_buffer_ac_to_final_place(int64_t* gpu_buffer, } } +__global__ void get_features_kernel(GpuPsCommGraphFea graph, + GpuPsFeaInfo* fea_info_array, + int* actual_size, + uint64_t* feature, + int slot_num, + int n) { + int idx = blockIdx.x * blockDim.y + threadIdx.y; + if (idx < n) { + int feature_size = fea_info_array[idx].feature_size; + int offset = idx * slot_num; + if (feature_size == 0) { + for (int k = 0; k < slot_num; ++k) { + feature[offset + k] = 0; + } + actual_size[idx] = slot_num; + return; + } + + uint64_t* feature_start = + &(graph.feature_list[fea_info_array[idx].feature_offset]); + uint8_t* slot_id_start = + &(graph.slot_id_list[fea_info_array[idx].feature_offset]); + int m = 0; + for (int k = 0; k < slot_num; ++k) { + if (m >= fea_info_array[idx].feature_size || k < slot_id_start[m]) { + feature[offset + k] = 0; + } else if (k == slot_id_start[m]) { + feature[offset + k] = feature_start[m]; + ++m; + } else { + assert(0); + } + } + actual_size[idx] = slot_num; + } +} + template -__global__ void neighbor_sample_example_v2(GpuPsCommGraph graph, - int64_t* node_index, - int* actual_size, - int64_t* res, - int sample_len, - int n, - int default_value) { +__global__ void neighbor_sample_kernel(GpuPsCommGraph graph, + GpuPsNodeInfo* node_info_list, + int* actual_size, + uint64_t* res, + int sample_len, + int n, + int default_value) { assert(blockDim.x == WARP_SIZE); assert(blockDim.y == BLOCK_WARPS); @@ -92,17 +130,16 @@ __global__ void neighbor_sample_example_v2(GpuPsCommGraph graph, const int last_idx = min(static_cast(blockIdx.x + 1) * TILE_SIZE, n); curandState rng; curand_init(blockIdx.x, threadIdx.y * WARP_SIZE + threadIdx.x, 0, &rng); - while (i < last_idx) { - if (node_index[i] == -1) { + if (node_info_list[i].neighbor_size == 0) { actual_size[i] = default_value; i += BLOCK_WARPS; continue; } - int neighbor_len = (int)graph.node_list[node_index[i]].neighbor_size; - int64_t data_offset = graph.node_list[node_index[i]].neighbor_offset; + int neighbor_len = (int)node_info_list[i].neighbor_size; + uint32_t data_offset = node_info_list[i].neighbor_offset; int offset = i * sample_len; - int64_t* data = graph.neighbor_list; + uint64_t* data = graph.neighbor_list; if (neighbor_len <= sample_len) { for (int j = threadIdx.x; j < neighbor_len; j += WARP_SIZE) { res[offset + j] = data[data_offset + j]; @@ -131,89 +168,10 @@ __global__ void neighbor_sample_example_v2(GpuPsCommGraph graph, } } -__global__ void neighbor_sample_example(GpuPsCommGraph graph, - int64_t* node_index, - int* actual_size, - int64_t* res, - int sample_len, - int* sample_status, - int n, - int from) { - int id = blockIdx.x * blockDim.y + threadIdx.y; - if (id < n) { - if (node_index[id] == -1) { - actual_size[id] = 0; - return; - } - curandState rng; - curand_init(blockIdx.x, threadIdx.x, threadIdx.y, &rng); - int64_t index = threadIdx.x; - int64_t offset = id * sample_len; - int64_t* data = graph.neighbor_list; - int64_t data_offset = graph.node_list[node_index[id]].neighbor_offset; - int64_t neighbor_len = graph.node_list[node_index[id]].neighbor_size; - int ac_len; - if (sample_len > neighbor_len) - ac_len = neighbor_len; - else { - ac_len = sample_len; - } - if (4 * ac_len >= 3 * neighbor_len) { - if (index == 0) { - res[offset] = curand(&rng) % (neighbor_len - ac_len + 1); - } - __syncwarp(); - int start = res[offset]; - while (index < ac_len) { - res[offset + index] = data[data_offset + start + index]; - index += blockDim.x; - } - actual_size[id] = ac_len; - } else { - while (index < ac_len) { - int num = curand(&rng) % neighbor_len; - int* addr = sample_status + data_offset + num; - int expected = *addr; - if (!(expected & (1 << from))) { - int old = atomicCAS(addr, expected, expected | (1 << from)); - if (old == expected) { - res[offset + index] = num; - index += blockDim.x; - } - } - } - __syncwarp(); - index = threadIdx.x; - while (index < ac_len) { - int* addr = sample_status + data_offset + res[offset + index]; - int expected, old = *addr; - do { - expected = old; - old = atomicCAS(addr, expected, expected & (~(1 << from))); - } while (old != expected); - res[offset + index] = data[data_offset + res[offset + index]]; - index += blockDim.x; - } - actual_size[id] = ac_len; - } - } - // const size_t i = blockIdx.x * blockDim.x + threadIdx.x; - // if (i < n) { - // auto node_index = index[i]; - // actual_size[i] = graph.node_list[node_index].neighbor_size < sample_size - // ? graph.node_list[node_index].neighbor_size - // : sample_size; - // int offset = graph.node_list[node_index].neighbor_offset; - // for (int j = 0; j < actual_size[i]; j++) { - // sample_result[sample_size * i + j] = graph.neighbor_list[offset + j]; - // } - // } -} - int GpuPsGraphTable::init_cpu_table( const paddle::distributed::GraphParameter& graph) { - cpu_graph_table.reset(new paddle::distributed::GraphTable); - cpu_table_status = cpu_graph_table->Initialize(graph); + cpu_graph_table_.reset(new paddle::distributed::GraphTable); + cpu_table_status = cpu_graph_table_->Initialize(graph); // if (cpu_table_status != 0) return cpu_table_status; // std::function&)> callback = // [this](std::vector& res) { @@ -227,17 +185,6 @@ int GpuPsGraphTable::init_cpu_table( return cpu_table_status; } -// int GpuPsGraphTable::load(const std::string& path, const std::string& param) -// { -// int status = cpu_graph_table->load(path, param); -// if (status != 0) { -// return status; -// } -// std::unique_lock lock(mutex_); -// cpu_graph_table->start_graph_sampling(); -// cv_.wait(lock); -// return 0; -// } /* comment 1 gpu i triggers a neighbor_sample task, @@ -263,36 +210,37 @@ void GpuPsGraphTable::display_sample_res(void* key, void* val, int len, int sample_len) { - char key_buffer[len * sizeof(int64_t)]; + char key_buffer[len * sizeof(uint64_t)]; char val_buffer[sample_len * sizeof(int64_t) * len + - (len + len % 2) * sizeof(int) + len * sizeof(int64_t)]; - cudaMemcpy(key_buffer, key, sizeof(int64_t) * len, cudaMemcpyDeviceToHost); + (len + len % 2) * sizeof(int) + len * sizeof(uint64_t)]; + cudaMemcpy(key_buffer, key, sizeof(uint64_t) * len, cudaMemcpyDeviceToHost); cudaMemcpy(val_buffer, val, sample_len * sizeof(int64_t) * len + - (len + len % 2) * sizeof(int) + len * sizeof(int64_t), + (len + len % 2) * sizeof(int) + len * sizeof(uint64_t), cudaMemcpyDeviceToHost); - int64_t* sample_val = (int64_t*)(val_buffer + (len + len % 2) * sizeof(int) + - len * sizeof(int64_t)); + uint64_t* sample_val = + (uint64_t*)(val_buffer + (len + len % 2) * sizeof(int) + + len * sizeof(int64_t)); for (int i = 0; i < len; i++) { - printf("key %lld\n", *(int64_t*)(key_buffer + i * sizeof(int64_t))); - printf("index %lld\n", *(int64_t*)(val_buffer + i * sizeof(int64_t))); + printf("key %llu\n", *(int64_t*)(key_buffer + i * sizeof(uint64_t))); + printf("index %llu\n", *(int64_t*)(val_buffer + i * sizeof(uint64_t))); int ac_size = *(int*)(val_buffer + i * sizeof(int) + len * sizeof(int64_t)); printf("sampled %d neigbhors\n", ac_size); for (int j = 0; j < ac_size; j++) { - printf("%lld ", sample_val[i * sample_len + j]); + printf("%llu ", sample_val[i * sample_len + j]); } printf("\n"); } } -void GpuPsGraphTable::move_neighbor_sample_result_to_source_gpu( - int start_index, - int gpu_num, - int sample_size, - int* h_left, - int* h_right, - int64_t* src_sample_res, - int* actual_sample_size) { + +void GpuPsGraphTable::move_result_to_source_gpu(int start_index, + int gpu_num, + int sample_size, + int* h_left, + int* h_right, + uint64_t* src_sample_res, + int* actual_sample_size) { int shard_len[gpu_num]; for (int i = 0; i < gpu_num; i++) { if (h_left[i] == -1 || h_right[i] == -1) { @@ -301,144 +249,44 @@ void GpuPsGraphTable::move_neighbor_sample_result_to_source_gpu( shard_len[i] = h_right[i] - h_left[i] + 1; int cur_step = (int)path_[start_index][i].nodes_.size() - 1; for (int j = cur_step; j > 0; j--) { - cudaMemcpyAsync(path_[start_index][i].nodes_[j - 1].val_storage, - path_[start_index][i].nodes_[j].val_storage, - path_[start_index][i].nodes_[j - 1].val_bytes_len, - cudaMemcpyDefault, - path_[start_index][i].nodes_[j - 1].out_stream); + CUDA_CHECK( + cudaMemcpyAsync(path_[start_index][i].nodes_[j - 1].val_storage, + path_[start_index][i].nodes_[j].val_storage, + path_[start_index][i].nodes_[j - 1].val_bytes_len, + cudaMemcpyDefault, + path_[start_index][i].nodes_[j - 1].out_stream)); } auto& node = path_[start_index][i].nodes_.front(); - cudaMemcpyAsync( + CUDA_CHECK(cudaMemcpyAsync( reinterpret_cast(src_sample_res + h_left[i] * sample_size), node.val_storage + sizeof(int64_t) * shard_len[i] + sizeof(int) * (shard_len[i] + shard_len[i] % 2), - sizeof(int64_t) * shard_len[i] * sample_size, + sizeof(uint64_t) * shard_len[i] * sample_size, cudaMemcpyDefault, - node.out_stream); - cudaMemcpyAsync(reinterpret_cast(actual_sample_size + h_left[i]), - node.val_storage + sizeof(int64_t) * shard_len[i], - sizeof(int) * shard_len[i], - cudaMemcpyDefault, - node.out_stream); + node.out_stream)); + CUDA_CHECK( + cudaMemcpyAsync(reinterpret_cast(actual_sample_size + h_left[i]), + node.val_storage + sizeof(int64_t) * shard_len[i], + sizeof(int) * shard_len[i], + cudaMemcpyDefault, + node.out_stream)); } for (int i = 0; i < gpu_num; ++i) { if (h_left[i] == -1 || h_right[i] == -1) { continue; } auto& node = path_[start_index][i].nodes_.front(); - cudaStreamSynchronize(node.out_stream); + CUDA_CHECK(cudaStreamSynchronize(node.out_stream)); // cudaStreamSynchronize(resource_->remote_stream(i, start_index)); } - /* - std::queue que; - // auto& node = path_[gpu_id][i].nodes_.front(); - // cudaMemcpyAsync( - // reinterpret_cast(src_sample_res + h_left[i] * sample_size), - // node.val_storage + sizeof(int64_t) * shard_len, - // node.val_bytes_len - sizeof(int64_t) * shard_len, cudaMemcpyDefault, - // node.out_stream); - // cudaMemcpyAsync(reinterpret_cast(actual_sample_size + h_left[i]), - // node.val_storage + sizeof(int) * shard_len, - // sizeof(int) * shard_len, cudaMemcpyDefault, - // node.out_stream); - int cur_step = path_[start_index][i].nodes_.size() - 1; - auto& node = path_[start_index][i].nodes_[cur_step]; - if (cur_step == 0) { - // cudaMemcpyAsync(reinterpret_cast(src_val + h_left[i]), - // node.val_storage, node.val_bytes_len, - // cudaMemcpyDefault, - // node.out_stream); - // VLOG(0)<<"copy "<(src_sample_res + h_left[i] * sample_size), - node.val_storage + sizeof(int64_t) * shard_len[i], - node.val_bytes_len - sizeof(int64_t) * shard_len[i], - cudaMemcpyDefault, - node.out_stream); - //resource_->remote_stream(i, start_index)); - cudaMemcpyAsync(reinterpret_cast(actual_sample_size + h_left[i]), - node.val_storage + sizeof(int) * shard_len[i], - sizeof(int) * shard_len[i], cudaMemcpyDefault, - node.out_stream); - //resource_->remote_stream(i, start_index)); - } else { - CopyTask t(&path_[start_index][i], cur_step - 1); - que.push(t); - // VLOG(0)<<"copy "<remote_stream(i, start_index)); - } - } - while (!que.empty()) { - CopyTask& cur_task = que.front(); - que.pop(); - int cur_step = cur_task.step; - if (cur_task.path->nodes_[cur_step].sync) { - cudaStreamSynchronize(cur_task.path->nodes_[cur_step].out_stream); - //cudaStreamSynchronize(resource_->remote_stream(cur_task.path->nodes_.back().gpu_num, - start_index)); - } - if (cur_step > 0) { - CopyTask c(cur_task.path, cur_step - 1); - que.push(c); - cudaMemcpyAsync(cur_task.path->nodes_[cur_step - 1].val_storage, - cur_task.path->nodes_[cur_step].val_storage, - cur_task.path->nodes_[cur_step - 1].val_bytes_len, - cudaMemcpyDefault, - cur_task.path->nodes_[cur_step - 1].out_stream); - //resource_->remote_stream(cur_task.path->nodes_.back().gpu_num, - start_index)); - } else if (cur_step == 0) { - int end_index = cur_task.path->nodes_.back().gpu_num; - // cudaMemcpyAsync(reinterpret_cast(src_val + h_left[end_index]), - // cur_task.path->nodes_[cur_step].val_storage, - // cur_task.path->nodes_[cur_step].val_bytes_len, - // cudaMemcpyDefault, - // cur_task.path->nodes_[cur_step].out_stream); - //VLOG(0)<<"copy "<nodes_[cur_step].gpu_num<< " to - "<(src_sample_res + - h_left[end_index] * sample_size), - cur_task.path->nodes_[cur_step].val_storage + - sizeof(int64_t) * shard_len[end_index], - cur_task.path->nodes_[cur_step].val_bytes_len - - sizeof(int64_t) * shard_len[end_index], - cudaMemcpyDefault, - cur_task.path->nodes_[cur_step].out_stream); - //resource_->remote_stream(cur_task.path->nodes_.back().gpu_num, - start_index)); - cudaMemcpyAsync( - reinterpret_cast(actual_sample_size + h_left[end_index]), - cur_task.path->nodes_[cur_step].val_storage + - sizeof(int) * shard_len[end_index], - sizeof(int) * shard_len[end_index], cudaMemcpyDefault, - cur_task.path->nodes_[cur_step].out_stream); - //resource_->remote_stream(cur_task.path->nodes_.back().gpu_num, - start_index)); - } - } - for (int i = 0; i < gpu_num; ++i) { - if (h_left[i] == -1 || h_right[i] == -1) { - continue; - } - auto& node = path_[start_index][i].nodes_.front(); - cudaStreamSynchronize(node.out_stream); - //cudaStreamSynchronize(resource_->remote_stream(i, start_index)); - } - */ } /* TODO: how to optimize it to eliminate the for loop */ -__global__ void fill_dvalues(int64_t* d_shard_vals, - int64_t* d_vals, +__global__ void fill_dvalues(uint64_t* d_shard_vals, + uint64_t* d_vals, int* d_shard_actual_sample_size, int* d_actual_sample_size, int* idx, @@ -453,8 +301,22 @@ __global__ void fill_dvalues(int64_t* d_shard_vals, } } -__global__ void fill_actual_vals(int64_t* vals, - int64_t* actual_vals, +__global__ void fill_dvalues(uint64_t* d_shard_vals, + uint64_t* d_vals, + int* d_shard_actual_sample_size, + int* idx, + int sample_size, + int len) { + const size_t i = blockIdx.x * blockDim.x + threadIdx.x; + if (i < len) { + for (int j = 0; j < sample_size; j++) { + d_vals[idx[i] * sample_size + j] = d_shard_vals[i * sample_size + j]; + } + } +} + +__global__ void fill_actual_vals(uint64_t* vals, + uint64_t* actual_vals, int* actual_sample_size, int* cumsum_actual_sample_size, int sample_size, @@ -470,40 +332,141 @@ __global__ void fill_actual_vals(int64_t* vals, __global__ void node_query_example(GpuPsCommGraph graph, int start, int size, - int64_t* res) { + uint64_t* res) { const size_t i = blockIdx.x * blockDim.x + threadIdx.x; if (i < size) { - res[i] = graph.node_list[start + i].node_id; + res[i] = graph.node_list[start + i]; + } +} + +void GpuPsGraphTable::clear_feature_info(int gpu_id) { + int idx = 0; + if (idx >= feature_table_num_) return; + int offset = get_table_offset(gpu_id, GraphTableType::FEATURE_TABLE, idx); + if (offset < tables_.size()) { + delete tables_[offset]; + tables_[offset] = NULL; + } + + int graph_fea_idx = gpu_id * feature_table_num_ + idx; + if (graph_fea_idx >= gpu_graph_fea_list_.size()) { + return; + } + auto& graph = gpu_graph_fea_list_[graph_fea_idx]; + if (graph.feature_list != NULL) { + cudaFree(graph.feature_list); + graph.feature_list = NULL; + } + + if (graph.slot_id_list != NULL) { + cudaFree(graph.slot_id_list); + graph.slot_id_list = NULL; } } -void GpuPsGraphTable::clear_graph_info(int gpu_id) { - if (tables_.size() && tables_[gpu_id] != NULL) { - delete tables_[gpu_id]; +void GpuPsGraphTable::clear_graph_info(int gpu_id, int idx) { + if (idx >= graph_table_num_) return; + int offset = get_table_offset(gpu_id, GraphTableType::EDGE_TABLE, idx); + if (offset < tables_.size()) { + delete tables_[offset]; + tables_[offset] = NULL; } - auto& graph = gpu_graph_list[gpu_id]; + auto& graph = gpu_graph_list_[gpu_id * graph_table_num_ + idx]; if (graph.neighbor_list != NULL) { cudaFree(graph.neighbor_list); + graph.neighbor_list = nullptr; } if (graph.node_list != NULL) { cudaFree(graph.node_list); + graph.node_list = nullptr; } } -void GpuPsGraphTable::clear_graph_info() { - if (tables_.size()) { - for (auto table : tables_) delete table; +void GpuPsGraphTable::clear_graph_info(int idx) { + for (int i = 0; i < gpu_num; i++) clear_graph_info(i, idx); +} +/* +the parameter std::vector cpu_graph_list is generated by cpu. +it saves the graph to be saved on each gpu. +for the ith GpuPsCommGraph, any the node's key satisfies that key % gpu_number +== i +In this function, memory is allocated on each gpu to save the graphs, +gpu i saves the ith graph from cpu_graph_list +*/ +void GpuPsGraphTable::build_graph_fea_on_single_gpu(const GpuPsCommGraphFea& g, + int gpu_id) { + clear_feature_info(gpu_id); + int ntype_id = 0; + + platform::CUDADeviceGuard guard(resource_->dev_id(gpu_id)); + + int offset = gpu_id * feature_table_num_ + ntype_id; + gpu_graph_fea_list_[offset] = GpuPsCommGraphFea(); + + int table_offset = + get_table_offset(gpu_id, GraphTableType::FEATURE_TABLE, ntype_id); + + size_t capacity = std::max((uint64_t)1, g.node_size) / load_factor_; + tables_[table_offset] = new Table(capacity); + if (g.node_size > 0) { + build_ps(gpu_id, + g.node_list, + (uint64_t*)g.fea_info_list, + g.node_size, + 1024, + 8, + table_offset); + gpu_graph_fea_list_[offset].node_list = NULL; + gpu_graph_fea_list_[offset].node_size = g.node_size; + } else { + build_ps(gpu_id, NULL, NULL, 0, 1024, 8, table_offset); + gpu_graph_fea_list_[offset].node_list = NULL; + gpu_graph_fea_list_[offset].node_size = 0; } - tables_.clear(); - for (auto graph : gpu_graph_list) { - if (graph.neighbor_list != NULL) { - cudaFree(graph.neighbor_list); - } - if (graph.node_list != NULL) { - cudaFree(graph.node_list); - } + if (g.feature_size) { + // TODO + cudaError_t cudaStatus = + cudaMalloc((void**)&gpu_graph_fea_list_[offset].feature_list, + g.feature_size * sizeof(uint64_t)); + PADDLE_ENFORCE_EQ( + cudaStatus, + cudaSuccess, + platform::errors::InvalidArgument( + "ailed to allocate memory for graph-feature on gpu ")); + VLOG(0) << "sucessfully allocate " << g.feature_size * sizeof(uint64_t) + << " bytes of memory for graph-feature on gpu " + << resource_->dev_id(gpu_id); + CUDA_CHECK(cudaMemcpy(gpu_graph_fea_list_[offset].feature_list, + g.feature_list, + g.feature_size * sizeof(uint64_t), + cudaMemcpyHostToDevice)); + + // TODO + cudaStatus = cudaMalloc((void**)&gpu_graph_fea_list_[offset].slot_id_list, + g.feature_size * sizeof(uint8_t)); + PADDLE_ENFORCE_EQ( + cudaStatus, + cudaSuccess, + platform::errors::InvalidArgument( + "ailed to allocate memory for graph-feature on gpu ")); + VLOG(0) << "sucessfully allocate " << g.feature_size * sizeof(uint8_t) + << " bytes of memory for graph-feature on gpu " + << resource_->dev_id(gpu_id); + cudaMemcpy(gpu_graph_fea_list_[offset].slot_id_list, + g.slot_id_list, + g.feature_size * sizeof(uint8_t), + cudaMemcpyHostToDevice); + + gpu_graph_fea_list_[offset].feature_size = g.feature_size; + } else { + gpu_graph_fea_list_[offset].feature_list = NULL; + gpu_graph_fea_list_[offset].slot_id_list = NULL; + gpu_graph_fea_list_[offset].feature_size = 0; } - gpu_graph_list.clear(); + VLOG(0) << "gpu node_feature info card :" << gpu_id << " ,node_size is " + << gpu_graph_fea_list_[offset].node_size << ", feature_size is " + << gpu_graph_fea_list_[offset].feature_size; } + /* the parameter std::vector cpu_graph_list is generated by cpu. it saves the graph to be saved on each gpu. @@ -512,78 +475,131 @@ for the ith GpuPsCommGraph, any the node's key satisfies that key % gpu_number In this function, memory is allocated on each gpu to save the graphs, gpu i saves the ith graph from cpu_graph_list */ - -void GpuPsGraphTable::build_graph_on_single_gpu(GpuPsCommGraph& g, int i) { - clear_graph_info(i); +void GpuPsGraphTable::build_graph_on_single_gpu(const GpuPsCommGraph& g, + int i, + int idx) { + clear_graph_info(i, idx); platform::CUDADeviceGuard guard(resource_->dev_id(i)); - // platform::CUDADeviceGuard guard(i); - gpu_graph_list[i] = GpuPsCommGraph(); - sample_status[i] = NULL; - tables_[i] = new Table(std::max((int64_t)1, g.node_size) / load_factor_); + int offset = i * graph_table_num_ + idx; + gpu_graph_list_[offset] = GpuPsCommGraph(); + int table_offset = get_table_offset(i, GraphTableType::EDGE_TABLE, idx); + size_t capacity = std::max((uint64_t)1, (uint64_t)g.node_size) / load_factor_; + tables_[table_offset] = new Table(capacity); if (g.node_size > 0) { - std::vector keys; - std::vector offset; - cudaMalloc((void**)&gpu_graph_list[i].node_list, - g.node_size * sizeof(GpuPsGraphNode)); - cudaMemcpy(gpu_graph_list[i].node_list, - g.node_list, - g.node_size * sizeof(GpuPsGraphNode), - cudaMemcpyHostToDevice); - for (int64_t j = 0; j < g.node_size; j++) { - keys.push_back(g.node_list[j].node_id); - offset.push_back(j); + if (FLAGS_gpugraph_load_node_list_into_hbm) { + CUDA_CHECK(cudaMalloc((void**)&gpu_graph_list_[offset].node_list, + g.node_size * sizeof(uint64_t))); + CUDA_CHECK(cudaMemcpy(gpu_graph_list_[offset].node_list, + g.node_list, + g.node_size * sizeof(uint64_t), + cudaMemcpyHostToDevice)); } - build_ps(i, (uint64_t*)keys.data(), offset.data(), keys.size(), 1024, 8); - gpu_graph_list[i].node_size = g.node_size; + + build_ps(i, + g.node_list, + (uint64_t*)(g.node_info_list), + g.node_size, + 1024, + 8, + table_offset); + gpu_graph_list_[offset].node_size = g.node_size; } else { - build_ps(i, NULL, NULL, 0, 1024, 8); - gpu_graph_list[i].node_list = NULL; - gpu_graph_list[i].node_size = 0; + build_ps(i, NULL, NULL, 0, 1024, 8, table_offset); + gpu_graph_list_[offset].node_list = NULL; + gpu_graph_list_[offset].node_size = 0; } if (g.neighbor_size) { cudaError_t cudaStatus = - cudaMalloc((void**)&gpu_graph_list[i].neighbor_list, - g.neighbor_size * sizeof(int64_t)); + cudaMalloc((void**)&gpu_graph_list_[offset].neighbor_list, + g.neighbor_size * sizeof(uint64_t)); PADDLE_ENFORCE_EQ(cudaStatus, cudaSuccess, platform::errors::InvalidArgument( "ailed to allocate memory for graph on gpu ")); - VLOG(0) << "sucessfully allocate " << g.neighbor_size * sizeof(int64_t) + VLOG(0) << "sucessfully allocate " << g.neighbor_size * sizeof(uint64_t) << " bytes of memory for graph-edges on gpu " << resource_->dev_id(i); - cudaMemcpy(gpu_graph_list[i].neighbor_list, - g.neighbor_list, - g.neighbor_size * sizeof(int64_t), - cudaMemcpyHostToDevice); - gpu_graph_list[i].neighbor_size = g.neighbor_size; + CUDA_CHECK(cudaMemcpy(gpu_graph_list_[offset].neighbor_list, + g.neighbor_list, + g.neighbor_size * sizeof(uint64_t), + cudaMemcpyHostToDevice)); + gpu_graph_list_[offset].neighbor_size = g.neighbor_size; } else { - gpu_graph_list[i].neighbor_list = NULL; - gpu_graph_list[i].neighbor_size = 0; + gpu_graph_list_[offset].neighbor_list = NULL; + gpu_graph_list_[offset].neighbor_size = 0; } + VLOG(0) << " gpu node_neighbor info card: " << i << " ,node_size is " + << gpu_graph_list_[offset].node_size << ", neighbor_size is " + << gpu_graph_list_[offset].neighbor_size; } -void GpuPsGraphTable::init_sample_status() { - for (int i = 0; i < gpu_num; i++) { - if (gpu_graph_list[i].neighbor_size) { - platform::CUDADeviceGuard guard(resource_->dev_id(i)); - int* addr; - cudaMalloc((void**)&addr, gpu_graph_list[i].neighbor_size * sizeof(int)); - cudaMemset(addr, 0, gpu_graph_list[i].neighbor_size * sizeof(int)); - sample_status[i] = addr; +void GpuPsGraphTable::build_graph_fea_from_cpu( + const std::vector& cpu_graph_fea_list, int ntype_id) { + PADDLE_ENFORCE_EQ( + cpu_graph_fea_list.size(), + resource_->total_device(), + platform::errors::InvalidArgument("the cpu node list size doesn't match " + "the number of gpu on your machine.")); + clear_feature_info(ntype_id); + for (int i = 0; i < cpu_graph_fea_list.size(); i++) { + int table_offset = + get_table_offset(i, GraphTableType::FEATURE_TABLE, ntype_id); + int offset = i * feature_table_num_ + ntype_id; + platform::CUDADeviceGuard guard(resource_->dev_id(i)); + gpu_graph_fea_list_[offset] = GpuPsCommGraphFea(); + tables_[table_offset] = new Table( + std::max((uint64_t)1, (uint64_t)cpu_graph_fea_list[i].node_size) / + load_factor_); + if (cpu_graph_fea_list[i].node_size > 0) { + build_ps(i, + cpu_graph_fea_list[i].node_list, + (uint64_t*)cpu_graph_fea_list[i].fea_info_list, + cpu_graph_fea_list[i].node_size, + 1024, + 8, + table_offset); + gpu_graph_fea_list_[offset].node_size = cpu_graph_fea_list[i].node_size; + } else { + build_ps(i, NULL, NULL, 0, 1024, 8, table_offset); + gpu_graph_fea_list_[offset].node_list = NULL; + gpu_graph_fea_list_[offset].node_size = 0; } - } -} - -void GpuPsGraphTable::free_sample_status() { - for (int i = 0; i < gpu_num; i++) { - if (sample_status[i] != NULL) { - platform::CUDADeviceGuard guard(resource_->dev_id(i)); - cudaFree(sample_status[i]); + if (cpu_graph_fea_list[i].feature_size) { + // TODO + CUDA_CHECK( + cudaMalloc((void**)&gpu_graph_fea_list_[offset].feature_list, + cpu_graph_fea_list[i].feature_size * sizeof(uint64_t))); + + CUDA_CHECK( + cudaMemcpy(gpu_graph_fea_list_[offset].feature_list, + cpu_graph_fea_list[i].feature_list, + cpu_graph_fea_list[i].feature_size * sizeof(uint64_t), + cudaMemcpyHostToDevice)); + + // TODO + CUDA_CHECK( + cudaMalloc((void**)&gpu_graph_fea_list_[offset].slot_id_list, + cpu_graph_fea_list[i].feature_size * sizeof(uint8_t))); + + CUDA_CHECK( + cudaMemcpy(gpu_graph_fea_list_[offset].slot_id_list, + cpu_graph_fea_list[i].slot_id_list, + cpu_graph_fea_list[i].feature_size * sizeof(uint8_t), + cudaMemcpyHostToDevice)); + + gpu_graph_fea_list_[offset].feature_size = + cpu_graph_fea_list[i].feature_size; + } else { + gpu_graph_fea_list_[offset].feature_list = NULL; + gpu_graph_fea_list_[offset].slot_id_list = NULL; + gpu_graph_fea_list_[offset].feature_size = 0; } } + cudaDeviceSynchronize(); } + void GpuPsGraphTable::build_graph_from_cpu( - std::vector& cpu_graph_list) { + const std::vector& cpu_graph_list, int idx) { VLOG(0) << "in build_graph_from_cpu cpu_graph_list size = " << cpu_graph_list.size(); PADDLE_ENFORCE_EQ( @@ -591,240 +607,77 @@ void GpuPsGraphTable::build_graph_from_cpu( resource_->total_device(), platform::errors::InvalidArgument("the cpu node list size doesn't match " "the number of gpu on your machine.")); - clear_graph_info(); + clear_graph_info(idx); for (int i = 0; i < cpu_graph_list.size(); i++) { + int table_offset = get_table_offset(i, GraphTableType::EDGE_TABLE, idx); + int offset = i * graph_table_num_ + idx; platform::CUDADeviceGuard guard(resource_->dev_id(i)); - gpu_graph_list[i] = GpuPsCommGraph(); - sample_status[i] = NULL; - tables_[i] = new Table(std::max((int64_t)1, cpu_graph_list[i].node_size) / - load_factor_); + gpu_graph_list_[offset] = GpuPsCommGraph(); + tables_[table_offset] = + new Table(std::max((uint64_t)1, (uint64_t)cpu_graph_list[i].node_size) / + load_factor_); if (cpu_graph_list[i].node_size > 0) { - std::vector keys; - std::vector offset; - cudaMalloc((void**)&gpu_graph_list[i].node_list, - cpu_graph_list[i].node_size * sizeof(GpuPsGraphNode)); - cudaMemcpy(gpu_graph_list[i].node_list, - cpu_graph_list[i].node_list, - cpu_graph_list[i].node_size * sizeof(GpuPsGraphNode), - cudaMemcpyHostToDevice); - for (int64_t j = 0; j < cpu_graph_list[i].node_size; j++) { - keys.push_back(cpu_graph_list[i].node_list[j].node_id); - offset.push_back(j); - } - build_ps( - i, (uint64_t*)(keys.data()), offset.data(), keys.size(), 1024, 8); - gpu_graph_list[i].node_size = cpu_graph_list[i].node_size; + CUDA_CHECK(cudaMalloc((void**)&gpu_graph_list_[offset].node_list, + cpu_graph_list[i].node_size * sizeof(uint64_t))); + CUDA_CHECK(cudaMemcpy(gpu_graph_list_[offset].node_list, + cpu_graph_list[i].node_list, + cpu_graph_list[i].node_size * sizeof(uint64_t), + cudaMemcpyHostToDevice)); + build_ps(i, + cpu_graph_list[i].node_list, + (uint64_t*)(cpu_graph_list[i].node_info_list), + cpu_graph_list[i].node_size, + 1024, + 8, + table_offset); + gpu_graph_list_[offset].node_size = cpu_graph_list[i].node_size; } else { - build_ps(i, NULL, NULL, 0, 1024, 8); - gpu_graph_list[i].node_list = NULL; - gpu_graph_list[i].node_size = 0; + build_ps(i, NULL, NULL, 0, 1024, 8, table_offset); + gpu_graph_list_[offset].node_list = NULL; + gpu_graph_list_[offset].node_size = 0; } if (cpu_graph_list[i].neighbor_size) { - cudaMalloc((void**)&gpu_graph_list[i].neighbor_list, - cpu_graph_list[i].neighbor_size * sizeof(int64_t)); - - cudaMemcpy(gpu_graph_list[i].neighbor_list, - cpu_graph_list[i].neighbor_list, - cpu_graph_list[i].neighbor_size * sizeof(int64_t), - cudaMemcpyHostToDevice); - gpu_graph_list[i].neighbor_size = cpu_graph_list[i].neighbor_size; + CUDA_CHECK( + cudaMalloc((void**)&gpu_graph_list_[offset].neighbor_list, + cpu_graph_list[i].neighbor_size * sizeof(uint64_t))); + + CUDA_CHECK(cudaMemcpy(gpu_graph_list_[offset].neighbor_list, + cpu_graph_list[i].neighbor_list, + cpu_graph_list[i].neighbor_size * sizeof(uint64_t), + cudaMemcpyHostToDevice)); + gpu_graph_list_[offset].neighbor_size = cpu_graph_list[i].neighbor_size; } else { - gpu_graph_list[i].neighbor_list = NULL; - gpu_graph_list[i].neighbor_size = 0; + gpu_graph_list_[offset].neighbor_list = NULL; + gpu_graph_list_[offset].neighbor_size = 0; } } - cudaDeviceSynchronize(); + CUDA_CHECK(cudaDeviceSynchronize()); } NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v3( NeighborSampleQuery q, bool cpu_switch) { - return graph_neighbor_sample_v2( - global_device_map[q.gpu_id], q.key, q.sample_size, q.len, cpu_switch); + return graph_neighbor_sample_v2(global_device_map[q.gpu_id], + q.table_idx, + q.src_nodes, + q.sample_size, + q.len, + cpu_switch); } + NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample(int gpu_id, - int64_t* key, + uint64_t* key, int sample_size, int len) { - /* - comment 2 - this function shares some kernels with heter_comm_inl.h - arguments definitions: - gpu_id:the id of gpu. - len:how many keys are used,(the length of array key) - sample_size:how many neighbors should be sampled for each node in key. - the code below shuffle the key array to make the keys - that belong to a gpu-card stay together, - the shuffled result is saved on d_shard_keys, - if ith element in d_shard_keys_ptr is - from jth element in the original key array, then idx[i] = j, - idx could be used to recover the original array. - if keys in range [a,b] belong to ith-gpu, then h_left[i] = a, h_right[i] = - b, - if no keys are allocated for ith-gpu, then h_left[i] == h_right[i] == -1 - for example, suppose key = [0,1,2,3,4,5,6,7,8], gpu_num = 2 - when we run this neighbor_sample function, - the key is shuffled to [0,2,4,6,8,1,3,5,7] - the first part (0,2,4,6,8) % 2 == 0,thus should be handled by gpu 0, - the rest part should be handled by gpu1, because (1,3,5,7) % 2 == 1, - h_left = [0,5],h_right = [4,8] - */ - - NeighborSampleResult result; - result.initialize(sample_size, len, resource_->dev_id(gpu_id)); - if (len == 0) { - return result; - } - platform::CUDAPlace place = platform::CUDAPlace(resource_->dev_id(gpu_id)); - platform::CUDADeviceGuard guard(resource_->dev_id(gpu_id)); - int* actual_sample_size = result.actual_sample_size; - int64_t* val = result.val; - int total_gpu = resource_->total_device(); - auto stream = resource_->local_stream(gpu_id, 0); - - int grid_size = (len - 1) / block_size_ + 1; - - int h_left[total_gpu]; // NOLINT - int h_right[total_gpu]; // NOLINT - - auto d_left = memory::Alloc(place, total_gpu * sizeof(int)); - auto d_right = memory::Alloc(place, total_gpu * sizeof(int)); - int* d_left_ptr = reinterpret_cast(d_left->ptr()); - int* d_right_ptr = reinterpret_cast(d_right->ptr()); - - cudaMemsetAsync(d_left_ptr, -1, total_gpu * sizeof(int), stream); - cudaMemsetAsync(d_right_ptr, -1, total_gpu * sizeof(int), stream); - // - auto d_idx = memory::Alloc(place, len * sizeof(int)); - int* d_idx_ptr = reinterpret_cast(d_idx->ptr()); - - auto d_shard_keys = memory::Alloc(place, len * sizeof(int64_t)); - int64_t* d_shard_keys_ptr = reinterpret_cast(d_shard_keys->ptr()); - auto d_shard_vals = memory::Alloc(place, sample_size * len * sizeof(int64_t)); - int64_t* d_shard_vals_ptr = reinterpret_cast(d_shard_vals->ptr()); - auto d_shard_actual_sample_size = memory::Alloc(place, len * sizeof(int)); - int* d_shard_actual_sample_size_ptr = - reinterpret_cast(d_shard_actual_sample_size->ptr()); - - split_input_to_shard( - (uint64_t*)(key), d_idx_ptr, len, d_left_ptr, d_right_ptr, gpu_id); - - heter_comm_kernel_->fill_shard_key( - d_shard_keys_ptr, key, d_idx_ptr, len, stream); - cudaStreamSynchronize(stream); - - cudaMemcpy( - h_left, d_left_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost); - cudaMemcpy( - h_right, d_right_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost); - // auto start1 = std::chrono::steady_clock::now(); - for (int i = 0; i < total_gpu; ++i) { - int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; - if (shard_len == 0) { - continue; - } - /* - comment 3 - shard_len denotes the size of keys on i-th gpu here, - when we sample on i-th gpu, we allocate shard_len * (1 + sample_size) - int64_t units - of memory, we use alloc_mem_i to denote it, the range [0,shard_len) is saved - for the respective nodes' indexes - and acutal sample_size. - with nodes' indexes we could get the nodes to sample. - since size of int64_t is 8 bits, while size of int is 4, - the range of [0,shard_len) contains shard_len * 2 int uinits; - The values of the first half of this range will be updated by - the k-v map on i-th-gpu. - The second half of this range is saved for actual sample size of each node. - For node x, - its sampling result is saved on the range - [shard_len + sample_size * x,shard_len + sample_size * x + - actual_sample_size_of_x) - of alloc_mem_i, actual_sample_size_of_x equals ((int - *)alloc_mem_i)[shard_len + x] - */ - - create_storage(gpu_id, - i, - shard_len * sizeof(int64_t), - shard_len * (1 + sample_size) * sizeof(int64_t) + - sizeof(int) * (shard_len + shard_len % 2)); - // auto& node = path_[gpu_id][i].nodes_[0]; - } - walk_to_dest( - gpu_id, total_gpu, h_left, h_right, (uint64_t*)(d_shard_keys_ptr), NULL); - - for (int i = 0; i < total_gpu; ++i) { - if (h_left[i] == -1) { - continue; - } - int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; - auto& node = path_[gpu_id][i].nodes_.back(); - cudaMemsetAsync( - node.val_storage, -1, shard_len * sizeof(int64_t), node.in_stream); - cudaStreamSynchronize(node.in_stream); - platform::CUDADeviceGuard guard(resource_->dev_id(i)); - tables_[i]->get(reinterpret_cast(node.key_storage), - reinterpret_cast(node.val_storage), - h_right[i] - h_left[i] + 1, - resource_->remote_stream(i, gpu_id)); - // node.in_stream); - auto graph = gpu_graph_list[i]; - int64_t* id_array = reinterpret_cast(node.val_storage); - int* actual_size_array = (int*)(id_array + shard_len); - int64_t* sample_array = - (int64_t*)(actual_size_array + shard_len + shard_len % 2); - int sample_grid_size = (shard_len - 1) / dim_y + 1; - dim3 block(parallel_sample_size, dim_y); - dim3 grid(sample_grid_size); - neighbor_sample_example<<remote_stream(i, gpu_id)>>>( - graph, - id_array, - actual_size_array, - sample_array, - sample_size, - sample_status[i], - shard_len, - gpu_id); - } - - for (int i = 0; i < total_gpu; ++i) { - if (h_left[i] == -1) { - continue; - } - cudaStreamSynchronize(resource_->remote_stream(i, gpu_id)); - } - move_neighbor_sample_result_to_source_gpu(gpu_id, - total_gpu, - sample_size, - h_left, - h_right, - d_shard_vals_ptr, - d_shard_actual_sample_size_ptr); - fill_dvalues<<>>( - d_shard_vals_ptr, - val, - d_shard_actual_sample_size_ptr, - actual_sample_size, - d_idx_ptr, - sample_size, - len); - for (int i = 0; i < total_gpu; ++i) { - int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; - if (shard_len == 0) { - continue; - } - destroy_storage(gpu_id, i); - } - cudaStreamSynchronize(stream); - return result; + return graph_neighbor_sample_v2(gpu_id, 0, key, sample_size, len, false); } NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( - int gpu_id, int64_t* key, int sample_size, int len, bool cpu_query_switch) { + int gpu_id, + int idx, + uint64_t* key, + int sample_size, + int len, + bool cpu_query_switch) { NeighborSampleResult result; result.initialize(sample_size, len, resource_->dev_id(gpu_id)); @@ -834,8 +687,9 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( platform::CUDAPlace place = platform::CUDAPlace(resource_->dev_id(gpu_id)); platform::CUDADeviceGuard guard(resource_->dev_id(gpu_id)); + int* actual_sample_size = result.actual_sample_size; - int64_t* val = result.val; + uint64_t* val = result.val; int total_gpu = resource_->total_device(); auto stream = resource_->local_stream(gpu_id, 0); @@ -853,16 +707,17 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( default_value = -1; } - cudaMemsetAsync(d_left_ptr, -1, total_gpu * sizeof(int), stream); - cudaMemsetAsync(d_right_ptr, -1, total_gpu * sizeof(int), stream); + CUDA_CHECK(cudaMemsetAsync(d_left_ptr, -1, total_gpu * sizeof(int), stream)); + CUDA_CHECK(cudaMemsetAsync(d_right_ptr, -1, total_gpu * sizeof(int), stream)); // auto d_idx = memory::Alloc(place, len * sizeof(int)); int* d_idx_ptr = reinterpret_cast(d_idx->ptr()); - auto d_shard_keys = memory::Alloc(place, len * sizeof(int64_t)); - int64_t* d_shard_keys_ptr = reinterpret_cast(d_shard_keys->ptr()); - auto d_shard_vals = memory::Alloc(place, sample_size * len * sizeof(int64_t)); - int64_t* d_shard_vals_ptr = reinterpret_cast(d_shard_vals->ptr()); + auto d_shard_keys = memory::Alloc(place, len * sizeof(uint64_t)); + uint64_t* d_shard_keys_ptr = reinterpret_cast(d_shard_keys->ptr()); + auto d_shard_vals = + memory::Alloc(place, sample_size * len * sizeof(uint64_t)); + uint64_t* d_shard_vals_ptr = reinterpret_cast(d_shard_vals->ptr()); auto d_shard_actual_sample_size = memory::Alloc(place, len * sizeof(int)); int* d_shard_actual_sample_size_ptr = reinterpret_cast(d_shard_actual_sample_size->ptr()); @@ -873,12 +728,12 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( heter_comm_kernel_->fill_shard_key( d_shard_keys_ptr, key, d_idx_ptr, len, stream); - cudaStreamSynchronize(stream); + CUDA_CHECK(cudaStreamSynchronize(stream)); - cudaMemcpy( - h_left, d_left_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost); - cudaMemcpy( - h_right, d_right_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost); + CUDA_CHECK(cudaMemcpy( + h_left, d_left_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost)); + CUDA_CHECK(cudaMemcpy( + h_right, d_right_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost)); for (int i = 0; i < total_gpu; ++i) { int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; if (shard_len == 0) { @@ -886,8 +741,9 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( } create_storage(gpu_id, i, - shard_len * sizeof(int64_t), - shard_len * (1 + sample_size) * sizeof(int64_t) + + shard_len * sizeof(uint64_t), + shard_len * sample_size * sizeof(uint64_t) + + shard_len * sizeof(uint64_t) + sizeof(int) * (shard_len + shard_len % 2)); } walk_to_dest( @@ -899,30 +755,35 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( } int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; auto& node = path_[gpu_id][i].nodes_.back(); - cudaMemsetAsync( - node.val_storage, -1, shard_len * sizeof(int64_t), node.in_stream); - cudaStreamSynchronize(node.in_stream); + + CUDA_CHECK(cudaMemsetAsync( + node.val_storage, 0, shard_len * sizeof(int64_t), node.in_stream)); + CUDA_CHECK(cudaStreamSynchronize(node.in_stream)); platform::CUDADeviceGuard guard(resource_->dev_id(i)); // If not found, val is -1. - tables_[i]->get(reinterpret_cast(node.key_storage), - reinterpret_cast(node.val_storage), - h_right[i] - h_left[i] + 1, - resource_->remote_stream(i, gpu_id)); - - auto graph = gpu_graph_list[i]; - int64_t* id_array = reinterpret_cast(node.val_storage); - int* actual_size_array = (int*)(id_array + shard_len); - int64_t* sample_array = - (int64_t*)(actual_size_array + shard_len + shard_len % 2); + int table_offset = get_table_offset(i, GraphTableType::EDGE_TABLE, idx); + int offset = i * graph_table_num_ + idx; + tables_[table_offset]->get(reinterpret_cast(node.key_storage), + reinterpret_cast(node.val_storage), + (size_t)(h_right[i] - h_left[i] + 1), + resource_->remote_stream(i, gpu_id)); + + auto graph = gpu_graph_list_[offset]; + GpuPsNodeInfo* node_info_list = + reinterpret_cast(node.val_storage); + int* actual_size_array = (int*)(node_info_list + shard_len); + uint64_t* sample_array = + (uint64_t*)(actual_size_array + shard_len + shard_len % 2); constexpr int WARP_SIZE = 32; constexpr int BLOCK_WARPS = 128 / WARP_SIZE; constexpr int TILE_SIZE = BLOCK_WARPS * 16; const dim3 block(WARP_SIZE, BLOCK_WARPS); const dim3 grid((shard_len + TILE_SIZE - 1) / TILE_SIZE); - neighbor_sample_example_v2 + + neighbor_sample_kernel <<remote_stream(i, gpu_id)>>>( graph, - id_array, + node_info_list, actual_size_array, sample_array, sample_size, @@ -934,16 +795,15 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( if (h_left[i] == -1) { continue; } - cudaStreamSynchronize(resource_->remote_stream(i, gpu_id)); + CUDA_CHECK(cudaStreamSynchronize(resource_->remote_stream(i, gpu_id))); } - - move_neighbor_sample_result_to_source_gpu(gpu_id, - total_gpu, - sample_size, - h_left, - h_right, - d_shard_vals_ptr, - d_shard_actual_sample_size_ptr); + move_result_to_source_gpu(gpu_id, + total_gpu, + sample_size, + h_left, + h_right, + d_shard_vals_ptr, + d_shard_actual_sample_size_ptr); fill_dvalues<<>>( d_shard_vals_ptr, val, @@ -953,11 +813,11 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( sample_size, len); - cudaStreamSynchronize(stream); + CUDA_CHECK(cudaStreamSynchronize(stream)); if (cpu_query_switch) { // Get cpu keys and corresponding position. - thrust::device_vector t_cpu_keys(len); + thrust::device_vector t_cpu_keys(len); thrust::device_vector t_index(len + 1, 0); get_cpu_id_index<<>>( key, @@ -967,52 +827,52 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( thrust::raw_pointer_cast(t_index.data()) + 1, len); - cudaStreamSynchronize(stream); + CUDA_CHECK(cudaStreamSynchronize(stream)); int number_on_cpu = 0; - cudaMemcpy(&number_on_cpu, - thrust::raw_pointer_cast(t_index.data()), - sizeof(int), - cudaMemcpyDeviceToHost); + CUDA_CHECK(cudaMemcpy(&number_on_cpu, + thrust::raw_pointer_cast(t_index.data()), + sizeof(int), + cudaMemcpyDeviceToHost)); if (number_on_cpu > 0) { - int64_t* cpu_keys = new int64_t[number_on_cpu]; - cudaMemcpy(cpu_keys, - thrust::raw_pointer_cast(t_cpu_keys.data()), - number_on_cpu * sizeof(int64_t), - cudaMemcpyDeviceToHost); + uint64_t* cpu_keys = new uint64_t[number_on_cpu]; + CUDA_CHECK(cudaMemcpy(cpu_keys, + thrust::raw_pointer_cast(t_cpu_keys.data()), + number_on_cpu * sizeof(uint64_t), + cudaMemcpyDeviceToHost)); std::vector> buffers(number_on_cpu); std::vector ac(number_on_cpu); - auto status = cpu_graph_table->random_sample_neighbors( - 0, cpu_keys, sample_size, buffers, ac, false); + auto status = cpu_graph_table_->random_sample_neighbors( + idx, cpu_keys, sample_size, buffers, ac, false); int total_cpu_sample_size = std::accumulate(ac.begin(), ac.end(), 0); - total_cpu_sample_size /= sizeof(int64_t); + total_cpu_sample_size /= sizeof(uint64_t); - // Merge buffers into one int64_t vector. - int64_t* merge_buffers = new int64_t[total_cpu_sample_size]; + // Merge buffers into one uint64_t vector. + uint64_t* merge_buffers = new uint64_t[total_cpu_sample_size]; int start = 0; for (int j = 0; j < number_on_cpu; j++) { - memcpy(merge_buffers + start, (int64_t*)(buffers[j].get()), ac[j]); - start += ac[j] / sizeof(int64_t); + memcpy(merge_buffers + start, (uint64_t*)(buffers[j].get()), ac[j]); + start += ac[j] / sizeof(uint64_t); } // Copy merge_buffers to gpu. - thrust::device_vector gpu_buffers(total_cpu_sample_size); + thrust::device_vector gpu_buffers(total_cpu_sample_size); thrust::device_vector gpu_ac(number_on_cpu); - int64_t* gpu_buffers_ptr = thrust::raw_pointer_cast(gpu_buffers.data()); + uint64_t* gpu_buffers_ptr = thrust::raw_pointer_cast(gpu_buffers.data()); int* gpu_ac_ptr = thrust::raw_pointer_cast(gpu_ac.data()); - cudaMemcpyAsync(gpu_buffers_ptr, - merge_buffers, - total_cpu_sample_size * sizeof(int64_t), - cudaMemcpyHostToDevice, - stream); - cudaMemcpyAsync(gpu_ac_ptr, - ac.data(), - number_on_cpu * sizeof(int), - cudaMemcpyHostToDevice, - stream); + CUDA_CHECK(cudaMemcpyAsync(gpu_buffers_ptr, + merge_buffers, + total_cpu_sample_size * sizeof(uint64_t), + cudaMemcpyHostToDevice, + stream)); + CUDA_CHECK(cudaMemcpyAsync(gpu_ac_ptr, + ac.data(), + number_on_cpu * sizeof(int), + cudaMemcpyHostToDevice, + stream)); // Copy gpu_buffers and gpu_ac using kernel. // Kernel divide for gpu_ac_ptr. @@ -1020,7 +880,7 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( get_actual_gpu_ac<<>>(gpu_ac_ptr, number_on_cpu); - cudaStreamSynchronize(stream); + CUDA_CHECK(cudaStreamSynchronize(stream)); thrust::device_vector cumsum_gpu_ac(number_on_cpu); thrust::exclusive_scan( @@ -1048,7 +908,7 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( } { - cudaStreamSynchronize(stream); + CUDA_CHECK(cudaStreamSynchronize(stream)); platform::CUDAPlace place = platform::CUDAPlace(resource_->dev_id(gpu_id)); platform::CUDADeviceGuard guard(resource_->dev_id(gpu_id)); @@ -1060,11 +920,10 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( t_actual_sample_size.end()); result.actual_val_mem = - memory::AllocShared(place, total_sample_size * sizeof(int64_t)); - result.actual_val = (int64_t*)(result.actual_val_mem)->ptr(); + memory::AllocShared(place, total_sample_size * sizeof(uint64_t)); + result.actual_val = (uint64_t*)(result.actual_val_mem)->ptr(); result.set_total_sample_size(total_sample_size); - thrust::device_vector cumsum_actual_sample_size(len); thrust::exclusive_scan(t_actual_sample_size.begin(), t_actual_sample_size.end(), @@ -1085,7 +944,6 @@ NeighborSampleResult GpuPsGraphTable::graph_neighbor_sample_v2( } destroy_storage(gpu_id, i); } - cudaStreamSynchronize(stream); return result; } @@ -1096,32 +954,13 @@ NodeQueryResult GpuPsGraphTable::graph_node_sample(int gpu_id, } NodeQueryResult GpuPsGraphTable::query_node_list(int gpu_id, + int idx, int start, int query_size) { NodeQueryResult result; + result.actual_sample_size = 0; if (query_size <= 0) return result; - int& actual_size = result.actual_sample_size; - actual_size = 0; - // int dev_id = resource_->dev_id(gpu_id); - // platform::CUDADeviceGuard guard(dev_id); - std::vector idx, gpu_begin_pos, local_begin_pos; - int sample_size; - /* - if idx[i] = a, gpu_begin_pos[i] = p1, - gpu_local_begin_pos[i] = p2; - sample_size[i] = s; - then on gpu a, the nodes of positions [p1,p1 + s) should be returned - and saved from the p2 position on the sample_result array - for example: - suppose - gpu 0 saves [0,2,4,6,8], gpu1 saves [1,3,5,7] - start = 3, query_size = 5 - we know [6,8,1,3,5] should be returned; - idx = [0,1] - gpu_begin_pos = [3,0] - local_begin_pos = [0,3] - sample_size = [2,3] - */ + std::vector gpu_begin_pos, local_begin_pos; std::function range_check = [](int x, int y, int x1, int y1, int& x2, int& y2) { if (y <= x1 || x >= y1) return 0; @@ -1129,7 +968,9 @@ NodeQueryResult GpuPsGraphTable::query_node_list(int gpu_id, x2 = max(x1, x); return y2 - x2; }; - auto graph = gpu_graph_list[gpu_id]; + + int offset = gpu_id * graph_table_num_ + idx; + const auto& graph = gpu_graph_list_[offset]; if (graph.node_size == 0) { return result; } @@ -1139,69 +980,159 @@ NodeQueryResult GpuPsGraphTable::query_node_list(int gpu_id, if (len == 0) { return result; } - int64_t* val; - sample_size = len; + result.initialize(len, resource_->dev_id(gpu_id)); - actual_size = len; - val = result.val; + result.actual_sample_size = len; + uint64_t* val = result.val; + int dev_id_i = resource_->dev_id(gpu_id); platform::CUDADeviceGuard guard(dev_id_i); - // platform::CUDADeviceGuard guard(i); int grid_size = (len - 1) / block_size_ + 1; node_query_example<<remote_stream(gpu_id, gpu_id)>>>( - gpu_graph_list[gpu_id], x2, len, (int64_t*)val); - cudaStreamSynchronize(resource_->remote_stream(gpu_id, gpu_id)); + graph, x2, len, (uint64_t*)val); + CUDA_CHECK(cudaStreamSynchronize(resource_->remote_stream(gpu_id, gpu_id))); return result; - /* - for (int i = 0; i < gpu_graph_list.size() && query_size != 0; i++) { - auto graph = gpu_graph_list[i]; - if (graph.node_size == 0) { +} + +int GpuPsGraphTable::get_feature_of_nodes(int gpu_id, + uint64_t* d_nodes, + uint64_t* d_feature, + int node_num, + int slot_num) { + if (node_num == 0) { + return -1; + } + + platform::CUDAPlace place = platform::CUDAPlace(resource_->dev_id(gpu_id)); + platform::CUDADeviceGuard guard(resource_->dev_id(gpu_id)); + int total_gpu = resource_->total_device(); + auto stream = resource_->local_stream(gpu_id, 0); + + auto d_left = memory::Alloc(place, total_gpu * sizeof(int)); + auto d_right = memory::Alloc(place, total_gpu * sizeof(int)); + int* d_left_ptr = reinterpret_cast(d_left->ptr()); + int* d_right_ptr = reinterpret_cast(d_right->ptr()); + + CUDA_CHECK(cudaMemsetAsync(d_left_ptr, -1, total_gpu * sizeof(int), stream)); + CUDA_CHECK(cudaMemsetAsync(d_right_ptr, -1, total_gpu * sizeof(int), stream)); + // + auto d_idx = memory::Alloc(place, node_num * sizeof(int)); + int* d_idx_ptr = reinterpret_cast(d_idx->ptr()); + + auto d_shard_keys = memory::Alloc(place, node_num * sizeof(uint64_t)); + uint64_t* d_shard_keys_ptr = reinterpret_cast(d_shard_keys->ptr()); + auto d_shard_vals = + memory::Alloc(place, slot_num * node_num * sizeof(uint64_t)); + uint64_t* d_shard_vals_ptr = reinterpret_cast(d_shard_vals->ptr()); + auto d_shard_actual_size = memory::Alloc(place, node_num * sizeof(int)); + int* d_shard_actual_size_ptr = + reinterpret_cast(d_shard_actual_size->ptr()); + + split_input_to_shard( + d_nodes, d_idx_ptr, node_num, d_left_ptr, d_right_ptr, gpu_id); + + heter_comm_kernel_->fill_shard_key( + d_shard_keys_ptr, d_nodes, d_idx_ptr, node_num, stream); + CUDA_CHECK(cudaStreamSynchronize(stream)); + + int h_left[total_gpu]; // NOLINT + CUDA_CHECK(cudaMemcpy( + h_left, d_left_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost)); + int h_right[total_gpu]; // NOLINT + CUDA_CHECK(cudaMemcpy( + h_right, d_right_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost)); + for (int i = 0; i < total_gpu; ++i) { + int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; + if (shard_len == 0) { continue; } - int x2, y2; - int len = range_check(start, start + query_size, size, - size + graph.node_size, x2, y2); - if (len > 0) { - idx.push_back(i); - gpu_begin_pos.emplace_back(x2 - size); - local_begin_pos.emplace_back(actual_size); - sample_size.push_back(len); - actual_size += len; - create_storage(gpu_id, i, 1, len * sizeof(int64_t)); - } - size += graph.node_size; - } - for (int i = 0; i < idx.size(); i++) { - int dev_id_i = resource_->dev_id(idx[i]); - platform::CUDADeviceGuard guard(dev_id_i); - // platform::CUDADeviceGuard guard(i); - auto& node = path_[gpu_id][idx[i]].nodes_.front(); - int grid_size = (sample_size[i] - 1) / block_size_ + 1; - node_query_example<<remote_stream(idx[i], gpu_id)>>>( - gpu_graph_list[idx[i]], gpu_begin_pos[i], sample_size[i], - (int64_t*)node.val_storage); + create_storage(gpu_id, + i, + shard_len * sizeof(uint64_t), + shard_len * slot_num * sizeof(uint64_t) + + shard_len * sizeof(uint64_t) + + sizeof(int) * (shard_len + shard_len % 2)); } - for (int i = 0; i < idx.size(); i++) { - cudaStreamSynchronize(resource_->remote_stream(idx[i], gpu_id)); - auto& node = path_[gpu_id][idx[i]].nodes_.front(); - cudaMemcpyAsync(reinterpret_cast(val + local_begin_pos[i]), - node.val_storage, node.val_bytes_len, cudaMemcpyDefault, - node.out_stream); + walk_to_dest( + gpu_id, total_gpu, h_left, h_right, (uint64_t*)(d_shard_keys_ptr), NULL); + + for (int i = 0; i < total_gpu; ++i) { + if (h_left[i] == -1) { + continue; + } + int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; + auto& node = path_[gpu_id][i].nodes_.back(); + + CUDA_CHECK(cudaMemsetAsync( + node.val_storage, 0, shard_len * sizeof(uint64_t), node.in_stream)); + CUDA_CHECK(cudaStreamSynchronize(node.in_stream)); + platform::CUDADeviceGuard guard(resource_->dev_id(i)); + // If not found, val is -1. + int table_offset = get_table_offset(i, GraphTableType::FEATURE_TABLE, 0); + tables_[table_offset]->get(reinterpret_cast(node.key_storage), + reinterpret_cast(node.val_storage), + (size_t)(h_right[i] - h_left[i] + 1), + resource_->remote_stream(i, gpu_id)); + + int offset = i * feature_table_num_; + auto graph = gpu_graph_fea_list_[offset]; + + GpuPsFeaInfo* val_array = reinterpret_cast(node.val_storage); + int* actual_size_array = (int*)(val_array + shard_len); + uint64_t* feature_array = + (uint64_t*)(actual_size_array + shard_len + shard_len % 2); + dim3 grid((shard_len - 1) / dim_y + 1); + dim3 block(1, dim_y); + get_features_kernel<<remote_stream(i, gpu_id)>>>( + graph, + val_array, + actual_size_array, + feature_array, + slot_num, + shard_len); } - for (int i = 0; i < idx.size(); i++) { - auto& node = path_[gpu_id][idx[i]].nodes_.front(); - cudaStreamSynchronize(node.out_stream); + + for (int i = 0; i < total_gpu; ++i) { + if (h_left[i] == -1) { + continue; + } + CUDA_CHECK(cudaStreamSynchronize(resource_->remote_stream(i, gpu_id))); } - for (auto x : idx) { - destroy_storage(gpu_id, x); + + move_result_to_source_gpu(gpu_id, + total_gpu, + slot_num, + h_left, + h_right, + d_shard_vals_ptr, + d_shard_actual_size_ptr); + + int grid_size = (node_num - 1) / block_size_ + 1; + fill_dvalues<<>>(d_shard_vals_ptr, + d_feature, + d_shard_actual_size_ptr, + d_idx_ptr, + slot_num, + node_num); + + for (int i = 0; i < total_gpu; ++i) { + int shard_len = h_left[i] == -1 ? 0 : h_right[i] - h_left[i] + 1; + if (shard_len == 0) { + continue; + } + destroy_storage(gpu_id, i); } - return result; - */ + + CUDA_CHECK(cudaStreamSynchronize(stream)); + + return 0; } } // namespace framework }; // namespace paddle diff --git a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.cu b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.cu index 8de0b11fdb0608f60e217a343e60fb5367abced1..fafb5ef26698e19816f05b121c37f771775627ad 100644 --- a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.cu +++ b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.cu @@ -13,6 +13,8 @@ // limitations under the License. #include "paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.h" +#include +#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h" #include "paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table.h" #include "paddle/fluid/framework/fleet/heter_ps/heter_resource.h" namespace paddle { @@ -25,12 +27,46 @@ void GraphGpuWrapper::set_device(std::vector ids) { device_id_mapping.push_back(device_id); } } -std::vector> GraphGpuWrapper::get_all_id(int type, - int idx, - int slice_num) { + +int GraphGpuWrapper::get_all_id(int type, + int slice_num, + std::vector> *output) { + return ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->get_all_id(type, slice_num, output); +} + +int GraphGpuWrapper::get_all_neighbor_id( + int type, int slice_num, std::vector> *output) { + return ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->get_all_neighbor_id(type, slice_num, output); +} + +int GraphGpuWrapper::get_all_id(int type, + int idx, + int slice_num, + std::vector> *output) { + return ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->get_all_id(type, idx, slice_num, output); +} + +int GraphGpuWrapper::get_all_neighbor_id( + int type, + int idx, + int slice_num, + std::vector> *output) { return ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->get_all_id(type, idx, slice_num); + ->cpu_graph_table_->get_all_neighbor_id(type, idx, slice_num, output); } + +int GraphGpuWrapper::get_all_feature_ids( + int type, + int idx, + int slice_num, + std::vector> *output) { + return ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->get_all_feature_ids(type, idx, slice_num, output); +} + void GraphGpuWrapper::set_up_types(std::vector &edge_types, std::vector &node_types) { id_to_edge = edge_types; @@ -49,32 +85,40 @@ void GraphGpuWrapper::set_up_types(std::vector &edge_types, this->table_feat_conf_feat_shape.resize(node_types.size()); } +void GraphGpuWrapper::set_feature_separator(std::string ch) { + feature_separator_ = ch; + if (graph_table != nullptr) { + ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->set_feature_separator(feature_separator_); + } +} + void GraphGpuWrapper::make_partitions(int idx, int64_t byte_size, int device_len) { ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->make_partitions(idx, byte_size, device_len); + ->cpu_graph_table_->make_partitions(idx, byte_size, device_len); } int32_t GraphGpuWrapper::load_next_partition(int idx) { return ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->load_next_partition(idx); + ->cpu_graph_table_->load_next_partition(idx); } void GraphGpuWrapper::set_search_level(int level) { - ((GpuPsGraphTable *)graph_table)->cpu_graph_table->set_search_level(level); + ((GpuPsGraphTable *)graph_table)->cpu_graph_table_->set_search_level(level); } -std::vector GraphGpuWrapper::get_partition(int idx, int num) { +std::vector GraphGpuWrapper::get_partition(int idx, int num) { return ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->get_partition(idx, num); + ->cpu_graph_table_->get_partition(idx, num); } int32_t GraphGpuWrapper::get_partition_num(int idx) { return ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->get_partition_num(idx); + ->cpu_graph_table_->get_partition_num(idx); } void GraphGpuWrapper::make_complementary_graph(int idx, int64_t byte_size) { ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->make_complementary_graph(idx, byte_size); + ->cpu_graph_table_->make_complementary_graph(idx, byte_size); } void GraphGpuWrapper::load_edge_file(std::string name, std::string filepath, @@ -90,7 +134,7 @@ void GraphGpuWrapper::load_edge_file(std::string name, } if (edge_to_id.find(name) != edge_to_id.end()) { ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->Load(std::string(filepath), params); + ->cpu_graph_table_->Load(std::string(filepath), params); } } @@ -101,10 +145,21 @@ void GraphGpuWrapper::load_node_file(std::string name, std::string filepath) { if (feature_to_id.find(name) != feature_to_id.end()) { ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->Load(std::string(filepath), params); + ->cpu_graph_table_->Load(std::string(filepath), params); } } +void GraphGpuWrapper::load_node_and_edge(std::string etype, + std::string ntype, + std::string epath, + std::string npath, + int part_num, + bool reverse) { + ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->load_node_and_edge_file( + etype, ntype, epath, npath, part_num, reverse); +} + void GraphGpuWrapper::add_table_feat_conf(std::string table_name, std::string feat_name, std::string feat_dtype, @@ -137,8 +192,10 @@ void GraphGpuWrapper::init_search_level(int level) { search_level = level; } void GraphGpuWrapper::init_service() { table_proto.set_task_pool_size(24); + table_proto.set_shard_num(1000); + table_proto.set_build_sampler_on_cpu(false); table_proto.set_search_level(search_level); - table_proto.set_table_name("cpu_graph_table"); + table_proto.set_table_name("cpu_graph_table_"); table_proto.set_use_cache(false); for (int i = 0; i < id_to_edge.size(); i++) table_proto.add_edge_types(id_to_edge[i]); @@ -155,76 +212,122 @@ void GraphGpuWrapper::init_service() { std::shared_ptr resource = std::make_shared(device_id_mapping); resource->enable_p2p(); - GpuPsGraphTable *g = new GpuPsGraphTable(resource, 1); + GpuPsGraphTable *g = new GpuPsGraphTable(resource, 1, id_to_edge.size()); g->init_cpu_table(table_proto); + g->cpu_graph_table_->set_feature_separator(feature_separator_); graph_table = (char *)g; + upload_task_pool.reset(new ::ThreadPool(upload_num)); +} + +void GraphGpuWrapper::finalize() { + ((GpuPsGraphTable *)graph_table)->show_table_collisions(); } -void GraphGpuWrapper::upload_batch(int idx, - std::vector> &ids) { +void GraphGpuWrapper::upload_batch(int type, + int idx, + int slice_num, + const std::string &edge_type) { + VLOG(0) << "begin upload edge, type[" << edge_type << "]"; + std::vector> ids; + ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->get_all_id(type, idx, slice_num, &ids); + debug_gpu_memory_info("upload_batch node start"); GpuPsGraphTable *g = (GpuPsGraphTable *)graph_table; - // std::vector vec; + std::vector> tasks; + for (int i = 0; i < ids.size(); i++) { - // vec.push_back(g->cpu_graph_table->make_gpu_ps_graph(idx, ids[i])); - GpuPsCommGraph sub_graph = - g->cpu_graph_table->make_gpu_ps_graph(idx, ids[i]); - g->build_graph_on_single_gpu(sub_graph, i); - sub_graph.release_on_cpu(); - VLOG(0) << "sub graph on gpu " << i << " is built"; + tasks.push_back(upload_task_pool->enqueue([&, i, idx, this]() -> int { + VLOG(0) << "begin make_gpu_ps_graph, node_id[" << i << "]_size[" + << ids[i].size() << "]"; + GpuPsCommGraph sub_graph = + g->cpu_graph_table_->make_gpu_ps_graph(idx, ids[i]); + g->build_graph_on_single_gpu(sub_graph, i, idx); + sub_graph.release_on_cpu(); + VLOG(0) << "sub graph on gpu " << i << " is built"; + return 0; + })); } + for (size_t i = 0; i < tasks.size(); i++) tasks[i].get(); + debug_gpu_memory_info("upload_batch node end"); +} + +// feature table +void GraphGpuWrapper::upload_batch(int type, int slice_num, int slot_num) { + std::vector> node_ids; + ((GpuPsGraphTable *)graph_table) + ->cpu_graph_table_->get_all_id(type, slice_num, &node_ids); + debug_gpu_memory_info("upload_batch feature start"); + GpuPsGraphTable *g = (GpuPsGraphTable *)graph_table; + std::vector> tasks; + for (int i = 0; i < node_ids.size(); i++) { + tasks.push_back(upload_task_pool->enqueue([&, i, this]() -> int { + VLOG(0) << "begin make_gpu_ps_graph_fea, node_ids[" << i << "]_size[" + << node_ids[i].size() << "]"; + GpuPsCommGraphFea sub_graph = + g->cpu_graph_table_->make_gpu_ps_graph_fea(node_ids[i], slot_num); + // sub_graph.display_on_cpu(); + VLOG(0) << "begin build_graph_fea_on_single_gpu, node_ids[" << i + << "]_size[" << node_ids[i].size() << "]"; + g->build_graph_fea_on_single_gpu(sub_graph, i); + sub_graph.release_on_cpu(); + VLOG(0) << "sub graph fea on gpu " << i << " is built"; + return 0; + })); + } + for (size_t i = 0; i < tasks.size(); i++) tasks[i].get(); // g->build_graph_from_cpu(vec); + debug_gpu_memory_info("upload_batch feature end"); } -// void GraphGpuWrapper::test() { -// int64_t cpu_key[3] = {0, 1, 2}; -// void *key; -// platform::CUDADeviceGuard guard(0); -// cudaMalloc((void **)&key, 3 * sizeof(int64_t)); -// cudaMemcpy(key, cpu_key, 3 * sizeof(int64_t), cudaMemcpyHostToDevice); -// auto neighbor_sample_res = -// ((GpuPsGraphTable *)graph_table) -// ->graph_neighbor_sample(0, (int64_t *)key, 2, 3); -// int64_t *res = new int64_t[7]; -// cudaMemcpy(res, neighbor_sample_res.val, 3 * 2 * sizeof(int64_t), -// cudaMemcpyDeviceToHost); -// int *actual_sample_size = new int[3]; -// cudaMemcpy(actual_sample_size, neighbor_sample_res.actual_sample_size, -// 3 * sizeof(int), -// cudaMemcpyDeviceToHost); // 3, 1, 3 - -// //{0,9} or {9,0} is expected for key 0 -// //{0,2} or {2,0} is expected for key 1 -// //{1,3} or {3,1} is expected for key 2 -// for (int i = 0; i < 3; i++) { -// VLOG(0) << "actual sample size for " << i << " is " -// << actual_sample_size[i]; -// for (int j = 0; j < actual_sample_size[i]; j++) { -// VLOG(0) << "sampled an neighbor for node" << i << " : " << res[i * 2 + -// j]; -// } -// } -// } NeighborSampleResult GraphGpuWrapper::graph_neighbor_sample_v3( NeighborSampleQuery q, bool cpu_switch) { return ((GpuPsGraphTable *)graph_table) ->graph_neighbor_sample_v3(q, cpu_switch); } +int GraphGpuWrapper::get_feature_of_nodes(int gpu_id, + uint64_t *d_walk, + uint64_t *d_offset, + uint32_t size, + int slot_num) { + platform::CUDADeviceGuard guard(gpu_id); + PADDLE_ENFORCE_NOT_NULL(graph_table, + paddle::platform::errors::InvalidArgument( + "graph_table should not be null")); + return ((GpuPsGraphTable *)graph_table) + ->get_feature_of_nodes(gpu_id, d_walk, d_offset, size, slot_num); +} + +NeighborSampleResult GraphGpuWrapper::graph_neighbor_sample( + int gpu_id, uint64_t *device_keys, int walk_degree, int len) { + platform::CUDADeviceGuard guard(gpu_id); + auto neighbor_sample_res = + ((GpuPsGraphTable *)graph_table) + ->graph_neighbor_sample(gpu_id, device_keys, walk_degree, len); + + return neighbor_sample_res; +} + // this function is contributed by Liwb5 -std::vector GraphGpuWrapper::graph_neighbor_sample( - int gpu_id, std::vector &key, int sample_size) { - int64_t *cuda_key; +std::vector GraphGpuWrapper::graph_neighbor_sample( + int gpu_id, int idx, std::vector &key, int sample_size) { + std::vector res; + if (key.size() == 0) { + return res; + } + uint64_t *cuda_key; platform::CUDADeviceGuard guard(gpu_id); - cudaMalloc(&cuda_key, key.size() * sizeof(int64_t)); + cudaMalloc(&cuda_key, key.size() * sizeof(uint64_t)); cudaMemcpy(cuda_key, key.data(), - key.size() * sizeof(int64_t), + key.size() * sizeof(uint64_t), cudaMemcpyHostToDevice); - + VLOG(0) << "key_size: " << key.size(); auto neighbor_sample_res = ((GpuPsGraphTable *)graph_table) - ->graph_neighbor_sample(gpu_id, cuda_key, sample_size, key.size()); + ->graph_neighbor_sample_v2( + gpu_id, idx, cuda_key, sample_size, key.size(), false); int *actual_sample_size = new int[key.size()]; cudaMemcpy(actual_sample_size, neighbor_sample_res.actual_sample_size, @@ -235,12 +338,12 @@ std::vector GraphGpuWrapper::graph_neighbor_sample( cumsum += actual_sample_size[i]; } - std::vector cpu_key, res; + std::vector cpu_key; cpu_key.resize(key.size() * sample_size); cudaMemcpy(cpu_key.data(), neighbor_sample_res.val, - key.size() * sample_size * sizeof(int64_t), + key.size() * sample_size * sizeof(uint64_t), cudaMemcpyDeviceToHost); for (int i = 0; i < key.size(); i++) { for (int j = 0; j < actual_sample_size[i]; j++) { @@ -256,27 +359,26 @@ std::vector GraphGpuWrapper::graph_neighbor_sample( return res; } -void GraphGpuWrapper::init_sample_status() { - ((GpuPsGraphTable *)graph_table)->init_sample_status(); -} - -void GraphGpuWrapper::free_sample_status() { - ((GpuPsGraphTable *)graph_table)->free_sample_status(); -} NodeQueryResult GraphGpuWrapper::query_node_list(int gpu_id, + int idx, int start, int query_size) { + PADDLE_ENFORCE_EQ(FLAGS_gpugraph_load_node_list_into_hbm, + true, + paddle::platform::errors::PreconditionNotMet( + "when use query_node_list should set " + "gpugraph_load_node_list_into_hbm true")); return ((GpuPsGraphTable *)graph_table) - ->query_node_list(gpu_id, start, query_size); + ->query_node_list(gpu_id, idx, start, query_size); } void GraphGpuWrapper::load_node_weight(int type_id, int idx, std::string path) { return ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->load_node_weight(type_id, idx, path); + ->cpu_graph_table_->load_node_weight(type_id, idx, path); } void GraphGpuWrapper::export_partition_files(int idx, std::string file_path) { return ((GpuPsGraphTable *)graph_table) - ->cpu_graph_table->export_partition_files(idx, file_path); + ->cpu_graph_table_->export_partition_files(idx, file_path); } #endif } // namespace framework diff --git a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.h b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.h index 99c25e2e66803ecd6bf9611f9ddc2eb7d6baf4f7..9f3448714653e2fd09242c99ab044042068abe46 100644 --- a/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.h +++ b/paddle/fluid/framework/fleet/heter_ps/graph_gpu_wrapper.h @@ -32,39 +32,76 @@ class GraphGpuWrapper { } static std::shared_ptr s_instance_; void initialize(); - void test(); + void finalize(); void set_device(std::vector ids); void init_service(); void set_up_types(std::vector& edge_type, std::vector& node_type); - void upload_batch(int idx, std::vector>& ids); + void upload_batch(int type, + int idx, + int slice_num, + const std::string& edge_type); + void upload_batch(int type, int slice_num, int slot_num); void add_table_feat_conf(std::string table_name, std::string feat_name, std::string feat_dtype, int feat_shape); void load_edge_file(std::string name, std::string filepath, bool reverse); void load_node_file(std::string name, std::string filepath); + void load_node_and_edge(std::string etype, + std::string ntype, + std::string epath, + std::string npath, + int part_num, + bool reverse); int32_t load_next_partition(int idx); int32_t get_partition_num(int idx); void load_node_weight(int type_id, int idx, std::string path); void export_partition_files(int idx, std::string file_path); - std::vector get_partition(int idx, int num); + std::vector get_partition(int idx, int num); void make_partitions(int idx, int64_t byte_size, int device_len); void make_complementary_graph(int idx, int64_t byte_size); void set_search_level(int level); void init_search_level(int level); - std::vector> get_all_id(int type, - int idx, - int slice_num); - NodeQueryResult query_node_list(int gpu_id, int start, int query_size); + int get_all_id(int type, + int slice_num, + std::vector>* output); + int get_all_neighbor_id(int type, + int slice_num, + std::vector>* output); + int get_all_id(int type, + int idx, + int slice_num, + std::vector>* output); + int get_all_neighbor_id(int type, + int idx, + int slice_num, + std::vector>* output); + int get_all_feature_ids(int type, + int idx, + int slice_num, + std::vector>* output); + NodeQueryResult query_node_list(int gpu_id, + int idx, + int start, + int query_size); NeighborSampleResult graph_neighbor_sample_v3(NeighborSampleQuery q, bool cpu_switch); - std::vector graph_neighbor_sample(int gpu_id, - std::vector& key, - int sample_size); + NeighborSampleResult graph_neighbor_sample(int gpu_id, + uint64_t* device_keys, + int walk_degree, + int len); + std::vector graph_neighbor_sample(int gpu_id, + int idx, + std::vector& key, + int sample_size); + void set_feature_separator(std::string ch); + int get_feature_of_nodes(int gpu_id, + uint64_t* d_walk, + uint64_t* d_offset, + uint32_t size, + int slot_num); - void init_sample_status(); - void free_sample_status(); std::unordered_map edge_to_id, feature_to_id; std::vector id_to_feature, id_to_edge; std::vector> table_feat_mapping; @@ -75,6 +112,9 @@ class GraphGpuWrapper { std::vector device_id_mapping; int search_level = 1; void* graph_table; + int upload_num = 8; + std::shared_ptr<::ThreadPool> upload_task_pool; + std::string feature_separator_ = std::string(" "); }; #endif } // namespace framework diff --git a/paddle/fluid/framework/fleet/heter_ps/graph_sampler.h b/paddle/fluid/framework/fleet/heter_ps/graph_sampler.h index 7cec4fcfb8311894823db2de54773b2f1eba8b0a..fdde8eb064ba41ef32231de0a18025c04b0ed978 100644 --- a/paddle/fluid/framework/fleet/heter_ps/graph_sampler.h +++ b/paddle/fluid/framework/fleet/heter_ps/graph_sampler.h @@ -83,10 +83,10 @@ class CommonGraphSampler : public GraphSampler { virtual void init(GpuPsGraphTable *g, std::vector args); GpuPsGraphTable *gpu_table; paddle::distributed::GraphTable *table; - std::vector gpu_edges_count; - int64_t cpu_edges_count; - int64_t gpu_edges_limit, cpu_edges_limit, gpu_edges_each_limit; - std::vector> gpu_set; + std::vector gpu_edges_count; + uint64_t cpu_edges_count; + uint64_t gpu_edges_limit, cpu_edges_limit, gpu_edges_each_limit; + std::vector> gpu_set; int gpu_num; }; @@ -102,8 +102,9 @@ class AllInGpuGraphSampler : public GraphSampler { protected: paddle::distributed::GraphTable *graph_table; GpuPsGraphTable *gpu_table; - std::vector> sample_nodes; - std::vector> sample_neighbors; + std::vector> sample_node_ids; + std::vector> sample_node_infos; + std::vector> sample_neighbors; std::vector sample_res; // std::shared_ptr random; int gpu_num; diff --git a/paddle/fluid/framework/fleet/heter_ps/graph_sampler_inl.h b/paddle/fluid/framework/fleet/heter_ps/graph_sampler_inl.h index a0cce8c32da92fff4dd55d64b15889c149ca11b7..9ad5898757cd488880ac9de8d8399f120506ce76 100644 --- a/paddle/fluid/framework/fleet/heter_ps/graph_sampler_inl.h +++ b/paddle/fluid/framework/fleet/heter_ps/graph_sampler_inl.h @@ -24,7 +24,7 @@ int CommonGraphSampler::load_from_ssd(std::string path) { std::cout << values.size(); if (values.size() < 2) continue; auto neighbors = paddle::string::split_string(values[1], ";"); - std::vector neighbor_data; + std::vector neighbor_data; for (auto x : neighbors) { neighbor_data.push_back(std::stoll(x)); } @@ -33,7 +33,7 @@ int CommonGraphSampler::load_from_ssd(std::string path) { (char *)&src_id, sizeof(uint64_t), (char *)neighbor_data.data(), - sizeof(int64_t) * neighbor_data.size()); + sizeof(uint64_t) * neighbor_data.size()); int gpu_shard = src_id % gpu_num; if (gpu_edges_count[gpu_shard] + neighbor_data.size() <= gpu_edges_each_limit) { @@ -52,7 +52,7 @@ int CommonGraphSampler::load_from_ssd(std::string path) { } std::vector graph_list; for (int i = 0; i < gpu_num; i++) { - std::vector ids(gpu_set[i].begin(), gpu_set[i].end()); + std::vector ids(gpu_set[i].begin(), gpu_set[i].end()); graph_list.push_back(table->make_gpu_ps_graph(ids)); } gpu_table->build_graph_from_cpu(graph_list); @@ -72,26 +72,29 @@ void CommonGraphSampler::init(GpuPsGraphTable *g, gpu_edges_each_limit = gpu_edges_limit / gpu_num; if (gpu_edges_each_limit > INT_MAX) gpu_edges_each_limit = INT_MAX; table = g->cpu_graph_table.get(); - gpu_edges_count = std::vector(gpu_num, 0); + gpu_edges_count = std::vector(gpu_num, 0); cpu_edges_count = 0; - gpu_set = std::vector>(gpu_num); + gpu_set = std::vector>(gpu_num); } int AllInGpuGraphSampler::run_graph_sampling() { return 0; } int AllInGpuGraphSampler::load_from_ssd(std::string path) { graph_table->load_edges(path, false); - sample_nodes.clear(); - sample_neighbors.clear(); + sample_node_ids.clear() sample_node_infos.clear() sample_neighbors.clear(); sample_res.clear(); - sample_nodes.resize(gpu_num); + sample_node_ids.resize(gpu_num); + sample_node_infos.resize(gpu_num); sample_neighbors.resize(gpu_num); sample_res.resize(gpu_num); - std::vector>> - sample_nodes_ex(graph_table->task_pool_size_); - std::vector>> sample_neighbors_ex( + std::vector>> sample_node_ids_ex( + graph_table->task_pool_size_); + std::vector>> + sample_node_infos_ex(graph_table->task_pool_size_); + std::vector>> sample_neighbors_ex( graph_table->task_pool_size_); for (int i = 0; i < graph_table->task_pool_size_; i++) { - sample_nodes_ex[i].resize(gpu_num); + sample_node_ids_ex[i].resize(gpu_num); + sample_node_infos_ex[i].resize(gpu_num); sample_neighbors_ex[i].resize(gpu_num); } std::vector> tasks; @@ -100,17 +103,16 @@ int AllInGpuGraphSampler::load_from_ssd(std::string path) { graph_table->_shards_task_pool[i % graph_table->task_pool_size_] ->enqueue([&, i, this]() -> int { if (this->status == GraphSamplerStatus::terminating) return 0; - paddle::framework::GpuPsGraphNode node; + paddle::framework::GpuPsNodeInfo info; std::vector &v = this->graph_table->shards[i]->get_bucket(); size_t ind = i % this->graph_table->task_pool_size_; for (size_t j = 0; j < v.size(); j++) { - size_t location = v[j]->get_id() % this->gpu_num; - node.node_id = v[j]->get_id(); - node.neighbor_size = v[j]->get_neighbor_size(); - node.neighbor_offset = - (int)sample_neighbors_ex[ind][location].size(); - sample_nodes_ex[ind][location].emplace_back(node); + info.neighbor_size = v[j]->get_neighbor_size(); + info.neighbor_offset = + sample_neighbors_ex[ind][location].size(); + sample_node_infos_ex[ind][location].emplace_back(info); + sample_node_ids_ex[ind][location].emplace_back(v[j]->get_id()); for (int k = 0; k < node.neighbor_size; k++) sample_neighbors_ex[ind][location].push_back( v[j]->get_neighbor_id(k)); @@ -128,9 +130,11 @@ int AllInGpuGraphSampler::load_from_ssd(std::string path) { int total_offset = 0; size_t ind = i; for (int j = 0; j < this->graph_table->task_pool_size_; j++) { - for (size_t k = 0; k < sample_nodes_ex[j][ind].size(); k++) { - sample_nodes[ind].push_back(sample_nodes_ex[j][ind][k]); - sample_nodes[ind].back().neighbor_offset += total_offset; + for (size_t k = 0; k < sample_node_ids_ex[j][ind].size(); k++) { + sample_node_ids[ind].push_back(sample_node_ids_ex[j][ind][k]); + sample_node_infos[ind].push_back( + sample_node_infos_ex[j][ind][k]); + sample_node_infos[ind].back().neighbor_offset += total_offset; } size_t neighbor_size = sample_neighbors_ex[j][ind].size(); total_offset += neighbor_size; @@ -144,9 +148,10 @@ int AllInGpuGraphSampler::load_from_ssd(std::string path) { } for (size_t i = 0; i < tasks.size(); i++) tasks[i].get(); for (size_t i = 0; i < gpu_num; i++) { - sample_res[i].node_list = sample_nodes[i].data(); + sample_res[i].node_list = sample_node_ids[i].data(); + sample_res[i].node_info_list = sample_node_infos[i].data(); sample_res[i].neighbor_list = sample_neighbors[i].data(); - sample_res[i].node_size = sample_nodes[i].size(); + sample_res[i].node_size = sample_node_ids[i].size(); sample_res[i].neighbor_size = sample_neighbors[i].size(); } diff --git a/paddle/fluid/framework/fleet/heter_ps/hashtable.h b/paddle/fluid/framework/fleet/heter_ps/hashtable.h index 43192df0c71f037f9ab91ab6b6585ed17801c06c..18fb2eca5b752eb8ee381b898d9a8bb32b7bafd0 100644 --- a/paddle/fluid/framework/fleet/heter_ps/hashtable.h +++ b/paddle/fluid/framework/fleet/heter_ps/hashtable.h @@ -76,6 +76,7 @@ class XPUCacheArray { } void print() {} + void print_collision(int i) {} #if defined(__xpu__) __device__ ValType* find(const KeyType& key) { @@ -137,12 +138,12 @@ class HashTable { size_t len, StreamType stream); - template + template void get(const KeyType* d_keys, char* d_vals, size_t len, StreamType stream, - FVAccessor& fv_accessor); + GPUAccessor& fv_accessor); void show(); @@ -193,6 +194,8 @@ class HashTable { << " push value size: " << push_grad_value_size_; } + void show_collision(int id) { return container_->print_collision(id); } + std::unique_ptr rwlock_{nullptr}; private: diff --git a/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu b/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu index 2f5d5697e7c385ba33cc2056c26b7fd992fac73c..1fda5a586a2e81841091dd6f7d9d7cbe40290f9a 100644 --- a/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu +++ b/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu @@ -83,25 +83,22 @@ __global__ void search_kernel(Table* table, } } -template +template __global__ void dy_mf_search_kernel(Table* table, const typename Table::key_type* const keys, char* vals, size_t len, size_t pull_feature_value_size, - FVAccessor feature_value_accessor) { + GPUAccessor gpu_accessor) { const size_t i = blockIdx.x * blockDim.x + threadIdx.x; + // return; if (i < len) { auto it = table->find(keys[i]); - if (it != table->end()) { uint64_t offset = i * pull_feature_value_size; float* cur = (float*)(vals + offset); float* input = it->second; - int mf_dim = - int(input[feature_value_accessor.common_feature_value.MfDimIndex()]); - - feature_value_accessor.FeatureValueFill(cur, input, mf_dim); + gpu_accessor.PullValueFill(cur, input); } } } @@ -137,9 +134,7 @@ __global__ void dy_mf_update_kernel(Table* table, float* cur = (float*)(grads + i * grad_value_size); sgd.dy_mf_update_value(optimizer_config, (it.getter())->second, cur); } else { - if (keys[i] != 0) { - printf("warning::push miss key: %llu", keys[i]); - } + printf("warning: push miss key: %lu", keys[i]); } } } @@ -147,11 +142,12 @@ __global__ void dy_mf_update_kernel(Table* table, template HashTable::HashTable(size_t capacity) { container_ = new TableContainer(capacity); - cudaMalloc((void**)&device_optimizer_config_, sizeof(OptimizerConfig)); - cudaMemcpy((void*)device_optimizer_config_, - &host_optimizer_config_, - sizeof(OptimizerConfig), - cudaMemcpyHostToDevice); + CUDA_RT_CALL( + cudaMalloc((void**)&device_optimizer_config_, sizeof(OptimizerConfig))); + CUDA_RT_CALL(cudaMemcpy((void*)device_optimizer_config_, + &host_optimizer_config_, + sizeof(OptimizerConfig), + cudaMemcpyHostToDevice)); rwlock_.reset(new phi::RWLock); } @@ -201,12 +197,12 @@ void HashTable::get(const KeyType* d_keys, } template -template +template void HashTable::get(const KeyType* d_keys, char* d_vals, size_t len, StreamType stream, - FVAccessor& fv_accessor) { + GPUAccessor& fv_accessor) { if (len == 0) { return; } @@ -345,6 +341,7 @@ template class HashTable; template class HashTable; template class HashTable; template class HashTable; +template class HashTable; template class HashTable; template class HashTable; template class HashTable; @@ -377,7 +374,8 @@ template void HashTable::get( unsigned long* d_vals, size_t len, cudaStream_t stream); - +template void HashTable::get( + const unsigned long* d_keys, long* d_vals, size_t len, cudaStream_t stream); template void HashTable::get( const long* d_keys, unsigned long* d_vals, size_t len, cudaStream_t stream); template void HashTable::get(const long* d_keys, @@ -386,8 +384,6 @@ template void HashTable::get(const long* d_keys, cudaStream_t stream); template void HashTable::get( const long* d_keys, unsigned int* d_vals, size_t len, cudaStream_t stream); -template void HashTable::get( - const unsigned long* d_keys, long* d_vals, size_t len, cudaStream_t stream); // template void // HashTable::get( // const unsigned long* d_keys, char* d_vals, size_t len, cudaStream_t @@ -421,6 +417,13 @@ template void HashTable::insert( const int* d_vals, size_t len, cudaStream_t stream); + +template void HashTable::insert( + const unsigned long* d_keys, + const long* d_vals, + size_t len, + cudaStream_t stream); + template void HashTable::insert( const long* d_keys, const unsigned long* d_vals, @@ -433,12 +436,6 @@ template void HashTable::insert( size_t len, cudaStream_t stream); -template void HashTable::insert( - const unsigned long* d_keys, - const long* d_vals, - size_t len, - cudaStream_t stream); - template void HashTable::insert( const unsigned long* d_keys, const unsigned long* d_vals, @@ -448,26 +445,26 @@ template void HashTable::insert( template void HashTable::dump_to_cpu( int devid, cudaStream_t stream); -template void -HashTable::update( - const unsigned long* d_keys, - const char* d_grads, - size_t len, - SparseAdagradOptimizer sgd, - cudaStream_t stream); -template void -HashTable::update( - const unsigned long* d_keys, - const char* d_grads, - size_t len, - SparseAdamOptimizer sgd, - cudaStream_t stream); template void HashTable::update< - SparseAdamSharedOptimizer, + SparseAdagradOptimizer, + cudaStream_t>(const unsigned long* d_keys, + const char* d_grads, + size_t len, + SparseAdagradOptimizer sgd, + cudaStream_t stream); +template void HashTable::update< + SparseAdamOptimizer, + cudaStream_t>(const unsigned long* d_keys, + const char* d_grads, + size_t len, + SparseAdamOptimizer sgd, + cudaStream_t stream); +template void HashTable::update< + SparseAdamSharedOptimizer, cudaStream_t>(const unsigned long* d_keys, const char* d_grads, size_t len, - SparseAdamSharedOptimizer sgd, + SparseAdamSharedOptimizer sgd, cudaStream_t stream); // template void HashTable #include "paddle/fluid/platform/device/xpu/enforce_xpu.h" @@ -49,14 +48,46 @@ namespace framework { template + typename GPUAccessor> class HeterComm { public: HeterComm(size_t capacity, std::shared_ptr resource); + HeterComm(size_t capacity, + std::shared_ptr resource, + GPUAccessor& gpu_accessor); virtual ~HeterComm(); HeterComm(const HeterComm&) = delete; HeterComm& operator=(const HeterComm&) = delete; + void merge_keys(int gpu_num, + const KeyType* d_keys, + size_t len, + KeyType* d_sorted_keys, + KeyType* d_merged_keys, + uint32_t* d_restore_idx, + size_t& uniq_len); + void dynamic_merge_grad(int gpu_num, + KeyType* d_keys, + float* d_grads, + size_t len, + int& uniq_len, + size_t& segment_len, + bool enable_segment_merge_grad); + void segment_merge_grad(int gpu_num, + KeyType* d_keys, + float* d_grads, + const uint32_t* d_index, + size_t len, + const uint32_t* d_fea_num_info, + size_t uniq_len, + size_t& segment_len); + void build_ps(int num, + KeyType* h_keys, + ValType* h_vals, + size_t len, + size_t chunk_size, + int stream_num, + int offset = -1); void split_input_to_shard(KeyType* d_keys, int* d_idx_ptr, size_t len, @@ -71,12 +102,6 @@ class HeterComm { void dynamic_merge_grad( int gpu_num, KeyType* d_keys, float* d_grads, size_t len, int& uniq_len); void pull_sparse(int num, KeyType* d_keys, float* d_vals, size_t len); - void build_ps(int num, - KeyType* h_keys, - ValType* h_vals, - size_t len, - size_t chunk_size, - int stream_num); void build_ps(int num, KeyType* h_keys, char* pool, @@ -86,6 +111,7 @@ class HeterComm { int stream_num); void dump(); void show_one_table(int gpu_num); + void show_table_collisions(); int get_index_by_devid(int devid); #if defined(PADDLE_WITH_CUDA) @@ -150,12 +176,6 @@ class HeterComm { max_mf_dim_ = max_mf_dim; } - void set_accessor(FVAccessor& accessor) { - feature_value_accessor_ = accessor; - // for (auto& ptr_table: ptr_tables_) { - // ptr_table->set_accessor(feature_value_accessor_); - // } - } #endif bool need_transfer(int send_id, int receive_id) { @@ -167,6 +187,19 @@ class HeterComm { int get_transfer_devid(int send_id) { return (send_id + 4) % 8; } void end_pass(); +#if defined(PADDLE_WITH_CUDA) + // dedup + int dedup_keys_and_fillidx(const int gpu_id, + const int total_fea_num, + const KeyType* d_keys, // input + KeyType* d_merged_keys, // output + KeyType* d_sorted_keys, + uint32_t* d_restore_idx, + uint32_t* d_sorted_idx, + uint32_t* d_offset, + uint32_t* d_merged_cnts, + bool filter_zero); +#endif struct Node { ppStream in_stream; @@ -262,7 +295,10 @@ class HeterComm { #endif } - void create_storage(int start_index, int end_index, int keylen, int vallen); + void create_storage(int start_index, + int end_index, + size_t keylen, + size_t vallen); void destroy_storage(int start_index, int end_index); void walk_to_dest(int start_index, int gpu_num, @@ -289,9 +325,10 @@ class HeterComm { char* src_val, size_t val_size); - FVAccessor feature_value_accessor_; - protected: + void pull_merge_sparse(int num, KeyType* d_keys, float* d_vals, size_t len); + void pull_normal_sparse(int num, KeyType* d_keys, float* d_vals, size_t len); + using Table = HashTable; using PtrTable = HashTable; std::vector tables_; @@ -302,6 +339,8 @@ class HeterComm { int block_size_{256}; std::unique_ptr heter_comm_kernel_; + GPUAccessor gpu_accessor_; + private: int topo_aware_{0}; std::vector storage_; diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h b/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h index f8657c8e895ad3b30e490f57b5fdb19cea5fe1fb..eb55209f856aa6714406f775b1972add166b5654 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h @@ -16,25 +16,34 @@ limitations under the License. */ #include #include "paddle/fluid/framework/fleet/heter_ps/feature_value.h" +#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h" #include "paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h" #include "paddle/fluid/platform/device_context.h" #ifdef PADDLE_WITH_XPU_KP #include "paddle/fluid/platform/device/xpu/xpu_info.h" #endif +DECLARE_double(gpugraph_hbm_table_load_factor); +DECLARE_bool(gpugraph_enable_gpu_direct_access); +DECLARE_bool(gpugraph_enable_segment_merge_grads); +DECLARE_uint64(gpugraph_merge_grads_segment_size); +DECLARE_int32(gpugraph_dedup_pull_push_mode); + namespace paddle { namespace framework { template -HeterComm::HeterComm( + typename GPUAccessor> +HeterComm::HeterComm( size_t capacity, std::shared_ptr resource) { VLOG(1) << "Construct new HeterComm"; resource_ = resource; storage_.resize(resource_->total_device()); multi_mf_dim_ = resource->multi_mf(); + load_factor_ = FLAGS_gpugraph_hbm_table_load_factor; + VLOG(0) << "load_factor = " << load_factor_; for (int i = 0; i < resource_->total_device(); ++i) { #if defined(PADDLE_WITH_CUDA) platform::CUDADeviceGuard guard(resource_->dev_id(i)); @@ -47,15 +56,19 @@ HeterComm::HeterComm( } else { max_mf_dim_ = resource_->max_mf_dim(); auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); size_t val_type_size = accessor_wrapper_ptr->GetFeatureValueSize(max_mf_dim_); size_t grad_type_size = accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); + size_t pull_type_size = + accessor_wrapper_ptr->GetPullValueSize(max_mf_dim_); + VLOG(0) << " HeterComm init, max feature_value_size:" << val_type_size - << ", feature_value_push_size:" << grad_type_size; + << ", feature_value_push_size:" << grad_type_size + << ", feature_pull_type_size:" << pull_type_size; auto ptr_table = new PtrTable(capacity / load_factor_); - ptr_table->set_feature_value_size(val_type_size, grad_type_size); + ptr_table->set_feature_value_size(pull_type_size, grad_type_size); ptr_tables_.push_back(ptr_table); } if (multi_node_) { @@ -69,8 +82,58 @@ HeterComm::HeterComm( template -void HeterComm::init_path() { + typename GPUAccessor> +HeterComm::HeterComm( + size_t capacity, + std::shared_ptr resource, + GPUAccessor& gpu_accessor) { + VLOG(1) << "Construct new HeterComm"; + resource_ = resource; + storage_.resize(resource_->total_device()); + multi_mf_dim_ = resource->multi_mf(); + gpu_accessor_ = gpu_accessor; + load_factor_ = FLAGS_gpugraph_hbm_table_load_factor; + VLOG(0) << "load_factor = " << load_factor_; + for (int i = 0; i < resource_->total_device(); ++i) { +#if defined(PADDLE_WITH_CUDA) + platform::CUDADeviceGuard guard(resource_->dev_id(i)); + allocators_.push_back(std::make_shared( + 8, 1, (unsigned int)-1, (size_t)-1, false, false)); // NOLINT +#endif + if (!multi_mf_dim_) { + auto table = new Table(capacity / load_factor_); + tables_.push_back(table); + } else { + max_mf_dim_ = resource_->max_mf_dim(); + auto accessor_wrapper_ptr = + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + size_t val_type_size = + accessor_wrapper_ptr->GetFeatureValueSize(max_mf_dim_); + size_t grad_type_size = + accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); + size_t pull_type_size = + accessor_wrapper_ptr->GetPullValueSize(max_mf_dim_); + + VLOG(0) << " HeterComm init, max feature_value_size:" << val_type_size + << ", feature_value_push_size:" << grad_type_size + << ", feature_pull_type_size:" << pull_type_size; + auto ptr_table = new PtrTable(capacity / load_factor_); + ptr_table->set_feature_value_size(pull_type_size, grad_type_size); + ptr_tables_.push_back(ptr_table); + } + if (multi_node_) { + storage_[i].init(feanum_, resource_->dev_id(i)); + } + } + heter_comm_kernel_ = std::make_unique(block_size_); + init_path(); +} + +template +void HeterComm::init_path() { int total_device = resource_->total_device(); path_.resize(total_device); if (!topo_aware_) { @@ -125,9 +188,9 @@ void HeterComm::init_path() { template + typename GPUAccessor> template -void HeterComm::memory_copy( +void HeterComm::memory_copy( DstPlace dst_place, void* dst, SrcPlace src_place, @@ -135,9 +198,9 @@ void HeterComm::memory_copy( size_t count, StreamType stream) { #if defined(PADDLE_WITH_CUDA) - cudaMemcpyAsync(dst, src, count, cudaMemcpyDefault, stream); + CUDA_CHECK(cudaMemcpyAsync(dst, src, count, cudaMemcpyDefault, stream)); if (stream == 0) { - cudaStreamSynchronize(0); + CUDA_CHECK(cudaStreamSynchronize(0)); } #elif defined(PADDLE_WITH_XPU_KP) memory::Copy(dst_place, dst, src_place, src, count); @@ -147,24 +210,24 @@ void HeterComm::memory_copy( template -void HeterComm::create_storage( - int start_index, int end_index, int keylen, int vallen) { + typename GPUAccessor> +void HeterComm::create_storage( + int start_index, int end_index, size_t keylen, size_t vallen) { #if defined(PADDLE_WITH_CUDA) auto& allocator = allocators_[start_index]; auto& nodes = path_[start_index][end_index].nodes_; for (size_t i = 0; i < nodes.size(); ++i) { platform::CUDADeviceGuard guard(resource_->dev_id(nodes[i].dev_num)); - allocator->DeviceAllocate( + PADDLE_ENFORCE_GPU_SUCCESS(allocator->DeviceAllocate( resource_->dev_id(nodes[i].dev_num), (void**)&(nodes[i].key_storage), // NOLINT keylen, - resource_->remote_stream(nodes[i].dev_num, start_index)); - allocator->DeviceAllocate( + resource_->remote_stream(nodes[i].dev_num, start_index))); + PADDLE_ENFORCE_GPU_SUCCESS(allocator->DeviceAllocate( resource_->dev_id(nodes[i].dev_num), (void**)&(nodes[i].val_storage), // NOLINT vallen, - resource_->remote_stream(nodes[i].dev_num, start_index)); + resource_->remote_stream(nodes[i].dev_num, start_index))); nodes[i].key_bytes_len = keylen; nodes[i].val_bytes_len = vallen; } @@ -186,8 +249,8 @@ void HeterComm::create_storage( template -void HeterComm::destroy_storage( + typename GPUAccessor> +void HeterComm::destroy_storage( int start_index, int end_index) { #if defined(PADDLE_WITH_CUDA) auto& allocator = allocators_[start_index]; @@ -195,10 +258,10 @@ void HeterComm::destroy_storage( for (size_t i = 0; i < nodes.size(); ++i) { platform::CUDADeviceGuard guard(resource_->dev_id(nodes[i].dev_num)); - allocator->DeviceFree(resource_->dev_id(nodes[i].dev_num), - nodes[i].key_storage); - allocator->DeviceFree(resource_->dev_id(nodes[i].dev_num), - nodes[i].val_storage); + PADDLE_ENFORCE_GPU_SUCCESS(allocator->DeviceFree( + resource_->dev_id(nodes[i].dev_num), nodes[i].key_storage)); + PADDLE_ENFORCE_GPU_SUCCESS(allocator->DeviceFree( + resource_->dev_id(nodes[i].dev_num), nodes[i].val_storage)); } #endif } @@ -206,8 +269,8 @@ void HeterComm::destroy_storage( template -void HeterComm::walk_to_dest( + typename GPUAccessor> +void HeterComm::walk_to_dest( int start_index, int num, int* h_left, @@ -293,8 +356,8 @@ void HeterComm::walk_to_dest( template -void HeterComm::walk_to_dest( + typename GPUAccessor> +void HeterComm::walk_to_dest( int start_index, int gpu_num, int* h_left, @@ -315,40 +378,44 @@ void HeterComm::walk_to_dest( auto& node = path_[start_index][i].nodes_[0]; CopyTask t(&path_[start_index][i], 0); que.push(t); - cudaMemcpyAsync(node.key_storage, - reinterpret_cast(src_key + h_left[i]), - node.key_bytes_len, - cudaMemcpyDefault, - node.in_stream); + CUDA_CHECK(cudaMemcpyAsync(node.key_storage, + reinterpret_cast(src_key + h_left[i]), + node.key_bytes_len, + cudaMemcpyDefault, + node.in_stream)); if (need_copy_val) { - cudaMemcpyAsync(node.val_storage, - src_val + uint64_t(h_left[i]) * uint64_t(val_size), - node.val_bytes_len, - cudaMemcpyDefault, - node.in_stream); + CUDA_CHECK( + cudaMemcpyAsync(node.val_storage, + src_val + uint64_t(h_left[i]) * uint64_t(val_size), + node.val_bytes_len, + cudaMemcpyDefault, + node.in_stream)); } } while (!que.empty()) { CopyTask& cur_task = que.front(); que.pop(); if (cur_task.path->nodes_[cur_task.step].sync) { - cudaStreamSynchronize(cur_task.path->nodes_[cur_task.step].in_stream); + CUDA_CHECK(cudaStreamSynchronize( + cur_task.path->nodes_[cur_task.step].in_stream)); } if (cur_task.step != cur_task.path->nodes_.size() - 1) { int cur_step = cur_task.step; CopyTask c(cur_task.path, cur_step + 1); que.push(c); - cudaMemcpyAsync(cur_task.path->nodes_[cur_step + 1].key_storage, - cur_task.path->nodes_[cur_step].key_storage, - cur_task.path->nodes_[cur_step + 1].key_bytes_len, - cudaMemcpyDefault, - cur_task.path->nodes_[cur_step + 1].in_stream); + CUDA_CHECK( + cudaMemcpyAsync(cur_task.path->nodes_[cur_step + 1].key_storage, + cur_task.path->nodes_[cur_step].key_storage, + cur_task.path->nodes_[cur_step + 1].key_bytes_len, + cudaMemcpyDefault, + cur_task.path->nodes_[cur_step + 1].in_stream)); if (need_copy_val) { - cudaMemcpyAsync(cur_task.path->nodes_[cur_step + 1].val_storage, - cur_task.path->nodes_[cur_step].val_storage, - cur_task.path->nodes_[cur_step + 1].val_bytes_len, - cudaMemcpyDefault, - cur_task.path->nodes_[cur_step + 1].in_stream); + CUDA_CHECK( + cudaMemcpyAsync(cur_task.path->nodes_[cur_step + 1].val_storage, + cur_task.path->nodes_[cur_step].val_storage, + cur_task.path->nodes_[cur_step + 1].val_bytes_len, + cudaMemcpyDefault, + cur_task.path->nodes_[cur_step + 1].in_stream)); } } } @@ -357,8 +424,8 @@ void HeterComm::walk_to_dest( template -void HeterComm::walk_to_src( + typename GPUAccessor> +void HeterComm::walk_to_src( int start_index, int gpu_num, int* h_left, @@ -373,19 +440,20 @@ void HeterComm::walk_to_src( int cur_step = path_[start_index][i].nodes_.size() - 1; auto& node = path_[start_index][i].nodes_[cur_step]; if (cur_step == 0) { - cudaMemcpyAsync(src_val + uint64_t(h_left[i]) * val_size, - node.val_storage, - node.val_bytes_len, - cudaMemcpyDefault, - node.out_stream); + CUDA_CHECK(cudaMemcpyAsync(src_val + uint64_t(h_left[i]) * val_size, + node.val_storage, + node.val_bytes_len, + cudaMemcpyDefault, + node.out_stream)); } else { CopyTask t(&path_[start_index][i], cur_step - 1); que.push(t); - cudaMemcpyAsync(path_[start_index][i].nodes_[cur_step - 1].val_storage, - node.val_storage, - path_[start_index][i].nodes_[cur_step - 1].val_bytes_len, - cudaMemcpyDefault, - path_[start_index][i].nodes_[cur_step - 1].out_stream); + CUDA_CHECK(cudaMemcpyAsync( + path_[start_index][i].nodes_[cur_step - 1].val_storage, + node.val_storage, + path_[start_index][i].nodes_[cur_step - 1].val_bytes_len, + cudaMemcpyDefault, + path_[start_index][i].nodes_[cur_step - 1].out_stream)); } } while (!que.empty()) { @@ -398,18 +466,20 @@ void HeterComm::walk_to_src( if (cur_step > 0) { CopyTask c(cur_task.path, cur_step - 1); que.push(c); - cudaMemcpyAsync(cur_task.path->nodes_[cur_step - 1].val_storage, - cur_task.path->nodes_[cur_step].val_storage, - cur_task.path->nodes_[cur_step - 1].val_bytes_len, - cudaMemcpyDefault, - cur_task.path->nodes_[cur_step - 1].out_stream); + CUDA_CHECK( + cudaMemcpyAsync(cur_task.path->nodes_[cur_step - 1].val_storage, + cur_task.path->nodes_[cur_step].val_storage, + cur_task.path->nodes_[cur_step - 1].val_bytes_len, + cudaMemcpyDefault, + cur_task.path->nodes_[cur_step - 1].out_stream)); } else if (cur_step == 0) { int end_index = cur_task.path->nodes_.back().dev_num; - cudaMemcpyAsync(src_val + uint64_t(h_left[end_index]) * val_size, - cur_task.path->nodes_[cur_step].val_storage, - cur_task.path->nodes_[cur_step].val_bytes_len, - cudaMemcpyDefault, - cur_task.path->nodes_[cur_step].out_stream); + CUDA_CHECK( + cudaMemcpyAsync(src_val + uint64_t(h_left[end_index]) * val_size, + cur_task.path->nodes_[cur_step].val_storage, + cur_task.path->nodes_[cur_step].val_bytes_len, + cudaMemcpyDefault, + cur_task.path->nodes_[cur_step].out_stream)); } } } @@ -417,8 +487,8 @@ void HeterComm::walk_to_src( template -HeterComm::~HeterComm() { + typename GPUAccessor> +HeterComm::~HeterComm() { if (!multi_mf_dim_) { for (auto& table : tables_) { delete table; @@ -439,8 +509,8 @@ HeterComm::~HeterComm() { template -void HeterComm::show_one_table( + typename GPUAccessor> +void HeterComm::show_one_table( int gpu_num) { if (!multi_mf_dim_) { tables_[gpu_num]->show(); @@ -450,8 +520,28 @@ void HeterComm::show_one_table( template -int HeterComm::log2i(int x) { + typename GPUAccessor> +void HeterComm:: + show_table_collisions() { + size_t idx = 0; + for (auto& table : tables_) { + if (table != nullptr) { + table->show_collision(idx++); + } + } + idx = 0; + for (auto& table : ptr_tables_) { + if (table != nullptr) { + table->show_collision(idx++); + } + } +} + +template +int HeterComm::log2i(int x) { unsigned res = 0; while (x >>= 1) { ++res; @@ -462,8 +552,8 @@ int HeterComm::log2i(int x) { template -int HeterComm::get_index_by_devid( + typename GPUAccessor> +int HeterComm::get_index_by_devid( int devid) { return resource_->get_index_by_devid(devid); } @@ -471,8 +561,8 @@ int HeterComm::get_index_by_devid( template -void HeterComm::set_sparse_sgd( + typename GPUAccessor> +void HeterComm::set_sparse_sgd( const OptimizerConfig& optimizer_config) { for (int i = 0; i < resource_->total_device(); ++i) { AnyDeviceGuard guard(resource_->dev_id(i)); @@ -487,8 +577,8 @@ void HeterComm::set_sparse_sgd( template -void HeterComm::set_embedx_sgd( + typename GPUAccessor> +void HeterComm::set_embedx_sgd( const OptimizerConfig& optimizer_config) { for (int i = 0; i < resource_->total_device(); ++i) { AnyDeviceGuard guard(resource_->dev_id(i)); @@ -503,14 +593,15 @@ void HeterComm::set_embedx_sgd( template -void HeterComm::build_ps( + typename GPUAccessor> +void HeterComm::build_ps( int dev_num, KeyType* h_keys, ValType* h_vals, size_t len, size_t chunk_size, - int stream_num) { + int stream_num, + int offset) { if (len <= 0) { return; } @@ -557,11 +648,11 @@ void HeterComm::build_ps( h_vals + cur_len, sizeof(ValType) * tmp_len, cur_use_stream); - - tables_[dev_num]->insert( + if (offset == -1) offset = dev_num; + tables_[offset]->insert( reinterpret_cast(d_key_bufs[cur_stream]->ptr()), reinterpret_cast(d_val_bufs[cur_stream]->ptr()), - tmp_len, + (size_t)tmp_len, cur_use_stream); cur_stream += 1; @@ -576,8 +667,8 @@ void HeterComm::build_ps( template -void HeterComm::build_ps( + typename GPUAccessor> +void HeterComm::build_ps( int num, KeyType* h_keys, char* pool, @@ -642,8 +733,8 @@ void HeterComm::build_ps( template -void HeterComm::merge_grad( + typename GPUAccessor> +void HeterComm::merge_grad( int dev_num, KeyType* d_keys, GradType* d_grads, @@ -719,34 +810,36 @@ void HeterComm::merge_grad( template -void HeterComm::dynamic_merge_grad( - int gpu_num, KeyType* d_keys, float* d_grads, size_t len, int& uniq_len) { + typename GPUAccessor> +void HeterComm::dynamic_merge_grad( + int gpu_num, + KeyType* d_keys, + float* d_grads, + size_t len, + int& uniq_len, + size_t& segment_len, + bool enable_segment_merge_grad) { int dev_id = resource_->dev_id(gpu_num); platform::CUDAPlace place = platform::CUDAPlace(dev_id); platform::CUDADeviceGuard guard(dev_id); auto stream = resource_->local_stream(gpu_num, 0); size_t temp_storage_bytes; - + size_t grad_dim = max_mf_dim_; auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); size_t grad_value_size = accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); auto d_merge_keys = memory::Alloc(place, len * sizeof(KeyType)); KeyType* d_merge_keys_ptr = reinterpret_cast(d_merge_keys->ptr()); - - auto d_merge_grads = memory::Alloc(place, len * grad_value_size); - float* d_merge_grads_ptr = reinterpret_cast(d_merge_grads->ptr()); - auto d_fea_num_info = memory::Alloc(place, sizeof(uint32_t) * (len * 3 + 1)); uint32_t* d_fea_num_info_ptr = reinterpret_cast(d_fea_num_info->ptr()); uint32_t* d_index = (uint32_t*)&d_fea_num_info_ptr[len]; uint32_t* d_idx = (uint32_t*)&d_index[len]; int* d_merged_size = (int*)&d_idx[len]; - int grid_size = (len - 1) / block_size_ + 1; heter_comm_kernel_->fill_idx(d_idx, len, stream); + PADDLE_ENFORCE_GPU_SUCCESS( cub::DeviceRadixSort::SortPairs(NULL, temp_storage_bytes, @@ -758,7 +851,6 @@ void HeterComm::dynamic_merge_grad( 0, 8 * sizeof(KeyType), stream)); - void* d_buff = NULL; auto d_temp_storage = memory::Alloc(place, temp_storage_bytes); PADDLE_ENFORCE_GPU_SUCCESS( cub::DeviceRadixSort::SortPairs(d_temp_storage->ptr(), @@ -772,6 +864,7 @@ void HeterComm::dynamic_merge_grad( 8 * sizeof(KeyType), stream)); PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + temp_storage_bytes = 0; PADDLE_ENFORCE_GPU_SUCCESS( cub::DeviceRunLengthEncode::Encode(NULL, @@ -824,20 +917,194 @@ void HeterComm::dynamic_merge_grad( uniq_len, stream)); PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); - heter_comm_kernel_->merge_gradient(d_offset, - d_fea_num_info_ptr, + + if (enable_segment_merge_grad) { + segment_merge_grad(gpu_num, + d_merge_keys_ptr, + d_grads, + d_index, + len, + d_fea_num_info_ptr, + uniq_len, + segment_len); + PADDLE_ENFORCE_GPU_SUCCESS(cudaMemcpyAsync(d_keys, + d_merge_keys_ptr, + sizeof(KeyType) * segment_len, + cudaMemcpyDeviceToDevice, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + } else { + auto d_merge_grads = memory::Alloc(place, len * grad_value_size); + float* d_merge_grads_ptr = reinterpret_cast(d_merge_grads->ptr()); + + heter_comm_kernel_->merge_gradient(d_keys, + d_offset, + d_fea_num_info_ptr, + d_index, + (char*)d_grads, + (char*)d_merge_grads_ptr, + uniq_len, + grad_dim, + grad_value_size, + merger_, + stream, + gpu_accessor_); + PADDLE_ENFORCE_GPU_SUCCESS(cudaMemcpyAsync(d_grads, + d_merge_grads_ptr, + grad_value_size * uniq_len, + cudaMemcpyDeviceToDevice, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + } +} + +template +void HeterComm::segment_merge_grad( + int gpu_num, // the device number + KeyType* + d_keys, // the sorted keys list, which will be modified after merged + float* d_grads, // the raw grads list, which will be modified after merged + const uint32_t* + d_index, // the storage position of d_keys, its length is len. + size_t len, // the number of raw input keys + const uint32_t* + d_fea_num_info, // prefix sum array, its length is uniq_len+1 + size_t uniq_len, // the number of unique keys + size_t& segments_num) { // the number of segment merged keys + + int dev_id = resource_->dev_id(gpu_num); + platform::CUDAPlace place = platform::CUDAPlace(dev_id); + platform::CUDADeviceGuard guard(dev_id); + auto stream = resource_->local_stream(gpu_num, 0); + + auto grad_dim = max_mf_dim_; + auto accessor_wrapper_ptr = + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + size_t grad_value_size = accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); + + auto d_buffer1 = memory::Alloc(place, sizeof(uint32_t) * len); + auto d_segments = reinterpret_cast(d_buffer1->ptr()); + auto d_buffer2 = memory::Alloc(place, sizeof(uint32_t) * len); + auto d_segments_offset = reinterpret_cast(d_buffer2->ptr()); + auto d_buffer3 = memory::Alloc(place, sizeof(uint32_t) * len); + auto d_segments_fea_num_info = reinterpret_cast(d_buffer3->ptr()); + auto d_buffer4 = memory::Alloc(place, sizeof(uint32_t) * len); + auto d_segments_fea_num_offset = + reinterpret_cast(d_buffer4->ptr()); + auto d_buffer5 = memory::Alloc(place, sizeof(uint32_t)); + auto d_segments_num = reinterpret_cast(d_buffer5->ptr()); + CUDA_CHECK(cudaMemsetAsync(d_segments_num, 0, sizeof(uint32_t), stream)); + + uint32_t segment_size = FLAGS_gpugraph_merge_grads_segment_size; + heter_comm_kernel_->split_segments(d_fea_num_info, + uniq_len, + d_segments, + d_segments_num, + segment_size, + stream); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + size_t temp_storage_bytes = 0; + PADDLE_ENFORCE_GPU_SUCCESS(cub::DeviceReduce::Sum( + NULL, temp_storage_bytes, d_segments, d_segments_num, uniq_len, stream)); + auto d_temp_storage = memory::Alloc(place, temp_storage_bytes); + PADDLE_ENFORCE_GPU_SUCCESS(cub::DeviceReduce::Sum(d_temp_storage->ptr(), + temp_storage_bytes, + d_segments, + d_segments_num, + uniq_len, + stream)); + CUDA_CHECK(cudaMemcpyAsync(&segments_num, + d_segments_num, + sizeof(uint32_t), + cudaMemcpyDeviceToHost, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + temp_storage_bytes = 0; + PADDLE_ENFORCE_GPU_SUCCESS(cub::DeviceScan::ExclusiveSum(NULL, + temp_storage_bytes, + d_segments, + d_segments_offset, + uniq_len, + stream)); + if (d_temp_storage->size() < temp_storage_bytes) { + d_temp_storage = NULL; + d_temp_storage = memory::Alloc(place, temp_storage_bytes); + } + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceScan::ExclusiveSum(d_temp_storage->ptr(), + temp_storage_bytes, + d_segments, + d_segments_offset, + uniq_len, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + heter_comm_kernel_->expand_segments(d_fea_num_info, + d_segments_offset, + uniq_len, + d_segments_fea_num_info, + segment_size, + stream); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceScan::ExclusiveSum(NULL, + temp_storage_bytes, + d_segments_fea_num_info, + d_segments_fea_num_offset, + segments_num, + stream)); + if (d_temp_storage->size() < temp_storage_bytes) { + d_temp_storage = NULL; + d_temp_storage = memory::Alloc(place, temp_storage_bytes); + } + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceScan::ExclusiveSum(d_temp_storage->ptr(), + temp_storage_bytes, + d_segments_fea_num_info, + d_segments_fea_num_offset, + segments_num, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + auto d_segments_keys = memory::Alloc(place, sizeof(KeyType) * segments_num); + auto d_segments_keys_ptr = reinterpret_cast(d_segments_keys->ptr()); + heter_comm_kernel_->shrink_keys(d_keys, + d_segments_fea_num_offset, + d_segments_keys_ptr, + segments_num, + stream); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + auto d_segment_grads = memory::Alloc(place, segments_num * grad_value_size); + auto d_segment_grads_ptr = reinterpret_cast(d_segment_grads->ptr()); + heter_comm_kernel_->merge_gradient(d_segments_keys_ptr, + d_segments_fea_num_offset, + d_segments_fea_num_info, d_index, (char*)d_grads, - (char*)d_merge_grads_ptr, - uniq_len, + (char*)d_segment_grads_ptr, + segments_num, + grad_dim, grad_value_size, merger_, stream, - feature_value_accessor_); + gpu_accessor_); PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + PADDLE_ENFORCE_GPU_SUCCESS(cudaMemcpyAsync(d_keys, + d_segments_keys_ptr, + sizeof(KeyType) * segments_num, + cudaMemcpyDeviceToDevice, + stream)); PADDLE_ENFORCE_GPU_SUCCESS(cudaMemcpyAsync(d_grads, - d_merge_grads_ptr, - grad_value_size * uniq_len, + d_segment_grads_ptr, + grad_value_size * segments_num, cudaMemcpyDeviceToDevice, stream)); PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); @@ -846,8 +1113,8 @@ void HeterComm::dynamic_merge_grad( template -void HeterComm::split_input_to_shard( + typename GPUAccessor> +void HeterComm::split_input_to_shard( KeyType* d_keys, int* d_idx_ptr, size_t len, @@ -869,15 +1136,12 @@ void HeterComm::split_input_to_shard( auto d_shard_index_tmp = memory::Alloc(place, len * sizeof(int)); int* d_shard_index_tmp_ptr = reinterpret_cast(d_shard_index_tmp->ptr()); - // int grid_size = (len - 1) / block_size_ + 1; - heter_comm_kernel_->fill_idx(d_idx_tmp_ptr, len, stream); heter_comm_kernel_->calc_shard_index( d_keys, len, d_shard_index_tmp_ptr, total_device, stream); size_t temp_storage_bytes; const int num_bits = 1 + log2i(total_device); - heter_comm_kernel_->sort_pairs(NULL, temp_storage_bytes, d_shard_index_tmp_ptr, @@ -890,7 +1154,6 @@ void HeterComm::split_input_to_shard( stream); auto d_temp_storage = memory::Alloc(place, temp_storage_bytes); - heter_comm_kernel_->sort_pairs(d_temp_storage->ptr(), temp_storage_bytes, d_shard_index_tmp_ptr, @@ -910,13 +1173,309 @@ void HeterComm::split_input_to_shard( template -void HeterComm::pull_sparse( + typename GPUAccessor> +void HeterComm::merge_keys( + int gpu_num, + const KeyType* d_keys, + size_t len, // input + KeyType* d_sorted_keys, // output + KeyType* d_merged_keys, // output + uint32_t* d_restore_idx, // output + size_t& uniq_len) { // output + int dev_id = resource_->dev_id(gpu_num); + platform::CUDAPlace place = platform::CUDAPlace(dev_id); + platform::CUDADeviceGuard guard(dev_id); + auto stream = resource_->local_stream(gpu_num, 0); + + size_t grad_dim = max_mf_dim_; + auto accessor_wrapper_ptr = + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + size_t grad_value_size = accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); + + auto d_fea_num_info = memory::Alloc(place, sizeof(uint32_t) * (len * 4 + 1)); + uint32_t* d_fea_num_info_ptr = + reinterpret_cast(d_fea_num_info->ptr()); + uint32_t* d_idx = (uint32_t*)&d_fea_num_info_ptr[len]; + uint32_t* d_index = (uint32_t*)&d_idx[len]; + uint32_t* d_offset = (uint32_t*)&d_index[len]; + uint32_t* d_merged_size = (uint32_t*)&d_offset[len]; + heter_comm_kernel_->fill_idx(d_idx, len, stream); + + size_t temp_storage_bytes; + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRadixSort::SortPairs(NULL, + temp_storage_bytes, + d_keys, + d_sorted_keys, + d_idx, + d_index, + len, + 0, + 8 * sizeof(KeyType), + stream)); + auto d_temp_storage = memory::Alloc(place, temp_storage_bytes); + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRadixSort::SortPairs(d_temp_storage->ptr(), + temp_storage_bytes, + d_keys, + d_sorted_keys, + d_idx, + d_index, + len, + 0, + 8 * sizeof(KeyType), + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + temp_storage_bytes = 0; + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRunLengthEncode::Encode(NULL, + temp_storage_bytes, + d_sorted_keys, + d_merged_keys, + d_fea_num_info_ptr, + d_merged_size, + len, + stream)); + if (d_temp_storage->size() < temp_storage_bytes) { + d_temp_storage = NULL; + d_temp_storage = memory::Alloc(place, temp_storage_bytes); + } + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRunLengthEncode::Encode(d_temp_storage->ptr(), + temp_storage_bytes, + d_sorted_keys, + d_merged_keys, + d_fea_num_info_ptr, + d_merged_size, + len, + stream)); + cudaMemcpyAsync((void*)&uniq_len, + d_merged_size, + sizeof(int), + cudaMemcpyDeviceToHost, + stream); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + temp_storage_bytes = 0; + PADDLE_ENFORCE_GPU_SUCCESS(cub::DeviceScan::ExclusiveSum(NULL, + temp_storage_bytes, + d_fea_num_info_ptr, + d_offset, + uniq_len, + stream)); + if (d_temp_storage->size() < temp_storage_bytes) { + d_temp_storage = NULL; + d_temp_storage = memory::Alloc(place, temp_storage_bytes); + } + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceScan::ExclusiveSum(d_temp_storage->ptr(), + temp_storage_bytes, + d_fea_num_info_ptr, + d_offset, + uniq_len, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + heter_comm_kernel_->fill_restore_idx(true, + len, + uniq_len, + d_merged_keys, + d_index, + d_offset, + d_fea_num_info_ptr, + d_restore_idx, + stream); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); +} + +template +void HeterComm::pull_merge_sparse( int num, KeyType* d_keys, float* d_vals, size_t len) { - if (len == 0) { - return; + int total_device = resource_->total_device(); + int dev_id = resource_->dev_id(num); + DevPlace place = DevPlace(dev_id); + AnyDeviceGuard guard(dev_id); + auto stream = resource_->local_stream(num, 0); + + int h_left[total_device]; // NOLINT + int h_right[total_device]; // NOLINT + + auto d_left = memory::Alloc(place, total_device * sizeof(int)); + auto d_right = memory::Alloc(place, total_device * sizeof(int)); + int* d_left_ptr = reinterpret_cast(d_left->ptr()); + int* d_right_ptr = reinterpret_cast(d_right->ptr()); + +#if defined(PADDLE_WITH_CUDA) + cudaMemsetAsync(d_left_ptr, -1, total_device * sizeof(int), stream); + cudaMemsetAsync(d_right_ptr, -1, total_device * sizeof(int), stream); + +#elif defined(PADDLE_WITH_XPU_KP) + // get XPUDeviceContext according to xpu place + paddle::platform::XPUDeviceContext xpu_dev_ctx(place); + auto xpu_context = xpu_dev_ctx.x_context(); + + int r = xpu::constant(xpu_context, d_left_ptr, total_device, -1); + PADDLE_ENFORCE_EQ(r, + XPU_SUCCESS, + platform::errors::External( + "XPU constant kernel return wrong value[%d %s]", + r, + XPUAPIErrorMsg[r])); + int r2 = xpu::constant(xpu_context, d_right_ptr, total_device, -1); + PADDLE_ENFORCE_EQ(r2, + XPU_SUCCESS, + platform::errors::External( + "XPU constant kernel return wrong value[%d %s]", + r2, + XPUAPIErrorMsg[r2])); +#endif + + auto accessor_wrapper_ptr = + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + size_t val_type_size = accessor_wrapper_ptr->GetPullValueSize(max_mf_dim_); + VLOG(3) << "pull_sparse len:" << len << " val_type_size: " << val_type_size; + auto d_sorted_keys = memory::Alloc(place, len * sizeof(KeyType)); + auto d_sorted_keys_ptr = reinterpret_cast(d_sorted_keys->ptr()); + auto d_merged_keys = memory::Alloc(place, len * sizeof(KeyType)); + auto d_merged_keys_ptr = reinterpret_cast(d_merged_keys->ptr()); + auto d_restore_idx = memory::Alloc(place, len * sizeof(uint32_t)); + auto d_restore_idx_ptr = reinterpret_cast(d_restore_idx->ptr()); + auto d_shard_keys = memory::Alloc(place, len * sizeof(KeyType)); + auto d_shard_keys_ptr = reinterpret_cast(d_shard_keys->ptr()); + auto d_shard_vals = memory::Alloc(place, len * val_type_size); + auto d_shard_vals_ptr = reinterpret_cast(d_shard_vals->ptr()); + + size_t uniq_len = 0; + merge_keys(num, + d_keys, + len, + d_sorted_keys_ptr, + d_merged_keys_ptr, + d_restore_idx_ptr, + uniq_len); + sync_stream(stream); + + auto d_idx = memory::Alloc(place, uniq_len * sizeof(int)); + auto d_idx_ptr = reinterpret_cast(d_idx->ptr()); + split_input_to_shard( + d_merged_keys_ptr, d_idx_ptr, uniq_len, d_left_ptr, d_right_ptr, num); + heter_comm_kernel_->fill_shard_key( + d_shard_keys_ptr, d_merged_keys_ptr, d_idx_ptr, uniq_len, stream); + sync_stream(stream); + + auto dst_place = platform::CPUPlace(); + auto src_place = place; + + memory_copy(dst_place, + h_left, + src_place, + d_left_ptr, + total_device * sizeof(int), + stream); + memory_copy(dst_place, + h_right, + src_place, + d_right_ptr, + total_device * sizeof(int), + stream); + + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + for (int i = 0; i < total_device; ++i) { + int shard_len = h_right[i] - h_left[i] + 1; + if (h_left[i] == -1 || h_right[i] == -1) { + continue; + } + create_storage( + num, i, shard_len * sizeof(KeyType), shard_len * val_type_size); + } + walk_to_dest(num, total_device, h_left, h_right, d_shard_keys_ptr, NULL); + } + + for (int i = 0; i < total_device; ++i) { + if (h_left[i] == -1) { + continue; + } + auto& node = path_[num][i].nodes_.back(); + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + sync_stream(node.in_stream); + } + AnyDeviceGuard guard(resource_->dev_id(i)); + ptr_tables_[i]->rwlock_->RDLock(); + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + ptr_tables_[i]->get(reinterpret_cast(node.key_storage), + node.val_storage, + h_right[i] - h_left[i] + 1, + resource_->remote_stream(i, num), + gpu_accessor_); + } else { + ptr_tables_[i]->get( + d_shard_keys_ptr + h_left[i], + reinterpret_cast(d_shard_vals_ptr) + h_left[i] * val_type_size, + h_right[i] - h_left[i] + 1, + resource_->remote_stream(i, num), + gpu_accessor_); + } } + for (int i = 0; i < total_device; ++i) { + sync_stream(resource_->remote_stream(i, num)); + if (h_left[i] == -1) { + continue; + } + ptr_tables_[i]->rwlock_->UNLock(); + } + + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + walk_to_src(num, + total_device, + h_left, + h_right, + reinterpret_cast(d_shard_vals_ptr), + val_type_size); + for (int i = 0; i < total_device; ++i) { + auto& node = path_[num][i].nodes_.front(); + sync_stream(node.out_stream); + } + } + + auto d_merged_vals = memory::Alloc(place, uniq_len * val_type_size); + auto d_merged_vals_ptr = reinterpret_cast(d_merged_vals->ptr()); + heter_comm_kernel_->dy_mf_fill_dvals(d_shard_vals_ptr, + d_merged_vals_ptr, + d_idx_ptr, + uniq_len, + val_type_size, + stream); + sync_stream(stream); + + heter_comm_kernel_->unpack_merged_vals(len, + d_keys, + d_merged_vals_ptr, + d_restore_idx_ptr, + d_vals, + val_type_size, + stream); + sync_stream(stream); + + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + for (int i = 0; i < total_device; ++i) { + if (h_left[i] == -1 || h_right[i] == -1) { + continue; + } + destroy_storage(num, i); + } + } +} +template +void HeterComm::pull_normal_sparse( + int num, KeyType* d_keys, float* d_vals, size_t len) { int total_device = resource_->total_device(); int dev_id = resource_->dev_id(num); DevPlace place = DevPlace(dev_id); @@ -960,8 +1519,8 @@ void HeterComm::pull_sparse( int* d_idx_ptr = reinterpret_cast(d_idx->ptr()); auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); - size_t val_type_size = accessor_wrapper_ptr->GetFeatureValueSize(max_mf_dim_); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + size_t val_type_size = accessor_wrapper_ptr->GetPullValueSize(max_mf_dim_); VLOG(3) << "pull_sparse len:" << len << " val_type_size: " << val_type_size; auto d_shard_keys = memory::Alloc(place, len * sizeof(KeyType)); KeyType* d_shard_keys_ptr = reinterpret_cast(d_shard_keys->ptr()); @@ -991,29 +1550,41 @@ void HeterComm::pull_sparse( total_device * sizeof(int), stream); - for (int i = 0; i < total_device; ++i) { - int shard_len = h_right[i] - h_left[i] + 1; - if (h_left[i] == -1 || h_right[i] == -1) { - continue; + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + for (int i = 0; i < total_device; ++i) { + int shard_len = h_right[i] - h_left[i] + 1; + if (h_left[i] == -1 || h_right[i] == -1) { + continue; + } + create_storage( + num, i, shard_len * sizeof(KeyType), shard_len * val_type_size); } - create_storage( - num, i, shard_len * sizeof(KeyType), shard_len * val_type_size); + walk_to_dest(num, total_device, h_left, h_right, d_shard_keys_ptr, NULL); } - walk_to_dest(num, total_device, h_left, h_right, d_shard_keys_ptr, NULL); - for (int i = 0; i < total_device; ++i) { if (h_left[i] == -1) { continue; } auto& node = path_[num][i].nodes_.back(); - sync_stream(node.in_stream); + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + sync_stream(node.in_stream); + } AnyDeviceGuard guard(resource_->dev_id(i)); ptr_tables_[i]->rwlock_->RDLock(); - ptr_tables_[i]->get(reinterpret_cast(node.key_storage), - node.val_storage, - h_right[i] - h_left[i] + 1, - resource_->remote_stream(i, num), - feature_value_accessor_); + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + ptr_tables_[i]->get(reinterpret_cast(node.key_storage), + node.val_storage, + h_right[i] - h_left[i] + 1, + resource_->remote_stream(i, num), + gpu_accessor_); + } else { + ptr_tables_[i]->get( + d_shard_keys_ptr + h_left[i], + reinterpret_cast(d_shard_vals_ptr) + h_left[i] * val_type_size, + h_right[i] - h_left[i] + 1, + resource_->remote_stream(i, num), + gpu_accessor_); + } } for (int i = 0; i < total_device; ++i) { @@ -1023,31 +1594,46 @@ void HeterComm::pull_sparse( } ptr_tables_[i]->rwlock_->UNLock(); } - walk_to_src(num, - total_device, - h_left, - h_right, - reinterpret_cast(d_shard_vals_ptr), - val_type_size); - for (int i = 0; i < total_device; ++i) { - auto& node = path_[num][i].nodes_.front(); - sync_stream(node.out_stream); + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + walk_to_src(num, + total_device, + h_left, + h_right, + reinterpret_cast(d_shard_vals_ptr), + val_type_size); + for (int i = 0; i < total_device; ++i) { + auto& node = path_[num][i].nodes_.front(); + sync_stream(node.out_stream); + } } - heter_comm_kernel_->dy_mf_fill_dvals(d_shard_vals_ptr, - d_vals, - d_idx_ptr, - len, - val_type_size, - stream, - feature_value_accessor_); + heter_comm_kernel_->dy_mf_fill_dvals( + d_shard_vals_ptr, d_vals, d_idx_ptr, len, val_type_size, stream); sync_stream(stream); - for (int i = 0; i < total_device; ++i) { - if (h_left[i] == -1 || h_right[i] == -1) { - continue; + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + for (int i = 0; i < total_device; ++i) { + if (h_left[i] == -1 || h_right[i] == -1) { + continue; + } + destroy_storage(num, i); } - destroy_storage(num, i); + } +} + +template +void HeterComm::pull_sparse( + int num, KeyType* d_keys, float* d_vals, size_t len) { + if (len == 0) { + return; + } + if (!FLAGS_gpugraph_dedup_pull_push_mode) { + pull_merge_sparse(num, d_keys, d_vals, len); + } else { + pull_normal_sparse(num, d_keys, d_vals, len); } } @@ -1055,9 +1641,9 @@ void HeterComm::pull_sparse( template + typename GPUAccessor> template -void HeterComm::push_sparse( +void HeterComm::push_sparse( int dev_num, KeyType* d_keys, float* d_grads, @@ -1071,7 +1657,7 @@ void HeterComm::push_sparse( int dev_id = resource_->dev_id(dev_num); auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); size_t grad_value_size = accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); DevPlace place = DevPlace(dev_id); AnyDeviceGuard guard(dev_id); @@ -1116,13 +1702,30 @@ void HeterComm::push_sparse( auto d_shard_keys = memory::Alloc(place, len * sizeof(KeyType)); KeyType* d_shard_keys_ptr = reinterpret_cast(d_shard_keys->ptr()); + float* d_shard_grads_ptr; auto d_shard_grads = memory::Alloc(place, len * grad_value_size); - float* d_shard_grads_ptr = reinterpret_cast(d_shard_grads->ptr()); + d_shard_grads_ptr = reinterpret_cast(d_shard_grads->ptr()); int uniq_len = len; - dynamic_merge_grad(dev_num, d_keys, d_grads, len, uniq_len); - - int grid_size = (uniq_len - 1) / block_size_ + 1; + if (!FLAGS_gpugraph_dedup_pull_push_mode) { + size_t segment_len = 0; + if (FLAGS_gpugraph_enable_segment_merge_grads) { + // do two gradient merge + // 1st. do segmented gradient merge + // 2nd. do global gradient merge + dynamic_merge_grad( + dev_num, d_keys, d_grads, len, uniq_len, segment_len, true); + len = segment_len; + uniq_len = 0; + segment_len = 0; + dynamic_merge_grad( + dev_num, d_keys, d_grads, len, uniq_len, segment_len, false); + } else { + // Perform gradient merge only once + dynamic_merge_grad( + dev_num, d_keys, d_grads, len, uniq_len, segment_len, false); + } + } split_input_to_shard( d_keys, d_idx_ptr, uniq_len, d_left_ptr, d_right_ptr, dev_num); @@ -1135,7 +1738,7 @@ void HeterComm::push_sparse( uniq_len, grad_value_size, stream, - feature_value_accessor_); + gpu_accessor_); sync_stream(stream); @@ -1154,37 +1757,50 @@ void HeterComm::push_sparse( total_device * sizeof(int), stream); - for (int i = 0; i < total_device; ++i) { - int shard_len = h_right[i] - h_left[i] + 1; - if (h_left[i] == -1 || h_right[i] == -1) { - continue; + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + for (int i = 0; i < total_device; ++i) { + int shard_len = h_right[i] - h_left[i] + 1; + if (h_left[i] == -1 || h_right[i] == -1) { + continue; + } + create_storage( + dev_num, i, shard_len * sizeof(KeyType), shard_len * grad_value_size); } - create_storage( - dev_num, i, shard_len * sizeof(KeyType), shard_len * grad_value_size); - } - walk_to_dest(dev_num, - total_device, - h_left, - h_right, - d_shard_keys_ptr, - reinterpret_cast(d_shard_grads_ptr), - grad_value_size); + walk_to_dest(dev_num, + total_device, + h_left, + h_right, + d_shard_keys_ptr, + reinterpret_cast(d_shard_grads_ptr), + grad_value_size); + } for (int i = 0; i < total_device; ++i) { if (h_left[i] == -1 || h_right[i] == -1) { continue; } auto& node = path_[dev_num][i].nodes_.back(); - sync_stream(node.in_stream); + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + sync_stream(node.in_stream); + } AnyDeviceGuard guard(resource_->dev_id(i)); ptr_tables_[i]->rwlock_->WRLock(); - ptr_tables_[i]->update(reinterpret_cast(node.key_storage), - node.val_storage, - h_right[i] - h_left[i] + 1, - sgd, - resource_->remote_stream(i, dev_num)); + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + ptr_tables_[i]->update(reinterpret_cast(node.key_storage), + node.val_storage, + h_right[i] - h_left[i] + 1, + sgd, + resource_->remote_stream(i, dev_num)); + } else { + ptr_tables_[i]->update(d_shard_keys_ptr + h_left[i], + reinterpret_cast(d_shard_grads_ptr) + + grad_value_size * h_left[i], + h_right[i] - h_left[i] + 1, + sgd, + resource_->remote_stream(i, dev_num)); + } } for (int i = 0; i < total_device; ++i) { @@ -1198,11 +1814,13 @@ void HeterComm::push_sparse( } } - for (int i = 0; i < total_device; ++i) { - if (h_left[i] == -1 || h_right[i] == -1) { - continue; + if (!FLAGS_gpugraph_enable_gpu_direct_access) { + for (int i = 0; i < total_device; ++i) { + if (h_left[i] == -1 || h_right[i] == -1) { + continue; + } + destroy_storage(dev_num, i); } - destroy_storage(dev_num, i); } } @@ -1210,8 +1828,8 @@ void HeterComm::push_sparse( template -void HeterComm::push_sparse( + typename GPUAccessor> +void HeterComm::push_sparse( int dev_num, KeyType* d_keys, GradType* d_grads, size_t len) { if (len == 0) { return; @@ -1269,8 +1887,6 @@ void HeterComm::push_sparse( int uniq_len = len; merge_grad(dev_num, d_keys, d_grads, len, uniq_len); - // int grid_size = (uniq_len - 1) / block_size_ + 1; - split_input_to_shard( d_keys, d_idx_ptr, uniq_len, d_left_ptr, d_right_ptr, dev_num); @@ -1351,9 +1967,9 @@ void HeterComm::push_sparse( template + typename GPUAccessor> template -void HeterComm::update_one_table( +void HeterComm::update_one_table( int gpu_num, KeyType* d_keys, GradType* d_grads, @@ -1375,9 +1991,9 @@ void HeterComm::update_one_table( template + typename GPUAccessor> template -void HeterComm::push_sparse_multi_node( +void HeterComm::push_sparse_multi_node( int gpu_num, KeyType* d_keys, GradType* d_grads, @@ -1407,8 +2023,8 @@ void HeterComm::push_sparse_multi_node( template -int HeterComm::gather_one_node_grad( + typename GPUAccessor> +int HeterComm::gather_one_node_grad( int gpu_num, KeyType* d_keys, GradType* d_grads, int len) { int total_gpu = resource_->total_device(); int dev_id = resource_->dev_id(gpu_num); @@ -1493,7 +2109,6 @@ int HeterComm::gather_one_node_grad( cudaMemcpy( h_right, d_right_ptr, total_gpu * sizeof(int), cudaMemcpyDeviceToHost); - // int grid_size = (h_node_len[i] - 1) / block_size_ + 1; heter_comm_kernel_->fill_shard_grads(storage.local_keys + merge_num, storage.all_keys + index, storage.local_grads + merge_num, @@ -1512,8 +2127,8 @@ int HeterComm::gather_one_node_grad( template -int HeterComm::gather_multi_node_grad( + typename GPUAccessor> +int HeterComm::gather_multi_node_grad( int gpu_num, KeyType* d_keys, GradType* d_grads, int len) { int dev_id = resource_->dev_id(gpu_num); auto& storage = storage_[gpu_num]; @@ -1586,8 +2201,8 @@ int HeterComm::gather_multi_node_grad( template -void HeterComm::end_pass() { + typename GPUAccessor> +void HeterComm::end_pass() { int total_device = resource_->total_device(); std::vector threads; @@ -1608,10 +2223,127 @@ void HeterComm::end_pass() { } } -// template -// void HeterComm::dump_to_cpu(int -// index) { +#if defined(PADDLE_WITH_CUDA) +template +int HeterComm::dedup_keys_and_fillidx( + const int gpu_id, + const int total_fea_num, + const KeyType* d_keys, // input + KeyType* d_merged_keys, // output + KeyType* d_sorted_keys, + uint32_t* d_restore_idx, + uint32_t* d_sorted_idx, + uint32_t* d_offset, + uint32_t* d_merged_cnts, + bool filter_zero) { + int dev_id = resource_->dev_id(gpu_id); + platform::CUDAPlace place = platform::CUDAPlace(dev_id); + platform::CUDADeviceGuard guard(dev_id); + auto stream = resource_->local_stream(gpu_id, 0); + + assert(total_fea_num > 0); + int merged_size = 0; + size_t byte_size = sizeof(uint32_t) * (total_fea_num + 1); + + auto d_index_ptr = memory::Alloc(place, byte_size); + uint32_t* d_index_in = reinterpret_cast(d_index_ptr->ptr()); + int* d_merged_size = reinterpret_cast(&d_index_in[total_fea_num]); + + heter_comm_kernel_->fill_idx(d_index_in, total_fea_num, stream); + + void* d_buf = NULL; + size_t temp_storage_bytes = 0; + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRadixSort::SortPairs(NULL, + temp_storage_bytes, + d_keys, + d_sorted_keys, + d_index_in, + d_sorted_idx, + total_fea_num, + 0, + 8 * sizeof(KeyType), + stream, + false)); + auto d_cache_ptr = memory::Alloc(place, temp_storage_bytes); + d_buf = reinterpret_cast(d_cache_ptr->ptr()); + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRadixSort::SortPairs(d_buf, + temp_storage_bytes, + d_keys, + d_sorted_keys, + d_index_in, + d_sorted_idx, + total_fea_num, + 0, + 8 * sizeof(KeyType), + stream, + false)); + + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRunLengthEncode::Encode(NULL, + temp_storage_bytes, + d_sorted_keys, + d_merged_keys, + d_merged_cnts, + d_merged_size, + total_fea_num, + stream)); + if (d_cache_ptr->size() < temp_storage_bytes) { + d_cache_ptr = NULL; + d_cache_ptr = memory::Alloc(place, temp_storage_bytes); + } + d_buf = reinterpret_cast(d_cache_ptr->ptr()); + PADDLE_ENFORCE_GPU_SUCCESS( + cub::DeviceRunLengthEncode::Encode(d_buf, + temp_storage_bytes, + d_sorted_keys, + d_merged_keys, + d_merged_cnts, + d_merged_size, + total_fea_num, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaMemcpyAsync((void*)&merged_size, + (void*)d_merged_size, + sizeof(int), + cudaMemcpyDeviceToHost, + stream)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + PADDLE_ENFORCE_GPU_SUCCESS(cub::DeviceScan::ExclusiveSum( + NULL, temp_storage_bytes, d_merged_cnts, d_offset, merged_size, stream)); + if (d_cache_ptr->size() < temp_storage_bytes) { + d_cache_ptr = NULL; + d_cache_ptr = memory::Alloc(place, temp_storage_bytes); + } + d_buf = reinterpret_cast(d_cache_ptr->ptr()); + PADDLE_ENFORCE_GPU_SUCCESS(cub::DeviceScan::ExclusiveSum( + d_buf, temp_storage_bytes, d_merged_cnts, d_offset, merged_size, stream)); + + if (filter_zero) { + cudaMemsetAsync(d_restore_idx, 0, total_fea_num * sizeof(uint32_t), stream); + } + // fill restore idx [1,3,5,2,4,6] = [1,2,1,3,2,1] + heter_comm_kernel_->fill_restore_idx(filter_zero, + total_fea_num, + merged_size, + d_merged_keys, + d_sorted_idx, + d_offset, + d_merged_cnts, + d_restore_idx, + stream); + + PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream)); + + return merged_size; +} +#endif +// template +// void HeterComm::dump_to_cpu(int index) { // auto stream = resource_->local_stream(index, 0); // int dev_id = resource_->dev_id(index); // platform::CUDADeviceGuard guard(dev_id); diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.cu b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.cu index ebf7e76527af0e31a66d598aa66f990e014c8138..c885b77d2e1da8b1fbace3a4302ad0a0cf655516 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.cu +++ b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.cu @@ -128,69 +128,177 @@ __global__ void fill_dvals_kernel(ValType* d_shard_vals, } } -template -__global__ void dy_mf_fill_shard_grads_kernel( - KeyType* d_shard_keys, - KeyType* d_keys, - float* d_shard_grads, - float* d_grads, - T* idx, - size_t len, - size_t grad_value_size, - FVAccessor feature_value_accessor) { +template +__global__ void merge_gradients_basic_kernel(const KeyType* d_keys, + const uint32_t* offset, + const uint32_t* fea_num, + const uint32_t* index, + const char* input, + char* output, + int n, + size_t grad_value_size, + DynamicGradMerger& merger, + GPUAccessor& gpu_accessor) { const size_t i = blockIdx.x * blockDim.x + threadIdx.x; - if (i < len) { - d_shard_keys[i] = d_keys[idx[i]]; - float* cur = (float*)((char*)d_shard_grads + i * grad_value_size); - float* shard_val = - (float*)((char*)d_grads + uint64_t(idx[i]) * grad_value_size); - feature_value_accessor.PushValueFill(cur, shard_val); + if (i < n) { + uint32_t start = offset[i]; + uint32_t num = fea_num[i]; + int ori_index = index[start]; + float* out = (float*)(output + i * grad_value_size); + float* in = (float*)(input + size_t(ori_index) * grad_value_size); + merger.update_basic(out, in, gpu_accessor); + KeyType key = d_keys[i]; + if (key != 0) { + for (int j = 1; j < num; ++j) { + ori_index = index[start + j]; + in = (float*)(input + size_t(ori_index) * grad_value_size); + merger.merge_basic(out, in, gpu_accessor); + } + } } } -template -__global__ void merge_gradients_kernel(const uint32_t* offset, - const uint32_t* fea_num, - const uint32_t* index, - const char* input, - char* output, - int n, - size_t grad_value_size, - DynamicGradMerger& merger, - FVAccessor& feature_value_accessor) { +template +__global__ void merge_gradients_embedx_kernel(const KeyType* d_keys, + const uint32_t* offset, + const uint32_t* fea_num, + const uint32_t* index, + const char* input, + char* output, + int n, + size_t grad_dim, + size_t grad_value_size, + DynamicGradMerger& merger, + GPUAccessor& gpu_accessor) { const size_t i = blockIdx.x * blockDim.x + threadIdx.x; + if (i < n) { - uint32_t start = offset[i]; - uint32_t num = fea_num[i]; + size_t value_idx = i / grad_dim; + size_t field_idx = i % grad_dim; + uint32_t start = offset[value_idx]; + uint32_t num = fea_num[value_idx]; int ori_index = index[start]; - float* out = (float*)(output + i * grad_value_size); float* in = (float*)(input + size_t(ori_index) * grad_value_size); - merger.update_one(out, in, feature_value_accessor); - for (int j = 1; j < num; ++j) { - ori_index = index[start + j]; - in = (float*)(input + size_t(ori_index) * grad_value_size); - merger.merge_one(out, in, feature_value_accessor); + float* out = (float*)(output + value_idx * grad_value_size); + merger.update_embedx(out, in, field_idx, gpu_accessor); + KeyType key = d_keys[value_idx]; + if (key != 0) { + for (int j = 1; j < num; ++j) { + int ori_index = index[start + j]; + float* in = (float*)(input + size_t(ori_index) * grad_value_size); + merger.merge_embedx(out, in, field_idx, gpu_accessor); + } } } } -template -__global__ void dy_mf_fill_dvals_kernel(float* d_shard_vals, - float* d_vals, - T* idx, - size_t len, - size_t val_size, - FVAccessor feature_value_accessor) { +__global__ void split_segments_kernel(const uint32_t* d_fea_num_info, + size_t n, + uint32_t* d_segments, + uint32_t* d_segments_num, + uint32_t segment_size) { + const size_t tx = blockIdx.x * blockDim.x + threadIdx.x; + if (tx >= n) { + return; + } + + auto fea_num = d_fea_num_info[tx]; + auto seg_num = (uint32_t)((fea_num - 1) / segment_size + 1); + d_segments[tx] = seg_num; +} + +__global__ void expand_segments_kernel(const uint32_t* d_fea_num_info, + const uint32_t* d_segments_offset, + size_t n, + uint32_t* d_segments_fea_num_info, + uint32_t segment_size) { + const size_t tx = blockIdx.x * blockDim.x + threadIdx.x; + if (tx >= n) { + return; + } + + auto fea_num = d_fea_num_info[tx]; + auto seg_num = (uint32_t)((fea_num - 1) / segment_size + 1); + auto start_pos = d_segments_offset[tx]; + auto remains = fea_num; + int cur_seg_size = 0; + for (size_t i = 0; i < seg_num; ++i) { + if (remains >= segment_size) { + cur_seg_size = segment_size; + } else { + cur_seg_size = remains; + } + d_segments_fea_num_info[start_pos + i] = cur_seg_size; + remains -= cur_seg_size; + } +} + +template +__global__ void shrink_keys_kernel(const KeyType* d_keys, + const uint32_t* d_segments_offset, + KeyType* d_segments_keys, + size_t n) { + const size_t tx = blockIdx.x * blockDim.x + threadIdx.x; + if (tx >= n) { + return; + } + + d_segments_keys[tx] = d_keys[d_segments_offset[tx]]; +} + +template +__global__ void unpack_merged_vals_kernel(const KeyType* d_keys, + const float* d_merged_vals, + const uint32_t* d_restored_idx, + float* d_out, + size_t val_size, + const size_t n) { + const size_t tx = blockIdx.x * blockDim.x + threadIdx.x; + if (tx >= n) { + return; + } + + size_t src_val_idx = 0; + const KeyType& key = d_keys[tx]; + if (key != 0) { + src_val_idx = d_restored_idx[tx]; + } + + uint64_t dst_offset = uint64_t(tx) * val_size; + float* dst = (float*)((char*)d_out + dst_offset); + float* src_val = + (float*)((char*)d_merged_vals + uint64_t(src_val_idx) * val_size); + + size_t n_float = val_size / sizeof(float); + for (size_t k = 0; k < n_float; ++k) { + dst[k] = src_val[k]; + } +} + +template +__global__ void scatter_dvals_by_unit_kernel(TUnit* d_dest_vals, + const TUnit* d_src_vals, + T* idx, + size_t len, + size_t val_size_unit) { const size_t i = blockIdx.x * blockDim.x + threadIdx.x; if (i < len) { - uint64_t new_offset = uint64_t(idx[i]) * val_size; - float* cur = (float*)((char*)d_vals + new_offset); - float* shard_val = (float*)((char*)d_shard_vals + uint64_t(i) * val_size); - int mf_dim = int( - shard_val[feature_value_accessor.common_feature_value.MfDimIndex()]); + size_t pos = idx[i / val_size_unit] * val_size_unit + (i % val_size_unit); + d_dest_vals[i] = d_src_vals[pos]; + } +} - feature_value_accessor.FeatureValueFill(cur, shard_val, mf_dim); +template +__global__ void gather_dvals_by_unit_kernel(TUnit* d_dest_vals, + const TUnit* d_src_vals, + T* idx, + size_t len, + const size_t val_size_unit) { + const size_t i = blockIdx.x * blockDim.x + threadIdx.x; + if (i < len) { + size_t pos = idx[i / val_size_unit] * val_size_unit + (i % val_size_unit); + d_dest_vals[pos] = d_src_vals[i]; } } @@ -325,43 +433,47 @@ void HeterCommKernel::reduce_by_key(void* d_temp_storage, template -void HeterCommKernel::dy_mf_fill_shard_grads( - KeyType* d_shard_keys, - KeyType* d_keys, - float* d_shard_grads, - float* d_grads, - T* idx, - long long len, - size_t grad_value_size, - const StreamType& stream, - FVAccessor& feature_value_accessor) { + typename GPUAccessor> +void HeterCommKernel::dy_mf_fill_shard_grads(KeyType* d_shard_keys, + KeyType* d_keys, + float* d_shard_grads, + float* d_grads, + T* idx, + long long len, + size_t grad_value_size, + const StreamType& stream, + GPUAccessor& gpu_accessor) { int grid_size = (len - 1) / block_size_ + 1; size_t c_len = (size_t)len; - dy_mf_fill_shard_grads_kernel<<>>( - d_shard_keys, - d_keys, - d_shard_grads, - d_grads, - idx, - c_len, - grad_value_size, - feature_value_accessor); + + const size_t grad_value_size_float = grad_value_size / sizeof(float); + // d_keys to d_shard_keys + fill_shard_key_kernel<<>>( + d_shard_keys, d_keys, idx, c_len); + + CHECK((grad_value_size % sizeof(float)) == 0); + size_t N = len * grad_value_size_float; + grid_size = (N - 1) / block_size_ + 1; + scatter_dvals_by_unit_kernel<<>>( + d_shard_grads, d_grads, idx, N, grad_value_size_float); } -template -void HeterCommKernel::merge_gradient(const uint32_t* offset, +template +void HeterCommKernel::merge_gradient(const KeyType* d_keys, + const uint32_t* offset, const uint32_t* fea_num, const uint32_t* index, const char* input, char* output, int n, + size_t grad_dim, size_t grad_value_size, - DynamicGradMerger& merger_, + DynamicGradMerger& merger, const StreamType& stream, - FVAccessor& feature_value_accessor) { - int grid_size = (n - 1) / block_size_ + 1; - merge_gradients_kernel<<>>( + GPUAccessor& gpu_accessor) { + int grid_size1 = (n - 1) / block_size_ + 1; + merge_gradients_basic_kernel<<>>( + d_keys, offset, fea_num, index, @@ -369,22 +481,189 @@ void HeterCommKernel::merge_gradient(const uint32_t* offset, output, n, grad_value_size, - merger_, - feature_value_accessor); + merger, + gpu_accessor); + if (grad_dim > 0) { + int grid_size2 = (n * grad_dim - 1) / block_size_ + 1; + merge_gradients_embedx_kernel<<>>( + d_keys, + offset, + fea_num, + index, + input, + output, + n * grad_dim, + grad_dim, + grad_value_size, + merger, + gpu_accessor); + } } -template +template void HeterCommKernel::dy_mf_fill_dvals(float* d_shard_vals, float* d_vals, T* idx, long long len, size_t val_size, - const StreamType& stream, - FVAccessor& feature_value_accessor) { - int grid_size = (len - 1) / block_size_ + 1; - size_t c_len = (size_t)len; - dy_mf_fill_dvals_kernel<<>>( - d_shard_vals, d_vals, idx, c_len, val_size, feature_value_accessor); + const StreamType& stream) { + const size_t val_size_float = val_size / sizeof(float); + CHECK((val_size % sizeof(float)) == 0); + size_t N = len * val_size_float; + const int grid_size = (N - 1) / block_size_ + 1; + // fill by float, d_shard_vals to d_vals + gather_dvals_by_unit_kernel<<>>( + d_vals, d_shard_vals, idx, N, val_size_float); +} + +template +void HeterCommKernel::split_segments(const uint32_t* d_fea_num_info, + size_t n, + uint32_t* d_segments, + uint32_t* d_segments_num, + size_t segment_size, + const StreamType& stream) { + int grid_size = (n - 1) / block_size_ + 1; + split_segments_kernel<<>>( + d_fea_num_info, n, d_segments, d_segments_num, segment_size); +} + +template +void HeterCommKernel::expand_segments(const uint32_t* d_fea_num_info, + const uint32_t* d_segments_offset, + size_t n, + uint32_t* d_segments_fea_num_info, + uint32_t segment_size, + const StreamType& stream) { + int grid_size = (n - 1) / block_size_ + 1; + expand_segments_kernel<<>>( + d_fea_num_info, + d_segments_offset, + n, + d_segments_fea_num_info, + segment_size); +} + +template +void HeterCommKernel::shrink_keys(const KeyType* d_keys, + const uint32_t* d_segments_offset, + KeyType* d_segments_keys, + size_t n, + const StreamType& stream) { + int grid_size = (n - 1) / block_size_ + 1; + shrink_keys_kernel<<>>( + d_keys, d_segments_offset, d_segments_keys, n); +} +template +__global__ void kernel_fill_restore_idx(const size_t N, + const T* d_sorted_idx, + const T* d_offset, + const T* d_merged_cnts, + T* d_restore_idx) { + const size_t i = blockIdx.x * blockDim.x + threadIdx.x; + if (i < N) { + const T& off = d_offset[i]; + const T& num = d_merged_cnts[i]; + for (size_t k = 0; k < num; ++k) { + d_restore_idx[d_sorted_idx[off + k]] = i; + } + } +} +template +__global__ void kernel_fill_restore_idx_filter_zero(const size_t N, + const KeyType* d_keys, + const T* d_sorted_idx, + const T* d_offset, + const T* d_merged_cnts, + T* d_restore_idx) { + const size_t i = blockIdx.x * blockDim.x + threadIdx.x; + if (i < N) { + if (d_keys[i] == 0) { + return; + } + const T& off = d_offset[i]; + const T& num = d_merged_cnts[i]; + for (size_t k = 0; k < num; ++k) { + d_restore_idx[d_sorted_idx[off + k]] = i; + } + } +} +template +__global__ void kernel_fill_restore_idx_by_search(const size_t N, + const T* d_sorted_idx, + const size_t merge_num, + const T* d_offset, + T* d_restore_idx) { + const size_t i = blockIdx.x * blockDim.x + threadIdx.x; + if (i < N) { + if (i < d_offset[1]) { + d_restore_idx[d_sorted_idx[i]] = 0; + return; + } + int high = merge_num - 1; + int low = 1; + while (low < high) { + int mid = (low + high) / 2; + if (i < d_offset[mid + 1]) { + high = mid; + } else { + low = mid + 1; + } + } + d_restore_idx[d_sorted_idx[i]] = low; + } +} +template +void HeterCommKernel::fill_restore_idx(bool filter_zero, + const size_t total_num, + const size_t merge_size, + const KeyType* d_keys, + const uint32_t* d_sorted_idx, + const uint32_t* d_offset, + const uint32_t* d_merged_cnts, + uint32_t* d_restore_idx, + const StreamType& stream) { + // fill restore idx [1,3,5,2,4,6] = [1,2,1,3,2,1] + if (merge_size * 3 > total_num) { + // repetition rate is not very high + size_t grid_size = (merge_size - 1) / block_size_ + 1; + if (filter_zero) { + kernel_fill_restore_idx_filter_zero<<>>(merge_size, + d_keys, + d_sorted_idx, + d_offset, + d_merged_cnts, + d_restore_idx); + } else { + kernel_fill_restore_idx<<>>( + merge_size, d_sorted_idx, d_offset, d_merged_cnts, d_restore_idx); + } + } else { + size_t grid_size = (total_num - 1) / block_size_ + 1; + // mid search + kernel_fill_restore_idx_by_search<<>>( + total_num, d_sorted_idx, merge_size, d_offset, d_restore_idx); + } +} +template +void HeterCommKernel::unpack_merged_vals(size_t n, + const KeyType* d_keys, + const void* d_merged_vals, + const uint32_t* d_restore_idx, + void* d_vals, + size_t val_size, + const StreamType& stream) { + int grid_size = (n - 1) / block_size_ + 1; + unpack_merged_vals_kernel<<>>( + d_keys, + (const float*)d_merged_vals, + d_restore_idx, + (float*)d_vals, + val_size, + n); } template void HeterCommKernel::fill_idx( @@ -491,43 +770,127 @@ template void HeterCommKernel::reduce_by_key< cudaStream_t stream, bool debug_synchronous); -template void -HeterCommKernel::dy_mf_fill_shard_grads( - unsigned long* d_shard_keys, - unsigned long* d_keys, - float* d_shard_grads, - float* d_grads, - int* idx, - long long len, - size_t grad_value_size, - const cudaStream_t& stream, - CommonFeatureValueAccessor& feature_value_accessor); +template void HeterCommKernel::dy_mf_fill_shard_grads< + unsigned long, + int, + cudaStream_t, + CommonFeatureValueAccessor>(unsigned long* d_shard_keys, + unsigned long* d_keys, + float* d_shard_grads, + float* d_grads, + int* idx, + long long len, + size_t grad_value_size, + const cudaStream_t& stream, + CommonFeatureValueAccessor& gpu_accessor); -template void -HeterCommKernel::merge_gradient( - const uint32_t* offset, - const uint32_t* fea_num, - const uint32_t* index, - const char* input, - char* output, - int n, - size_t grad_value_size, - DynamicGradMerger& merger_, - const cudaStream_t& stream, - CommonFeatureValueAccessor& feature_value_accessor); +template void HeterCommKernel:: + merge_gradient( + const uint32_t* d_keys, + const uint32_t* offset, + const uint32_t* fea_num, + const uint32_t* index, + const char* input, + char* output, + int n, + size_t grad_dim, + size_t grad_value_size, + DynamicGradMerger& merger_, + const cudaStream_t& stream, + CommonFeatureValueAccessor& gpu_accessor); template void HeterCommKernel:: - dy_mf_fill_dvals( - float* d_shard_vals, - float* d_vals, - int* idx, - long long len, - size_t val_size, + merge_gradient( + const uint64_t* d_keys, + const uint32_t* offset, + const uint32_t* fea_num, + const uint32_t* index, + const char* input, + char* output, + int n, + size_t grad_dim, + size_t grad_value_size, + DynamicGradMerger& merger_, const cudaStream_t& stream, - CommonFeatureValueAccessor& feature_value_accessor); + CommonFeatureValueAccessor& gpu_accessor); + +template void HeterCommKernel::dy_mf_fill_dvals( + float* d_shard_vals, + float* d_vals, + int* idx, + long long len, + size_t val_size, + const cudaStream_t& stream); + +template void HeterCommKernel::split_segments( + const uint32_t* d_fea_num_info, + size_t n, + uint32_t* d_segment, + uint32_t* d_segments_num, + size_t segment_size, + const cudaStream_t& stream); + +template void HeterCommKernel::expand_segments( + const uint32_t* d_fea_num_info, + const uint32_t* d_segments_offset, + size_t n, + uint32_t* d_segments_fea_num_info, + uint32_t segment_size, + const cudaStream_t& stream); + +template void HeterCommKernel::shrink_keys( + const uint32_t* d_keys, + const uint32_t* d_segments_offset, + uint32_t* d_segments_keys, + size_t segment_num, + const cudaStream_t& stream); + +template void HeterCommKernel::shrink_keys( + const uint64_t* d_keys, + const uint32_t* d_segments, + uint64_t* d_segments_keys, + size_t total_segment_num, + const cudaStream_t& stream); + +template void HeterCommKernel::fill_restore_idx( + bool filter_zero, + const size_t total_num, + const size_t merge_size, + const uint64_t* d_keys, + const uint32_t* d_sorted_idx, + const uint32_t* d_offset, + const uint32_t* d_merged_cnts, + uint32_t* d_restore_idx, + const cudaStream_t& stream); + +template void HeterCommKernel::fill_restore_idx( + bool filter_zero, + const size_t total_num, + const size_t merge_size, + const uint32_t* d_keys, + const uint32_t* d_sorted_idx, + const uint32_t* d_offset, + const uint32_t* d_merged_cnts, + uint32_t* d_restore_idx, + const cudaStream_t& stream); + +template void HeterCommKernel::unpack_merged_vals( + size_t n, + const uint64_t* d_keys, + const void* d_merged_vals, + const uint32_t* d_restore_idx, + void* d_vals, + size_t val_size, + const cudaStream_t& stream); + +template void HeterCommKernel::unpack_merged_vals( + size_t n, + const uint32_t* d_keys, + const void* d_merged_vals, + const uint32_t* d_restore_idx, + void* d_vals, + size_t val_size, + const cudaStream_t& stream); #endif } // namespace framework diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h index 57f0aff4b6e56bfcf50e62d4f2c84ccb30aed0b1..affde16713c627c0bc23ae6596f1a8d81469d251 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h @@ -41,16 +41,54 @@ struct DynamicGradMerger { return out; } - template - __device__ __forceinline__ void update_one( - float* output, const float* input, FVAccessor& feature_value_accessor) { - feature_value_accessor.PushValueFill(output, input); + template + __device__ __forceinline__ void update_one(float* output, + const float* input, + GPUAccessor& gpu_accessor) { + gpu_accessor.PushValueFill(output, input); } - template - __device__ __forceinline__ void merge_one( - float* output, const float* input, FVAccessor& feature_value_accessor) { - feature_value_accessor.MergePushValue(output, input); + template + __device__ __forceinline__ void merge_one(float* output, + const float* input, + GPUAccessor& gpu_accessor) { + gpu_accessor.MergePushValue(output, input); + } + + template + __device__ __forceinline__ void update_basic(float* output, + const float* input, + GPUAccessor& fv_accessor) { + fv_accessor.PushValueFillBasic(output, input); + } + + template + __device__ __forceinline__ void merge_basic(float* output, + const float* input, + GPUAccessor& fv_accessor) { + fv_accessor.MergePushValueBasic(output, input); + } + + template + __device__ __forceinline__ void update_embedx(float* output, + const float* input, + size_t embedx_idx, + GPUAccessor& fv_accessor) { + if (embedx_idx < output[fv_accessor.common_push_value.MfDimIndex()]) { + output[fv_accessor.common_push_value.EmbedxGIndex() + embedx_idx] = + input[fv_accessor.common_push_value.EmbedxGIndex() + embedx_idx]; + } + } + + template + __device__ __forceinline__ void merge_embedx(float* output, + const float* input, + size_t embedx_idx, + GPUAccessor& fv_accessor) { + if (embedx_idx < output[fv_accessor.common_push_value.MfDimIndex()]) { + output[fv_accessor.common_push_value.EmbedxGIndex() + embedx_idx] += + input[fv_accessor.common_push_value.EmbedxGIndex() + embedx_idx]; + } } }; @@ -139,7 +177,7 @@ class HeterCommKernel { template + typename GPUAccessor> void dy_mf_fill_shard_grads(KeyType* d_shard_keys, KeyType* d_keys, float* d_shard_grads, @@ -148,28 +186,72 @@ class HeterCommKernel { long long len, size_t grad_value_size, const StreamType& stream, - FVAccessor& feature_value_accessor); + GPUAccessor& gpu_accessor); - template - void merge_gradient(const uint32_t* offset, + template + void merge_gradient(const KeyType* d_shard_keys, + const uint32_t* offset, const uint32_t* fea_num, const uint32_t* index, const char* input, char* output, int n, + size_t grad_dim, size_t grad_value_size, - DynamicGradMerger& merger_, + DynamicGradMerger& merger, const StreamType& stream, - FVAccessor& feature_value_accessor); + GPUAccessor& gpu_accessor); - template + template void dy_mf_fill_dvals(float* d_shard_vals, float* d_vals, T* idx, long long len, size_t val_size, - const StreamType& stream, - FVAccessor& feature_value_accessor); + const StreamType& stream); + + template + void split_segments(const uint32_t* d_fea_num_info, + size_t len, + uint32_t* d_segments, + uint32_t* d_segments_num, + size_t segment_size, + const StreamType& stream); + + template + void expand_segments(const uint32_t* d_fea_num_info, + const uint32_t* d_segments_offset, + size_t segments_num, + uint32_t* d_segments_fea_num_info, + uint32_t segment_size, + const StreamType& stream); + + template + void shrink_keys(const KeyType* d_keys, + const uint32_t* d_segments_offset, + KeyType* d_segments_keys, + size_t segments_num, + const StreamType& stream); + + template + void fill_restore_idx(bool filter_zero, + const size_t total_num, + const size_t merge_size, + const KeyType* d_keys, + const uint32_t* d_sorted_idx, + const uint32_t* d_offset, + const uint32_t* d_merged_cnts, + uint32_t* d_restore_idx, + const StreamType& stream); + + template + void unpack_merged_vals(size_t n, + const KeyType* d_keys, + const void* d_merged_vals, + const uint32_t* d_restore_idx, + void* d_vals, + size_t val_size, + const StreamType& stream); private: int block_size_{256}; diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc index 4eff4a8ad55b94ef441ad930491f1072eef206ae..59c31c5bc27359637a6c00f93a86161b116e8a71 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/framework/fleet/heter_ps/heter_ps.h" - #include #ifdef PADDLE_WITH_HETERPS @@ -27,58 +26,83 @@ HeterPsBase* HeterPsBase::get_instance( std::unordered_map fleet_config, std::string accessor_type, int optimizer_type) { - if (accessor_type == "CtrDymfAccessor" && - (optimizer_type == 1 || optimizer_type == 3 || optimizer_type == 4)) { - return new HeterPs( - capacity, resource, accessor_type, fleet_config, optimizer_type); + if (accessor_type == "CtrDymfAccessor") { + auto* accessor_wrapper_ptr = + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + CommonFeatureValueAccessor* gpu_accessor = + ((AccessorWrapper*)accessor_wrapper_ptr) + ->AccessorPtr(); + if (optimizer_type == 1) { + return new HeterPs( + capacity, resource, *gpu_accessor); + } else if (optimizer_type == 3) { + return new HeterPs( + capacity, resource, *gpu_accessor); + } else if (optimizer_type == 4) { + return new HeterPs( + capacity, resource, *gpu_accessor); + } } else { VLOG(0) << " HeterPsBase get_instance Warning: now only support " "CtrDymfAccessor, but get " - << accessor_type_; - return new HeterPs( - capacity, resource, accessor_type, fleet_config, optimizer_type); + << accessor_type; + return new HeterPs( + capacity, resource, fleet_config, accessor_type, optimizer_type); } } -HeterPs::HeterPs(size_t capacity, - std::shared_ptr resource, - std::unordered_map fleet_config, - std::string accessor_type, - int optimizer_type) { - comm_ = std::make_shared>( +template class GPUOptimizer> +HeterPs::HeterPs( + size_t capacity, + std::shared_ptr resource, + GPUAccessor& gpu_accessor) { + comm_ = std::make_shared>( capacity, resource); - optimizer_type_ = optimizer_type; + opt_ = GPUOptimizer(gpu_accessor); } -HeterPs::~HeterPs() {} +template class GPUOptimizer> +HeterPs::~HeterPs() {} -void HeterPs::pull_sparse(int num, - FeatureKey* d_keys, - float* d_vals, - size_t len) { +template class GPUOptimizer> +void HeterPs::pull_sparse(int num, + FeatureKey* d_keys, + float* d_vals, + size_t len) { comm_->pull_sparse(num, d_keys, d_vals, len); } -int HeterPs::get_index_by_devid(int devid) { +template class GPUOptimizer> +int HeterPs::get_index_by_devid(int devid) { return comm_->get_index_by_devid(devid); } -void HeterPs::set_sparse_sgd(const OptimizerConfig& optimizer_config) { +template class GPUOptimizer> +void HeterPs::set_sparse_sgd( + const OptimizerConfig& optimizer_config) { comm_->set_sparse_sgd(optimizer_config); } -void HeterPs::set_embedx_sgd(const OptimizerConfig& optimizer_config) { +void HeterPs::set_embedx_sgd( + const OptimizerConfig& optimizer_config) { comm_->set_embedx_sgd(optimizer_config); } -void HeterPs::end_pass() { comm_->end_pass(); } +template class GPUOptimizer> +void HeterPs::end_pass() { + comm_->end_pass(); +} -void HeterPs::show_one_table(int gpu_num) { comm_->show_one_table(gpu_num); } +template class GPUOptimizer> +void HeterPs::show_one_table(int gpu_num) { + comm_->show_one_table(gpu_num); +} -void HeterPs::push_sparse(int num, - FeatureKey* d_keys, - float* d_grads, - size_t len) { +template class GPUOptimizer> +void HeterPs::push_sparse(int num, + FeatureKey* d_keys, + float* d_grads, + size_t len) { comm_->push_sparse(num, d_keys, d_grads, len); // comm_->push_sparse_multi_node(num, d_keys, d_grads, len, opt_); } diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu index b059690990370e780a0d3a889d47a58afa731fe0..92934e961f14913bc5302d8bda6413ed26352273 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu @@ -27,132 +27,138 @@ HeterPsBase* HeterPsBase::get_instance( std::unordered_map fleet_config, std::string accessor_type, int optimizer_type) { - if (accessor_type == "CtrDymfAccessor" && - (optimizer_type == 1 || optimizer_type == 3 || optimizer_type == 4)) { - return new HeterPs( - capacity, resource, fleet_config, accessor_type, optimizer_type); + if (accessor_type == "CtrDymfAccessor") { + auto* accessor_wrapper_ptr = + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + CommonFeatureValueAccessor* gpu_accessor = + ((AccessorWrapper*)accessor_wrapper_ptr) + ->AccessorPtr(); + if (optimizer_type == 1) { + return new HeterPs( + capacity, resource, *gpu_accessor); + } else if (optimizer_type == 3) { + return new HeterPs( + capacity, resource, *gpu_accessor); + } else if (optimizer_type == 4) { + return new HeterPs( + capacity, resource, *gpu_accessor); + } } else { VLOG(0) << " HeterPsBase get_instance Warning: now only support " "CtrDymfAccessor, but get " << accessor_type; - return new HeterPs( - capacity, resource, fleet_config, accessor_type, optimizer_type); } } -template -HeterPs::HeterPs( +template class GPUOptimizer> +HeterPs::HeterPs( size_t capacity, std::shared_ptr resource, - std::unordered_map fleet_config, - std::string accessor_type, - int optimizer_type) { - comm_ = std::make_shared>( - capacity, resource); - feature_value_accessor_.Configure(fleet_config); - set_accessor(feature_value_accessor_); - accessor_type_ = accessor_type; - optimizer_type_ = optimizer_type; + GPUAccessor& gpu_accessor) { + comm_ = std::make_shared>( + capacity, resource, gpu_accessor); + opt_ = GPUOptimizer(gpu_accessor); } -template -HeterPs::~HeterPs() {} +template class GPUOptimizer> +HeterPs::~HeterPs() {} -template -void HeterPs::pull_sparse(int num, - FeatureKey* d_keys, - float* d_vals, - size_t len) { +template class GPUOptimizer> +void HeterPs::pull_sparse(int num, + FeatureKey* d_keys, + float* d_vals, + size_t len) { comm_->pull_sparse(num, d_keys, d_vals, len); } -template -void HeterPs::build_ps(int num, - FeatureKey* h_keys, - char* pool, - size_t len, - size_t feature_value_size, - size_t chunk_size, - int stream_num) { +template class GPUOptimizer> +void HeterPs::build_ps(int num, + FeatureKey* h_keys, + char* pool, + size_t len, + size_t feature_value_size, + size_t chunk_size, + int stream_num) { comm_->build_ps( num, h_keys, pool, len, feature_value_size, chunk_size, stream_num); } -template -int HeterPs::get_index_by_devid(int devid) { +template class GPUOptimizer> +int HeterPs::get_index_by_devid(int devid) { return comm_->get_index_by_devid(devid); } -template -void HeterPs::set_sparse_sgd( +template class GPUOptimizer> +void HeterPs::set_sparse_sgd( const OptimizerConfig& optimizer_config) { comm_->set_sparse_sgd(optimizer_config); } -template -void HeterPs::set_embedx_sgd( +template class GPUOptimizer> +void HeterPs::set_embedx_sgd( const OptimizerConfig& optimizer_config) { comm_->set_embedx_sgd(optimizer_config); } -template -void HeterPs::end_pass() { +template class GPUOptimizer> +void HeterPs::end_pass() { comm_->end_pass(); } -template -void HeterPs::show_one_table(int gpu_num) { +template class GPUOptimizer> +void HeterPs::show_one_table(int gpu_num) { comm_->show_one_table(gpu_num); } -template -void HeterPs::push_sparse(int num, - FeatureKey* d_keys, - float* d_grads, - size_t len) { - if (accessor_type_ == "CtrDymfAccessor") { - if (optimizer_type_ == 3) { // adam - auto optimizer = SparseAdamOptimizer(feature_value_accessor_); - VLOG(5) << "INTO push_sparse SparseAdamOptimizer, EmbedDim():" - << optimizer.EmbedDim(); - comm_->push_sparse(num, d_keys, d_grads, len, optimizer); - } else if (optimizer_type_ == 4) { // shared_adam - auto optimizer = SparseAdamSharedOptimizer(feature_value_accessor_); - VLOG(5) << "INTO push_sparse SparseAdamSharedOptimizer, EmbedDim():" - << optimizer.EmbedDim(); - comm_->push_sparse(num, d_keys, d_grads, len, optimizer); - } else if (optimizer_type_ == 1) { // adagrad { - auto optimizer = SparseAdagradOptimizer(feature_value_accessor_); - VLOG(5) << "INTO push_sparse SparseAdagradOptimizer, EmbedDim():" - << optimizer.EmbedDim(); - comm_->push_sparse(num, d_keys, d_grads, len, optimizer); - } else { - VLOG(0) << " push sparse Error: CtrDymfAccessor only support adagrad(1)," - "adam(3) or shared_adam(4), bug get optimizer type:" - << optimizer_type_; - } - } else { - VLOG(0) << " push sparse Error: now only support CtrDymfAccessor, but get " - << accessor_type_; - } +template class GPUOptimizer> +void HeterPs::push_sparse(int num, + FeatureKey* d_keys, + float* d_grads, + size_t len) { + comm_->push_sparse(num, d_keys, d_grads, len, opt_); } -template -void HeterPs::set_nccl_comm_and_size( +template class GPUOptimizer> +void HeterPs::set_nccl_comm_and_size( const std::vector& inner_comms, const std::vector& inter_comms, int comm_size) { comm_->set_nccl_comm_and_size(inner_comms, inter_comms, comm_size); } -template -void HeterPs::set_multi_mf_dim(int multi_mf_dim, int max_mf_dim) { +template class GPUOptimizer> +void HeterPs::set_multi_mf_dim(int multi_mf_dim, + int max_mf_dim) { comm_->set_multi_mf_dim(multi_mf_dim, max_mf_dim); } -template -void HeterPs::set_accessor(FVAccessor& accessor) { - comm_->set_accessor(accessor); +template class GPUOptimizer> +void HeterPs::show_table_collisions() { + comm_->show_table_collisions(); +} + +template class GPUOptimizer> +int HeterPs::dedup_keys_and_fillidx( + const int gpu_id, + const int total_fea_num, + const FeatureKey* d_keys, // input + FeatureKey* d_merged_keys, // output + FeatureKey* d_sorted_keys, + uint32_t* d_restore_idx, + uint32_t* d_sorted_idx, + uint32_t* d_offset, + uint32_t* d_merged_cnts, + bool filter_zero) { + return comm_->dedup_keys_and_fillidx(gpu_id, + total_fea_num, + d_keys, // input + d_merged_keys, // output + d_sorted_keys, + d_restore_idx, + d_sorted_idx, + d_offset, + d_merged_cnts, + filter_zero); } } // end namespace framework diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps.h b/paddle/fluid/framework/fleet/heter_ps/heter_ps.h index 439f5d6c818544016fe79e429fd6d876242ef139..20292a4df36332e129e4fc309613c7131fa908f6 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps.h @@ -26,15 +26,13 @@ limitations under the License. */ namespace paddle { namespace framework { -template +template class GPUOptimizer> class HeterPs : public HeterPsBase { public: HeterPs() {} HeterPs(size_t capacity, std::shared_ptr resource, - std::unordered_map fleet_config, - std::string accessor_type, - int optimizer_type); + GPUAccessor& gpu_accessor); virtual ~HeterPs(); HeterPs(const HeterPs&) = delete; HeterPs& operator=(const HeterPs&) = delete; @@ -43,6 +41,8 @@ class HeterPs : public HeterPsBase { FeatureKey* d_keys, float* d_vals, size_t len) override; + // void build_ps(int num, FeatureKey* h_keys, float* h_vals, size_t len, + // size_t chunk_size, int stream_num) override; void build_ps(int num, FeatureKey* h_keys, char* pool, @@ -56,7 +56,6 @@ class HeterPs : public HeterPsBase { int comm_size) override; void set_multi_mf_dim(int multi_mf_dim, int max_mf_dim) override; - void set_accessor(FVAccessor& accessor); #endif void set_sparse_sgd(const OptimizerConfig& optimizer_config) override; @@ -65,17 +64,25 @@ class HeterPs : public HeterPsBase { void end_pass() override; int get_index_by_devid(int devid) override; void show_one_table(int gpu_num) override; - void push_sparse(int num, - FeatureKey* d_keys, - float* d_grads, - size_t len) override; - + void push_sparse(int num, FeatureKey* d_keys, float* d_grads, size_t len); + void show_table_collisions() override; +#if defined(PADDLE_WITH_CUDA) + // dedup + int dedup_keys_and_fillidx(const int gpu_id, + const int total_fea_num, + const FeatureKey* d_keys, // input + FeatureKey* d_merged_keys, // output + FeatureKey* d_sorted_keys, + uint32_t* d_restore_idx, + uint32_t* d_sorted_idx, + uint32_t* d_offset, + uint32_t* d_merged_cnts, + bool filter_zero); +#endif private: - std::shared_ptr> comm_; + std::shared_ptr> comm_; #if defined(PADDLE_WITH_CUDA) - FVAccessor feature_value_accessor_; - std::string accessor_type_; - int optimizer_type_; + GPUOptimizer opt_; #endif }; diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h b/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h index e45d1db71ccae911fcbf9314d245c4c77693b00b..af1a1261d7341b74bead2bdb13313cc9fed95053 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h @@ -54,6 +54,7 @@ class HeterPsBase { #endif virtual void end_pass() = 0; virtual void show_one_table(int gpu_num) = 0; + virtual void show_table_collisions() = 0; virtual void push_sparse(int num, FeatureKey* d_keys, float* d_grads, @@ -65,10 +66,22 @@ class HeterPsBase { static HeterPsBase* get_instance( size_t capacity, std::shared_ptr resource, - // CommonFeatureValueAccessor feature_value_accessor, std::unordered_map fleet_config, std::string accessor_type, int optimizer_type); +#if defined(PADDLE_WITH_CUDA) + // dedup + virtual int dedup_keys_and_fillidx(const int gpu_id, + const int total_fea_num, + const FeatureKey* d_keys, // input + FeatureKey* d_merged_keys, // output + FeatureKey* d_sorted_keys, + uint32_t* d_restore_idx, + uint32_t* d_sorted_idx, + uint32_t* d_offset, + uint32_t* d_merged_cnts, + bool filter_zero) = 0; +#endif }; } // end namespace framework diff --git a/paddle/fluid/framework/fleet/heter_ps/mem_pool.h b/paddle/fluid/framework/fleet/heter_ps/mem_pool.h index 05e252b2afe44eb870c735332e16a223eb5dd294..4696a7cc91b5aec51b07750dff9a1d8cb70f379f 100644 --- a/paddle/fluid/framework/fleet/heter_ps/mem_pool.h +++ b/paddle/fluid/framework/fleet/heter_ps/mem_pool.h @@ -20,6 +20,7 @@ limitations under the License. */ #include #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/framework/fleet/heter_ps/cudf/managed.cuh" +#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h" namespace paddle { namespace framework { @@ -60,9 +61,9 @@ class HBMMemoryPool : public managed { block_size_ = mem_pool->block_size(); VLOG(3) << "hbm memory pool with capacity" << capacity_ << " bs: " << block_size_; - cudaMalloc(&mem_, block_size_ * capacity_); - cudaMemcpy( - mem_, mem_pool->mem(), mem_pool->byte_size(), cudaMemcpyHostToDevice); + CUDA_CHECK(cudaMalloc(&mem_, block_size_ * capacity_)); + CUDA_CHECK(cudaMemcpy( + mem_, mem_pool->mem(), mem_pool->byte_size(), cudaMemcpyHostToDevice)); } ~HBMMemoryPool() { @@ -78,8 +79,8 @@ class HBMMemoryPool : public managed { cudaFree(mem_); mem_ = NULL; capacity_ = capacity; - cudaMalloc(&mem_, (block_size_ * capacity / 8 + 1) * 8); - cudaMemset(mem_, 0, block_size_ * capacity); + CUDA_CHECK(cudaMalloc(&mem_, (block_size_ * capacity / 8 + 1) * 8)); + CUDA_CHECK(cudaMemset(mem_, 0, block_size_ * capacity)); } char* mem() { return mem_; } diff --git a/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h b/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h index 3a6f60fef858ba30501bebf1a7de4d39f1aa2c02..1e95284869856482d122471039e85a2cfe0e210c 100644 --- a/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h +++ b/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h @@ -19,7 +19,6 @@ limitations under the License. */ #include #endif #include - #include "paddle/fluid/framework/fleet/heter_ps/feature_value.h" #include "paddle/fluid/framework/fleet/heter_ps/optimizer_conf.h" @@ -28,50 +27,35 @@ namespace framework { #if defined(PADDLE_WITH_CUDA) -class Optimizer { - public: - __host__ Optimizer(CommonFeatureValueAccessor feature_value_accessor) { - feature_value_accessor_ = feature_value_accessor; - } - __host__ ~Optimizer() {} - - __device__ void update_value(const OptimizerConfig& optimizer_config, - float& val, // NOLINT - const float& grad) { - printf( - "Warning: update_value will not used. Please use dy_mf_update_value\n"); - } - - __device__ void dy_mf_update_value(const OptimizerConfig& optimizer_config, - float* ptr, - const float* grad) {} - - CommonFeatureValueAccessor feature_value_accessor_; - - size_t _embedding_dim; - size_t _lr_embedding_dim; -}; - -class SparseAdagradOptimizer : public Optimizer { +template +class SparseAdagradOptimizer { public: - __host__ SparseAdagradOptimizer( - CommonFeatureValueAccessor feature_value_accessor) - : Optimizer(feature_value_accessor) { + SparseAdagradOptimizer() {} + SparseAdagradOptimizer(GPUAccessor gpu_accessor) { + gpu_accessor_ = gpu_accessor; _lr_embedding_dim = 1; - _embedding_dim = feature_value_accessor_.common_feature_value.EmbedWDim(); + _embedding_dim = gpu_accessor_.common_feature_value.EmbedWDim(); } + ~SparseAdagradOptimizer() {} + __device__ void update_value_work(const OptimizerConfig& optimizer_config, int n, float* w, float* sgd, // NOLINT const float* g, - float scale) { + float scale, + float slot) { float& g2sum = sgd[G2SumIndex()]; double add_g2sum = 0; - double ratio = optimizer_config.mf_learning_rate * - sqrt(optimizer_config.mf_initial_g2sum / - (optimizer_config.mf_initial_g2sum + g2sum)); + + float learning_rate = optimizer_config.mf_learning_rate; + if (slot != optimizer_config.nodeid_slot) { + learning_rate = optimizer_config.feature_learning_rate; + } + double ratio = + learning_rate * sqrt(optimizer_config.mf_initial_g2sum / + (optimizer_config.mf_initial_g2sum + g2sum)); for (int i = 0; i < n; ++i) { double scaled_grad = g[i] / scale; @@ -96,47 +80,43 @@ class SparseAdagradOptimizer : public Optimizer { __device__ void dy_mf_update_value(const OptimizerConfig& optimizer_config, float* ptr, const float* grad) { - float g_show = grad[feature_value_accessor_.common_push_value.ShowIndex()]; - float g_click = - grad[feature_value_accessor_.common_push_value.ClickIndex()]; - - ptr[feature_value_accessor_.common_feature_value.SlotIndex()] = - grad[feature_value_accessor_.common_push_value.SlotIndex()]; - ptr[feature_value_accessor_.common_feature_value.ShowIndex()] += g_show; - ptr[feature_value_accessor_.common_feature_value.ClickIndex()] += g_click; - ptr[feature_value_accessor_.common_feature_value.DeltaScoreIndex()] += + float g_show = grad[gpu_accessor_.common_push_value.ShowIndex()]; + float g_click = grad[gpu_accessor_.common_push_value.ClickIndex()]; + + ptr[gpu_accessor_.common_feature_value.SlotIndex()] = + grad[gpu_accessor_.common_push_value.SlotIndex()]; + ptr[gpu_accessor_.common_feature_value.ShowIndex()] += g_show; + ptr[gpu_accessor_.common_feature_value.ClickIndex()] += g_click; + ptr[gpu_accessor_.common_feature_value.DeltaScoreIndex()] += optimizer_config.nonclk_coeff * (g_show - g_click) + optimizer_config.clk_coeff * g_click; + float slot = ptr[gpu_accessor_.common_feature_value.SlotIndex()]; update_value_work( optimizer_config, 1, - ptr + feature_value_accessor_.common_feature_value.EmbedWIndex(), - ptr + feature_value_accessor_.common_feature_value.EmbedG2SumIndex(), - grad + feature_value_accessor_.common_push_value.EmbedGIndex(), - g_show); - - int mf_dim = - int(ptr[feature_value_accessor_.common_feature_value.MfDimIndex()]); - if (ptr[feature_value_accessor_.common_feature_value.MfSizeIndex()] == 0) { + ptr + gpu_accessor_.common_feature_value.EmbedWIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedG2SumIndex(), + grad + gpu_accessor_.common_push_value.EmbedGIndex(), + g_show, + slot); + + int mf_dim = int(ptr[gpu_accessor_.common_feature_value.MfDimIndex()]); + if (ptr[gpu_accessor_.common_feature_value.MfSizeIndex()] == 0) { if (optimizer_config.mf_create_thresholds <= optimizer_config.nonclk_coeff * - (ptr[feature_value_accessor_.common_feature_value - .ShowIndex()] - - ptr[feature_value_accessor_.common_feature_value - .ClickIndex()]) + + (ptr[gpu_accessor_.common_feature_value.ShowIndex()] - + ptr[gpu_accessor_.common_feature_value.ClickIndex()]) + optimizer_config.clk_coeff * - ptr[feature_value_accessor_.common_feature_value - .ClickIndex()]) { - ptr[feature_value_accessor_.common_feature_value.MfSizeIndex()] = - feature_value_accessor_.common_feature_value.MFSize(mf_dim) / - sizeof(float); + ptr[gpu_accessor_.common_feature_value.ClickIndex()]) { + ptr[gpu_accessor_.common_feature_value.MfSizeIndex()] = + gpu_accessor_.common_feature_value.MFSize(mf_dim) / sizeof(float); int tid_x = blockIdx.x * blockDim.x + threadIdx.x; curandState state; curand_init(clock64(), tid_x, 0, &state); for (int i = 0; i < mf_dim; ++i) { - ptr[feature_value_accessor_.common_feature_value.EmbedxWIndex() + i] = + ptr[gpu_accessor_.common_feature_value.EmbedxWIndex() + i] = (curand_uniform(&state)) * optimizer_config.mf_initial_range; } } @@ -144,10 +124,11 @@ class SparseAdagradOptimizer : public Optimizer { update_value_work( optimizer_config, mf_dim, - ptr + feature_value_accessor_.common_feature_value.EmbedxWIndex(), - ptr + feature_value_accessor_.common_feature_value.EmbedxG2SumIndex(), - grad + feature_value_accessor_.common_push_value.EmbedxGIndex(), - g_show); + ptr + gpu_accessor_.common_feature_value.EmbedxWIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedxG2SumIndex(), + grad + gpu_accessor_.common_push_value.EmbedxGIndex(), + g_show, + slot); } } @@ -156,17 +137,25 @@ class SparseAdagradOptimizer : public Optimizer { __host__ __device__ size_t EmbedxDim() { return _embedding_dim; } __host__ __device__ size_t G2SumIndex() { return 0; } __host__ __device__ size_t EmbedxG2SumIndex() { return 0; } + + private: + GPUAccessor gpu_accessor_; + size_t _embedding_dim; + size_t _lr_embedding_dim; }; -class SparseAdamOptimizer : public Optimizer { +template +class SparseAdamOptimizer { public: - __host__ SparseAdamOptimizer( - CommonFeatureValueAccessor feature_value_accessor) - : Optimizer(feature_value_accessor) { + SparseAdamOptimizer() {} + SparseAdamOptimizer(GPUAccessor gpu_accessor) { + gpu_accessor_ = gpu_accessor; _lr_embedding_dim = 1; - _embedding_dim = feature_value_accessor_.common_feature_value.EmbedWDim(); + _embedding_dim = gpu_accessor_.common_feature_value.EmbedWDim(); } + ~SparseAdamOptimizer() {} + __device__ void update_lr(const OptimizerConfig& optimizer_config, int n, float* w, @@ -256,65 +245,57 @@ class SparseAdamOptimizer : public Optimizer { __device__ void dy_mf_update_value(const OptimizerConfig& optimizer_config, float* ptr, const float* grad) { - float g_show = grad[feature_value_accessor_.common_push_value.ShowIndex()]; - float g_click = - grad[feature_value_accessor_.common_push_value.ClickIndex()]; - - ptr[feature_value_accessor_.common_feature_value.SlotIndex()] = - grad[feature_value_accessor_.common_push_value.SlotIndex()]; - ptr[feature_value_accessor_.common_feature_value.ShowIndex()] += g_show; - ptr[feature_value_accessor_.common_feature_value.ClickIndex()] += g_click; - ptr[feature_value_accessor_.common_feature_value.DeltaScoreIndex()] += + float g_show = grad[gpu_accessor_.common_push_value.ShowIndex()]; + float g_click = grad[gpu_accessor_.common_push_value.ClickIndex()]; + + ptr[gpu_accessor_.common_feature_value.SlotIndex()] = + grad[gpu_accessor_.common_push_value.SlotIndex()]; + ptr[gpu_accessor_.common_feature_value.ShowIndex()] += g_show; + ptr[gpu_accessor_.common_feature_value.ClickIndex()] += g_click; + ptr[gpu_accessor_.common_feature_value.DeltaScoreIndex()] += optimizer_config.nonclk_coeff * (g_show - g_click) + optimizer_config.clk_coeff * g_click; - update_lr( - optimizer_config, - 1, - ptr + feature_value_accessor_.common_feature_value.EmbedWIndex(), - ptr + feature_value_accessor_.common_feature_value.EmbedG2SumIndex(), - grad + feature_value_accessor_.common_push_value.EmbedGIndex(), - g_show); - int mf_dim = - int(ptr[feature_value_accessor_.common_feature_value.MfDimIndex()]); - if (ptr[feature_value_accessor_.common_feature_value.MfSizeIndex()] == 0) { + update_lr(optimizer_config, + 1, + ptr + gpu_accessor_.common_feature_value.EmbedWIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedG2SumIndex(), + grad + gpu_accessor_.common_push_value.EmbedGIndex(), + g_show); + int mf_dim = int(ptr[gpu_accessor_.common_feature_value.MfDimIndex()]); + if (ptr[gpu_accessor_.common_feature_value.MfSizeIndex()] == 0) { if (optimizer_config.mf_create_thresholds <= optimizer_config.nonclk_coeff * - (ptr[feature_value_accessor_.common_feature_value - .ShowIndex()] - - ptr[feature_value_accessor_.common_feature_value - .ClickIndex()]) + + (ptr[gpu_accessor_.common_feature_value.ShowIndex()] - + ptr[gpu_accessor_.common_feature_value.ClickIndex()]) + optimizer_config.clk_coeff * - ptr[feature_value_accessor_.common_feature_value - .ClickIndex()]) { - ptr[feature_value_accessor_.common_feature_value.MfSizeIndex()] = - feature_value_accessor_.common_feature_value.MFSize(mf_dim) / - sizeof(float); + ptr[gpu_accessor_.common_feature_value.ClickIndex()]) { + ptr[gpu_accessor_.common_feature_value.MfSizeIndex()] = + gpu_accessor_.common_feature_value.MFSize(mf_dim) / sizeof(float); int tid_x = blockIdx.x * blockDim.x + threadIdx.x; curandState state; curand_init(clock64(), tid_x, 0, &state); for (int i = 0; i < mf_dim; ++i) { - ptr[feature_value_accessor_.common_feature_value.EmbedxWIndex() + i] = + ptr[gpu_accessor_.common_feature_value.EmbedxWIndex() + i] = (curand_uniform(&state)) * optimizer_config.mf_initial_range; } - ptr[feature_value_accessor_.common_feature_value.EmbedxG2SumIndex() + + ptr[gpu_accessor_.common_feature_value.EmbedxG2SumIndex() + EmbedxBeta1PowIndex()] = optimizer_config.beta1_decay_rate; - ptr[feature_value_accessor_.common_feature_value.EmbedxG2SumIndex() + + ptr[gpu_accessor_.common_feature_value.EmbedxG2SumIndex() + EmbedxBeta2PowIndex()] = optimizer_config.beta2_decay_rate; } } else { - update_mf( - optimizer_config, - mf_dim, - ptr + feature_value_accessor_.common_feature_value.EmbedxWIndex(), - ptr + feature_value_accessor_.common_feature_value.EmbedxG2SumIndex(), - grad + feature_value_accessor_.common_push_value.EmbedxGIndex(), - g_show); + update_mf(optimizer_config, + mf_dim, + ptr + gpu_accessor_.common_feature_value.EmbedxWIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedxG2SumIndex(), + grad + gpu_accessor_.common_push_value.EmbedxGIndex(), + g_show); } // printf("EmbedxGIndex: %f, mf_gsum: %f, ", - // feature_value_accessor_.common_push_value.EmbedxGIndex(), - // ptr[feature_value_accessor_.common_feature_value.EmbedxG2SumIndex()]); + // gpu_accessor_.common_push_value.EmbedxGIndex(), + // ptr[gpu_accessor_.common_feature_value.EmbedxG2SumIndex()]); } __host__ __device__ size_t Dim() { return EmbedDim() + EmbedxDim(); } @@ -338,17 +319,25 @@ class SparseAdamOptimizer : public Optimizer { __host__ __device__ size_t EmbedxBeta2PowIndex() { return EmbedxBeta1PowIndex() + 1; } + + private: + GPUAccessor gpu_accessor_; + size_t _embedding_dim; + size_t _lr_embedding_dim; }; -class SparseAdamSharedOptimizer : public Optimizer { +template +class SparseAdamSharedOptimizer { public: - __host__ SparseAdamSharedOptimizer( - CommonFeatureValueAccessor feature_value_accessor) - : Optimizer(feature_value_accessor) { + SparseAdamSharedOptimizer() {} + SparseAdamSharedOptimizer(GPUAccessor gpu_accessor) { + gpu_accessor_ = gpu_accessor; _lr_embedding_dim = 1; - _embedding_dim = feature_value_accessor_.common_feature_value.EmbedWDim(); + _embedding_dim = gpu_accessor_.common_feature_value.EmbedWDim(); } + ~SparseAdamSharedOptimizer() {} + __device__ void update_value_work(const OptimizerConfig& optimizer_config, int n, float* w, @@ -406,60 +395,54 @@ class SparseAdamSharedOptimizer : public Optimizer { __device__ void dy_mf_update_value(const OptimizerConfig& optimizer_config, float* ptr, const float* grad) { - float g_show = grad[feature_value_accessor_.common_push_value.ShowIndex()]; - float g_click = - grad[feature_value_accessor_.common_push_value.ClickIndex()]; - - ptr[feature_value_accessor_.common_feature_value.SlotIndex()] = - grad[feature_value_accessor_.common_push_value.SlotIndex()]; - ptr[feature_value_accessor_.common_feature_value.ShowIndex()] += g_show; - ptr[feature_value_accessor_.common_feature_value.ClickIndex()] += g_click; - ptr[feature_value_accessor_.common_feature_value.DeltaScoreIndex()] += + float g_show = grad[gpu_accessor_.common_push_value.ShowIndex()]; + float g_click = grad[gpu_accessor_.common_push_value.ClickIndex()]; + + ptr[gpu_accessor_.common_feature_value.SlotIndex()] = + grad[gpu_accessor_.common_push_value.SlotIndex()]; + ptr[gpu_accessor_.common_feature_value.ShowIndex()] += g_show; + ptr[gpu_accessor_.common_feature_value.ClickIndex()] += g_click; + ptr[gpu_accessor_.common_feature_value.DeltaScoreIndex()] += optimizer_config.nonclk_coeff * (g_show - g_click) + optimizer_config.clk_coeff * g_click; update_value_work( optimizer_config, 1, - ptr + feature_value_accessor_.common_feature_value.EmbedWIndex(), - ptr + feature_value_accessor_.common_feature_value.EmbedG2SumIndex(), - grad + feature_value_accessor_.common_push_value.EmbedGIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedWIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedG2SumIndex(), + grad + gpu_accessor_.common_push_value.EmbedGIndex(), g_show); - int mf_dim = - int(ptr[feature_value_accessor_.common_feature_value.MfDimIndex()]); - if (ptr[feature_value_accessor_.common_feature_value.MfSizeIndex()] == 0) { + int mf_dim = int(ptr[gpu_accessor_.common_feature_value.MfDimIndex()]); + if (ptr[gpu_accessor_.common_feature_value.MfSizeIndex()] == 0) { if (optimizer_config.mf_create_thresholds <= optimizer_config.nonclk_coeff * - (ptr[feature_value_accessor_.common_feature_value - .ShowIndex()] - - ptr[feature_value_accessor_.common_feature_value - .ClickIndex()]) + + (ptr[gpu_accessor_.common_feature_value.ShowIndex()] - + ptr[gpu_accessor_.common_feature_value.ClickIndex()]) + optimizer_config.clk_coeff * - ptr[feature_value_accessor_.common_feature_value - .ClickIndex()]) { - ptr[feature_value_accessor_.common_feature_value.MfSizeIndex()] = - feature_value_accessor_.common_feature_value.MFSize(mf_dim) / - sizeof(float); + ptr[gpu_accessor_.common_feature_value.ClickIndex()]) { + ptr[gpu_accessor_.common_feature_value.MfSizeIndex()] = + gpu_accessor_.common_feature_value.MFSize(mf_dim) / sizeof(float); int tid_x = blockIdx.x * blockDim.x + threadIdx.x; curandState state; curand_init(clock64(), tid_x, 0, &state); for (int i = 0; i < mf_dim; ++i) { - ptr[feature_value_accessor_.common_feature_value.EmbedxWIndex() + i] = + ptr[gpu_accessor_.common_feature_value.EmbedxWIndex() + i] = (curand_uniform(&state)) * optimizer_config.mf_initial_range; } - ptr[feature_value_accessor_.common_feature_value.EmbedxG2SumIndex() + + ptr[gpu_accessor_.common_feature_value.EmbedxG2SumIndex() + EmbedxBeta1PowIndex()] = optimizer_config.beta1_decay_rate; - ptr[feature_value_accessor_.common_feature_value.EmbedxG2SumIndex() + + ptr[gpu_accessor_.common_feature_value.EmbedxG2SumIndex() + EmbedxBeta2PowIndex()] = optimizer_config.beta2_decay_rate; } } else { update_value_work( optimizer_config, mf_dim, - ptr + feature_value_accessor_.common_feature_value.EmbedxWIndex(), - ptr + feature_value_accessor_.common_feature_value.EmbedxG2SumIndex(), - grad + feature_value_accessor_.common_push_value.EmbedxGIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedxWIndex(), + ptr + gpu_accessor_.common_feature_value.EmbedxG2SumIndex(), + grad + gpu_accessor_.common_push_value.EmbedxGIndex(), g_show); } } @@ -481,6 +464,11 @@ class SparseAdamSharedOptimizer : public Optimizer { __host__ __device__ size_t EmbedxBeta2PowIndex() { return EmbedxBeta1PowIndex() + 1; } + + private: + GPUAccessor gpu_accessor_; + size_t _embedding_dim; + size_t _lr_embedding_dim; }; #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/optimizer_conf.h b/paddle/fluid/framework/fleet/heter_ps/optimizer_conf.h index 2db259941c873a4a4d5885232b553c6296d8fe5e..ba76f2ff914b22668e14fb1c823df256ba5b787a 100644 --- a/paddle/fluid/framework/fleet/heter_ps/optimizer_conf.h +++ b/paddle/fluid/framework/fleet/heter_ps/optimizer_conf.h @@ -41,6 +41,9 @@ class OptimizerConfig { float mf_max_bound = 10; float mf_ada_epsilon = 1e-8; + float nodeid_slot = 9008; + float feature_learning_rate = 0.05; + void set_sparse_sgd(float nonclk_coeff, float clk_coeff, float min_bound, @@ -84,7 +87,9 @@ class OptimizerConfig { float mf_max_bound, float mf_beta1_decay_rate, float mf_beta2_decay_rate, - float mf_ada_epsilon) { + float mf_ada_epsilon, + float nodeid_slot, + float feature_learning_rate) { this->mf_create_thresholds = mf_create_thresholds; this->mf_learning_rate = mf_learning_rate; this->mf_initial_g2sum = mf_initial_g2sum; @@ -94,6 +99,9 @@ class OptimizerConfig { this->mf_beta1_decay_rate = mf_beta1_decay_rate; this->mf_beta2_decay_rate = mf_beta2_decay_rate; this->mf_ada_epsilon = mf_ada_epsilon; + + this->nodeid_slot = nodeid_slot; + this->feature_learning_rate = feature_learning_rate; } void set_embedx_sgd(const OptimizerConfig& optimizer_config) { @@ -106,6 +114,9 @@ class OptimizerConfig { this->mf_beta1_decay_rate = optimizer_config.mf_beta1_decay_rate; this->mf_beta2_decay_rate = optimizer_config.mf_beta2_decay_rate; this->mf_ada_epsilon = optimizer_config.mf_ada_epsilon; + + this->nodeid_slot = nodeid_slot; + this->feature_learning_rate = feature_learning_rate; } }; diff --git a/paddle/fluid/framework/fleet/heter_ps/test_cpu_query.cu b/paddle/fluid/framework/fleet/heter_ps/test_cpu_query.cu index 7287830b6fef01186f835718acb29a4801d9ecfb..c4e77d65203beb9c987b246ce1473ccfccaf3c45 100644 --- a/paddle/fluid/framework/fleet/heter_ps/test_cpu_query.cu +++ b/paddle/fluid/framework/fleet/heter_ps/test_cpu_query.cu @@ -27,9 +27,6 @@ using namespace paddle::framework; namespace platform = paddle::platform; -// paddle::framework::GpuPsCommGraph GraphTable::make_gpu_ps_graph -// paddle::framework::GpuPsCommGraph GraphTable::make_gpu_ps_graph( -// std::vector ids) std::string edges[] = { std::string("0\t1"), @@ -121,13 +118,13 @@ TEST(TEST_FLEET, test_cpu_cache) { std::make_shared(device_id_mapping); resource->enable_p2p(); int use_nv = 1; - GpuPsGraphTable g(resource, use_nv); + GpuPsGraphTable g(resource, 1, 2); g.init_cpu_table(table_proto); - g.cpu_graph_table->Load(node_file_name, "nuser"); - g.cpu_graph_table->Load(node_file_name, "nitem"); + g.cpu_graph_table_->Load(node_file_name, "nuser"); + g.cpu_graph_table_->Load(node_file_name, "nitem"); std::remove(node_file_name); std::vector vec; - std::vector node_ids; + std::vector node_ids; node_ids.push_back(37); node_ids.push_back(96); std::vector> node_feat(2, @@ -135,38 +132,29 @@ TEST(TEST_FLEET, test_cpu_cache) { std::vector feature_names; feature_names.push_back(std::string("c")); feature_names.push_back(std::string("d")); - g.cpu_graph_table->get_node_feat(0, node_ids, feature_names, node_feat); + g.cpu_graph_table_->get_node_feat(0, node_ids, feature_names, node_feat); VLOG(0) << "get_node_feat: " << node_feat[0][0]; VLOG(0) << "get_node_feat: " << node_feat[0][1]; VLOG(0) << "get_node_feat: " << node_feat[1][0]; VLOG(0) << "get_node_feat: " << node_feat[1][1]; int n = 10; - std::vector ids0, ids1; + std::vector ids0, ids1; for (int i = 0; i < n; i++) { - g.cpu_graph_table->add_comm_edge(0, i, (i + 1) % n); - g.cpu_graph_table->add_comm_edge(0, i, (i - 1 + n) % n); + g.cpu_graph_table_->add_comm_edge(0, i, (i + 1) % n); + g.cpu_graph_table_->add_comm_edge(0, i, (i - 1 + n) % n); if (i % 2 == 0) ids0.push_back(i); } - g.cpu_graph_table->build_sampler(0); + g.cpu_graph_table_->build_sampler(0); ids1.push_back(5); ids1.push_back(7); - vec.push_back(g.cpu_graph_table->make_gpu_ps_graph(0, ids0)); - vec.push_back(g.cpu_graph_table->make_gpu_ps_graph(0, ids1)); + vec.push_back(g.cpu_graph_table_->make_gpu_ps_graph(0, ids0)); + vec.push_back(g.cpu_graph_table_->make_gpu_ps_graph(0, ids1)); vec[0].display_on_cpu(); vec[1].display_on_cpu(); // g.build_graph_from_cpu(vec); - g.build_graph_on_single_gpu(vec[0], 0); - g.build_graph_on_single_gpu(vec[1], 1); - int64_t cpu_key[3] = {0, 1, 2}; - /* - std::vector> buffers(3); - std::vector actual_sizes(3,0); - g.cpu_graph_table->random_sample_neighbors(cpu_key,2,buffers,actual_sizes,false); - for(int i = 0;i < 3;i++){ - VLOG(0)<<"sample from cpu key->"<set_search_level(2); - // g.cpu_graph_table->Load_to_ssd(edge_file_name,"e>u2u"); - g.cpu_graph_table->Load(edge_file_name, "e>u2u"); - g.cpu_graph_table->make_partitions(0, 64, 2); + g.cpu_graph_table_->clear_graph(0); + g.cpu_graph_table_->set_search_level(2); + g.cpu_graph_table_->Load(edge_file_name, "e>u2u"); + g.cpu_graph_table_->make_partitions(0, 64, 2); int index = 0; - while (g.cpu_graph_table->load_next_partition(0) != -1) { - auto all_ids = g.cpu_graph_table->get_all_id(0, 0, device_len); + /* + while (g.cpu_graph_table_->load_next_partition(0) != -1) { + auto all_ids = g.cpu_graph_table_->get_all_id(0, 0, device_len); for (auto x : all_ids) { for (auto y : x) { VLOG(0) << "part " << index << " " << y; @@ -207,19 +196,19 @@ TEST(TEST_FLEET, test_cpu_cache) { } for (int i = 0; i < all_ids.size(); i++) { GpuPsCommGraph sub_graph = - g.cpu_graph_table->make_gpu_ps_graph(0, all_ids[i]); - g.build_graph_on_single_gpu(sub_graph, i); + g.cpu_graph_table_->make_gpu_ps_graph(0, all_ids[i]); + g.build_graph_on_single_gpu(sub_graph, i, 0); VLOG(2) << "sub graph on gpu " << i << " is built"; } VLOG(0) << "start to iterate gpu graph node"; - g.cpu_graph_table->make_complementary_graph(0, 64); + g.cpu_graph_table_->make_complementary_graph(0, 64); for (int i = 0; i < 2; i++) { // platform::CUDADeviceGuard guard(i); LOG(0) << "query on card " << i; int step = 2; int cur = 0; while (true) { - auto node_query_res = g.query_node_list(i, cur, step); + auto node_query_res = g.query_node_list(i, 0, cur, step); node_query_res.display(); if (node_query_res.get_len() == 0) { VLOG(0) << "no more ids,break"; @@ -227,23 +216,23 @@ TEST(TEST_FLEET, test_cpu_cache) { } cur += node_query_res.get_len(); NeighborSampleQuery query, q1; - query.initialize( - i, node_query_res.get_val(), 4, node_query_res.get_len()); + query.initialize(i, 0, node_query_res.get_val(), 4, + node_query_res.get_len()); query.display(); auto c = g.graph_neighbor_sample_v3(query, true); c.display(); platform::CUDADeviceGuard guard(i); - int64_t *key; + uint64_t *key; VLOG(0) << "sample key 1 globally"; - g.cpu_graph_table->set_search_level(2); - cudaMalloc((void **)&key, sizeof(int64_t)); - int64_t t_key = 1; - cudaMemcpy(key, &t_key, sizeof(int64_t), cudaMemcpyHostToDevice); - q1.initialize(i, (int64_t)key, 2, 1); + g.cpu_graph_table_->set_search_level(2); + cudaMalloc((void **)&key, sizeof(uint64_t)); + uint64_t t_key = 1; + cudaMemcpy(key, &t_key, sizeof(uint64_t), cudaMemcpyHostToDevice); + q1.initialize(i, 0, (uint64_t)key, 2, 1); auto d = g.graph_neighbor_sample_v3(q1, true); d.display(); cudaFree(key); - g.cpu_graph_table->set_search_level(1); + g.cpu_graph_table_->set_search_level(1); } } index++; @@ -253,4 +242,5 @@ TEST(TEST_FLEET, test_cpu_cache) { device.push_back(0); device.push_back(1); iter->set_device(device); + */ } diff --git a/paddle/fluid/framework/fleet/heter_ps/test_graph.cu b/paddle/fluid/framework/fleet/heter_ps/test_graph.cu index 837c1fb94089e25cd0e4745c66a441189cb0b9a4..788cf932737b5382363ef5f783bde044545770b5 100644 --- a/paddle/fluid/framework/fleet/heter_ps/test_graph.cu +++ b/paddle/fluid/framework/fleet/heter_ps/test_graph.cu @@ -50,15 +50,16 @@ TEST(TEST_FLEET, graph_comm) { } std::vector neighbor_offset(gpu_count, 0), node_index(gpu_count, 0); for (int i = 0; i < graph_list.size(); i++) { - graph_list[i].node_list = new GpuPsGraphNode[graph_list[i].node_size]; + graph_list[i].node_list = new uint64_t[graph_list[i].node_size]; + graph_list[i].node_info_list = new GpuPsNodeInfo[graph_list[i].node_size]; graph_list[i].neighbor_list = new int64_t[graph_list[i].neighbor_size]; } for (int i = 0; i < node_count; i++) { ind = i % gpu_count; - graph_list[ind].node_list[node_index[ind]].node_id = i; - graph_list[ind].node_list[node_index[ind]].neighbor_offset = + graph_list[ind].node_list[node_index[ind]] = i; + graph_list[ind].node_info_list[node_index[ind]].neighbor_offset = neighbor_offset[ind]; - graph_list[ind].node_list[node_index[ind]].neighbor_size = + graph_list[ind].node_info_list[node_index[ind]].neighbor_size = neighbors[i].size(); for (auto x : neighbors[i]) { graph_list[ind].neighbor_list[neighbor_offset[ind]++] = x; diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc index 622793653dcab08fbe29f91d82e8d21512138afb..bbeb5977635e9780317eaaf6bce365dd4ff43910 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc @@ -25,7 +25,6 @@ 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. */ - #ifdef PADDLE_WITH_HETERPS #include "paddle/fluid/framework/fleet/ps_gpu_wrapper.h" @@ -34,11 +33,14 @@ limitations under the License. */ #include #include "paddle/fluid/framework/data_set.h" +#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h" #include "paddle/fluid/platform/timer.h" #if defined(PADDLE_WITH_PSCORE) #include "paddle/fluid/distributed/ps/table/depends/feature_value.h" #endif +DECLARE_int32(gpugraph_dedup_pull_push_mode); + namespace paddle { namespace framework { @@ -117,7 +119,6 @@ void PSGPUWrapper::PreBuildTask(std::shared_ptr gpu_task) { gpu_task->init(thread_keys_shard_num_, device_num, multi_mf_dim_); std::vector threads; - // data should be in input channel thread_dim_keys_.resize(thread_keys_thread_num_); @@ -135,94 +136,161 @@ void PSGPUWrapper::PreBuildTask(std::shared_ptr gpu_task) { std::string data_set_name = std::string(typeid(*dataset_).name()); - if (data_set_name.find("SlotRecordDataset") != std::string::npos) { - SlotRecordDataset* dataset = (SlotRecordDataset*)(dataset_); - auto input_channel = dataset->GetInputChannel(); - VLOG(0) << "psgpu wrapperinputslotchannle size: " << input_channel->Size(); - const std::deque& vec_data = input_channel->GetData(); - total_len = vec_data.size(); - len_per_thread = total_len / thread_keys_thread_num_; - remain = total_len % thread_keys_thread_num_; - VLOG(0) << "total len: " << total_len; - auto gen_dynamic_mf_func = [this](const std::deque& total_data, - int begin_index, - int end_index, - int i) { - for (auto iter = total_data.begin() + begin_index; - iter != total_data.begin() + end_index; - iter++) { - const auto& ins = *iter; - const auto& feasign_v = ins->slot_uint64_feasigns_.slot_values; - const auto& slot_offset = ins->slot_uint64_feasigns_.slot_offsets; - for (size_t slot_idx = 0; slot_idx < slot_offset_vector_.size(); - slot_idx++) { - for (size_t j = slot_offset[slot_offset_vector_[slot_idx]]; - j < slot_offset[slot_offset_vector_[slot_idx] + 1]; - j++) { - int shard_id = feasign_v[j] % thread_keys_shard_num_; - int dim_id = slot_index_vec_[slot_idx]; - if (feasign_v[j] != 0) { - this->thread_dim_keys_[i][shard_id][dim_id].insert(feasign_v[j]); + VLOG(0) << "gpu_graph_mode_:" << gpu_graph_mode_; + if (!gpu_graph_mode_) { + if (data_set_name.find("SlotRecordDataset") != std::string::npos) { + VLOG(0) << "ps_gpu_wrapper use SlotRecordDataset"; + SlotRecordDataset* dataset = (SlotRecordDataset*)(dataset_); + auto input_channel = dataset->GetInputChannel(); + VLOG(0) << "psgpu wrapperinputslotchannle size: " + << input_channel->Size(); + const std::deque& vec_data = input_channel->GetData(); + total_len = vec_data.size(); + len_per_thread = total_len / thread_keys_thread_num_; + remain = total_len % thread_keys_thread_num_; + VLOG(0) << "total len: " << total_len; + auto gen_dynamic_mf_func = [this]( + const std::deque& total_data, + int begin_index, + int end_index, + int i) { + for (auto iter = total_data.begin() + begin_index; + iter != total_data.begin() + end_index; + iter++) { + const auto& ins = *iter; + const auto& feasign_v = ins->slot_uint64_feasigns_.slot_values; + const auto& slot_offset = ins->slot_uint64_feasigns_.slot_offsets; + for (size_t slot_idx = 0; slot_idx < slot_offset_vector_.size(); + slot_idx++) { + for (size_t j = slot_offset[slot_offset_vector_[slot_idx]]; + j < slot_offset[slot_offset_vector_[slot_idx] + 1]; + j++) { + int shard_id = feasign_v[j] % thread_keys_shard_num_; + int dim_id = slot_index_vec_[slot_idx]; + if (feasign_v[j] != 0) { + this->thread_dim_keys_[i][shard_id][dim_id].insert( + feasign_v[j]); + } } } } + }; + for (int i = 0; i < thread_keys_thread_num_; i++) { + threads.push_back( + std::thread(gen_dynamic_mf_func, + std::ref(vec_data), + begin, + begin + len_per_thread + (i < remain ? 1 : 0), + i)); + + begin += len_per_thread + (i < remain ? 1 : 0); + } + for (std::thread& t : threads) { + t.join(); + } + timeline.Pause(); + VLOG(0) << "GpuPs build task cost " << timeline.ElapsedSec() + << " seconds."; + } else { + CHECK(data_set_name.find("MultiSlotDataset") != std::string::npos); + VLOG(0) << "ps_gpu_wrapper use MultiSlotDataset"; + MultiSlotDataset* dataset = (MultiSlotDataset*)(dataset_); + auto input_channel = dataset->GetInputChannel(); + + const std::deque& vec_data = input_channel->GetData(); + total_len = vec_data.size(); + len_per_thread = total_len / thread_keys_thread_num_; + remain = total_len % thread_keys_thread_num_; + auto gen_func = [this](const std::deque& total_data, + int begin_index, + int end_index, + int i) { + for (auto iter = total_data.begin() + begin_index; + iter != total_data.begin() + end_index; + iter++) { + const auto& ins = *iter; + const auto& feasign_v = ins.uint64_feasigns_; + for (const auto feasign : feasign_v) { + uint64_t cur_key = feasign.sign().uint64_feasign_; + int shard_id = cur_key % thread_keys_shard_num_; + this->thread_keys_[i][shard_id].insert(cur_key); + } + } + }; + for (int i = 0; i < thread_keys_thread_num_; i++) { + threads.push_back( + std::thread(gen_func, + std::ref(vec_data), + begin, + begin + len_per_thread + (i < remain ? 1 : 0), + i)); + begin += len_per_thread + (i < remain ? 1 : 0); } - }; - for (int i = 0; i < thread_keys_thread_num_; i++) { - threads.push_back( - std::thread(gen_dynamic_mf_func, - std::ref(vec_data), - begin, - begin + len_per_thread + (i < remain ? 1 : 0), - i)); - - begin += len_per_thread + (i < remain ? 1 : 0); - } - for (std::thread& t : threads) { - t.join(); + for (std::thread& t : threads) { + t.join(); + } + timeline.Pause(); + VLOG(0) << "GpuPs build task cost " << timeline.ElapsedSec() + << " seconds."; } - timeline.Pause(); - VLOG(0) << "GpuPs build task cost " << timeline.ElapsedSec() << " seconds."; } else { - CHECK(data_set_name.find("MultiSlotDataset") != std::string::npos); - VLOG(0) << "ps_gpu_wrapper use MultiSlotDataset"; - MultiSlotDataset* dataset = (MultiSlotDataset*)(dataset_); - auto input_channel = dataset->GetInputChannel(); + VLOG(0) << "PreBuild in GpuGraph mode"; + SlotRecordDataset* dataset = (SlotRecordDataset*)(dataset_); + const std::vector& vec_data = dataset->GetGpuGraphTotalKeys(); - const std::deque& vec_data = input_channel->GetData(); total_len = vec_data.size(); len_per_thread = total_len / thread_keys_thread_num_; + VLOG(0) << "GpuGraphTotalKeys: " << total_len; remain = total_len % thread_keys_thread_num_; - auto gen_func = [this](const std::deque& total_data, - int begin_index, - int end_index, - int i) { + auto gen_graph_data_func = [this](const std::vector& total_data, + int begin_index, + int end_index, + int i) { for (auto iter = total_data.begin() + begin_index; iter != total_data.begin() + end_index; iter++) { - const auto& ins = *iter; - const auto& feasign_v = ins.uint64_feasigns_; - for (const auto feasign : feasign_v) { - uint64_t cur_key = feasign.sign().uint64_feasign_; - int shard_id = cur_key % thread_keys_shard_num_; - this->thread_keys_[i][shard_id].insert(cur_key); - } + uint64_t cur_key = *iter; + int shard_id = cur_key % thread_keys_shard_num_; + this->thread_keys_[i][shard_id].insert(cur_key); } }; + auto gen_graph_dynamic_mf_func = + [this](const std::vector& total_data, + int begin_index, + int end_index, + int i) { + for (auto iter = total_data.begin() + begin_index; + iter != total_data.begin() + end_index; + iter++) { + uint64_t cur_key = *iter; + int shard_id = cur_key % thread_keys_shard_num_; + // TODO: feasign <-> slot <-> multi_dim + this->thread_dim_keys_[i][shard_id][0].insert(cur_key); + } + }; for (int i = 0; i < thread_keys_thread_num_; i++) { - threads.push_back( - std::thread(gen_func, - std::ref(vec_data), - begin, - begin + len_per_thread + (i < remain ? 1 : 0), - i)); + if (!multi_mf_dim_) { + VLOG(1) << "psgpu graph wrapper genfunc"; + threads.push_back( + std::thread(gen_graph_data_func, + std::ref(vec_data), + begin, + begin + len_per_thread + (i < remain ? 1 : 0), + i)); + } else { + VLOG(1) << "psgpu graph wrapper genfunc with dynamic mf"; + threads.push_back( + std::thread(gen_graph_dynamic_mf_func, + std::ref(vec_data), + begin, + begin + len_per_thread + (i < remain ? 1 : 0), + i)); + } begin += len_per_thread + (i < remain ? 1 : 0); } for (std::thread& t : threads) { t.join(); } - timeline.Pause(); - VLOG(0) << "GpuPs build task cost " << timeline.ElapsedSec() << " seconds."; } timeline.Start(); @@ -255,6 +323,9 @@ void PSGPUWrapper::PreBuildTask(std::shared_ptr gpu_task) { VLOG(0) << "GpuPs task unique cost " << timeline.ElapsedSec() << " seconds."; for (int i = 0; i < thread_keys_shard_num_; i++) { for (int j = 0; j < multi_mf_dim_; j++) { + if (i == 0 && j == multi_mf_dim_ - 1) { + gpu_task->feature_dim_keys_[i][j].push_back(0); + } VLOG(0) << "GpuPs shard: " << i << "mf dim: " << index_dim_vec_[j] << " key len: " << gpu_task->feature_dim_keys_[i][j].size(); gpu_task->value_dim_ptr_[i][j].resize( @@ -640,7 +711,7 @@ void PSGPUWrapper::BuildGPUTask(std::shared_ptr gpu_task) { } std::vector threads(device_num); auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); HeterPs_ = HeterPsBase::get_instance( size_max, resource_, fleet_config_, accessor_class_, optimizer_type_); #ifdef PADDLE_WITH_CUDA @@ -824,6 +895,7 @@ void PSGPUWrapper::LoadIntoMemory(bool is_shuffle) { dataset_->LocalShuffle(); } InitSlotInfo(); + gpu_graph_mode_ = dataset_->GetGpuGraphMode(); std::shared_ptr gpu_task = gpu_task_pool_.Get(); gpu_task->Reset(); @@ -890,15 +962,22 @@ void PSGPUWrapper::BeginPass() { platform::errors::Fatal("[BeginPass] current task is not ended.")); } + debug_gpu_memory_info("befor build task"); build_task(); + debug_gpu_memory_info("after build task"); timer.Pause(); if (current_task_ == nullptr) { PADDLE_THROW(platform::errors::Fatal( "[BeginPass] after build_task, current task is not null.")); } - - VLOG(0) << "BeginPass end, cost time: " << timer.ElapsedSec() << "s"; + if (FLAGS_gpugraph_dedup_pull_push_mode) { + VLOG(0) << "BeginPass end, cost time: " << timer.ElapsedSec() + << "s, enable pull push dedup mode=" + << FLAGS_gpugraph_dedup_pull_push_mode; + } else { + VLOG(0) << "BeginPass end, cost time: " << timer.ElapsedSec() << "s"; + } } void PSGPUWrapper::EndPass() { @@ -919,7 +998,7 @@ void PSGPUWrapper::EndPass() { } int thread_num = 8; auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); auto dump_pool_to_cpu_func = [this, thread_num, &accessor_wrapper_ptr]( int i, int j, int z) { PADDLE_ENFORCE_GPU_SUCCESS(cudaSetDevice(this->resource_->dev_id(i))); @@ -961,30 +1040,7 @@ void PSGPUWrapper::EndPass() { size_t local_offset = (i - left) * feature_value_size; float* gpu_val = (float*)(test_build_values + local_offset); #ifdef PADDLE_WITH_PSLIB - auto* downpour_value = - (paddle::ps::DownpourFixedFeatureValue*)(gpu_val->cpu_ptr); - int downpour_value_size = downpour_value->size(); - if (gpu_val->mf_size > 0 && downpour_value_size == 8) { - downpour_value->resize(gpu_val->mf_dim + 1 + downpour_value_size); - } - float* cpu_val = downpour_value->data(); - cpu_val[paddle::ps::DownpourCtrDymfAccessor::DownpourCtrDymfFeatureValue:: - delta_score_index()] = gpu_val->delta_score; - cpu_val[paddle::ps::DownpourCtrDymfAccessor::DownpourCtrDymfFeatureValue:: - show_index()] = gpu_val->show; - cpu_val[paddle::ps::DownpourCtrDymfAccessor::DownpourCtrDymfFeatureValue:: - click_index()] = gpu_val->clk; - cpu_val[paddle::ps::DownpourCtrDymfAccessor::DownpourCtrDymfFeatureValue:: - embed_w_index()] = gpu_val->lr; - cpu_val[paddle::ps::DownpourCtrDymfAccessor::DownpourCtrDymfFeatureValue:: - embed_g2sum_index()] = gpu_val->lr_g2sum; - cpu_val[paddle::ps::DownpourCtrDymfAccessor::DownpourCtrDymfFeatureValue:: - slot_index()] = gpu_val->slot; - if (gpu_val->mf_size > 0) { - for (int x = 0; x < gpu_val->mf_dim + 1; x++) { - cpu_val[x + 8] = gpu_val->mf[x]; - } - } + // TODO: PSLIB DumpFill #endif #ifdef PADDLE_WITH_PSCORE accessor_wrapper_ptr->DumpFill(gpu_val, cpu_table_accessor_, mf_dim); @@ -1043,102 +1099,220 @@ void PSGPUWrapper::PullSparse(const paddle::platform::Place& place, platform::Timer all_timer; platform::Timer pull_gpups_timer; all_timer.Start(); - size_t total_length = - std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL); - size_t feature_value_size = 0; auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); - feature_value_size = accessor_wrapper_ptr->GetFeatureValueSize(max_mf_dim_); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); + size_t feature_value_size = + accessor_wrapper_ptr->GetPullValueSize(max_mf_dim_); VLOG(3) << "PullSparse max_dim:" << max_mf_dim_ - << " feature_value_size:" << feature_value_size; + << " pull_feature_value_size:" << pull_type_size_; -#ifdef PADDLE_WITH_CUDA - VLOG(3) << "Begine Gpu Ps PullSparse"; - auto buf = memory::Alloc(place, total_length * feature_value_size); - float* total_values_gpu = reinterpret_cast(buf->ptr()); -#endif -#ifdef PADDLE_WITH_XPU_KP - VLOG(3) << "Begine Xpu Ps PullSparse"; - FeatureValue* total_values_gpu = nullptr; - xpu_malloc(reinterpret_cast(&total_values_gpu), - total_length * feature_value_size); -#endif if (platform::is_cpu_place(place)) { PADDLE_THROW(platform::errors::Unimplemented( "Warning:: CPUPlace is not supported in GpuPs now.")); } else if (platform::is_gpu_place(place)) { - VLOG(3) << "Begin copy keys, key_num[" << total_length << "]"; +#ifdef PADDLE_WITH_CUDA int device_id = place.GetDeviceId(); int devid_2_index = HeterPs_->get_index_by_devid(device_id); - LoDTensor& total_keys_tensor = keys_tensor[devid_2_index]; - uint64_t* total_keys = - reinterpret_cast(total_keys_tensor.mutable_data( - {int64_t(total_length), 1}, place)); - - // construct slot_level lod info - auto slot_lengths_lod = slot_lengths; - for (size_t i = 1; i < slot_lengths_lod.size(); i++) { - slot_lengths_lod[i] += slot_lengths_lod[i - 1]; + if (FLAGS_gpugraph_dedup_pull_push_mode > 0) { + auto& dev = device_caches_[devid_2_index]; + int slot_num = static_cast(slot_lengths.size()); + std::vector slot_lengths_lod; + slot_lengths_lod.reserve(slot_num + 1); + slot_lengths_lod.push_back(0); + + int64_t total_length = 0; + for (int i = 0; i < slot_num; ++i) { + total_length += slot_lengths[i]; + slot_lengths_lod.push_back(total_length); + } + dev.total_key_length = total_length; + VLOG(3) << "[" << device_id << "]Begin copy keys, key_num[" + << total_length << "] dedup mode"; + + auto stream = dynamic_cast( + platform::DeviceContextPool::Instance().Get(place)) + ->stream(); + + uint64_t* total_keys = dev.keys_tensor.mutable_data( + (total_length * 3) * sizeof(uint64_t), place); + + int* gpu_slot_dims = dev.dims_tensor.mutable_data( + slot_dim.size() * sizeof(int), place); + uint64_t** gpu_keys = dev.keys_ptr_tensor.mutable_data( + keys.size() * sizeof(uint64_t*), place); + + int64_t* slot_lens = dev.slot_lens.mutable_data( + (slot_num + 1) * sizeof(int64_t), place); + cudaMemcpyAsync(gpu_keys, + keys.data(), + keys.size() * sizeof(uint64_t*), + cudaMemcpyHostToDevice, + stream); + cudaMemcpyAsync(slot_lens, + slot_lengths_lod.data(), + slot_lengths_lod.size() * sizeof(int64_t), + cudaMemcpyHostToDevice, + stream); + + cudaMemcpyAsync(gpu_slot_dims, + slot_dim.data(), + slot_dim.size() * sizeof(int), + cudaMemcpyHostToDevice, + stream); + float** gpu_values = dev.values_ptr_tensor.mutable_data( + values.size() * sizeof(float*), place); + cudaMemcpyAsync(gpu_values, + values.data(), + values.size() * sizeof(float*), + cudaMemcpyHostToDevice, + stream); + + int* key2slot = dev.keys2slot.mutable_data( + (total_length * 5) * sizeof(int), place); + + this->CopyKeys(place, + gpu_keys, + total_keys, + slot_lens, + slot_num, + static_cast(total_length), + key2slot); + + uint32_t* d_restore_idx = + reinterpret_cast(&key2slot[total_length]); + uint32_t* d_sorted_idx = + reinterpret_cast(&d_restore_idx[total_length]); + uint32_t* d_offset = + reinterpret_cast(&d_sorted_idx[total_length]); + uint32_t* d_merged_cnts = + reinterpret_cast(&d_offset[total_length]); + uint64_t* d_merged_keys = + reinterpret_cast(&total_keys[total_length]); + uint64_t* d_sorted_keys = + reinterpret_cast(&d_merged_keys[total_length]); + + int dedup_size = HeterPs_->dedup_keys_and_fillidx( + devid_2_index, + static_cast(total_length), + total_keys, // input + d_merged_keys, // output + d_sorted_keys, // sort keys + d_restore_idx, // pull fill idx + d_sorted_idx, // sort old idx + d_offset, // offset + d_merged_cnts, + FLAGS_gpugraph_dedup_pull_push_mode & 0x02); + // printf("device %d, end dedup_keys_and_fillidx total %d, " + // "dedup_size %d, slot num: %d, value size: %d\n", + // device_id, int(total_length), dedup_size, slot_num, + // int(feature_value_size)); + + PADDLE_ENFORCE_GT(dedup_size, + 0, + platform::errors::PreconditionNotMet( + "dedup keys need more than zero failed in BoxPS.")); + dev.dedup_key_length = dedup_size; + + int64_t total_bytes = dedup_size * feature_value_size; + float* total_values_gpu = + dev.pull_push_tensor.mutable_data(total_bytes, place); + pull_gpups_timer.Start(); + HeterPs_->pull_sparse( + devid_2_index, d_merged_keys, total_values_gpu, dedup_size); + + // values.size() not sure equal slot_num + accessor_wrapper_ptr->CopyForPull(place, + total_keys, + gpu_values, + total_values_gpu, + slot_lens, + key2slot, + max_mf_dim_ + 3, + total_length, + gpu_slot_dims, + d_restore_idx, + feature_value_size); + } else { + size_t total_length = + std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL); + auto buf = memory::Alloc(place, total_length * feature_value_size); + float* total_values_gpu = reinterpret_cast(buf->ptr()); + VLOG(3) << "Begin copy keys, key_num[" << total_length << "]"; + LoDTensor& total_keys_tensor = keys_tensor[devid_2_index]; + uint64_t* total_keys = + reinterpret_cast(total_keys_tensor.mutable_data( + {int64_t(total_length), 1}, place)); + // construct slot_level lod info + auto slot_lengths_lod = slot_lengths; + for (size_t i = 1; i < slot_lengths_lod.size(); i++) { + slot_lengths_lod[i] += slot_lengths_lod[i - 1]; + } + auto buf_key = memory::Alloc(place, keys.size() * sizeof(uint64_t*)); + auto buf_length = + memory::Alloc(place, slot_lengths.size() * sizeof(int64_t)); + uint64_t** gpu_keys = reinterpret_cast(buf_key->ptr()); + int64_t* gpu_len = reinterpret_cast(buf_length->ptr()); + cudaMemcpy(gpu_keys, + keys.data(), + keys.size() * sizeof(uint64_t*), + cudaMemcpyHostToDevice); + cudaMemcpy(gpu_len, + slot_lengths_lod.data(), + slot_lengths.size() * sizeof(int64_t), + cudaMemcpyHostToDevice); + + auto buf_dim = memory::Alloc(place, slot_dim.size() * sizeof(int)); + int* gpu_dim = reinterpret_cast(buf_dim->ptr()); + cudaMemcpy(gpu_dim, + slot_dim.data(), + slot_dim.size() * sizeof(int), + cudaMemcpyHostToDevice); + + this->CopyKeys(place, + gpu_keys, + total_keys, + gpu_len, + static_cast(slot_lengths.size()), + static_cast(total_length)); + VLOG(3) << "Begin call PullSparseGPU in GPUPS, dev: " << devid_2_index + << " len: " << total_length; + + pull_gpups_timer.Start(); + HeterPs_->pull_sparse( + devid_2_index, total_keys, total_values_gpu, total_length); + + VLOG(3) << "Begin Copy result to tensor, total_length[" << total_length + << "]"; + + accessor_wrapper_ptr->CopyForPull(place, + gpu_keys, + values, + total_values_gpu, + gpu_len, + static_cast(slot_lengths.size()), + hidden_size, + total_length, + gpu_dim, + feature_value_size); } - auto buf_key = memory::Alloc(place, keys.size() * sizeof(uint64_t*)); - auto buf_length = - memory::Alloc(place, slot_lengths.size() * sizeof(int64_t)); - uint64_t** gpu_keys = reinterpret_cast(buf_key->ptr()); - int64_t* gpu_len = reinterpret_cast(buf_length->ptr()); - cudaMemcpy(gpu_keys, - keys.data(), - keys.size() * sizeof(uint64_t*), - cudaMemcpyHostToDevice); - cudaMemcpy(gpu_len, - slot_lengths_lod.data(), - slot_lengths.size() * sizeof(int64_t), - cudaMemcpyHostToDevice); - - auto buf_dim = memory::Alloc(place, slot_dim.size() * sizeof(int)); - int* gpu_dim = reinterpret_cast(buf_dim->ptr()); - cudaMemcpy(gpu_dim, - slot_dim.data(), - slot_dim.size() * sizeof(int), - cudaMemcpyHostToDevice); - - this->CopyKeys(place, - gpu_keys, - total_keys, - gpu_len, - static_cast(slot_lengths.size()), - static_cast(total_length)); - VLOG(3) << "Begin call PullSparseGPU in GPUPS, dev: " << devid_2_index - << " len: " << total_length; - - pull_gpups_timer.Start(); - HeterPs_->pull_sparse( - devid_2_index, total_keys, total_values_gpu, total_length); - - VLOG(3) << "Begin Copy result to tensor, total_length[" << total_length - << "]"; - - accessor_wrapper_ptr->CopyForPull(place, - gpu_keys, - values, - total_values_gpu, - gpu_len, - static_cast(slot_lengths.size()), - hidden_size, - total_length, - gpu_dim, - val_type_size_); - pull_gpups_timer.Pause(); - +#endif } else if (platform::is_xpu_place(place)) { #ifdef PADDLE_WITH_XPU_KP + VLOG(3) << "Begine Xpu Ps PullSparse"; + size_t total_length = + std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL); + FeatureValue* total_values_gpu = nullptr; + xpu_malloc(reinterpret_cast(&total_values_gpu), + total_length * feature_value_size); VLOG(3) << "Begin copy keys, key_num[" << total_length << "]"; int device_id = place.GetDeviceId(); int devid_2_index = HeterPs_->get_index_by_devid(device_id); LoDTensor& total_keys_tensor = keys_tensor[devid_2_index]; - uint64_t* total_keys = reinterpret_cast( - total_keys_tensor.mutable_data({total_length, 1}, place)); + uint64_t* total_keys = + reinterpret_cast(total_keys_tensor.mutable_data( + {int64_t(total_length), 1}, place)); // construct slot_level lod info auto slot_lengths_lod = slot_lengths; @@ -1185,7 +1359,7 @@ void PSGPUWrapper::PullSparse(const paddle::platform::Place& place, static_cast(slot_lengths.size()), hidden_size, total_length, - val_type_size_); + feature_value_size); #endif } else { PADDLE_THROW(platform::errors::PreconditionNotMet( @@ -1208,17 +1382,10 @@ void PSGPUWrapper::PushSparseGrad(const paddle::platform::Place& place, platform::Timer all_timer; platform::Timer push_gpups_timer; all_timer.Start(); - int64_t total_length = - std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL); - // #ifdef PADDLE_WITH_CUDA - VLOG(3) << "Begin GPUPS PushSparseGrad"; auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); size_t grad_value_size = accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); - auto buf = memory::Alloc(place, total_length * grad_value_size); - VLOG(3) << "Push Sparse Max mf dimention: " << max_mf_dim_ - << "grad_value_size:" << grad_value_size; - float* total_grad_values_gpu = reinterpret_cast(buf->ptr()); + if (platform::is_cpu_place(place)) { PADDLE_THROW(platform::errors::Unimplemented( "Warning:: CPUPlace is not supported in GPUPS now.")); @@ -1226,36 +1393,142 @@ void PSGPUWrapper::PushSparseGrad(const paddle::platform::Place& place, #ifdef PADDLE_WITH_CUDA int device_id = place.GetDeviceId(); int devid_2_index = HeterPs_->get_index_by_devid(device_id); - LoDTensor& cached_total_keys_tensor = keys_tensor[devid_2_index]; - uint64_t* total_keys = - reinterpret_cast(cached_total_keys_tensor.data()); - VLOG(3) << "Begin copy grad tensor to gpups struct"; - accessor_wrapper_ptr->CopyForPush(place, - grad_values, - total_grad_values_gpu, - slot_lengths, - total_length, - batch_size, - grad_value_size, - slot_vector_, - slot_mf_dim_vector_); + if (FLAGS_gpugraph_dedup_pull_push_mode > 0) { + auto& dev = device_caches_[devid_2_index]; + int64_t total_length = dev.total_key_length; + VLOG(3) << "Begin push sparse, key_num[" << total_length + << "] dedup mode, device:" << device_id << ", index" + << devid_2_index; + auto stream = dynamic_cast( + platform::DeviceContextPool::Instance().Get(place)) + ->stream(); + uint64_t* total_keys = dev.keys_tensor.data(); + int* slot_dims = dev.dims_tensor.data(); + int slot_num = static_cast(slot_lengths.size()); + if (!dev.d_slot_vector.IsInitialized()) { + int* buf_slot_vector = + dev.d_slot_vector.mutable_data(slot_num * sizeof(int), place); + cudaMemcpyAsync(buf_slot_vector, + slot_vector_.data(), + slot_num * sizeof(int), + cudaMemcpyHostToDevice, + stream); + } - VLOG(3) << "Begin call PushSparseGPU in GPUPS, dev: " << devid_2_index - << " len: " << total_length; - push_gpups_timer.Start(); - HeterPs_->push_sparse(devid_2_index, - total_keys, - total_grad_values_gpu, - static_cast(total_length)); + const int64_t* slot_lens = dev.slot_lens.data(); + const int* d_slot_vector = dev.d_slot_vector.data(); + const int* key2slot = dev.keys2slot.data(); + float** gpu_values = dev.values_ptr_tensor.data(); + cudaMemcpyAsync(gpu_values, + grad_values.data(), + grad_values.size() * sizeof(float*), + cudaMemcpyHostToDevice, + stream); + + uint64_t* d_merged_keys = &total_keys[total_length]; + + int64_t dedup_size = dev.dedup_key_length; + int64_t total_bytes = dedup_size * grad_value_size; + float* total_grad_values_gpu = + dev.pull_push_tensor.mutable_data(total_bytes, place); + // dedup rate more than 3 + if (total_length > dedup_size * 3) { + const uint32_t* d_restore_idx = + reinterpret_cast(&key2slot[total_length]); + accessor_wrapper_ptr->CopyForPush(place, + total_keys, + gpu_values, + total_grad_values_gpu, + d_slot_vector, + slot_lens, + max_mf_dim_ + 3, + total_length, + dedup_size, + batch_size, + slot_dims, + key2slot, + d_restore_idx, + grad_value_size); + } else { + const uint32_t* d_sorted_idx = + reinterpret_cast(&key2slot[total_length * 2]); + const uint32_t* d_offset = + reinterpret_cast(&d_sorted_idx[total_length]); + const uint32_t* d_merged_cnts = + reinterpret_cast(&d_offset[total_length]); + accessor_wrapper_ptr->CopyForPush(place, + d_merged_keys, + gpu_values, + total_grad_values_gpu, + d_slot_vector, + slot_lens, + max_mf_dim_ + 3, + total_length, + dedup_size, + batch_size, + slot_dims, + key2slot, + d_sorted_idx, + d_offset, + d_merged_cnts, + grad_value_size); + } + + push_gpups_timer.Start(); + HeterPs_->push_sparse(devid_2_index, + d_merged_keys, + total_grad_values_gpu, + static_cast(dedup_size)); + } else { + int64_t total_length = + std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL); + VLOG(3) << "Begin GPUPS PushSparseGrad"; + + auto buf = memory::Alloc(place, total_length * grad_value_size); + VLOG(3) << "Push Sparse Max mf dimention: " << max_mf_dim_ + << "grad_value_size:" << grad_value_size; + float* total_grad_values_gpu = reinterpret_cast(buf->ptr()); + + LoDTensor& total_keys_tensor = keys_tensor[devid_2_index]; + uint64_t* total_keys = + reinterpret_cast(total_keys_tensor.data()); + VLOG(3) << "Begin copy grad tensor to gpups struct"; + + accessor_wrapper_ptr->CopyForPush(place, + grad_values, + total_grad_values_gpu, + slot_lengths, + total_length, + batch_size, + grad_value_size, + slot_vector_, + slot_mf_dim_vector_); + + VLOG(3) << "Begin call PushSparseGPU in GPUPS, dev: " << devid_2_index + << " len: " << total_length; + push_gpups_timer.Start(); + HeterPs_->push_sparse(devid_2_index, + total_keys, + total_grad_values_gpu, + static_cast(total_length)); + } push_gpups_timer.Pause(); #endif } else if (platform::is_xpu_place(place)) { #ifdef PADDLE_WITH_XPU_KP int device_id = place.GetDeviceId(); int devid_2_index = HeterPs_->get_index_by_devid(device_id); - LoDTensor& cached_total_keys_tensor = keys_tensor[devid_2_index]; + int64_t total_length = + std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL); + VLOG(3) << "Begin GPUPS PushSparseGrad"; + + auto buf = memory::Alloc(place, total_length * grad_value_size); + VLOG(3) << "Push Sparse Max mf dimention: " << max_mf_dim_ + << "grad_value_size:" << grad_value_size; + float* total_grad_values_gpu = reinterpret_cast(buf->ptr()); + LoDTensor& total_keys_tensor = keys_tensor[devid_2_index]; uint64_t* total_keys = - reinterpret_cast(cached_total_keys_tensor.data()); + reinterpret_cast(total_keys_tensor.data()); VLOG(3) << "Begin copy grad tensor to xpups struct"; accessor_wrapper_ptr->CopyForPush(place, grad_values, @@ -1288,6 +1561,6 @@ void PSGPUWrapper::PushSparseGrad(const paddle::platform::Place& place, VLOG(3) << "End PushSparseGrad"; } -} // end namespace framework +} // namespace framework } // end namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu index f8624f48d08f3f895752f566d2b562925fca6fd8..0e806fdb5f50960c3ec8e562062617643bd72e0d 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu @@ -22,10 +22,15 @@ limitations under the License. */ #include "paddle/fluid/framework/fleet/ps_gpu_wrapper.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/platform/device/gpu/gpu_info.h" +#include "paddle/fluid/platform/device/gpu/gpu_primitives.h" namespace paddle { namespace framework { +const int CUDA_NUM_THREADS = platform::PADDLE_CUDA_NUM_THREADS; +#define GET_BLOCK(N) ((N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS) +#define CUDA_BLOCK(N) GET_BLOCK(N), CUDA_NUM_THREADS, 0 + __global__ void CopyKeysKernel(uint64_t** src_keys, uint64_t* dest_total_keys, const int64_t* len, @@ -93,6 +98,44 @@ void PSGPUWrapper::CopyKeys(const paddle::platform::Place& place, cudaStreamSynchronize(stream); } +__global__ void CopyKeysKernel2(const int total_len, + uint64_t** src_keys, + uint64_t* dest_total_keys, + const int slot_num, + const int64_t* slot_lens, + int* key2slots) { + CUDA_KERNEL_LOOP(i, total_len) { + int low = 0; + int high = slot_num - 1; + while (low < high) { + int mid = (low + high) / 2; + if (i < slot_lens[mid + 1]) { + high = mid; + } else { + low = mid + 1; + } + } + key2slots[i] = low; + int y = i - slot_lens[low]; + dest_total_keys[i] = src_keys[low][y]; + } +} + +void PSGPUWrapper::CopyKeys(const paddle::platform::Place& place, + uint64_t** origin_keys, + uint64_t* total_keys, + const int64_t* slot_lens, + int slot_num, + int total_len, + int* key2slot) { + auto stream = dynamic_cast( + platform::DeviceContextPool::Instance().Get(place)) + ->stream(); + CopyKeysKernel2<<>>( + total_len, origin_keys, total_keys, slot_num, slot_lens, key2slot); + cudaStreamSynchronize(stream); +} + void PSGPUWrapper::SetSparseSGD(float nonclk_coeff, float clk_coeff, float min_bound, @@ -123,7 +166,9 @@ void PSGPUWrapper::SetEmbedxSGD(float mf_create_thresholds, float mf_max_bound, float mf_beta1_decay_rate, float mf_beta2_decay_rate, - float mf_ada_epsilon) { + float mf_ada_epsilon, + float nodeid_slot, + float feature_learning_rate) { optimizer_config_.set_embedx_sgd(mf_create_thresholds, mf_learning_rate, mf_initial_g2sum, @@ -132,7 +177,9 @@ void PSGPUWrapper::SetEmbedxSGD(float mf_create_thresholds, mf_max_bound, mf_beta1_decay_rate, mf_beta2_decay_rate, - mf_ada_epsilon); + mf_ada_epsilon, + nodeid_slot, + feature_learning_rate); } } // end namespace framework diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.h b/paddle/fluid/framework/fleet/ps_gpu_wrapper.h index cce120bcef747d315afae464ed2563a52a690aca..c48cf3347573a05dcbfd6c2af0255728fe492dcc 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.h +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.h @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. */ #pragma once - #ifdef PADDLE_WITH_HETERPS #include @@ -98,20 +97,61 @@ class AfsWrapper { #endif class PSGPUWrapper { + class DCacheBuffer { + public: + DCacheBuffer() : buf_(nullptr) {} + ~DCacheBuffer() {} + /** + * @Brief get data + */ + template + T* mutable_data(const size_t total_bytes, + const paddle::platform::Place& place) { + if (buf_ == nullptr) { + buf_ = memory::AllocShared(place, total_bytes); + } else if (buf_->size() < total_bytes) { + buf_.reset(); + buf_ = memory::AllocShared(place, total_bytes); + } + return reinterpret_cast(buf_->ptr()); + } + template + T* data() { + return reinterpret_cast(buf_->ptr()); + } + size_t memory_size() { + if (buf_ == nullptr) { + return 0; + } + return buf_->size(); + } + bool IsInitialized(void) { return (buf_ != nullptr); } + + private: + std::shared_ptr buf_ = nullptr; + }; + struct PSDeviceData { + DCacheBuffer keys_tensor; + DCacheBuffer dims_tensor; + DCacheBuffer keys_ptr_tensor; + DCacheBuffer values_ptr_tensor; + DCacheBuffer pull_push_tensor; + + DCacheBuffer slot_lens; + DCacheBuffer d_slot_vector; + DCacheBuffer keys2slot; + + int64_t total_key_length = 0; + int64_t dedup_key_length = 0; + }; + PSDeviceData* device_caches_ = nullptr; + public: ~PSGPUWrapper(); PSGPUWrapper() { HeterPs_ = NULL; sleep_seconds_before_fail_exit_ = 300; - pull_thread_pool_.resize(thread_keys_shard_num_); - for (size_t i = 0; i < pull_thread_pool_.size(); i++) { - pull_thread_pool_[i].reset(new ::ThreadPool(1)); - } - hbm_thread_pool_.resize(thread_keys_shard_num_); - for (size_t i = 0; i < hbm_thread_pool_.size(); i++) { - hbm_thread_pool_[i].reset(new ::ThreadPool(1)); - } } void PullSparse(const paddle::platform::Place& place, @@ -140,6 +180,13 @@ class PSGPUWrapper { const int64_t* gpu_len, int slot_num, int total_len); + void CopyKeys(const paddle::platform::Place& place, + uint64_t** origin_keys, + uint64_t* total_keys, + const int64_t* gpu_len, + int slot_num, + int total_len, + int* key2slot); void BuildGPUTask(std::shared_ptr gpu_task); void PreBuildTask(std::shared_ptr gpu_task); @@ -164,6 +211,11 @@ class PSGPUWrapper { pre_build_threads_.join(); s_instance_ = nullptr; VLOG(3) << "PSGPUWrapper Finalize Finished."; + HeterPs_->show_table_collisions(); + if (device_caches_ != nullptr) { + delete[] device_caches_; + device_caches_ = nullptr; + } } void InitializeGPU(const std::vector& dev_ids) { @@ -173,6 +225,7 @@ class PSGPUWrapper { resource_ = std::make_shared(dev_ids); resource_->enable_p2p(); keys_tensor.resize(resource_->total_device()); + device_caches_ = new PSDeviceData[resource_->total_device()]; #ifdef PADDLE_WITH_GLOO auto gloo = paddle::framework::GlooWrapper::GetInstance(); if (gloo->Size() > 1) { @@ -256,7 +309,9 @@ class PSGPUWrapper { float mf_max_bound, float mf_beta1_decay_rate, float mf_beta2_decay_rate, - float mf_ada_epsilon); + float mf_ada_epsilon, + float nodeid_slot, + float feature_learning_rate); #ifdef PADDLE_WITH_PSCORE void add_sparse_optimizer( @@ -308,6 +363,21 @@ class PSGPUWrapper { void InitializeGPUServer(paddle::distributed::PSParameter ps_param) { auto sparse_table = ps_param.server_param().downpour_server_param().downpour_table_param(0); + // set build thread_num and shard_num + thread_keys_thread_num_ = sparse_table.shard_num(); + thread_keys_shard_num_ = sparse_table.shard_num(); + VLOG(1) << "ps_gpu build phase thread_num:" << thread_keys_thread_num_ + << " shard_num:" << thread_keys_shard_num_; + + pull_thread_pool_.resize(thread_keys_shard_num_); + for (size_t i = 0; i < pull_thread_pool_.size(); i++) { + pull_thread_pool_[i].reset(new ::ThreadPool(1)); + } + hbm_thread_pool_.resize(thread_keys_shard_num_); + for (size_t i = 0; i < hbm_thread_pool_.size(); i++) { + hbm_thread_pool_[i].reset(new ::ThreadPool(1)); + } + auto sparse_table_accessor = sparse_table.accessor(); auto sparse_table_accessor_parameter = sparse_table_accessor.ctr_accessor_param(); @@ -319,6 +389,11 @@ class PSGPUWrapper { config["clk_coeff"] = sparse_table_accessor_parameter.click_coeff(); config["mf_create_thresholds"] = sparse_table_accessor.embedx_threshold(); + config["nodeid_slot"] = + sparse_table_accessor.graph_sgd_param().nodeid_slot(); + config["feature_learning_rate"] = + sparse_table_accessor.graph_sgd_param().feature_learning_rate(); + if (accessor_class_ == "CtrDymfAccessor") { // optimizer config for embed_w and embedx add_sparse_optimizer(config, sparse_table_accessor.embed_sgd_param()); @@ -327,8 +402,8 @@ class PSGPUWrapper { } fleet_config_ = config; - GlobalAccessorTransfor::GetInstance().Init(accessor_class_); - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper()->Configure( + GlobalAccessorFactory::GetInstance().Init(accessor_class_); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper()->Configure( config); InitializeGPUServer(config); } @@ -394,6 +469,16 @@ class PSGPUWrapper { float mf_ada_epsilon = (config.find("mf_ada_epsilon") == config.end()) ? 1e-8 : config["mf_ada_epsilon"]; + + float feature_learning_rate = + (config.find("feature_learning_rate") == config.end()) + ? 0.05 + : config["feature_learning_rate"]; + + float nodeid_slot = (config.find("nodeid_slot") == config.end()) + ? 9008 + : config["nodeid_slot"]; + this->SetSparseSGD(nonclk_coeff, clk_coeff, min_bound, @@ -412,12 +497,18 @@ class PSGPUWrapper { mf_max_bound, mf_beta1_decay_rate, mf_beta2_decay_rate, - mf_ada_epsilon); + mf_ada_epsilon, + nodeid_slot, + feature_learning_rate); // set optimizer type(naive,adagrad,std_adagrad,adam,share_adam) optimizer_type_ = (config.find("optimizer_type") == config.end()) ? 1 - : static_cast(config["optimizer_type"]); + : int(config["optimizer_type"]); + + VLOG(0) << "InitializeGPUServer optimizer_type_:" << optimizer_type_ + << " nodeid_slot:" << nodeid_slot + << " feature_learning_rate:" << feature_learning_rate; } void SetDate(int year, int month, int day) { @@ -508,11 +599,13 @@ class PSGPUWrapper { } auto accessor_wrapper_ptr = - GlobalAccessorTransfor::GetInstance().GetAccessorWrapper(); + GlobalAccessorFactory::GetInstance().GetAccessorWrapper(); val_type_size_ = accessor_wrapper_ptr->GetFeatureValueSize(max_mf_dim_); grad_type_size_ = accessor_wrapper_ptr->GetPushValueSize(max_mf_dim_); + pull_type_size_ = accessor_wrapper_ptr->GetPullValueSize(max_mf_dim_); VLOG(0) << "InitSlotInfo: val_type_size_" << val_type_size_ - << " grad_type_size_:" << grad_type_size_; + << " grad_type_size_:" << grad_type_size_ + << " pull_type_size_:" << pull_type_size_; slot_info_initialized_ = true; } #endif @@ -564,6 +657,7 @@ class PSGPUWrapper { int max_mf_dim_{0}; size_t val_type_size_{0}; size_t grad_type_size_{0}; + size_t pull_type_size_{0}; double time_1 = 0.0; double time_2 = 0.0; @@ -573,6 +667,7 @@ class PSGPUWrapper { int multi_node_{0}; int node_size_; uint64_t table_id_; + int gpu_graph_mode_ = 0; #ifdef PADDLE_WITH_CUDA std::vector inner_comms_; std::vector inter_comms_; diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps b/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps index 3505bff72e90a1c01b60a46a5d45c68928d4a456..3a8a4cbeaa0f5514cd312771c4338712fee14c8d 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps @@ -220,52 +220,6 @@ void PSGPUWrapper::CopyKeys(const paddle::platform::Place& place, xpu_wait(stream); } -void PSGPUWrapper::SetSparseSGD(float nonclk_coeff, - float clk_coeff, - float min_bound, - float max_bound, - float learning_rate, - float initial_g2sum, - float initial_range, - float beta1_decay_rate, - float beta2_decay_rate, - float ada_epsilon) { - OptimizerConfig optimizer_config; - optimizer_config.set_sparse_sgd(nonclk_coeff, - clk_coeff, - min_bound, - max_bound, - learning_rate, - initial_g2sum, - initial_range, - beta1_decay_rate, - beta2_decay_rate, - ada_epsilon); - HeterPs_->set_sparse_sgd(optimizer_config); -} - -void PSGPUWrapper::SetEmbedxSGD(float mf_create_thresholds, - float mf_learning_rate, - float mf_initial_g2sum, - float mf_initial_range, - float mf_min_bound, - float mf_max_bound, - float mf_beta1_decay_rate, - float mf_beta2_decay_rate, - float mf_ada_epsilon) { - OptimizerConfig optimizer_config; - optimizer_config.set_embedx_sgd(mf_create_thresholds, - mf_learning_rate, - mf_initial_g2sum, - mf_initial_range, - mf_min_bound, - mf_max_bound, - mf_beta1_decay_rate, - mf_beta2_decay_rate, - mf_ada_epsilon); - HeterPs_->set_embedx_sgd(optimizer_config); -} - } // end namespace framework } // end namespace paddle #endif diff --git a/paddle/fluid/framework/hogwild_worker.cc b/paddle/fluid/framework/hogwild_worker.cc index a3daa37e0318bd1542f531b65332643bc37d317c..777ca26e3eb7d2750b5a1f7ccd482f7f8f98a72f 100644 --- a/paddle/fluid/framework/hogwild_worker.cc +++ b/paddle/fluid/framework/hogwild_worker.cc @@ -119,6 +119,12 @@ void HogwildWorker::CreateDeviceResource(const ProgramDesc &main_prog) { void HogwildWorker::TrainFilesWithProfiler() { platform::SetNumThreads(1); +#if defined(PADDLE_WITH_HETERPS) && \ + (defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)) + platform::SetDeviceId(thread_id_); +#elif defined(PADDLE_WITH_HETERPS) && defined(PADDLE_WITH_XPU_BKCL) + platform::SetXPUDeviceId(thread_id_); +#endif device_reader_->Start(); std::vector op_total_time; std::vector op_name; @@ -175,8 +181,6 @@ void HogwildWorker::TrainFilesWithProfiler() { PrintFetchVars(); #ifdef PADDLE_WITH_HETERPS dev_ctx_->Wait(); - VLOG(1) << "GpuPs worker " << thread_id_ << " train cost " << total_time - << " seconds, ins_num: " << total_inst; for (size_t i = 0; i < op_name.size(); ++i) { VLOG(1) << "card:" << thread_id_ << ", op: " << op_name[i] << ", mean time: " << op_total_time[i] / total_inst @@ -201,6 +205,9 @@ void HogwildWorker::TrainFilesWithProfiler() { thread_scope_->DropKids(); timeline.Start(); } + VLOG(0) << "GpuPs worker " << thread_id_ << " train cost " << total_time + << " seconds, ins_num: " << total_inst << " read time: " << read_time + << "seconds "; if (need_dump_field_ || need_dump_param_) { writer_.Flush(); @@ -217,16 +224,19 @@ void HogwildWorker::TrainFiles() { platform::SetNumThreads(1); platform::Timer timeline; timeline.Start(); +#if defined(PADDLE_WITH_HETERPS) && \ + (defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)) + platform::SetDeviceId(thread_id_); +#elif defined(PADDLE_WITH_HETERPS) && defined(PADDLE_WITH_XPU_BKCL) + platform::SetXPUDeviceId(thread_id_); +#endif - int total_ins_num = 0; + int total_batch_num = 0; // how to accumulate fetched values here device_reader_->Start(); int cur_batch; int batch_cnt = 0; -#if defined(PADDLE_WITH_HETERPS) && defined(PADDLE_WITH_CUDA) - platform::SetDeviceId(thread_id_); -#endif while ((cur_batch = device_reader_->Next()) > 0) { for (auto &op : ops_) { bool need_skip = false; @@ -248,7 +258,7 @@ void HogwildWorker::TrainFiles() { DumpParam(*thread_scope_, batch_cnt); } - total_ins_num += cur_batch; + total_batch_num += cur_batch; ++batch_cnt; PrintFetchVars(); thread_scope_->DropKids(); @@ -257,8 +267,8 @@ void HogwildWorker::TrainFiles() { #endif } timeline.Pause(); - VLOG(1) << "worker " << thread_id_ << " train cost " << timeline.ElapsedSec() - << " seconds, ins_num: " << total_ins_num; + VLOG(0) << "worker " << thread_id_ << " train cost " << timeline.ElapsedSec() + << " seconds, batch_num: " << total_batch_num; if (need_dump_field_ || need_dump_param_) { writer_.Flush(); diff --git a/paddle/fluid/framework/io/fs.cc b/paddle/fluid/framework/io/fs.cc index da87bd33f0d4b8accebeea49935ea16bab4fe9d6..285ce2ddb2791f72ddde62afd1d6d516d05a3d0d 100644 --- a/paddle/fluid/framework/io/fs.cc +++ b/paddle/fluid/framework/io/fs.cc @@ -157,7 +157,7 @@ std::vector localfs_list(const std::string& path) { std::shared_ptr pipe; int err_no = 0; pipe = shell_popen( - string::format_string("find %s -type f -maxdepth 1", path.c_str()), + string::format_string("find %s -type f -maxdepth 1 | sort", path.c_str()), "r", &err_no); string::LineFileReader reader; diff --git a/paddle/fluid/framework/ps_gpu_worker.cc b/paddle/fluid/framework/ps_gpu_worker.cc index ad1ddbfabd0911226f4c2e58975567d425428396..b7674e06b9f73d54df6e5c5e9388d0f4737e7c43 100644 --- a/paddle/fluid/framework/ps_gpu_worker.cc +++ b/paddle/fluid/framework/ps_gpu_worker.cc @@ -128,16 +128,16 @@ void PSGPUWorker::TrainFiles() { timeline.Start(); int total_ins_num = 0; - - // how to accumulate fetched values here - device_reader_->Start(); - int cur_batch; - int batch_cnt = 0; #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) platform::SetDeviceId(thread_id_); #elif defined(PADDLE_WITH_XPU_BKCL) platform::SetXPUDeviceId(thread_id_); #endif + + // how to accumulate fetched values here + device_reader_->Start(); + int cur_batch; + int batch_cnt = 0; while ((cur_batch = device_reader_->Next()) > 0) { total_ins_num += cur_batch; for (auto& op : ops_) { diff --git a/paddle/fluid/framework/trainer.cc b/paddle/fluid/framework/trainer.cc index dc48a8f8d8f2feaa94dcb7f2f692b9ff8e8c737f..2d8e567b65a7d799cdba1b51c964074be39d2501 100644 --- a/paddle/fluid/framework/trainer.cc +++ b/paddle/fluid/framework/trainer.cc @@ -58,7 +58,6 @@ void TrainerBase::DumpWork(int tid) { int err_no = 0; // GetDumpPath is implemented in each Trainer std::string path = GetDumpPath(tid); - std::shared_ptr fp = fs_open_write(path, &err_no, dump_converter_); while (1) { std::string out_str; diff --git a/paddle/fluid/framework/trainer_desc.proto b/paddle/fluid/framework/trainer_desc.proto index 6fe33545aa22d3f17234dbb1b6cd8ad1bb719409..daded21ec62d9522211106b2c074064fc42e6d96 100644 --- a/paddle/fluid/framework/trainer_desc.proto +++ b/paddle/fluid/framework/trainer_desc.proto @@ -68,7 +68,7 @@ message TrainerDesc { // add for gpu optional string fleet_desc = 37; - + optional bool is_dump_in_simple_mode = 38 [ default = false ]; // device worker parameters optional HogwildWorkerParameter hogwild_param = 101; optional DownpourWorkerParameter downpour_param = 103; diff --git a/paddle/fluid/jit/CMakeLists.txt b/paddle/fluid/jit/CMakeLists.txt index c17ad0a30eb601cd4b88de958c349b1750681bcb..cbbd1baf8f63a798346fb48695df094b571621f6 100644 --- a/paddle/fluid/jit/CMakeLists.txt +++ b/paddle/fluid/jit/CMakeLists.txt @@ -32,7 +32,7 @@ cc_library( if(WITH_TESTING AND NOT WIN32) add_custom_target( jit_download_program - COMMAND wget -nc -q + COMMAND wget -nc -q --no-check-certificate https://paddle-ci.gz.bcebos.com/dy2st/multi_program_load.tar.gz COMMAND tar zxf multi_program_load.tar.gz) set(JIT_DEPS diff --git a/paddle/fluid/memory/allocation/CMakeLists.txt b/paddle/fluid/memory/allocation/CMakeLists.txt index 042efa3349aba35ec1547be1f2add49deb79705e..13a405c7d3d3098f9d914f114fc805df2dc2804d 100644 --- a/paddle/fluid/memory/allocation/CMakeLists.txt +++ b/paddle/fluid/memory/allocation/CMakeLists.txt @@ -170,7 +170,7 @@ if(WITH_TESTING) if(NOT WIN32) add_custom_target( download_data - COMMAND wget -nc + COMMAND wget -nc --no-check-certificate https://paddle-ci.cdn.bcebos.com/buddy_allocator_test_data.tar COMMAND tar -xf buddy_allocator_test_data.tar) add_dependencies(buddy_allocator_test download_data) diff --git a/paddle/fluid/platform/flags.cc b/paddle/fluid/platform/flags.cc index b3b16356b9de3b3d54d74b957e251735508f4136..02f93dcaf09705c387e228158445278a83a92af5 100644 --- a/paddle/fluid/platform/flags.cc +++ b/paddle/fluid/platform/flags.cc @@ -68,6 +68,20 @@ PADDLE_DEFINE_EXPORTED_bool( "Checking whether operator produce NAN/INF or not. It will be " "extremely slow so please use this flag wisely."); +/** + * Operator related FLAG + * Name: FLAGS_check_nan_inf + * Since Version: 0.13.0 + * Value Range: bool, default=false + * Example: + * Note: Used to debug. Checking whether operator produce NAN/INF or not. + */ +PADDLE_DEFINE_EXPORTED_bool( + enable_opt_get_features, + false, + "Checking whether operator produce NAN/INF or not. It will be " + "extremely slow so please use this flag wisely."); + // NOTE(zhiqiu): better to share the flags, otherwise we will have too many // flags. #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) || \ @@ -785,6 +799,34 @@ PADDLE_DEFINE_EXPORTED_bool( false, "It controls whether to apply IR pass to program when using Fleet APIs"); +/** + * Distributed related FLAG + * Name: FLAGS_graph_load_in_parallel + * Since Version: 2.2.0 + * Value Range: bool, default=false + * Example: + * Note: Control whether load graph node and edge with multi threads parallely + * If it is not set, load graph data with one thread + */ +PADDLE_DEFINE_EXPORTED_bool(graph_load_in_parallel, + false, + "It controls whether load graph node and edge with " + "mutli threads parallely."); + +/** + * Distributed related FLAG + * Name: FLAGS_graph_get_neighbor_id + * Since Version: 2.2.0 + * Value Range: bool, default=false + * Example: + * Note: Control get all neighbor id when running sub part graph + * If it is not set, do not need get neighbor id when run all part graph + */ +PADDLE_DEFINE_EXPORTED_bool( + graph_get_neighbor_id, + false, + "It controls get all neighbor id when running sub part graph."); + /** * KP kernel related FLAG * Name: FLAGS_run_kp_kernel @@ -893,7 +935,33 @@ DEFINE_bool(enable_slotrecord_reset_shrink, "enable slotrecord obejct reset shrink memory, default false"); DEFINE_bool(enable_ins_parser_file, false, - "enable parser ins file , default false"); + "enable parser ins file, default false"); +PADDLE_DEFINE_EXPORTED_bool( + gpugraph_enable_hbm_table_collision_stat, + false, + "enable hash collisions stat for hbm table, default false"); +PADDLE_DEFINE_EXPORTED_double(gpugraph_hbm_table_load_factor, + 0.75, + "the load factor of hbm table, default 0.75"); +PADDLE_DEFINE_EXPORTED_bool( + gpugraph_enable_gpu_direct_access, + false, + "enable direct access bwtween multi gpu cards, default false"); +PADDLE_DEFINE_EXPORTED_bool( + gpugraph_enable_segment_merge_grads, + false, + "enable segment merge gradients while push sparse, default false"); +PADDLE_DEFINE_EXPORTED_uint64( + gpugraph_merge_grads_segment_size, + 128, + "segment size with segment gradient merge, default 128"); +PADDLE_DEFINE_EXPORTED_int32( + gpugraph_dedup_pull_push_mode, + 0, + "enable dedup keys while pull push sparse, default 0"); +PADDLE_DEFINE_EXPORTED_bool(gpugraph_load_node_list_into_hbm, + true, + "enable load_node_list_into_hbm, default true"); /** * ProcessGroupNCCL related FLAG diff --git a/paddle/fluid/pybind/data_set_py.cc b/paddle/fluid/pybind/data_set_py.cc index e1950ade92fb2297132f7a140e57a445f59ca48d..e902baa13532e522bddb213aae6fea7be9df3252 100644 --- a/paddle/fluid/pybind/data_set_py.cc +++ b/paddle/fluid/pybind/data_set_py.cc @@ -365,6 +365,9 @@ void BindDataset(py::module *m) { py::call_guard()) .def("enable_pv_merge", &framework::Dataset::EnablePvMerge, + py::call_guard()) + .def("set_gpu_graph_mode", + &framework::Dataset::SetGpuGraphMode, py::call_guard()); py::class_(*m, "IterableDatasetWrapper") diff --git a/paddle/fluid/pybind/fleet_py.cc b/paddle/fluid/pybind/fleet_py.cc index f8501efde05ad0c4c242c69ce9e8faf0b040eaeb..b11f5832d8c8a6519518d0f0af80dc72fa9e5207 100755 --- a/paddle/fluid/pybind/fleet_py.cc +++ b/paddle/fluid/pybind/fleet_py.cc @@ -199,13 +199,13 @@ void BindHeterClient(py::module* m) { void BindGraphNode(py::module* m) { py::class_(*m, "GraphNode") .def(py::init<>()) - .def("get_id", &GraphNode::get_id) + .def("get_id", &GraphNode::get_py_id) .def("get_feature", &GraphNode::get_feature); } void BindGraphPyFeatureNode(py::module* m) { py::class_(*m, "FeatureNode") .def(py::init<>()) - .def("get_id", &GraphNode::get_id) + .def("get_id", &GraphNode::get_py_id) .def("get_feature", &GraphNode::get_feature); } @@ -359,17 +359,32 @@ void BindGraphGpuWrapper(py::module* m) { *m, "GraphGpuWrapper") .def(py::init([]() { return GraphGpuWrapper::GetInstance(); })) .def("neighbor_sample", &GraphGpuWrapper::graph_neighbor_sample_v3) - .def("graph_neighbor_sample", &GraphGpuWrapper::graph_neighbor_sample) + .def("graph_neighbor_sample", + py::overload_cast( + &GraphGpuWrapper::graph_neighbor_sample)) + .def("graph_neighbor_sample", + py::overload_cast&, int>( + &GraphGpuWrapper::graph_neighbor_sample)) .def("set_device", &GraphGpuWrapper::set_device) + .def("set_feature_separator", &GraphGpuWrapper::set_feature_separator) .def("init_service", &GraphGpuWrapper::init_service) .def("set_up_types", &GraphGpuWrapper::set_up_types) .def("query_node_list", &GraphGpuWrapper::query_node_list) .def("add_table_feat_conf", &GraphGpuWrapper::add_table_feat_conf) .def("load_edge_file", &GraphGpuWrapper::load_edge_file) - .def("upload_batch", &GraphGpuWrapper::upload_batch) - .def("get_all_id", &GraphGpuWrapper::get_all_id) - .def("init_sample_status", &GraphGpuWrapper::init_sample_status) - .def("free_sample_status", &GraphGpuWrapper::free_sample_status) + .def("load_node_and_edge", &GraphGpuWrapper::load_node_and_edge) + .def("upload_batch", + py::overload_cast( + &GraphGpuWrapper::upload_batch)) + .def("upload_batch", + py::overload_cast(&GraphGpuWrapper::upload_batch)) + .def( + "get_all_id", + py::overload_cast>*>( + &GraphGpuWrapper::get_all_id)) + .def("get_all_id", + py::overload_cast>*>( + &GraphGpuWrapper::get_all_id)) .def("load_next_partition", &GraphGpuWrapper::load_next_partition) .def("make_partitions", &GraphGpuWrapper::make_partitions) .def("make_complementary_graph", @@ -380,7 +395,8 @@ void BindGraphGpuWrapper(py::module* m) { .def("get_partition", &GraphGpuWrapper::get_partition) .def("load_node_weight", &GraphGpuWrapper::load_node_weight) .def("export_partition_files", &GraphGpuWrapper::export_partition_files) - .def("load_node_file", &GraphGpuWrapper::load_node_file); + .def("load_node_file", &GraphGpuWrapper::load_node_file) + .def("finalize", &GraphGpuWrapper::finalize); } #endif diff --git a/paddle/utils/string/string_helper.h b/paddle/utils/string/string_helper.h index f34ae49fcfa1511d3dd1d9a48c12ce77d8428506..b84c7fa75209df87b199055fcd479bf69047db8d 100644 --- a/paddle/utils/string/string_helper.h +++ b/paddle/utils/string/string_helper.h @@ -18,6 +18,7 @@ #include #include +#include #include #include #include @@ -221,6 +222,117 @@ std::string join_strings(const Container& strs, return ss.str(); } +struct str_ptr { + const char* ptr; + size_t len; + str_ptr(const char* p, size_t n) : ptr(p), len(n) {} + str_ptr(str_ptr& other) { + ptr = other.ptr; + len = other.len; + } + str_ptr(str_ptr&& other) { + ptr = other.ptr; + len = other.len; + } + size_t find_ptr(const char c) { + for (size_t i = 0; i < len; ++i) { + if (ptr[i] == c) { + return i; + } + } + return -1; + } + std::string to_string(void) { return std::string(ptr, len); } +}; + +struct str_ptr_stream { + char* ptr = NULL; + char* end = NULL; + str_ptr_stream() {} + str_ptr_stream(const str_ptr& p) { reset(p.ptr, p.len); } + void reset(const str_ptr& p) { reset(p.ptr, p.len); } + void reset(const char* p, size_t len) { + ptr = const_cast(p); + end = ptr + len; + } + char* cursor(void) { return ptr; } + char* finish(void) { return end; } + void set_cursor(char* p) { ptr = p; } + bool is_finish(void) { return (ptr == end); } + template + str_ptr_stream& operator>>(T& x) { + *this >> x; + return *this; + } +}; +inline str_ptr_stream& operator>>(str_ptr_stream& ar, float& c) { + char* next = NULL; + c = strtof(ar.cursor(), &next); + ar.set_cursor(std::min(++next, ar.finish())); + return ar; +} +inline str_ptr_stream& operator>>(str_ptr_stream& ar, double& c) { + char* next = NULL; + c = strtod(ar.cursor(), &next); + ar.set_cursor(std::min(++next, ar.finish())); + return ar; +} +inline str_ptr_stream& operator>>(str_ptr_stream& ar, int32_t& c) { + char* next = NULL; + c = strtol(ar.cursor(), &next, 10); + ar.set_cursor(std::min(++next, ar.finish())); + return ar; +} +inline str_ptr_stream& operator>>(str_ptr_stream& ar, uint32_t& c) { + char* next = NULL; + c = strtoul(ar.cursor(), &next, 10); + ar.set_cursor(std::min(++next, ar.finish())); + return ar; +} +inline str_ptr_stream& operator>>(str_ptr_stream& ar, uint64_t& c) { + char* next = NULL; + c = strtoul(ar.cursor(), &next, 10); + ar.set_cursor(std::min(++next, ar.finish())); + return ar; +} +inline str_ptr_stream& operator>>(str_ptr_stream& ar, int64_t& c) { + char* next = NULL; + c = strtoll(ar.cursor(), &next, 10); + ar.set_cursor(std::min(++next, ar.finish())); + return ar; +} +inline int split_string_ptr(const char* str, + size_t len, + char delim, + std::vector* values) { + if (len <= 0) { + return 0; + } + + int num = 0; + const char* p = str; + const char* end = str + len; + const char* last = str; + while (p < end) { + if (*p != delim) { + ++p; + continue; + } + values->emplace_back(last, (size_t)(p - last)); + ++num; + ++p; + // skip continue delim + while (*p == delim) { + ++p; + } + last = p; + } + if (p > last) { + values->emplace_back(last, (size_t)(p - last)); + ++num; + } + return num; +} // A helper class for reading lines from file. A line buffer is maintained. It // doesn't need to know the maximum possible length of a line. diff --git a/python/paddle/distributed/fleet/base/distributed_strategy.py b/python/paddle/distributed/fleet/base/distributed_strategy.py index 6f8e2926abe0b113a71ee2cc4a2b8a0ce3467810..d58770dd714ff3b35660d38008cfff3624dfed5b 100755 --- a/python/paddle/distributed/fleet/base/distributed_strategy.py +++ b/python/paddle/distributed/fleet/base/distributed_strategy.py @@ -530,7 +530,7 @@ class DistributedStrategy(object): 'embed_sparse_initial_range', 'embed_sparse_initial_g2sum', 'embed_sparse_beta1_decay_rate', \ 'embed_sparse_beta2_decay_rate', 'embedx_sparse_optimizer', 'embedx_sparse_learning_rate', \ 'embedx_sparse_weight_bounds', 'embedx_sparse_initial_range', 'embedx_sparse_initial_g2sum', \ - 'embedx_sparse_beta1_decay_rate', 'embedx_sparse_beta2_decay_rate'] + 'embedx_sparse_beta1_decay_rate', 'embedx_sparse_beta2_decay_rate', 'feature_learning_rate', 'nodeid_slot'] support_sparse_table_class = ['DownpourSparseTable'] support_sparse_accessor_class = [ 'DownpourSparseValueAccessor', 'DownpourCtrAccessor', @@ -540,6 +540,11 @@ class DistributedStrategy(object): from google.protobuf.descriptor import FieldDescriptor table_param = self.strategy.downpour_table_param + def add_graph_config(graph, strategy): + graph.feature_learning_rate = strategy.get('feature_learning_rate', + 0.05) + graph.nodeid_slot = strategy.get('nodeid_slot', 9008) + def sparse_optimizer_config(sgd, strategy, prefix): optimizer_name = strategy.get(prefix + "sparse_optimizer", "adagrad") @@ -691,6 +696,7 @@ class DistributedStrategy(object): config, 'embed_') sparse_optimizer_config(table_data.accessor.embedx_sgd_param, config, 'embedx_') + add_graph_config(table_data.accessor.graph_sgd_param, config) if not configs: print("fleet desc config is empty") diff --git a/python/paddle/distributed/ps/the_one_ps.py b/python/paddle/distributed/ps/the_one_ps.py index bee1ee169ef721815a6cbba25cabe947c74a5dd6..a99bd6649f0fdfba2fed51a93cfcfd3a0d292a59 100755 --- a/python/paddle/distributed/ps/the_one_ps.py +++ b/python/paddle/distributed/ps/the_one_ps.py @@ -155,6 +155,12 @@ class Accessor: if not accessor_proto.HasField("embedx_threshold"): accessor_proto.embedx_threshold = 0 + graph_sgd_param = accessor_proto.graph_sgd_param + if not graph_sgd_param.HasField("nodeid_slot"): + graph_sgd_param.nodeid_slot = 9008 + if not graph_sgd_param.HasField("feature_learning_rate"): + graph_sgd_param.feature_learning_rate = 0.05 + ctr_accessor_param = accessor_proto.ctr_accessor_param if not ctr_accessor_param.HasField("nonclk_coeff"): ctr_accessor_param.nonclk_coeff = 0.1 diff --git a/python/paddle/fluid/contrib/layers/nn.py b/python/paddle/fluid/contrib/layers/nn.py index 36bdffdda78d09b81426c2f3c7252f258854fb8f..44b622807bcc785f7124d2eb08e0883a7104b46a 100644 --- a/python/paddle/fluid/contrib/layers/nn.py +++ b/python/paddle/fluid/contrib/layers/nn.py @@ -933,7 +933,7 @@ def shuffle_batch(x, seed=None): seed = helper.create_variable( name=unique_name.generate("shuffle_batch_seed"), dtype="int64", - persistable=True) + persistable=False) helper.append_op(type='shuffle_batch', inputs={ 'X': x, diff --git a/python/paddle/fluid/dataset.py b/python/paddle/fluid/dataset.py index 8ea3e15ca4d3c894a2e01c13c4e0cf30b1105ded..9fba7bb70f189e9914792b221f733e3578f81460 100644 --- a/python/paddle/fluid/dataset.py +++ b/python/paddle/fluid/dataset.py @@ -1037,6 +1037,51 @@ class InMemoryDataset(DatasetBase): """ self.dataset.set_heter_ps(enable_heter_ps) + def set_graph_config(self, config): + """ + Set graph config, user can set graph config in gpu graph mode. + + Args: + config(dict): config dict. + + Returns: + The size of shuffle data. + + Examples: + .. code-block:: python + + # required: skiptest + import paddle.fluid as fluid + from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + graph_config = {"walk_len": 24, + "walk_degree": 10, + "once_sample_startid_len": 80000, + "sample_times_one_chunk": 5, + "window": 3, + "debug_mode": 0, + "batch_size": 800, + "meta_path": "cuid2clk-clk2cuid;cuid2conv-conv2cuid;clk2cuid-cuid2clk;clk2cuid-cuid2conv", + "gpu_graph_training": 1} + dataset.set_graph_config(graph_config) + + """ + self.proto_desc.graph_config.walk_degree = config.get("walk_degree", 1) + self.proto_desc.graph_config.walk_len = config.get("walk_len", 20) + self.proto_desc.graph_config.window = config.get("window", 5) + self.proto_desc.graph_config.once_sample_startid_len = config.get( + "once_sample_startid_len", 8000) + self.proto_desc.graph_config.sample_times_one_chunk = config.get( + "sample_times_one_chunk", 10) + self.proto_desc.graph_config.batch_size = config.get("batch_size", 1) + self.proto_desc.graph_config.debug_mode = config.get("debug_mode", 0) + self.proto_desc.graph_config.first_node_type = config.get( + "first_node_type", "") + self.proto_desc.graph_config.meta_path = config.get("meta_path", "") + self.proto_desc.graph_config.gpu_graph_training = config.get( + "gpu_graph_training", True) + self.dataset.set_gpu_graph_mode(True) + class QueueDataset(DatasetBase): """ diff --git a/python/paddle/fluid/tests/unittests/test_dataset.py b/python/paddle/fluid/tests/unittests/test_dataset.py index ed01e7e06f6a9328a7190a6ccb8eb8631a4ba293..65baffc8300fdf8f3be33903e72fb77cfebc6fc0 100644 --- a/python/paddle/fluid/tests/unittests/test_dataset.py +++ b/python/paddle/fluid/tests/unittests/test_dataset.py @@ -744,6 +744,65 @@ class TestDataset(unittest.TestCase): temp_dir.cleanup() + def test_run_with_inmemory_dataset_train_debug_mode(self): + """ + Testcase for InMemoryDataset from create to run. + """ + + temp_dir = tempfile.TemporaryDirectory() + dump_a_path = os.path.join(temp_dir.name, 'test_run_with_dump_a.txt') + dump_b_path = os.path.join(temp_dir.name, 'test_run_with_dump_b.txt') + + with open(dump_a_path, "w") as f: + data = "1 a 1 a 1 1 2 3 3 4 5 5 5 5 1 1\n" + data += "1 b 1 b 1 2 2 3 4 4 6 6 6 6 1 2\n" + data += "1 c 1 c 1 3 2 3 5 4 7 7 7 7 1 3\n" + f.write(data) + with open(dump_b_path, "w") as f: + data = "1 d 1 d 1 4 2 3 3 4 5 5 5 5 1 4\n" + data += "1 e 1 e 1 5 2 3 4 4 6 6 6 6 1 5\n" + data += "1 f 1 f 1 6 2 3 5 4 7 7 7 7 1 6\n" + data += "1 g 1 g 1 7 2 3 6 4 8 8 8 8 1 7\n" + f.write(data) + + slots = ["slot1", "slot2", "slot3", "slot4"] + slots_vars = [] + for slot in slots: + var = fluid.layers.data(name=slot, + shape=[1], + dtype="int64", + lod_level=1) + slots_vars.append(var) + + dataset = paddle.distributed.InMemoryDataset() + dataset.init(batch_size=32, + thread_num=1, + pipe_command="cat", + data_feed_type="SlotRecordInMemoryDataFeed", + use_var=slots_vars) + dataset._init_distributed_settings(parse_ins_id=True, + parse_content=True, + fea_eval=True, + candidate_size=10000) + dataset.set_filelist([dump_a_path, dump_b_path]) + dataset.load_into_memory() + + paddle.enable_static() + + exe = paddle.static.Executor(paddle.CPUPlace()) + startup_program = paddle.static.Program() + main_program = paddle.static.Program() + exe.run(startup_program) + for i in range(2): + try: + exe.train_from_dataset(main_program, dataset, debug=True) + except ImportError as e: + pass + except Exception as e: + self.assertTrue(False) + + temp_dir.cleanup() + class TestDatasetWithDataLoader(TestDataset): """ diff --git a/python/paddle/fluid/tests/unittests/test_trainer_desc.py b/python/paddle/fluid/tests/unittests/test_trainer_desc.py index f2724ea22b006c786576a3a3a2d02e99a43722b7..79b3f4e5f375d7c6e83e0cefaa70a8c3953a6116 100644 --- a/python/paddle/fluid/tests/unittests/test_trainer_desc.py +++ b/python/paddle/fluid/tests/unittests/test_trainer_desc.py @@ -45,6 +45,17 @@ class TestTrainerDesc(unittest.TestCase): self.assertEqual(mpi_rank, 1) self.assertEqual(dump_fields_path, "path") + def test_config_dump_simple(self): + """ + Testcase for dump_in_simple_mode + """ + trainer_desc = fluid.trainer_desc.TrainerDesc() + trainer_desc._set_dump_fields(["a", "b"]) + trainer_desc._set_is_dump_in_simple_mode(True) + + is_dump_in_simple_mode = trainer_desc.proto_desc.is_dump_in_simple_mode + self.assertEqual(is_dump_in_simple_mode, 1) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/trainer_desc.py b/python/paddle/fluid/trainer_desc.py index 613d04a7f69e9654a0b91c714cbdb01819cfc9cb..c4c17c7095aa093276086b375d742e8e8b1f33ac 100644 --- a/python/paddle/fluid/trainer_desc.py +++ b/python/paddle/fluid/trainer_desc.py @@ -156,6 +156,9 @@ class TrainerDesc(object): for field in dump_fields: self.proto_desc.dump_fields.append(field) + def _set_is_dump_in_simple_mode(self, is_dump_in_simple_mode): + self.proto_desc.is_dump_in_simple_mode = is_dump_in_simple_mode + def _set_dump_fields_path(self, path): self.proto_desc.dump_fields_path = path diff --git a/python/paddle/fluid/trainer_factory.py b/python/paddle/fluid/trainer_factory.py index a34fb2dea7dc50a65d65b5e386a9207778f43280..3ba9f9eea46d1bbf759cc5414a595d00aa148460 100644 --- a/python/paddle/fluid/trainer_factory.py +++ b/python/paddle/fluid/trainer_factory.py @@ -84,6 +84,9 @@ class TrainerFactory(object): trainer._set_worker_places(opt_info["worker_places"]) if opt_info.get("use_ps_gpu") is not None: trainer._set_use_ps_gpu(opt_info["use_ps_gpu"]) + if opt_info.get("is_dump_in_simple_mode") is not None: + trainer._set_is_dump_in_simple_mode( + opt_info["is_dump_in_simple_mode"]) if opt_info.get("enable_random_dump") is not None: trainer._set_enable_random_dump( opt_info["enable_random_dump"])