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change arg of GenerateSampleGraph to be vector #289

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May 16, 2023
Merged

change arg of GenerateSampleGraph to be vector #289

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249 changes: 126 additions & 123 deletions paddle/fluid/framework/data_feed.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -3337,7 +3337,8 @@ void GraphDataGenerator::DoSageForTrain() {
int sage_pass_end = 0;
uint64_t *ins_buf, *ins_cursor;
while (is_sage_pass_continue) {
for (int tensor_pair_idx = 0; tensor_pair_idx < conf_.tensor_pair_num;
for (int tensor_pair_idx = 0;
tensor_pair_idx < conf_.tensor_pair_num && is_sage_pass_continue;
++tensor_pair_idx) {
while (ins_buf_pair_len_[tensor_pair_idx] < conf_.batch_size) {
int32_t *pair_label_buf = NULL;
Expand Down Expand Up @@ -3374,80 +3375,79 @@ void GraphDataGenerator::DoSageForTrain() {
}
}
}
}

// check whether reach sage pass end
if (conf_.is_multi_node) {
int res = multi_node_sync_sample(
sage_pass_end, ncclProd, place_, &multi_node_sync_stat_);
if (res) {
is_sage_pass_continue = false;
// check whether reach sage pass end
if (conf_.is_multi_node) {
int res = multi_node_sync_sample(
sage_pass_end, ncclProd, place_, &multi_node_sync_stat_);
if (res) {
is_sage_pass_continue = false;
break;
}
}
}

if (!is_sage_pass_continue) {
break;
}

total_instance = ins_buf_pair_len_[0] < conf_.batch_size ? ins_buf_pair_len_[0]
: conf_.batch_size;
total_instance *= 2;
total_instance = ins_buf_pair_len_[tensor_pair_idx] < conf_.batch_size ?
ins_buf_pair_len_[tensor_pair_idx] : conf_.batch_size;
total_instance *= 2;

ins_buf = reinterpret_cast<uint64_t *>(d_ins_buf_[0]->ptr());
ins_cursor = ins_buf + ins_buf_pair_len_[0] * 2 - total_instance;
auto final_sage_nodes = GenerateSampleGraph(ins_cursor,
total_instance,
&uniq_instance,
conf_,
&inverse_vec_,
&graph_edges_vec_,
&edges_split_num_vec_,
&edge_type_graph_,
place_,
sample_stream_);
final_sage_nodes_vec_.emplace_back(final_sage_nodes);
uniq_instance_vec_.emplace_back(uniq_instance);
total_instance_vec_.emplace_back(total_instance);

if (conf_.get_degree) {
auto node_degrees = GetNodeDegree(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
conf_,
place_,
sample_stream_);
node_degree_vec_.emplace_back(node_degrees);
ins_buf = reinterpret_cast<uint64_t *>(d_ins_buf_[tensor_pair_idx]->ptr());
ins_cursor = ins_buf + ins_buf_pair_len_[tensor_pair_idx] * 2 - total_instance;
auto final_sage_nodes = GenerateSampleGraph(ins_cursor,
total_instance,
&uniq_instance,
conf_,
&inverse_vec_,
&graph_edges_vec_,
&edges_split_num_vec_,
&edge_type_graph_,
place_,
sample_stream_);
final_sage_nodes_vec_.emplace_back(final_sage_nodes);
uniq_instance_vec_.emplace_back(uniq_instance);
total_instance_vec_.emplace_back(total_instance);

if (conf_.get_degree) {
auto node_degrees = GetNodeDegree(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
conf_,
place_,
sample_stream_);
node_degree_vec_.emplace_back(node_degrees);
}

if (conf_.enable_pair_label) {
auto pair_label = memory::AllocShared(
place_,
total_instance / 2 * sizeof(int),
phi::Stream(reinterpret_cast<phi::StreamId>(sample_stream_)));
int32_t *pair_label_buf =
reinterpret_cast<int32_t *>(d_pair_label_buf_[tensor_pair_idx]->ptr());
int32_t *pair_label_cursor =
pair_label_buf + ins_buf_pair_len_[tensor_pair_idx] - total_instance / 2;
cudaMemcpyAsync(pair_label->ptr(),
pair_label_cursor,
sizeof(int32_t) * total_instance / 2,
cudaMemcpyDeviceToDevice,
sample_stream_);
pair_label_vec_.emplace_back(pair_label);
}

cudaStreamSynchronize(sample_stream_);
InsertTable(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
&d_uniq_node_num_[tensor_pair_idx],
conf_,
&copy_unique_len_[tensor_pair_idx],
place_,
table_,
&host_vec_,
sample_stream_);

ins_buf_pair_len_[tensor_pair_idx] -= total_instance / 2;
}
if (is_sage_pass_continue) {
sage_batch_num_ += 1;
}

if (conf_.enable_pair_label) {
auto pair_label = memory::AllocShared(
place_,
total_instance / 2 * sizeof(int),
phi::Stream(reinterpret_cast<phi::StreamId>(sample_stream_)));
int32_t *pair_label_buf =
reinterpret_cast<int32_t *>(d_pair_label_buf_[0]->ptr());
int32_t *pair_label_cursor =
pair_label_buf + ins_buf_pair_len_[0] - total_instance / 2;
cudaMemcpyAsync(pair_label->ptr(),
pair_label_cursor,
sizeof(int32_t) * total_instance / 2,
cudaMemcpyDeviceToDevice,
sample_stream_);
pair_label_vec_.emplace_back(pair_label);
}

cudaStreamSynchronize(sample_stream_);
InsertTable(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
&d_uniq_node_num_[0],
conf_,
&copy_unique_len_[0],
place_,
table_,
&host_vec_,
sample_stream_);

ins_buf_pair_len_[0] -= total_instance / 2;
sage_batch_num_ += 1;
}
}

Expand All @@ -3465,60 +3465,63 @@ void GraphDataGenerator::DoSageForInfer() {
: infer_node_end_ - infer_node_start_;
total_instance *= 2;
while (total_instance != 0) {
uint64_t *d_type_keys = reinterpret_cast<uint64_t *>(
d_device_keys_[0][infer_cursor_]->ptr());
d_type_keys += infer_node_start_;
infer_node_start_ += total_instance / 2;
auto node_buf = memory::AllocShared(
place_,
total_instance * sizeof(uint64_t),
phi::Stream(reinterpret_cast<phi::StreamId>(sample_stream_)));
int64_t *node_buf_ptr = reinterpret_cast<int64_t *>(node_buf->ptr());
CopyDuplicateKeys<<<GET_BLOCKS(total_instance / 2),
CUDA_NUM_THREADS,
0,
sample_stream_>>>(
node_buf_ptr, d_type_keys, total_instance / 2);
auto final_sage_nodes = GenerateSampleGraph(reinterpret_cast<uint64_t *>(node_buf->ptr()),
total_instance,
&uniq_instance,
conf_,
&inverse_vec_,
&graph_edges_vec_,
&edges_split_num_vec_,
&edge_type_graph_,
place_,
sample_stream_);
final_sage_nodes_vec_.emplace_back(final_sage_nodes);
uniq_instance_vec_.emplace_back(uniq_instance);
total_instance_vec_.emplace_back(total_instance);

if (conf_.get_degree) {
auto node_degrees = GetNodeDegree(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
conf_,
place_,
sample_stream_);
node_degree_vec_.emplace_back(node_degrees);
}
cudaStreamSynchronize(sample_stream_);
InsertTable(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
&d_uniq_node_num_[0],
conf_,
&copy_unique_len_[0],
place_,
table_,
&host_vec_,
sample_stream_);

sage_batch_num_ += 1;
for (int tensor_pair_idx = 0; tensor_pair_idx < conf_.tensor_pair_num;
++tensor_pair_idx) {
uint64_t *d_type_keys = reinterpret_cast<uint64_t *>(
d_device_keys_[tensor_pair_idx][infer_cursor_]->ptr());
d_type_keys += infer_node_start_;
infer_node_start_ += total_instance / 2;
auto node_buf = memory::AllocShared(
place_,
total_instance * sizeof(uint64_t),
phi::Stream(reinterpret_cast<phi::StreamId>(sample_stream_)));
int64_t *node_buf_ptr = reinterpret_cast<int64_t *>(node_buf->ptr());
CopyDuplicateKeys<<<GET_BLOCKS(total_instance / 2),
CUDA_NUM_THREADS,
0,
sample_stream_>>>(
node_buf_ptr, d_type_keys, total_instance / 2);
auto final_sage_nodes = GenerateSampleGraph(reinterpret_cast<uint64_t *>(node_buf->ptr()),
total_instance,
&uniq_instance,
conf_,
&inverse_vec_,
&graph_edges_vec_,
&edges_split_num_vec_,
&edge_type_graph_,
place_,
sample_stream_);
final_sage_nodes_vec_.emplace_back(final_sage_nodes);
uniq_instance_vec_.emplace_back(uniq_instance);
total_instance_vec_.emplace_back(total_instance);

if (conf_.get_degree) {
auto node_degrees = GetNodeDegree(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
conf_,
place_,
sample_stream_);
node_degree_vec_.emplace_back(node_degrees);
}
cudaStreamSynchronize(sample_stream_);
InsertTable(reinterpret_cast<uint64_t *>(final_sage_nodes->ptr()),
uniq_instance,
&d_uniq_node_num_[tensor_pair_idx],
conf_,
&copy_unique_len_[tensor_pair_idx],
place_,
table_,
&host_vec_,
sample_stream_);

total_instance =
(infer_node_start_ + conf_.batch_size <= infer_node_end_)
? conf_.batch_size
: infer_node_end_ - infer_node_start_;
total_instance *= 2;
}

total_instance =
(infer_node_start_ + conf_.batch_size <= infer_node_end_)
? conf_.batch_size
: infer_node_end_ - infer_node_start_;
total_instance *= 2;
sage_batch_num_ += 1;
}
}

Expand Down