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[multi-node] Fix sage infer hang #287

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May 17, 2023
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61 changes: 54 additions & 7 deletions paddle/fluid/framework/data_feed.cu
Original file line number Diff line number Diff line change
Expand Up @@ -3563,11 +3563,25 @@ int GraphDataGenerator::FillInferBuf() {
}

size_t device_key_size = h_device_keys_len_[0][infer_cursor];
total_row_[0] =
(global_infer_node_type_start[infer_cursor] + conf_.buf_size <=
device_key_size)
? conf_.buf_size
: device_key_size - global_infer_node_type_start[infer_cursor];
if (conf_.is_multi_node) {
int local_reach_end = global_infer_node_type_start[infer_cursor] + conf_.buf_size >=
device_key_size;
int global_reach_end = dynamic_adjust_total_row_for_infer(local_reach_end);
if (global_reach_end) {
total_row_[0] = device_key_size - global_infer_node_type_start[infer_cursor];
} else {
if (local_reach_end) {
conf_.buf_size /= 2;
}
total_row_[0] = conf_.buf_size;
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再提一个建议,这里最好不要修改buf_size_。
如果后面还有其他类型节点需要infer,这里buf_size_得不到reset,不友好。
建议这样,
if (global_reach_end)
total_row_[0] = device_key_size - global_infer_node_type_start[infer_cursor];
else
remain = device_key_size - global_infer_node_type_start[infer_cursor];
if local_reach_end
total_row_[0] = remain / 2;
else
total_row_[0] = remain;

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done,不过最后那个else那里,应该是total_row_[0] = conf_.buf_size;

}
} else {
total_row_[0] =
(global_infer_node_type_start[infer_cursor] + conf_.buf_size <=
device_key_size)
? conf_.buf_size
: device_key_size - global_infer_node_type_start[infer_cursor];
}

uint64_t *d_type_keys =
reinterpret_cast<uint64_t *>(d_device_keys_[0][infer_cursor]->ptr());
Expand All @@ -3580,9 +3594,11 @@ int GraphDataGenerator::FillInferBuf() {
sample_stream_);
cudaStreamSynchronize(sample_stream_);
}
VLOG(1) << "cursor: " << infer_cursor
VLOG(1) << "gpuid: " << conf_.gpuid
<< " cursor: " << infer_cursor
<< " start: " << global_infer_node_type_start[infer_cursor]
<< " num: " << total_row_[0];
<< " num: " << total_row_[0]
<< " device_key_size: " << device_key_size;
infer_node_start_ = global_infer_node_type_start[infer_cursor];
global_infer_node_type_start[infer_cursor] += total_row_[0];
infer_node_end_ = global_infer_node_type_start[infer_cursor];
Expand Down Expand Up @@ -4042,6 +4058,37 @@ int GraphDataGenerator::dynamic_adjust_batch_num_for_sage() {
return new_batch_size;
}

int GraphDataGenerator::dynamic_adjust_total_row_for_infer(int local_reach_end) {
auto send_buff = memory::Alloc(
place_,
2 * sizeof(int),
phi::Stream(reinterpret_cast<phi::StreamId>(sample_stream_)));
int *send_buff_ptr = reinterpret_cast<int *>(send_buff->ptr());
cudaMemcpyAsync(send_buff_ptr,
&local_reach_end,
sizeof(int),
cudaMemcpyHostToDevice,
sample_stream_);
cudaStreamSynchronize(sample_stream_);
auto comm =
platform::NCCLCommContext::Instance().Get(0, place_.GetDeviceId());
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(&send_buff_ptr[0],
&send_buff_ptr[1],
1,
ncclInt,
ncclProd,
comm->comm(),
sample_stream_));
int global_reach_end = 0;
cudaMemcpyAsync(&global_reach_end,
&send_buff_ptr[1],
sizeof(int),
cudaMemcpyDeviceToHost,
sample_stream_);
cudaStreamSynchronize(sample_stream_);
return global_reach_end;
}

} // namespace framework
} // namespace paddle
#endif
1 change: 1 addition & 0 deletions paddle/fluid/framework/data_feed.h
Original file line number Diff line number Diff line change
Expand Up @@ -981,6 +981,7 @@ class GraphDataGenerator {
int get_pass_end() { return pass_end_; }
void clear_gpu_mem();
int dynamic_adjust_batch_num_for_sage();
int dynamic_adjust_total_row_for_infer(int local_reach_end);

protected:
void DoSageForInfer();
Expand Down