support float16 tensor (#45)

* support float16 tensor

* use tensor.data_ptr directly

* add fp16 test

* fix

* add MAG240M Example

* fix bugs

* add fp16 test

* fix bugs

* update

* update distribute training

* update parameters

* add readme

csrc/include/qvf/dist_tensor_client.h

@@ -11,6 +11,7 @@
 #include <infinity/queues/QueuePairFactory.h>
 #include <infinity/requests/RequestToken.h>
 #include <torch/extension.h>
+#include <ATen/ATen.h>
 #include <chrono>
 #include <deque>
 #include <thread>
@@ -98,15 +99,17 @@ class DistTensorClient {
                             {tensor_shape[0], tensor_shape[1]}, tensor_option);
   }
 
-  void register_float32_tensor(torch::Tensor& float_tensor) {
+  void register_float_tensor(torch::Tensor& float_tensor) {
     QUIVER_FEATURE_ASSERT(
         float_tensor.dim() == 2,
         "Only support 2-dimensional tensor, But got %d-dimensional tensor\n",
         float_tensor.dim());
-    uint64_t size_in_bytes = 4 * float_tensor.numel();
+
+    uint64_t size_in_bytes = float_tensor.element_size() * float_tensor.numel();
 
     tensor_buffer = new infinity::memory::Buffer(
-        context, float_tensor.data_ptr<float>(), size_in_bytes);
+        context, float_tensor.data_ptr(), size_in_bytes);
+
     tensor_token = tensor_buffer->createRegionToken();
   }
 
@@ -134,12 +137,12 @@ class DistTensorClient {
                  torch::Tensor& local_offsets,
                  torch::Tensor& remote_offsets) {
     QUIVER_FEATURE_ASSERT(
-        reinterpret_cast<uint64_t>(res_tensor.data_ptr<float>()) ==
+        reinterpret_cast<uint64_t>(res_tensor.data_ptr()) ==
             tensor_buffer->getAddress(),
         "Result Tensor is not created from registered buffer");
 
     pipes[server_rank]->read(tensor_buffer, local_offsets, remote_offsets,
-                             res_tensor.size(1) * 4);
+                             res_tensor.size(1) * res_tensor.element_size());
   }
 
   void collect_inner(CollectionTask collection_task) {

csrc/include/qvf/dist_tensor_server.h

@@ -16,6 +16,7 @@
 #include <vector>
 
 #include <torch/extension.h>
+#include <ATen/ATen.h>
 
 namespace qvf {
 class DistTensorServer {
@@ -51,9 +52,10 @@ class DistTensorServer {
 
   void serve_tensor(torch::Tensor& data) {
     std::cout << "Registering Buffer, Please Wait..." << std::endl;
-    uint64_t size_in_bytes = data.numel() * 4;
+    uint64_t size_in_bytes = data.numel() * data.element_size();
+
     feature_buffer = new infinity::memory::Buffer(
-        context, data.data_ptr<float>(), size_in_bytes);
+        context, data.data_ptr(), size_in_bytes);
     bufferToken = feature_buffer->createRegionToken();
     server_thread = std::thread(run, qpFactory, bufferToken,
                                 qp_per_pipe * (world_size - 1));

csrc/src/register.cpp

@@ -47,8 +47,8 @@ void register_DistTensorClient(pybind11::module& m) {
       .def("create_registered_float32_tensor",
            &qvf::DistTensorClient::create_registered_float32_tensor,
            py::call_guard<py::gil_scoped_release>())
-      .def("register_float32_tensor",
-           &qvf::DistTensorClient::register_float32_tensor,
+      .def("register_float_tensor",
+           &qvf::DistTensorClient::register_float_tensor,
            py::call_guard<py::gil_scoped_release>())
       .def("create_registered_float32_tensor_cuda",
            &qvf::DistTensorClient::create_registered_float32_tensor_cuda,

examples/mag240m/README.md

@@ -0,0 +1,45 @@
+# Introduction
+
+Distributed training setting on MAG240M dataset is almost the same as the [official example in DGL](https://github.com/dmlc/dgl/tree/master/examples/pytorch/ogb_lsc/MAG240M) except that we use `Quiver-Feature` for distributed feature collection.
+
+Our implementation is much faster than DGL's offical example while achieved similar accuracy.
+
+# Data Preprocess & Partition
+
+First, please run [preprocess.py](./preprocess.py) to generate `graph.dgl` and `full.npy`, you can check [DGL's official guide](https://github.com/dmlc/dgl/tree/master/examples/pytorch/ogb_lsc/MAG240M) for more details.
+
+Then we use [Range Partition](../../docs/partition_methods.md) to partition feature data, it is very easy to understand, you can check [process_quiver.py](./process_quiver.py) for more details.
+
+![](../../docs/imgs/range_partition.png)
+
+
+# Running Training Script
+
+On each machine, please run:
+
+    python3 distributed_training.py \
+            --rootdir . \
+            --graph-path ./graph.dgl \
+            --feature-partition-path ./feature_part.pt \
+            --server_world_size 2
+            --server_rank 0
+
+Remember to:
+
+- Set shm size limit as large as your physical memory size. You can set by:
+
+        sudo mount -o remount,size=300G /dev/shm
+
+- Set `MASTER_IP` as your master node's IP
+
+
+The validation accuracy is 0.680. We do not have ground truth test labels so we do not report test accuracy.
+
+# Performance
+
+With 2 machines and 1 GPU per machine, we need 2 minutes 10 seconds to train and 15 seconds to validate for each epoch. This is 3x faster than [DGL's performance result](https://github.com/dmlc/dgl/tree/master/examples/pytorch/ogb_lsc/MAG240M).
+
+
+# Hardware configurations
+
+We have 2 machines, each have 377G memory and they are connected by 100Gbps IB. Running training script will consume around 256GB memory.

examples/mag240m/config.py

@@ -0,0 +1,18 @@
+PORT_NUMBER = 3344
+MASTER_IP = "155.198.152.17"
+#MASTER_IP = "127.0.0.1"
+HLPER_PORT = 5678
+NODE_COUNT = 1200000
+FEATURE_DIM = 128
+FEATURE_TYPE_SIZE = 4
+SAMPLE_NUM = 80000
+ITER_NUM = 10
+POST_LIST_SIZE = 128
+QP_NUM = 8
+TX_DEPTH = 2048
+CTX_POLL_BATCH = TX_DEPTH // POST_LIST_SIZE
+TEST_TLB_OPTIMIZATION = True
+
+# For MAG240M Training
+SAMPLE_PARAM = [15, 25]
+BATCH_SIZE = 1024