[ProcessGroup]add new comm primitive

paddle/fluid/distributed/collective/ProcessGroup.h

@@ -96,7 +96,25 @@ class ProcessGroup {
       std::vector<Tensor>& /* tensors */,
       const BroadcastOptions& = BroadcastOptions()) {
     PADDLE_THROW(platform::errors::InvalidArgument(
-        "ProcessGroup%s does not support allreduce", GetBackendName()));
+        "ProcessGroup%s does not support broadcast", GetBackendName()));
+  }
+
+  virtual std::shared_ptr<ProcessGroup::Task> Barrier(
+      const BarrierOptions& = BarrierOptions()) {
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "ProcessGroup%s does not support barrier", GetBackendName()));
+  }
+
+  virtual std::shared_ptr<ProcessGroup::Task> Send(
+      std::vector<Tensor>& tensors /* tensors */, int dst_rank) {  // NOLINT
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "ProcessGroup%s does not support send", GetBackendName()));
+  }
+
+  virtual std::shared_ptr<ProcessGroup::Task> Recv(
+      std::vector<Tensor>& tensors /* tensors */, int src_rank) {  // NOLINT
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "ProcessGroup%s does not support receive", GetBackendName()));
   }
 
  protected:

paddle/fluid/distributed/collective/ProcessGroupNCCL.cc

@@ -14,6 +14,9 @@
 
 #include "paddle/fluid/distributed/collective/ProcessGroupNCCL.h"
 #include "paddle/fluid/platform/device/gpu/nccl_helper.h"
+#include "paddle/fluid/platform/place.h"
+#include "paddle/phi/api/include/api.h"
+#include "paddle/phi/common/place.h"
 
 DECLARE_bool(nccl_blocking_wait);
 DECLARE_bool(use_stream_safe_cuda_allocator);
@@ -139,6 +142,14 @@ bool ProcessGroupNCCL::NCCLTask::Wait(std::chrono::milliseconds timeout) {
       std::this_thread::sleep_for(std::chrono::milliseconds(kWaitBlockTImeout));
     }
   }
+
+  if (!barrierTensors_.empty()) {
+    // If we use the work to do barrier, we should block cpu
+    for (auto& place : places_) {
+      platform::CUDADeviceGuard gpuGuard(place);
+      PADDLE_ENFORCE_GPU_SUCCESS(cudaDeviceSynchronize());
+    }
+  }
   return true;
 }
 
@@ -193,6 +204,10 @@ void ProcessGroupNCCL::CreateNCCLManagerCache(
   nccl_ids.resize(1);
   auto& nccl_id = nccl_ids.front();
 
+  for (auto& place : places) {
+    used_place_ids_.insert(place.GetDeviceId());
+  }
+
   if (rank_ == 0) {
     PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGetUniqueId(&nccl_id));
   }
@@ -274,6 +289,54 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Collective(
   return task;
 }
 
+template <typename Fn>
+std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::PointToPoint(
+    std::vector<Tensor>& tensors, Fn fn, int dst_rank, CommType op_type) {
+  const auto places = GetPlaceList(tensors);
+  const auto key = GetKeyFromPlaces(places);
+
+  {
+    std::lock_guard<std::mutex> lock(mutex_);
+    if (places_to_ncclcomm_.find(key) == places_to_ncclcomm_.end()) {
+      CreateNCCLManagerCache(key, places);
+    }
+  }
+
+  auto& nccl_comms = places_to_ncclcomm_[key];
+
+  SyncDefaultStream(places, places_to_events_[key], places_to_ctx_[key]);
+
+  auto task = CreateTask(places, rank_, op_type, tensors);
+
+  // construct uninitialize guard for device
+  platform::CUDADeviceGuard cuda_guard;
+
+  if (FLAGS_use_stream_safe_cuda_allocator) {
+    for (size_t i = 0; i < tensors.size(); ++i) {
+      cuda_guard.SetDevice(places[i]);
+      auto dense_tensor =
+          std::dynamic_pointer_cast<phi::DenseTensor>(tensors[i].impl());
+      memory::RecordStream(dense_tensor->Holder(),
+                           places_to_ctx_[key][i]->stream());
+    }
+  }
+
+  {
+    platform::NCCLGroupGuard nccl_guard;
+    for (size_t i = 0; i < tensors.size(); ++i) {
+      cuda_guard.SetDevice(places[i]);
+      const auto& nccl_stream = places_to_ctx_[key][i]->stream();
+      fn(tensors[i], nccl_comms[i]->GetNcclComm(), nccl_stream, dst_rank);
+    }
+  }
+
+  for (size_t i = 0; i < tensors.size(); ++i) {
+    cuda_guard.SetDevice(places[i]);
+    task->control_events_[i].Record(*places_to_ctx_[key][i]);
+  }
+  return task;
+}
+
 std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllReduce(
     std::vector<Tensor>& tensors, const AllreduceOptions& opts) {
   PADDLE_ENFORCE_EQ(
@@ -317,5 +380,98 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Broadcast(
       CommType::BROADCAST);
 }
 
+std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Barrier(
+    const BarrierOptions& opts) {
+  std::vector<phi::GPUPlace> places;
+
+  if (!opts.place_ids.empty()) {
+    for (auto place_id : opts.place_ids) {
+      places.emplace_back(place_id);
+    }
+  } else if (!used_place_ids_.empty()) {
+    for (auto place_id : used_place_ids_) {
+      places.emplace_back(place_id);
+    }
+  } else {
+    auto numGPUs = GetSize();
+    int place_id = static_cast<int>(rank_ % numGPUs);
+    places.emplace_back(place_id);
+  }
+
+  std::vector<Tensor> barrierTensors;
+  barrierTensors.reserve(places.size());
+
+  platform::CUDADeviceGuard gpuGuard;
+  for (auto& place : places) {
+    gpuGuard.SetDeviceIndex(place.GetDeviceId());
+    auto dt = full({1}, 0, phi::DataType::FLOAT32, phi::Backend::GPU);
+    barrierTensors.push_back(dt);
+  }
+  auto task = ProcessGroupNCCL::AllReduce(barrierTensors);
+  auto nccl_task = dynamic_cast<ProcessGroupNCCL::NCCLTask*>(task.get());
+  nccl_task->barrierTensors_ = std::move(barrierTensors);
+  return task;
+}
+
+void CheckTensorsInDifferentDevices(const std::vector<Tensor>& tensors,
+                                    const size_t num_devices) {
+  PADDLE_ENFORCE_EQ(
+      tensors.size() == 0, false,
+      platform::errors::InvalidArgument("Tensor list must be nonempty."));
+  PADDLE_ENFORCE_LE(
+      tensors.size(), num_devices,
+      platform::errors::InvalidArgument(
+          "Tensor list mustn't be larger than the number of available GPUs."));
+
+  std::set<Place> used_devices;
+
+  for (const auto& t : tensors) {
+    PADDLE_ENFORCE_EQ(t.is_cuda() && t.is_dense_tensor(), true,
+                      platform::errors::InvalidArgument(
+                          "Tensors must be CUDA and dense tensor."));
+
+    const auto inserted = used_devices.insert(t.inner_place()).second;
+    PADDLE_ENFORCE_EQ(inserted, true,
+                      platform::errors::InvalidArgument(
+                          "Tensors must be on distinct GPU devices."));
+  }
+}
+
+std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Send(
+    std::vector<Tensor>& tensors, int dst_rank) {
+  CheckTensorsInDifferentDevices(tensors, static_cast<size_t>(GetSize()));
+
+  auto task = PointToPoint(
+      tensors,
+      [&](Tensor& input, ncclComm_t comm, const gpuStream_t& stream,
+          int dst_rank) {
+        auto input_tensor =
+            std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
+        return platform::dynload::ncclSend(
+            input_tensor->data(), input_tensor->numel(),
+            platform::ToNCCLDataType(input.type()), dst_rank, comm, stream);
+      },
+      dst_rank, CommType::SEND);
+  return task;
+}
+
+std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Recv(
+    std::vector<Tensor>& tensors, int src_rank) {
+  CheckTensorsInDifferentDevices(tensors, static_cast<size_t>(GetSize()));
+
+  auto task = PointToPoint(
+      tensors,
+      [&](Tensor& output, ncclComm_t comm, const gpuStream_t& stream,
+          int src_rank) {
+        auto output_tensor =
+            std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
+        return platform::dynload::ncclRecv(
+            output_tensor->data(), output_tensor->numel(),
+            platform::ToNCCLDataType(output.type()), src_rank, comm, stream);
+      },
+      src_rank, CommType::RECV);
+  return task;
+}
+
 }  //  namespace distributed
 }  //  namespace paddle

paddle/fluid/distributed/collective/ProcessGroupNCCL.h

@@ -65,6 +65,7 @@ class ProcessGroupNCCL : public ProcessGroup {
     virtual ~NCCLTask();
 
     std::vector<EventManager> control_events_;
+    std::vector<Tensor> barrierTensors_;
 
    protected:
     std::vector<Place> places_;
@@ -88,6 +89,15 @@ class ProcessGroupNCCL : public ProcessGroup {
       std::vector<Tensor>& tensors,
       const BroadcastOptions& = BroadcastOptions()) override;
 
+  std::shared_ptr<ProcessGroup::Task> Barrier(
+      const BarrierOptions& = BarrierOptions()) override;
+
+  std::shared_ptr<ProcessGroup::Task> Send(std::vector<Tensor>& tensors,
+                                           int dst_rank) override;
+
+  std::shared_ptr<ProcessGroup::Task> Recv(std::vector<Tensor>& tensors,
+                                           int src_rank) override;
+
  protected:
   virtual std::shared_ptr<ProcessGroupNCCL::NCCLTask> CreateTask(
       std::vector<Place> places, int rank, CommType opType,
@@ -106,6 +116,8 @@ class ProcessGroupNCCL : public ProcessGroup {
                      std::vector<std::unique_ptr<CUDADeviceContext>>>
       places_to_ctx_;
 
+  std::set<int> used_place_ids_;
+
  private:
   void BcastNCCLId(std::vector<ncclUniqueId>& nccl_ids, int root,  // NOLINT
                    int server_fd);
@@ -118,6 +130,11 @@ class ProcessGroupNCCL : public ProcessGroup {
       std::vector<Tensor>& outputs,  // NOLINT
       Fn fn, CommType op_type);
 
+  template <typename Fn>
+  std::shared_ptr<ProcessGroup::Task> PointToPoint(
+      std::vector<Tensor>& tensors,  // NOLINT
+      Fn fn, int dst_rank, CommType op_type);
+
   void CreateNCCLManagerCache(const std::string& places_key,
                               const std::vector<Place>& places);
 };

paddle/fluid/distributed/collective/Types.h

@@ -32,5 +32,9 @@ struct BroadcastOptions {
   int source_root = 0;
 };
 
+struct BarrierOptions {
+  std::vector<int> place_ids;
+};
+
 }  //  namespace distributed
 }  //  namespace paddle

paddle/fluid/pybind/distributed_py.cc

@@ -59,6 +59,10 @@ void BindDistributed(py::module *m) {
       .def_readwrite("source_root",
                      &distributed::BroadcastOptions::source_root);
 
+  py::class_<distributed::BarrierOptions>(*m, "BarrierOptions")
+      .def(py::init<>())
+      .def_readwrite("place_ids", &distributed::BarrierOptions::place_ids);
+
   auto ProcessGroup =
       py::class_<distributed::ProcessGroup,
                  std::shared_ptr<distributed::ProcessGroup>>(*m, "ProcessGroup")
@@ -87,6 +91,35 @@ void BindDistributed(py::module *m) {
                  return self.Broadcast(tensors, opts);
                },
                py::arg("tensor"), py::arg("source_rank"),
+               py::call_guard<py::gil_scoped_release>())
+
+          .def("barrier",
+               [](distributed::ProcessGroup &self, std::vector<int> place_ids) {
+                 distributed::BarrierOptions opts;
+                 opts.place_ids = place_ids;
+                 return self.Barrier(opts);
+               },
+               py::arg("place_ids") = std::vector<int>{},
+               py::call_guard<py::gil_scoped_release>())
+
+          .def("send",
+               [](distributed::ProcessGroup &self, py::handle py_tensor,
+                  int dst) {
+                 auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
+                 std::vector<Tensor> tensors = {tensor};
+                 return self.Send(tensors, dst);
+               },
+               py::arg("tensor"), py::arg("dst"),
+               py::call_guard<py::gil_scoped_release>())
+
+          .def("recv",
+               [](distributed::ProcessGroup &self, py::handle py_tensor,
+                  int src) {
+                 auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
+                 std::vector<Tensor> tensors = {tensor};
+                 return self.Recv(tensors, src);
+               },
+               py::arg("tensor"), py::arg("src"),
                py::call_guard<py::gil_scoped_release>());
 
 #if defined(PADDLE_WITH_NCCL)

python/paddle/fluid/tests/unittests/process_group_nccl.py

@@ -132,6 +132,36 @@ def test_create_process_group_nccl(self):
 
             print("test broadcast api ok")
 
+            # test barrier
+            # rank 0
+            if pg.rank() == 0:
+                task = pg.barrier()
+                task.wait()
+            # rank 1
+            else:
+                task = pg.barrier()
+                task.wait()
+
+            print("test barrier api ok\n")
+
+            # test send/recv
+            # rank 0
+            x = np.random.random(self.shape).astype(self.dtype)
+            tensor_x = paddle.to_tensor(x)
+            if pg.rank() == 0:
+                task = pg.send(tensor_x, dst=1)
+                task.wait()
+                paddle.device.cuda.synchronize()
+            # rank 1
+            else:
+                y = np.random.random(self.shape).astype(self.dtype)
+                tensor_y = paddle.to_tensor(y)
+                task = pg.recv(tensor_y, src=0)
+                task.wait()
+                paddle.device.cuda.synchronize()
+                assert np.array_equal(tensor_x, tensor_y)
+                print("test send/recv api ok\n")
+
 
 class TestProcessGroupFp16(TestProcessGroupFp32):
     def setUp(self):