Add nproc_per_node for DistributedFusedLamb (#43295)

* add nproc_per_node for DistributedFusedLamb

* fix nproc_per_node communicator bug

* fix ring_id = 1 init bug

* fix ci

* fix test_parallel_executor_mnist.py

paddle/fluid/framework/parallel_executor.cc

@@ -793,8 +793,8 @@ void ParallelExecutor::BCastParamsToDevices(
       std::vector<void *> buffers;
       buffers.reserve(member_->places_.size());
       size_t numel = main_tensor.numel();
-      ncclDataType_t data_type = platform::ToNCCLDataType(
-          framework::TransToProtoVarType(main_tensor.dtype()));
+      auto dtype = framework::TransToProtoVarType(main_tensor.dtype());
+      ncclDataType_t data_type = platform::ToNCCLDataType(dtype);
       for (size_t i = 0; i < member_->places_.size(); ++i) {
         auto place = member_->places_[i];
         void *buffer;
@@ -815,7 +815,7 @@ void ParallelExecutor::BCastParamsToDevices(
                             "variables' buffer size to bcast is %d, which is "
                             "NOT equal to places size %d",
                             buffers.size(), member_->places_.size()));
-      {
+      if (member_->nccl_ctxs_ != nullptr) {
         auto *nccl_ctxs = member_->nccl_ctxs_->DefaultFlatCtx();
         platform::NCCLGroupGuard guard;
         for (size_t i = 0; i < member_->places_.size(); ++i) {
@@ -824,6 +824,22 @@ void ParallelExecutor::BCastParamsToDevices(
                                        nccl_ctx.comm_, nccl_ctx.stream());
         }
         nccl_ctxs->WaitAll();
+      } else {
+        auto src_place = member_->places_[0];
+        auto src_dev_ctx = static_cast<platform::CUDADeviceContext *>(
+            platform::DeviceContextPool::Instance().Get(src_place));
+        auto sizeof_dtype = framework::SizeOfType(dtype) * numel;
+        for (size_t i = 1; i < member_->places_.size(); ++i) {
+          auto dst_place = member_->places_[i];
+          auto dst_dev_ctx = static_cast<platform::CUDADeviceContext *>(
+              platform::DeviceContextPool::Instance().Get(dst_place));
+          src_dev_ctx->Wait();
+          dst_dev_ctx->Wait();
+          memory::Copy(dst_place, buffers[i], src_place, buffers[0],
+                       sizeof_dtype, src_dev_ctx->stream());
+          src_dev_ctx->Wait();
+          dst_dev_ctx->Wait();
+        }
       }
 #endif
     } else if (paddle::platform::is_xpu_place(main_tensor.place())) {
@@ -1348,6 +1364,11 @@ std::vector<ir::Graph *> ParallelExecutor::CloneGraphToMultiDevices(
 }
 
 void ParallelExecutor::PrepareNCCLCommunicator(Scope *global_scope) {
+  if (member_->build_strategy_.reduce_ ==
+      BuildStrategy::ReduceStrategy::kNoReduce) {
+    return;
+  }
+
   if (member_->IsUseCUDA(member_->use_device_) && member_->nranks_ > 1) {
 #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
     member_->InitOrGetNCCLCommunicator(global_scope, &member_->build_strategy_);

paddle/fluid/operators/optimizers/distributed_fused_lamb_op.cc

@@ -147,8 +147,10 @@ class DistributedFusedLambOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<bool>("is_grad_scaled_by_nranks",
                   "Whether the input gradient has been scaled by nranks.")
         .SetDefault(true);
-    AddAttr<int>("ring_id", "The ring id of the NCCL communicator.")
-        .SetDefault(0);
+    AddAttr<int64_t>("nranks", "The world size.").SetDefault(1);
+    AddAttr<std::vector<int>>("ring_id",
+                              "The ring id of the NCCL communicator.")
+        .SetDefault({0});
     AddComment("The DistributedFusedLamb optimizer.");
   }
 };

paddle/fluid/operators/optimizers/distributed_fused_lamb_op.cu

@@ -806,23 +806,24 @@ static void LaunchScaleKernel(const platform::CUDADeviceContext &dev_ctx,
 #undef PD_LAMB_VEC_SCALE_KERNEL_CASE
 }
 
-template <typename T>
-static void NCCLReduceScatterWithScale(
-    const T *sendbuff, T *recvbuff, size_t recvcount, size_t nranks,
-    ncclComm_t comm, gpuStream_t stream,
-    const platform::CUDADeviceContext &dev_ctx, const T *scale = nullptr) {
+template <typename T, bool UseReduceScatter>
+static void NCCLSumWithScaleBase(const T *sendbuff, T *recvbuff,
+                                 size_t recvcount, size_t nranks,
+                                 ncclComm_t comm, gpuStream_t stream,
+                                 const platform::CUDADeviceContext &dev_ctx,
+                                 const T *scale = nullptr) {
   static_assert(std::is_same<T, float>::value ||
                     std::is_same<T, platform::float16>::value,
                 "T must be either float32 or float16.");
   if (recvcount == 0) return;
 
+  auto numel = UseReduceScatter ? (recvcount * nranks) : recvcount;
   if (comm == nullptr) {
     if (scale != nullptr) {
       PADDLE_ENFORCE_EQ(nranks, 1,
                         platform::errors::InvalidArgument(
                             "nranks must be 1 when scale != nullptr."));
-      LaunchScaleKernel(dev_ctx, sendbuff, scale, recvbuff, recvcount * nranks,
-                        stream);
+      LaunchScaleKernel(dev_ctx, sendbuff, scale, recvbuff, numel, stream);
     }
     return;
   }
@@ -834,14 +835,18 @@ static void NCCLReduceScatterWithScale(
       scale && CreatePreMulScaleOpIfSupported(dtype, comm, scale, &op);
   memory::Buffer buffer(dev_ctx.GetPlace());
   if (scale && !should_destroy_op) {
-    size_t numel = recvcount * nranks;
     T *new_sendbuff = buffer.Alloc<T>(numel);
     LaunchScaleKernel(dev_ctx, sendbuff, scale, new_sendbuff, numel, stream);
     sendbuff = new_sendbuff;
   }
 
-  PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclReduceScatter(
-      sendbuff, recvbuff, recvcount, dtype, op, comm, stream));
+  if (UseReduceScatter) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclReduceScatter(
+        sendbuff, recvbuff, recvcount, dtype, op, comm, stream));
+  } else {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(
+        sendbuff, recvbuff, recvcount, dtype, op, comm, stream));
+  }
 
 #if NCCL_VERSION_CODE >= 21100
   if (should_destroy_op) {
@@ -851,6 +856,26 @@ static void NCCLReduceScatterWithScale(
   }
 #endif
 }
+
+template <typename T>
+static void NCCLReduceScatterWithScale(
+    const T *sendbuff, T *recvbuff, size_t recvcount, size_t nranks,
+    ncclComm_t comm, gpuStream_t stream,
+    const platform::CUDADeviceContext &dev_ctx, const T *scale = nullptr) {
+  NCCLSumWithScaleBase<T, true>(sendbuff, recvbuff, recvcount, nranks, comm,
+                                stream, dev_ctx, scale);
+}
+
+template <typename T>
+static void NCCLAllReduceWithScale(const T *sendbuff, T *recvbuff,
+                                   size_t recvcount, size_t nranks,
+                                   ncclComm_t comm, gpuStream_t stream,
+                                   const platform::CUDADeviceContext &dev_ctx,
+                                   const T *scale = nullptr) {
+  NCCLSumWithScaleBase<T, false>(sendbuff, recvbuff, recvcount, nranks, comm,
+                                 stream, dev_ctx, scale);
+}
+
 #endif
 
 template <typename InputIteratorT, typename OutputIteratorT, typename ReduceOpT,
@@ -1321,6 +1346,9 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
                           "exactly by the element number %d of Moment1.",
                           numel, partial_numel));
 
+    // The num_devices means the number of devices that shard a complete set
+    // of all parameters. It may be num_devices < nranks or num_devices ==
+    // nranks.
     int64_t num_devices = numel / partial_numel;
     VLOG(1) << "num_devices = " << num_devices
             << " , partial_numel = " << partial_numel;
@@ -1354,22 +1382,43 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
     auto epsilon = ctx.Attr<float>("epsilon");
     auto max_global_grad_norm = ctx.Attr<float>("max_global_grad_norm");
     auto clip_after_allreduce = ctx.Attr<bool>("clip_after_allreduce");
-    auto ring_id = ctx.Attr<int>("ring_id");
+    auto nranks = ctx.Attr<int64_t>("nranks");
+    PADDLE_ENFORCE_GE(nranks, num_devices,
+                      phi::errors::InvalidArgument(
+                          "The nranks must be not less than num_devices."));
+    PADDLE_ENFORCE_EQ(
+        nranks % num_devices, 0,
+        phi::errors::InvalidArgument(
+            "The nranks must be exactly divided by num_devices."));
+    bool local_shard = (nranks > num_devices);
+
+    const auto &ring_ids = ctx.Attr<std::vector<int>>("ring_id");
     auto use_master_param_norm = ctx.Attr<bool>("use_master_param_norm");
     auto is_grad_scaled_by_nranks = ctx.Attr<bool>("is_grad_scaled_by_nranks");
     VLOG(10) << "max_global_grad_norm = " << max_global_grad_norm
              << " , clip_after_allreduce = " << clip_after_allreduce
              << " , use_master_param_norm = " << use_master_param_norm
-             << " , is_grad_scaled_by_nranks = " << is_grad_scaled_by_nranks;
+             << " , is_grad_scaled_by_nranks = " << is_grad_scaled_by_nranks
+             << " , local_shard = " << local_shard;
 
     // Step 6: allreduce + global norm gradient clip
-    int rank = 0;
-    ncclComm_t comm = nullptr;
-    if (num_devices > 1) {
+    int64_t global_rank = 0, local_rank = 0;
+    ncclComm_t global_comm = nullptr, local_comm = 0;
+    if (nranks > 1) {
       auto *nccl_comm_handle =
-          platform::NCCLCommContext::Instance().Get(ring_id, place);
-      comm = nccl_comm_handle->comm();
-      rank = nccl_comm_handle->rank();
+          platform::NCCLCommContext::Instance().Get(ring_ids[0], place);
+      global_comm = nccl_comm_handle->comm();
+      global_rank = nccl_comm_handle->rank();
+
+      if (local_shard) {
+        auto *local_nccl_comm_handle =
+            platform::NCCLCommContext::Instance().Get(ring_ids[1], place);
+        local_comm = local_nccl_comm_handle->comm();
+        local_rank = local_nccl_comm_handle->rank();
+      } else {
+        local_comm = global_comm;
+        local_rank = global_rank;
+      }
     }
 
     memory::Buffer grad_norm_square_buffer(place);
@@ -1381,8 +1430,15 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
     platform::float16 *fp16_sum_grad;
     auto fp32_numel_each_device = fp32_numel / num_devices;
     auto fp16_numel_each_device = fp16_numel / num_devices;
-    if (num_devices > 1 ||
-        (max_global_grad_norm > 0 && !clip_after_allreduce)) {
+    if (local_shard) {
+      auto ptr = sum_grad_buffer.Alloc<uint8_t>(
+          fp32_numel * sizeof(float) + fp16_numel * sizeof(platform::float16));
+      fp32_sum_grad = has_fp32_param ? reinterpret_cast<float *>(ptr) : nullptr;
+      fp16_sum_grad = has_fp16_param ? reinterpret_cast<platform::float16 *>(
+                                           ptr + fp32_numel * sizeof(float))
+                                     : nullptr;
+    } else if (nranks > 1 ||
+               (max_global_grad_norm > 0 && !clip_after_allreduce)) {
       auto ptr = sum_grad_buffer.Alloc<uint8_t>(
           fp32_numel_each_device * sizeof(float) +
           fp16_numel_each_device * sizeof(platform::float16));
@@ -1404,18 +1460,27 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
 
     float rescale_grad = 1.0f;
     if (!is_grad_scaled_by_nranks) {
-      rescale_grad /= num_devices;
+      rescale_grad /= nranks;
     }
 
     if (max_global_grad_norm > 0) {
       if (clip_after_allreduce) {
         // (1) ReduceScater first
-        NCCLReduceScatterWithScale(fp32_grad, fp32_sum_grad,
-                                   fp32_numel_each_device, num_devices, comm,
-                                   stream, dev_ctx);
-        NCCLReduceScatterWithScale(fp16_grad, fp16_sum_grad,
-                                   fp16_numel_each_device, num_devices, comm,
-                                   stream, dev_ctx);
+        if (local_shard) {
+          NCCLAllReduceWithScale(fp32_grad, fp32_sum_grad, fp32_numel, nranks,
+                                 global_comm, stream, dev_ctx);
+          NCCLAllReduceWithScale(fp16_grad, fp16_sum_grad, fp16_numel, nranks,
+                                 global_comm, stream, dev_ctx);
+          fp32_sum_grad += (local_rank * fp32_numel_each_device);
+          fp16_sum_grad += (local_rank * fp16_numel_each_device);
+        } else {
+          NCCLReduceScatterWithScale(fp32_grad, fp32_sum_grad,
+                                     fp32_numel_each_device, nranks,
+                                     global_comm, stream, dev_ctx);
+          NCCLReduceScatterWithScale(fp16_grad, fp16_sum_grad,
+                                     fp16_numel_each_device, nranks,
+                                     global_comm, stream, dev_ctx);
+        }
         // (2) Calculate the global grad norm
         GetSquareGradNorm(fp32_sum_grad, fp32_numel_each_device, fp16_sum_grad,
                           fp16_numel_each_device, fp32_square_grad_norm, stream,
@@ -1425,7 +1490,7 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
         if (num_devices > 1) {
           PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(
               fp32_square_grad_norm, fp32_square_grad_norm, 1, ncclFloat32,
-              ncclSum, comm, stream));
+              ncclSum, local_comm, stream));
         }
         VLOG(1) << "Grad square norm after all reduce: "
                 << FlattenToString(fp32_square_grad_norm, 1, place);
@@ -1452,7 +1517,7 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
 
         float clip_scale = 1.0f;
         if (is_grad_scaled_by_nranks) {
-          clip_scale *= num_devices;
+          clip_scale *= nranks;
         }
         CalcGradNormClipBeforeAllReduceScale<float, platform::float16>
             <<<1, 1, 0, stream>>>(global_scale, max_global_grad_norm,
@@ -1463,36 +1528,54 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
         } else {
           VLOG(1) << "Grad scale: " << FlattenToString(fp16_scale, 1, place);
         }
-        if (num_devices > 1) {
+        if (nranks > 1) {
           PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(
               fp32_square_grad_norm, fp32_square_grad_norm, 1, ncclFloat32,
-              ncclSum, comm, stream));
+              ncclSum, global_comm, stream));
         }
         // (3) Do ReduceScatter with scale
-        NCCLReduceScatterWithScale(fp32_grad, fp32_sum_grad,
-                                   fp32_numel_each_device, num_devices, comm,
-                                   stream, dev_ctx, fp32_scale);
-        NCCLReduceScatterWithScale(fp16_grad, fp16_sum_grad,
-                                   fp16_numel_each_device, num_devices, comm,
-                                   stream, dev_ctx, fp16_scale);
+        if (local_shard) {
+          NCCLAllReduceWithScale(fp32_grad, fp32_sum_grad, fp32_numel, nranks,
+                                 global_comm, stream, dev_ctx, fp32_scale);
+          NCCLAllReduceWithScale(fp16_grad, fp16_sum_grad, fp16_numel, nranks,
+                                 global_comm, stream, dev_ctx, fp16_scale);
+          fp32_sum_grad += (local_rank * fp32_numel_each_device);
+          fp16_sum_grad += (local_rank * fp16_numel_each_device);
+        } else {
+          NCCLReduceScatterWithScale(fp32_grad, fp32_sum_grad,
+                                     fp32_numel_each_device, nranks,
+                                     global_comm, stream, dev_ctx, fp32_scale);
+          NCCLReduceScatterWithScale(fp16_grad, fp16_sum_grad,
+                                     fp16_numel_each_device, nranks,
+                                     global_comm, stream, dev_ctx, fp16_scale);
+        }
         // (4) mark max_global_grad_norm as 0, meaning that clip has been
         // already performed
         max_global_grad_norm = 0;
       }
     } else {
-      NCCLReduceScatterWithScale(fp32_grad, fp32_sum_grad,
-                                 fp32_numel_each_device, num_devices, comm,
-                                 stream, dev_ctx);
-      NCCLReduceScatterWithScale(fp16_grad, fp16_sum_grad,
-                                 fp16_numel_each_device, num_devices, comm,
-                                 stream, dev_ctx);
+      if (local_shard) {
+        NCCLAllReduceWithScale(fp32_grad, fp32_sum_grad, fp32_numel, nranks,
+                               global_comm, stream, dev_ctx);
+        NCCLAllReduceWithScale(fp16_grad, fp16_sum_grad, fp16_numel, nranks,
+                               global_comm, stream, dev_ctx);
+        fp32_sum_grad += (local_rank * fp32_numel_each_device);
+        fp16_sum_grad += (local_rank * fp16_numel_each_device);
+      } else {
+        NCCLReduceScatterWithScale(fp32_grad, fp32_sum_grad,
+                                   fp32_numel_each_device, num_devices,
+                                   global_comm, stream, dev_ctx);
+        NCCLReduceScatterWithScale(fp16_grad, fp16_sum_grad,
+                                   fp16_numel_each_device, num_devices,
+                                   global_comm, stream, dev_ctx);
+      }
       CheckHasNanInfGrad(fp32_sum_grad, fp32_numel_each_device, fp16_sum_grad,
                          fp16_numel_each_device, fp32_square_grad_norm, stream,
                          &cub_tmp_buffer);
       if (num_devices > 1) {
         PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(
             fp32_square_grad_norm, fp32_square_grad_norm, 1, ncclFloat32,
-            ncclSum, comm, stream));
+            ncclSum, local_comm, stream));
       }
       max_global_grad_norm = 0;
     }
@@ -1526,8 +1609,8 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
 
     memory::Buffer trust_ratio_div_buffer(place);
     auto *trust_ratio_div = trust_ratio_div_buffer.Alloc<float>(partial_numel);
-    auto fp32_offset = rank * fp32_numel_each_device;
-    auto fp16_offset = rank * fp16_numel_each_device;
+    auto fp32_offset = local_rank * fp32_numel_each_device;
+    auto fp16_offset = local_rank * fp16_numel_each_device;
     if (has_fp32_param) {
       VLOG(10) << "Update FP32 Moment and TrustRatioDiv starts";
       MultiTensorUpdateLambMomentAndTrustRatioDiv(
@@ -1598,12 +1681,12 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
         PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(
             param_square_norm + fp32_global_param_num,
             param_square_norm + fp32_global_param_num,
-            2 * param_num - fp32_global_param_num, ncclFloat32, ncclSum, comm,
-            stream));
+            2 * param_num - fp32_global_param_num, ncclFloat32, ncclSum,
+            local_comm, stream));
       } else {
         PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(
             trust_ratio_div_square_norm, trust_ratio_div_square_norm, param_num,
-            ncclFloat32, ncclSum, comm, stream));
+            ncclFloat32, ncclSum, local_comm, stream));
       }
       VLOG(10) << "ncclAllReduce done";
     }
@@ -1623,7 +1706,7 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
         // ncclAllGather
         PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllGather(
             fp32_param + fp32_offset, fp32_param, fp32_numel_each_device,
-            ncclFloat32, comm, stream));
+            ncclFloat32, local_comm, stream));
       }
 
       beta1pow = nullptr;
@@ -1641,7 +1724,7 @@ class DistributedFusedLambOpKernel<platform::CUDADeviceContext, T>
         // ncclAllGather
         PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllGather(
             fp16_param + fp16_offset, fp16_param, fp16_numel_each_device,
-            ncclFloat16, comm, stream));
+            ncclFloat16, local_comm, stream));
       }
     }
     VLOG(10) << "Update Param done";

python/paddle/distributed/fleet/meta_optimizers/graph_execution_optimizer.py

@@ -69,6 +69,9 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
         if trainer_id == 0 and not paddle.is_compiled_with_npu():
             wait_server_ready(other_trainers)
 
+        if build_strategy.reduce_strategy == BuildStrategy.ReduceStrategy._NoReduce:
+            return
+
         if core.is_compiled_with_cuda():
             comm_id_var = startup_program.global_block().create_var(
                 name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)

python/paddle/incubate/optimizer/distributed_fused_lamb.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 from paddle.fluid import framework, core, layers, unique_name
 from paddle.fluid.framework import Variable
 from paddle.fluid.clip import ClipGradByGlobalNorm
@@ -19,11 +20,69 @@ from paddle.fluid.initializer import Constant
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.optimizer import Optimizer
 from paddle.distributed import get_rank, get_world_size
+from paddle.distributed.collective import new_group
 from paddle.fluid.executor import global_scope
 from paddle.fluid.framework import name_scope
+from paddle.fluid import core, unique_name
 import numpy as np
 
 
+def init_communicator(block, rank, ranks, ring_id):
+    eps = os.environ['PADDLE_TRAINER_ENDPOINTS']
+    eps = [ep.strip() for ep in eps.split(",") if ep.strip()]
+    cur_ep = eps[rank]
+    other_eps = [eps[r] for r in ranks if r != rank]
+
+    local_rank = ranks.index(rank)
+    comm_var_name = unique_name.generate('comm_id')
+    comm_id_var = block.create_var(name=comm_var_name,
+                                   persistable=True,
+                                   type=core.VarDesc.VarType.RAW)
+    block.append_op(type='c_gen_nccl_id',
+                    inputs={},
+                    outputs={'Out': comm_id_var},
+                    attrs={
+                        'rank': local_rank,
+                        'endpoint': cur_ep,
+                        'other_endpoints': other_eps,
+                        'ring_id': ring_id
+                    })
+    block.append_op(type='c_comm_init',
+                    inputs={'X': comm_id_var},
+                    outputs={},
+                    attrs={
+                        'nranks': len(ranks),
+                        'rank': local_rank,
+                        'ring_id': ring_id
+                    })
+    tmp_var = block.create_var(name=unique_name.generate('tmp'))
+    block.append_op(type='fill_constant',
+                    outputs={'Out': tmp_var},
+                    attrs={'value': 1})
+    block.append_op(type='c_allreduce_sum',
+                    inputs={'X': tmp_var},
+                    outputs={'Out': tmp_var},
+                    attrs={
+                        'ring_id': ring_id,
+                        'use_calc_stream': True
+                    })
+    block.append_op(type='c_sync_calc_stream',
+                    inputs={'X': tmp_var},
+                    outputs={'Out': tmp_var})
+    return ring_id
+
+
+def broadcast_parameters(block, parameters, ring_id):
+    for p in parameters:
+        block.append_op(type='c_broadcast',
+                        inputs={'X': p},
+                        outputs={'Out': p},
+                        attrs={
+                            'ring_id': ring_id,
+                            'use_calc_stream': True
+                        })
+
+
 class DistributedFusedLamb(Optimizer):
 
     def __init__(self,
@@ -41,6 +100,7 @@ class DistributedFusedLamb(Optimizer):
                  use_master_param_norm=True,
                  gradient_accumulation_steps=1,
                  use_master_acc_grad=True,
+                 nproc_per_node=None,
                  name=None):
         assert not framework._non_static_mode(
         ), "DistributedFusedLamb does not support dygraph mode"
@@ -65,10 +125,10 @@ class DistributedFusedLamb(Optimizer):
         self._is_grad_scaled_by_nranks = is_grad_scaled_by_nranks
         self._exclude_from_weight_decay_fn = exclude_from_weight_decay_fn
         self._scale = None
-        self._ring_id = 0
         self._use_master_param_norm = use_master_param_norm
         self._gradient_accumulation_steps = gradient_accumulation_steps
         self._use_master_acc_grad = use_master_acc_grad
+        self._nproc_per_node = nproc_per_node
         assert self._gradient_accumulation_steps >= 1
 
         self.helper = LayerHelper('distributed_fused_lamb')
@@ -228,6 +288,30 @@ class DistributedFusedLamb(Optimizer):
 
         rank = get_rank()
         nranks = get_world_size()
+        if self._nproc_per_node is None:
+            nproc_per_node = nranks
+        else:
+            nproc_per_node = self._nproc_per_node
+        assert nranks % nproc_per_node == 0, "nranks should be exactly divided by nproc_per_node"
+
+        shard_inside_node = (nranks > nproc_per_node)
+        local_rank = rank % nproc_per_node
+        node_id = int(rank / nproc_per_node)
+        node_num = int(nranks / nproc_per_node)
+        ring_ids = []
+        startup_block = self.helper.startup_program.global_block()
+        if nranks > 1:
+            ring_id = init_communicator(startup_block, rank,
+                                        list(range(nranks)), 0)
+            ring_ids.append(ring_id)
+
+        if node_num > 1 and len(ring_ids) <= 1 and shard_inside_node:
+            local_group_ranks = list(
+                range(node_id * nproc_per_node, (node_id + 1) * nproc_per_node))
+            ring_id = init_communicator(startup_block, rank, local_group_ranks,
+                                        1)
+            ring_ids.append(ring_id)
+
         scale = self._get_or_create_scale()
 
         params = [p for p, _ in params_grads]
@@ -238,7 +322,6 @@ class DistributedFusedLamb(Optimizer):
                 if self._exclude_from_weight_decay_fn(p):
                     apply_weight_decay[i] = 0
 
-        startup_block = self.helper.startup_program.global_block()
         for g in grads:
             startup_block.create_var(name=g.name,
                                      type=g.type,
@@ -246,46 +329,45 @@ class DistributedFusedLamb(Optimizer):
                                      persistable=g.persistable,
                                      shape=g.shape)
 
-        startup_block.append_op(type='distributed_fused_lamb_init',
-                                inputs={
-                                    'Param': params,
-                                    'Grad': grads,
-                                },
-                                outputs={
-                                    'FP32FusedParam': [fp32_fused_param],
-                                    'FP32FusedGrad': [fp32_fused_grad],
-                                    'FP16FusedParam': [fp16_fused_param],
-                                    'FP16FusedGrad': [fp16_fused_grad],
-                                    'Moment1': [moment1],
-                                    'Moment2': [moment2],
-                                    'Beta1Pow': [beta1pow],
-                                    'Beta2Pow': [beta2pow],
-                                    'GlobalScale': [scale],
-                                    'ParamInfo': [param_info],
-                                    'ParamOut':
-                                    params,
-                                    'MasterParamOut':
-                                    master_params,
-                                    'GradOut':
-                                    grads,
-                                    'FP32ShardFusedParamOffsets':
-                                    [fp32_partial_fused_offsets],
-                                    'FP16ShardFusedParamOffsets':
-                                    [fp16_partial_fused_offsets],
-                                    'FusedParamOffsets': [fused_offsets],
-                                    'ParamOrder': [param_order],
-                                    'Step': [step],
-                                },
-                                attrs={
-                                    'alignment': self._alignment,
-                                    'rank': rank,
-                                    'nranks': nranks,
-                                    'apply_weight_decay': apply_weight_decay,
-                                    'moment1': 0.0,
-                                    'moment2': 0.0,
-                                    'beta1': self._beta1,
-                                    'beta2': self._beta2,
-                                })
+        if nranks > 1:
+            broadcast_parameters(startup_block, params, ring_ids[0])
+
+        startup_block.append_op(
+            type='distributed_fused_lamb_init',
+            inputs={
+                'Param': params,
+                'Grad': grads,
+            },
+            outputs={
+                'FP32FusedParam': [fp32_fused_param],
+                'FP32FusedGrad': [fp32_fused_grad],
+                'FP16FusedParam': [fp16_fused_param],
+                'FP16FusedGrad': [fp16_fused_grad],
+                'Moment1': [moment1],
+                'Moment2': [moment2],
+                'Beta1Pow': [beta1pow],
+                'Beta2Pow': [beta2pow],
+                'GlobalScale': [scale],
+                'ParamInfo': [param_info],
+                'ParamOut': params,
+                'MasterParamOut': master_params,
+                'GradOut': grads,
+                'FP32ShardFusedParamOffsets': [fp32_partial_fused_offsets],
+                'FP16ShardFusedParamOffsets': [fp16_partial_fused_offsets],
+                'FusedParamOffsets': [fused_offsets],
+                'ParamOrder': [param_order],
+                'Step': [step],
+            },
+            attrs={
+                'alignment': self._alignment,
+                'rank': local_rank if shard_inside_node else rank,
+                'nranks': nproc_per_node if shard_inside_node else nranks,
+                'apply_weight_decay': apply_weight_decay,
+                'moment1': 0.0,
+                'moment2': 0.0,
+                'beta1': self._beta1,
+                'beta2': self._beta2,
+            })
 
         main_block = self.helper.main_program.global_block()
         self._create_global_learning_rate()
@@ -351,7 +433,8 @@ class DistributedFusedLamb(Optimizer):
                 'max_global_grad_norm': self._max_global_grad_norm,
                 'clip_after_allreduce': self._clip_after_allreduce,
                 'rank': rank,
-                'ring_id': self._ring_id,
+                'nranks': nranks,
+                'ring_id': ring_ids,
                 'use_master_param_norm': self._use_master_param_norm,
                 'is_grad_scaled_by_nranks': self._is_grad_scaled_by_nranks,
                 'acc_steps': self._gradient_accumulation_steps,